36
Average Days in Review
99%
Percent of Research Articles Cited within Two Years of Publication
37
Average Days to Publication from Acceptance
Annual HTML+PDF Usage

CURRENT ISSUE

Volume 12Issue 3June 2021

EDITOR IN CHIEF: Dr. Arturo Casadevall

Explore mBio

Editor in Chief

mBio EiC Casadevall
Dr. Arturo Casadevall

Editor in Chief (2021) | Johns Hopkins Bloomberg School of Public Health

Arturo Casadevall is the Alfred and Jill Sommer Professor and Chair of the W. Harry Feinstone Department of Molecular Microbiology and Immunology at Johns Hopkins Bloomberg School of Public Health in Baltimore, Maryland.

Board of Editors

  • mBioArticle
    Single-Dose, Intranasal Immunization with Recombinant Parainfluenza Virus 5 Expressing Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Spike Protein Protects Mice from Fatal MERS-CoV Infection

    Single-Dose, Intranasal Immunization with Recombinant Parainfluenza Virus 5 Expressing Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Spike Protein Protects Mice from Fatal MERS-CoV Infection

    ABSTRACT

    Middle East respiratory syndrome coronavirus (MERS-CoV) can cause severe and fatal acute respiratory disease in humans and remains endemic in the Middle East since first being identified in 2012. There are currently no approved vaccines or therapies available for MERS-CoV. In this study, we evaluated parainfluenza virus 5 (PIV5)-based vaccine expressing the MERS-CoV envelope spike protein (PIV5/MERS-S) in a human DPP4 knockin C57BL/6 congenic mouse model (hDPP4 KI). Following a single-dose intranasal immunization, PIV5-MERS-S induced neutralizing antibody and robust T cell responses in hDPP4 KI mice. A single intranasal administration of 104 PFU PIV5-MERS-S provided complete protection against a lethal challenge with mouse-adapted MERS-CoV (MERSMA6.1.2) and improved virus clearance in the lung. In comparison, single-dose intramuscular immunization with 106 PFU UV-inactivated MERSMA6.1.2 mixed with Imject alum provided protection to only 25% of immunized mice. Intriguingly, an influx of eosinophils was observed only in the lungs of mice immunized with inactivated MERS-CoV, suggestive of a hypersensitivity-type response. Overall, our study indicated that PIV5-MERS-S is a promising effective vaccine candidate against MERS-CoV infection.
    IMPORTANCE MERS-CoV causes lethal infection in humans, and there is no vaccine. Our work demonstrates that PIV5 is a promising vector for developing a MERS vaccine. Furthermore, success of PIV5-based MERS vaccine can be employed to develop a vaccine for emerging CoVs such as SARS-CoV-2, which causes COVID-19.

    REFERENCES

    1.
    Zaki AM, van Boheemen S, Bestebroer TM, Osterhaus AD, Fouchier RA. 2012. Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia. N Engl J Med 367:1814–1820.
    2.
    Yusof MF, Eltahir YM, Serhan WS, Hashem FM, Elsayed EA, Marzoug BA, Abdelazim AS, Bensalah OK, Al Muhairi SS. 2015. Prevalence of Middle East respiratory syndrome coronavirus (MERS-CoV) in dromedary camels in Abu Dhabi Emirate, United Arab Emirates. Virus Genes 50:509–513.
    3.
    Chan RW, Hemida MG, Kayali G, Chu DK, Poon LL, Alnaeem A, Ali MA, Tao KP, Ng HY, Chan MC, Guan Y, Nicholls JM, Peiris JS. 2014. Tropism and replication of Middle East respiratory syndrome coronavirus from dromedary camels in the human respiratory tract: an in-vitro and ex-vivo study. Lancet Respir Med 2:813–822.
    4.
    Wernery U, Corman VM, Wong EY, Tsang AK, Muth D, Lau SK, Khazanehdari K, Zirkel F, Ali M, Nagy P, Juhasz J, Wernery R, Joseph S, Syriac G, Elizabeth SK, Patteril NA, Woo PC, Drosten C. 2015. Acute Middle East respiratory syndrome coronavirus infection in livestock dromedaries, Dubai, 2014. Emerg Infect Dis 21:1019–1022.
    5.
    Azhar EI, El-Kafrawy SA, Farraj SA, Hassan AM, Al-Saeed MS, Hashem AM, Madani TA. 2014. Evidence for camel-to-human transmission of MERS coronavirus. N Engl J Med 370:2499–2505.
    6.
    Assiri A, McGeer A, Perl TM, Price CS, Al Rabeeah AA, Cummings DA, Alabdullatif ZN, Assad M, Almulhim A, Makhdoom H, Madani H, Alhakeem R, Al-Tawfiq JA, Cotten M, Watson SJ, Kellam P, Zumla AI, Memish ZA, KSA MERS-CoV Investigation Team. 2013. Hospital outbreak of Middle East respiratory syndrome coronavirus. N Engl J Med 369:407–416.
    7.
    Cho SY, Kang JM, Ha YE, Park GE, Lee JY, Ko JH, Lee JY, Kim JM, Kang CI, Jo IJ, Ryu JG, Choi JR, Kim S, Huh HJ, Ki CS, Kang ES, Peck KR, Dhong HJ, Song JH, Chung DR, Kim YJ. 2016. MERS-CoV outbreak following a single patient exposure in an emergency room in South Korea: an epidemiological outbreak study. Lancet 388:994–1001.
    8.
    Zhang N, Jiang S, Du L. 2014. Current advancements and potential strategies in the development of MERS-CoV vaccines. Expert Rev Vaccines 13:761–774.
    9.
    Li F. 2015. Receptor recognition mechanisms of coronaviruses: a decade of structural studies. J Virol 89:1954–1964.
    10.
    Raj VS, Mou H, Smits SL, Dekkers DH, Muller MA, Dijkman R, Muth D, Demmers JA, Zaki A, Fouchier RA, Thiel V, Drosten C, Rottier PJ, Osterhaus AD, Bosch BJ, Haagmans BL. 2013. Dipeptidyl peptidase 4 is a functional receptor for the emerging human coronavirus-EMC. Nature 495:251–254.
    11.
    Du L, Tai W, Zhou Y, Jiang S. 2016. Vaccines for the prevention against the threat of MERS-CoV. Expert Rev Vaccines 15:1123–1134.
    12.
    Du L, Jiang S. 2015. Middle East respiratory syndrome: current status and future prospects for vaccine development. Expert Opin Biol Ther 15:1647–1651.
    13.
    Jia W, Channappanavar R, Zhang C, Li M, Zhou H, Zhang S, Zhou P, Xu J, Shan S, Shi X, Wang X, Zhao J, Zhou D, Perlman S, Zhang L. 2019. Single intranasal immunization with chimpanzee adenovirus-based vaccine induces sustained and protective immunity against MERS-CoV infection. Emerg Microbes Infect 8:760–772.
    14.
    Hotez PJ, Bottazzi ME, Tseng CT, Zhan B, Lustigman S, Du L, Jiang S. 2014. Calling for rapid development of a safe and effective MERS vaccine. Microbes Infect 16:529–531.
    15.
    Lamb RA, Kolakofsky D. 2001. Paramyxoviridae: the viruses and their replication, ch 41, p 1305–1340. In Knipe DM, Howley PM (ed), Fields virology, 4th ed. Lippincott, Williams and Wilkins, Philadelphia, PA.
    16.
    Binn LN, Eddy GA, Lazar EC, Helms J, Murnane T. 1967. Viruses recovered from laboratory dogs with respiratory disease. Proc Soc Exp Biol Med 126:140–145.
    17.
    Rosenberg FJ, Lief FS, Todd JD, Reif JS. 1971. Studies of canine respiratory viruses. I. Experimental infection of dogs with an SV5-like canine parainfluenza agent. Am J Epidemiol 94:147–165.
    18.
    Cornwell HJ, McCandlish IA, Thompson H, Laird HM, Wright NG. 1976. Isolation of parainfluenza virus SV5 from dogs with respiratory disease. Vet Rec 98:301–302.
    19.
    McCandlish IA, Thompson H, Cornwell HJ, Wright NG. 1978. A study of dogs with kennel cough. Vet Rec 102:293–301.
    20.
    Azetaka M, Konishi S. 1988. Kennel cough complex: confirmation and analysis of the outbreak in Japan. Nippon Juigaku Zasshi 50:851–858.
    21.
    Phan SI, Adam CM, Chen Z, Citron M, Liang X, Espeseth AS, Wang D, He B. 2017. Genetic stability of parainfluenza virus 5-vectored human respiratory syncytial virus vaccine candidates after in vitro and in vivo passage. J Virol 91:e00559-17.
    22.
    Tompkins SM, Lin Y, Leser GP, Kramer KA, Haas DL, Howerth EW, Xu J, Kennett MJ, Durbin RK, Durbin JE, Tripp R, Lamb RA, He B. 2007. Recombinant parainfluenza virus 5 (PIV5) expressing the influenza A virus hemagglutinin provides immunity in mice to influenza A virus challenge. Virology 362:139–150.
    23.
    Mooney AJ, Li Z, Gabbard JD, He B, Tompkins SM. 2013. Recombinant parainfluenza virus 5 vaccine encoding the influenza virus hemagglutinin protects against H5N1 highly pathogenic avian influenza virus infection following intranasal or intramuscular vaccination of BALB/c mice. J Virol 87:363–371.
    24.
    Li Z, Mooney AJ, Gabbard JD, Gao X, Xu P, Place RJ, Hogan RJ, Tompkins SM, He B. 2013. Recombinant parainfluenza virus 5 expressing hemagglutinin of influenza A virus H5N1 protected mice against lethal highly pathogenic avian influenza virus H5N1 challenge. J Virol 87:354–362.
    25.
    Li Z, Gabbard JD, Mooney A, Gao X, Chen Z, Place RJ, Tompkins SM, He B. 2013. Single dose vaccination of a recombinant parainfluenza virus 5 expressing NP from H5N1 provides broad immunity against influenza A viruses. J Virol 87:5985–5993.
    26.
    Li Z, Gabbard JD, Mooney A, Chen Z, Tompkins SM, He B. 2013. Efficacy of parainfluenza virus 5 mutants expressing HA from H5N1 influenza A virus in mice. J Virol 87:9604–9609.
    27.
    Phan SI, Chen Z, Xu P, Li Z, Gao X, Foster SL, Teng MN, Tripp RA, Sakamoto K, He B. 2014. A respiratory syncytial virus (RSV) vaccine based on parainfluenza virus 5 (PIV5). Vaccine 32:3050–3057.
    28.
    Chen Z, Zhou M, Gao X, Zhang G, Ren G, Gnanadurai CW, Fu ZF, He B. 2013. A novel rabies vaccine based on a recombinant parainfluenza virus 5 expressing rabies virus glycoprotein. J Virol 87:2986–2993.
    29.
    Agrawal AS, Tao X, Algaissi A, Garron T, Narayanan K, Peng BH, Couch RB, Tseng CT. 2016. Immunization with inactivated Middle East Respiratory Syndrome coronavirus vaccine leads to lung immunopathology on challenge with live virus. Hum Vaccin Immunother 12:2351–2356.
    30.
    Lamirande EW, DeDiego ML, Roberts A, Jackson JP, Alvarez E, Sheahan T, Shieh WJ, Zaki SR, Baric R, Enjuanes L, Subbarao K. 2008. A live attenuated severe acute respiratory syndrome coronavirus is immunogenic and efficacious in golden Syrian hamsters. J Virol 82:7721–7724.
    31.
    Modjarrad K, Roberts CC, Mills KT, Castellano AR, Paolino K, Muthumani K, Reuschel EL, Robb ML, Racine T, Oh MD, Lamarre C, Zaidi FI, Boyer J, Kudchodkar SB, Jeong M, Darden JM, Park YK, Scott PT, Remigio C, Parikh AP, Wise MC, Patel A, Duperret EK, Kim KY, Choi H, White S, Bagarazzi M, May JM, Kane D, Lee H, Kobinger G, Michael NL, Weiner DB, Thomas SJ, Maslow JN. 2019. Safety and immunogenicity of an anti-Middle East respiratory syndrome coronavirus DNA vaccine: a phase 1, open-label, single-arm, dose-escalation trial. Lancet Infect Dis 19:1013–1022.
    32.
    Haagmans BL, van den Brand JM, Raj VS, Volz A, Wohlsein P, Smits SL, Schipper D, Bestebroer TM, Okba N, Fux R, Bensaid A, Solanes Foz D, Kuiken T, Baumgartner W, Segales J, Sutter G, Osterhaus AD. 2016. An orthopoxvirus-based vaccine reduces virus excretion after MERS-CoV infection in dromedary camels. Science 351:77–81.
    33.
    Volz A, Kupke A, Song F, Jany S, Fux R, Shams-Eldin H, Schmidt J, Becker C, Eickmann M, Becker S, Sutter G. 2015. Protective efficacy of recombinant modified vaccinia virus Ankara Delivering Middle East respiratory syndrome coronavirus spike glycoprotein. J Virol 89:8651–8656.
    34.
    Malczyk AH, Kupke A, Prüfer S, Scheuplein VA, Hutzler S, Kreuz D, Beissert T, Bauer S, Hubich-Rau S, Tondera C, Eldin HS, Schmidt J, Vergara-Alert J, Süzer Y, Seifried J, Hanschmann K-M, Kalinke U, Herold S, Sahin U, Cichutek K, Waibler Z, Eickmann M, Becker S, Mühlebach MD. 2015. A highly immunogenic and protective Middle East respiratory syndrome coronavirus vaccine based on a recombinant measles virus vaccine platform. J Virol 89:11654–11667.
    35.
    Wirblich C, Coleman CM, Kurup D, Abraham TS, Bernbaum JG, Jahrling PB, Hensley LE, Johnson RF, Frieman MB, Schnell MJ. 2017. One-Health: a safe, efficient, dual-use vaccine for humans and animals against Middle East respiratory syndrome coronavirus and rabies virus. J Virol 91:e02040-16.
    36.
    Kim E, Okada K, Kenniston T, Raj VS, AlHajri MM, Farag EA, AlHajri F, Osterhaus AD, Haagmans BL, Gambotto A. 2014. Immunogenicity of an adenoviral-based Middle East Respiratory Syndrome coronavirus vaccine in BALB/c mice. Vaccine 32:5975–5982.
    37.
    Neutra MR, Kozlowski PA. 2006. Mucosal vaccines: the promise and the challenge. Nat Rev Immunol 6:148–158.
    38.
    Ko SY, Cheng C, Kong WP, Wang L, Kanekiyo M, Einfeld D, King CR, Gall JG, Nabel GJ. 2009. Enhanced induction of intestinal cellular immunity by oral priming with enteric adenovirus 41 vectors. J Virol 83:748–756.
    39.
    Guo X, Deng Y, Chen H, Lan J, Wang W, Zou X, Hung T, Lu Z, Tan W. 2015. Systemic and mucosal immunity in mice elicited by a single immunization with human adenovirus type 5 or 41 vector-based vaccines carrying the spike protein of Middle East respiratory syndrome coronavirus. Immunology 145:476–484.
    40.
    Alharbi NK, Qasim I, Almasoud A, Aljami HA, Alenazi MW, Alhafufi A, Aldibasi OS, Hashem AM, Kasem S, Albrahim R, Aldubaib M, Almansour A, Temperton NJ, Kupke A, Becker S, Abu-Obaidah A, Alkarar A, Yoon IK, Azhar E, Lambe T, Bayoumi F, Aldowerij A, Ibrahim OH, Gilbert SC, Balkhy HH. 2019. Humoral immunogenicity and efficacy of a single dose of ChAdOx1 MERS vaccine candidate in dromedary camels. Sci Rep 9:16292.
    41.
    Buchholz UJ, Bukreyev A, Yang L, Lamirande EW, Murphy BR, Subbarao K, Collins PL. 2004. Contributions of the structural proteins of severe acute respiratory syndrome coronavirus to protective immunity. Proc Natl Acad Sci U S A 101:9804–9809.
    42.
    Tseng CT, Sbrana E, Iwata-Yoshikawa N, Newman PC, Garron T, Atmar RL, Peters CJ, Couch RB. 2012. Immunization with SARS coronavirus vaccines leads to pulmonary immunopathology on challenge with the SARS virus. PLoS One 7:e35421.
    43.
    Bolles M, Deming D, Long K, Agnihothram S, Whitmore A, Ferris M, Funkhouser W, Gralinski L, Totura A, Heise M, Baric RS. 2011. A double-inactivated severe acute respiratory syndrome coronavirus vaccine provides incomplete protection in mice and induces increased eosinophilic proinflammatory pulmonary response upon challenge. J Virol 85:12201–12215.
    44.
    Anderson LJ. 2013. Respiratory syncytial virus vaccine development. Semin Immunol 25:160–171.
    45.
    Phan SI, Zengel JR, Wei H, Li Z, Wang D, He B. 2017. Parainfluenza virus 5 expressing wild-type or prefusion respiratory syncytial virus (RSV) fusion protein protects mice and cotton rats from RSV challenge. J Virol 91:e00560-17.
    46.
    Wang D, Phan S, DiStefano DJ, Citron MP, Callahan CL, Indrawati L, Dubey SA, Heidecker GJ, Govindarajan D, Liang X, He B, Espeseth AS. 2017. A single-dose recombinant parainfluenza virus 5-vectored vaccine expressing respiratory syncytial virus (RSV) F or G protein protected cotton rats and African green monkeys from RSV challenge. J Virol 91:e00066-17.
    47.
    Li Z, Gabbard JD, Johnson S, Dlugolenski D, Phan S, Tompkins SM, He B. 2015. Efficacy of a parainfluenza virus 5 (PIV5)-based H7N9 vaccine in mice and guinea pigs: antibody titer towards HA was not a good indicator for protection. PLoS One 10:e0120355.
    48.
    Chen Z, Xu P, Salyards GW, Harvey SB, Rada B, Fu ZF, He B. 2012. Evaluating a parainfluenza virus 5-based vaccine in a host with pre-existing immunity against parainfluenza virus 5. PLoS One 7:e50144.
    49.
    Mooney AJ, Gabbard JD, Li Z, Dlugolenski DA, Johnson SK, Tripp RA, He B, Tompkins SM. 2017. Vaccination with recombinant parainfluenza virus 5 expressing neuraminidase protects against homologous and heterologous influenza virus challenge. J Virol 91:e01579-17.
    50.
    Li K, Wohlford-Lenane CL, Channappanavar R, Park JE, Earnest JT, Bair TB, Bates AM, Brogden KA, Flaherty HA, Gallagher T, Meyerholz DK, Perlman S, McCray PB, Jr. 2017. Mouse-adapted MERS coronavirus causes lethal lung disease in human DPP4 knockin mice. Proc Natl Acad Sci U S A 114:E3119–E3128.
    51.
    Meyerholz DK, Beck AP. 2018. Principles and approaches for reproducible scoring of tissue stains in research. Lab Invest 98:844–855.
    52.
    Fuentes S, Coyle EM, Golding H, Khurana S. 2015. Nonglycosylated G-protein vaccine protects against homologous and heterologous respiratory syncytial virus (RSV) challenge, while glycosylated G enhances RSV lung pathology and cytokine levels. J Virol 89:8193–8205.
    53.
    Zhao J, Li K, Wohlford-Lenane C, Agnihothram SS, Fett C, Zhao J, Gale MJ, Jr, Baric RS, Enjuanes L, Gallagher T, McCray PB, Jr, Perlman S. 2014. Rapid generation of a mouse model for Middle East respiratory syndrome. Proc Natl Acad Sci U S A 111:4970–4975.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 11Number 228 April 2020
    eLocator: e00554-20
    Editor: Kanta Subbarao
    The Peter Doherty Institute for Infection and Immunity

    History

    Received: 6 March 2020
    Accepted: 24 March 2020
    Published online: 7 April 2020

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. COVID-19
    2. MERS
    3. coronavirus
    4. vaccine

    Contributors

    Authors

    Kun Li
    Department of Pediatrics, Pappajohn Biomedical Institute, University of Iowa, Iowa City, Iowa, USA
    Zhuo Li
    Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia, USA
    Present address: Zhuo Li, Center for Inflammation, Immunity & Infection, Institute for Biomedical Sciences, Georgia State University, Atlanta, Georgia, USA; Rudragouda Channappanavar, Departments of Acute and Tertiary Care and of Microbiology and Immunology, University of Tennessee Health Sciences Center, Memphis, Tennessee, USA.
    Christine Wohlford-Lenane
    Department of Pediatrics, Pappajohn Biomedical Institute, University of Iowa, Iowa City, Iowa, USA
    David K. Meyerholz
    Department of Pathology, University of Iowa, Iowa City, Iowa, USA
    Rudragouda Channappanavar
    Department of Microbiology and Immunology, University of Iowa, Iowa City, Iowa, USA
    Present address: Zhuo Li, Center for Inflammation, Immunity & Infection, Institute for Biomedical Sciences, Georgia State University, Atlanta, Georgia, USA; Rudragouda Channappanavar, Departments of Acute and Tertiary Care and of Microbiology and Immunology, University of Tennessee Health Sciences Center, Memphis, Tennessee, USA.
    Dong An
    Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia, USA
    Department of Pediatrics, Pappajohn Biomedical Institute, University of Iowa, Iowa City, Iowa, USA
    Department of Microbiology and Immunology, University of Iowa, Iowa City, Iowa, USA
    Paul B. McCray Jr.
    Department of Pediatrics, Pappajohn Biomedical Institute, University of Iowa, Iowa City, Iowa, USA
    Department of Microbiology and Immunology, University of Iowa, Iowa City, Iowa, USA
    Biao He
    Department of Infectious Diseases, College of Veterinary Medicine, University of Georgia, Athens, Georgia, USA

    Editor

    Kanta Subbarao
    Editor
    The Peter Doherty Institute for Infection and Immunity

    Notes

    Address correspondence to Paul B. McCray, Jr., [email protected], or Biao He, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Lipid-Specific Labeling of Enveloped Viruses with Quantum Dots for Single-Virus Tracking

    Lipid-Specific Labeling of Enveloped Viruses with Quantum Dots for Single-Virus Tracking

    ABSTRACT

    Quantum dots (QDs) possess optical properties of superbright fluorescence, excellent photostability, narrow emission spectra, and optional colors. Labeled with QDs, single molecules/viruses can be rapidly and continuously imaged for a long time, providing more detailed information than when labeled with other fluorophores. While they are widely used to label proteins in single-molecule-tracking studies, QDs have rarely been used to study virus infection, mainly due to a lack of accepted labeling strategies. Here, we report a general method to mildly and readily label enveloped viruses with QDs. Lipid-biotin conjugates were used to recognize and mark viral lipid membranes, and streptavidin-QD conjugates were used to light them up. Such a method allowed enveloped viruses to be labeled in 2 h with specificity and efficiency up to 99% and 98%, respectively. The intact morphology and the native infectivity of viruses were preserved. With the aid of this QD labeling method, we lit wild-type and mutant Japanese encephalitis viruses up, tracked their infection in living Vero cells, and found that H144A and Q258A substitutions in the envelope protein did not affect the virus intracellular trafficking. The lipid-specific QD labeling method described in this study provides a handy and practical tool to readily “see” the viruses and follow their infection, facilitating the widespread use of single-virus tracking and the uncovering of complex infection mechanisms.
    IMPORTANCE Virus infection in host cells is a complex process comprising a large number of dynamic molecular events. Single-virus tracking is a versatile technique to study these events. To perform this technique, viruses must be fluorescently labeled to be visible to fluorescence microscopes. The quantum dot is a kind of fluorescent tag that has many unique optical properties. It has been widely used to label proteins in single-molecule-tracking studies but rarely used to study virus infection, mainly due to the lack of an accepted labeling method. In this study, we developed a lipid-specific method to readily, mildly, specifically, and efficiently label enveloped viruses with quantum dots by recognizing viral envelope lipids with lipid-biotin conjugates and recognizing these lipid-biotin conjugates with streptavidin-quantum dot conjugates. It is not only applicable to normal viruses, but also competent to label the key protein-mutated viruses and the inactivated highly virulent viruses, providing a powerful tool for single-virus tracking.

    REFERENCES

    1.
    Liu SL, Wang ZG, Xie HY, Liu AA, Lamb DC, Pang DW. 2020. Single-virus tracking: from imaging methodologies to virological applications. Chem Rev 120:1936–1979.
    2.
    Hoornweg TE, van Duijl-Richter MKS, Nunez NVA, Albulescu IC, van Hemert MJ, Smit JM. 2016. Dynamics of chikungunya virus cell entry unraveled by single-virus tracking in living cells. J Virol 90:4745–4756.
    3.
    Liu AA, Zhang ZF, Sun EZ, Zheng ZH, Zhang ZL, Hu QX, Wang HZ, Pang DW. 2016. Simultaneous visualization of parental and progeny viruses by a capsid-specific HaloTag labeling strategy. ACS Nano 10:1147–1155.
    4.
    Suddala KC, Lee CC, Meraner P, Marin M, Markosyan RM, Desai TM, Cohen FS, Brass AL, Melikyan GB. 2019. Interferon-induced transmembrane protein 3 blocks fusion of sensitive but not resistant viruses by partitioning into virus-carrying endosomes. PLoS Pathog 15:e1007532.
    5.
    Ma Y, He Z, Tan T, Li W, Zhang Z, Song S, Zhang X, Hu Q, Zhou P, Wu Y, Zhang XE, Cui Z. 2016. Real-time imaging of single HIV-1 disassembly with multicolor viral particles. ACS Nano 10:6273–6282.
    6.
    Varela JA, Dupuis JP, Etchepare L, Espana A, Cognet L, Groc L. 2016. Targeting neurotransmitter receptors with nanoparticles in vivo allows single-molecule tracking in acute brain slices. Nat Commun 7:10947.
    7.
    Pinaud F, Clarke S, Sittner A, Dahan M. 2010. Probing cellular events, one quantum dot at a time. Nat Methods 7:275–285.
    8.
    Sun EZ, Liu AA, Zhang ZL, Liu SL, Tian ZQ, Pang DW. 2017. Real-time dissection of distinct dynamin-dependent endocytic routes of influenza A virus by quantum dot-based single-virus tracking. ACS Nano 11:4395–4406.
    9.
    Zhang LJ, Xia L, Xie HY, Zhang ZL, Pang DW. 2019. Quantum dot based biotracking and biodetection. Anal Chem 91:532–547.
    10.
    Popp M-L, Karssemeijer RA, Ploegh HL. 2012. Chemoenzymatic site-specific labeling of influenza glycoproteins as a tool to observe virus budding in real time. PLoS Pathog 8:e1002604.
    11.
    Gluska S, Zahavi EE, Chein M, Gradus T, Bauer A, Finke S, Perlson E. 2014. Rabies virus hijacks and accelerates the p75NTR retrograde axonal transport machinery. PLoS Pathog 10:e1004348.
    12.
    You C, Marquez-Lago TT, Richter CP, Wilmes S, Moraga I, Garcia KC, Leier A, Piehler J. 2016. Receptor dimer stabilization by hierarchical plasma membrane microcompartments regulates cytokine signaling. Sci Adv 2:e1600452.
    13.
    Katrukha EA, Mikhaylova M, van Brakel HX, Henegouwen P, Akhmanova A, Hoogenraad CC, Kapitein LC. 2017. Probing cytoskeletal modulation of passive and active intracellular dynamics using nanobody-functionalized quantum dots. Nat Commun 8:14772.
    14.
    Cantaut-Belarif Y, Antri M, Pizzarelli R, Colasse S, Vaccari I, Soares S, Renner M, Dallel R, Triller A, Bessis A. 2017. Microglia control the glycinergic but not the GABAergic synapses via prostaglandin E2 in the spinal cord. J Cell Biol 216:2979–2989.
    15.
    Lee S, Tan HY, Geneva II, Kruglov A, Calvert PD. 2018. Actin filaments partition primary cilia membranes into distinct fluid corrals. J Cell Biol 217:2831–2849.
    16.
    Ibarlucea-Benitez I, Ferro LS, Drubin DG, Barnes G. 2018. Kinesins relocalize the chromosomal passenger complex to the midzone for spindle disassembly. J Cell Biol 217:1687–1700.
    17.
    Olenick MA, Dominguez R, Holzbaur E. 2019. Dynein activator Hook1 is required for trafficking of BDNF-signaling endosomes in neurons. J Cell Biol 218:220–233.
    18.
    Joo KI, Fang Y, Liu Y, Xiao L, Gu Z, Tai A, Lee CL, Tang Y, Wang P. 2011. Enhanced real-time monitoring of adeno-associated virus trafficking by virus-quantum dot conjugates. ACS Nano 5:3523–3535.
    19.
    Hao J, Huang LL, Zhang R, Wang HZ, Xie HY. 2012. A mild and reliable method to label enveloped virus with quantum dots by copper-free click chemistry. Anal Chem 84:8364–8370.
    20.
    Hong ZY, Lv C, Liu AA, Liu SL, Sun EZ, Zhang ZL, Lei AW, Pang DW. 2015. Clicking hydrazine and aldehyde: the way to labeling of viruses with quantum dots. ACS Nano 9:11750–11760.
    21.
    Liu SL, Tian ZQ, Zhang ZL, Wu QM, Zhao HS, Ren B, Pang DW. 2012. High-efficiency dual labeling of influenza virus for single-virus imaging. Biomaterials 33:7828–7833.
    22.
    Wen L, Lin Y, Zhang ZL, Lu W, Lv C, Chen ZL, Wang HZ, Pang DW. 2016. Intracellular self-assembly based multi-labeling of key viral components: envelope, capsid and nucleic acids. Biomaterials 99:24–33.
    23.
    Li Q, Li W, Yin W, Guo J, Zhang ZP, Zeng D, Zhang X, Wu Y, Zhang XE, Cui Z. 2017. Single-particle tracking of human immunodeficiency virus type 1 productive entry into human primary macrophages. ACS Nano 11:3890–3903.
    24.
    Ke XL, Zhang Y, Zheng FL, Liu Y, Zheng ZH, Xu Y, Wang HZ. 2018. SpyCatcher-SpyTag mediated in situ labelling of progeny baculovirus with quantum dots for tracking viral infection in living cells. Chem Commun (Camb) 54:1189–1192.
    25.
    Joo KI, Lei Y, Lee CL, Lo J, Xie J, Hamm-Alvarez SF, Wang P. 2008. Site-specific labeling of enveloped viruses with quantum dots for single virus tracking. ACS Nano 2:1553–1562.
    26.
    Lv C, Lin Y, Liu AA, Hong ZY, Wen L, Zhang Z, Zhang ZL, Wang H, Pang DW. 2016. Labeling viral envelope lipids with quantum dots by harnessing the biotinylated lipid-self-inserted cellular membrane. Biomaterials 106:69–77.
    27.
    Zheng LL, Li CM, Zhen SJ, Li YF, Huang CZ. 2016. His-tag based in situ labelling of progeny viruses for real-time single virus tracking in living cells. Nanoscale 8:18635–18639.
    28.
    Zheng LL, Yang XX, Liu Y, Wan XY, Wu WB, Wang TT, Wang Q, Zhen SJ, Huang CZ. 2014. In situ labelling chemistry of respiratory syncytial viruses by employing the biotinylated host-cell membrane protein for tracking the early stage of virus entry. Chem Commun (Camb) 50:15776–15779.
    29.
    Hong ZY, Zhang ZL, Tang B, Ao J, Wang C, Yu C, Pang DW. 2018. Equipping inner central components of influenza A virus with quantum dots. Anal Chem 90:14020–14028.
    30.
    Zhang FX, Zheng ZH, Liu SL, Lu W, Zhang ZF, Zhang CL, Zhou P, Zhang Y, Long G, He ZK, Pang DW, Hu QX, Wang HZ. 2013. Self-biotinylation and site-specific double labeling of baculovirus using quantum dots for single-virus in-situ tracking. Biomaterials 34:7506–7518.
    31.
    Dixit SK, Goicochea NL, Daniel MC, Murali A, Bronstein L, De M, Stein B, Rotello VM, Kao CC, Dragnea B. 2006. Quantum dot encapsulation in viral capsids. Nano Lett 6:1993–1999.
    32.
    Cui ZQ, Ren Q, Wei HP, Chen Z, Deng JY, Zhang ZP, Zhang XE. 2011. Quantum dot-aptamer nanoprobes for recognizing and labeling influenza A virus particles. Nanoscale 3:2454–2457.
    33.
    Zhang Y, Ke X, Zheng Z, Zhang C, Zhang Z, Zhang F, Hu Q, He Z, Wang H. 2013. Encapsulating quantum dots into enveloped virus in living cells for tracking virus infection. ACS Nano 7:3896–3904.
    34.
    Zhao X, Shen Y, Adogla EA, Viswanath A, Tan R, Benicewicz BC, Greytak AB, Lin Y, Wang Q. 2016. Surface labeling of enveloped virus with polymeric imidazole ligand-capped quantum dots via the metabolic incorporation of phospholipids into host cells. J Mater Chem B 4:2421–2427.
    35.
    Bolte S, Cordelieres F. 2006. A guided tour into subcellular colocalization analysis in light microscopy. J Microsc 224:213–232.
    36.
    Zhang P, Liu S, Gao D, Hu D, Gong P, Sheng Z, Deng J, Ma Y, Cai L. 2012. Click-functionalized compact quantum dots protected by multidentate-imidazole ligands: conjugation-ready nanotags for living-virus labeling and imaging. J Am Chem Soc 134:8388–8391.
    37.
    Lakadamyali M, Rust MJ, Babcock HP, Zhuang X. 2003. Visualizing infection of individual influenza viruses. Proc Natl Acad Sci U S A 100:9280–9285.
    38.
    Liu HB, Liu Y, Liu SL, Pang DW, Xiao GF. 2011. Clathrin-mediated endocytosis in living host cells visualized through quantum dot labeling of infectious hematopoietic necrosis virus. J Virol 85:6252–6262.
    39.
    Liu HB, Liu Y, Wang SB, Zhang YJ, Zu XY, Zhou Z, Zhang B, Xiao GF. 2015. Structure-based mutational analysis of several sites in the E protein: implications for understanding the entry mechanism of Japanese encephalitis virus. J Virol 89:5668–5686.
    40.
    Zhu YZ, Xu QQ, Wu DG, Ren H, Zhao P, Lao WG, Wang Y, Tao QY, Qian XJ, Wei YH, Cao MM, Qi ZT. 2012. Japanese encephalitis virus enters rat neuroblastoma cells via a pH-dependent, dynamin and caveola-mediated endocytosis pathway. J Virol 86:13407–13422.
    41.
    Höök P, Vallee RB. 2006. The dynein family at a glance. J Cell Sci 119:4369–4371.
    42.
    Zhang LJ, Xia L, Liu SL, Sun EZ, Wu QM, Wen L, Zhang ZL, Pang DW. 2018. A “driver switchover” mechanism of influenza virus transport from microfilaments to microtubules. ACS Nano 12:474–484.
    43.
    Liu SL, Zhang LJ, Wang ZG, Zhang ZL, Wu QM, Sun EZ, Shi YB, Pang DW. 2014. Globally visualizing the microtubule-dependent transport behaviors of influenza virus in live cells. Anal Chem 86:3902–3908.
    44.
    Ruthardt N, Lamb DC, Bräuchle C. 2011. Single-particle tracking as a quantitative microscopy-based approach to unravel cell entry mechanisms of viruses and pharmaceutical nanoparticles. Mol Ther 19:1199–1211.
    45.
    Kural C, Serpinskaya AS, Chou YH, Goldman RD, Gelfand VI, Selvin PR. 2007. Tracking melanosomes inside a cell to study molecular motors and their interaction. Proc Natl Acad Sci U S A 104:5378–5382.
    46.
    Sakai T, Ohuchi M, Imai M, Mizuno T, Kawasaki K, Kuroda K, Yamashina S. 2006. Dual wavelength imaging allows analysis of membrane fusion of influenza virus inside cells. J Virol 80:2013–2018.
    47.
    Krzyzaniak MA, Zumstein MT, Gerez JA, Picotti P, Helenius A. 2013. Host cell entry of respiratory syncytial virus involves macropinocytosis followed by proteolytic activation of the F protein. PLoS Pathog 9:e1003309.
    48.
    Pelkmans L, Kartenbeck J, Helenius A. 2001. Caveolar endocytosis of simian virus 40 reveals a new two-step vesicular-transport pathway to the ER. Nat Cell Biol 3:473–483.
    49.
    van der Schaar HM, Rust MJ, Waarts B-L, van der Ende-Metselaar H, Kuhn RJ, Wilschut J, Zhuang X, Smit JM. 2007. Characterization of the early events in dengue virus cell entry by biochemical assays and single-virus tracking. J Virol 81:12019–12028.
    50.
    Liu SL, Zhang ZL, Tian ZQ, Zhao HS, Liu H, Sun EZ, Xiao GF, Zhang W, Wang HZ, Pang DW. 2012. Effectively and efficiently dissecting the infection of influenza virus by quantum-dot-based single-particle tracking. ACS Nano 6:141–150.
    51.
    Eisfeld AJ, Neumann G, Kawaoka Y. 2014. Influenza A virus isolation, culture and identification. Nat Protoc 9:2663–2681.
    52.
    Li Q, Lau A, Morris TJ, Guo L, Fordyce CB, Stanley EF. 2004. A syntaxin 1, Gαo, and N-type calcium channel complex at a presynaptic nerve terminal: analysis by quantitative immunocolocalization. J Neurosci 24:4070–4081.
    53.
    Khanna R, Li Q, Sun L, Collins TJ, Stanley EF. 2006. N type Ca2+ channels and rim scaffold protein covary at the presynaptic transmitter release face but are components of independent protein complexes. Neuroscience 140:1201–1208.
    54.
    Sbalzarini IF, Koumoutsakos P. 2005. Feature point tracking and trajectory analysis for video imaging in cell biology. J Struct Biol 151:182–195.
    55.
    Brandenburg B, Zhuang XW. 2007. Virus trafficking - learning from single-virus tracking. Nat Rev Microbiol 5:197–208.
    56.
    Levi V, Gratton E. 2007. Exploring dynamics in living cells by tracking single particles. Cell Biochem Biophys 48:1–15.
    57.
    Saxton MJ, Jacobson K. 1997. Single-particle tracking: applications to membrane dynamics. Annu Rev Biophys Biomol Struct 26:373–399.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 11Number 330 June 2020
    eLocator: e00135-20
    Editors: Thomas J. Hope
    Northwestern University, Feinberg School of Medicine
    and Stephen P. Goff
    Columbia University/ HHMI

    History

    Received: 27 January 2020
    Accepted: 21 April 2020
    Published online: 19 May 2020

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. enveloped virus
    2. lipid-specific labeling
    3. quantum dot
    4. single-virus tracking

    Contributors

    Authors

    Li-Juan Zhang
    College of Chemistry and Molecular Sciences, State Key Laboratory of Virology, The Institute for Advanced Studies, Wuhan Institute of Biotechnology, Wuhan University, Wuhan, People’s Republic of China
    Shaobo Wang
    Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, People’s Republic of China
    Li Xia
    College of Chemistry and Molecular Sciences, State Key Laboratory of Virology, The Institute for Advanced Studies, Wuhan Institute of Biotechnology, Wuhan University, Wuhan, People’s Republic of China
    Cheng Lv
    College of Chemistry and Molecular Sciences, State Key Laboratory of Virology, The Institute for Advanced Studies, Wuhan Institute of Biotechnology, Wuhan University, Wuhan, People’s Republic of China
    Hong-Wu Tang
    College of Chemistry and Molecular Sciences, State Key Laboratory of Virology, The Institute for Advanced Studies, Wuhan Institute of Biotechnology, Wuhan University, Wuhan, People’s Republic of China
    Zhenpu Liang
    College of Life Sciences, Henan Agricultural University, Zhengzhou, People’s Republic of China
    Gengfu Xiao
    Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan, People’s Republic of China
    College of Chemistry and Molecular Sciences, State Key Laboratory of Virology, The Institute for Advanced Studies, Wuhan Institute of Biotechnology, Wuhan University, Wuhan, People’s Republic of China
    State Key Laboratory of Medicinal Chemical Biology, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, Nankai University, Tianjin, People’s Republic of China

    Editors

    Thomas J. Hope
    Invited Editor
    Northwestern University, Feinberg School of Medicine
    Stephen P. Goff
    Editor
    Columbia University/ HHMI

    Notes

    Address correspondence to Gengfu Xiao, [email protected], or Dai-Wen Pang, [email protected].
    Li-Juan Zhang and Shaobo Wang contributed equally to this work. Author order was determined through consultation.

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Species-Specific Recognition of Sulfolobales Mediated by UV-Inducible Pili and S-Layer Glycosylation Patterns

    Species-Specific Recognition of Sulfolobales Mediated by UV-Inducible Pili and S-Layer Glycosylation Patterns

    ABSTRACT

    The UV-inducible pili system of Sulfolobales (Ups) mediates the formation of species-specific cellular aggregates. Within these aggregates, cells exchange DNA to repair DNA double-strand breaks via homologous recombination. Substitution of the Sulfolobus acidocaldarius pilin subunits UpsA and UpsB with their homologs from Sulfolobus tokodaii showed that these subunits facilitate species-specific aggregation. A region of low conservation within the UpsA homologs is primarily important for this specificity. Aggregation assays in the presence of different sugars showed the importance of N-glycosylation in the recognition process. In addition, the N-glycan decorating the S-layer of S. tokodaii is different from the one of S. acidocaldarius. Therefore, each Sulfolobus species seems to have developed a unique UpsA binding pocket and unique N-glycan composition to ensure aggregation and, consequently, also DNA exchange with cells from only the same species, which is essential for DNA repair by homologous recombination.
    IMPORTANCE Type IV pili can be found on the cell surface of many archaea and bacteria where they play important roles in different processes. The UV-inducible pili system of Sulfolobales (Ups) pili from the crenarchaeal Sulfolobales species are essential in establishing species-specific mating partners, thereby assisting in genome stability. With this work, we show that different Sulfolobus species have specific regions in their Ups pili subunits, which allow them to interact only with cells from the same species. Additionally, different Sulfolobus species have unique surface-layer N-glycosylation patterns. We propose that the unique features of each species allow the recognition of specific mating partners. This knowledge for the first time gives insights into the molecular basis of archaeal self-recognition.

