Free access
Spotlight Selection
Research Article
5 May 2015

Highly Divergent Hepaciviruses from African Cattle

This article has a companion.


The hepatitis C virus (HCV; genus Hepacivirus) is a highly relevant human pathogen. Unique hepaciviruses (HV) were discovered recently in animal hosts. The direct ancestor of HCV has not been found, but the genetically most closely related animal HVs exist in horses. To investigate whether other peridomestic animals also carry HVs, we analyzed sera from Ghanaian cattle for HVs by reverse transcription-PCR (RT-PCR). Nine of 106 specimens from different sampling sites contained HV RNA (8.5%) at median viral loads of 1.6 × 105 copies/ml. Infection seemed unrelated to cattle age and gender. Near-full-genome sequencing of five representative viruses confirmed taxonomic classifications. Cattle HVs formed two distinct phylogenetic lineages that differed by up to 17.7% on the nucleotide level in the polyprotein-encoding region, suggesting cocirculation of different virus subtypes. A conserved microRNA122-binding site in the 5′ internal ribosomal entry site suggested liver tropism of cattle HVs. Phylogenetic analyses suggested the circulation of HVs in cattle for several centuries. Cattle HVs were genetically highly divergent from all other HVs, including HCV. HVs from genetically related equine and bovine hosts were not monophyletic, corroborating host shifts during the evolution of the genus Hepacivirus. Similar to equine HVs, the genetic diversity of cattle HVs was low compared to that of HCV genotypes. This suggests an influence of the human-modified ecology of peridomestic animals on virus diversity. Further studies should investigate the occurrence of cattle HVs in other geographic areas and breeds, virus pathogenicity in cattle, and the potential exposure of human risk groups, such as farmers, butchers, and abattoir workers.
IMPORTANCE HCV (genus Hepacivirus) is a major human pathogen, causing liver failure and cancer. Unique hepaciviruses (HVs) were discovered over the last few years in animals, but the direct ancestor of HCV has not been found. The animal HV most closely related to HCV so far originated from horses, suggesting that other livestock animals also harbor HVs. Therefore, we investigated African cattle and discovered previously unknown HVs at high prevalence and viral loads. Because of the agricultural importance of cattle, it may be relevant to investigate HV pathogenicity. The frequent exposure of humans to cattle also may warrant investigations of the zoonotic potential of these viruses. Evolutionary analyses suggested that cattle HVs have existed for centuries. Despite the genetic relatedness of their animal hosts, HVs from cattle and horses were not phylogenetically related, corroborating frequent host shifts during the evolution of the genus Hepacivirus.


The hepatitis C virus (HCV) is a major human pathogen, causing liver failure, hepatitis, and hepatocellular carcinoma (1). Up to 185 million people are infected worldwide (2). The economic burden of HCV-associated costs amounts to several billion dollars in the United States alone (3). There is no vaccine to prevent HCV infection, partly due to the lack of accessible animal models at early stages of vaccine development (4). However, treatment options have improved recently by the availability of several direct-acting antivirals (5).
HCV belongs to the genus Hepacivirus within the family Flaviviridae (6). Contrary to the human immunodeficiency virus (HIV), which originated in African nonhuman primates, no direct ancestor of HCV is known (6, 7). It was long unclear whether nonprimate hepaciviruses (HVs) exist. Recently, unique HVs were identified in equine, rodent, and chiropteran hosts (810). In addition, equine HVs were detected in dogs in a single study (11). Altogether, these data suggest that an animal origin of HCV is plausible (6). While the animal HVs discovered so far are unlikely to be direct ancestors of HCV, the most closely related animal HVs exist in horses sampled worldwide (12). This suggests that other livestock animals, such as goat, sheep, and cattle, also harbor animal HVs. These animals belong to the genetically diversified family Bovidae and are frequent sources of zoonotic infections, unlike other peridomestic animals (13, 14).
To investigate whether HVs exist in bovids, we investigated cattle in western Africa. Highly diversified HVs were identified and characterized on a full-genome level.