    REFERENCES

    1.
    Craig L, Forest KT, Maier B. 2019. Type IV pili: dynamics, biophysics and functional consequences. Nat Rev Microbiol 17:429–440.
    2.
    Giltner CL, Nguyen Y, Burrows LL. 2012. Type IV pilin proteins: versatile molecular modules. Microbiol Mol Biol Rev 76:740–772.
    3.
    Maier B, Wong GCL. 2015. How bacteria use type IV pili machinery on surfaces. Trends Microbiol 23:775–788.
    4.
    Denise R, Abby SS, Rocha EPC. 2019. Diversification of the type IV filament superfamily into machines for adhesion, protein secretion, DNA uptake, and motility. PLoS Biol 17:e3000390.
    5.
    Coureuil M, Join-Lambert O, Lécuyer H, Bourdoulous S, Marullo S, Nassif X. 2012. Mechanism of meningeal invasion by Neisseria meningitidis. Virulence 3:164–172.
    6.
    Bernard SC, Simpson N, Join-Lambert O, Federici C, Laran-Chich M-P, Maïssa N, Bouzinba-Ségard H, Morand PC, Chretien F, Taouji S, Chevet E, Janel S, Lafont F, Coureuil M, Segura A, Niedergang F, Marullo S, Couraud P-O, Nassif X, Bourdoulous S. 2014. Pathogenic Neisseria meningitidis utilizes CD147 for vascular colonization. Nat Med 20:725–731.
    7.
    Kolappan S, Coureuil M, Yu X, Nassif X, Egelman EH, Craig L. 2016. Structure of the Neisseria meningitidis yype IV pilus. Nat Commun 7:13015.
    8.
    Hung M-C, Christodoulides M. 2013. The biology of Neisseria adhesins. Biology (Basel) 2:1054–1109.
    9.
    Scheuerpflug I, Rudel T, Ryll R, Pandit J, Meyer TF. 1999. Roles of PilC and PilE proteins in pilus-mediated adherence of Neisseria gonorrhoeae and Neisseria meningitidis to human erythrocytes and endothelial and epithelial cells. Infect Immun 67:834–843.
    10.
    Winther-Larsen HC, Hegge FT, Wolfgang M, Hayes SF, van Putten JPM, Koomey M. 2001. Neisseria gonorrhoeae PilV, a type IV pilus-associated protein essential to human epithelial cell adherence. Proc Natl Acad Sci U S A 98:15276–15281.
    11.
    Hu W, Yang Z, Lux R, Zhao M, Wang J, He X, Shi W. 2012. Direct visualization of the interaction between pilin and exopolysaccharides of Myxococcus xanthus with eGFP-fused PilA protein. FEMS Microbiol Lett 326:23–30.
    12.
    Li Y, Sun H, Ma X, Lu A, Lux R, Zusman D, Shi W. 2003. Extracellular polysaccharides mediate pilus retraction during social motility of Myxococcus xanthus. Proc Natl Acad Sci U S A 100:5443–5448.
    13.
    Adams DW, Stutzmann S, Stoudmann C, Blokesch M. 2019. DNA-uptake pili of Vibrio cholerae are required for chitin colonization and capable of kin recognition via sequence-specific self-interaction. Nat Microbiol 4:1545–1557.
    14.
    Carter MQ, Chen J, Lory S. 2010. The Pseudomonas aeruginosa pathogenicity island PAPI-1 is transferred via a novel type IV pilus. J Bacteriol 192:3249–3258.
    15.
    Hong TP, Carter MQ, Struffi P, Casonato S, Hao Y, Lam JS, Lory S, Jousson O. 2017. Conjugative type IVb pilus recognizes lipopolysaccharide of recipient cells to initiate PAPI-1 pathogenicity island transfer in Pseudomonas aeruginosa. BMC Microbiol 17:31.
    16.
    Albers S-V, Pohlschröder M. 2009. Diversity of archaeal type IV pilin-like structures. Extremophiles 13:403–410.
    17.
    Ng SYM, Zolghadr B, Driessen AJM, Albers S-V, Jarrell KF. 2008. Cell surface structures of archaea. J Bacteriol 190:6039–6047.
    18.
    Szabó Z, Stahl AO, Albers SV, Kissinger JC, Driessen AJ, Pohlschroder M. 2007. Identification of diverse archaeal proteins with class III signal peptides cleaved by distinct archaeal prepilin peptidases. J Bacteriol 189:772–778.
    19.
    Makarova KS, Koonin EV, Albers S-V. 2016. Diversity and evolution of type IV pili systems in Archaea. Front Microbiol 7:667.
    20.
    Chaudhury P, Quax TEF, Albers S-V. 2018. Versatile cell surface structures of archaea. Mol Microbiol 107:298–311.
    21.
    Jarrell KF, Albers S-V. 2012. The archaellum: an old motility structure with a new name. Trends Microbiol 20:307–312.
    22.
    Albers S-V, Jarrell KF. 2018. The archaellum: an update on the unique archaeal motility structure. Trends Microbiol 26:351–362.
    23.
    Tripepi M, Imam S, Pohlschröder M. 2010. Haloferax volcanii flagella are required for motility but are not involved in PibD-dependent surface adhesion. J Bacteriol 192:3093–3102.
    24.
    Esquivel R, Xu R, Pohlschroder M. 2013. Novel, archaeal adhesion pilins with a conserved N-terminus. J Bacteriol 195:3808–3818.
    25.
    Henche A-L, Ghosh A, Yu X, Jeske T, Egelman E, Albers S-V. 2012. Structure and function of the adhesive type IV pilus of Sulfolobus acidocaldarius. Environ Microbiol 14:3188–3202.
    26.
    Zolghadr B, Klingl A, Koerdt A, Driessen AJM, Rachel R, Albers S-V. 2010. Appendage-mediated surface adherence of Sulfolobus solfataricus. J Bacteriol 192:104–110.
    27.
    Bardy SL, Eichler J, Jarrell KF. 2003. Archaeal signal peptides—a comparative survey at the genome level. Protein Sci 12:1833–1843.
    28.
    Jarrell KF, Stark M, Nair DB, Chong JPJ. 2011. Flagella and pili are both necessary for efficient attachment of Methanococcus maripaludis to surfaces. FEMS Microbiol Lett 319:44–50.
    29.
    Fröls S, Gordon PMK, Panlilio MA, Duggin IG, Bell SD, Sensen CW, Schleper C. 2007. Response of the hyperthermophilic archaeon Sulfolobus solfataricus to UV damage. J Bacteriol 189:8708–8718.
    30.
    Fröls S, Ajon M, Wagner M, Teichmann D, Zolghadr B, Folea M, Boekema EJ, Driessen AJM, Schleper C, Albers S-V. 2008. UV-inducible cellular aggregation of the hyperthermophilic archaeon Sulfolobus solfataricus is mediated by pili formation. Mol Microbiol 70:938–952.
    31.
    Götz D, Paytubi S, Munro S, Lundgren M, Bernander R, White MF. 2007. Responses of hyperthermophilic crenarchaea to UV irradiation. Genome Biol 8:R220.
    32.
    van Wolferen M, Ajon M, Driessen AJM, Albers S-V. 2013. Molecular analysis of the UV-inducible pili operon from Sulfolobus acidocaldarius. Microbiologyopen 2:928–937.
    33.
    Ajon M, Fröls S, van Wolferen M, Stoecker K, Teichmann D, Driessen AJM, Grogan DW, Albers S-V, Schleper C. 2011. UV-inducible DNA exchange in hyperthermophilic archaea mediated by type IV pili. Mol Microbiol 82:807–817.
    34.
    Allers T. 2011. Swapping genes to survive—a new role for archaeal type IV pili. Mol Microbiol 82:789–791.
    35.
    Wagner A, Whitaker RJ, Krause DJ, Heilers J-H, Van Wolferen M, van der Does C, Albers S-V. 2017. Mechanisms of gene flow in archaea. Nat Rev Microbiol 15:492–501.
    36.
    van Wolferen M, Wagner A, van der Does C, Albers S-V. 2016. The archaeal Ced system imports DNA. Proc Natl Acad Sci U S A 113:2496–2501.
    37.
    Wagner M, van Wolferen M, Wagner A, Lassak K, Meyer BH, Reimann J, Albers S-V. 2012. Versatile genetic tool box for the crenarchaeote Sulfolobus acidocaldarius. Front Microbiol 3:214.
    38.
    Albers S-V, Meyer BH. 2011. The archaeal cell envelope. Nat Rev Microbiol 9:414–426.
    39.
    Peyfoon E, Meyer B, Hitchen PG, Panico M, Morris HR, Haslam SM, Albers SV, Dell A. 2010. The S-layer glycoprotein of the crenarchaeote Sulfolobus acidocaldarius is glycosylated at multiple sites with chitobiose-linked N-glycans. Archaea 2010:1–10.
    40.
    Hartman R, Eilers BJ, Bollschweiler D, Munson-McGee JH, Engelhardt H, Young MJ, Lawrence CM. 2019. The molecular mechanism of cellular attachment for an archaeal virus. Structure 27:1634–1646.e3.
    41.
    Shajahan A, Heiss C, Ishihara M, Azadi P. 2017. Glycomic and glycoproteomic analysis of glycoproteins—a tutorial. Anal Bioanal Chem 409:4483–4505.
    42.
    Palmieri G, Balestrieri M, Peter-Katalinić J, Pohlentz G, Rossi M, Fiume I, Pocsfalvi G. 2013. Surface-exposed glycoproteins of hyperthermophilic Sulfolobus solfataricus P2 Show a common N-glycosylation profile. J Proteome Res 12:2779–2790.
    43.
    Nizet V, Varki A, Aebi M. 2015. Microbial lectins: hemagglutinins, adhesins, and toxins. In Essentials of glycobiology. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY.
    44.
    Tytgat HLP, de Vos WM. 2016. Sugar coating the envelope: glycoconjugates for microbe-host crosstalk. Trends Microbiol 24:853–861.
    45.
    National Research Council. 2012. Transforming glycoscience: a roadmap for the future. National Academies Press, Washington DC.
    46.
    Soares EV. 2011. Flocculation in Saccharomyces cerevisiae: a review. J Appl Microbiol 110:1–18.
    47.
    Masy CL, Henquinet A, Mestdagh MM. 1992. Flocculation of Saccharomyces cerevisiae: inhibition by sugars. Can J Microbiol 38:1298–1306.
    48.
    Varki A, Lowe JB. 2009. Biological roles of glycans. In Essentials of glycobiology, 2nd edition. Cold Spring Harbor Press, Cold Spring Harbor, NY.
    49.
    Shalev Y, Turgeman-Grott I, Tamir A, Eichler J, Gophna U. 2017. Cell surface glycosylation is required for efficient mating of Haloferax volcanii. Front Microbiol 8:1253.
    50.
    Kaminski L, Naparstek S, Kandiba L, Cohen-Rosenzweig C, Arbiv A, Konrad Z, Eichler J. 2013. Add salt, add sugar: N-glycosylation in Haloferax volcanii. Biochem Soc Trans 41:432–435.
    51.
    Naor A, Lapierre P, Mevarech M, Papke RT, Gophna U. 2012. Low species barriers in halophilic archaea and the formation of recombinant hybrids. Curr Biol 22:1444–1448.
    52.
    Guan Z, Naparstek S, Calo D, Eichler J. 2012. Protein glycosylation as an adaptive response in Archaea: growth at different salt concentrations leads to alterations in Haloferax volcanii S-layer glycoprotein N-glycosylation. Environ Microbiol 14:743–753.
    53.
    Cadillo-Quiroz H, Didelot X, Held NL, Herrera A, Darling A, Reno ML, Krause DJ, Whitaker RJ. 2012. Patterns of gene flow define species of thermophilic archaea. PLoS Biol 10:e1001265.
    54.
    Makarova K, Wolf Y, Koonin E. 2015. Archaeal Clusters of Orthologous Genes (arCOGs): an update and application for analysis of shared features between Thermococcales, Methanococcales, and Methanobacteriales. Life (Basel) 5:818–840.
    55.
    Ellison CK, Kan J, Dillard RS, Kysela DT, Ducret A, Berne C, Hampton CM, Ke Z, Wright ER, Biais N, Dalia AB, Brun YV. 2017. Obstruction of pilus retraction stimulates bacterial surface sensing. Science 358:535–538.
    56.
    Ng D, Harn T, Altindal T, Kolappan S, Marles JM, Lala R, Spielman I, Gao Y, Hauke CA, Kovacikova G, Verjee Z, Taylor RK, Biais N, Craig L. 2016. The Vibrio cholerae minor pilin TcpB initiates assembly and retraction of the toxin-coregulated pilus. PLoS Pathog 12:e1006109.
    57.
    Zöllner R, Cronenberg T, Maier B. 2019. Motor properties of PilT-independent type 4 pilus retraction in gonococci. J Bacteriol 201:e00778-18.
    58.
    Ellison CK, Dalia TN, Vidal Ceballos A, Wang J-Y, Biais N, Brun YV, Dalia AB. 2018. Retraction of DNA-bound type IV competence pili initiates DNA uptake during natural transformation in Vibrio cholerae. Nat Microbiol 3:773–780.
    59.
    Clausen M, Jakovljevic V, Sogaard-Andersen L, Maier B. 2009. High-force generation is a conserved property of type IV pilus systems. J Bacteriol 191:4633–4638.
    60.
    Brock TD, Brock KM, Belly RT, Weiss RL. 1972. Sulfolobus: a new genus of sulfur-oxidizing bacteria living at low pH and high temperature. Arch Mikrobiol 84:54–68.
    61.
    Kurosawa N, Grogan DW. 2005. Homologous recombination of exogenous DNA with the Sulfolobus acidocaldarius genome: properties and uses. FEMS Microbiol Lett 253:141–149.
    62.
    Suzuki T, Iwasaki T, Uzawa T, Hara K, Nemoto N, Kon T, Ueki T, Yamagishi A, Oshima T. 2002. Sulfolobus tokodaii sp. nov. (f. Sulfolobus sp. strain 7), a new member of the genus Sulfolobus isolated from Beppu Hot Springs, Japan. Extremophiles 6:39–44.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 11Number 228 April 2020
    eLocator: e03014-19
    Editor: Christa M. Schleper
    University of Vienna

    History

    Received: 14 November 2019
    Accepted: 21 January 2020
    Published online: 10 March 2020

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. type IV pili
    2. archaea
    3. Sulfolobus
    4. DNA exchange
    5. glycosylation
    6. species-specific recognition

    Contributors

    Authors

    Marleen van Wolferen
    Molecular Biology of Archaea, Institute of Biology II—Microbiology, University of Freiburg, Freiburg, Germany
    Asif Shajahan
    Complex Carbohydrate Research Center, The University of Georgia, Athens, Georgia, USA
    Kristina Heinrich
    Molecular Biology of Archaea, Institute of Biology II—Microbiology, University of Freiburg, Freiburg, Germany
    Susanne Brenzinger
    Institute of Biology, Leiden University, Leiden, The Netherlands
    Ian M. Black
    Complex Carbohydrate Research Center, The University of Georgia, Athens, Georgia, USA
    Alexander Wagner
    Molecular Biology of Archaea, Institute of Biology II—Microbiology, University of Freiburg, Freiburg, Germany
    Present address: Alexander Wagner, Biozentrum, University of Basel, Basel, Switzerland.
    Ariane Briegel
    Institute of Biology, Leiden University, Leiden, The Netherlands
    Parastoo Azadi
    Complex Carbohydrate Research Center, The University of Georgia, Athens, Georgia, USA
    Molecular Biology of Archaea, Institute of Biology II—Microbiology, University of Freiburg, Freiburg, Germany
    BIOSS Centre for Biological Signaling Studies, University of Freiburg, Freiburg, Germany

    Editor

    Christa M. Schleper
    Editor
    University of Vienna

    Notes

    Address correspondence to Sonja-Verena Albers, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Increased Production of Outer Membrane Vesicles by Salmonella Interferes with Complement-Mediated Innate Immune Attack

    ABSTRACT

    Bacterial outer membrane vesicles (OMVs) enriched with bioactive proteins, toxins, and virulence factors play a critical role in host-pathogen and microbial interactions. The two-component system PhoP-PhoQ (PhoPQ) of Salmonella enterica orchestrates the remodeling of outer membrane lipopolysaccharide (LPS) molecules and concomitantly upregulates OMV production. In this study, we document a novel use of nanoparticle tracking analysis to determine bacterial OMV size and number. Among the PhoPQ-activated genes tested, pagC expression had the most significant effect on the upregulation of OMV production. We provide the first evidence that PhoPQ-mediated upregulation of OMV production contributes to bacterial survival by interfering with complement activation. OMVs protected bacteria in a dose-dependent manner, and bacteria were highly susceptible to complement-mediated killing in their absence. OMVs from bacteria expressing PagC bound to complement component C3b in a dose-dependent manner and inactivated it by recruiting complement inhibitor Factor H. As we also found that Factor H binds to PagC, we propose that PagC interferes with complement-mediated killing of Salmonella in the following two steps: first by engaging Factor H, and second, through the production of PagC-enriched OMVs that divert and inactivate the complement away from the bacteria. Since PhoPQ activation occurs intracellularly, the resultant increase in PagC expression and OMV production is suggested to contribute to the local and systemic spread of Salmonella released from dying host cells that supports the infection of new cells.
    IMPORTANCE Bacterial outer membrane vesicles (OMVs) mediate critical bacterium-bacterium and host-microbial interactions that influence pathogenesis through multiple mechanisms, including the elicitation of inflammatory responses, delivery of virulence factors, and enhancement of biofilm formation. As such, there is a growing interest in understanding the underlying mechanisms of OMV production. Recent studies have revealed that OMV biogenesis is a finely tuned physiological process that requires structural organization and selective sorting of outer membrane components into the vesicles. In Salmonella, outer membrane remodeling and OMV production are tightly regulated by its PhoPQ system. In this study, we demonstrate that PhoPQ-regulated OMV production plays a significant role in defense against host innate immune attack. PhoPQ-activated PagC expression recruits the complement inhibitor Factor H and degrades the active C3 component of complement. Our results provide valuable insight into the combination of tools and environmental signals that Salmonella employs to evade complement-mediated lysis, thereby suggesting a strong evolutionary adaptation of this facultative intracellular pathogen to protect itself during its extracellular stage in the host.

    REFERENCES

    1.
    Geddes K, Cruz F, Heffron F. 2007. Analysis of cells targeted by Salmonella type III secretion in vivo. PLoS Pathog 3:e196.
    2.
    Richter-Dahlfors A, Buchan AM, Finlay BB. 1997. Murine salmonellosis studied by confocal microscopy: Salmonella Typhimurium resides intracellularly inside macrophages and exerts a cytotoxic effect on phagocytes in vivo. J Exp Med 186:569–580.
    3.
    Groisman EA. 2001. The pleiotropic two-component regulatory system PhoP-PhoQ. J Bacteriol 183:1835–1842.
    4.
    Garcia VE, Soncini FC, Groisman EA. 1996. Mg2+ as an extracellular signal: environmental regulation of Salmonella virulence. Cell 84:165–174.
    5.
    Bader MW, Sanowar S, Daley ME, Schneider AR, Cho U, Xu W, Klevit RE, Le Moual H, Miller SI. 2005. Recognition of antimicrobial peptides by a bacterial sensor kinase. Cell 122:461–472.
    6.
    Prost LR, Daley ME, Le Sage V, Bader MW, Le Moual H, Klevit RE, Miller SI. 2007. Activation of the bacterial sensor kinase PhoQ by acidic pH. Mol Cell 26:165–174.
    7.
    Bonnington KE, Kuehn MJ. 2016. Outer membrane vesicle production facilitates LPS remodeling and outer membrane maintenance in Salmonella during environmental transitions. mBio 7:e01532-16.
    8.
    Bonnington KE, Kuehn MJ. 2017. Breaking the bilayer: OMV formation during environmental transitions. Microb Cell 4:64–66.
    9.
    Elhenawy W, Bording-Jorgensen M, Valguarnera E, Haurat MF, Wine E, Feldman MF. 2016. LPS remodeling triggers formation of outer membrane vesicles in Salmonella. mBio 7:e00940-16.
    10.
    Kulp A, Kuehn MJ. 2010. Biological functions and biogenesis of secreted bacterial outer membrane vesicles. Annu Rev Microbiol 64:163–184.
    11.
    Jan AT. 2017. Outer membrane vesicles (OMVs) of Gram-negative bacteria: a perspective update. Front Microbiol 8:1053.
    12.
    Kulkarni HM, Jagannadham MV. 2014. Biogenesis and multifaceted roles of outer membrane vesicles from Gram-negative bacteria. Microbiology 160:2109–2121.
    13.
    Ellis TN, Kuehn MJ. 2010. Virulence and immunomodulatory roles of bacterial outer membrane vesicles. Microbiol Mol Biol Rev 74:81–94.
    14.
    Haurat MF, Elhenawy W, Feldman MF. 2015. Prokaryotic membrane vesicles: new insights on biogenesis and biological roles. Biol Chem 396:95–109.
    15.
    Schwechheimer C, Kuehn MJ. 2015. Outer-membrane vesicles from Gram-negative bacteria: biogenesis and functions. Nat Rev Microbiol 13:605–619.
    16.
    Pathirana RD, Kaparakis-Liaskos M. 2016. Bacterial membrane vesicles: biogenesis, immune regulation and pathogenesis. Cell Microbiol 18:1518–1524.
    17.
    Kitagawa R, Takaya A, Ohya M, Mizunoe Y, Takade A, Yoshida S, Isogai E, Yamamoto T. 2010. Biogenesis of Salmonella enterica serovar Typhimurium membrane vesicles provoked by induction of PagC. J Bacteriol 192:5645–5656.
    18.
    Singh C, Lee H, Tian Y, Schesser Bartra S, Hower S, Fujimoto LM, Yao Y, Ivanov SA, Shaikhutdinova RZ, Anisimov AP, Plano GV, Im W, Marassi FM. 2020. Mutually constructive roles of Ail and LPS in Yersinia pestis serum survival. Mol Microbiol 114:510–520.
    19.
    Dutta SK, Yao Y, Marassi FM. 2017. Structural insights into the Yersinia pestis outer membrane protein Ail in lipid bilayers. J Phys Chem B 121:7561–7570.
    20.
    Heffernan EJ, Harwood J, Fierer J, Guiney D. 1992. The Salmonella typhimurium virulence plasmid complement resistance gene rck is homologous to a family of virulence-related outer membrane protein genes, including pagC and ail. J Bacteriol 174:84–91.
    21.
    Rosselin M, Virlogeux-Payant I, Roy C, Bottreau E, Sizaret PY, Mijouin L, Germon P, Caron E, Velge P, Wiedemann A. 2010. Rck of Salmonella enterica, subspecies enterica serovar Enteritidis, mediates zipper-like internalization. Cell Res 20:647–664.
    22.
    Pulkkinen WS, Miller SI. 1991. A Salmonella Typhimurium virulence protein is similar to a Yersinia enterocolitica invasion protein and a bacteriophage lambda outer membrane protein. J Bacteriol 173:86–93.
    23.
    Bartra SS, Styer KL, O'Bryant DM, Nilles ML, Hinnebusch BJ, Aballay A, Plano GV. 2008. Resistance of Yersinia pestis to complement-dependent killing is mediated by the Ail outer membrane protein. Infect Immun 76:612–622.
    24.
    Mecsas J, Welch R, Erickson JW, Gross CA. 1995. Identification and characterization of an outer membrane protein, OmpX, in Escherichia coli that is homologous to a family of outer membrane proteins including Ail of Yersinia enterocolitica. J Bacteriol 177:799–804.
    25.
    Li B, Huang Q, Cui A, Liu X, Hou B, Zhang L, Liu M, Meng X, Li S. 2018. Overexpression of outer membrane protein X (OmpX) compensates for the effect of TolC inactivation on biofilm formation and curli production in extraintestinal pathogenic Escherichia coli (ExPEC). Front Cell Infect Microbiol 8:208.
    26.
    Vica PS, Garcia GO, Paniagua CG. 1997. The lom gene of bacteriophage lambda is involved in Escherichia coli K12 adhesion to human buccal epithelial cells. FEMS Microbiol Lett 156:129–132.
    27.
    Lu J, Li L, Pan F, Zuo G, Yu D, Liu R, Fan H, Ma Z. 2020. PagC is involved in salmonella pullorum OMVs production and affects biofilm production. Vet Microbiol 247:108778.
    28.
    Heffernan EJ, Reed S, Hackett J, Fierer J, Roudier C, Guiney D. 1992. Mechanism of resistance to complement-mediated killing of bacteria encoded by the Salmonella Typhimurium virulence plasmid gene rck. J Clin Invest 90:953–964.
    29.
    Ho DK, Jarva H, Meri S. 2010. Human complement factor H binds to outer membrane protein Rck of Salmonella. J Immunol 185:1763–1769.
    30.
    Ho DK, Skurnik M, Blom AM, Meri S. 2014. Yersinia pestis Ail recruitment of C4b-binding protein leads to factor I-mediated inactivation of covalently and noncovalently bound C4b. Eur J Immunol 44:742–751.
    31.
    Ho DK, Tissari J, Jarvinen HM, Blom AM, Meri S, Jarva H. 2011. Functional recruitment of human complement inhibitor C4B-binding protein to outer membrane protein Rck of Salmonella. PLoS One 6:e27546.
    32.
    Bartra SS, Ding Y, Miya Fujimoto L, Ring JG, Jain V, Ram S, Marassi FM, Plano GV. 2015. Yersinia pestis uses the Ail outer membrane protein to recruit vitronectin. Microbiology 161:2174–2183.
    33.
    Heffernan EJ, Wu L, Louie J, Okamoto S, Fierer J, Guiney DG. 1994. Specificity of the complement resistance and cell association phenotypes encoded by the outer membrane protein genes rck from Salmonella typhimurium and ail from Yersinia enterocolitica. Infect Immun 62:5183–5186.
    34.
    Nishio M, Okada N, Miki T, Haneda T, Danbara H. 2005. Identification of the outer-membrane protein PagC required for the serum resistance phenotype in Salmonella enterica serovar Choleraesuis. Microbiology 151:863–873.
    35.
    Ramu P, Tanskanen R, Holmberg M, Lahteenmaki K, Korhonen TK, Meri S. 2007. The surface protease PgtE of Salmonella enterica affects complement activity by proteolytically cleaving C3b, C4b and C5. FEBS Lett 581:1716–1720.
    36.
    Riva R, Korhonen TK, Meri S. 2015. The outer membrane protease PgtE of Salmonella enterica interferes with the alternative complement pathway by cleaving factors B and H. Front Microbiol 6:63.
    37.
    Ricklin D, Hajishengallis G, Yang K, Lambris JD. 2010. Complement: a key system for immune surveillance and homeostasis. Nat Immunol 11:785–797.
    38.
    Parente R, Clark SJ, Inforzato A, Day AJ. 2017. Complement factor H in host defense and immune evasion. Cell Mol Life Sci 74:1605–1624.
    39.
    Ueda Y, Mohammed I, Song D, Gullipalli D, Zhou L, Sato S, Wang Y, Gupta S, Cheng Z, Wang H, Bao J, Mao Y, Brass L, Zheng XL, Miwa T, Palmer M, Dunaief J, Song WC. 2017. Murine systemic thrombophilia and hemolytic uremic syndrome from a factor H point mutation. Blood 129:1184–1196.
    40.
    Lambris JD, Ricklin D, Geisbrecht BV. 2008. Complement evasion by human pathogens. Nat Rev Microbiol 6:132–142.
    41.
    Bishop RE, Gibbons HS, Guina T, Trent MS, Miller SI, Raetz CR. 2000. Transfer of palmitate from phospholipids to lipid A in outer membranes of gram-negative bacteria. EMBO J 19:5071–5080.
    42.
    Trent MS, Pabich W, Raetz CR, Miller SI. 2001. A PhoP/PhoQ-induced Lipase (PagL) that catalyzes 3-O-deacylation of lipid A precursors in membranes of Salmonella Typhimurium. J Biol Chem 276:9083–9092.
    43.
    Kawasaki K, China K, Nishijima M. 2007. Release of the lipopolysaccharide deacylase PagL from latency compensates for a lack of lipopolysaccharide aminoarabinose modification-dependent resistance to the antimicrobial peptide polymyxin B in Salmonella enterica. J Bacteriol 189:4911–4919.
    44.
    Park SY, Groisman EA. 2014. Signal-specific temporal response by the Salmonella PhoP/PhoQ regulatory system. Mol Microbiol 91:135–144.
    45.
    Bai J, Kim SI, Ryu S, Yoon H. 2014. Identification and characterization of outer membrane vesicle-associated proteins in Salmonella enterica serovar Typhimurium. Infect Immun 82:4001–4010.
    46.
    Schweder T, Lee KH, Lomovskaya O, Matin A. 1996. Regulation of Escherichia coli starvation sigma factor (sigma s) by ClpXP protease. J Bacteriol 178:470–476.
    47.
    Gottesman S. 2003. Proteolysis in bacterial regulatory circuits. Annu Rev Cell Dev Biol 19:565–587.
    48.
    Maurizi MR. 1992. Proteases and protein degradation in Escherichia coli. Experientia 48:178–201.
    49.
    Tu X, Latifi T, Bougdour A, Gottesman S, Groisman EA. 2006. The PhoP/PhoQ two-component system stabilizes the alternative sigma factor RpoS in Salmonella enterica. Proc Natl Acad Sci U S A 103:13503–13508.
    50.
    Klein G, Raina S. 2017. Small regulatory bacterial RNAs regulating the envelope stress response. Biochem Soc Trans 45:417–425.
    51.
    Colgan AM, Kroger C, Diard M, Hardt WD, Puente JL, Sivasankaran SK, Hokamp K, Hinton JC. 2016. The impact of 18 ancestral and horizontally-acquired regulatory proteins upon the transcriptome and sRNA landscape of Salmonella enterica serovar Typhimurium. PLoS Genet 12:e1006258.
    52.
    Yamamoto T, Sashinami H, Takaya A, Tomoyasu T, Matsui H, Kikuchi Y, Hanawa T, Kamiya S, Nakane A. 2001. Disruption of the genes for ClpXP protease in Salmonella enterica serovar Typhimurium results in persistent infection in mice, and development of persistence requires endogenous gamma interferon and tumor necrosis factor alpha. Infect Immun 69:3164–3174.
    53.
    Aung KM, Sjostrom AE, von Pawel-Rammingen U, Riesbeck K, Uhlin BE, Wai SN. 2016. Naturally occurring IgG antibodies provide innate protection against Vibrio cholerae bacteremia by recognition of the outer membrane protein U. J Innate Immun 8:269–283.
    54.
    Tan TT, Morgelin M, Forsgren A, Riesbeck K. 2007. Haemophilus influenzae survival during complement-mediated attacks is promoted by Moraxella catarrhalis outer membrane vesicles. J Infect Dis 195:1661–1670.
    55.
    Pramoonjago P, Kaneko M, Kinoshita T, Ohtsubo E, Takeda J, Hong KS, Inagi R, Inoue K. 1992. Role of TraT protein, an anticomplementary protein produced in Escherichia coli by R100 factor, in serum resistance. J Immunol 148:827–836.
    56.
    Grossman N, Leive L. 1984. Complement activation via the alternative pathway by purified Salmonella lipopolysaccharide is affected by its structure but not its O-antigen length. J Immunol 132:376–385.
    57.
    Murray GL, Attridge SR, Morona R. 2006. Altering the length of the lipopolysaccharide O antigen has an impact on the interaction of Salmonella enterica serovar Typhimurium with macrophages and complement. J Bacteriol 188:2735–2739.
    58.
    Grossman N, Schmetz MA, Foulds J, Klima EN, Jimenez-Lucho VE, Leive LL, Joiner KA, Jiminez V. 1987. Lipopolysaccharide size and distribution determine serum resistance in Salmonella montevideo. J Bacteriol 169:856–863.
    59.
    Geiser P, Di Martino ML, Samperio Ventayol P, Eriksson J, Sima E, Al-Saffar AK, Ahl D, Phillipson M, Webb DL, Sundbom M, Hellstrom PM, Sellin ME. 2021. Salmonella enterica serovar Typhimurium exploits cycling through epithelial cells to colonize human and murine Enteroids. mBio 12:e02684-20.
    60.
    Tagkopoulos I, Liu YC, Tavazoie S. 2008. Predictive behavior within microbial genetic networks. Science 320:1313–1317.
    61.
    Bordi C, Theraulaz L, Mejean V, Jourlin-Castelli C. 2003. Anticipating an alkaline stress through the Tor phosphorelay system in Escherichia coli. Mol Microbiol 48:211–223.
    62.
    Ahmer BM, van Reeuwijk J, Timmers CD, Valentine PJ, Heffron F. 1998. Salmonella typhimurium encodes an SdiA homolog, a putative quorum sensor of the LuxR family, that regulates genes on the virulence plasmid. J Bacteriol 180:1185–1193.
    63.
    Miller SI, Kukral AM, Mekalanos JJ. 1989. A two-component regulatory system (phoP phoQ) controls Salmonella typhimurium virulence. Proc Natl Acad Sci U S A 86:5054–5058.
    64.
    Miller VL, Beer KB, Loomis WP, Olson JA, Miller SI. 1992. An unusual pagC::TnphoA mutation leads to an invasion- and virulence-defective phenotype in Salmonellae. Infect Immun 60:3763–3770.
    65.
    Alix E, Miki T, Felix C, Rang C, Figueroa-Bossi N, Demettre E, Blanc-Potard AB. 2008. Interplay between MgtC and PagC in Salmonella enterica serovar Typhimurium. Microb Pathog 45:236–240.
    66.
    Harris JB, Baresch-Bernal A, Rollins SM, Alam A, LaRocque RC, Bikowski M, Peppercorn AF, Handfield M, Hillman JD, Qadri F, Calderwood SB, Hohmann E, Breiman RF, Brooks WA, Ryan ET. 2006. Identification of in vivo-induced bacterial protein antigens during human infection with Salmonella enterica serovar Typhi. Infect Immun 74:5161–5168.
    67.
    Cunrath O, Meinel DM, Maturana P, Fanous J, Buyck JM, Saint Auguste P, Seth-Smith HMB, Korner J, Dehio C, Trebosc V, Kemmer C, Neher R, Egli A, Bumann D. 2019. Quantitative contribution of efflux to multi-drug resistance of clinical Escherichia coli and Pseudomonas aeruginosa strains. EBioMedicine 41:479–487.
    68.
    Edwards RA, Keller LH, Schifferli DM. 1998. Improved allelic exchange vectors and their use to analyze 987P fimbria gene expression. Gene 207:149–157.
    69.
    Gunn JS, Hohmann EL, Miller SI. 1996. Transcriptional regulation of Salmonella virulence: a PhoQ periplasmic domain mutation results in increased net phosphotransfer to PhoP. J Bacteriol 178:6369–6373.
    70.
    Chutkan H, Macdonald I, Manning A, Kuehn MJ. 2013. Quantitative and qualitative preparations of bacterial outer membrane vesicles. Methods Mol Biol 966:259–272.
    71.
    Lee J, Kim OY, Gho YS. 2016. Proteomic profiling of Gram-negative bacterial outer membrane vesicles: current perspectives. Prot Clin Appl 10:897–909.
    72.
    Qing G, Gong N, Chen X, Chen J, Zhang H, Wang Y, Wang R, Zhang S, Zhang Z, Zhao X, Luo Y, Liang X-J. 2019. Natural and engineered bacterial outer membrane vesicles. Biophys Rep 5:184–198.
    73.
    Bachurski D, Schuldner M, Nguyen PH, Malz A, Reiners KS, Grenzi PC, Babatz F, Schauss AC, Hansen HP, Hallek M, Pogge von Strandmann E. 2019. Extracellular vesicle measurements with nanoparticle tracking analysis—an accuracy and repeatability comparison between NanoSight NS300 and ZetaView. J Extracell Vesicles 8:1596016.
    74.
    Guo J, Nair MK, Galvan EM, Liu SL, Schifferli DM. 2011. Tn5AraOut mutagenesis for the identification of Yersinia pestis genes involved in resistance towards cationic antimicrobial peptides. Microb Pathog 51:121–132.
    75.
    Guo A, Cao S, Tu L, Chen P, Zhang C, Jia A, Yang W, Liu Z, Chen H, Schifferli DM. 2009. FimH alleles direct preferential binding of Salmonella to distinct mammalian cells or to avian cells. Microbiology 155:1623–1633.
    76.
    Ferreira VP, Pangburn MK, Cortés C. 2010. Complement control protein factor H: the good, the bad, and the inadequate. Mol Immunol 47:2187–2197.
    77.
    Hoiseth SK, Stocker BA. 1981. Aromatic-dependent Salmonella Typhimurium are non-virulent and effective as live vaccines. Nature 291:238–239.
    78.
    Gewirtz AT, Simon PO, Schmitt CK, Taylor LJ, Hagedorn CH, O’Brien AD, Neish AS, Madara JL. 2001. Salmonella Typhimurium translocates flagellin across intestinal epithelia, inducing a proinflammatory response. J Clin Invest 107:99–109.
    79.
    Wu Y, Hu Q, Dehinwal R, Rakov AV, Grams N, Clemens EC, Hofmann J, Okeke IN, Schifferli DM. 2020. The not so good, the bad and the ugly: differential bacterial adhesion and invasion mediated by Salmonella PagN allelic variants. Microorganisms 8:489.
    80.
    Harms A, Liesch M, Korner J, Quebatte M, Engel P, Dehio C. 2017. A bacterial toxin-antitoxin module is the origin of inter-bacterial and inter-kingdom effectors of Bartonella. PLoS Genet 13:e1007077.
    81.
    Cianfanelli FR, Cunrath O, Bumann D. 2020. Efficient dual-negative selection for bacterial genome editing. BMC Microbiol 20:129.
    82.
    Felek S, Krukonis ES. 2009. The Yersinia pestis Ail protein mediates binding and Yop delivery to host cells required for plague virulence. Infect Immun 77:825–836.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 12Number 37 July 2021
    eLocator: e00869-21
    Editor: Samuel I. Miller
    University of Washington

    History

    Received: 23 March 2021
    Accepted: 22 April 2021
    Published online: 1 June 2021

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. Salmonella
    2. S. Typhimurium
    3. PagC
    4. Rck
    5. outer membrane vesicles
    6. PhoPQ
    7. C3b
    8. Factor H
    9. complement resistance

    Contributors

    Authors

    Ruchika Dehinwal
    Department of Pathobiology, University of Pennsylvania, School of Veterinary Medicine, Philadelphia, Pennsylvania, USA
    Danielle Cooley
    Department of Pathobiology, University of Pennsylvania, School of Veterinary Medicine, Philadelphia, Pennsylvania, USA
    Alexey V. Rakov
    Department of Pathobiology, University of Pennsylvania, School of Veterinary Medicine, Philadelphia, Pennsylvania, USA
    Present address: Alexey V. Rakov, Somov Institute of Epidemiology and Microbiology, Vladivostok, Russia; Olivier Cunrath, University of Oxford, Department of Zoology, Oxford, United Kingdom; Prashanth Vallabhajosyula, Department of Surgery, Yale University School of Medicine, New Haven, Connecticut, USA.
    Akhil S. Alugupalli
    Department of Pathobiology, University of Pennsylvania, School of Veterinary Medicine, Philadelphia, Pennsylvania, USA
    Joey Harmon
    Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
    Olivier Cunrath
    Biozentrum, University of Basel, Basel, Switzerland
    Present address: Alexey V. Rakov, Somov Institute of Epidemiology and Microbiology, Vladivostok, Russia; Olivier Cunrath, University of Oxford, Department of Zoology, Oxford, United Kingdom; Prashanth Vallabhajosyula, Department of Surgery, Yale University School of Medicine, New Haven, Connecticut, USA.
    Prashanth Vallabhajosyula
    Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
    Present address: Alexey V. Rakov, Somov Institute of Epidemiology and Microbiology, Vladivostok, Russia; Olivier Cunrath, University of Oxford, Department of Zoology, Oxford, United Kingdom; Prashanth Vallabhajosyula, Department of Surgery, Yale University School of Medicine, New Haven, Connecticut, USA.
    Dirk Bumann
    Biozentrum, University of Basel, Basel, Switzerland
    Department of Pathobiology, University of Pennsylvania, School of Veterinary Medicine, Philadelphia, Pennsylvania, USA

    Editor

    Samuel I. Miller
    Editor
    University of Washington

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Evaluation of Preexisting Anti-Hemagglutinin Stalk Antibody as a Correlate of Protection in a Healthy Volunteer Challenge with Influenza A/H1N1pdm Virus

    Evaluation of Preexisting Anti-Hemagglutinin Stalk Antibody as a Correlate of Protection in a Healthy Volunteer Challenge with Influenza A/H1N1pdm Virus

    ABSTRACT

    Influenza virus hemagglutinin (HA) surface glycoprotein is currently the primary target of licensed influenza vaccines. Recently, broadly reactive antibodies that target the stalk region of the HA have become a major focus of current novel vaccine development. These antibodies have been observed in humans after natural infection with influenza A virus, but the data are limited. Using samples and data from the uniquely controlled setting of an influenza A/H1N1 virus human challenge study of healthy volunteers, we performed a secondary analysis that for the first time explores the role of anti-HA stalk antibody as a human correlate of protection. An anti-HA stalk antibody enzyme-linked immunosorbent assay (ELISA) was performed on samples from 65 participants challenged with a 2009 H1N1pdm virus. Pre- and postchallenge anti-HA stalk titers were then correlated with multiple outcome measures to evaluate anti-HA stalk antibody titer as a correlate of protection. Anti-HA stalk antibody titers were present before challenge and rose in response to challenge in 64% of individuals. Those individuals with higher titers at baseline were less likely to develop shedding, but not less likely to develop symptoms. Similar to the hemagglutination inhibition (HAI) titer, the baseline anti-HA stalk antibody titer did not independently predict a decrease in the severity of influenza disease, while the antineuraminidase (neuraminidase inhibition [NAI]) titer did. As a correlate of protection, the naturally occurring anti-HA stalk antibody titer is predictive of a reduction of certain aspects of disease similar to HAI titer, but the NAI titer is the only identified correlate that is an independent predictor of a reduction of all assessed influenza clinical outcome measures.
    IMPORTANCE This is the first study to evaluate preexisting anti-HA stalk antibodies as a predictor of protection. We use a healthy volunteer influenza challenge trial for an examination of the role such antibodies play in protection. This study demonstrates that anti-HA stalk antibodies are naturally generated in response to an infection, but there is significant variability in response. Similar to antibodies that target the HA head, baseline anti-HA stalk antibody titer is a correlate of protection in terms of reduced shedding, but it is not a predictor of reduced clinical disease or an independent predictor of disease severity. These results, in the context of the limited data available in humans, suggest that vaccines that induce anti-HA stalk antibodies could play a role in future vaccine strategies, but alone, this target may be insufficient to induce a fully protective vaccine and overcome some of the issues identified with current vaccines.

    REFERENCES

    1.
    Neu KE, Henry Dunand CJ, Wilson PC. 2016. Heads, stalks and everything else: how can antibodies eradicate influenza as a human disease? Curr Opin Immunol 42:48–55.
    2.
    Zhou L, Ren R, Yang L, Bao C, Wu J, Wang D, Li C, Xiang N, Wang Y, Li D, Sui H, Shu Y, Feng Z, Li Q, Ni D. 2017. Sudden increase in human infection with avian influenza A(H7N9) virus in China, September-December 2016. Western Pac Surveill Response J 8:6–14.
    3.
    Pica N, Hai R, Krammer F, Wang TT, Maamary J, Eggink D, Tan GS, Krause JC, Moran T, Stein CR, Banach D, Wrammert J, Belshe RB, García-Sastre A, Palese P. 2012. Hemagglutinin stalk antibodies elicited by the 2009 pandemic influenza virus as a mechanism for the extinction of seasonal H1N1 viruses. Proc Natl Acad Sci U S A 109:2573–2578.
    4.
    Impagliazzo A, Milder F, Kuipers H, Wagner MV, Zhu X, Hoffman RM, van Meersbergen R, Huizingh J, Wanningen P, Verspuij J, de Man M, Ding Z, Apetri A, Kükrer B, Sneekes-Vriese E, Tomkiewicz D, Laursen NS, Lee PS, Zakrzewska A, Dekking L, Tolboom J, Tettero L, van Meerten S, Yu W, Koudstaal W, Goudsmit J, Ward AB, Meijberg W, Wilson IA, Radošević K. 2015. A stable trimeric influenza hemagglutinin stem as a broadly protective immunogen. Science 349:1301–1306.
    5.
    Nachbagauer R, Krammer F. 2017. Universal influenza virus vaccines and therapeutic antibodies. Clin Microbiol Infect 23:222–228.
    6.
    Margine I, Hai R, Albrecht RA, Obermoser G, Harrod AC, Banchereau J, Palucka K, García-Sastre A, Palese P, Treanor JJ, Krammer F. 2013. H3N2 influenza virus infection induces broadly reactive hemagglutinin stalk antibodies in humans and mice. J Virol 87:4728–4737.
    7.
    Moody MA, Zhang R, Walter EB, Woods CW, Ginsburg GS, McClain MT, Denny TN, Chen X, Munshaw S, Marshall DJ, Whitesides JF, Drinker MS, Amos JD, Gurley TC, Eudailey JA, Foulger A, DeRosa KR, Parks R, Meyerhoff RR, Yu JS, Kozink DM, Barefoot BE, Ramsburg EA, Khurana S, Golding H, Vandergrift NA, Alam SM, Tomaras GD, Kepler TB, Kelsoe G, Liao HX, Haynes BF. 2011. H3N2 influenza infection elicits more cross-reactive and less clonally expanded anti-hemagglutinin antibodies than influenza vaccination. PLoS One 6:e25797.
    8.
    Liu L, Nachbagauer R, Zhu L, Huang Y, Xie X, Jin S, Zhang A, Wan Y, Hirsh A, Tian D, Shi X, Dong Z, Yuan S, Hu Y, Krammer F, Zhang X, Xu J. 2017. Induction of broadly cross-reactive stalk-specific antibody responses to influenza group 1 and group 2 hemagglutinins by natural H7N9 virus infection in humans. J Infect Dis 215:518–528.
    9.
    Memoli MJ, Czajkowski L, Reed S, Athota R, Bristol T, Proudfoot K, Fargis S, Stein M, Dunfee RL, Shaw PA, Davey RT, Taubenberger JK. 2015. Validation of the wild-type influenza A human challenge model H1N1pdMIST: an A(H1N1)pdm09 dose-finding investigational new drug study. Clin Infect Dis 60:693–702.
    10.
    Memoli MJ, Shaw PA, Han A, Czajkowski L, Reed S, Athota R, Bristol T, Fargis S, Risos K, Powers JH, Davey RT, Jr, Taubenberger JK. 2016. Evaluation of antihemagglutinin and antineuraminidase antibodies as correlates of protection in an influenza A/H1N1 virus healthy human challenge model. mBio 7:e00417-16.
    11.
    Jacobson RM, Grill DE, Oberg AL, Tosh PK, Ovsyannikova IG, Poland GA. 2015. Profiles of influenza A/H1N1 vaccine response using hemagglutination inhibition titers. Hum Vaccin Immunother 11:961–969.
    12.
    Ohmit SE, Petrie JG, Cross RT, Johnson E, Monto AS. 2011. Influenza hemagglutination inhibition antibody titer as a correlate of vaccine-induced protection. J Infect Dis 204:1879–1885.
    13.
    Beyer WE, Palache AM, Baljet M, Masurel N. 1989. Antibody induction by influenza vaccines in the elderly: a review of the literature. Vaccine 7:385–394.
    14.
    Kanekiyo M, Wei CJ, Yassine HM, McTamney PM, Boyington JC, Whittle JR, Rao SS, Kong WP, Wang L, Nabel GJ. 2013. Self-assembling influenza nanoparticle vaccines elicit broadly neutralizing H1N1 antibodies. Nature 499:102–106.
    15.
    Nabel GJ, Fauci AS. 2010. Induction of unnatural immunity: prospects for a broadly protective universal influenza vaccine. Nat Med 16:1389–1391.
    16.
    Yassine HM, Boyington JC, McTamney PM, Wei CJ, Kanekiyo M, Kong WP, Gallagher JR, Wang L, Zhang Y, Joyce MG, Lingwood D, Moin SM, Andersen H, Okuno Y, Rao SS, Harris AK, Kwong PD, Mascola JR, Nabel GJ, Graham BS. 2015. Hemagglutinin-stem nanoparticles generate heterosubtypic influenza protection. Nat Med 21:1065–1070.
    17.
    Powers JH, Guerrero ML, Leidy NK, Fairchok MP, Rosenberg A, Hernández A, Stringer S, Schofield C, Rodríguez-Zulueta P, Kim K, Danaher PJ, Ortega-Gallegos H, Bacci ED, Stepp N, Galindo-Fraga A, St Clair K, Rajnik M, McDonough EA, Ridoré M, Arnold JC, Millar EV, Ruiz-Palacios GM. 2016. Development of the Flu-PRO: a patient-reported outcome (PRO) instrument to evaluate symptoms of influenza. BMC Infect Dis 16:1.
    18.
    Powers JH, Bacci ED, Leidy NK, Stringer S, Kim K, Memoli MJ, Han A, Fairchok MP, Chen W, Arnold JC, Danaher PJ, Lalani T, Hansen EA, Ridore M, Burgess TH, Millar EV, Hernández A, Rodríguez-Zulueta P, Ortega-Gallegos H, Galindo-Fraga A, Ruiz-Palacios GM, Pett S, Fischer W, Gillor D, Macias LM, DuVal A, Rothman R, Dugas A, Guerrero ML. 2016. Evaluation of the performance properties of the influenza patient-reported outcomes instrument (Flu-Pro). Value Health 19:A220–A221.
    19.
    Wrammert J, Koutsonanos D, Li GM, Edupuganti S, Sui J, Morrissey M, McCausland M, Skountzou I, Hornig M, Lipkin WI, Mehta A, Razavi B, Del Rio C, Zheng NY, Lee JH, Huang M, Ali Z, Kaur K, Andrews S, Amara RR, Wang Y, Das SR, O’Donnell CD, Yewdell JW, Subbarao K, Marasco WA, Mulligan MJ, Compans R, Ahmed R, Wilson PC. 2011. Broadly cross-reactive antibodies dominate the human B cell response against 2009 pandemic H1N1 influenza virus infection. J Exp Med 208:181–193.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 9Number 17 March 2018
    eLocator: e02284-17
    Editor: W. Ian Lipkin
    Mailman School of Public Health, Columbia University

    History

    Received: 8 December 2017
    Accepted: 12 December 2017
    Published online: 23 January 2018

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. CHIM
    2. HA stalk
    3. NA
    4. antibody
    5. human challenge
    6. influenza
    7. influenza A
    8. neuraminidase
    9. universal vaccine

    Contributors

    Authors

    Jae-Keun Park
    Viral Pathogenesis and Evolution Section, Laboratory of Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
    Alison Han
    Viral Pathogenesis and Evolution Section, Laboratory of Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
    Lindsay Czajkowski
    Viral Pathogenesis and Evolution Section, Laboratory of Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
    Susan Reed
    Viral Pathogenesis and Evolution Section, Laboratory of Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
    Rani Athota
    Viral Pathogenesis and Evolution Section, Laboratory of Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
    Tyler Bristol
    Viral Pathogenesis and Evolution Section, Laboratory of Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
    Luz Angela Rosas
    Viral Pathogenesis and Evolution Section, Laboratory of Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
    Adriana Cervantes-Medina
    Viral Pathogenesis and Evolution Section, Laboratory of Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
    Jeffery K. Taubenberger
    Viral Pathogenesis and Evolution Section, Laboratory of Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
    Matthew J. Memoli
    Viral Pathogenesis and Evolution Section, Laboratory of Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA

    Editor

    W. Ian Lipkin
    Editor
    Mailman School of Public Health, Columbia University

    Reviewers

    Robert Webster
    Solicited external reviewer
    St. Jude Children's Research Hospital
    Arnold Monto
    Solicited external reviewer
    University of Michigan–Ann Arbor

    Notes

    Address correspondence to Matthew J. Memoli, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    The Potent and Broadly Neutralizing Human Dengue Virus-Specific Monoclonal Antibody 1C19 Reveals a Unique Cross-Reactive Epitope on the bc Loop of Domain II of the Envelope Protein

    The Potent and Broadly Neutralizing Human Dengue Virus-Specific Monoclonal Antibody 1C19 Reveals a Unique Cross-Reactive Epitope on the bc Loop of Domain II of the Envelope Protein

    ABSTRACT

    Following natural dengue virus (DENV) infection, humans produce some antibodies that recognize only the serotype of infection (type specific) and others that cross-react with all four serotypes (cross-reactive). Recent studies with human antibodies indicate that type-specific antibodies at high concentrations are often strongly neutralizing in vitro and protective in animal models. In general, cross-reactive antibodies are poorly neutralizing and can enhance the ability of DENV to infect Fc receptor-bearing cells under some conditions. Type-specific antibodies at low concentrations also may enhance infection. There is an urgent need to determine whether there are conserved antigenic sites that can be recognized by cross-reactive potently neutralizing antibodies. Here, we describe the isolation of a large panel of naturally occurring human monoclonal antibodies (MAbs) directed to the DENV domain II fusion loop (FL) envelope protein region from subjects following vaccination or natural infection. Most of the FL-specific antibodies exhibited a conventional phenotype, characterized by low-potency neutralizing function and antibody-dependent enhancing activity. One clone, however, recognized the bc loop of domain II adjacent to the FL and exhibited a unique phenotype of ultrahigh potency, neutralizing all four serotypes better than any other previously described MAb recognizing this region. This antibody not only neutralized DENV effectively but also competed for binding against the more prevalent poor-quality antibodies whose binding was focused on the FL. The 1C19 human antibody could be a promising component of a preventative or therapeutic intervention. Furthermore, the unique epitope revealed by 1C19 suggests a focus for rational vaccine design based on novel immunogens presenting cross-reactive neutralizing determinants.
    IMPORTANCE With no effective vaccine available, the incidence of dengue virus (DENV) infections worldwide continues to rise, with more than 390 million infections estimated to occur each year. Due to the unique roles that antibodies are postulated to play in the pathogenesis of DENV infection and disease, there is consensus that a successful DENV vaccine must protect against all four serotypes. If conserved epitopes recognized by naturally occurring potently cross-neutralizing human antibodies could be identified, monovalent subunit vaccine preparations might be developed. We characterized 30 DENV cross-neutralizing human monoclonal antibodies (MAbs) and identified one (1C19) that recognized a novel conserved site, known as the bc loop. This antibody has several desirable features, as it neutralizes DENV effectively and competes for binding against the more common low-potency fusion loop (FL) antibodies, which are believed to contribute to antibody-mediated disease. To our knowledge, this is the first description of a potent serotype cross-neutralizing human antibody to DENV.

    REFERENCES

    1.
    Bhatt S, Gething PW, Brady OJ, Messina JP, Farlow AW, Moyes CL, Drake JM, Brownstein JS, Hoen AG, Sankoh O, Myers MF, George DB, Jaenisch T, Wint GR, Simmons CP, Scott TW, Farrar JJ, and Hay SI. 2013. The global distribution and burden of dengue. Nature 496:504–507.
    2.
    Gubler DJ. 2002. Epidemic dengue/dengue hemorrhagic fever as a public health, social and economic problem in the 21st century. Trends Microbiol. 10:100–103.
    3.
    Halstead SB and O’Rourke EJ. 1977. Antibody-enhanced dengue virus infection in primate leukocytes. Nature 265:739–741.
    4.
    Zellweger RM, Prestwood TR, and Shresta S. 2010. Enhanced infection of liver sinusoidal endothelial cells in a mouse model of antibody-induced severe dengue disease. Cell Host Microbe 7:128–139.
    5.
    Balsitis SJ, Williams KL, Lachica R, Flores D, Kyle JL, Mehlhop E, Johnson S, Diamond MS, Beatty PR, and Harris E. 2010. Lethal antibody enhancement of dengue disease in mice is prevented by Fc modification. PLoS Pathog. 6:e1000790.
    6.
    Goncalvez AP, Engle RE, St, Claire M, Purcell RH, and Lai CJ. 2007. Monoclonal antibody-mediated enhancement of dengue virus infection in vitro and in vivo and strategies for prevention. Proc. Natl. Acad. Sci. U. S. A. 104:9422–9427.
    7.
    Crill WD and Roehrig JT. 2001. Monoclonal antibodies that bind to domain III of dengue virus E glycoprotein are the most efficient blockers of virus adsorption to Vero cells. J. Virol. 75:7769–7773.
    8.
    Gromowski GD and Barrett AD. 2007. Characterization of an antigenic site that contains a dominant, type-specific neutralization determinant on the envelope protein domain III (ED3) of dengue 2 virus. Virology 366:349–360.
    9.
    Gromowski GD, Barrett ND, and Barrett AD. 2008. Characterization of dengue virus complex-specific neutralizing epitopes on envelope protein domain III of dengue 2 virus. J. Virol. 82:8828–8837.
    10.
    Lin B, Parrish CR, Murray JM, and Wright PJ. 1994. Localization of a neutralizing epitope on the envelope protein of dengue virus type 2. Virology 202:885–890.
    11.
    Sukupolvi-Petty S, Austin SK, Purtha WE, Oliphant T, Nybakken GE, Schlesinger JJ, Roehrig JT, Gromowski GD, Barrett AD, Fremont DH, and Diamond MS. 2007. Type and subcomplex-specific neutralizing antibodies against domain III of dengue virus type 2 envelope protein recognize adjacent epitopes. J. Virol. 81:12816–12826.
    12.
    Wahala WM, Huang C, Butrapet S, White LJ, and de Silva AM. 2012. Recombinant dengue type 2 viruses with altered e protein domain III epitopes are efficiently neutralized by human immune sera. J. Virol. 86:4019–4023.
    13.
    Wahala WM, Kraus AA, Haymore LB, Accavitti-Loper MA, and de Silva AM. 2009. Dengue virus neutralization by human immune sera: role of envelope protein domain III-reactive antibody. Virology 392:103–113.
    14.
    Crill WD, Hughes HR, Delorey MJ, and Chang GJ. 2009. Humoral immune responses of dengue fever patients using epitope-specific serotype-2 virus-like particle antigens. PLoS One 4:e4991.
    15.
    Smith SA, Zhou Y, Olivarez NP, Broadwater AH, de Silva AM, and Crowe JE Jr.. 2012. Persistence of circulating memory B cell clones with potential for dengue virus disease enhancement for decades following infection. J. Virol. 86:2665–2675.
    16.
    de Alwis R, Smith SA, Olivarez NP, Messer WB, Huynh JP, Wahala WM, White LJ, Diamond MS, Baric RS, Crowe JE Jr, and de Silva AM. 2012. Identification of human neutralizing antibodies that bind to complex epitopes on dengue virions. Proc. Natl. Acad. Sci. U. S. A. 109:7439–7444.
    17.
    de Alwis R, Beltramello M, Messer WB, Sukupolvi-Petty S, Wahala WM, Kraus A, Olivarez NP, Pham Q, Brien JD, Tsai WY, Wang WK, Halstead S, Kliks S, Diamond MS, Baric R, Lanzavecchia A, Sallusto F, de Silva AM, and de Silva AM. 2011. In-depth analysis of the antibody response of individuals exposed to primary dengue virus infection. PLoS Negl. Trop. Dis. 5:e1188.
    18.
    Dejnirattisai W, Jumnainsong A, Onsirisakul N, Fitton P, Vasanawathana S, Limpitikul W, Puttikhunt C, Edwards C, Duangchinda T, Supasa S, Chawansuntati K, Malasit P, Mongkolsapaya J, and Screaton G. 2010. Cross-reacting antibodies enhance dengue virus infection in humans. Science 328:745–748.
    19.
    Beltramello M, Williams KL, Simmons CP, Macagno A, Simonelli L, Quyen NT, Sukupolvi-Petty S, Navarro-Sanchez E, Young PR, de Silva AM, Rey FA, Varani L, Whitehead SS, Diamond MS, Harris E, Lanzavecchia A, and Sallusto F. 2010. The human immune response to dengue virus is dominated by highly cross-reactive antibodies endowed with neutralizing and enhancing activity. Cell Host Microbe 8:271–283.
    20.
    Teoh EP, Kukkaro P, Teo EW, Lim AP, Tan TT, Yip A, Schul W, Aung M, Kostyuchenko VA, Leo YS, Chan SH, Smith KG, Chan AH, Zou G, Ooi EE, Kemeny DM, Tan GK, Ng JK, Ng ML, Alonso S, Fisher D, Shi PY, Hanson BJ, Lok SM, and MacAry PA. 2012. The structural basis for serotype-specific neutralization of dengue virus by a human antibody. Sci. Transl. Med. 4:139–183.
    21.
    Deng YQ, Dai JX, Ji GH, Jiang T, Wang HJ, Yang HO, Tan WL, Liu R, Yu M, Ge BX, Zhu QY, Qin ED, Guo YJ, and Qin CF. 2011. A broadly flavivirus cross-neutralizing monoclonal antibody that recognizes a novel epitope within the fusion loop of E protein. PLoS One 6:e16059.
    22.
    Lin HE, Tsai WY, Liu IJ, Li PC, Liao MY, Tsai JJ, Wu YC, Lai CY, Lu CH, Huang JH, Chang GJ, Wu HC, and Wang WK. 2012. Analysis of epitopes on dengue virus envelope protein recognized by monoclonal antibodies and polyclonal human sera by a high throughput assay. PLoS Negl. Trop. Dis. 6:e1447.
    23.
    Rodenhuis-Zybert IA, Moesker B, da Silva Voorham JM, van der Ende-Metselaar H, Diamond MS, Wilschut J, and Smit JM. 2011. A fusion-loop antibody enhances the infectious properties of immature flavivirus particles. J. Virol. 85:11800–11808.
    24.
    Costin JM, Zaitseva E, Kahle KM, Nicholson CO, Rowe DK, Graham AS, Bazzone LE, Hogancamp G, Figueroa Sierra M, Fong RH, Yang ST, Lin L, Robinson JE, Doranz BJ, Chernomordik LV, Michael SF, Schieffelin JS, and Isern S. 2013. Mechanistic study of broadly neutralizing human monoclonal antibodies against dengue virus that target the FL. J. Virol. 87:52–66.
    25.
    Williams KL, Sukupolvi-Petty S, Beltramello M, Johnson S, Sallusto F, Lanzavecchia A, Diamond MS, and Harris E. 2013. Therapeutic efficacy of antibodies lacking FcgammaR against lethal dengue virus infection is due to neutralizing potency and blocking of enhancing antibodies. PLoS Pathog. 9:e1003157.
    26.
    Lai CY, Tsai WY, Lin SR, Kao CL, Hu HP, King CC, Wu HC, Chang GJ, and Wang WK. 2008. Antibodies to envelope glycoprotein of dengue virus during the natural course of infection are predominantly cross-reactive and recognize epitopes containing highly conserved residues at the fusion loop of domain II. J. Virol. 82:6631–6643.
    27.
    Tsai WY, Lai CY, Wu YC, Lin HE, Edwards C, Jumnainsong A, Kliks S, Halstead S, Mongkolsapaya J, Screaton GR, and Wang WK. 11 September 2013. High avidity and potent neutralizing cross-reactive human monoclonal antibodies derived from secondary dengue virus infection. J. Virol. doi:
    28.
    Lok SM, Kostyuchenko V, Nybakken GE, Holdaway HA, Battisti AJ, Sukupolvi-Petty S, Sedlak D, Fremont DH, Chipman PR, Roehrig JT, Diamond MS, Kuhn RJ, and Rossmann MG. 2008. Binding of a neutralizing antibody to dengue virus alters the arrangement of surface glycoproteins. Nat. Struct. Mol. Biol. 15:312–317.
    29.
    Dowd KA, Jost CA, Durbin AP, Whitehead SS, and Pierson TC. 2011. A dynamic landscape for antibody binding modulates antibody-mediated neutralization of West Nile virus. PLoS Pathog. 7:e1002111.
    30.
    Austin SK, Dowd KA, Shrestha B, Nelson CA, Edeling MA, Johnson S, Pierson TC, Diamond MS, and Fremont DH. 2012. Structural basis of differential neutralization of DENV-1 genotypes by an antibody that recognizes a cryptic epitope. PLOS Pathog. 8:e1002930.
    31.
    Sukupolvi-Petty S, Brien JD, Austin SK, Shrestha B, Swayne S, Kahle K, Doranz BJ, Johnson S, Pierson TC, Fremont DH, and Diamond MS. 2013. Functional analysis of antibodies against dengue virus type 4 reveals strain-dependent epitope exposure that impacts neutralization and protection. J. Virol. 87:8826–8842.
    32.
    Vogt MR, Dowd KA, Engle M, Tesh RB, Johnson S, Pierson TC, and Diamond MS. 2011. Poorly neutralizing cross-reactive antibodies against the FL of West Nile virus envelope protein protect in vivo via Fcgamma receptor and complement-dependent effector mechanisms. J. Virol. 85:11567–11580.
    33.
    Oliphant T, Nybakken GE, Engle M, Xu Q, Nelson CA, Sukupolvi-Petty S, Marri A, Lachmi BE, Olshevsky U, Fremont DH, Pierson TC, and Diamond MS. 2006. Antibody recognition and neutralization determinants on domains I and II of West Nile virus envelope protein. J. Virol. 80:12149–12159.
    34.
    Sukupolvi-Petty S, Austin SK, Engle M, Brien JD, Dowd KA, Williams KL, Johnson S, Rico-Hesse R, Harris E, Pierson TC, Fremont DH, and Diamond MS. 2010. Structure and function analysis of therapeutic monoclonal antibodies against dengue virus type 2. J. Virol. 84:9227–9239.
    35.
    Durbin AP, Whitehead SS, Shaffer D, Wanionek K, Thumar B, Blaney JE, Murphy BR, Schmidt AC, and Schmidt AC. 2011. A single dose of the DENV-1 candidate vaccine rDEN1Delta30 is strongly immunogenic and induces resistance to a second dose in a randomized trial. PLoS Negl. Trop. Dis. 5:e1267.
    36.
    Smith SA, de Alwis R, Kose N, Durbin AP, Whitehead SS, de Silva AM, and Crowe JE Jr.. 2013. Human monoclonal antibodies derived from memory B cells following live attenuated dengue virus vaccination or natural infection exhibit similar characteristics. J. Infect. Dis. 207:1898–1908.
    37.
    Oliphant T, Nybakken GE, Austin SK, Xu Q, Bramson J, Loeb M, Throsby M, Fremont DH, Pierson TC, and Diamond MS. 2007. Induction of epitope-specific neutralizing antibodies against West Nile virus. J. Virol. 81:11828–11839.
    38.
    Lambeth CR, White LJ, Johnston RE, and de Silva AM. 2005. Flow cytometry-based assay for titrating dengue virus. J. Clin. Microbiol. 43:3267–3272.
    39.
    Kraus AA, Messer W, Haymore LB, and de Silva AM. 2007. Comparison of plaque- and flow cytometry-based methods for measuring dengue virus neutralization. J. Clin. Microbiol. 45:3777–3780.
    40.
    Modis Y, Ogata S, Clements D, and Harrison SC. 2005. Variable surface epitopes in the crystal structure of dengue virus type 3 envelope glycoprotein. J. Virol. 79:1223–1231.
    41.
    Shresta S, Kyle JL, Robert Beatty P, and Harris E. 2004. Early activation of natural killer and B cells in response to primary dengue virus infection in A/J mice. Virology 319:262–273.
    42.
    Diamond MS, Edgil D, Roberts TG, Lu B, and Harris E. 2000. Infection of human cells by dengue virus is modulated by different cell types and viral strains. J. Virol. 74:7814–7823.
    43.
    van den Broek MF, Müller U, Huang S, Aguet M, and Zinkernagel RM. 1995. Antiviral defense in mice lacking both alpha/beta and gamma interferon receptors. J. Virol. 69:4792–4796.
    44.
    Johnson BW, Russell BJ, and Lanciotti RS. 2005. Serotype-specific detection of dengue viruses in a fourplex real-time reverse transcriptase PCR assay. J. Clin. Microbiol. 43:4977–4983.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 4Number 631 December 2013
    eLocator: e00873-13
    Editor: W. Ian Lipkin
    Columbia University

    History

    Received: 13 October 2013
    Accepted: 15 October 2013
    Published online: 19 November 2013

    Permissions

    Request permissions for this article.

    Contributors

    Authors

    Scott A. Smith
    Department of Medicine, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA
    The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA
    A. Ruklanthi de Alwis
    Department of Microbiology and Immunology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
    Nurgun Kose
    The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA
    Eva Harris
    Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, California, USA
    Kristie D. Ibarra
    Division of Infectious Diseases and Vaccinology, School of Public Health, University of California, Berkeley, Berkeley, California, USA
    Kristen M. Kahle
    Integral Molecular Inc., Philadelphia, Pennsylvania, USA
    Jennifer M. Pfaff
    Integral Molecular Inc., Philadelphia, Pennsylvania, USA
    Xiaoxiao Xiang
    Integral Molecular Inc., Philadelphia, Pennsylvania, USA
    Benjamin J. Doranz
    Integral Molecular Inc., Philadelphia, Pennsylvania, USA
    Aravinda M. de Silva
    Department of Microbiology and Immunology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
    S. Kyle Austin
    Departments of Medicine, Molecular Microbiology, Pathology, and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA
    Soila Sukupolvi-Petty
    Departments of Medicine, Molecular Microbiology, Pathology, and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA
    Michael S. Diamond
    Departments of Medicine, Molecular Microbiology, Pathology, and Immunology, Washington University School of Medicine, St. Louis, Missouri, USA
    James E. Crowe Jr.
    The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA
    Department of Pediatrics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA
    Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA

    Editor

    W. Ian Lipkin
    Editor
    Columbia University

    Notes

    Address correspondence to James E. Crowe, Jr., [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Host-Microbe Coevolution: Applying Evidence from Model Systems to Complex Marine Invertebrate Holobionts

    Host-Microbe Coevolution: Applying Evidence from Model Systems to Complex Marine Invertebrate Holobionts

    ABSTRACT

    Marine invertebrates often host diverse microbial communities, making it difficult to identify important symbionts and to understand how these communities are structured. This complexity has also made it challenging to assign microbial functions and to unravel the myriad of interactions among the microbiota. Here we propose to address these issues by applying evidence from model systems of host-microbe coevolution to complex marine invertebrate microbiomes. Coevolution is the reciprocal adaptation of one lineage in response to another and can occur through the interaction of a host and its beneficial symbiont. A classic indicator of coevolution is codivergence of host and microbe, and evidence of this is found in both corals and sponges. Metabolic collaboration between host and microbe is often linked to codivergence and appears likely in complex holobionts, where microbial symbionts can interact with host cells through production and degradation of metabolic compounds. Neutral models are also useful to distinguish selected microbes against a background population consisting predominately of random associates. Enhanced understanding of the interactions between marine invertebrates and their microbial communities is urgently required as coral reefs face unprecedented local and global pressures and as active restoration approaches, including manipulation of the microbiome, are proposed to improve the health and tolerance of reef species. On the basis of a detailed review of the literature, we propose three research criteria for examining coevolution in marine invertebrates: (i) identifying stochastic and deterministic components of the microbiome, (ii) assessing codivergence of host and microbe, and (iii) confirming the intimate association based on shared metabolic function.

    REFERENCES

    1.
    Zaneveld J, Turnbaugh PJ, Lozupone C, Ley RE, Hamady M, Gordon JI, Knight R. 2008. Host-bacterial coevolution and the search for new drug targets. Curr Opin Chem Biol 12:109–114.
    2.
    Van den Abbeele P, Van de Wiele T, Verstraete W, Possemiers S. 2011. The host selects mucosal and luminal associations of coevolved gut microorganisms: a novel concept. FEMS Microbiol Rev 35:681–704.
    3.
    Archibald JM. 2015. Endosymbiosis and eukaryotic cell evolution. Curr Biol 25:R911–R921.
    4.
    McFall-Ngai M, Hadfield MG, Bosch TCG, Carey HV, Domazet-Lošo T, Douglas AE, Dubilier N, Eberl G, Fukami T, Gilbert SF, Hentschel U, King N, Kjelleberg S, Knoll AH, Kremer N, Mazmanian SK, Metcalf JL, Nealson K, Pierce NE, Rawls JF, Reid A, Ruby EG, Rumpho M, Sanders JG, Tautz D, Wernegreen JJ. 2013. Animals in a bacterial world, a new imperative for the life sciences. Proc Natl Acad Sci U S A 110:3229–3236.
    5.
    Friedman WE. 2009. The meaning of Darwin’s ‘abominable mystery’. Am J Bot 96:5–21.
    6.
    Ehrlich PR, Raven PH. 1964. Butterflies and plants: a study in coevolution. Evolution 18:586–608.
    7.
    Janz N, Nylin S. 1998. Butterflies and plants: a phylogenetic study. Evolution 52:486–502.
    8.
    Ryan MF, Byrne O. 1988. Plant-insect coevolution and inhibition of acetylcholinesterase. J Chem Ecol 14:1965–1975.
    9.
    Van Valen L. 1974. Molecular evolution as predicted by natural selection. J Mol Evol 3:89–101.
    10.
    Paterson S, Vogwill T, Buckling A, Benmayor R, Spiers AJ, Thomson NR, Quail M, Smith F, Walker D, Libberton B, Fenton A, Hall N, Brockhurst MA. 2010. Antagonistic coevolution accelerates molecular evolution. Nature 464:275–278.
    11.
    Herre EA, Knowlton N, Mueller UG, Rehner SA. 1999. The evolution of mutualisms: exploring the paths between conflict and cooperation. Trends Ecol Evol 14:49–53.
    12.
    Theis KR, Dheilly NM, Klassen JL, Brucker RM, Baines JF, Bosch TCG, Cryan JF, Gilbert SF, Goodnight CJ, Lloyd EA, Sapp J, Vandenkoornhuyse P, Zilber-Rosenberg I, Rosenberg E, Bordenstein SR. 2016. Getting the hologenome concept right: an eco-evolutionary framework for hosts and their microbiomes. mSystems 1:e00028-16.
    13.
    Bordenstein SR, Theis KR. 2015. Host biology in light of the microbiome: ten principles of holobionts and hologenomes. PLoS Biol 13:e1002226.
    14.
    Zilber-Rosenberg I, Rosenberg E. 2008. Role of microorganisms in the evolution of animals and plants: the hologenome theory of evolution. FEMS Microbiol Rev 32:723–735.
    15.
    Wilson ACC, Duncan RP. 2015. Signatures of host/symbiont genome coevolution in insect nutritional endosymbioses. Proc Natl Acad Sci U S A 112:10255–10261.
    16.
    Baumann P, Moran NA, Baumann L. 1997. The evolution and genetics of aphid endosymbionts. Bioscience 47:12–20.
    17.
    Russell CW, Bouvaine S, Newell PD, Douglas AE. 2013. Shared metabolic pathways in a coevolved insect-bacterial symbiosis. Appl Environ Microbiol 79:6117–6123.
    18.
    Collins SM, Surette M, Bercik P. 2012. The interplay between the intestinal microbiota and the brain. Nat Rev Microbiol 10:735–742.
    19.
    Kennedy PJ, Cryan JF, Dinan TG, Clarke G. 2017. Kynurenine pathway metabolism and the microbiota-gut-brain axis. Neuropharmacology 112:399–412.
    20.
    Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz-Ponten T, Turner K, Zhu H, Yu C, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, Wang J, Brunak S, Doré J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J, et al. 2010. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464:59–65.
    21.
    Brune A, Dietrich C. 2015. The gut microbiota of termites: digesting the diversity in the light of ecology and evolution. Annu Rev Microbiol 69:145–166.
    22.
    Fenn K, Blaxter M. 2004. Are filarial nematode Wolbachia obligate mutualist symbionts? Trends Ecol Evol 19:163–166.
    23.
    Wu D, Daugherty SC, Van Aken SE, Pai GH, Watkins KL, Khouri H, Tallon LJ, Zaborsky JM, Dunbar HE, Tran PL, Moran NA, Eisen JA. 2006. Metabolic complementarity and genomics of the dual bacterial symbiosis of sharpshooters. PLoS Biol 4:e188.
    24.
    Clark MA, Moran NA, Baumann P, Wernegreen JJ. 2000. Cospeciation between bacterial endosymbionts (Buchnera) and a recent radiation of aphids (Uroleucon) and pitfalls of testing for phylogenetic congruence. Evolution 54:517–525.
    25.
    Moeller AH, Caro-Quintero A, Mjungu D, Georgiev AV, Lonsdorf EV, Muller MN, Pusey AE, Peeters M, Hahn BH, Ochman H. 2016. Cospeciation of gut microbiota with hominids. Science 353:380–382.
    26.
    Moran NA. 2006. Symbiosis. Curr Biol 16:R866–R871.
    27.
    McFall-Ngai M. 2008. Hawaiian bobtail squid. Curr Biol 18:R1043–R1044.
    28.
    Chen C, Tseng C, Chen CA, Tang S. 2011. The dynamics of microbial partnerships in the coral Isopora palifera. ISME J 5:728–740.
    29.
    Gil-Agudelo DL, Myers C, Smith GW, Kim K. 2006. Changes in the microbial communities associated with Gorgonia ventalina during aspergillosis infection. Dis Aquat Organ 69:89–94.
    30.
    Koren O, Rosenberg E. 2006. Bacteria associated with mucus and tissues of the coral Oculina patagonica in summer and winter. Appl Environ Microbiol 72:5254–5259.
    31.
    Littman RA, Willis BL, Pfeffer C, Bourne DG. 2009. Diversities of coral-associated bacteria differ with location, but not species, for three acroporid corals on the Great Barrier Reef. FEMS Microbiol Ecol 68:152–163.
    32.
    Thomas T, Moitinho-Silva L, Lurgi M, Björk JR, Easson C, Astudillo-García C, Olson JB, Erwin PM, López-Legentil S, Luter H, Chaves-Fonnegra A, Costa R, Schupp PJ, Steindler L, Erpenbeck D, Gilbert J, Knight R, Ackermann G, Victor Lopez J, Taylor MW, Thacker RW, Montoya JM, Hentschel U, Webster NS. 2016. Diversity, structure and convergent evolution of the global sponge microbiome. Nat Commun 7:11870.
    33.
    Webster NS, Thomas T. 2016. The sponge hologenome. mBio 7:e00135-16.
    34.
    Röthig T, Costa RM, Simona F, Baumgarten S, Torres AF, Radhakrishnan A, Aranda M, Voolstra CR. 2016. Distinct bacterial communities associated with the coral model Aiptasia in aposymbiotic and symbiotic states with Symbiodinium. Front Mar Sci 3.
    35.
    Erwin PM, Pineda MC, Webster N, Turon X, López-Legentil S. 2014. Down under the tunic: bacterial biodiversity hotspots and widespread ammonia-oxidizing archaea in coral reef ascidians. ISME J 8:575–588.
    36.
    Moran NA, Baumann P. 2000. Bacterial endosymbionts in animals. Curr Opin Microbiol 3:270–275.
    37.
    Moran NA, Sloan DB. 2015. The hologenome concept: helpful or hollow? PLoS Biol 13:e1002311.
    38.
    Douglas AE, Werren JH. 2016. Holes in the hologenome: why host-microbe symbioses are not holobionts. mBio 7:e02099-15.
    39.
    Mazel F, Davis KM, Loudon A, Kwong WK, Groussin M, Parfrey LW. 2018. Is host filtering the main driver of phylosymbiosis across the tree of life? mSystems 3:e00097-18.
    40.
    Sieber M, Pita L, Weiland-Bräuer N, Dirksen P, Wang J, Mortzfeld B, Franzenburg S, Schmitz RA, Baines JF, Fraune S, Hentschel U, Schulenburg H, Bosch TCG, Traulsen A. 2018. The neutral metaorganism. bioRxiv https://doi.org/10.1101/367243.
    41.
    Sloan WT, Lunn M, Woodcock S, Head IM, Nee S, Curtis TP. 2006. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ Microbiol 8:732–740.
    42.
    Brooks AW, Kohl KD, Brucker RM, van Opstal EJ, Bordenstein SR. 2016. Phylosymbiosis: relationships and functional effects of microbial communities across host evolutionary history. PLoS Biol 14:e2000225.
    43.
    Kohl KD, Dearing MD, Bordenstein SR. 2018. Microbial communities exhibit host species distinguishability and phylosymbiosis along the length of the gastrointestinal tract. Mol Ecol 27:1874–1883.
    44.
    Ross AA, Müller KM, Weese JS, Neufeld JD. 2018. Comprehensive skin microbiome analysis reveals the uniqueness of human skin and evidence for phylosymbiosis within the class Mammalia. Proc Natl Acad Sci U S A 115:E5786–E5795.
    45.
    Ochman H, Worobey M, Kuo CH, Ndjango JBN, Peeters M, Hahn BH, Hugenholtz P. 2010. Evolutionary relationships of wild hominids recapitulated by gut microbial communities. PLoS Biol 8:e1000546.
    46.
    Yeoh YK, Dennis PG, Paungfoo-Lonhienne C, Weber L, Brackin R, Ragan MA, Schmidt S, Hugenholtz P. 2017. Evolutionary conservation of a core root microbiome across plant phyla along a tropical soil chronosequence. Nat Commun 8:215.
    47.
    Pollock FJ, McMinds R, Smith S, Bourne DG, Willis BL, Medina M, Thurber RV, Zaneveld JR. 2018. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat Commun 9:4921.
    48.
    Schöttner S, Hoffmann F, Cárdenas P, Rapp HT, Boetius A, Ramette A. 2013. Relationships between host phylogeny, host type and bacterial community diversity in cold-water coral reef sponges. PLoS One 8:e55505.
    49.
    Easson CG, Thacker RW. 2014. Phylogenetic signal in the community structure of host-specific microbiomes of tropical marine sponges. Front Microbiol 5:532.
    50.
    Rivett DW, Bell T. 2018. Abundance determines the functional role of bacterial phylotypes in complex communities. Nat Microbiol 3:767–772.
    51.
    Nishiguchi MK, Ruby EG, McFall-Ngai MJ. 1998. Competitive dominance among strains of luminous bacteria provides an unusual form of evidence for parallel evolution in sepiolid squid-vibrio symbioses. Appl Environ Microbiol 64:3209–3213.
    52.
    Bandi C, Anderson TJC, Genchi C, Blaxter ML. 1998. Phylogeny of Wolbachia in filarial nematodes. Proc Biol Sci 265:2407–2413.
    53.
    Deines P, Bosch TCG. 2016. Transitioning from microbiome composition to microbial community interactions: the potential of the metaorganism hydra as an experimental model. Front Microbiol 7:1610.
    54.
    Mews LK, Smith DC. 1980. The green hydra symbiosis. III. The biotrophic transport of carbohydrate from alga to animal. Proc R Soc Lond B Biol Sci 209:377–401.
    55.
    Kawaida H, Ohba K, Koutake Y, Shimizu H, Tachida H, Kobayakawa Y. 2013. Symbiosis between hydra and chlorella: molecular phylogenetic analysis and experimental study provide insight into its origin and evolution. Mol Phylogenet Evol 66:906–914.
    56.
    Matcher GF, Waterworth SC, Walmsley TA, Matsatsa T, Parker-Nance S, Davies-Coleman MT, Dorrington RA. 2017. Keeping it in the family: coevolution of latrunculid sponges and their dominant bacterial symbionts. Microbiologyopen 6:e00417.
    57.
    Neave MJ, Apprill A, Ferrier-Pagès C, Voolstra CR. 2016. Diversity and function of prevalent symbiotic marine bacteria in the genus Endozoicomonas. Appl Microbiol Biotechnol 100:8315–8324.
    58.
    Neave MJ, Michell CT, Apprill A, Voolstra CR. 2017. Endozoicomonas genomes reveal functional adaptation and plasticity in bacterial strains symbiotically associated with diverse marine hosts. Sci Rep 7:40579.
    59.
    Neave MJ, Rachmawati R, Xun L, Michell CT, Bourne DG, Apprill A, Voolstra CR. 2017. Differential specificity between closely related corals and abundant Endozoicomonas endosymbionts across global scales. ISME J 11:186–200.
    60.
    Brune A. 2014. Symbiotic digestion of lignocellulose in termite guts. Nat Rev Microbiol 12:168–180.
    61.
    Ikeda-Ohtsubo W, Brune A. 2009. Cospeciation of termite gut flagellates and their bacterial endosymbionts: Trichonympha species and ‘Candidatus Endomicrobium trichonymphae’. Mol Ecol 18:332–342.
    62.
    Raina JB, Tapiolas D, Willis BL, Bourne DG. 2009. Coral-associated bacteria and their role in the biogeochemical cycling of sulfur. Appl Environ Microbiol 75:3492–3501.
    63.
    Lema KA, Willis BL, Bourne DG. 2012. Corals form characteristic associations with symbiotic nitrogen-fixing bacteria. Appl Environ Microbiol 78:3136–3144.
    64.
    Rädecker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C. 2015. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol 23:490–497.
    65.
    Lawson CA, Raina JB, Kahlke T, Seymour JR, Suggett DJ. 2018. Defining the core microbiome of the symbiotic dinoflagellate, Symbiodinium. Environ Microbiol Rep 10:7–11.
    66.
    Takiya DM, Tran PL, Dietrich CH, Moran NA. 2006. Co-cladogenesis spanning three phyla: leafhoppers (Insecta: Hemiptera: Cicadellidae) and their dual bacterial symbionts. Mol Ecol 15:4175–4191.
    67.
    Moitinho-Silva L, Díez-Vives C, Batani G, Esteves AIS, Jahn MT, Thomas T. 2017. Integrated metabolism in sponge-microbe symbiosis revealed by genome-centered metatranscriptomics. ISME J 11:1651–1666.
    68.
    Lackner G, Peters EE, Helfrich EJN, Piel J. 2017. Insights into the lifestyle of uncultured bacterial natural product factories associated with marine sponges. Proc Natl Acad Sci U S A 114:E347–E356.
    69.
    Nguyen MTHD, Liu M, Thomas T. 2014. Ankyrin-repeat proteins from sponge symbionts modulate amoebal phagocytosis. Mol Ecol 23:1635–1645.
    70.
    Reynolds D, Thomas T. 2016. Evolution and function of eukaryotic-like proteins from sponge symbionts. Mol Ecol 25:5242–5253.
    71.
    Díez-Vives C, Moitinho-Silva L, Nielsen S, Reynolds D, Thomas T. 2017. Expression of eukaryotic-like protein in the microbiome of sponges. Mol Ecol 26:1432–1451.
    72.
    Berry D, Loy A. 2018. Stable-isotope probing of human and animal microbiome function. Trends Microbiol 13:999-1007.
    73.
    Volland JM, Schintlmeister A, Zambalos H, Reipert S, Mozetič P, Espada-Hinojosa S, Turk V, Wagner M, Bright M. 2018. NanoSIMS and tissue autoradiography reveal symbiont carbon fixation and organic carbon transfer to giant ciliate host. ISME J 12:714–727.
    74.
    Hernandez-Agreda A, Gates RD, Ainsworth TD. 2017. Defining the core microbiome in corals’ microbial soup. Trends Microbiol 25:125–140.
    75.
    Ainsworth TD, Krause L, Bridge T, Torda G, Raina JB, Zakrzewski M, Gates RD, Padilla-Gamiño JL, Spalding HL, Smith C, Woolsey ES, Bourne DG, Bongaerts P, Hoegh-Guldberg O, Leggat W. 2015. The coral core microbiome identifies rare bacterial taxa as ubiquitous endosymbionts. ISME J 9:2261–2274.
    76.
    Weynberg KD, Wood-Charlson EM, Suttle CA, van Oppen MJH. 2014. Generating viral metagenomes from the coral holobiont. Front Microbiol 5:206.
    77.
    Wommack KE, Colwell RR. 2000. Virioplankton: viruses in aquatic ecosystems. Microbiol Mol Biol Rev 64:69–114.
    78.
    Ochman H, Moran NA. 2001. Genes lost and genes found: evolution of bacterial pathogenesis and symbiosis. Science 292:1096–1099.
    79.
    Oliver KM, Degnan PH, Hunter MS, Moran NA. 2009. Bacteriophages encode factors required for protection in a symbiotic mutualism. Science 325:992–994.
    80.
    Bettarel Y, Bouvier T, Nguyen HK, Thu PT. 2015. The versatile nature of coral-associated viruses. Environ Microbiol 17:3433–3439.
    81.
    Laffy PW, Wood-Charlson EM, Turaev D, Jutz S, Pascelli C, Bell SC, Peirce TE, Weynberg KD, Van OMJH, Rattei T, Webster NS. 25 March 2018. Reef invertebrate viromics : diversity, host specificity and functional capacity. Environ Microbiol doi:
    82.
    LaJeunesse TC, Parkinson JE, Gabrielson PW, Jeong HJ, Reimer JD, Voolstra CR, Santos SR. 2018. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr Biol 28:2570–2580.e6.
    83.
    Stat M, Carter D, Hoegh GO. 2006. The evolutionary history of Symbiodinium and scleractinian hosts—symbiosis, diversity, and the effect of climate change. Perspect Plant Ecol Evol Syst 8:23–43.
    84.
    Rowan R. 1998. Diversity and Ecology of Zooxanthellae on coral reefs. J Phycol 34:407–417.
    85.
    Krueger T, Gates RD. 2012. Cultivating endosymbionts - host environmental mimics support the survival of Symbiodinium C15 ex hospite. J Exp Mar Bio Ecol 413:169–176.
    86.
    Fisher RM, Henry LM, Cornwallis CK, Kiers ET, West SA. 2017. The evolution of host-symbiont dependence. Nat Commun 8:15973.
    87.
    Ceh J, van Keulen M, Bourne DG. 2013. Intergenerational transfer of specific bacteria in corals and possible implications for offspring fitness. Microb Ecol 65:227–231.
    88.
    Leite DCA, Leão P, Garrido AG, Lins U, Santos HF, Pires DO, Castro CB, van Elsas JD, Zilberberg C, Rosado AS, Peixoto RS. 2017. Broadcast spawning coral Mussismilia hispida can vertically transfer its associated bacterial core. Front Microbiol 8:176.
    89.
    Sharp KH, Distel D, Paul VJ. 2012. Diversity and dynamics of bacterial communities in early life stages of the Caribbean coral Porites astreoides. ISME J 6:790–801.
    90.
    Sharp KH, Ritchie KB, Schupp PJ, Ritson-Williams R, Paul VJ. 2010. Bacterial acquisition in juveniles of several broadcast spawning coral species. PLoS One 5:e10898.
    91.
    Sharp KH, Eam B, Faulkner DJ, Haygood MG. 2007. Vertical transmission of diverse microbes in the tropical sponge Corticium sp. Appl Environ Microbiol 73:622–629.
    92.
    Brucker RM, Bordenstein SR. 2012. Speciation by symbiosis. Trends Ecol Evol 27:443–451.
    93.
    Hughes TP, Kerry JT, Álvarez-Noriega M, Álvarez-Romero JG, Anderson KD, Baird AH, Babcock RC, Beger M, Bellwood DR, Berkelmans R, Bridge TC, Butler IR, Byrne M, Cantin NE, Comeau S, Connolly SR, Cumming GS, Dalton SJ, Diaz-Pulido G, Eakin CM, Figueira WF, Gilmour JP, Harrison HB, Heron SF, Hoey AS, Hobbs JPA, Hoogenboom MO, Kennedy EV, Kuo CY, Lough JM, Lowe RJ, Liu G, McCulloch MT, Malcolm HA, McWilliam MJ, Pandolfi JM, Pears RJ, Pratchett MS, Schoepf V, Simpson T, Skirving WJ, Sommer B, Torda G, Wachenfeld DR, Willis BL, Wilson SK. 2017. Global warming and recurrent mass bleaching of corals. Nature 543:373–377.
    94.
    Hughes TP, Barnes ML, Bellwood DR, Cinner JE, Cumming GS, Jackson JBC, Kleypas J, Van De Leemput IA, Lough JM, Morrison TH, Palumbi SR, Van Nes EH, Scheffer M. 2017. Coral reefs in the Anthropocene. Nature 546:82–90.
    95.
    Fan L, Reynolds D, Liu M, Stark M, Kjelleberg S, Webster NS, Thomas T. 2012. Functional equivalence and evolutionary convergence in complex communities of microbial sponge symbionts. Proc Natl Acad Sci 109:E1878–E1887.
    96.
    Bourne DG, Dennis PG, Uthicke S, Soo RM, Tyson GW, Webster N. 2013. Coral reef invertebrate microbiomes correlate with the presence of photosymbionts. ISME J 7:1452–1458.
    97.
    Li J, Chen Q, Long LJ, Dong J De, Yang J, Zhang S. 2014. Bacterial dynamics within the mucus, tissue and skeleton of the coral Porites lutea during different seasons. Sci Rep 4:1–8.
    98.
    Hakim JA, Koo H, Kumar R, Lefkowitz EJ, Morrow CD, Powell ML, Watts SA, Bej AK. 2016. The gut microbiome of the sea urchin, Lytechinus variegatus, from its natural habitat demonstrates selective attributes of microbial taxa and predictive metabolic profiles. FEMS Microbiol Ecol 92:1–12.
    99.
    Wessels W, Sprungala S, Watson SA, Miller DJ, Bourne DG. 2017. The microbiome of the octocoral Lobophytum pauciflorum: Minor differences between sexes and resilience to short-term stress. FEMS Microbiol Ecol 93:1–13.
    100.
    Ngangbam AK, Baten A, Waters DLE, Whalan S, Benkendorff K. 2015. Characterization of bacterial communities associated with the Tyrian purple producing gland in a marine gastropod. PLoS One 10:1–19.
    101.
    Shinzato C, Shoguchi E, Kawashima T, Hamada M, Hisata K, Tanaka M, Fujie M, Fujiwara M, Koyanagi R, Ikuta T, Fujiyama A, Miller DJ, Satoh N. 2011. Using the Acropora digitifera genome to understand coral responses to environmental change. Nature 476:320–323.
    102.
    Work TM, Aeby GS. 2014. Microbial aggregates within tissues infect a diversity of corals throughout the Indo-Pacific. Mar Ecol Prog Ser 500:1–9.
    103.
    Maldonado M. 2007. Intergenerational transmission of symbiotic bacteria in oviparous and viviparous demosponges, with emphasis on intracytoplasmically-compartmented bacterial types. J Mar Biol Assoc United Kingdom 87:1701–1713.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 10Number 126 February 2019
    eLocator: e02241-18
    Editor: Danielle A. Garsin
    University of Texas Health Science Center at Houston