Licenses for sampling, ethical review, and clearances of animal handling procedures were obtained from the forestry commission of the Ghanaian Ministry of Food and Agriculture.
Screening for HVs and genomic characterizations were done using broadly reactive and highly sensitive nested reverse transcription-PCR (RT-PCR) assays as described previously (8). Illumina NGS was done on a HiSeq2500 as described previously (15). Genome ends were determined following a 5′/3′-rapid amplification of cDNA ends (RACE) strategy using a 5′/3′-RACE kit (Roche, Penzberg, Germany). For the exclusion of poly(A) tails, 3′-RACEs were done in parallel on polyadenylated and native RNA extracts. RNA secondary structures in viral genome ends were inferred manually based on covariant base pairing and thermodynamic predictions from mfold (16). The quantification of viral loads relied on photometrically quantified cRNA controls in vitro transcribed from cloned PCR amplicons containing the target sites within the NS3 gene as described previously (17). For quantification, a 25-μl real-time RT-PCR was set up using a SuperScript III (SSIII) one-step RT-PCR kit (Invitrogen, Karlsruhe, Germany) with 5 μl of template RNA, 400 nM both primers cattleHV-FWD (CTYAAATTGGCNTCCTAYAAAACWGG) and cattleHV-REV (GCICGRATGCCCCTAGAACG), 200 nM the probe cattleHV-P (6-carboxyfluorescein [FAM]-AYTGTGARGCKCTCGCTGCTGACCT-black hole quencher 1), 1 μg bovine serum albumin, 0.2 mM each deoxynucleoside triphosphate (dNTP), and 2.4 mM MgSO4. Amplification involved 15 min at 50°C and then 3 min at 95°C, followed by 45 cycles of 15 s at 94°C and 25 s at 58°C. Fluorescence was measured at the 58°C annealing/extension step.
Phylogenetic analyses were done using MrBayes V3.1 (18), SSE V1.1 (19), and MEGA5 (20). Distance matrices were calculated using MEGA5 (20) and a pairwise deletion option. Signal peptidase cleavage sites were predicted using Geneious V6 ( and SignalP 4.1 (21). N- and O-glycosylation sites were predicted using NetNGlyc 1.0 and NetOGlyc 4.0 (22 and Exclusion of recombination was done using GARD (23), and estimation of the ratios of nonsynonymous to synonymous evolutionary changes (dN/dS) was done using PARRIS (24), both from the DataMonkey webserver (25).
Statistical analyses were done using EpiInfo V7 ( and SPSS V22 (IBM, Ehningen, Germany).

Nucleotide sequence accession numbers.

All cattle HV genomic sequences were deposited in GenBank (accession numbers KP265942 to KP265950).


Sampling and hepacivirus detection.