    History

    Published online: 5 February 2019

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. codivergence
    2. coevolution
    3. marine invertebrates
    4. microbiome
    5. phylosymbiosis

    Contributors

    Authors

    Paul A. O’Brien
    College of Science and Engineering, James Cook University, Townsville, QLD, Australia
    Australian Institute of Marine Science, Townsville, QLD, Australia
    [email protected], Townsville, QLD, Australia
    Nicole S. Webster
    Australian Institute of Marine Science, Townsville, QLD, Australia
    [email protected], Townsville, QLD, Australia
    Australian Centre for Ecogenomics, University of Queensland, Brisbane, QLD, Australia
    David J. Miller
    ARC Centre of Excellence for Coral Reef Studies, James Cook University, Townsville, QLD, Australia
    Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Townsville, QLD, Australia
    David G. Bourne
    College of Science and Engineering, James Cook University, Townsville, QLD, Australia
    Australian Institute of Marine Science, Townsville, QLD, Australia
    [email protected], Townsville, QLD, Australia

    Editor

    Danielle A. Garsin
    Editor
    University of Texas Health Science Center at Houston

    Notes

    Address correspondence to David G. Bourne, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    CRISPR Spacers Indicate Preferential Matching of Specific Virioplankton Genes

    ABSTRACT

    Viral infection exerts selection pressure on marine microbes, as virus-induced cell lysis causes 20 to 50% of cell mortality, resulting in fluxes of biomass into oceanic dissolved organic matter. Archaeal and bacterial populations can defend against viral infection using the clustered regularly interspaced short palindromic repeat (CRISPR)-associated (Cas) system, which relies on specific matching between a spacer sequence and a viral gene. If a CRISPR spacer match to any gene within a viral genome is equally effective in preventing lysis, no viral genes should be preferentially matched by CRISPR spacers. However, if there are differences in effectiveness, certain viral genes may demonstrate a greater frequency of CRISPR spacer matches. Indeed, homology search analyses of bacterioplankton CRISPR spacer sequences against virioplankton sequences revealed preferential matching of replication proteins, nucleic acid binding proteins, and viral structural proteins. Positive selection pressure for effective viral defense is one parsimonious explanation for these observations. CRISPR spacers from virioplankton metagenomes preferentially matched methyltransferase and phage integrase genes within virioplankton sequences. These virioplankton CRISPR spacers may assist infected host cells in defending against competing phage. Analyses also revealed that half of the spacer-matched viral genes were unknown, some genes matched several spacers, and some spacers matched multiple genes, a many-to-many relationship. Thus, CRISPR spacer matching may be an evolutionary algorithm, agnostically identifying those genes under stringent selection pressure for sustaining viral infection and lysis. Investigating this subset of viral genes could reveal those genetic mechanisms essential to virus-host interactions and provide new technologies for optimizing CRISPR defense in beneficial microbes.
    IMPORTANCE The CRISPR-Cas system is one means by which bacterial and archaeal populations defend against viral infection which causes 20 to 50% of cell mortality in the ocean. We tested the hypothesis that certain viral genes are preferentially targeted for the initial attack of the CRISPR-Cas system on a viral genome. Using CASC, a pipeline for CRISPR spacer discovery, and metagenome data from oceanic microbes and viruses, we found a clear subset of viral genes with high match frequencies to CRISPR spacers. Moreover, we observed a many-to-many relationship of spacers and viral genes. These high-match viral genes were involved in nucleotide metabolism, DNA methylation, and viral structure. It is possible that CRISPR spacer matching is an evolutionary algorithm pointing to those viral genes most important to sustaining infection and lysis. Studying these genes may advance the understanding of virus-host interactions in nature and provide new technologies for leveraging CRISPR-Cas systems in beneficial microbes.

    REFERENCES

    1.
    Weitz JS, Stock CA, Wilhelm SW, Bourouiba L, Coleman ML, Buchan A, Follows MJ, Fuhrman JA, Jover LF, Lennon JT, Middelboe M, Sonderegger DL, Suttle CA, Taylor BP, Frede Thingstad T, Wilson WH, Eric Wommack K. 2015. A multitrophic model to quantify the effects of marine viruses on microbial food webs and ecosystem processes. ISME J 9:1352–1364.
    2.
    Poorvin L, Rinta-Kanto JM, Hutchins DA, Wilhelm SW. 2004. Viral release of iron and its bioavailability to marine plankton. Limnol Oceanogr 49:1734–1741.
    3.
    Labrie SJ, Samson JE, Moineau S. 2010. Bacteriophage resistance mechanisms. Nat Rev Microbiol 8:317–327.
    4.
    Dorman CJ. 2004. H-NS: a universal regulator for a dynamic genome. Nat Rev Microbiol 2:391–400.
    5.
    Sorek R, Kunin V, Hugenholtz P. 2008. CRISPR–a widespread system that provides acquired resistance against phages in bacteria and archaea. Nat Rev Microbiol 6:181–186.
    6.
    Ran FA, Hsu PDP, Wright J, Agarwala V, Scott DA, Zhang F. 2013. Genome engineering using the CRISPR-Cas9 system. Nat Protoc 8:2281–2308.
    7.
    Grissa I, Vergnaud G, Pourcel C. 2007. The CRISPRdb database and tools to display CRISPRs and to generate dictionaries of spacers and repeats. BMC Bioinformatics 8:172.
    8.
    Barrangou R, Fremaux C, Deveau H, Richards M, Boyaval P, Moineau S, Romero DA, Horvath P. 2007. CRISPR provides against viruses resistance acquired in prokaryotes. Science 315:1709–1712.
    9.
    Andersson AF, Banfield JF. 2008. Virus population dynamics and acquired virus resistance in natural microbial communities. Science 320:1047–1050.
    10.
    Berg Miller ME, Yeoman CJ, Chia N, Tringe SG, Angly FE, Edwards RA, Flint HJ, Lamed R, Bayer EA, White BA. 2012. Phage-bacteria relationships and CRISPR elements revealed by a metagenomic survey of the rumen microbiome. Environ Microbiol 14:207–227.
    11.
    Anderson RE, Brazelton WJ, Baross JA. 2011. Using CRISPRs as a metagenomic tool to identify microbial hosts of a diffuse flow hydrothermal vent viral assemblage. FEMS Microbiol Ecol 77:120–133.
    12.
    Paez-Espino D, Chen IMA, Palaniappan K, Ratner A, Chu K, Szeto E, Pillay M, Huang J, Markowitz VM, Nielsen T, Huntemann M, Reddy TBK, Pavlopoulos GA, Sullivan MB, Campbell BJ, Chen F, McMahon K, Hallam SJ, Denef V, Cavicchioli R, Caffrey SM, Streit WR, Webster J, Handley KM, Salekdeh GH, Tsesmetzis N, Setubal JC, Pope PB, Liu WT, Rivers AR, Ivanova NN, Kyrpides NC. 2017. IMG/VR: A database of cultured and uncultured DNA viruses and retroviruses. Nucleic Acids Res 45:D457–D465.
    13.
    Horvath P, Barrangou R. 2010. CRISPR-Cas, the immune system of bacteria and archaea. Science 327:167–170.
    14.
    Paez-Espino D, Morovic W, Sun CL, Thomas BC, Ueda K, Stahl B, Barrangou R, Banfield JF. 2013. Strong bias in the bacterial CRISPR elements that confer immunity to phage. Nat Commun 4:1430.
    15.
    Rosario K, Breitbart M. 2011. Exploring the viral world through metagenomics. Curr Opin Virol 1:289–297.
    16.
    Bland C, Ramsey TL, Sabree F, Lowe M, Brown K, Kyrpides NC, Hugenholtz P. 2007. CRISPR recognition tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats. BMC Bioinformatics 8:209.
    17.
    Yooseph S, Sutton G, Rusch DB, Halpern AL, Williamson SJ, Remington K, Eisen JA, Heidelberg KB, Manning G, Li W, Jaroszewski L, Cieplak P, Miller CS, Li H, Mashiyama ST, Joachimiak MP, van Belle C, Chandonia JM, Soergel DA, Zhai Y, Natarajan K, Lee S, Raphael BJ, Bafna V, Friedman R, Brenner SE, Godzik A, Eisenberg D, Dixon JE, Taylor SS, Strausberg RL, Frazier M, Venter JC. 2007. The Sorcerer II Global Ocean Sampling expedition: expanding the universe of protein families. PLoS Biol 5:e16.
    18.
    Pesant S, Not F, Picheral M, Kandels-Lewis S, Le Bescot N, Gorsky G, Iudicone D, Karsenti E, Speich S, Troublé R, Dimier C, Searson S, Acinas SG, Bork P, Boss E, Bowler C, De Vargas C, Follows M, Gorsky G, Grimsley N, Hingamp P, Iudicone D, Jaillon O, Kandels-Lewis S, Karp-Boss L, Karsenti E, Krzic U, Not F, Ogata H, Pesant S, Raes J, Reynaud EG, Sardet C, Sieracki M, Speich S, Stemmann L, Sullivan MB, Sunagawa S, Velayoudon D, Weissenbach J, Wincker P. 2015. Open science resources for the discovery and analysis of Tara Oceans data. Sci Data 2:150023.
    19.
    Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV. 2012. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Computational Biol 19:455–477.
    20.
    Edgar RC. 2007. PILER-CR: fast and accurate identification of CRISPR repeats. BMC Bioinformatics 8:18.
    21.
    Grissa I, Vergnaud G, Pourcel C. 2007. CRISPRFinder: a web tool to identify clustered regularly interspaced short palindromic repeats. Nucleic Acids Res 35:W52–W57.
    22.
    Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, Djahanschiri B, Zeller G, Mende DR, Alberti A, Cornejo-Castillo FM, Costea PI, Cruaud C, d’Ovidio F, Engelen S, Ferrera I, Gasol JM, Guidi L, Hildebrand F, Kokoszka F, Lepoivre C, Lima-Mendez G, Poulain J, Poulos BT, Royo-Llonch M, Sarmento H, Vieira-Silva S, Dimier C, Picheral M, Searson S, Kandels-Lewis S, Bowler C, de Vargas C, Gorsky G, Grimsley N, Hingamp P, Iudicone D, Jaillon O, Not F, Ogata H, Pesant S, Speich S, Stemmann L, Sullivan MB, Weissenbach J, Wincker P, Karsenti E, Raes J, Acinas SG, Bork P, et al. 2015. Ocean plankton. Structure and function of the global ocean microbiome. Science 348:1261359.
    23.
    Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang HY, Cohoon M, de Crécy-Lagard V, Diaz N, Disz T, Edwards R, Fonstein M, Frank ED, Gerdes S, Glass EM, Goesmann A, Hanson A, Iwata RD, Jensen R, Jamshidi N, Krause L, Kubal M, Larsen N, Linke B, McHardy AC, Meyer F, Neuweger H, Olsen G, Olson R, Osterman A, Portnoy V, Pusch GD, Rodionov DA, Rückert C, Steiner J, Stevens R, Thiele I, Vassieva O, Ye Y, Zagnitko O, Vonstein V. 2005. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 33:5691–5702.
    24.
    Seed KD, Lazinski DW, Calderwood SB, Camilli A. 2013. A bacteriophage encodes its own CRISPR-Cas adaptive response to evade host innate immunity. Nature 494:489–491.
    25.
    Chénard C, Wirth JF, Suttle CA. 2016. Viruses infecting a freshwater filamentous cyanobacterium (Nostoc sp.) encode a functional CRISPR array and a proteobacterial DNA polymerase B. mBio 7:e00667-16.
    26.
    Marraffini LA, Sontheimer EJ. 2010. Self versus non-self discrimination during CRISPR RNA-directed immunity. Nature 463:568–571.
    27.
    Garrett RA, Vestergaard G, Shah SA. 2011. Archaeal CRISPR-based immune systems: Exchangeable functional modules. Trends Microbiol 19:549–556.
    28.
    Heler R, Samai P, Modell JW, Weiner C, Goldberg GW, Bikard D, Marraffini LA. 2015. Cas9 specifies functional viral targets during CRISPR-Cas adaptation. Nature 519:199–202.
    29.
    Iranzo J, Lobkovsky AE, Wolf YI, Koonin EV. 2013. Evolutionary dynamics of the prokaryotic adaptive immunity system CRISPR-Cas in an explicit ecological context. J Bacteriol 195:3834–3844.
    30.
    Sun CL, Thomas BC, Barrangou R, Banfield JF. 2016. Metagenomic reconstructions of bacterial CRISPR loci constrain population histories. ISME J 10:858–870.
    31.
    Ummat A, Bashir A. 2014. Resolving complex tandem repeats with long reads. Bioinformatics 30:3491–3498.
    32.
    Hara S, Koike I, Terauchi K, Kamiya H, Tanoue E. 1996. Abundance of viruses in deep oceanic waters. Mar Ecol Prog Ser 145:269–277.
    33.
    Westra ER, Van Houte S, Oyesiku-Blakemore S, Makin B, Broniewski JM, Best A, Bondy-Denomy J, Davidson A, Boots M, Buckling A. 2015. Parasite exposure drives selective evolution of constitutive versus inducible defense. Curr Biol 25:1043–1049.
    34.
    Morris RM, Rappé MS, Connon SA, Vergin KL, Siebold WA, Carlson CA, Giovannoni SJ. 2002. SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420:806–810.
    35.
    Zhao Y, Temperton B, Thrash JC, Schwalbach MS, Vergin KL, Landry ZC, Ellisman M, Deerinck T, Sullivan MB, Giovannoni SJ. 2013. Abundant SAR11 viruses in the ocean. Nature 494:357–360.
    36.
    Shah SA, Erdmann S, Mojica FJM, Garrett RA. 2013. Protospacer recognition motifs: mixed identities and functional diversity. RNA Biol 10:891–899.
    37.
    Minot S, Grunberg S, Wu GD, Lewis JD, Bushman FD. 2012. Hypervariable loci in the human gut virome. Proc Natl Acad Sci U S A 109:3962–3966.
    38.
    Doulatov S, Hodes A, Dai L, Mandhana N, Zimmerly S, Miller JF, Liu M, Deora R, Simons RW, Zimmerly S, Miller JF. 2004. Tropism switching in Bordetella bacteriophage defines a family of diversity-generating retroelements. Nature 431:476–481.
    39.
    Roux S, Hallam SJ, Woyke T, Sullivan MB. 2015. Viral dark matter and virus-host interactions resolved from publicly available microbial genomes. Elife 4:e08490.
    40.
    Emerson JB, Andrade K, Thomas BC, Norman A, Allen EE, Heidelberg KB, Banfield JF. 2013. Virus-host and CRISPR dynamics in Archaea-dominated hypersaline Lake Tyrrell, Victoria, Australia. Archaea 2013:370871.
    41.
    Tschitschko B, Williams TJ, Allen MA, Páez-Espino D, Kyrpides N, Zhong L, Raftery MJ, Cavicchioli R. 2015. Antarctic archaea–virus interactions: metaproteome-led analysis of invasion, evasion and adaptation. ISME J 9:2094–2107.
    42.
    Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, Rubin E, Ivanova NN, Kyrpides NC. 2016. Uncovering Earth’s virome. Nature 536:425–430.
    43.
    Suzek BE, Huang H, McGarvey P, Mazumder R, Wu CH. 2007. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23:1282–1288.
    44.
    Angly FE, Willner D, Rohwer F, Hugenholtz P, Tyson GW. 2012. Grinder: a versatile amplicon and shotgun sequence simulator. Nucleic Acids Res 40:e94.
    45.
    Rho M, Wu YW, Tang H, Doak TG, Ye Y. 2012. Diverse CRISPRs evolving in human microbiomes. PLoS Genet 8:e1002441.
    46.
    Li W, Godzik A. 2006. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22:1658–1659.
    47.
    Langmead B, Salzberg SL. 2012. Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359.
    48.
    Li H. 2011. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27:2987–2993.
    49.
    Wommack KE, Bhavsar J, Ravel J. 2008. Metagenomics: read length matters. Appl Environ Microbiol 74:1453–1463.
    50.
    Skennerton CT, Imelfort M, Tyson GW. 2013. Crass: identification and reconstruction of CRISPR from unassembled metagenomic data. Nucleic Acids Res 41:e105.
    51.
    Sorokin VA, Gelfand MS, Artamonova II. 2010. Evolutionary dynamics of clustered irregularly interspaced short palindromic repeat systems in the ocean metagenome. Appl Environ Microbiol 76:2136–2144.
    52.
    Karsenti E, Acinas SG, Bork P, Bowler C, De Vargas C, Raes J, Sullivan M, Arendt D, Benzoni F, Claverie J-M, Follows M, Gorsky G, Hingamp P, Iudicone D, Jaillon O, Kandels-Lewis S, Krzic U, Not F, Ogata H, Pesant S, Reynaud EG, Sardet C, Sieracki ME, Speich S, Velayoudon D, Weissenbach J, Wincker P, Tara Oceans Consortium. 2011. A holistic approach to marine eco-systems biology. PLoS Biol 9:e1001177.
    53.
    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley G. a, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone C. a, Mcdonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters W. a, Widmann J, Yatsunenko T, Zaneveld J, Knight R. 2010. QIIME allows analysis of high- throughput community sequencing data. Nat Methods 7:335–336.
    54.
    Wommack KE, Bhavsar J, Polson SW, Chen J, Dumas M, Srinivasiah S, Furman M, Jamindar S, Nasko DJ. 2012. VIROME: a standard operating procedure for analysis of viral metagenome sequences. Stand Genomic Sci 6:427–439.
    55.
    Pride DT, Salzman J, Relman DA. 2012. Comparisons of clustered regularly interspaced short palindromic repeats and viromes in human saliva reveal bacterial adaptations to salivary viruses. Environ Microbiol 14:2564–2576.
    56.
    Manica A, Zebec Z, Steinkellner J, Schleper C. 2013. Unexpectedly broad target recognition of the CRISPR-mediated virus defence system in the archaeon sulfolobus solfataricus. Nucleic Acids Res 41:10509–10517.
    57.
    Horvath P, Romero DA, Coute-Monvoisin A-C, Richards M, Deveau H, Moineau S, Boyaval P, Fremaux C, Barrangou R. 2008. Diversity, activity, and evolution of CRISPR loci in Streptococcus thermophilus. J Bacteriol 190:1401–1412.
    58.
    Noguchi H, Park J, Takagi T. 2006. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res 34:5623–5630.
    59.
    R Core Team. 2005. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
    60.
    Hijmans RJ. 2014. Introduction to the “geosphere” package (version 1.3-8).

    Information & Contributors

    Information

    Published In

    mBio
    Volume 10Number 230 April 2019
    eLocator: e02651-18
    Editor: Jennifer Martiny
    University of California, Irvine

    History

    Received: 4 December 2018
    Accepted: 14 December 2018
    Published online: 5 March 2019

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. bacteriophage
    2. biogeography
    3. bioinformatics
    4. metagenomics
    5. oceanography
    6. viral ecology

    Contributors

    Authors

    Daniel J. Nasko
    Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
    Present address: Daniel J. Nasko, Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland, USA.
    Barbra D. Ferrell
    Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
    Ryan M. Moore
    Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
    Jaysheel D. Bhavsar
    Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
    Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA
    K. Eric Wommack
    Delaware Biotechnology Institute, University of Delaware, Newark, Delaware, USA

    Editor

    Jennifer Martiny
    Editor
    University of California, Irvine

    Reviewers

    Steven Wilhelm
    Solicited external reviewer
    University of Tennessee at Knoxville
    Graham Hatfull
    Solicited external reviewer
    University of Pittsburgh

    Notes

    Address correspondence to K. Eric Wommack, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Cell-to-Cell Variation in Defective Virus Expression and Effects on Host Responses during Influenza Virus Infection

    Cell-to-Cell Variation in Defective Virus Expression and Effects on Host Responses during Influenza Virus Infection

    ABSTRACT

    Virus and host factors contribute to cell-to-cell variation in viral infections and determine the outcome of the overall infection. However, the extent of the variability at the single-cell level and how it impacts virus-host interactions at a system level are not well understood. To characterize the dynamics of viral transcription and host responses, we used single-cell RNA sequencing to quantify at multiple time points the host and viral transcriptomes of human A549 cells and primary bronchial epithelial cells infected with influenza A virus. We observed substantial variability in viral transcription between cells, including the accumulation of defective viral genomes (DVGs) that impact viral replication. We show (i) a correlation between DVGs and virus-induced variation of the host transcriptional program and (ii) an association between differential inductions of innate immune response genes and attenuated viral transcription in subpopulations of cells. These observations at the single-cell level improve our understanding of the complex virus-host interplay during influenza virus infection.
    IMPORTANCE Defective influenza virus particles generated during viral replication carry incomplete viral genomes and can interfere with the replication of competent viruses. These defective genomes are thought to modulate the disease severity and pathogenicity of an influenza virus infection. Different defective viral genomes also introduce another source of variation across a heterogeneous cell population. Evaluating the impact of defective virus genomes on host cell responses cannot be fully resolved at the population level, requiring single-cell transcriptional profiling. Here, we characterized virus and host transcriptomes in individual influenza virus-infected cells, including those of defective viruses that arise during influenza A virus infection. We established an association between defective virus transcription and host responses and validated interfering and immunostimulatory functions of identified dominant defective viral genome species in vitro. This study demonstrates the intricate effects of defective viral genomes on host transcriptional responses and highlights the importance of capturing host-virus interactions at the single-cell level.

    REFERENCES

    1.
    Heldt FS, Kupke SY, Dorl S, Reichl U, Frensing T. 2015. Single-cell analysis and stochastic modelling unveil large cell-to-cell variability in influenza A virus infection. Nat Commun 6:8938.
    2.
    Guo F, Li S, Caglar MU, Mao Z, Liu W, Woodman A, Arnold JJ, Wilke CO, Huang TJ, Cameron CE. 2017. Single-cell virology: on-chip investigation of viral infection dynamics. Cell Rep 21:1692–1704.
    3.
    Raj A, van Oudenaarden A. 2008. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135:216–226.
    4.
    Lwoff A, Dulbecco R, Vogt M, Lwoff M. 1955. Kinetics of the release of poliomyelitis virus from single cells. Virology 1:128–139.
    5.
    Zhu Y, Yongky A, Yin J. 2009. Growth of an RNA virus in single cells reveals a broad fitness distribution. Virology 385:39–46.
    6.
    Timm A, Yin J. 2012. Kinetics of virus production from single cells. Virology 424:11–17.
    7.
    Genoyer E, López CB, Genoyer E, López CB. 2019. Defective viral genomes alter how Sendai virus interacts with cellular trafficking machinery, leading to heterogeneity in the production of viral particles among infected cells. J Virol 93:e01579-18.
    8.
    Xu J, Sun Y, Li Y, Ruthel G, Weiss SR, Raj A, Beiting D, López CB. 2017. Replication defective viral genomes exploit a cellular pro-survival mechanism to establish paramyxovirus persistence. Nat Commun 8:799.
    9.
    Zanini F, Robinson ML, Croote D, Sahoo MK, Sanz AM, Ortiz-Lasso E, Albornoz LL, Rosso F, Montoya JG, Goo L, Pinsky BA, Quake SR, Einav S. 2018. Virus-inclusive single-cell RNA sequencing reveals the molecular signature of progression to severe dengue. Proc Natl Acad Sci U S A 115:E12363–E12369.
    10.
    Zanini F, Pu S-Y, Bekerman E, Einav S, Quake SR. 2018. Single-cell transcriptional dynamics of flavivirus infection. Elife 7:e32942.
    11.
    Dou D, Hernández-Neuta I, Wang H, Östbye H, Qian X, Thiele S, Resa-Infante P, Kouassi NM, Sender V, Hentrich K, Mellroth P, Henriques-Normark B, Gabriel G, Nilsson M, Daniels R. 2017. Analysis of IAV replication and co-infection dynamics by a versatile RNA viral genome labeling method. Cell Rep 20:251–263.
    12.
    Russell AB, Trapnell C, Bloom JD. 2018. Extreme heterogeneity of influenza virus infection in single cells. Elife 7:e32303.
    13.
    Russell AB, Elshina E, Kowalsky JR, Te Velthuis AJW, Bloom JD. 2019. Single-cell virus sequencing of influenza infections that trigger innate immunity. J Virol 93:e00500-19.
    14.
    Steuerman Y, Cohen M, Peshes-Yaloz N, Valadarsky L, Cohn O, David E, Frishberg A, Mayo L, Bacharach E, Amit I, Gat-Viks I. 2018. Dissection of influenza infection in vivo by single-cell RNA sequencing. Cell Syst 6:679–691.
    15.
    Ramos I, Smith G, Ruf-Zamojski F, Martínez-Romero C, Fribourg M, Carbajal EA, Hartmann BM, Nair VD, Marjanovic N, Monteagudo PL, DeJesus VA, Mutetwa T, Zamojski M, Tan GS, Jayaprakash C, Zaslavsky E, Albrecht RA, Sealfon SC, García-Sastre A, Fernandez-Sesma A. 2019. Innate immune response to influenza virus at single-cell resolution in human epithelial cells revealed paracrine induction of interferon lambda 1. J Virol 93:e00559-19.
    16.
    Brooke CB, Ince WL, Wrammert J, Ahmed R, Wilson PC, Bennink JR, Yewdell JW. 2013. Most influenza A virions fail to express at least one essential viral protein. J Virol 87:3155–3162.
    17.
    Henle W, Henle G. 1943. Interference of inactive virus with the propagation of virus of influenza A. Science 98:87–89.
    18.
    von Magnus P. 1954. Incomplete forms of influenza virus. Adv Virus Res 2:59–79.
    19.
    Davis AR, Nayak DP. 1979. Sequence relationships among defective interfering influenza viral RNAs. Proc Natl Acad Sci U S A 76:3092–3096.
    20.
    Nayak DP, Chambers TM, Akkina RK. 1985. Defective-interfering (DI) RNAs of influenza viruses: origin, structure, expression, and interference. Curr Top Microbiol Immunol 114:103–151.
    21.
    Kantorovich-Prokudina EN, Semyonova NP, Berezina ON, Zhdanov VM. 1980. Gradual changes of influenza virions during passage of undiluted material. J Gen Virol 50:23–31.
    22.
    Saira K, Lin X, DePasse JV, Halpin R, Twaddle A, Stockwell T, Angus B, Cozzi-Lepri A, Delfino M, Dugan V, Dwyer DE, Freiberg M, Horban A, Losso M, Lynfield R, Wentworth DN, Holmes EC, Davey R, Wentworth DE, Ghedin E. 2013. Sequence analysis of in vivo defective interfering-like RNA of influenza A H1N1 pandemic virus. J Virol 87:8064–8074.
    23.
    Vasilijevic J, Zamarreño N, Oliveros JC, Rodriguez-Frandsen A, Gómez G, Rodriguez G, Pérez-Ruiz M, Rey S, Barba I, Pozo F, Casas I, Nieto A, Falcón A. 2017. Reduced accumulation of defective viral genomes contributes to severe outcome in influenza virus infected patients. PLoS Pathog 13:e1006650.
    24.
    Davis AR, Hiti AL, Nayak DP. 1980. Influenza defective interfering viral RNA is formed by internal deletion of genomic RNA. Proc Natl Acad Sci U S A 77:215–219.
    25.
    Jennings PA, Finch JT, Winter G, Robertson JR. 1983. Does the higher order structure of the influenza virus ribonucleoprotein guide sequence rearrangements in influenza viral RNA. Cell 34:619–627.
    26.
    Lazzarini RA, Keene JD, Schubert M. 1981. The origins of defective interfering particles of the negative-strand RNA viruses. Cell 26:145–154.
    27.
    Dimmock NJ, Easton AJ. 2014. Defective interfering influenza virus RNAs: time to reevaluate their clinical potential as broad-spectrum antivirals? J Virol 88:5217–5227.
    28.
    Manzoni TB, López CB. 2018. Defective (interfering) viral genomes re-explored: impact on antiviral immunity and virus persistence. Future Virol 13:493–504.
    29.
    Vignuzzi M, López CB. 2019. Defective viral genomes are key drivers of the virus-host interaction. Nat Microbiol 4:1075–1087.
    30.
    Genoyer E, López CB. 2019. The impact of defective viruses on infection and immunity. Annu Rev Virol 6:547–566.
    31.
    Dimmock NJ, Rainsford EW, Scott PD, Marriott AC. 2008. Influenza virus protecting RNA: an effective prophylactic and therapeutic antiviral. J Virol 82:8570–8578.
    32.
    Scott PD, Meng B, Marriott AC, Easton AJ, Dimmock NJ. 2011. Defective interfering influenza virus confers only short-lived protection against influenza virus disease: evidence for a role for adaptive immunity in DI virus-mediated protection in vivo. Vaccine 29:6584–6591.
    33.
    Dimmock NJ, Dove BK, Scott PD, Meng B, Taylor I, Cheung L, Hallis B, Marriott AC, Carroll MW, Easton AJ. 2012. Cloned defective interfering influenza virus protects ferrets from pandemic 2009 influenza A virus and allows protective immunity to be established. PLoS One 7:e49394.
    34.
    Dimmock NJ, Dove BK, Meng B, Scott PD, Taylor I, Cheung L, Hallis B, Marriott AC, Carroll MW, Easton AJ. 2012. Comparison of the protection of ferrets against pandemic 2009 influenza A virus (H1N1) by 244 DI influenza virus and oseltamivir. Antiviral Res 96:376–385.
    35.
    Baum A, Sachidanandam R, García-Sastre A. 2010. Preference of RIG-I for short viral RNA molecules in infected cells revealed by next-generation sequencing. Proc Natl Acad Sci U S A 107:16303–16308.
    36.
    Killip MJ, Fodor E, Randall RE. 2015. Influenza virus activation of the interferon system. Virus Res 209:11–22.
    37.
    Frensing T, Pflugmacher A, Bachmann M, Peschel B, Reichl U. 2014. Impact of defective interfering particles on virus replication and antiviral host response in cell culture-based influenza vaccine production. Appl Microbiol Biotechnol 98:8999–9008.
    38.
    Ngunjiri JM, Buchek GM, Mohni KN, Sekellick MJ, Marcus PI. 2013. Influenza virus subpopulations: exchange of lethal H5N1 virus NS for H1N1 virus NS triggers de novo generation of defective-interfering particles and enhances interferon-inducing particle efficiency. J Interferon Cytokine Res 33:99–107.
    39.
    Pérez-Cidoncha M, Killip MJ, Oliveros JC, Asensio VJ, Fernández Y, Bengoechea JA, Randall RE, Ortín J. 2014. An unbiased genetic screen reveals the polygenic nature of the influenza virus anti-interferon response. J Virol 88:4632–4646.
    40.
    Tapia K, Kim W-K, Sun Y, Mercado-López X, Dunay E, Wise M, Adu M, López CB. 2013. Defective viral genomes arising in vivo provide critical danger signals for the triggering of lung antiviral immunity. PLoS Pathog 9:e1003703.
    41.
    Janda MJ, Davis AR, Nayak DP, De BK. 1979. Diversity and generation of defective interfering influenza virus particles. Virology 95:48–58.
    42.
    Chanda PK, Chambers TM, Nayak DP. 1983. In vitro transcription of defective interfering particles of influenza virus produces polyadenylic acid-containing complementary RNAs. J Virol 45:55–61.
    43.
    Alnaji FG, Holmes JR, Rendon G, Vera JC, Fields CJ, Martin BE, Brooke CB. 2019. Sequencing framework for the sensitive detection and precise mapping of defective interfering particle-associated deletions across influenza A and B viruses. J Virol 93:e00354-19.
    44.
    Iwasaki A, Pillai PS. 2014. Innate immunity to influenza virus infection. Nat Rev Immunol 14:315–328.
    45.
    Chen S, Short JAL, Young DF, Killip MJ, Schneider M, Goodbourn S, Randall RE. 2010. Heterocellular induction of interferon by negative-sense RNA viruses. Virology 407:247–255.
    46.
    He Y, Xu K, Keiner B, Zhou J, Czudai V, Li T, Chen Z, Liu J, Klenk H-D, Shu YL, Sun B. 2010. Influenza A virus replication induces cell cycle arrest in G0/G1 phase. J Virol 84:12832–12840.
    47.
    Jiang W, Wang Q, Chen S, Gao S, Song L, Liu P, Huang W. 2013. Influenza A virus NS1 induces G0/G1 cell cycle arrest by inhibiting the expression and activity of RhoA protein. J Virol 87:3039–3052.
    48.
    Song W-M, Zhang B. 2015. Multiscale embedded gene co-expression network analysis. PLoS Comput Biol 11:e1004574.
    49.
    Poirier EZ, Vignuzzi M. 2017. Virus population dynamics during infection. Curr Opin Virol 23:82–87.
    50.
    Brooke CB. 2017. Population diversity and collective interactions during influenza virus infection. J Virol 91:e01164-17.
    51.
    Rezelj VV, Levi LI, Vignuzzi M. 2018. The defective component of viral populations. Curr Opin Virol 33:74–80.
    52.
    Marriott AC, Dimmock NJ. 2010. Defective interfering viruses and their potential as antiviral agents. Rev Med Virol 20:51–62.
    53.
    López CB. 2014. Defective viral genomes: critical danger signals of viral infections. J Virol 88:8720–8723.
    54.
    Easton AJ, Scott PD, Edworthy NL, Meng B, Marriott AC, Dimmock NJ. 2011. A novel broad-spectrum treatment for respiratory virus infections: influenza-based defective interfering virus provides protection against pneumovirus infection in vivo. Vaccine 29:2777–2784.
    55.
    Schmid S, Mordstein M, Kochs G, García-Sastre A, tenOever BR. 2010. Transcription factor redundancy ensures induction of the antiviral state. J Biol Chem 285:42013–42022.
    56.
    Wang N, Dong Q, Li J, Jangra RK, Fan M, Brasier AR, Lemon SM, Pfeffer LM, Li K. 2010. Viral induction of the zinc finger antiviral protein is IRF3-dependent but NF-κB-independent. J Biol Chem 285:6080–6090.
    57.
    Ourthiague DR, Birnbaum H, Ortenlof N, Vargas JD, Wollman R, Hoffmann A. 2015. Limited specificity of IRF3 and ISGF3 in the transcriptional innate-immune response to double-stranded RNA. J Leukoc Biol 98:119–128.
    58.
    Grandvaux N, Servant MJ, tenOever B, Sen GC, Balachandran S, Barber GN, Lin R, Hiscott J. 2002. Transcriptional profiling of interferon regulatory factor 3 target genes: direct involvement in the regulation of interferon-stimulated genes. J Virol 76:5532–5539.
    59.
    Ning S, Pagano JS, Barber GN. 2011. IRF7: activation, regulation, modification and function. Genes Immun 12:399–414.
    60.
    Yanai H, Negishi H, Taniguchi T. 2012. The IRF family of transcription factors. Oncoimmunology 1:1376–1386.
    61.
    Zemke NR, Berk AJ. 2017. The adenovirus E1A C terminus suppresses a delayed antiviral response and modulates Ras signaling. Cell Host Microbe 22:789–800.
    62.
    Re GG, Gupta KC, Kingsbury DW. 1983. Genomic and copy-back 3′ termini in Sendai virus defective interfering RNA species. J Virol 45:659–664.
    63.
    Strahle L, Garcin D, Kolakofsky D. 2006. Sendai virus defective-interfering genomes and the activation of interferon-beta. Virology 351:101–111.
    64.
    Strähle L, Marq J-B, Brini A, Hausmann S, Kolakofsky D, Garcin D. 2007. Activation of the beta interferon promoter by unnatural Sendai virus infection requires RIG-I and is inhibited by viral C proteins. J Virol 81:12227–12237.
    65.
    Xu J, Mercado-López X, Grier JT, Kim W-K, Chun LF, Irvine EB, Del Toro Duany Y, Kell A, Hur S, Gale M, Raj A, López CB. 2015. Identification of a natural viral RNA motif that optimizes sensing of viral RNA by RIG-I. mBio 6:e01265-15.
    66.
    Mercado-López X, Cotter CR, Kim W-K, Sun Y, Muñoz L, Tapia K, López CB. 2013. Highly immunostimulatory RNA derived from a Sendai virus defective viral genome. Vaccine 31:5713–5721.
    67.
    Te Velthuis AJW, Long JC, Bauer DLV, Fan RLY, Yen H-L, Sharps J, Siegers JY, Killip MJ, French H, Oliva-Martín MJ, Randall RE, de Wit E, van Riel D, Poon LLM, Fodor E. 2018. Mini viral RNAs act as innate immune agonists during influenza virus infection. Nat Microbiol 3:1234–1242.
    68.
    Turrell L, Lyall JW, Tiley LS, Fodor E, Vreede FT. 2013. The role and assembly mechanism of nucleoprotein in influenza A virus ribonucleoprotein complexes. Nat Commun 4:1591.
    69.
    Boergeling Y, Rozhdestvensky TS, Schmolke M, Resa-Infante P, Robeck T, Randau G, Wolff T, Gabriel G, Brosius J, Ludwig S. 2015. Evidence for a novel mechanism of influenza virus-induced type I interferon expression by a defective RNA-encoded protein. PLoS Pathog 11:e1004924.
    70.
    O’Neal JT, Upadhyay AA, Wolabaugh A, Patel NB, Bosinger SE, Suthar MS. 2018. West Nile virus-inclusive single-cell RNA sequencing reveals heterogeneity in the type I interferon response within single cells. J Virol 93:e01778-18.
    71.
    Drayman N, Patel P, Vistain L, Tay S. 2019. HSV-1 single-cell analysis reveals the activation of anti-viral and developmental programs in distinct sub-populations. Elife 8:e46339.
    72.
    Xin G, Zander R, Schauder DM, Chen Y, Weinstein JS, Drobyski WR, Tarakanova V, Craft J, Cui W. 2018. Single-cell RNA sequencing unveils an IL-10-producing helper subset that sustains humoral immunity during persistent infection. Nat Commun 9:5037.
    73.
    Sjaastad LE, Fay EJ, Fiege JK, Macchietto MG, Stone IA, Markman MW, Shen S, Langlois RA. 2018. Distinct antiviral signatures revealed by the magnitude and round of influenza virus replication in vivo. Proc Natl Acad Sci U S A 115:9610–9615.
    74.
    Killip MJ, Jackson D, Pérez-Cidoncha M, Fodor E, Randall RE. 2017. Single-cell studies of IFN-β promoter activation by wild-type and NS1-defective influenza A viruses. J Gen Virol 98:357–363.
    75.
    Cristinelli S, Ciuffi A. 2018. The use of single-cell RNA-Seq to understand virus-host interactions. Curr Opin Virol 29:39–50.
    76.
    Ozawa M, Victor ST, Taft AS, Yamada S, Li C, Hatta M, Das SC, Takashita E, Kakugawa S, Maher EA, Neumann G, Kawaoka Y. 2011. Replication-incompetent influenza A viruses that stably express a foreign gene. J Gen Virol 92:2879–2888.
    77.
    Zhou B, Donnelly ME, Scholes DT, St George K, Hatta M, Kawaoka Y, Wentworth DE. 2009. Single-reaction genomic amplification accelerates sequencing and vaccine production for classical and swine origin human influenza A viruses. J Virol 83:10309–10313.
    78.
    Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. 2013. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21.
    79.
    Bercovich-Kinori A, Tai J, Gelbart IA, Shitrit A, Ben-Moshe S, Drori Y, Itzkovitz S, Mandelboim M, Stern-Ginossar N, Bercovich-Kinori A, Tai J, Gelbart IA, Shitrit A, Ben-Moshe S, Drori Y, Itzkovitz S, Mandelboim M, Stern-Ginossar N. 2016. A systematic view on influenza induced host shutoff. Elife 5:e18311.
    80.
    Satija R, Farrell JA, Gennert D, Schier AF, Regev A. 2015. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33:495–502.
    81.
    McDavid A, Finak G, Chattopadyay PK, Dominguez M, Lamoreaux L, Ma SS, Roederer M, Gottardo R. 2013. Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments. Bioinformatics 29:461–467.
    82.
    Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, Slichter CK, Miller HW, McElrath MJ, Prlic M, Linsley PS, Gottardo R. 2015. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol 16:278.
    83.
    Huang DW, Sherman BT, Lempicki RA. 2009. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat Protoc 4:44–57.
    84.
    Huang DW, Sherman BT, Lempicki RA. 2009. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37:1–13.
    85.
    Kowalczyk MS, Tirosh I, Heckl D, Rao TN, Dixit A, Haas BJ, Schneider RK, Wagers AJ, Ebert BL, Regev A. 2015. Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells. Genome Res 25:1860–1872.
    86.
    Bolger A, Lohse M, Usadel B. 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120.
    87.
    Liao Y, Smyth G, Shi W. 2014. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–930.
    88.
    Liao Y, Smyth G, Shi W. 2013. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res 41:e108.
    89.
    Love M, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550.
    90.
    Robinson M, McCarthy D, Smyth G. 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–140.
    91.
    McCarthy D, Chen Y, Smyth G. 2012. Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res 40:4288–4297.
    92.
    Zhou B, Wentworth DE. 2012. Influenza A virus molecular virology techniques. Methods Protoc 865:175–192.
    93.
    Cheon H, Holvey-Bates EG, Schoggins JW, Forster S, Hertzog P, Imanaka N, Rice CM, Jackson MW, Junk DJ, Stark GR. 2013. IFNβ-dependent increases in STAT1, STAT2, and IRF9 mediate resistance to viruses and DNA damage. EMBO J 32:2751–2763.
    94.
    Zhou B, Li Y, Belser JA, Pearce MB, Schmolke M, Subba AX, Shi Z, Zaki SR, Blau DM, García-Sastre A, Tumpey TM, Wentworth DE. 2010. NS-based live attenuated H1N1 pandemic vaccines protect mice and ferrets. Vaccine 28:8015–8025.
    95.
    Mayanagi T, Morita T, Hayashi K, Fukumoto K, Sobue K. 2008. Glucocorticoid receptor-mediated expression of caldesmon regulates cell migration via the reorganization of the actin cytoskeleton. J Biol Chem 283:31183–31196.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 11Number 125 February 2020
    eLocator: e02880-19
    Editor: Mark R. Denison
    Vanderbilt University Medical Center

    History

    Received: 31 October 2019
    Accepted: 21 November 2019
    Published online: 14 January 2020

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. influenza A virus
    2. defective viral genome
    3. host immune response
    4. single-cell RNA-seq
    5. viral transcription

    Contributors

    Authors

    Chang Wang
    Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA
    Christian V. Forst
    Mount Sinai Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Tsui-wen Chou
    Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA
    Adam Geber
    Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA
    Minghui Wang
    Mount Sinai Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Wissam Hamou
    Mount Sinai Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Melissa Smith
    Mount Sinai Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Robert Sebra
    Mount Sinai Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Bin Zhang
    Mount Sinai Center for Transformative Disease Modeling, Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Bin Zhou
    Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA
    Present address: Bin Zhou, Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
    Elodie Ghedin
    Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA
    College of Global Public Health, New York University, New York, New York, USA

    Editor

    Mark R. Denison
    Editor
    Vanderbilt University Medical Center

    Notes

    Address correspondence to Elodie Ghedin, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Analysis of Anti-Influenza Virus Neuraminidase Antibodies in Children, Adults, and the Elderly by ELISA and Enzyme Inhibition: Evidence for Original Antigenic Sin

    Analysis of Anti-Influenza Virus Neuraminidase Antibodies in Children, Adults, and the Elderly by ELISA and Enzyme Inhibition: Evidence for Original Antigenic Sin

    ABSTRACT

    Antibody responses to influenza virus hemagglutinin provide protection against infection and are well studied. Less is known about the human antibody responses to the second surface glycoprotein, neuraminidase. Here, we assessed human antibody reactivity to a panel of N1, N2, and influenza B virus neuraminidases in different age groups, including children, adults, and the elderly. Using enzyme-linked immunosorbent assays (ELISA), we determined the breadth, magnitude, and isotype distribution of neuraminidase antibody responses to historic, current, and avian strains, as well as to recent isolates to which these individuals have not been exposed. It appears that antibody levels against N1 neuraminidases were lower than those against N2 or B neuraminidases. The anti-neuraminidase antibody levels increased with age and were, in general, highest against strains that circulated during the childhood of the tested individuals, providing evidence for “original antigenic sin.” Titers measured by ELISA correlated well with titers measured by the neuraminidase inhibition assays. However, in the case of the 2009 pandemic H1N1 virus, we found evidence of interference from antibodies binding to the conserved stalk domain of the hemagglutinin. In conclusion, we found that antibodies against the neuraminidase differ in magnitude and breadth between subtypes and age groups in the human population. (This study has been registered at ClinicalTrials.gov under registration no. NCT00336453, NCT00539981, and NCT00395174.)
    IMPORTANCE Anti-neuraminidase antibodies can afford broad protection from influenza virus infection in animal models and humans. However, little is known about the breadth and magnitude of the anti-neuraminidase response in the human population. Here we assessed antibody levels of children, adults, and the elderly against a panel of N1, N2, and type B influenza virus neuraminidases. We demonstrated that antibody levels measured by ELISA correlate well with functional neuraminidase inhibition titers. This is an important finding since ELISA is a simpler method than functional assays and can be implemented in high-throughput settings to analyze large numbers of samples. Furthermore, we showed that low titers of broadly cross-reactive antibodies against neuraminidase are prevalent in humans. By the use of an appropriate vaccination strategy, these titers could potentially be boosted to levels that might provide broad protection from influenza virus infection.

    REFERENCES

    1.
    Krammer F and Palese P. 2015. Advances in the development of influenza virus vaccines. Nat Rev Drug Discov14:167–182.
    2.
    Heaton NS, Sachs D, Chen CJ, Hai R, and Palese P. 2013. Genome-wide mutagenesis of influenza virus reveals unique plasticity of the hemagglutinin and NS1 proteins. Proc Natl Acad Sci U S A110:20248–20253.
    3.
    Wohlbold TJ and Krammer F. 2014. In the shadow of hemagglutinin: a growing interest in influenza viral neuraminidase and its role as a vaccine antigen. Viruses6:2465–2494.
    4.
    Eichelberger MC and Wan H. 2015. Influenza neuraminidase as a vaccine antigen. Curr Top Microbiol Immunol386:275–299.
    5.
    Johansson BE, Matthews JT, and Kilbourne ED. 1998. Supplementation of conventional influenza A vaccine with purified viral neuraminidase results in a balanced and broadened immune response. Vaccine16:1009–1015.
    6.
    Wohlbold TJ, Nachbagauer R, Xu H, Tan GS, Hirsh A, Brokstad KA, Cox RJ, Palese P, and Krammer F. 2015. Vaccination with adjuvanted recombinant neuraminidase induces broad heterologous, but not heterosubtypic, cross-protection against influenza virus infection in mice. mBio6:e02556-14.
    7.
    Monto AS, Petrie JG, Cross RT, Johnson E, Liu M, Zhong W, Levine M, Katz JM, and Ohmit SE. 2015. Antibody to influenza virus neuraminidase: an independent correlate of protection. J Infect Dis212:1191–1199.
    8.
    Kilbourne ED, Cerini CP, Khan MW, Mitchell JW, and Ogra PL. 1987. Immunologic response to the influenza virus neuraminidase is influenced by prior experience with the associated viral hemagglutinin. I. Studies in human vaccinees. J Immunol138:3010–3013.
    9.
    Johansson BE, Moran TM, and Kilbourne ED. 1987. Antigen-presenting B cells and helper T cells cooperatively mediate intravirionic antigenic competition between influenza A virus surface glycoproteins. Proc Natl Acad Sci U S A84:6869–6873.
    10.
    Johansson BE and Kilbourne ED. 1993. Dissociation of influenza virus hemagglutinin and neuraminidase eliminates their intravirionic antigenic competition. J Virol67:5721–5723.
    11.
    Webster RG, Laver WG, and Kilbourne ED. 1968. Reactions of antibodies with surface antigens of influenza virus. J Gen Virol3:315–326.
    12.
    Schulman JL, Khakpour M, and Kilbourne ED. 1968. Protective effects of specific immunity to viral neuraminidase on influenza virus infection of mice. J Virol2:778–786.
    13.
    Johansson BE, Grajower B, and Kilbourne ED. 1993. Infection-permissive immunization with influenza virus neuraminidase prevents weight loss in infected mice. Vaccine11:1037–1039.
    14.
    Couch RB, Kasel JA, Gerin JL, Schulman JL, and Kilbourne ED. 1974. Induction of partial immunity to influenza by a neuraminidase-specific vaccine. J Infect Dis129:411–420.
    15.
    Wohlbold TJ, Hirsh A, and Krammer F. 2015. An H10N8 influenza virus vaccine strain and mouse challenge model based on the human isolate A/Jiangxi-Donghu/346/13. Vaccine33:1102–1106.
    16.
    Wohlbold TJ, Chromikova V, Tan GS, Meade P, Amanat F, Comella P, Hirsh A, and Krammer F. 2015. Hemagglutinin stalk- and neuraminidase-specific monoclonal antibodies protect against lethal H10N8 influenza virus infection in mice. J Virol90:851–861.
    17.
    Wan H, Yang H, Shore DA, Garten RJ, Couzens L, Gao J, Jiang L, Carney PJ, Villanueva J, Stevens J, and Eichelberger MC. 2015. Structural characterization of a protective epitope spanning A(H1N1)pdm09 influenza virus neuraminidase monomers. Nat Commun6:6114.
    18.
    Easterbrook JD, Schwartzman LM, Gao J, Kash JC, Morens DM, Couzens L, Wan H, Eichelberger MC, and Taubenberger JK. 2012. Protection against a lethal H5N1 influenza challenge by intranasal immunization with virus-like particles containing 2009 pandemic H1N1 neuraminidase in mice. Virology432:39–44.
    19.
    Wan H, Gao J, Xu K, Chen H, Couzens LK, Rivers KH, Easterbrook JD, Yang K, Zhong L, Rajabi M, Ye J, Sultana I, Wan XF, Liu X, Perez DR, Taubenberger JK, and Eichelberger MC. 2013. Molecular basis for broad neuraminidase immunity: conserved epitopes in seasonal and pandemic H1N1 as well as H5N1 influenza viruses. J Virol87:9290–9300.
    20.
    Liu WC, Lin CY, Tsou YT, Jan JT, and Wu SC. 2015. Cross-reactive neuraminidase-inhibiting antibodies elicited by immunization with recombinant neuraminidase proteins of H5N1 and pandemic H1N1 influenza A viruses. J Virol89:7224–7234.
    21.
    Sandbulte MR, Jimenez GS, Boon AC, Smith LR, Treanor JJ, and Webby RJ. 2007. Cross-reactive neuraminidase antibodies afford partial protection against H5N1 in mice and are present in unexposed humans. PLoS Med4:e59.
    22.
    Jiang L, Fantoni G, Couzens L, Gao J, Plant E, Ye Z, Eichelberger MC, and Wan H. 2015. Comparative efficacy of monoclonal antibodies that bind to different epitopes of the 2009 pandemic H1N1 influenza virus neuraminidase. J Virol90:117–128.
    23.
    Halbherr SJ, Ludersdorfer TH, Ricklin M, Locher S, Berger Rentsch M, Summerfield A, and Zimmer G. 2015. Biological and protective properties of immune sera directed to influenza virus neuraminidase. J Virol89:1550–1563.
    24.
    Memoli MJ, Shaw PA, Han A, Czajkowski L, Reed S, Athota R, Bristol T, Fargis S, Risos K, Powers JH, Davey RT, and Taubenberger JK. 2016. Evaluation of antihemagglutinin and antineuraminidase antibodies as correlates of protection in an influenza A/H1N1 virus healthy human challenge model. mBio7:e00417-16.
    25.
    Couzens L, Gao J, Westgeest K, Sandbulte M, Lugovtsev V, Fouchier R, and Eichelberger M. 2014. An optimized enzyme-linked lectin assay to measure influenza A virus neuraminidase inhibition antibody titers in human sera. J Virol Methods210:7–14.
    26.
    Gao J, Couzens L, and Eichelberger MC. 2016. Measuring influenza neuraminidase inhibition antibody titers by enzyme-linked lectin assay. J Vis Exp2016:54573.
    27.
    Fonville JM, Wilks SH, James SL, Fox A, Ventresca M, Aban M, Xue L, Jones TC, Le NM, Pham QT, Tran ND, Wong Y, Mosterin A, Katzelnick LC, Labonte D, Le TT, van der Net G, Skepner E, Russell CA, Kaplan TD, Rimmelzwaan GF, Masurel N, de Jong JC, Palache A, Beyer WE, Le QM, Nguyen TH, Wertheim HF, Hurt AC, Osterhaus AD, Barr IG, Fouchier RA, Horby PW, and Smith DJ. 2014. Antibody landscapes after influenza virus infection or vaccination. Science346:996–1000.
    28.
    Fazekas de St Groth WRG and Webster RG. 1966. Disquisitions on original antigenic sin. II. Proof in lower creatures. J Exp Med124:347–361.
    29.
    Fazekas de St Groth WRG and Webster RG. 1966. Disquisitions of original antigenic sin. I. Evidence in man. J Exp Med124:331–345.
    30.
    Nachbagauer R, Choi A, Izikson R, Cox MM, Palese P, and Krammer F. 2016. Age dependence and isotype specificity of influenza virus hemagglutinin stalk-reactive antibodies in humans. mBio7:e01996-15.
    31.
    Pedersen GK, Höschler K, Øie Solbak SM, Bredholt G, Pathirana RD, Afsar A, Breakwell L, Nøstbakken JK, Raae AJ, Brokstad KA, Sjursen H, Zambon M, and Cox RJ. 2014. Serum IgG titres, but not avidity, correlates with neutralizing antibody response after H5N1 vaccination. Vaccine32:4550–4557.
    32.
    Manenti A, Tete SM, Mohn KG, Jul-Larsen Å, Gianchecchi E, Montomoli E, Brokstad KA, and Cox RJ. 2017. Comparative analysis of influenza A(H3N2) virus hemagglutinin specific IgG subclass and IgA responses in children and adults after influenza vaccination. Vaccine35:191–198.
    33.
    Kosik I and Yewdell JW. 2017. Influenza A virus hemagglutinin specific antibodies interfere with virion neuraminidase activity via two distinct mechanisms. Virology500:178–183.
    34.
    Dreyfus C, Laursen NS, Kwaks T, Zuijdgeest D, Khayat R, Ekiert DC, Lee JH, Metlagel Z, Bujny MV, Jongeneelen M, van der Vlugt R, Lamrani M, Korse HJ, Geelen E, Sahin Ö, Sieuwerts M, Brakenhoff JP, Vogels R, Li OT, Poon LL, Peiris M, Koudstaal W, Ward AB, Wilson IA, Goudsmit J, and Friesen RH. 2012. Highly conserved protective epitopes on influenza B viruses. Science337:1343–1348.
    35.
    Heaton NS, Leyva-Grado VH, Tan GS, Eggink D, Hai R, and Palese P. 2013. In vivo bioluminescent imaging of influenza A virus infection and characterization of novel cross-protective monoclonal antibodies. J Virol87:8272–8281.
    36.
    Desbien AL, Van Hoeven N, Reed SJ, Casey AC, Laurance JD, Baldwin SL, Duthie MS, Reed SG, and Carter D. 2013. Development of a high density hemagglutinin protein microarray to determine the breadth of influenza antibody responses. Biotechniques54:345–348.
    37.
    Wang TT, Tan GS, Hai R, Pica N, Petersen E, Moran TM, and Palese P. 2010. Broadly protective monoclonal antibodies against H3 influenza viruses following sequential immunization with different hemagglutinins. PLoS Pathog6:e1000796.
    38.
    Tanimoto T, Nakatsu R, Fuke I, Ishikawa T, Ishibashi M, Yamanishi K, Takahashi M, and Tamura S. 2005. Estimation of the neuraminidase content of influenza viruses and split-product vaccines by immunochromatography. Vaccine23:4598–4609.
    39.
    McCullers JA and Hayden FG. 2012. Fatal influenza B infections: time to reexamine influenza research priorities. J Infect Dis205:870–872.
    40.
    Tan GS, Lee PS, Hoffman RM, Mazel-Sanchez B, Krammer F, Leon PE, Ward AB, Wilson IA, and Palese P. 2014. Characterization of a broadly neutralizing monoclonal antibody that targets the fusion domain of group 2 influenza A virus hemagglutinin. J Virol88:13580–13592.
    41.
    Krammer F, Schinko T, Palmberger D, Tauer C, Messner P, and Grabherr R. 2010. Trichoplusia ni cells (High Five) are highly efficient for the production of influenza A virus-like particles: a comparison of two insect cell lines as production platforms for influenza vaccines. Mol Biotechnol45:226–234.
    42.
    Margine I, Palese P, and Krammer F. 2013. Expression of functional recombinant hemagglutinin and neuraminidase proteins from the novel H7N9 influenza virus using the baculovirus expression system. J Vis Exp81:e51112.
    43.
    Krammer F, Margine I, Tan GS, Pica N, Krause JC, and Palese P. 2012. A carboxy-terminal trimerization domain stabilizes conformational epitopes on the stalk domain of soluble recombinant hemagglutinin substrates. PLoS One7:e43603.
    44.
    King JC, Cox MM, Reisinger K, Hedrick J, Graham I, and Patriarca P. 2009. Evaluation of the safety, reactogenicity and immunogenicity of FluBlok trivalent recombinant baculovirus-expressed hemagglutinin influenza vaccine administered intramuscularly to healthy children aged 6–59 months. Vaccine27:6589–6594.
    45.
    Treanor JJ, El Sahly H, King J, Graham I, Izikson R, Kohberger R, Patriarca P, and Cox M. 2011. Protective efficacy of a trivalent recombinant hemagglutinin protein vaccine (FluBlok®) against influenza in healthy adults: a randomized, placebo-controlled trial. Vaccine29:7733–7739.
    46.
    Keitel WA, Treanor JJ, El Sahly HM, Gilbert A, Meyer AL, Patriarca PA, and Cox MM. 2009. Comparative immunogenicity of recombinant influenza hemagglutinin (rHA) and trivalent inactivated vaccine (TIV) among persons > or =65 years old. Vaccine28:379–385.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 8Number 23 May 2017
    eLocator: e02281-16
    Editors: Joseph Bresee
    CDC
    and Keith P. Klugman
    Emory University

    History

    Received: 16 December 2016
    Accepted: 23 February 2017
    Published online: 21 March 2017

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. influenza
    2. influenza virus
    3. neuraminidase
    4. neuraminidase inhibition

    Contributors

    Authors

    Madhusudan Rajendran
    Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Megan E. Ermler
    Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Paul Bunduc
    Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Fatima Amanat
    Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Ruvim Izikson
    Protein Sciences Corp., Meriden, Connecticut, USA
    Manon Cox
    Protein Sciences Corp., Meriden, Connecticut, USA
    Peter Palese
    Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
    Maryna Eichelberger
    Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA
    Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA

    Editors

    Joseph Bresee
    Invited Editor
    CDC
    Keith P. Klugman
    Editor
    Emory University

    Notes

    Address correspondence to Raffael Nachbagauer, [email protected], or Florian Krammer, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Microbiota and Metatranscriptome Changes Accompanying the Onset of Gingivitis

    ABSTRACT

    Over half of adults experience gingivitis, a mild yet treatable form of periodontal disease caused by the overgrowth of oral microbes. Left untreated, gingivitis can progress to a more severe and irreversible disease, most commonly chronic periodontitis. While periodontal diseases are associated with a shift in the oral microbiota composition, it remains unclear how this shift impacts microbiota function early in disease progression. Here, we analyzed the transition from health to gingivitis through both 16S v4-v5 rRNA amplicon and metatranscriptome sequencing of subgingival plaque samples from individuals undergoing an experimental gingivitis treatment. Beta-diversity analysis of 16S rRNA reveals that samples cluster based on disease severity and patient but not by oral hygiene status. Significant shifts in the abundance of several genera occurred during disease transition, suggesting a dysbiosis due to development of gingivitis. Comparing taxonomic abundance with transcriptomic activity revealed concordance of bacterial diversity composition between the two quantification assays in samples originating from both healthy and diseased teeth. Metatranscriptome sequencing analysis indicates that during the early stages of transition to gingivitis, a number of virulence-related transcripts were significantly differentially expressed in individual and across pooled patient samples. Upregulated genes include those involved in proteolytic and nucleolytic processes, while expression levels of those involved in surface structure assembly and other general virulence functions leading to colonization or adaptation within the host are more dynamic. These findings help characterize the transition from health to periodontal disease and identify genes associated with early disease.
    IMPORTANCE Although more than 50% of adults have some form of periodontal disease, there remains a significant gap in our understanding of its underlying cause. We initiated this study in order to better characterize the progression from oral health to disease. We first analyzed changes in the abundances of specific microorganisms in dental plaque collected from teeth during health and gingivitis, the mildest form of periodontal disease. We found that the clinical score of disease and patient from whom the sample originated but not tooth brushing are significantly correlated with microbial community composition. While a number of virulence-related gene transcripts are differentially expressed in gingivitis samples relative to health, not all are increased, suggesting that the overall activity of the microbiota is dynamic during disease transition. Better understanding of which microbes are present and their function during early periodontal disease can potentially lead to more targeted prophylactic approaches to prevent disease progression.

    REFERENCES

    1.
    Paster BJ, Olsen I, Aas JA, Dewhirst FE. 2006. The breadth of bacterial diversity in the human periodontal pocket and other oral sites. Periodontol 2000 42:80–87.
    2.
    Jenkinson HF, Lamont RJ. 2005. Oral microbial communities in sickness and in health. Trends Microbiol 13:589–595.
    3.
    Kolenbrander PE, Palmer RJ, Jr, Periasamy S, Jakubovics NS. 2010. Oral multispecies biofilm development and the key role of cell-cell distance. Nat Rev Microbiol 8:471–480.
    4.
    Kolenbrander PE, Andersen RN, Blehert DS, Egland PG, Foster JS, Palmer RJ, Jr. 2002. Communication among oral bacteria. Microbiol Mol Biol Rev 66:486–505.
    5.
    Kolenbrander PE. 2000. Oral microbial communities: biofilms, interactions, and genetic systems. Annu Rev Microbiol 54:413–437.
    6.
    Huang S, Li Z, He T, Bo C, Chang J, Li L, He Y, Liu J, Charbonneau D, Li R, Xu J. 2016. Microbiota-based signature of gingivitis treatments: a randomized study. Sci Rep 6:24705.
    7.
    Moore LV, Moore WE, Cato EP, Smibert RM, Burmeister JA, Best AM, Ranney RR. 1987. Bacteriology of human gingivitis. J Dent Res 66:989–995.
    8.
    Huang S, Li R, Zeng X, He T, Zhao H, Chang A, Bo C, Chen J, Yang F, Knight R, Liu J, Davis C, Xu J. 2014. Predictive modeling of gingivitis severity and susceptibility via oral microbiota. ISME J 8:1768–1780.
    9.
    Kistler JO, Booth V, Bradshaw DJ, Wade WG. 2013. Bacterial community development in experimental gingivitis. PLoS One 8:e71227.
    10.
    Sheiham A. 1997. Is the chemical prevention of gingivitis necessary to prevent severe periodontitis? Periodontol 2000 15:15–24.
    11.
    Dumitrescu AL. 2015. Editorial. Periodontal disease—a public health problem. Front Public Health 3:278.
    12.
    Abusleme L, Dupuy AK, Dutzan N, Silva N, Burleson JA, Strausbaugh LD, Gamonal J, Diaz PI. 2013. The subgingival microbiome in health and periodontitis and its relationship with community biomass and inflammation. ISME J 7:1016–1025.
    13.
    Ai D, Huang R, Wen J, Li C, Zhu J, Xia LC. 2017. Integrated metagenomic data analysis demonstrates that a loss of diversity in oral microbiota is associated with periodontitis. BMC Genomics 18:1041.
    14.
    Dabdoub SM, Ganesan SM, Kumar PS. 2016. Comparative metagenomics reveals taxonomically idiosyncratic yet functionally congruent communities in periodontitis. Sci Rep 6:38993.
    15.
    Duran-Pinedo AE, Chen T, Teles R, Starr JR, Wang X, Krishnan K, Frias-Lopez J. 2014. Community-wide transcriptome of the oral microbiome in subjects with and without periodontitis. ISME J 8:1659–1672.
    16.
    Jorth P, Turner KH, Gumus P, Nizam N, Buduneli N, Whiteley M. 2014. Metatranscriptomics of the human oral microbiome during health and disease. mBio 5:e01012-14.
    17.
    Li Y, He J, He Z, Zhou Y, Yuan M, Xu X, Sun F, Liu C, Li J, Xie W, Deng Y, Qin Y, VanNostrand JD, Xiao L, Wu L, Zhou J, Shi W, Zhou X. 2014. Phylogenetic and functional gene structure shifts of the oral microbiomes in periodontitis patients. ISME J 8:1879–1891.
    18.
    Shi B, Chang M, Martin J, Mitreva M, Lux R, Klokkevold P, Sodergren E, Weinstock GM, Haake SK, Li H. 2015. Dynamic changes in the subgingival microbiome and their potential for diagnosis and prognosis of periodontitis. mBio 6:e01926-14.
    19.
    Szafranski SP, Wos-Oxley ML, Vilchez-Vargas R, Jáuregui R, Plumeier I, Klawonn F, Tomasch J, Meisinger C, Kühnisch J, Sztajer H, Pieper DH, Wagner-Döbler I. 2015. High-resolution taxonomic profiling of the subgingival microbiome for biomarker discovery and periodontitis diagnosis. Appl Environ Microbiol 81:1047–1058.
    20.
    Wang J, Qi J, Zhao H, He S, Zhang Y, Wei S, Zhao F. 2013. Metagenomic sequencing reveals microbiota and its functional potential associated with periodontal disease. Sci Rep 3:1843.
    21.
    Yost S, Duran-Pinedo AE, Teles R, Krishnan K, Frias-Lopez J. 2015. Functional signatures of oral dysbiosis during periodontitis progression revealed by microbial metatranscriptome analysis. Genome Med 7:27.
    22.
    Szafranski SP, Deng Z-L, Tomasch L, Jarek M, Bhuju S, Meisinger C, Kuhnisch J, Sztajer H, Wagner-Dobler I. 2015. Functional biomarkers for chronic periodontitis and insights into the roles of Prevotella nigrescens and Fusobacterium nucleatum; a metatranscriptome analysis. NPJ Biofilms Microbiomes 1:15017.
    23.
    Huang S, Yang F, Zeng X, Chen J, Li R, Wen T, Li C, Wei W, Liu J, Chen L, Davis C, Xu J. 2011. Preliminary characterization of the oral microbiota of Chinese adults with and without gingivitis. BMC Oral Health 11:33.
    24.
    Meuric V, Le Gall-David S, Boyer E, Acuña-Amador L, Martin B, Fong SB, Barloy-Hubler F, Bonnaure-Mallet M. 2017. Signature of microbial dysbiosis in periodontitis. Appl Environ Microbiol 83:e00462-17.
    25.
    Kim SH, Kang SR, Park HJ, Kim JM, Yi WJ, Kim TI. 2017. Improved accuracy in periodontal pocket depth measurement using optical coherence tomography. J Periodont Implant Sci 47:13–19.
    26.
    Blazewicz SJ, Barnard RL, Daly RA, Firestone MK. 2013. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. ISME J 7:2061–2068.
    27.
    Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550.
    28.
    Wassenaar TM, Gaastra W. 2001. Bacterial virulence: can we draw the line? FEMS Microbiol Lett 201:1–7.
    29.
    Dumitrescu AL. 2010. Etiology and pathogenesis of periodontal disease, p 39–76. Springer-Verlag, Berlin, Germany.
    30.
    Smith I. 2003. Mycobacterium tuberculosis pathogenesis and molecular determinants of virulence. Clin Microbiol Rev 16:463–496.
    31.
    Kajfasz JK, Rivera-Ramos I, Abranches J, Martinez AR, Rosalen PL, Derr AM, Quivey RG, Lemos JA. 2010. Two Spx proteins modulate stress tolerance, survival and virulence in Streptococcus mutans. J Bacteriol 192:2546–2556.
    32.
    Okoko T, Blagova EV, Whittingham JL, Dover LG, Wilkinson AJ. 2015. Structural characterisation of the virulence-associated protein VapG from the horse pathogen Rhodococcus equi. Vet Microbiol 179:42–52.
    33.
    Górska R, Gregorek H, Kowalski J, Laskus-Perendyk A, Syczewska M, Madaliński K. 2003. Relationship between clinical parameters and cytokine profiles in inflamed gingival tissue and serum samples from patients with chronic periodontitis. J Clin Periodontol 30:1046–1052.
    34.
    Sculley DV, Langley-Evans SC. 2003. Periodontal disease is associated with lower antioxidant capacity in whole saliva and evidence of increased protein oxidation. Clin Sci 105:167–172.
    35.
    Leggott PJ, Robertson PB, Rothman DL, Murray PA, Jacob RA. 1986. The effect of controlled ascorbic acid depletion and supplementation on periodontal health. J Periodontol 57:480–485.
    36.
    Singer RE, Buckner BA. 1981. Butyrate and propionate: important components of toxic dental plaque extracts. Infect Immun 32:458–463.
    37.
    Tancharoen S, Matsuyama T, Kawahara K, Tanaka K, Lee LJ, Machigashira M, Noguchi K, Ito T, Imamura T, Potempa J, Kikuchi K, Maruyama I. 2015. Cleavage of host cytokeratin-6 by lysine-specific gingipain induces gingival inflammation in periodontitis patients. PLoS One 10:e0117775.
    38.
    de Diego I, Veillard F, Sztukowska MN, Guevara T, Potempa B, Pomowski A, Huntington JA, Potempa J, Gomis-Rüth FX. 2014. Structure and mechanism of cysteine peptidase gingipain K (Kgp), a major virulence factor of Porphyromonas gingivalis in periodontitis. J Biol Chem 289:32291–32302.
    39.
    Bengtsson T, Khalaf A, Khalaf H. 2015. Secreted gingipains from Porphyromonas gingivalis colonies exert potent immunomodulatory effects on human gingival fibroblasts. Microbiol Res 178:18–26.
    40.
    Paige M, Wang K, Burdick M, Park S, Cha J, Jeffery E, Sherman N, Shim YM. 2014. Role of leukotriene A4 hydrolase aminopeptidase in the pathogenesis of emphysema. J Immunol 192:5059–5068.
    41.
    Sakihama Y, Mizoguchi H, Oshima T, Ogasawara N. 2012. YdfH identified as a repressor of rspA by the use of reduced genome Escherichia coli MGF-01. Biosci Biotechnol Biochem 76:1688–1693.
    42.
    Socransky SS, Haffajee AD, Cugini MA, Smith C, Kent RL, Jr. 1998. Microbial complexes in subgingival plaque. J Clin Periodontol 25:134–144.
    43.
    Socransky SS, Haffajee AD. 2005. Periodontal microbial ecology. Periodontol 2000 38:135–187.
    44.
    Couturier MR, Slechta ES, Goulston C, Fisher MA, Hanson KE. 2012. Leptotrichia bacteremia in patients receiving high-dose chemotherapy. J Clin Microbiol 50:1228–1232.
    45.
    Eribe ERK, Olsen I. 2017. Leptotrichia species in human infections II. J Oral Microbiol 9:1368848.
    46.
    Valour F, Sénéchal A, Dupieux C, Karsenty J, Lustig S, Breton P, Gleizal A, Boussel L, Laurent F, Braun E, Chidiac C, Ader F, Ferry T. 2014. Actinomycosis: etiology, clinical features, diagnosis, treatment, and management. Infect Drug Resist 7:183–197.
    47.
    Smalley JW, Birss AJ, Szmigielski B, Potempa J. 2007. Sequential action of R- and K-specific gingipains of Porphyromonas gingivalis in the generation of the haem-containing pigment from oxyhaemoglobin. Arch Biochem Biophys 465:44–49.
    48.
    Stathopoulou PG, Benakanakere MR, Galicia JC, Kinane DF. 2010. Epithelial cell pro-inflammatory cytokine response differs across dental plaque bacterial species. J Clin Periodontol 37:24–29.
    49.
    Oetjen J, Fives-Taylor P, Froeliger E. 2001. Characterization of a streptococcal endopeptidase with homology to human endothelin-converting enzyme. Infect Immun 69:58–64.
    50.
    Holmlund A, Lampa E, Lind L. 2017. Poor response to periodontal treatment may predict future cardiovascular disease. J Dent Res 96:768–773.
    51.
    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. 2010. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336.
    52.
    Dodt M, Roehr JT, Ahmed R, Dieterich C. 2012. FLEXBAR—flexible barcode and adapter processing for next-generation sequencing platforms. Biology 1:895–905.
    53.
    Chen T, Yu WH, Izard J, Baranova OV, Lakshmanan A, Dewhirst FE. 2010. The Human Oral Microbiome Database: a web accessible resource for investigating oral microbe taxonomic and genomic information. Database 2010:baq013.
    54.
    Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. 1999. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27:29–34.
    55.
    Langmead B, Salzberg SL. 2012. Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359.
    56.
    Hart SN, Therneau TM, Zhang Y, Poland GA, Kocher JP. 2013. Calculating sample size estimates for RNA sequencing data. J Comput Biol 20:970–978.
    57.
    Anders S, Pyl PT, Huber W. 2015. HTSeq—a python framework to work with high-throughput sequencing data. Bioinformatics 31:166–169.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 9Number 22 May 2018
    eLocator: e00575-18
    Editor: Vanessa Sperandio
    UT Southwestern Medical Center Dallas

    History

    Received: 15 March 2018
    Accepted: 19 March 2018
    Published online: 17 April 2018

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. dysbiosis
    2. gingivitis
    3. metatranscriptome
    4. oral microbiology
    5. periodontitis

    Contributors

    Authors

    Emily M. Nowicki
    John Ring LaMontagne Center for Infectious Disease, The University of Texas at Austin, Austin, Texas, USA
    Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, USA
    Raghav Shroff
    Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, USA
    Center for Systems and Synthetic Biology, College of Natural Sciences, The University of Texas at Austin, Austin, Texas, USA
    Jacqueline A. Singleton
    School of Dentistry, University of Louisville, Louisville, Kentucky, USA
    Diane E. Renaud
    School of Dentistry, University of Louisville, Louisville, Kentucky, USA
    Debra Wallace
    School of Dentistry, University of Louisville, Louisville, Kentucky, USA
    Julie Drury
    School of Dentistry, University of Louisville, Louisville, Kentucky, USA
    Jolene Zirnheld
    School of Dentistry, University of Louisville, Louisville, Kentucky, USA
    Brock Colleti
    School of Dentistry, University of Louisville, Louisville, Kentucky, USA
    Andrew D. Ellington
    Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, USA
    Center for Systems and Synthetic Biology, College of Natural Sciences, The University of Texas at Austin, Austin, Texas, USA
    Richard J. Lamont
    School of Dentistry, University of Louisville, Louisville, Kentucky, USA
    David A. Scott
    School of Dentistry, University of Louisville, Louisville, Kentucky, USA
    Marvin Whiteley
    School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA
    Emory-Children’s Cystic Fibrosis Center, Atlanta, Georgia, USA

    Editor

    Vanessa Sperandio
    Editor
    UT Southwestern Medical Center Dallas

    Reviewers

    Jorge Frias-Lopez
    Solicited external reviewer
    University of Florida
    Irene Wagner-Dobler
    Solicited external reviewer
    Helmholtz-Zentrum für Infektionsforschung mbH

    Notes

    Address correspondence to Marvin Whiteley, [email protected].
    E.M.N. and R.S. contributed equally to this work.