Individual sera were obtained from n = 106 cattle (Bos taurus) sampled in Kumasi, central Ghana, in December 2011 (Fig. 1A and Table 1). No information on cattle breeds was available. Testing for HVs by highly sensitive nested RT-PCR as described previously (8) yielded a high prevalence of 8.5% (9 of 106). There was no statistically significant difference between the HV detection rates in male (2 of 41; 4.9%) and female (7 of 65; 10.8%) cattle (P = 0.48 by Fisher's exact 2-tailed test). Similarly, there was no statistically significant difference between the median ages of infected (3.1 years; range, 1 to 4 years) and noninfected (3.3 years; range, 0.5 to 7 years) cattle (P = 0.73 by 2-tailed t test). These preliminary data suggest that HV infection was unrelated to cattle gender or age. However, these data need to be interpreted with caution. More accurate assessments regarding the exposure of cattle to HVs will require larger samples containing different age groups and even gender composition. Additionally, serological analyses of past infection, together with knowledge about the antibody kinetics against HVs in cattle, will be required. Finally, and in analogy to equine HVs (26), rates of spontaneous viral clearance may be much higher in cattle HVs than in HCV, further limiting our ability to infer exposure of cattle to HVs based on incidence alone.
FIG 1 Sampling sites. (A) Geographic location of Kumasi, central Ghana. (B) Twelve sampling sites in the metropolitan area of Kumasi were marked with dots and numbered from west to east. Gray background, Kumasi city limits. Positive sampling sites are given in red. The maps were created using QGIS ( and data freely available under an OpenStreetMap license (
TABLE 1 Sample characteristics
No.Sampling siteSampling date (day-mo-yr)Total no. tested (male/female)No. positive (male/female)% positiveSample code of positive samples (viral load [copies/ml])
1Afari12-12-20115 (3/2)0  
2Akrofrom12-12-20116 (5/1)0  
3Sepaase12-12-201110 (3/7)2 (1/1)20.0GHC52a (2.3 × 106), GHC55b (9.8 × 104)
4Adankwame13-12-201110 (4/6)0  
5Nyankyerenease7-12-201110 (5/5)0  
6Sobon Zongo8-12-201110 (4/6)1 (1/0)10.0GHC25a (3.7 × 105)
7Moshie Zongo8-12-20115 (1/4)2 (0/2)40.0GHC32 (1.6 × 104), GHC37 (1.6 × 105)
8Nima6-12-201110 (4/6)1 (0/1)10.0GHC11 (7.2 × 105)
9Adukrom9-12-201110 (3/7)0  
10Asotwe15-12-201110 (4/6)1 (0/1)10.0GHC100b (6.4 × 104)
11Onwe14-12-201110 (4/6)2 (0/2)20.0GHC85b (1.5 × 105), GHC87 (2.6 × 105)
12Essienimpong14-12-201110 (1/9)0  
 Total 106 (41/65)9 (2/7)8.5 
Full genome characterized.
Full polyprotein-encoding region characterized.
Cattle HV loads were quantified by strain-specific real-time RT-PCR. The median viral load was 1.6 × 105 copies/ml (range, 1.6 × 104 to 2.3 × 106; Table 1 lists individual HV loads). This was comparable to viral loads commonly observed in HCV, equine HVs, and rodent HVs (8, 27, 28). No organ specimens were available to determine liver tropism of cattle HVs.
Sampling sites were 12 nonindustrial farms spread across an area of approximately 600 km2 (Fig. 1B and Table 1). Each farm contained up to 40 animals, of which up to 10 animals were sampled. As shown in Fig. 1B, positive specimens originated from six different sampling sites that were not adjacent to each other. HV detection rates in individual farms ranged between 10 and 40% (Table 1). A 3,715-nucleotide (nt) data set encompassing the complete structural genome region of all cattle HVs was generated by PCR (primer sequences are available upon request). As shown in Fig. 2A, each farm (corresponding sampling sites are shown in red) harbored unique HVs differing by up to 18.8% in the 3,715-nt data set, excluding a common-source outbreak as an explanation for the high HV prevalence. The evolutionary rate of HCV is high, with about 10−3 substitutions per site per year (29). Extrapolation of this rate to the cattle HV data set represented in Fig. 2A implies about 160 years of evolution to yield the observed viral diversity. However, HVs are under strong noncoding constraints, including high levels of genome-scale ordered RNA structure (GORS) (30). Thus, it is possible that mere comparisons of substitution rates greatly underestimate the time scale of HV evolution in general (6). Because of these noncoding constraints, the limited data set available for cattle HVs, and the unknown level of host-defined selective pressure influencing cattle HV evolution, the true evolutionary history of cattle HVs remains to be determined. However, this exploratory analysis suggested a long evolutionary association between cattle HVs and their hosts.
FIG 2 Phylogenetic characterization of cattle hepaciviruses. (A) Neighbor-joining phylogenetic reconstruction of a 3,715-nt fragment encompassing the complete hepacivirus (HV) structural genome region using a complete deletion option. Numbers at nodes indicate support of grouping from 1,000 bootstrap replicates. Only values above 75% are shown. Corresponding sampling sites are given to the right. (B) Bayesian phylogeny of the complete HV polyprotein done in MrBayes using a WAG amino acid substitution model and 2,000,000 generations sampled every 100 steps with 25% discarded as burn-in. The branch leading to the bovine viral diarrhea virus 1 (BVDV) outgroup was truncated for graphical reasons. Filled circles, posterior probability support of 1.0. Hosts are depicted to the right of taxa, which are colored accordingly. The GenBank accession number of viruses used in this study were the following: NZP-1, JQ434001; A6-006, JQ434003; G5-077, JQ434006; G10-73, JQ434002; H3-011, JQ434008; F80-68, JQ434005; B10-022, JQ434004; H10-094, JQ434007; CHV, JQ434007; HCV1a, NC_004102; HCV2a, AB047639; HCV3a, X76918; HCV4a, Y11604; HCV5a, Y13184; HCV6a, AY859526; HCV7, EF108306; PDB829, KC796074; PDB491, KC796078; PDB452, KC796090; PDB445, KC796091; RMU10-3382, KC411777; NLR07-oct70, KC411784; NLR08-365, KC411796; SAR-3, KC411806; SAR-46, KC411807; RHV089, KC815312; NrHV-1, KJ950938; NrHV-2, KJ950939; RHV339, NC_021153; BWC08, KC551800; BWC05, KC551801; BWC04, KC551802; GBV-B, NC_001655; PDB112, KC796077. Branches leading to novel cattle HVs are in red.