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Leveraging Existing 16S rRNA Gene Surveys To Identify Reproducible Biomarkers in Individuals with Colorectal Tumors

    Leveraging Existing 16S rRNA Gene Surveys To Identify Reproducible Biomarkers in Individuals with Colorectal Tumors

    ABSTRACT

    An increasing body of literature suggests that both individual and collections of bacteria are associated with the progression of colorectal cancer. As the number of studies investigating these associations increases and the number of subjects in each study increases, a meta-analysis to identify the associations that are the most predictive of disease progression is warranted. We analyzed previously published 16S rRNA gene sequencing data collected from feces and colon tissue. We quantified the odds ratios (ORs) for individual bacterial taxa that were associated with an individual having tumors relative to a normal colon. Among the fecal samples, there were no taxa that had significant ORs associated with adenoma and there were 8 taxa with significant ORs associated with carcinoma. Similarly, among the tissue samples, there were no taxa that had a significant OR associated with adenoma and there were 3 taxa with significant ORs associated with carcinoma. Among the significant ORs, the association between individual taxa and tumor diagnosis was equal to or below 7.11. Because individual taxa had limited association with tumor diagnosis, we trained Random Forest classification models using only the taxa that had significant ORs, using the entire collection of taxa found in each study, and using operational taxonomic units defined based on a 97% similarity threshold. All training approaches yielded similar classification success as measured using the area under the curve. The ability to correctly classify individuals with adenomas was poor, and the ability to classify individuals with carcinomas was considerably better using sequences from feces or tissue.
    IMPORTANCE Colorectal cancer is a significant and growing health problem in which animal models and epidemiological data suggest that the colonic microbiota have a role in tumorigenesis. These observations indicate that the colonic microbiota is a reservoir of biomarkers that may improve our ability to detect colonic tumors using noninvasive approaches. This meta-analysis identifies and validates a set of 8 bacterial taxa that can be used within a Random Forest modeling framework to differentiate individuals as having normal colons or carcinomas. When models trained using one data set were tested on other data sets, the models performed well. These results lend support to the use of fecal biomarkers for the detection of tumors. Furthermore, these biomarkers are plausible candidates for further mechanistic studies into the role of the gut microbiota in tumorigenesis.

    REFERENCES

    1.
    Siegel RL, Miller KD, Jemal A. 2016. Cancer statistics, 2016. CA Cancer J Clin 66:7–30.
    2.
    Flynn KJ, Baxter NT, Schloss PD. 2016. Metabolic and community synergy of oral bacteria in colorectal cancer. mSphere 1:e00102-16.
    3.
    Goodwin AC, Destefano Shields CE, Wu S, Huso DL, Wu X, Murray-Stewart TR, Hacker-Prietz A, Rabizadeh S, Woster PM, Sears CL, Casero RA. 2011. Polyamine catabolism contributes to enterotoxigenic Bacteroides fragilis-induced colon tumorigenesis. Proc Natl Acad Sci U S A 108:15354–15359.
    4.
    Abed J, Emgård JEM, Zamir G, Faroja M, Almogy G, Grenov A, Sol A, Naor R, Pikarsky E, Atlan KA, Mellul A, Chaushu S, Manson AL, Earl AM, Ou N, Brennan CA, Garrett WS, Bachrach G. 2016. Fap2 mediates fusobacterium nucleatum colorectal adenocarcinoma enrichment by binding to tumor-expressed Gal-GalNAc. Cell Host Microbe 20:215–225.
    5.
    Arthur JC, Perez-Chanona E, Mühlbauer M, Tomkovich S, Uronis JM, Fan TJ, Campbell BJ, Abujamel T, Dogan B, Rogers AB, Rhodes JM, Stintzi A, Simpson KW, Hansen JJ, Keku TO, Fodor AA, Jobin C. 2012. Intestinal inflammation targets cancer-inducing activity of the microbiota. Science 338:120–123.
    6.
    Kostic AD, Chun E, Robertson L, Glickman JN, Gallini CA, Michaud M, Clancy TE, Chung DC, Lochhead P, Hold GL, El-Omar EM, Brenner D, Fuchs CS, Meyerson M, Garrett WS. 2013. Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe 14:207–215.
    7.
    Wu S, Rhee KJ, Albesiano E, Rabizadeh S, Wu X, Yen HR, Huso DL, Brancati FL, Wick E, McAllister F, Housseau F, Pardoll DM, Sears CL. 2009. A human colonic commensal promotes colon tumorigenesis via activation of T helper type 17 T cell responses. Nat Med 15:1016–1022.
    8.
    Zackular JP, Baxter NT, Chen GY, Schloss PD. 2016. Manipulation of the gut microbiota reveals role in colon tumorigenesis. mSphere 1:e00001-15.
    9.
    Zackular JP, Baxter NT, Iverson KD, Sadler WD, Petrosino JF, Chen GY, Schloss PD. 2013. The gut microbiome modulates colon tumorigenesis. mBio 4:e00692-13.
    10.
    Baxter NT, Zackular JP, Chen GY, Schloss PD. 2014. Structure of the gut microbiome following colonization with human feces determines colonic tumor burden. Microbiome 2:20.
    11.
    Ahn J, Sinha R, Pei Z, Dominianni C, Wu J, Shi J, Goedert JJ, Hayes RB, Yang L. 2013. Human gut microbiome and risk for colorectal cancer. J Natl Cancer Inst 105:1907–1911.
    12.
    Baxter NT, Ruffin MT, Rogers MAM, Schloss PD. 2016. Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions. Genome Med 8:37.
    13.
    Chen W, Liu F, Ling Z, Tong X, Xiang C. 2012. Human intestinal lumen and mucosa-associated microbiota in patients with colorectal cancer. PLoS One 7:e39743.
    14.
    Wang T, Cai G, Qiu Y, Fei N, Zhang M, Pang X, Jia W, Cai S, Zhao L. 2012. Structural segregation of gut microbiota between colorectal cancer patients and healthy volunteers. ISME J 6:320–329.
    15.
    Burns MB, Lynch J, Starr TK, Knights D, Blekhman R. 2015. Virulence genes are a signature of the microbiome in the colorectal tumor microenvironment. Genome Med 7:55.
    16.
    Zeller G, Tap J, Voigt AY, Sunagawa S, Kultima JR, Costea PI, Amiot A, Böhm J, Brunetti F, Habermann N, Hercog R, Koch M, Luciani A, Mende DR, Schneider MA, Schrotz-King P, Tournigand C, Tran Van Nhieu J, Yamada T, Zimmermann J, Benes V, Kloor M, Ulrich CM, von Knebel Doeberitz M, Sobhani I, Bork P. 2014. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol Syst Biol 10:766.
    17.
    Flemer B, Lynch DB, Brown JMR, Jeffery IB, Ryan FJ, Claesson MJ, O’Riordain M, Shanahan F, O’Toole PW. 2017. Tumour-associated and non-tumour-associated microbiota in colorectal cancer. Gut 66:633–643.
    18.
    Gimeno García AZ. 2012. Factors influencing colorectal cancer screening participation. Gastroenterol Res Pract 2012:483417.
    19.
    Geng J, Fan H, Tang X, Zhai H, Zhang Z. 2013. Diversified pattern of the human colorectal cancer microbiome. Gut Pathog 5:2.
    20.
    Dejea CM, Wick EC, Hechenbleikner EM, White JR, Mark Welch JL, Rossetti BJ, Peterson SN, Snesrud EC, Borisy GG, Lazarev M, Stein E, Vadivelu J, Roslani AC, Malik AA, Wanyiri JW, Goh KL, Thevambiga I, Fu K, Wan F, Llosa N, Housseau F, Romans K, Wu X, McAllister FM, Wu S, Vogelstein B, Kinzler KW, Pardoll DM, Sears CL. 2014. Microbiota organization is a distinct feature of proximal colorectal cancers. Proc Natl Acad Sci U S A 111:18321–18326.
    21.
    Arthur JC, Gharaibeh RZ, Mühlbauer M, Perez-Chanona E, Uronis JM, McCafferty J, Fodor AA, Jobin C. 2014. Microbial genomic analysis reveals the essential role of inflammation in bacteria-induced colorectal cancer. Nat Commun 5:4724.
    22.
    Aymeric L, Donnadieu F, Mulet C, du Merle L, Nigro G, Saffarian A, Bérard M, Poyart C, Robine S, Regnault B, Trieu-Cuot P, Sansonetti PJ, Dramsi S. 2018. Colorectal cancer specific conditions promote Streptococcus gallolyticus gut colonization. Proc Natl Acad Sci U S A 115:E283–E291.
    23.
    Weir TL, Manter DK, Sheflin AM, Barnett BA, Heuberger AL, Ryan EP. 2013. Stool microbiome and metabolome differences between colorectal cancer patients and healthy adults. PLoS One 8:e70803.
    24.
    Boleij A, Hechenbleikner EM, Goodwin AC, Badani R, Stein EM, Lazarev MG, Ellis B, Carroll KC, Albesiano E, Wick EC, Platz EA, Pardoll DM, Sears CL. 2015. The Bacteroides fragilis toxin gene is prevalent in the colon mucosa of colorectal cancer patients. Clin Infect Dis 60:208–215.
    25.
    Sanapareddy N, Legge RM, Jovov B, McCoy A, Burcal L, Araujo-Perez F, Randall TA, Galanko J, Benson A, Sandler RS, Rawls JF, Abdo Z, Fodor AA, Keku TO. 2012. Increased rectal microbial richness is associated with the presence of colorectal adenomas in humans. ISME J 6:1858–1868.
    26.
    Lu Y, Chen J, Zheng J, Hu G, Wang J, Huang C, Lou L, Wang X, Zeng Y. 2016. Mucosal adherent bacterial dysbiosis in patients with colorectal adenomas. Sci Rep 6:26337.
    27.
    Hale VL, Chen J, Johnson S, Harrington SC, Yab TC, Smyrk TC, Nelson H, Boardman LA, Druliner BR, Levin TR, Rex DK, Ahnen DJ, Lance P, Ahlquist DA, Chia N. 2017. Shifts in the fecal microbiota associated with adenomatous polyps. Cancer Epidemiol Biomarkers Prev 26:85–94.
    28.
    Shah MS, DeSantis TZ, Weinmaier T, McMurdie PJ, Cope JL, Altrichter A, Yamal JM, Hollister EB. 2018. Leveraging sequence-based faecal microbial community survey data to identify a composite biomarker for colorectal cancer. Gut 67:882–891.
    29.
    Brim H, Yooseph S, Zoetendal EG, Lee E, Torralbo M, Laiyemo AO, Shokrani B, Nelson K, Ashktorab H. 2013. Microbiome analysis of stool samples from African Americans with colon polyps. PLoS One 8:e81352.
    30.
    Sze MA, Baxter NT, Ruffin MT, Rogers MAM, Schloss PD. 2017. Normalization of the microbiota in patients after treatment for colonic lesions. Microbiome 5:150.
    31.
    Hannigan GD, Duhaime MB, Ruffin MT, Koumpouras CC, Schloss PD. 2017. Diagnostic potential and the interactive dynamics of the colorectal cancer virome. bioRxiv doi:
    32.
    Venkataraman A, Sieber JR, Schmidt AW, Waldron C, Theis KR, Schmidt TM. 2016. Variable responses of human microbiomes to dietary supplementation with resistant starch. Microbiome 4:33.
    33.
    Herrmann E, Young W, Reichert-Grimm V, Weis S, Riedel CU, Rosendale D, Stoklosinski H, Hunt M, Egert M. 2018. In vivo assessment of resistant starch degradation by the caecal microbiota of mice using RNA-based stable isotope probing—a proof-of-principle study. Nutrients 10:179.
    34.
    Reichardt N, Vollmer M, Holtrop G, Farquharson FM, Wefers D, Bunzel M, Duncan SH, Drew JE, Williams LM, Milligan G, Preston T, Morrison D, Flint HJ, Louis P. 2018. Specific substrate-driven changes in human faecal microbiota composition contrast with functional redundancy in short-chain fatty acid production. ISME J 12:610–622.
    35.
    Flynn KJ, Ruffin MT, Turgeon DK, Schloss PD. 2018. Spatial variation of the native colon microbiota in healthy adults. Cancer Prev Res doi:
    36.
    Repass J, Reproducibility Project: Cancer Biology, Iorns E, Denis A, Williams SR, Perfito N, Errington TM. 2018. Replication study: Fusobacterium nucleatum infection is prevalent in human colorectal carcinoma. Elife 7:e25801.
    37.
    Salter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO, Moffatt MF, Turner P, Parkhill J, Loman NJ, Walker AW. 2014. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol 12:87.
    38.
    Purcell RV, Pearson J, Aitchison A, Dixon L, Frizelle FA, Keenan JI. 2017. Colonization with enterotoxigenic Bacteroides fragilis is associated with early-stage colorectal neoplasia. PLoS One 12:e0171602.
    39.
    Sze MA, Schloss PD. 2016. Looking for a signal in the noise: revisiting obesity and the microbiome. mBio 7:e01018-16.
    40.
    Walters WA, Xu Z, Knight R. 2014. Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Lett 588:4223–4233.
    41.
    Finucane MM, Sharpton TJ, Laurent TJ, Pollard KS. 2014. A taxonomic signature of obesity in the microbiome? Getting to the guts of the matter. PLoS One 9:e84689.
    42.
    Hanage WP. 2014. Microbiology: microbiome science needs a healthy dose of scepticism. Nature 512:247–248.
    43.
    Keku TO, Dulal S, Deveaux A, Jovov B, Han X. 2015. The gastrointestinal microbiota and colorectal cancer. Am J Physiol Gastrointest Liver Physiol 308:G351–G363.
    44.
    Vogtmann E, Goedert JJ. 2016. Epidemiologic studies of the human microbiome and cancer. Br J Cancer 114:237–242.
    45.
    Kostic AD, Gevers D, Pedamallu CS, Michaud M, Duke F, Earl AM, Ojesina AI, Jung J, Bass AJ, Tabernero J, Baselga J, Liu C, Shivdasani RA, Ogino S, Birren BW, Huttenhower C, Garrett WS, Meyerson M. 2012. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res 22:292–298.
    46.
    Zackular JP, Rogers MAM, Ruffin MT, Schloss PD. 2014. The human gut microbiome as a screening tool for colorectal cancer. Cancer Prev Res 7:1112–1121.
    47.
    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF. 2009. Introducing Mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541.
    48.
    Rognes T, Flouri T, Nichols B, Quince C, Mahé F. 2016. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4:e2584.
    49.
    Westcott SL, Schloss PD. 2017. OptiClust, an improved method for assigning amplicon-based sequence data to operational taxonomic units. mSphere 2:e00073-17.
    50.
    Wang Q, Garrity GM, Tiedje JM, Cole JR. 2007. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73:5261–5267.
    51.
    Anderson MJ, Walsh DCI. 2013. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecol Monogr 83:557–574.
    52.
    Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B 57:289–300.
    53.
    Breiman L. 2001. Random forests. Mach Learn 45:5–32.
    54.
    R Core Team. 2017. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
    55.
    Mangiafico S. 2017. Rcompanion: functions to support extension education program evaluation.
    56.
    Fox J, Weisberg S. 2011. An R companion to applied regressionSecond. Sage, Thousand Oaks, CA.
    57.
    Bates D, Mächler M, Bolker B, Walker S. 2015. Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48.
    58.
    Nunes T, Heuer C, Marshall J, Sanchez J, Thornton R, Reiczigel J, Robison-Cox J, Sebastiani P, Solymos P, Yoshida K, Jones G, Pirikahu S, Firestone S, Kyle R. 2017. EpiR: tools for the analysis of epidemiological data.
    59.
    Viechtbauer W. 2010. Conducting meta-analyses in R with the metafor package. J Stat Softw 36:1–48.
    60.
    Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H. 2017. Vegan: community ecology package.
    61.
    Wing J, Weston S, Williams A, Keefer C, Engelhardt A, Cooper T, Mayer Z, Kenkel B, R Core Team, Benesty M, Lescarbeau R, Ziem A, Scrucca L, Tang Y, Candan C, Hunt T. 2017. Caret: classification and regression training.
    62.
    Liaw A, Wiener M. 2002. Classification and regression by randomForest. Res News 2:18–22.
    63.
    Wickham H. 2009. ggplot2: elegant graphics for data analysis. Springer-Verlag, New York, NY.
    64.
    Auguie B. 2017. GridExtra: miscellaneous functions for “grid” graphics.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 9Number 35 July 2018
    eLocator: e00630-18
    Editor: Claire M. Fraser
    University of Maryland, School of Medicine

    History

    Received: 21 March 2018
    Accepted: 10 May 2018
    Published online: 5 June 2018

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. 16S rRNA
    2. adenoma
    3. biomarkers
    4. carcinoma
    5. colorectal cancer
    6. diagnostic
    7. feces
    8. microbiome

    Contributors

    Authors

    Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA
    Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, USA

    Editor

    Claire M. Fraser
    Editor
    University of Maryland, School of Medicine

    Notes

    Address correspondence to Patrick D. Schloss, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Early Cyanobacteria and the Innovation of Microbial Sunscreens

    Early Cyanobacteria and the Innovation of Microbial Sunscreens

    ABSTRACT

    Metabolism drives life; thus, understanding how and when various branches of metabolism evolved provides a critical piece to understanding how life has integrated itself into the geochemical cycles of our planet over billions of years. Although the most transformative metabolisms that have significantly altered the trajectory of Earth are inherently linked to primary metabolism, natural products that stem from specialized metabolic pathways are also key components to many auxiliary facets of life. Cyanobacteria are primarily known as the original inventors of oxygenic photosynthesis, using sunlight to split water to create our dioxygen-filled atmosphere; however, many of them also have evolved to produce small molecules that function as sunscreens to protect themselves from ultraviolet radiation. Determining when cyanobacteria first evolved the ability to biosynthesize such compounds is an important piece to understanding the rise of oxygen and the eventual success of the phylum.

    REFERENCES

    1.
    Fischer W, Hemp J, Johnson JE. 2016. Evolution of oxygenic photosynthesis. Annu Rev Earth Planet Sci 44:647–683.
    2.
    Shih PM. 2015. Photosynthesis and early Earth. Curr Biol 25:R855–R859.
    3.
    Shih PM, Hemp J, Ward LM, Matzke NJ, Fischer WW. 2017. Crown group Oxyphotobacteria postdate the rise of oxygen. Geobiology 15:19–29.
    4.
    Fischer W, Hemp J, Valentine JS. 2016. How did life survive Earth’s Great Oxidation? Curr Opin Chem Biol 31:166–178.
    5.
    Kihara S, Hartzler DA, Savikhin S. 2014. Oxygen concentration inside a functioning photosynthetic cell. Biophys J 106:1882–1889.
    6.
    Garcia-Pichel F, Lombard J, Soule T, Dunaj S, Wu SH, Wojciechowski MF. 2019. Timing the evolutionary advent of cyanobacteria and the later Great Oxidation Event using gene phylogenies of a sunscreen. mBio 10:e00561-19.
    7.
    Schirrmeister BE, de Vos JM, Antonelli A, Bagheri HC. 2013. Evolution of multicellularity coincided with increased diversification of cyanobacteria and the Great Oxidation Event. Proc Natl Acad Sci U S A 110:1791–1796.
    8.
    Shih PM, Matzke NJ. 2013. Primary endosymbiosis events date to the later Proterozoic with cross-calibrated phylogenetic dating of duplicated ATPase proteins. Proc Natl Acad Sci U S A 110:12355–12360.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 10Number 325 June 2019
    eLocator: e01262-19

    History

    Published online: 11 June 2019

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. molecular clock
    2. photosynthesis
    3. sunscreen

    Contributors

    Author

    Department of Plant Biology, University of California, Davis, Davis, California, USA
    Genome Center, University of California, Davis, Davis, California, USA
    Feedstocks Division, Joint BioEnergy Institute, Emeryville, California, USA
    Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Notes

    Address correspondence to [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Metabolic and Bactericidal Effects of Targeted Suppression of NadD and NadE Enzymes in Mycobacteria

    ABSTRACT

    Mycobacterium tuberculosis remains a major cause of death due to the lack of treatment accessibility, HIV coinfection, and drug resistance. Development of new drugs targeting previously unexplored pathways is essential to shorten treatment time and eliminate persistent M. tuberculosis. A promising biochemical pathway which may be targeted to kill both replicating and nonreplicating M. tuberculosis is the biosynthesis of NAD(H), an essential cofactor in multiple reactions crucial for respiration, redox balance, and biosynthesis of major building blocks. NaMN adenylyltransferase (NadD) and NAD synthetase (NadE), the key enzymes of NAD biosynthesis, were selected as promising candidate drug targets for M. tuberculosis. Here we report for the first time kinetic characterization of the recombinant purified NadD enzyme, setting the stage for its structural analysis and inhibitor development. A protein knockdown approach was applied to validate bothNadD and NadE as target enzymes. Induced degradation of either target enzyme showed a strong bactericidal effect which coincided with anticipated changes in relative levels of NaMN and NaAD intermediates (substrates of NadD and NadE, respectively) and ultimate depletion of the NAD(H) pool. A metabolic catastrophe predicted as a likely result of NAD(H) deprivation of cellular metabolism was confirmed by 13C biosynthetic labeling followed by gas chromatography-mass spectrometry (GC-MS) analysis. A sharp suppression of metabolic flux was observed in multiple NAD(P)(H)-dependent pathways, including synthesis of many amino acids (serine, proline, aromatic amino acids) and fatty acids. Overall, these results provide strong validation of the essential NAD biosynthetic enzymes, NadD and NadE, as antimycobacterial drug targets.
    IMPORTANCE To address the problems of M. tuberculosis drug resistance and persistence of tuberculosis, new classes of drug targets need to be explored. The biogenesis of NAD cofactors was selected for target validation because of their indispensable role in driving hundreds of biochemical transformations. We hypothesized that the disruption of NAD production in the cell via genetic suppression of the essential enzymes (NadD and NadE) involved in the last two steps of NAD biogenesis would lead to cell death, even under dormancy conditions. In this study, we confirmed the hypothesis using a protein knockdown approach in the model system of Mycobacterium smegmatis. We showed that induced proteolytic degradation of either target enzyme leads to depletion of the NAD cofactor pool, which suppresses metabolic flux through numerous NAD(P)-dependent pathways of central metabolism of carbon and energy production. Remarkably, bactericidal effect was observed even for nondividing bacteria cultivated under carbon starvation conditions.

    REFERENCES

    1.
    Gerdes SY, Kurnasov OV, Shatalin K, Polanuyer B, Sloutsky R, Vonstein V, Overbeek R, and Osterman AL. 2006. Comparative genomics of NAD biosynthesis in Cyanobacteria. J. Bacteriol. 188:3012–3023.
    2.
    Osterman AL and Begley TP. 2007. A subsystems-based approach to the identification of drug targets in bacterial pathogens. Prog. Drug Res. 64:132, 133–170.
    3.
    Sorci L, Blaby I, De Ingeniis J, Gerdes S, Raffaelli N, de Crécy Lagard V, and Osterman A. 2010. Genomics-driven reconstruction of Acinetobacter NAD metabolism: insights for antibacterial target selection. J. Biol. Chem. 285:39490–39499.
    4.
    Sorci L, Blaby IK, Rodionova IA, De Ingeniis J, Tkachenko S, de Crecy-Lagard V, and Osterman AL. 2013. Quinolinate salvage and insights for targeting NAD biosynthesis in group A streptococci. J. Bacteriol. 195:726–732.
    5.
    Sorci L, Martynowski D, Rodionov DA, Eyobo Y, Zogaj X, Klose KE, Nikolaev EV, Magni G, Zhang H, and Osterman AL. 2009. Nicotinamide mononucleotide synthetase is the key enzyme for an alternative route of NAD biosynthesis in Francisella tularensis. Proc. Natl. Acad. Sci. U. S. A. 106:3083–3088.
    6.
    Sorci L, Pan Y, Eyobo Y, Rodionova I, Huang N, Kurnasov O, Zhong S, MacKerell AD Jr, Zhang H, and Osterman AL. 2009. Targeting NAD biosynthesis in bacterial pathogens: structure-based development of inhibitors of nicotinate mononucleotide adenylyltransferase NadD. Chem. Biol. 16:849–861.
    7.
    Gerdes SY, Scholle MD, D’Souza M, Bernal A, Baev MV, Farrell M, Kurnasov OV, Daugherty MD, Mseeh F, Polanuyer BM, Campbell JW, Anantha S, Shatalin KY, Chowdhury SA, Fonstein MY, and Osterman AL. 2002. From genetic footprinting to antimicrobial drug targets: examples in cofactor biosynthetic pathways. J. Bacteriol. 184:4555–4572.
    8.
    Kurnasov OV, Polanuyer BM, Ananta S, Sloutsky R, Tam A, Gerdes SY, and Osterman AL. 2002. Ribosylnicotinamide kinase domain of NadR protein: identification and implications in NAD biosynthesis. J. Bacteriol. 184:6906–6917.
    9.
    Boshoff HI, Xu X, Tahlan K, Dowd CS, Pethe K, Camacho LR, Park TH, Yun CS, Schnappinger D, Ehrt S, Williams KJ, and Barry CE III. 2008. Biosynthesis and recycling of nicotinamide cofactors in mycobacterium tuberculosis. An essential role for NAD in nonreplicating bacilli. J. Biol. Chem. 283:19329–19341.
    10.
    Bi J, Wang H, and Xie J. 2011. Comparative genomics of NAD(P) biosynthesis and novel antibiotic drug targets. J. Cell. Physiol. 226:331–340.
    11.
    Beste DJ, Hooper T, Stewart G, Bonde B, Avignone-Rossa C, Bushell ME, Wheeler P, Klamt S, Kierzek AM, and McFadden J. 2007. GSMN-TB: a web-based genome-scale network model of Mycobacterium tuberculosis metabolism. Genome Biol. 8:R89.
    12.
    Andries K, Verhasselt P, Guillemont J, Göhlmann HW, Neefs JM, Winkler H, Van Gestel J, Timmerman P, Zhu M, Lee E, Williams P, de Chaffoy D, Huitric E, Hoffner S, Cambau E, Truffot-Pernot C, Lounis N, and Jarlier V. 2005. A diarylquinoline drug active on the ATP synthase of Mycobacterium tuberculosis. Science 307:223–227.
    13.
    Cohen J. 2013. Approval of novel TB drug celebrated—with restraint. Science 339:130.
    14.
    Rao SP, Alonso S, Rand L, Dick T, and Pethe K. 2008. The protonmotive force is required for maintaining ATP homeostasis and viability of hypoxic, nonreplicating Mycobacterium tuberculosis. Proc. Natl. Acad. Sci. U. S. A. 105:11945–11950.
    15.
    Cheng W and Roth J. 1995. Isolation of NAD cycle mutants defective in nicotinamide mononucleotide deamidase in Salmonella typhimurium. J. Bacteriol. 177:6711–6717.
    16.
    Haferkamp I, Schmitz-Esser S, Linka N, Urbany C, Collingro A, Wagner M, Horn M, and Neuhaus HE. 2004. A candidate NAD+ transporter in an intracellular bacterial symbiont related to Chlamydiae. Nature 432:622–625.
    17.
    De Ingeniis J, Kazanov MD, Shatalin K, Gelfand MS, Osterman AL, and Sorci L. 2012. Glutamine versus ammonia utilization in the NAD synthetase family. PLoS One 7:e39115.
    18.
    Sorci L, Kurnasov O, Rodionov DA, and Osterman AL. 2010. Genomics and enzymology of NAD biosynthesis, p 213–257. In Lew M and Hung-Wen L (ed), Comprehensive natural products II. Elsevier, Oxford, United Kingdom.
    19.
    Huang N, Kolhatkar R, Eyobo Y, Sorci L, Rodionova I, Osterman AL, Mackerell AD, and Zhang H. 2010. Complexes of bacterial nicotinate mononucleotide adenylyltransferase with inhibitors: implication for structure-based drug design and improvement. J. Med. Chem. 53:5229–5239.
    20.
    Moro WB, Yang Z, Kane TA, Brouillette CG, and Brouillette WJ. 2009. Virtual screening to identify lead inhibitors for bacterial NAD synthetase (NADs). Bioorg. Med. Chem. Lett. 19:2001–2005.
    21.
    Moro WB, Yang Z, Kane TA, Zhou Q, Harville S, Brouillette CG, and Brouillette WJ. 2009. SAR studies for a new class of antibacterial NAD biosynthesis inhibitors. J. Comb. Chem. 11:617–625.
    22.
    Velu SE, Cristofoli WA, Garcia GJ, Brouillette CG, Pierson MC, Luan CH, DeLucas LJ, and Brouillette WJ. 2003. Tethered dimers as NAD synthetase inhibitors with antibacterial activity. J. Med. Chem. 46:3371–3381.
    23.
    Sassetti CM, Boyd DH, and Rubin EJ. 2001. Comprehensive identification of conditionally essential genes in mycobacteria. Proc. Natl. Acad. Sci. U. S. A. 98:12712–12717.
    24.
    Sassetti CM and Rubin EJ. 2003. Genetic requirements for mycobacterial survival during infection. Proc. Natl. Acad. Sci. U. S. A. 100:12989–12994.
    25.
    Griffin JE, Gawronski JD, Dejesus MA, Ioerger TR, Akerley BJ, and Sassetti CM. 2011. High-resolution phenotypic profiling defines genes essential for mycobacterial growth and cholesterol catabolism. PLoS Pathog. 7:e1002251.
    26.
    Bellinzoni M, Buroni S, Pasca MR, Guglierame P, Arcesi F, De Rossi E, and Riccardi G. 2005. Glutamine amidotransferase activity of NAD+ synthetase from Mycobacterium tuberculosis depends on an amino-terminal nitrilase domain. Res. Microbiol. 156:173–177.
    27.
    Bellinzoni M, De Rossi E, Branzoni M, Milano A, Peverali FA, Rizzi M, and Riccardi G. 2002. Heterologous expression, purification, and enzymatic activity of Mycobacterium tuberculosis NAD(+) synthetase. Protein Expr. Purif. 25:547–557.
    28.
    LaRonde-LeBlanc N, Resto M, and Gerratana B. 2009. Regulation of active site coupling in glutamine-dependent NAD(+) synthetase. Nat. Struct. Mol. Biol. 16:421–429.
    29.
    Wei JR, Krishnamoorthy V, Murphy K, Kim JH, Schnappinger D, Alber T, Sassetti CM, Rhee KY, and Rubin EJ. 2011. Depletion of antibiotic targets has widely varying effects on growth. Proc. Natl. Acad. Sci. U. S. A. 108:4176–4181.
    30.
    Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang HY, Cohoon M, de Crécy-Lagard V, Diaz N, Disz T, Edwards R, Fonstein M, Frank ED, Gerdes S, Glass EM, Goesmann A, Hanson A, Iwata-Reuyl D, Jensen R, Jamshidi N, Krause L, Kubal M, Larsen N, Linke B, McHardy AC, Meyer F, Neuweger H, Olsen G, Olson R, Osterman A, Portnoy V, Pusch GD, Rodionov DA, Rückert C, Steiner J, Stevens R, Thiele I, Vassieva O, Ye Y, Zagnitko O, and Vonstein V. 2005. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res. 33:5691–5702.
    31.
    Godek CP and Cynamon MH. 1990. In vitro evaluation of nicotinamide riboside analogs against Haemophilus influenzae. Antimicrob. Agents Chemother. 34:1473–1479.
    32.
    Zhang X, Kurnasov OV, Karthikeyan S, Grishin NV, Osterman AL, and Zhang H. 2003. Structural characterization of a human cytosolic NMN/NaMN adenylyltransferase and implication in human NAD biosynthesis. J. Biol. Chem. 278:13503–13511.
    33.
    Scott DA, Richardson AD, Filipp FV, Knutzen CA, Chiang GG, Ronai ZA, Osterman AL, and Smith JW. 2011. Comparative metabolic flux profiling of melanoma cell lines: beyond the Warburg effect. J. Biol. Chem. 286:42626–42634.
    34.
    Parish T and Stoker NG. 2002. The common aromatic amino acid biosynthesis pathway is essential in Mycobacterium tuberculosis. Microbiology 148:3069–3077.
    35.
    Reichau S, Jiao W, Walker SR, Hutton RD, Baker EN, and Parker EJ. 2011. Potent inhibitors of a shikimate pathway enzyme from Mycobacterium tuberculosis: combining mechanism- and modeling-based design. J. Biol. Chem. 286:16197–16207.
    36.
    Vilchèze C, Weinrick B, Wong KW, Chen B, and Jacobs WR Jr.. 2010. NAD+ auxotrophy is bacteriocidal for the tubercle bacilli. Mol. Microbiol. 76:365–377.
    37.
    Pan P and Tonge PJ. 2012. Targeting InhA, the FASII enoyl-ACP reductase: SAR studies on novel inhibitor scaffolds. Curr. Top. Med. Chem. 12:672–693.
    38.
    Ouellet H, Johnston JB, and de Montellano PR. 2011. Cholesterol catabolism as a therapeutic target in Mycobacterium tuberculosis. Trends Microbiol. 19:530–539.
    39.
    Siegrist MS, Unnikrishnan M, McConnell MJ, Borowsky M, Cheng TY, Siddiqi N, Fortune SM, Moody DB, and Rubin EJ. 2009. Mycobacterial Esx-3 is required for mycobactin-mediated iron acquisition. Proc. Natl. Acad. Sci. U. S. A. 106:18792–18797.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 5Number 128 February 2014
    eLocator: e00747-13
    Editor: George L. Drusano
    University of Florida

    History

    Received: 4 September 2013
    Accepted: 9 January 2014
    Published online: 18 February 2014

    Permissions

    Request permissions for this article.

    Contributors

    Authors

    Irina A. Rodionova
    Infectious and Inflammatory Disease Center, Sanford-Burnham Medical Research Institute, La Jolla, California, USA
    Brian M. Schuster
    Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, USA
    Kristine M. Guinn
    Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, USA
    Leonardo Sorci
    Infectious and Inflammatory Disease Center, Sanford-Burnham Medical Research Institute, La Jolla, California, USA
    Department of Clinical Sciences, Section of Biochemistry, Polytechnic University of Marche, Ancona, Italy
    David A. Scott
    Infectious and Inflammatory Disease Center, Sanford-Burnham Medical Research Institute, La Jolla, California, USA
    Xiaoqing Li
    Infectious and Inflammatory Disease Center, Sanford-Burnham Medical Research Institute, La Jolla, California, USA
    Indu Kheterpal
    Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
    Carolyn Shoen
    Department of Medicine, Veterans Affairs Medical Center, Syracuse, New York, USA
    Michael Cynamon
    Department of Medicine, Veterans Affairs Medical Center, Syracuse, New York, USA
    Christopher Locher
    Vertex Pharmaceuticals Incorporated, Cambridge, Massachusetts, USA
    Eric J. Rubin
    Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, Massachusetts, USA
    Andrei L. Osterman
    Infectious and Inflammatory Disease Center, Sanford-Burnham Medical Research Institute, La Jolla, California, USA

    Editor

    George L. Drusano
    Editor
    University of Florida

    Notes

    Address correspondence to Andrei L. Osterman, [email protected], and Eric J. Rubin, [email protected].
    I.A.R. and B.M.S. contributed equally to this article.

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    A Genus Definition for Bacteria and Archaea Based on a Standard Genome Relatedness Index

    A Genus Definition for Bacteria and Archaea Based on a Standard Genome Relatedness Index

    ABSTRACT

    Genus assignment is fundamental in the characterization of microbes, yet there is currently no unambiguous way to demarcate genera solely using standard genomic relatedness indices. Here, we propose an approach to demarcate genera that relies on the combined use of the average nucleotide identity, genome alignment fraction, and the distinction between type- and non-type species. More than 3,500 genomes representing type strains of species from >850 genera of either bacterial or archaeal lineages were tested. Over 140 genera were analyzed in detail within the taxonomic context of order/family. Significant genomic differences between members of a genus and type species of other genera in the same order/family were conserved in 94% of the cases. Nearly 90% (92% if polyphyletic genera are excluded) of the type strains were classified in agreement with current taxonomy. The 448 type strains that need reclassification directly impact 33% of the genera analyzed in detail. The results provide a first line of evidence that the combination of genomic indices provides added resolution to effectively demarcate genera within the taxonomic framework that is currently based on the 16S rRNA gene. We also identify the emergence of natural breakpoints at the genome level that can further help in the circumscription of taxa, increasing the proportion of directly impacted genera to at least 43% and pointing at inaccuracies on the use of the 16S rRNA gene as a taxonomic marker, despite its precision. Altogether, these results suggest that genomic coherence is an emergent property of genera in Bacteria and Archaea.
    IMPORTANCE In recent decades, the taxonomy of Bacteria and Archaea, and therefore genus designation, has been largely based on the use of a single ribosomal gene, the 16S rRNA gene, as a taxonomic marker. We propose an approach to delineate genera that excludes the direct use of the 16S rRNA gene and focuses on a standard genome relatedness index, the average nucleotide identity. Our findings are of importance to the microbiology community because the emergent properties of Bacteria and Archaea that are identified in this study will help assign genera with higher taxonomic resolution.

    REFERENCES

    1.
    Garrity GM. 2019. NamesforLife database 20190930 release. NamesforLife, East Lansing, MI.
    2.
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glöckner FO. 2013. The SILVA ribosomal RNA gene database project: improved data processing and Web-based tools. Nucleic Acids Res 41:D590–D596.
    3.
    Yarza P, Yilmaz P, Pruesse E, Glöckner FO, Ludwig W, Schleifer K, Whitman WB, Euzéby J, Amann R, Móra RR. 2014. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol 12:635–645.
    4.
    Markowitz VM, Chen IM, Chu K, Pati A, Ivanova NN, Kyrpides NC. 2015. Ten years of maintaining and expanding a microbial genome and metagenome analysis system. Trends Microbiol 23:730–741.
    5.
    Mukherjee S, Seshadri R, Varghese NJ, Eloe-Fadrosh EA, Meier-Kolthoff JP, Göker M, Coates RC, Hadjithomas M, Pavlopoulos GA, Paez-Espino D, Yoshikuni Y, Visel A, Whitman WB, Garrity GM, Eisen JA, Hugenholtz P, Pati A, Ivanova NN, Woyke T, Klenk H-P, Kyrpides NC. 2017. 1,003 reference genomes of bacterial and archaeal isolates expand coverage of the tree of life. Nat Biotechnol 35:676–683.
    6.
    Wu L, Ma J. 2019. The Global Catalogue of Microorganisms (GCM) 10K type strain sequencing project: providing services to taxonomists for standard genome sequencing and annotation. Int J Syst Evol Microbiol 69:895–898.
    7.
    Parker CT, Tindall BJ, Garrity GM. 2019. International Code of Nomenclature of Prokaryotes. Int J Syst Evol Microbiol 69:S1–S111.
    8.
    Tindall BJ, Rosselló-Móra R, Busse H-J, Ludwig W, Kämpfer P. 2010. Notes on the characterization of prokaryote strains for taxonomic purposes. Int J Syst Evol Microbiol 60:249–266.
    9.
    Stackebrandt E, Goebel BM. 1994. Taxonomic note: a place for DNA-DNA reassociation and 16S rRNA sequence analysis in the present species definition in Bacteriology. Int J Syst Bacteriol 44:846–849.
    10.
    Stackebrandt E, Ebers J. 2006. Taxonomic parameters revisited: tarnished gold standards. Microbiol Today 33:152–155.
    11.
    Konstantinidis KT, Tiedje JM. 2005. Genomic insights that advance the species definition for prokaryotes. Proc Natl Acad Sci U S A 102:2567–2572.
    12.
    Konstantinidis KT, Tiedje JM. 2005. Towards a genome-based taxonomy for prokaryotes. J Bacteriol 187:6258–6264.
    13.
    Meier-Kolthoff JP, Auch AF, Klenk H-P, Göker M. 2013. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinformatics 14:60.
    14.
    Kim M, Oh H-S, Park S-C, Chun J. 2014. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Int J Syst Evol Microbiol 64:346–351.
    15.
    Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P, Tiedje JM. 2007. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Microbiol 57:81–91.
    16.
    Richter M, Rosselló-Móra R. 2009. Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci U S A 106:19126–19131.
    17.
    Varghese NJ, Mukherjee S, Ivanova N, Konstantinidis KT, Mavrommatis K, Kyrpides NC, Pati A. 2015. Microbial species delineation using whole genome sequences. Nucleic Acids Res 43:6761–6771.
    18.
    Konstantinidis KT, Tiedje JM. 2007. Prokaryotic taxonomy and phylogeny in the genomic era: advancements and challenges ahead. Curr Opin Microbiol 10:504–509.
    19.
    Qin Q, Xie B, Zhang X, Chen X, Zhou B, Zhou J, Oren A, Zhang Y. 2014. A proposed genus boundary for the prokaryotes based on genomic insights. J Bacteriol 196:2210–2215.
    20.
    Boucher Y, Douady CJ, Sharma AK, Kamekura M, Doolittle WF. 2004. Intragenomic heterogeneity and intergenomic recombination among haloarchaeal rRNA genes. J Bacteriol 186:3980–3990.
    21.
    Sun D-L, Jiang X, Wu QL, Zhou N-Y. 2013. Intragenomic heterogeneity of 16S rRNA genes causes overestimation of prokaryotic diversity. Appl Environ Microbiol 79:5962–5969.
    22.
    Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil P-A, Hugenholtz P. 2018. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol 36:996–1004.
    23.
    Barco RA, Garrity GM, Scott JJ, Amend JP, Nealson KH, Emerson D. 2018. A genus definition for Bacteria and Archaea based on genome relatedness and taxonomic affiliation. bioRxiv doi:
    24.
    Takai K, Hirayama H, Nakagawa T, Suzuki Y, Nealson KH, Horikoshi K. 2004. Thiomicrospira thermophila sp. nov., a novel microaerobi, thermotolerant, sulfur-oxidizing chemolithomixotroph isolated from a deep-sea hydrothermal fumarole in the TOTO caldera, Mariana Arc, Western Pacific. Int J Syst Evol Microbiol 54:2325–2333.
    25.
    Tourova TP, Spiridonova EM, Berg IA, Kuznetsov BB, Sorokin DY. 2006. Occurrence, phylogeny and evolution of ribulose-1,5-bisphosphate carboxylase/oxygenase genes in obligately chemolithoautotrophic sulfur-oxidizing bacteria of the genera Thiomicrospira and Thioalkalimicrobium. Microbiology 152:2159–2169.
    26.
    Boden R, Scott KM, Williams J, Russel S, Antonen K, Rae AW, Hutt LP. 2017. An evaluation of Thiomicrospira, Hydrogenovibrio and Thioalkalimicrobium: reclassification of four species of Thiomicrospira to each Thiomicrorhabdus gen. nov. and Hydrogenovibrio, and reclassification of all four species of Thioalkalimicrobium to Thiomicrospira. Int J Syst Evol Microbiol 67:1140–1151.
    27.
    Sorokin DY, Tourova TP, Kolganova TV, Spiridonova EM, Berg IA, Muyzer G. 2006. Thiomicrospira halophila sp. nov., a moderately halophilic, obligately chemolithoautotrophic, sulfur-oxidizing bacterium from hypersaline lakes. Int J Syst Evol Microbiol 56:2375–2380.
    28.
    Scott KM, Williams J, Porter CMB, Russel S, Harmer TL, Paul JH, Antonen KM, Bridges MK, Camper GJ, Campla CK, Casella LG, Chase E, Conrad JW, Cruz MC, Dunlap DS, Duran L, Fahsbender EM, Goldsmith DB, Keeley RF, Kondoff MR, Kussy BI, Lane MK, Lawler S, Leigh BA, Lewis C, Lostal LM, Marking D, Mancera PA, McClenthan EC, McIntyre EA, Mine JA, Modi S, Moore BD, Morgan WA, Nelson KM, Nguyen KN, Ogburn N, Parrino DG, Pedapudi AD, Pelham RP, Preece AM, Rampersad EA, Richardson JC, Rodgers CM, Schaffer BL, Sheridan NE, Solone MR, Staley ZR, Tabuchi M, Waide RJ, et al. 2018. Genomes of ubiquitous marine and hypersaline Hydrogenovibrio, Thiomicrorhabdus and Thiomicrospira spp. encode a diversity of mechanisms to sustain chemolithoautotrophy in heterogeneous environments. Environ Microbiol 20:2686–2708.
    29.
    Kabisch J, Thürmer A, Hübel T, Popper L, Daniel R, Schweder T. 2013. Characterization and optimization of Bacillus subtilis ATCC 6051 as an expression host. J Biotechnol 163:97–104.
    30.
    Cohn F. 1872. Untersuchungen über Bakterien. Beitr Biol Pflanz 1:127–224.
    31.
    Skerman VBD, McGowan V, Sneath P. 1980. Approved Lists of Bacterial Names. Int J Syst Bacteriol 30:225–420.
    32.
    Pikuta E, Lysenko A, Chuvilskaya N, Mendrock U, Hippe H, Suzina N, Nikitin D, Osipov G, Laurinavichius K. 2000. Anoxybacillus pushchinensis gen. nov., sp. nov., a novel anaerobic, alkaliphilic, moderately thermophilic bacterium from manure, and description of Anoxybacillus flavithermus comb. nov. Int J Syst Evol Microbiol 50:2109–2117.
    33.
    Ahmed I, Yokota A, Yamazoe A, Fujiwara T. 2007. Proposal of Lysinibacillus boronitolerans gen. nov. sp. nov., and transfer of Bacillus fusiformis to Lysinibacillus fusiformis comb. nov. and Bacillus sphaericus to Lysinibacillus sphaericus comb. nov. Int J Syst Evol Microbiol 57:1117–1125.
    34.
    Caro-Quintero A, Konstantinidis KT. 2012. Bacterial species may exist, metagenomics reveal. Environ Microbiol 14:347–355.
    35.
    Konstantinidis KT, DeLong EF. 2008. Genomic patterns of recombination, clonal divergence and environment in marine microbial populations. ISME J 2:1052–1065.
    36.
    Rusch DB, Halpern AL, Sutton G, Heidelberg KB, Williamson S, Yooseph S, Wu D, Eisen JA, Hoffman JM, Remington K, Beeson K, Tran B, Smith H, Baden-Tillson H, Stewart C, Thorpe J, Freeman J, Andrews-Pfannkoch C, Venter JE, Li K, Kravitz S, Heidelberg JF, Utterback T, Rogers Y-H, Falcón LI, Souza V, Bonilla-Rosso G, Eguiarte LE, Karl DM, Sathyendranath S, Platt T, Bermingham E, Gallardo V, Tamayo-Castillo G, Ferrari MR, Strausberg RL, Nealson K, Friedman R, Frazier M, Venter JC. 2007. The Sorcerer II global ocean sampling expedition: Northwest Atlantic through Eastern Tropical Pacific. PLoS Biol 5:e77.
    37.
    Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. 2018. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun 9:5114.
    38.
    Delmont TO, Kiefl E, Kilinc O, Esen OC, Uysal I, Rappé MS, Giovannoni S, Eren AM, Delmont TO, Kiefl E, Kilinc O, Esen OC, Uysal I, Rappé MS, Giovannoni S, Eren AM. 2019. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. Elife 8:e46497.
    39.
    Palmer M, Venter SN, Coetzee MPA, Steenkamp ET. 2019. Prokaryotic species are sui generis evolutionary units. Syst Appl Microbiol 42:145–158.
    40.
    Tindall BJ. 2016. Priority of the genus name Clostridium Prazmowski 1880 (Approved Lists 1980) vs Sarcina Goodsir 1842 (Approved Lists 1980) and the creation of the illegitimate combinations Clostridium maximum (Lindner 1888) Lawson and Rainey 2016 and Clostridium ventriculi (Goodsir 1842) Lawson and Rainey 2016 that may not be used. Int J Syst Evol Microbiol 66:4890–4894.
    41.
    Cruz-Morales P, Orellana CA, Moutafis G, Moonen G, Rincon G, Nielsen LK, Marcellin E. 2019. Revisiting the evolution and taxonomy of Clostridia, a phylogenomic update. Genome Biol Evol 11:2035–2044.
    42.
    Thomas GM, Poinar GO. 1979. Xenorhabdus gen. nov., a genus of entomopathogenic, nematophilic bacteria of the family Enterobacteriaceae. Int J Syst Bacteriol 29:352–360.
    43.
    Boemare NE, Akhurst RJ, Mourant RG. 1993. DNA relatedness between Xenorhabdus spp. (Enterobacteriaceae), symbiotic bacteria of entomopathogenic nematodes, and a proposal to transfer Xenorhabdus luminescens to a new genus, Photorhabdus gen. nov. Int J Syst Bacteriol 43:249–255.
    44.
    Rainey FA, Ehlers R-U, Stackebrandt E. 1995. Inability of the polyphasic approach to systematics to determine the relatedness of the genera Xenorhabdus and Photorhabdus. Int J Syst Bacteriol 45:379–381.
    45.
    Liu J, Berry R, Poinar G, Moldenke A. 1997. Phylogeny of Photorhabdus and Xenorhabdus species and strains as determined by comparison of partial 16S rRNA gene sequences. Int J Syst Bacteriol 47:948–951.
    46.
    Akhurst RJ, Boemare NE. 1986. A non-luminescent strain of Xenorhabdus luminescens. J Gen Microbiol 132:1917–1922.
    47.
    Zheng J, Ruan L, Sun M, Gänzle M. 2015. A genomic view of lactobacilli and pediococci demonstrates that phylogeny matches ecology and physiology. Appl Environ Microbiol 81:7233–7243.
    48.
    Salvetti E, Harris HMB, Felis GE, O’Toole PW. 2018. Comparative genomics of the genus Lactobacillus reveals robust phylogroups that provide the basis for reclassification. Appl Environ Microbiol 84:e00993-18.
    49.
    Wittouck S, Wuyts S, Lebeer S. 2019. Towards a genome-based reclassification of the genus Lactobacillus. Appl Environ Microbiol 85:e02155-18.
    50.
    Morita H, Shimazu M, Shiono H, Toh H, Nakajima F, Akita H, Takagi M, Takami H, Murakami M, Masaoka T, Tanabe S, Hattori M. 2010. Lactobacillus equicursoris sp. nov., isolated from the faeces of a thoroughbred racehorse. Int J Syst Evol Microbiol 60:109–112.
    51.
    Sun Z, Harris HMB, McCann A, Guo C, Argimón S, Zhang W, Yang X, Jeffery IB, Cooney JC, Kagawa TF, Liu W, Song Y, Salvetti E, Wrobel A, Rasinkangas P, Parkhill J, Rea MC, O'Sullivan O, Ritari J, Douillard FP, Paul Ross R, Yang R, Briner AE, Felis GE, de Vos WM, Barrangou R, Klaenhammer TR, Caufield PW, Cui Y, Zhang H, O’Toole PW. 2015. Expanding the biotechnology potential of lactobacilli through comparative genomics of 213 strains and associated genera. Nat Commun 6:8322.
    52.
    Wittouck S, Wuyts S, Meehan CJ, van Noort V, Lebeer S. 2019. A genome-based species taxonomy of the Lactobacillus genus complex. mSystems 4:e00264-19.
    53.
    Schloss PD. 2018. Identifying and overcoming threats to reproducibility, replicability, robustness, and generalizability in microbiome research. mBio 9:e00525-18.
    54.
    Whitman WB. 2015. Genome sequences as the type material for taxonomic descriptions of prokaryotes. Syst Appl Microbiol 38:217–222.
    55.
    Konstantinidis KT, Rosselló-Móra R, Amann R. 2017. Uncultivated microbes in need of their own taxonomy. ISME J 11:2399–2406.
    56.
    Shaiber A, Eren AM, Shaiber A, Eren AM. 2019. Composite metagenome-assembled genomes reduce the quality of public genome repositories. mBio 10:e00725-19.
    57.
    Rodriguez-R LM, Konstantinidis KT. 2014. Bypassing cultivation to identify bacterial species. Microbe 9:111–118.
    58.
    Garrity GM. 2010. NamesforLife: BrowserTool takes expertise out of the database and puts it right in the browser. Microbiol Today 37:9.
    59.
    Wu D, Hugenholtz P, Mavromatis K, Pukall R, Dalin E, Ivanova NN, Kunin V, Goodwin L, Wu M, Tindall BJ, Hooper SD, Pati A, Lykidis A, Spring S, Anderson IJ, D'haeseleer P, Zemla A, Singer M, Lapidus A, Nolan M, Copeland A, Han C, Chen F, Cheng J-F, Lucas S, Kerfeld C, Lang E, Gronow S, Chain P, Bruce D, Rubin EM, Kyrpides NC, Klenk H-P, Eisen JA. 2009. A phylogeny-driven genomic encyclopaedia of Bacteria and Archaea. Nature 462:1056–1060.
    60.
    Yoon SH, Ha SM, Kwon S, Lim J, Kim Y, Seo H, Chun J. 2017. Introducing EzBioCloud: a taxonomically united database of 16S rRNA and whole genome assemblies. Int J Syst Evol Microbiol 67:1613–1617.
    61.
    Pruesse E, Peplies J, Glöckner FO. 2012. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28:1823–1829.
    62.
    Guindon S, Gascuel O. 2003. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 52:696–704.
    63.
    Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, Delmont TO. 2015. anvi’o: an advanced analysis and visualization platform for “omics” data. PeerJ 3:e1319.
    64.
    Delmont TO, Eren AM. 2018. Linking pangenomes and metagenomes: the Prochlorococcus metapangenome. PeerJ 6:e4320.
    65.
    Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11:119.
    66.
    Eddy SR. 2011. Accelerated profile HMM searches. PLoS Comput Biol 7:e1002195.
    67.
    van Dongen S, Abreu-Goodger C. 2012. Using MCL to extract clusters from networks. Methods Mol Biol 804:281–295.
    68.
    Backus L, Wels M, Boekhorst J, Dijkstra AR, Beerthuyzen M, Kelly WJ, Siezen RJ, van Hijum SAFT, Bachmann H. 2017. Draft genome sequences of 24 Lactococcus lactis strains. Genome Announc 5:e01737-16.
    69.
    Scott KM, Sievert SM, Abril FN, Ball LA, Barrett CJ, Blake RA, Boller AJ, Chain PS, Clark JA, Davis CR, Detter C, Do KF, Dobrinski KP, Faza BI, Fitzpatrick KA, Freyermuth SK, Harmer TL, Hauser LJ, Hügler M, Kerfeld CA, Klotz MG, Kong WW, Land M, Lapidus A, Larimer FW, Longo DL, Lucas S, Malfatti SA, Massey SE, Martin DD, McCuddin Z, Meyer F, Moore JL, Ocampo LH, Jr, Paul JH, Paulsen IT, Reep DK, Ren Q, Ross RL, Sato PY, Thomas P, Tinkham LE, Zeruth GT. 2006. The genome of deep-sea vent chemolithoautotroph Thiomicrospira crunogena XCL-2. PLoS Biol 4:e383.
    70.
    Chaston JM, Suen G, Tucker SL, Andersen AW, Bhasin A, Bode E, Bode HB, Brachmann AO, Cowles CE, Cowles KN, Darby C, de Léon L, Drace K, Du Z, Givaudan A, Herbert Tran EE, Jewell KA, Knack JJ, Krasomil-Osterfeld KC, Kukor R, Lanois A, Latreille P, Leimgruber NK, Lipke CM, Liu R, Lu X, Martens EC, Marri PR, Médigue C, Menard ML, Miller NM, Morales-Soto N, Norton S, Ogier J-C, Orchard SS, Park D, Park Y, Qurollo BA, Sugar DR, Richards GR, Rouy Z, Slominski B, Slominski K, Snyder H, Tjaden BC, van der Hoeven R, Welch RD, Wheeler C, Xiang B, Barbazuk B, Gaudriault S, Goodner B, Slater SC, Forst S, Goldman BS, Goodrich-Blair H. 2011. The entomopathogenic bacterial endosymbionts Xenorhabdus and Photorhabdus: convergent lifestyles from divergent genomes. PLoS One 6:e27909.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 11Number 125 February 2020
    eLocator: e02475-19
    Editor: Stephen J. Giovannoni
    Oregon State University

    History

    Received: 18 September 2019
    Accepted: 25 November 2019
    Published online: 14 January 2020

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. ANI
    2. Bacillus
    3. Clostridium
    4. Lactobacillus
    5. Photorhabdus
    6. Pseudomonas
    7. Xenorhabdus
    8. delineation
    9. demarcation
    10. genus
    11. systematics
    12. taxonomy

    Contributors

    Authors

    R. A. Barco
    Department of Earth Sciences, University of Southern California, Los Angeles, California, USA
    Department of Biological Sciences, University of Southern California, Los Angeles, California, USA
    Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine, USA
    Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, USA
    J. J. Scott
    Smithsonian Tropical Research Institute, Panama, Republic of Panama
    J. P. Amend
    Department of Earth Sciences, University of Southern California, Los Angeles, California, USA
    Department of Biological Sciences, University of Southern California, Los Angeles, California, USA
    K. H. Nealson
    Department of Earth Sciences, University of Southern California, Los Angeles, California, USA
    Department of Biological Sciences, University of Southern California, Los Angeles, California, USA
    D. Emerson
    Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine, USA

    Editor

    Stephen J. Giovannoni
    Editor
    Oregon State University

    Notes

    Address correspondence to R. A. Barco, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Gliotoxin, a Known Virulence Factor in the Major Human Pathogen Aspergillus fumigatus, Is Also Biosynthesized by Its Nonpathogenic Relative Aspergillus fischeri

    ABSTRACT

    Aspergillus fumigatus is a major opportunistic human pathogen. Multiple traits contribute to A. fumigatus pathogenicity, including its ability to produce specific secondary metabolites, such as gliotoxin. Gliotoxin is known to inhibit the host immune response, and genetic mutants that inactivate gliotoxin biosynthesis (or secondary metabolism in general) attenuate A. fumigatus virulence. The genome of Aspergillus fischeri, a very close nonpathogenic relative of A. fumigatus, contains a biosynthetic gene cluster that is homologous to the A. fumigatus gliotoxin cluster. However, A. fischeri is not known to produce gliotoxin. To gain further insight into the similarities and differences between the major pathogen A. fumigatus and the nonpathogen A. fischeri, we examined whether A. fischeri strain NRRL 181 biosynthesizes gliotoxin and whether the production of secondary metabolites influences the virulence profile of A. fischeri. We found that A. fischeri biosynthesizes gliotoxin under the same conditions as A. fumigatus. However, whereas loss of laeA, a master regulator of secondary metabolite production (including gliotoxin biosynthesis), has previously been shown to reduce A. fumigatus virulence, we found that laeA loss (and loss of secondary metabolite production) in A. fischeri does not influence its virulence. These results suggest that LaeA-regulated secondary metabolites are virulence factors in the genomic and phenotypic background of the major pathogen A. fumigatus but are much less important in the background of the nonpathogen A. fischeri. Understanding the observed spectrum of pathogenicity across closely related pathogenic and nonpathogenic Aspergillus species will require detailed characterization of their biological, chemical, and genomic similarities and differences.
    IMPORTANCE Aspergillus fumigatus is a major opportunistic fungal pathogen of humans, but most of its close relatives are nonpathogenic. Why is that so? This important, yet largely unanswered, question can be addressed by examining how A. fumigatus and its close nonpathogenic relatives are similar or different with respect to virulence-associated traits. We investigated whether Aspergillus fischeri, a nonpathogenic close relative of A. fumigatus, can produce gliotoxin, a mycotoxin known to contribute to A. fumigatus virulence. We discovered that the nonpathogenic A. fischeri produces gliotoxin under the same conditions as those of the major pathogen A. fumigatus. However, we also discovered that, in contrast to what has previously been observed in A. fumigatus, the loss of secondary metabolite production in A. fischeri does not alter its virulence. Our results are consistent with the “cards of virulence” model of opportunistic fungal disease, in which the ability to cause disease stems from the combination (“hand”) of virulence factors (“cards”) but not from individual factors per se.

    REFERENCES

    1.
    Bongomin F, Gago S, Oladele RO, Denning DW. 2017. Global and multi-national prevalence of fungal diseases—estimate precision. J Fungi (Basel) 3:57.
    2.
    Brown GD, Denning DW, Gow NA, Levitz SM, Netea MG, White TC. 2012. Hidden killers: human fungal infections. Sci Transl Med 4:165rv13.
    3.
    Raffa N, Keller NP. 2019. A call to arms: mustering secondary metabolites for success and survival of an opportunistic pathogen. PLoS Pathog 15:e1007606.
    4.
    Gardiner DM, Howlett BJ. 2005. Bioinformatic and expression analysis of the putative gliotoxin biosynthetic gene cluster of Aspergillus fumigatus. FEMS Microbiol Lett 248:241–248.
    5.
    Dolan SK, O'Keeffe G, Jones GW, Doyle S. 2015. Resistance is not futile: gliotoxin biosynthesis, functionality and utility. Trends Microbiol 23:419–428.
    6.
    Schrettl M, Carberry S, Kavanagh K, Haas H, Jones GW, O'Brien J, Nolan A, Stephens J, Fenelon O, Doyle S. 2010. Self-protection against gliotoxin—a component of the gliotoxin biosynthetic cluster, GliT, completely protects Aspergillus fumigatus against exogenous gliotoxin. PLoS Pathog 6:e1000952.
    7.
    Lewis RE, Wiederhold NP, Chi J, Han XY, Komanduri KV, Kontoyiannis DP, Prince RA. 2005. Detection of gliotoxin in experimental and human aspergillosis. Infect Immun 73:635–637.
    8.
    Sugui JA, Pardo J, Chang YC, Zarember KA, Nardone G, Galvez EM, Mullbacher A, Gallin JI, Simon MM, Kwon-Chung KJ. 2007. Gliotoxin is a virulence factor of Aspergillus fumigatus: gliP deletion attenuates virulence in mice immunosuppressed with hydrocortisone. Eukaryot Cell 6:1562–1569.
    9.
    Spikes S, Xu R, Nguyen CK, Chamilos G, Kontoyiannis DP, Jacobson RH, Ejzykowicz DE, Chiang LY, Filler SG, May GS. 2008. Gliotoxin production in Aspergillus fumigatus contributes to host-specific differences in virulence. J Infect Dis 197:479–486.
    10.
    Cramer RA, Jr, Gamcsik MP, Brooking RM, Najvar LK, Kirkpatrick WR, Patterson TF, Balibar CJ, Graybill JR, Perfect JR, Abraham SN, Steinbach WJ. 2006. Disruption of a nonribosomal peptide synthetase in Aspergillus fumigatus eliminates gliotoxin production. Eukaryot Cell 5:972–980.
    11.
    Bok JW, Balajee SA, Marr KA, Andes D, Nielsen KF, Frisvad JC, Keller NP. 2005. LaeA, a regulator of morphogenetic fungal virulence factors. Eukaryot Cell 4:1574–1582.
    12.
    Perrin RM, Fedorova ND, Bok JW, Cramer RA, Wortman JR, Kim HS, Nierman WC, Keller NP. 2007. Transcriptional regulation of chemical diversity in Aspergillus fumigatus by LaeA. PLoS Pathog 3:e50.
    13.
    Sugui JA, Peterson SW, Figat A, Hansen B, Samson RA, Mellado E, Cuenca-Estrella M, Kwon-Chung KJ. 2014. Genetic relatedness versus biological compatibility between Aspergillus fumigatus and related species. J Clin Microbiol 52:3707–3721.
    14.
    Mead ME, Knowles SL, Raja HA, Beattie SR, Kowalski CH, Steenwyk JL, Silva LP, Chiaratto J, Ries LNA, Goldman GH, Cramer RA, Oberlies NH, Rokas A. 2019. Characterizing the pathogenic, genomic, and chemical traits of Aspergillus fischeri, a close relative of the major human fungal pathogen Aspergillus fumigatus. mSphere 4:e00018-19.
    15.
    Hubka V, Barrs V, Dudová Z, Sklenář F, Kubátová A, Matsuzawa T, Yaguchi T, Horie Y, Nováková A, Frisvad JC, Talbot JJ, Kolařík M. 2018. Unravelling species boundaries in the Aspergillus viridinutans complex (section Fumigati): opportunistic human and animal pathogens capable of interspecific hybridization. Persoonia 41:142–174.
    16.
    Lonial S, Williams L, Carrum G, Ostrowski M, McCarthy P, Jr. 1997. Neosartorya fischeri: an invasive fungal pathogen in an allogeneic bone marrow transplant patient. Bone Marrow Transplant 19:753–755.
    17.
    Coriglione G, Stella G, Gafa L, Spata G, Oliveri S, Padhye AA, Ajello L. 1990. Neosartorya fischeri var fischeri (Wehmer) Malloch and Cain 1972 (anamorph: Aspergillus fischerianus Samson and Gams 1985) as a cause of mycotic keratitis. Eur J Epidemiol 6:382–385.
    18.
    Bok JW, Keller NP. 2004. LaeA, a regulator of secondary metabolism in Aspergillus spp. Eukaryot Cell 3:527–535.
    19.
    Frisvad JC, Larsen TO. 2015. Extrolites of Aspergillus fumigatus and other pathogenic species in Aspergillus section Fumigati. Front Microbiol 6:1485.
    20.
    Pena GA, Monge MP, Gonzalez Pereyra ML, Dalcero AM, Rosa CA, Chiacchiera SM, Cavaglieri LR. 2015. Gliotoxin production by Aspergillus fumigatus strains from animal environment. Micro-analytical sample treatment combined with a LC-MS/MS method for gliotoxin determination. Mycotoxin Res 31:145–150.
    21.
    Belkacemi L, Barton RC, Hopwood V, Evans EG. 1999. Determination of optimum growth conditions for gliotoxin production by Aspergillus fumigatus and development of a novel method for gliotoxin detection. Med Mycol 37:227–233.
    22.
    Kosalec I, Pepeljnjak S, Jandrlic M. 2005. Influence of media and temperature on gliotoxin production in Aspergillus fumigatus strains. Arh Hig Rada Toksikol 56:269–273.
    23.
    Fuchs BB, O'Brien E, Khoury JBE, Mylonakis E. 2010. Methods for using Galleria mellonella as a model host to study fungal pathogenesis. Virulence 1:475–482.
    24.
    Reeves EP, Messina CG, Doyle S, Kavanagh K. 2004. Correlation between gliotoxin production and virulence of Aspergillus fumigatus in Galleria mellonella. Mycopathologia 158:73–79.
    25.
    Bok JW, Chung D, Balajee SA, Marr KA, Andes D, Nielsen KF, Frisvad JC, Kirby KA, Keller NP. 2006. GliZ, a transcriptional regulator of gliotoxin biosynthesis, contributes to Aspergillus fumigatus virulence. Infect Immun 74:6761–6768.
    26.
    Casadevall A. 2006. Cards of virulence and the global virulome for humans. Microbe 1:359–364.
    27.
    Nierman WC, Pain A, Anderson MJ, Wortman JR, Kim HS, Arroyo J, Berriman M, Abe K, Archer DB, Bermejo C, Bennett J, Bowyer P, Chen D, Collins M, Coulsen R, Davies R, Dyer PS, Farman M, Fedorova N, Fedorova N, Feldblyum TV, Fischer R, Fosker N, Fraser A, García JL, García MJ, Goble A, Goldman GH, Gomi K, Griffith-Jones S, Gwilliam R, Haas B, Haas H, Harris D, Horiuchi H, Huang J, Humphray S, Jiménez J, Keller N, Khouri H, Kitamoto K, Kobayashi T, Konzack S, Kulkarni R, Kumagai T, Lafon A, Lafton A, Latgé J-P, Li W, Lord A, et al. 2005. Genomic sequence of the pathogenic and allergenic filamentous fungus Aspergillus fumigatus. Nature 438:1151–1156.
    28.
    Fedorova ND, Khaldi N, Joardar VS, Maiti R, Amedeo P, Anderson MJ, Crabtree J, Silva JC, Badger JH, Albarraq A, Angiuoli S, Bussey H, Bowyer P, Cotty PJ, Dyer PS, Egan A, Galens K, Fraser-Liggett CM, Haas BJ, Inman JM, Kent R, Lemieux S, Malavazi I, Orvis J, Roemer T, Ronning CM, Sundaram JP, Sutton G, Turner G, Venter JC, White OR, Whitty BR, Youngman P, Wolfe KH, Goldman GH, Wortman JR, Jiang B, Denning DW, Nierman WC. 2008. Genomic islands in the pathogenic filamentous fungus Aspergillus fumigatus. PLoS Genet 4:e1000046.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 11Number 125 February 2020
    eLocator: e03361-19
    Editor: Yong-Sun Bahn
    Yonsei University

    History

    Received: 20 December 2019
    Accepted: 3 January 2020
    Published online: 11 February 2020

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. fungal pathogenesis
    2. secondary metabolism
    3. gliotoxin
    4. specialized metabolism
    5. evolution of virulence
    6. laeA
    7. aspergillosis

    Contributors

    Authors

    Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina, USA
    Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
    Lilian Pereira Silva
    Faculdade de Ciencias Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brazil
    Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina, USA
    Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA
    Faculdade de Ciencias Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, São Paulo, Brazil
    Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, North Carolina, USA
    Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, USA

    Editor

    Yong-Sun Bahn
    Editor
    Yonsei University

    Reviewers

    Sean Doyle
    Solicited external reviewer
    Maynooth University, Ireland
    Jin Woo Bok
    Solicited external reviewer
    University of Wisconsin—Madison

    Notes

    Address correspondence to Gustavo H. Goldman, [email protected], Nicholas H. Oberlies, [email protected], or Antonis Rokas, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Flagellum-Mediated Mechanosensing and RflP Control Motility State of Pathogenic Escherichia coli

    Flagellum-Mediated Mechanosensing and RflP Control Motility State of Pathogenic Escherichia coli

    ABSTRACT

    Bacterial flagellar motility plays an important role in many processes that occur at surfaces or in hydrogels, including adhesion, biofilm formation, and bacterium-host interactions. Consequently, expression of flagellar genes, as well as genes involved in biofilm formation and virulence, can be regulated by the surface contact. In a few bacterial species, flagella themselves are known to serve as mechanosensors, where an increased load on flagella experienced during surface contact or swimming in viscous media controls gene expression. In this study, we show that gene regulation by motility-dependent mechanosensing is common among pathogenic Escherichia coli strains. This regulatory mechanism requires flagellar rotation, and it enables pathogenic E. coli to repress flagellar genes at low loads in liquid culture, while activating motility in porous medium (soft agar) or upon surface contact. It also controls several other cellular functions, including metabolism and signaling. The mechanosensing response in pathogenic E. coli depends on the negative regulator of motility, RflP (YdiV), which inhibits basal expression of flagellar genes in liquid. While no conditional inhibition of flagellar gene expression in liquid and therefore no upregulation in porous medium was observed in the wild-type commensal or laboratory strains of E. coli, mechanosensitive regulation could be recovered by overexpression of RflP in the laboratory strain. We hypothesize that this conditional activation of flagellar genes in pathogenic E. coli reflects adaptation to the dual role played by flagella and motility during infection.
    IMPORTANCE Flagella and motility are widespread virulence factors among pathogenic bacteria. Motility enhances the initial host colonization, but the flagellum is a major antigen targeted by the host immune system. Here, we demonstrate that pathogenic E. coli strains employ a mechanosensory function of the flagellar motor to activate flagellar expression under high loads, while repressing it in liquid culture. We hypothesize that this mechanism allows pathogenic E. coli to regulate its motility dependent on the stage of infection, activating flagellar expression upon initial contact with the host epithelium, when motility is beneficial, but reducing it within the host to delay the immune response.

    REFERENCES

    1.
    Soutourina OA, Bertin PN. 2003. Regulation cascade of flagellar expression in Gram-negative bacteria. FEMS Microbiol Rev 27:505–523.
    2.
    Chevance FFV, Hughes KT. 2008. Coordinating assembly of a bacterial macromolecular machine. Nat Rev Microbiol 6:455–465.
    3.
    Lertsethtakarn P, Ottemann KM, Hendrixson DR. 2011. Motility and chemotaxis in Campylobacter and Helicobacter. Annu Rev Microbiol 65:389–410.
    4.
    Josenhans C, Suerbaum S. 2002. The role of motility as a virulence factor in bacteria. Int J Med Microbiol 291:605–614.
    5.
    Yoon S, Kurnasov O, Natarajan V, Hong M, Gudkov AV, Osterman AL, Wilson IA. 2012. Structural basis of TLR5-flagellin recognition and signaling. Science 335:859–864.
    6.
    Bonifield HR, Hughes KT. 2003. Flagellar phase variation in Salmonella enterica is mediated by a posttranscriptional control mechanism. J Bacteriol 185:3567–3574.
    7.
    Ikeda JS, Schmitt CK, Darnell SC, Watson PR, Bispham J, Wallis TS, Weinstein DL, Metcalf ES, Adams P, O'Connor CD, O'Brien AD. 2001. Flagellar phase variation of Salmonella enterica serovar Typhimurium contributes to virulence in the murine typhoid infection model but does not influence Salmonella-induced enteropathogenesis. Infect Immun 69:3021–3030.
    8.
    Park SF, Purdy D, Leach S. 2000. Localized reversible frameshift mutation in the flhA gene confers phase variability to flagellin gene expression in Campylobacter coli. J Bacteriol 182:207–210.
    9.
    Josenhans C, Eaton KA, Thevenot T, Suerbaum S. 2000. Switching of flagellar motility in Helicobacter pylori by reversible length variation of a short homopolymeric sequence repeat in fliP, a gene encoding a basal body protein. Infect Immun 68:4598–4603.
    10.
    Caldwell MB, Guerry P, Lee EC, Burans JP, Walker RI. 1985. Reversible expression of flagella in Campylobacter jejuni. Infect Immun 50:941–943.
    11.
    Stewart MK, Cummings LA, Johnson ML, Berezow AB, Cookson BT. 2011. Regulation of phenotypic heterogeneity permits Salmonella evasion of the host caspase-1 inflammatory response. Proc Natl Acad Sci U S A 108:20742–20747.
    12.
    Simm R, Remminghorst U, Ahmad I, Zakikhany K, Römling U. 2009. A role for the EAL-like protein STM1344 in regulation of CsgD expression and motility in Salmonella enterica serovar Typhimurium. J Bacteriol 191:3928–3937.
    13.
    Stewart MK, Cookson BT. 2014. Mutually repressing repressor functions and multi-layered cellular heterogeneity regulate the bistable Salmonella fliC census. Mol Microbiol 94:1272–1284.
    14.
    Hengge R, Galperin MY, Ghigo J-M, Gomelsky M, Green J, Hughes KT, Jenal U, Landini P. 2016. Systematic nomenclature for GGDEF and EAL domain-containing cyclic di-GMP turnover proteins of Escherichia coli. J Bacteriol 198:7–11.
    15.
    Wada T, Morizane T, Abo T, Tominaga A, Inoue-Tanaka K, Kutsukake K. 2011. EAL domain protein YdiV acts as an anti-FlhD4C2 factor responsible for nutritional control of the flagellar regulon in Salmonella enterica serovar Typhimurium. 193:1600–1611.
    16.
    Koirala S, Mears P, Sim M, Golding I, Chemla YR, Aldridge PD, Rao CV. 2014. A nutrient-tunable bistable switch controls motility in Salmonella enterica serovar Typhimurium. mBio 5:e01611-14.
    17.
    Spöring I, Felgner S, Preuße M, Eckweiler D, Rohde M, Häussler S, Weiss S, Erhardt M. 2018. Regulation of flagellum biosynthesis in response to cell envelope stress in Salmonella enterica serovar Typhimurium. mBio 9:e00736-17.
    18.
    Macnab RM. 1992. Genetics and biogenesis of bacterial flagella. Annu Rev Genet 26:131–158.
    19.
    Feng L, Liu B, Liu Y, Ratiner YA, Hu B, Li D, Zong X, Xiong W, Wang L. 2008. A genomic islet mediates flagellar phase variation in Escherichia coli strains carrying the flagellin-specifying locus flk. J Bacteriol 190:4470–4477.
    20.
    Wada T, Hatamoto Y, Kutsukake K. 2012. Functional and expressional analyses of the anti-FlhD4C2 factor gene ydiV in Escherichia coli. Microbiology 158:1533–1542.
    21.
    Belas R. 2014. Biofilms, flagella, and mechanosensing of surfaces by bacteria. Trends Microbiol 22:517–527.
    22.
    Gordon VD, Wang L. 2019. Bacterial mechanosensing: the force will be with you, always. J Cell Sci 132:jcs227694.
    23.
    Lele PP, Hosu BG, Berg HC. 2013. Dynamics of mechanosensing in the bacterial flagellar motor. Proc Natl Acad Sci U S A 110:11839–11844.
    24.
    Hug I, Deshpande S, Sprecher KS, Pfohl T, Jenal U. 2017. Second messenger-mediated tactile response by a bacterial rotary motor. Science 358:531–534.
    25.
    Sperandio V, Torres AG, Kaper JB. 2002. Quorum sensing Escherichia coli regulators B and C (QseBC): a novel two-component regulatory system involved in the regulation of flagella and motility by quorum sensing in E. coli. Mol Microbiol 43:809–821.
    26.
    Moreira CG, Weinshenker D, Sperandio V. 2010. QseC mediates Salmonella enterica serovar Typhimurium virulence in vitro and in vivo. Infect Immun 78:914–926.
    27.
    Hughes DT, Clarke MB, Yamamoto K, Rasko DA, Sperandio V. 2009. The QseC adrenergic signaling cascade in enterohemorrhagic E. coli (EHEC). PLoS Pathog 5:e1000553.
    28.
    Simm R, Lusch A, Kader A, Andersson M, Römling U. 2007. Role of EAL-containing proteins in multicellular behavior of Salmonella enterica serovar Typhimurium. J Bacteriol 189:3613–3623.
    29.
    Guttenplan SB, Kearns DB. 2013. Regulation of flagellar motility during biofilm formation. FEMS Microbiol Rev 37:849–871.
    30.
    Cairns LS, Marlow VL, Bissett E, Ostrowski A, Stanley-Wall NR. 2013. A mechanical signal transmitted by the flagellum controls signalling in Bacillus subtilis. Mol Microbiol 90:6–21.
    31.
    Mukherjee S, Bree AC, Liu J, Patrick JE, Chien P, Kearns DB. 2015. Adaptor-mediated Lon proteolysis restricts Bacillus subtilis hyperflagellation. Proc Natl Acad Sci U S A 112:250–255.
    32.
    McCarter L, Hilmen M, Silverman M. 1988. Flagellar dynamometer controls swarmer cell differentiation of V. parahaemolyticus. Cell 54:345–351.
    33.
    Li G, Brown PJB, Tang JX, Xu J, Quardokus EM, Fuqua C, Brun YV. 2012. Surface contact stimulates the just-in-time deployment of bacterial adhesins. Mol Microbiol 83:41–51.
    34.
    Diethmaier C, Chawla R, Canzoneri A, Kearns DB, Lele PP, Dubnau D. 2017. Viscous drag on the flagellum activates Bacillus subtilis entry into the K-state. Mol Microbiol 106:367–380.
    35.
    Hölscher T, Schiklang T, Dragoš A, Dietel AK, Kost C, Kovács ÁT. 2018. Impaired competence in flagellar mutants of Bacillus subtilis is connected to the regulatory network governed by DegU. Environ Microbiol Rep 10:23–32.
    36.
    Alsharif G, Ahmad S, Islam MS, Shah R, Busby SJ, Krachler AM. 2015. Host attachment and fluid shear are integrated into a mechanical signal regulating virulence in Escherichia coli O157:H7. Proc Natl Acad Sci U S A 112:5503–5508.
    37.
    Tipping MJ, Delalez NJ, Lim R, Berry RM, Armitage JP. 2013. Load-dependent assembly of the bacterial flagellar motor. mBio 4:e00551-13.
    38.
    Kearns DB, Losick R. 2005. Cell population heterogeneity during growth of Bacillus subtilis. Genes Dev 19:3083–3094.
    39.
    Laventie BJ, Sangermani M, Estermann F, Manfredi P, Planes R, Hug I, Jaeger T, Meunier E, Broz P, Jenal U. 2019. A surface-induced asymmetric program promotes tissue colonization by Pseudomonas aeruginosa. Cell Host Microbe 25:140–152.e6.
    40.
    Zaslaver A, Bren A, Ronen M, Itzkovitz S, Kikoin I, Shavit S, Liebermeister W, Surette MG, Alon U. 2006. A comprehensive library of fluorescent transcriptional reporters for Escherichia coli. Nat Methods 3:623–628.
    41.
    Amann E, Ochs B, Abel K-J. 1988. Tightly regulated tac promoter vectors useful for the expression of unfused and fused proteins in Escherichia coli. Gene 69:301–315.
    42.
    Datsenko KA, Wanner BL. 2000. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc Natl Acad Sci U S A 97:6640–6645.
    43.
    Cherepanov PP, Wackernagel W. 1995. Gene disruption in Escherichia coli: TcR and KmR cassettes with the option of Flp-catalyzed excision of the antibiotic-resistance determinant. Gene 158:9–14.
    44.
    Yuan J, Jin F, Glatter T, Sourjik V. 2017. Osmosensing by the bacterial PhoQ/PhoP two-component system. Proc Natl Acad Sci U S A 114:E10792–E10798.
    45.
    Rudenko I, Ni B, Glatter T, Sourjik V. 2019. Inefficient secretion of anti-sigma factor FlgM inhibits bacterial motility at high temperature. iScience 16:145–154.
    46.
    Laganenka L, Colin R, Sourjik V. 2016. Chemotaxis towards autoinducer 2 mediates autoaggregation in Escherichia coli. Nat Commun 7:12984.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 11Number 228 April 2020
    eLocator: e02269-19
    Editor: Eduardo A. Groisman
    Yale School of Medicine

    History

    Received: 27 August 2019
    Accepted: 21 February 2020
    Published online: 24 March 2020

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. Escherichia coli
    2. bacterial physiology
    3. flagellar gene regulation
    4. flagellar motility
    5. mechanosensing
    6. virulence

    Contributors

    Authors

    Leanid Laganenka
    Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    LOEWE Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
    María Esteban López
    Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    LOEWE Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
    Remy Colin
    Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    LOEWE Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany
    Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
    LOEWE Center for Synthetic Microbiology (SYNMIKRO), Marburg, Germany

    Editor

    Eduardo A. Groisman
    Editor
    Yale School of Medicine

    Notes

    Address correspondence to Victor Sourjik, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Research on Highly Pathogenic H5N1 Influenza Virus: The Way Forward

    Research on Highly Pathogenic H5N1 Influenza Virus: The Way Forward

    ABSTRACT

    The voluntary moratorium on gain-of-function research related to the transmissibility of highly pathogenic H5N1 influenza virus should continue, pending the resolution of critical policy questions concerning the rationale for performing such experiments and how best to report their results. The potential benefits and risks of these experiments must be discussed and understood by multiple stakeholders, including the general public, and all decisions regarding such research must be made in a transparent manner.

    REFERENCES

    1.
    Fouchier RA et al. 2012. Pause on avian flu transmission research. Science 335:400–401.
    2.
    Herfst S et al. 2012. Airborne transmission of influenza A/H5N1 virus between ferrets. Science 336:1534–1541.
    3.
    Imai M et al. 2012. Experimental adaptation of an influenza H5 HA confers respiratory droplet transmission to a reassortant H5 HA/H1N1 virus in ferrets. Nature 486:420–428.
    4.
    NIH. 2012. United States Government policy for oversight of life sciences dual use research of concern. NIH, Bethesda, MD. http://oba.od.nih.gov/oba/biosecurity/PDF/United_States_Government_Policy_for_Oversight_of_DURC_FINAL_version_032812.pdf.
    5.
    Fauci A. S. 31 July 2012. The way forward in influenza research: a dialogue with the NIAID Director. Audio of presentation from the Sixth Annual Meeting of the Centers for Excellence for Influenza Research and Surveillance (CEIRS), New York, NY. http://www.niaid.nih.gov/about/directors/lectures/Documents/ASFCIERSDiscussion7312912.mp3.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 3Number 51 November 2012
    eLocator: e00359-12

    History

    Published online: 9 October 2012

    Permissions

    Request permissions for this article.

    Contributors

    Author

    Anthony S. Fauci
    National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA

    Notes

    Address correspondence to Anthony S. Fauci, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    A Bispecific Antibody That Simultaneously Recognizes the V2- and V3-Glycan Epitopes of the HIV-1 Envelope Glycoprotein Is Broader and More Potent than Its Parental Antibodies

    A Bispecific Antibody That Simultaneously Recognizes the V2- and V3-Glycan Epitopes of the HIV-1 Envelope Glycoprotein Is Broader and More Potent than Its Parental Antibodies

    ABSTRACT

    Broadly neutralizing antibodies (bNAbs) can prevent and control an HIV-1 infection, but their breadth is invariably too limited for use as monotherapy. To address this problem, bi- and trispecific antibody-like constructs have been developed. These engineered antibodies typically have greater breadth than the native bNAbs from which they were derived, but they are not more potent because they do not, in most cases, simultaneously engage more than a single epitope of the HIV-1 envelope glycoprotein (Env). Here, we describe a new class of bispecific antibodies targeting the V2-glycan (apex) and V3-glycan regions of the HIV-1 envelope glycoprotein (Env). Specifically, bispecific antibodies with a single-chain (scFv) form of the CAP256.VRC26.25 V2-glycan (apex) antibody on one antibody arm and a full V3-glycan Fab on the other arm neutralizes more HIV-1 isolates than the bNAbs from which they were derived. Moreover, these bispecific antibodies are markedly more potent than their parental bNAbs, likely because they simultaneously engage both the apex and V3-glycan epitopes of Env. Our data show that simultaneous engagement of two critical epitopes of a single Env trimer can markedly increase the potency of a bispecific antibody.
    IMPORTANCE Broadly neutralizing antibodies (bNAbs) can prevent a new HIV-1 infection and can at least temporarily suppress an established infection. However, antibody-resistant viruses rapidly emerge in infected persons treated with any single bNAb. Several bispecific antibodies have been developed to increase the breadth of these antibodies, but typically only one arm of these bispecific constructs binds the HIV-1 envelope glycoprotein trimer (Env). Here, we develop and characterize bispecific constructs based on well-characterized V2-glycan and V3-glycan bNAbs and show that at least one member of this class is more potent than its parental antibodies, indicating that they can simultaneously bind both of these epitopes of a single Env trimer. These data show that bispecific antibody-like proteins can achieve greater neutralization potency than the bNAbs from which they were derived.

    REFERENCES

    1.
    McCoy LE, Burton DR. 2017. Identification and specificity of broadly neutralizing antibodies against HIV. Immunol Rev 275:11–20.
    2.
    Burton DR, Barbas CF, III, Persson MA, Koenig S, Chanock RM, Lerner RA. 1991. A large array of human monoclonal antibodies to type 1 human immunodeficiency virus from combinatorial libraries of asymptomatic seropositive individuals. Proc Natl Acad Sci U S A 88:10134–10137.
    3.
    Buchacher A, Predl R, Strutzenberger K, Steinfellner W, Trkola A, Purtscher M, Gruber G, Tauer C, Steindl F, Jungbauer A. 1994. Generation of human monoclonal antibodies against HIV-1 proteins: electrofusion and Epstein-Barr virus transformation for peripheral blood lymphocyte immortalization. AIDS Res Hum Retroviruses 10:359–369.
    4.
    Huang J, Ofek G, Laub L, Louder MK, Doria-Rose NA, Longo NS, Imamichi H, Bailer RT, Chakrabarti B, Sharma SK, Alam SM, Wang T, Yang Y, Zhang B, Migueles SA, Wyatt R, Haynes BF, Kwong PD, Mascola JR, Connors M. 2012. Broad and potent neutralization of HIV-1 by a gp41-specific human antibody. Nature 491:406–412.
    5.
    Scheid JF, Mouquet H, Ueberheide B, Diskin R, Klein F, Oliveira TY, Pietzsch J, Fenyo D, Abadir A, Velinzon K, Hurley A, Myung S, Boulad F, Poignard P, Burton DR, Pereyra F, Ho DD, Walker BD, Seaman MS, Bjorkman PJ, Chait BT, Nussenzweig MC. 2011. Sequence and structural convergence of broad and potent HIV antibodies that mimic CD4 binding. Science 333:1633–1637.
    6.
    Wu X, Yang Z-Y, Li Y, Hogerkorp C-M, Schief WR, Seaman MS, Zhou T, Schmidt SD, Wu L, Xu L, Longo NS, McKee K, O’Dell S, Louder MK, Wycuff DL, Feng Y, Nason M, Doria-Rose N, Connors M, Kwong PD, Roederer M, Wyatt RT, Nabel GJ, Mascola JR. 2010. Rational design of envelope identifies broadly neutralizing human monoclonal antibodies to HIV-1. Science 329:856–861.
    7.
    Walker LM, Huber M, Doores KJ, Falkowska E, Pejchal R, Julien JP, Wang SK, Ramos A, Chan-Hui PY, Moyle M, Mitcham JL, Hammond PW, Olsen OA, Phung P, Fling S, Wong CH, Phogat S, Wrin T, Simek MD, Koff WC, Wilson IA, Burton DR, Poignard P. 2011. Broad neutralization coverage of HIV by multiple highly potent antibodies. Nature 477:466–470.
    8.
    Burton DR, Mascola JR. 2015. Antibody responses to envelope glycoproteins in HIV-1 infection. Nat Immunol 16:571–576.
    9.
    Schoofs T, Barnes CO, Suh-Toma N, Golijanin J, Schommers P, Gruell H, West AP, Jr, Bach F, Lee YE, Nogueira L, Georgiev IS, Bailer RT, Czartoski J, Mascola JR, Seaman MS, McElrath MJ, Doria-Rose NA, Klein F, Nussenzweig MC, Bjorkman PJ. 2019. Broad and potent neutralizing antibodies recognize the silent face of the HIV envelope. Immunity 50:1513–1529.e9.
    10.
    Scheid JF, Horwitz JA, Bar-On Y, Kreider EF, Lu CL, Lorenzi JC, Feldmann A, Braunschweig M, Nogueira L, Oliveira T, Shimeliovich I, Patel R, Burke L, Cohen YZ, Hadrigan S, Settler A, Witmer-Pack M, West AP, Jr, Juelg B, Keler T, Hawthorne T, Zingman B, Gulick RM, Pfeifer N, Learn GH, Seaman MS, Bjorkman PJ, Klein F, Schlesinger SJ, Walker BD, Hahn BH, Nussenzweig MC, Caskey M. 2016. HIV-1 antibody 3BNC117 suppresses viral rebound in humans during treatment interruption. Nature 535:556–560.
    11.
    Bar KJ, Sneller MC, Harrison LJ, Justement JS, Overton ET, Petrone ME, Salantes DB, Seamon CA, Scheinfeld B, Kwan RW, Learn GH, Proschan MA, Kreider EF, Blazkova J, Bardsley M, Refsland EW, Messer M, Clarridge KE, Tustin NB, Madden PJ, Oden K, O’Dell SJ, Jarocki B, Shiakolas AR, Tressler RL, Doria-Rose NA, Bailer RT, Ledgerwood JE, Capparelli EV, Lynch RM, Graham BS, Moir S, Koup RA, Mascola JR, Hoxie JA, Fauci AS, Tebas P, Chun T-W. 2016. Effect of HIV antibody VRC01 on viral rebound after treatment interruption. N Engl J Med 375:2037–2050.
    12.
    Lynch RM, Boritz E, Coates EE, DeZure A, Madden P, Costner P, Enama ME, Plummer S, Holman L, Hendel CS, Gordon I, Casazza J, Conan-Cibotti M, Migueles SA, Tressler R, Bailer RT, McDermott A, Narpala S, O’Dell S, Wolf G, Lifson JD, Freemire BA, Gorelick RJ, Pandey JP, Mohan S, Chomont N, Fromentin R, Chun TW, Fauci AS, Schwartz RM, Koup RA, Douek DC, Hu Z, Capparelli E, Graham BS, Mascola JR, Ledgerwood JE, VRC 601 Study Team. 2015. Virologic effects of broadly neutralizing antibody VRC01 administration during chronic HIV-1 infection. Sci Transl Med 7:319ra206.
    13.
    Caskey M, Klein F, Lorenzi JCC, Seaman MS, West AP, Buckley N, Kremer G, Nogueira L, Braunschweig M, Scheid JF, Horwitz JA, Shimeliovich I, Ben-Avraham S, Witmer-Pack M, Platten M, Lehmann C, Burke LA, Hawthorne T, Gorelick RJ, Walker BD, Keler T, Gulick RM, Fätkenheuer G, Schlesinger SJ, Nussenzweig MC. 2015. Viraemia suppressed in HIV-1-infected humans by broadly neutralizing antibody 3BNC117. Nature 522:487–491.
    14.
    Caskey M, Schoofs T, Gruell H, Settler A, Karagounis T, Kreider EF, Murrell B, Pfeifer N, Nogueira L, Oliveira TY, Learn GH, Cohen YZ, Lehmann C, Gillor D, Shimeliovich I, Unson-O’Brien C, Weiland D, Robles A, Kümmerle T, Wyen C, Levin R, Witmer-Pack M, Eren K, Ignacio C, Kiss S, West AP, Mouquet H, Zingman BS, Gulick RM, Keler T, Bjorkman PJ, Seaman MS, Hahn BH, Fätkenheuer G, Schlesinger SJ, Nussenzweig MC, Klein F. 2017. Antibody 10-1074 suppresses viremia in HIV-1-infected individuals. Nat Med 23:185–191.
    15.
    Wagh K, Seaman MS, Zingg M, Fitzsimons T, Barouch DH, Burton DR, Connors M, Ho DD, Mascola JR, Nussenzweig MC, Ravetch J, Gautam R, Martin MA, Montefiori DC, Korber B. 2018. Potential of conventional & bispecific broadly neutralizing antibodies for prevention of HIV-1 subtype A, C & D infections. PLoS Pathog 14:e1006860.
    16.
    Sather DN, Carbonetti S, Kehayia J, Kraft Z, Mikell I, Scheid JF, Klein F, Stamatatos L. 2012. Broadly neutralizing antibodies developed by an HIV-positive elite neutralizer exact a replication fitness cost on the contemporaneous virus. J Virol 86:12676–12685.
    17.
    Lynch RM, Wong P, Tran L, O’Dell S, Nason MC, Li Y, Wu X, Mascola JR. 2015. HIV-1 fitness cost associated with escape from the VRC01 class of CD4 binding site neutralizing antibodies. J Virol 89:4201–4213.
    18.
    Pietzsch J, Scheid JF, Mouquet H, Klein F, Seaman MS, Jankovic M, Corti D, Lanzavecchia A, Nussenzweig MC. 2010. Human anti-HIV-neutralizing antibodies frequently target a conserved epitope essential for viral fitness. J Exp Med 207:1995–2002.
    19.
    Wagh K, Bhattacharya T, Williamson C, Robles A, Bayne M, Garrity J, Rist M, Rademeyer C, Yoon H, Lapedes A, Gao H, Greene K, Louder MK, Kong R, Karim SA, Burton DR, Barouch DH, Nussenzweig MC, Mascola JR, Morris L, Montefiori DC, Korber B, Seaman MS. 2016. Optimal combinations of broadly neutralizing antibodies for prevention and treatment of HIV-1 clade C infection. PLoS Pathog 12:e1005520.
    20.
    Asokan M, Rudicell RS, Louder M, McKee K, O’Dell S, Stewart-Jones G, Wang K, Xu L, Chen X, Choe M, Chuang G, Georgiev IS, Joyce MG, Kirys T, Ko S, Pegu A, Shi W, Todd JP, Yang Z, Bailer RT, Rao S, Kwong PD, Nabel GJ, Mascola JR. 2015. Bispecific antibodies targeting different epitopes on the HIV-1 envelope exhibit broad and potent neutralization. J Virol 89:12501–12512.
    21.
    Khan SN, Sok D, Tran K, Movsesyan A, Dubrovskaya V, Burton DR, Wyatt RT. 2018. Targeting the HIV-1 spike and coreceptor with bi- and trispecific antibodies for single-component broad inhibition of entry. J Virol 92:e00384-18.
    22.
    Steinhardt JJ, Guenaga J, Turner HL, McKee K, Louder MK, O’Dell S, Chiang C-I, Lei L, Galkin A, Andrianov AK, Doria-Rose NA, Bailer RT, Ward AB, Mascola JR, Li Y. 2018. Rational design of a trispecific antibody targeting the HIV-1 Env with elevated anti-viral activity. Nat Commun 9:877.
    23.
    Bournazos S, Gazumyan A, Seaman MS, Nussenzweig MC, Ravetch JV. 2016. Bispecific anti-HIV-1 antibodies with enhanced breadth and potency. Cell 165:1609–1620.
    24.
    Mouquet H, Scharf L, Euler Z, Liu Y, Eden C, Scheid JF, Halper-Stromberg A, Gnanapragasam PN, Spencer DI, Seaman MS, Schuitemaker H, Feizi T, Nussenzweig MC, Bjorkman PJ. 2012. Complex-type N-glycan recognition by potent broadly neutralizing HIV antibodies. Proc Natl Acad Sci U S A 109:E3268–E3277.
    25.
    Doria-Rose NA, Bhiman JN, Roark RS, Schramm CA, Gorman J, Chuang G-Y, Pancera M, Cale EM, Ernandes MJ, Louder MK, Asokan M, Bailer RT, Druz A, Fraschilla IR, Garrett NJ, Jarosinski M, Lynch RM, McKee K, O’Dell S, Pegu A, Schmidt SD, Staupe RP, Sutton MS, Wang K, Wibmer CK, Haynes BF, Abdool-Karim S, Shapiro L, Kwong PD, Moore PL, Morris L, Mascola JR. 2016. New member of the V1V2-directed CAP256-VRC26 lineage that shows increased breadth and exceptional potency. J Virol 90:76–91.
    26.
    Merchant AM, Zhu Z, Yuan JQ, Goddard A, Adams CW, Presta LG, Carter P. 1998. An efficient route to human bispecific IgG. Nat Biotechnol 16:677–681.
    27.
    Schaefer W, Regula JT, Bahner M, Schanzer J, Croasdale R, Durr H, Gassner C, Georges G, Kettenberger H, Imhof-Jung S, Schwaiger M, Stubenrauch KG, Sustmann C, Thomas M, Scheuer W, Klein C. 2011. Immunoglobulin domain crossover as a generic approach for the production of bispecific IgG antibodies. Proc Natl Acad Sci U S A 108:11187–11192.
    28.
    McCoy LE, Falkowska E, Doores KJ, Le K, Sok D, van Gils MJ, Euler Z, Burger JA, Seaman MS, Sanders RW, Schuitemaker H, Poignard P, Wrin T, Burton DR. 2015. Incomplete neutralization and deviation from sigmoidal neutralization curves for HIV broadly neutralizing monoclonal antibodies. PLoS Pathog 11:e1005110.
    29.
    Kong R, Louder MK, Wagh K, Bailer RT, deCamp A, Greene K, Gao H, Taft JD, Gazumyan A, Liu C, Nussenzweig MC, Korber B, Montefiori DC, Mascola JR. 2015. Improving neutralization potency and breadth by combining broadly reactive HIV-1 antibodies targeting major neutralization epitopes. J Virol 89:2659–2671.
    30.
    Freund NT, Horwitz JA, Nogueira L, Sievers SA, Scharf L, Scheid JF, Gazumyan A, Liu C, Velinzon K, Goldenthal A, Sanders RW, Moore JP, Bjorkman PJ, Seaman MS, Walker BD, Klein F, Nussenzweig MC. 2015. A new glycan-dependent CD4-binding site neutralizing antibody exerts pressure on HIV-1 in vivo. PLoS Pathog 11:e1005238.
    31.
    Freund NT, Wang H, Scharf L, Nogueira L, Horwitz JA, Bar-On Y, Golijanin J, Sievers SA, Sok D, Cai H, Cesar Lorenzi JC, Halper-Stromberg A, Toth I, Piechocka-Trocha A, Gristick HB, van Gils MJ, Sanders RW, Wang LX, Seaman MS, Burton DR, Gazumyan A, Walker BD, West AP, Jr, Bjorkman PJ, Nussenzweig MC. 2017. Coexistence of potent HIV-1 broadly neutralizing antibodies and antibody-sensitive viruses in a viremic controller. Sci Transl Med 9:eaal2144.
    32.
    Hraber P, Seaman MS, Bailer RT, Mascola JR, Montefiori DC, Korber BT. 2014. Prevalence of broadly neutralizing antibody responses during chronic HIV-1 infection. AIDS 28:163–169.
    33.
    Webb NE, Montefiori DC, Lee B. 2015. Dose-response curve slope helps predict therapeutic potency and breadth of HIV broadly neutralizing antibodies. Nat Commun 6:8443.
    34.
    Xu L, Pegu A, Rao E, Doria-Rose N, Beninga J, McKee K, Lord DM, Wei RR, Deng G, Louder M, Schmidt SD, Mankoff Z, Wu L, Asokan M, Beil C, Lange C, Leuschner WD, Kruip J, Sendak R, Kwon YD, Zhou T, Chen X, Bailer RT, Wang K, Choe M, Tartaglia LJ, Barouch DH, O’Dell S, Todd J-P, Burton DR, Roederer M, Connors M, Koup RA, Kwong PD, Yang Z-Y, Mascola JR, Nabel GJ. 2017. Trispecific broadly neutralizing HIV antibodies mediate potent SHIV protection in macaques. Science 358:85–90.
    35.
    Yoon H, Macke J, West AP, Jr, Foley B, Bjorkman PJ, Korber B, Yusim K. 2015. CATNAP: a tool to compile, analyze and tally neutralizing antibody panels. Nucleic Acids Res 43:W213–W219.
    36.
    Platt EJ, Bilska M, Kozak SL, Kabat D, Montefiori DC. 2009. Evidence that ecotropic murine leukemia virus contamination in TZM-bl cells does not affect the outcome of neutralizing antibody assays with human immunodeficiency virus type 1. J Virol 83:8289–8292.
    37.
    Platt EJ, Wehrly K, Kuhmann SE, Chesebro B, Kabat D. 1998. Effects of CCR5 and CD4 cell surface concentrations on infections by macrophagetropic isolates of human immunodeficiency virus type 1. J Virol 72:2855–2864.
    38.
    Takeuchi Y, McClure MO, Pizzato M. 2008. Identification of gammaretroviruses constitutively released from cell lines used for human immunodeficiency virus research. J Virol 82:12585–12588.
    39.
    Wei X, Decker JM, Liu H, Zhang Z, Arani RB, Kilby JM, Saag MS, Wu X, Shaw GM, Kappes JC. 2002. Emergence of resistant human immunodeficiency virus type 1 in patients receiving fusion inhibitor (T-20) monotherapy. Antimicrob Agents Chemother 46:1896–1905.
    40.
    Derdeyn CA, Decker JM, Sfakianos JN, Wu X, O’Brien WA, Ratner L, Kappes JC, Shaw GM, Hunter E. 2000. Sensitivity of human immunodeficiency virus type 1 to the fusion inhibitor T-20 is modulated by coreceptor specificity defined by the V3 loop of gp120. J Virol 74:8358–8367.
    41.
    Gardner MR, Fellinger CH, Prasad NR, Zhou AS, Kondur HR, Joshi VR, Quinlan BD, Farzan M. 2016. CD4-induced antibodies promote association of the HIV-1 envelope glycoprotein with CD4-binding site antibodies. J Virol 90:7822–7832.
    42.
    Kavran JM, Leahy DJ. 2014. Coupling antibody to cyanogen bromide-activated sepharose. Methods Enzymol 541:27–34.
    43.
    Gardner MR, Kattenhorn LM, Kondur HR, von Schaewen M, Dorfman T, Chiang JJ, Haworth KG, Decker JM, Alpert MD, Bailey CC, Neale ES, Jr, Fellinger CH, Joshi VR, Fuchs SP, Martinez-Navio JM, Quinlan BD, Yao AY, Mouquet H, Gorman J, Zhang B, Poignard P, Nussenzweig MC, Burton DR, Kwong PD, Piatak M, Jr, Lifson JD, Gao G, Desrosiers RC, Evans DT, Hahn BH, Ploss A, Cannon PM, Seaman MS, Farzan M. 2015. AAV-expressed eCD4-Ig provides durable protection from multiple SHIV challenges. Nature 519:87–91.
    44.
    Chiang JJ, Gardner MR, Quinlan BD, Dorfman T, Choe H, Farzan M. 2012. Enhanced recognition and neutralization of HIV-1 by antibody-derived CCR5-mimetic peptide variants. J Virol 86:12417–12421.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 11Number 125 February 2020
    eLocator: e03080-19
    Editors: Richard A. Koup
    Vaccine Research Center, NIH
    and Stephen P. Goff
    Columbia University/HHMI

    History

    Received: 21 November 2019
    Accepted: 27 November 2019
    Published online: 14 January 2020

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. broadly neutralizing antibodies
    2. bispecific antibodies
    3. human immunodeficiency virus 1
    4. antibody neutralization
    5. broadly neutralizing antibodies
    6. human immunodeficiency virus

    Contributors

    Authors

    Meredith E. Davis-Gardner https://orcid.org/0000-0002-6133-3613
    Department of Microbiology and Immunology, The Scripps Research Institute, Jupiter, Florida, USA
    Barnett Alfant
    Department of Microbiology and Immunology, The Scripps Research Institute, Jupiter, Florida, USA
    Jesse A. Weber
    Department of Microbiology and Immunology, The Scripps Research Institute, Jupiter, Florida, USA
    Department of Microbiology and Immunology, The Scripps Research Institute, Jupiter, Florida, USA
    Department of Microbiology and Immunology, The Scripps Research Institute, Jupiter, Florida, USA

    Editors

    Richard A. Koup
    Invited Editor
    Vaccine Research Center, NIH
    Stephen P. Goff
    Editor
    Columbia University/HHMI

    Notes

    Address correspondence to Michael Farzan, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

  • mBioArticle
    Gamma Interferon Alters Junctional Integrity via Rho Kinase, Resulting in Blood-Brain Barrier Leakage in Experimental Viral Encephalitis

    Gamma Interferon Alters Junctional Integrity via Rho Kinase, Resulting in Blood-Brain Barrier Leakage in Experimental Viral Encephalitis

    ABSTRACT

    Blood-brain barrier (BBB) breakdown is a hallmark of many diseases of the central nervous system (CNS). Loss of BBB integrity in CNS diseases such as viral encephalitis results in the loss of nutrient/oxygen delivery, rapid infiltration of immune cells, and brain swelling that can exacerbate neuronal injury. Despite this, the cellular and molecular mechanisms that underlie BBB breakdown in viral encephalitis are incompletely understood. We undertook a comprehensive analysis of the cellular and molecular signaling events that induce BBB breakdown in an experimental model of virus-induced encephalitis in which neonatal mice are infected with reovirus (serotype 3 strain Abney). We show that BBB leakage during reovirus infection correlates with morphological changes in the vasculature, reductions in pericytes (BBB supporting cells), and disorganization of vascular junctions. Pathway analysis on RNA sequencing from brain endothelial cells identified the activation of interferon (IFN) signaling within the brain vasculature following reovirus infection. Our in vitro and in vivo studies show that type II IFN mediated by IFN-γ, a well known antiviral signal, is a major contributor to BBB leakage during reovirus infection. We show that IFN-γ reduces barrier properties in cultured brain endothelial cells through Rho kinase (ROCK)-mediated cytoskeletal contractions, resulting in junctional disorganization and cell-cell separations. In vivo neutralization of IFN-γ during reovirus infection significantly improved BBB integrity, pericyte coverage, attenuated vascular ROCK activity, and junctional disorganization. Our work supports a model in which IFN-γ acts directly on the brain endothelium to induce BBB breakdown through a mechanism involving ROCK-induced junctional disorganization.
    IMPORTANCE In an experimental viral encephalitis mouse model in which mice are infected with reovirus, we show that IFN-γ induces blood-brain barrier leakage. We show that IFN-γ promotes Rho kinase activity, resulting in actin cytoskeletal contractions in the brain endothelium that lead to vascular junctional disorganization and cell-cell separations. These studies now provide insight into a previously unknown mechanism for how blood-brain barrier breakdown occurs in viral encephalitis and implicates IFN-γ-Rho kinase activity as major contributor to this phenomenon. By identifying this mechanism of blood-brain barrier breakdown, we now provide potential therapeutic targets in treating patients with viral causes of encephalitis with the hope of limiting damage to the central nervous system.

    REFERENCES

    1.
    Beckham JD, Tyler KL. 2012. Neuro-intensive care of patients with acute CNS infections. Neurotherapeutics 9:124–138.
    2.
    Obermeier B, Daneman R, Ransohoff RM. 2013. Development, maintenance, and disruption of the blood-brain barrier. Nat Med 19:1584–1596.
    3.
    Daniels BP, Klein RS. 2015. Knocking on closed doors: host interferons dynamically regulate blood-brain barrier function during viral infections of the central nervous system. PLoS Pathog 11:e1005096.
    4.
    Hawkins BT, Davis TP. 2005. The blood-brain barrier/neurovascular unit in health and disease. Pharmacol Rev 57:173–185.
    5.
    Engelhardt B, Liebner S. 2014. Novel insights into the development and maintenance of the blood-brain barrier. Cell Tissue Res 355:687–699.
    6.
    Liebner S, Dijkhuizen RM, Reiss Y, Plate KH, Agalliu D, Constantin G. 2018. Functional morphology of the blood-brain barrier in health and disease. Acta Neuropathol 135:311–336.
    7.
    Armulik A, Genove G, Mae M, Nisancioglu MH, Wallgard E, Niaudet C, He L, Norlin J, Lindblom P, Strittmatter K, Johansson BR, Betsholtz C. 2010. Pericytes regulate the blood-brain barrier. Nature 468:557–561.
    8.
    Daneman R, Zhou L, Kebede AA, Barres BA. 2010. Pericytes are required for blood-brain barrier integrity during embryogenesis. Nature 468:562–566.
    9.
    Daneman R, Agalliu D, Zhou L, Kuhnert F, Kuo CJ, Barres BA. 2009. Wnt/beta-catenin signaling is required for CNS, but not non-CNS, angiogenesis. Proc Natl Acad Sci U S A 106:641–646.
    10.
    Ben-Zvi A, Lacoste B, Kur E, Andreone BJ, Mayshar Y, Yan H, Gu C. 2014. Mfsd2a is critical for the formation and function of the blood-brain barrier. Nature 509:507–511.
    11.
    Chai Q, He WQ, Zhou M, Lu H, Fu ZF. 2014. Enhancement of blood-brain barrier permeability and reduction of tight junction protein expression are modulated by chemokines/cytokines induced by rabies virus infection. J Virol 88:4698–4710.
    12.
    Bleau C, Filliol A, Samson M, Lamontagne L. 2015. Brain invasion by mouse hepatitis virus depends on impairment of tight junctions and beta interferon production in brain microvascular endothelial cells. J Virol 89:9896–9908.
    13.
    Li F, Wang Y, Yu L, Cao S, Wang K, Yuan J, Wang C, Wang K, Cui M, Fu ZF. 2015. Viral infection of the central nervous system and neuroinflammation precede blood-brain barrier disruption during Japanese encephalitis virus infection. J Virol 89:5602–5614.
    14.
    Kim JH, Hossain FM, Patil AM, Choi JY, Kim SB, Uyangaa E, Park SY, Lee JH, Kim B, Kim K, Eo SK. 2016. Ablation of CD11chi dendritic cells exacerbates Japanese encephalitis by regulating blood-brain barrier permeability and altering tight junction/adhesion molecules. Comp Immunol Microbiol Infect Dis 48:22–32.
    15.
    Daniels BP, Holman DW, Cruz-Orengo L, Jujjavarapu H, Durrant DM, Klein RS. 2014. Viral pathogen-associated molecular patterns regulate blood-brain barrier integrity via competing innate cytokine signals. mBio 5:e01476.
    16.
    Lazear HM, Daniels BP, Pinto AK, Huang AC, Vick SC, Doyle SE, Gale M, Jr, Klein RS, Diamond MS. 2015. Interferon-lambda restricts West Nile virus neuroinvasion by tightening the blood-brain barrier. Sci Transl Med 7:284ra59.
    17.
    Cain MD, Salimi H, Gong Y, Yang L, Hamilton SL, Heffernan JR, Hou J, Miller MJ, Klein RS. 2017. Virus entry and replication in the brain precedes blood-brain barrier disruption during intranasal alphavirus infection. J Neuroimmunol 308:118–130.
    18.
    Kapikian AZ, Shope RE. 1996. Rotaviruses, reoviruses, coltiviruses, and orbiviruses. In Baron S (ed), Medical microbiology, ch 63. University of Texas Medical Branch at Galveston, Galveston, TX.
    19.
    Nath A, Tyler KL. 2013. Novel approaches and challenges to treatment of central nervous system viral infections. Ann Neurol 74:412–422.
    20.
    Richardson-Burns SM, Kominsky DJ, Tyler KL. 2002. Reovirus-induced neuronal apoptosis is mediated by caspase 3 and is associated with the activation of death receptors. J Neurovirol 8:365–380.
    21.
    Clarke P, Beckham JD, Leser JS, Hoyt CC, Tyler KL. 2009. Fas-mediated apoptotic signaling in the mouse brain following reovirus infection. J Virol 83:6161–6170.
    22.
    Hoyt CC, Richardson-Burns SM, Goody RJ, Robinson BA, Debiasi RL, Tyler KL. 2005. Nonstructural protein sigma1s is a determinant of reovirus virulence and influences the kinetics and severity of apoptosis induction in the heart and central nervous system. J Virol 79:2743–2753.
    23.
    Hellstrom M, Gerhardt H, Kalen M, Li X, Eriksson U, Wolburg H, Betsholtz C. 2001. Lack of pericytes leads to endothelial hyperplasia and abnormal vascular morphogenesis. J Cell Biol 153:543–553.
    24.
    Knowland D, Arac A, Sekiguchi KJ, Hsu M, Lutz SE, Perrino J, Steinberg GK, Barres BA, Nimmerjahn A, Agalliu D. 2014. Stepwise recruitment of transcellular and paracellular pathways underlies blood-brain barrier breakdown in stroke. Neuron 82:603–617.
    25.
    Rajasekaran AK, Hojo M, Huima T, Rodriguez-Boulan E. 1996. Catenins and zonula occludens-1 form a complex during early stages in the assembly of tight junctions. J Cell Biol 132:451–463.
    26.
    Itoh M, Nagafuchi A, Moroi S, Tsukita S. 1997. Involvement of ZO-1 in cadherin-based cell adhesion through its direct binding to alpha catenin and actin filaments. J Cell Biol 138:181–192.
    27.
    Sorensen I, Adams RH, Gossler A. 2009. DLL1-mediated Notch activation regulates endothelial identity in mouse fetal arteries. Blood 113:5680–5688.
    28.
    Utech M, Ivanov AI, Samarin SN, Bruewer M, Turner JR, Mrsny RJ, Parkos CA, Nusrat A. 2005. Mechanism of IFN-gamma-induced endocytosis of tight junction proteins: myosin II-dependent vacuolarization of the apical plasma membrane. Mol Biol Cell 16:5040–5052.
    29.
    Stockton RA, Shenkar R, Awad IA, Ginsberg MH. 2010. Cerebral cavernous malformations proteins inhibit Rho kinase to stabilize vascular integrity. J Exp Med 207:881–896.
    30.
    Fisher OS, Deng H, Liu D, Zhang Y, Wei R, Deng Y, Zhang F, Louvi A, Turk BE, Boggon TJ, Su B. 2015. Structure and vascular function of MEKK3-cerebral cavernous malformations 2 complex. Nat Commun 6:7937.
    31.
    Manaenko A, Yang P, Nowrangi D, Budbazar E, Hartman RE, Obenaus A, Pearce WJ, Zhang JH, Tang J. 2018. Inhibition of stress fiber formation preserves blood-brain barrier after intracerebral hemorrhage in mice. J Cereb Blood Flow Metab 38:87–102.
    32.
    Shang X, Marchioni F, Sipes N, Evelyn CR, Jerabek-Willemsen M, Duhr S, Seibel W, Wortman M, Zheng Y. 2012. Rational design of small molecule inhibitors targeting RhoA subfamily Rho GTPases. Chem Biol 19:699–710.
    33.
    Ishizaki T, Uehata M, Tamechika I, Keel J, Nonomura K, Maekawa M, Narumiya S. 2000. Pharmacological properties of Y-27632, a specific inhibitor of rho-associated kinases. Mol Pharmacol 57:976–983.
    34.
    Davies SP, Reddy H, Caivano M, Cohen P. 2000. Specificity and mechanism of action of some commonly used protein kinase inhibitors. Biochem J 351:95–105.
    35.
    Amano M, Nakayama M, Kaibuchi K. 2010. Rho-kinase/ROCK: a key regulator of the cytoskeleton and cell polarity. Cytoskeleton (Hoboken) 67:545–554.
    36.
    Hirano M, Hirano K. 2016. Myosin di-phosphorylation and peripheral actin bundle formation as initial events during endothelial barrier disruption. Sci Rep 6:20989.
    37.
    Tyler KL, Leser JS, Phang TL, Clarke P. 2010. Gene expression in the brain during reovirus encephalitis. J Neurovirol 16:56–71.
    38.
    Tietz S, Engelhardt B. 2015. Brain barriers: crosstalk between complex tight junctions and adherens junctions. J Cell Biol 209:493–506.
    39.
    Shi Y, Zhang L, Pu H, Mao L, Hu X, Jiang X, Xu N, Stetler RA, Zhang F, Liu X, Leak RK, Keep RF, Ji X, Chen J. 2016. Rapid endothelial cytoskeletal reorganization enables early blood-brain barrier disruption and long-term ischaemic reperfusion brain injury. Nat Commun 7:10523.
    40.
    Winkler CW, Race B, Phillips K, Peterson KE. 2015. Capillaries in the olfactory bulb but not the cortex are highly susceptible to virus-induced vascular leak and promote viral neuroinvasion. Acta Neuropathol 130:233–245.
    41.
    Ma Z, Zhang J, Du R, Ji E, Chu L. 2011. Rho kinase inhibition by fasudil has anti-inflammatory effects in hypercholesterolemic rats. Biol Pharm Bull 34:1684–1689.
    42.
    Kreutzfeldt M, Bergthaler A, Fernandez M, Bruck W, Steinbach K, Vorm M, Coras R, Blumcke I, Bonilla WV, Fleige A, Forman R, Muller W, Becher B, Misgeld T, Kerschensteiner M, Pinschewer DD, Merkler D. 2013. Neuroprotective intervention by interferon-gamma blockade prevents CD8+ T cell-mediated dendrite and synapse loss. J Exp Med 210:2087–2103.
    43.
    Mizuno T, Zhang G, Takeuchi H, Kawanokuchi J, Wang J, Sonobe Y, Jin S, Takada N, Komatsu Y, Suzumura A. 2008. Interferon-gamma directly induces neurotoxicity through a neuron specific, calcium-permeable complex of IFN-gamma receptor and AMPA GluR1 receptor. FASEB J 22:1797–1806.
    44.
    Lindahl P, Johansson BR, Leveen P, Betsholtz C. 1997. Pericyte loss and microaneurysm formation in PDGF-B-deficient mice. Science 277:242–245.
    45.
    Winkler EA, Bell RD, Zlokovic BV. 2011. Central nervous system pericytes in health and disease. Nat Neurosci 14:1398–1405.
    46.
    Winkler EA, Sengillo JD, Bell RD, Wang J, Zlokovic BV. 2012. Blood-spinal cord barrier pericyte reductions contribute to increased capillary permeability. J Cereb Blood Flow Metab 32:1841–1852.
    47.
    Sagare AP, Bell RD, Zhao Z, Ma Q, Winkler EA, Ramanathan A, Zlokovic BV. 2013. Pericyte loss influences Alzheimer-like neurodegeneration in mice. Nat Commun 4:2932.
    48.
    Sengillo JD, Winkler EA, Walker CT, Sullivan JS, Johnson M, Zlokovic BV. 2013. Deficiency in mural vascular cells coincides with blood-brain barrier disruption in Alzheimer’s disease. Brain Pathol 23:303–310.
    49.
    Winkler EA, Sengillo JD, Sullivan JS, Henkel JS, Appel SH, Zlokovic BV. 2013. Blood-spinal cord barrier breakdown and pericyte reductions in amyotrophic lateral sclerosis. Acta Neuropathol 125:111–120.
    50.
    Winkler EA, Birk H, Burkhardt JK, Chen X, Yue JK, Guo D, Rutledge WC, Lasker GF, Partow C, Tihan T, Chang EF, Su H, Kim H, Walcott BP, Lawton MT. 2018. Reductions in brain pericytes are associated with arteriovenous malformation vascular instability. J Neurosurg 129:1377–1662.
    51.
    Lindblom P, Gerhardt H, Liebner S, Abramsson A, Enge M, Hellstrom M, Backstrom G, Fredriksson S, Landegren U, Nystrom HC, Bergstrom G, Dejana E, Ostman A, Lindahl P, Betsholtz C. 2003. Endothelial PDGF-B retention is required for proper investment of pericytes in the microvessel wall. Genes Dev 17:1835–1840.
    52.
    Behl Y, Krothapalli P, Desta T, DiPiazza A, Roy S, Graves DT. 2008. Diabetes-enhanced tumor necrosis factor-alpha production promotes apoptosis and the loss of retinal microvascular cells in type 1 and type 2 models of diabetic retinopathy. Am J Pathol 172:1411–1418.
    53.
    Zehendner CM, Wedler HE, Luhmann HJ. 2013. A novel in vitro model to study pericytes in the neurovascular unit of the developing cortex. PLoS One 8:e81637.
    54.
    Leibrand CR, Paris JJ, Ghandour MS, Knapp PE, Kim WK, Hauser KF, McRae M. 2017. HIV-1 Tat disrupts blood-brain barrier integrity and increases phagocytic perivascular macrophages and microglia in the dorsal striatum of transgenic mice. Neurosci Lett 640:136–143.
    55.
    Dallasta LM, Pisarov LA, Esplen JE, Werley JV, Moses AV, Nelson JA, Achim CL. 1999. Blood-brain barrier tight junction disruption in human immunodeficiency virus-1 encephalitis. Am J Pathol 155:1915–1927.
    56.
    Rahimy E, Li FY, Hagberg L, Fuchs D, Robertson K, Meyerhoff DJ, Zetterberg H, Price RW, Gisslen M, Spudich S. 2017. Blood-brain barrier disruption is initiated during primary HIV infection and not rapidly altered by antiretroviral therapy. J Infect Dis 215:1132–1140.
    57.
    Siegenthaler J, Ashique A, Zarbalis K, Patterson K, Hecht J, Kane M, Folias A, Choe Y, May S, Kume T, Napoli J, Peterson A, Pleasure S. 2009. Retinoic acid from the meninges regulates cortical neuron generation. Cell 139:597–609.
    58.
    Zarbalis K, Siegenthaler JA, Choe Y, May SR, Peterson AS, Pleasure SJ. 2007. Cortical dysplasia and skull defects in mice with a Foxc1 allele reveal the role of meningeal differentiation in regulating cortical development. Proc Natl Acad Sci U S A 104:14002–14007.
    59.
    Baird NL, Bowlin JL, Cohrs RJ, Gilden D, Jones KL. 2014. Comparison of varicella-zoster virus RNA sequences in human neurons and fibroblasts. J Virol 88:5877–5880.
    60.
    Wu TD, Nacu S. 2010. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26:873–881.
    61.
    Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, Salzberg SL, Wold BJ, Pachter L. 2010. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 28:511–515.
    62.
    Dionne KR, Zhuang Y, Leser JS, Tyler KL, Clarke P. 2013. Daxx upregulation within the cytoplasm of reovirus-infected cells is mediated by interferon and contributes to apoptosis. J Virol 87:3447–3460.

    Information & Contributors

    Information

    Published In

    mBio
    Volume 10Number 427 August 2019
    eLocator: e01675-19
    Editor: Mary K. Estes
    Baylor College of Medicine

    History

    Received: 26 June 2019
    Accepted: 12 July 2019
    Published online: 6 August 2019

    Permissions

    Request permissions for this article.

    KEYWORDS

    1. blood-brain barrier
    2. encephalitis
    3. viral encephalitis
    4. interferon gamma
    5. reovirus

    Contributors

    Authors

    Stephanie Bonney
    Department of Pediatrics, Section of Developmental Biology, University of Colorado, School of Medicine, Aurora, Colorado, USA
    Cell Biology, Stem Cells, and Development Graduate Program, University of Colorado, School of Medicine, Aurora, Colorado, USA
    Scott Seitz
    Microbiology Graduate Program, University of Colorado, School of Medicine, Aurora, Colorado, USA
    Department of Neurology, University of Colorado, School of Medicine, Aurora, Colorado, USA
    Caitlin A. Ryan
    Department of Pediatrics, Section of Developmental Biology, University of Colorado, School of Medicine, Aurora, Colorado, USA
    Kenneth L. Jones
    Department of Pediatrics, Section of Hematology, Oncology, and Bone Marrow Transplant, University of Colorado, School of Medicine, Aurora, Colorado, USA
    Penny Clarke
    Department of Neurology, University of Colorado, School of Medicine, Aurora, Colorado, USA
    Kenneth L. Tyler
    Department of Neurology, University of Colorado, School of Medicine, Aurora, Colorado, USA
    Julie A. Siegenthaler
    Department of Pediatrics, Section of Developmental Biology, University of Colorado, School of Medicine, Aurora, Colorado, USA

    Editor

    Mary K. Estes
    Editor
    Baylor College of Medicine

    Reviewers

    Avindra Nath
    Solicited external reviewer
    National Institutes of Health
    Robert Fujinami
    Solicited external reviewer
    University of Utah School of Medicine

    Notes

    Address correspondence to Kenneth L. Tyler, [email protected], or Julie A. Siegenthaler, [email protected].

    Metrics & Citations

    Metrics

    Citations

    View Options

    Media

    Figures

    Other

    Tables

    Share

There are no results at this time

American Society for Microbiology ("ASM") is committed to maintaining your confidence and trust with respect to the information we collect from you on websites owned and operated by ASM ("ASM Web Sites") and other sources. This Privacy Policy sets forth the information we collect about you, how we use this information and the choices you have about how we use such information.
FIND OUT MORE about the privacy policy