Hepacivirus genomic characterization.

The full polyprotein-encoding genomic regions of five cattle HVs were characterized using long-range PCR assays based on next-generation sequencing (NGS) data. As shown in Fig. 2B, HVs formed two distinct phylogenetic lineages that differed by up to 17.7% at the nucleotide level. Thus, cattle HVs showed a genetic diversity comparable to that between HCV subtypes (1). The genetic distance in cattle HVs was 3- to 4-fold higher at the nucleotide than at the amino acid level, respectively, with 19.4% and 6.7% in the structural (S) and 17.3% and 4.0% in the nonstructural (NS) genome regions. The large amount of synonymous mutations suggested by these comparisons was compatible with low mean dN/dS ratios of 0.062 and 0.033 for S and NS genome regions, respectively. Of note, these estimates were almost identical to those obtained for equine HVs and severalfold lower than those in HCV (9). The deep branch leading to cattle HVs clustering in basal sister relationships to other HVs was compatible with the ancient evolutionary history of cattle HVs suggested by sequence distance comparisons.
As shown in Fig. 3A, the predicted cattle HV polyprotein contained the 10 typical HV proteins in the order Core-E1-E2-p7-NS2-NS3-NS4A-NS4B-NS5A-NS5B. Similar to HCV, the E1 and E2 proteins of cattle HVs contained several predicted N-glycosylation sites (two and six, respectively; red arrows). These similarities confirmed taxonomic classification within the genus Hepacivirus.
FIG 3 Genomic characterization of cattle hepaciviruses. (A) Genome organization of cattle HVs. Black arrows on the top indicate predicted signal peptidase cleavage sites; red arrows indicate N-linked glycosylation sites. (B) Comparison of amino acid sequence identity within and between HV polyproteins, including five novel cattle HVs, one representative each of HCV genotypes 1 to 7, and all available equine HVs (see the legend to Fig. 2 for GenBank accession numbers), calculated using SSE V1.1 with a sliding window of 400 and a step size of 200 residues. (C) Mean minimum folding energy differences (MFED), calculated using SSE V1.1, comparing the original sequences of the five full cattle HV polyprotein genes against 50 replicates scrambled according to dinucleotide content and coding sequence (CDLR option) with a sliding window of 250 and a step size of 30 nucleotides.
Figure 3B shows variability across the cattle HV polyprotein-encoding region. Except for a small decrease of amino acid sequence identity in the E1 protein of cattle HVs, no genomic region contained a markedly higher degree of variation. This pattern was similar to that of globally sampled equine HVs. On the contrary, HCV genotypes showed high variability in the genome regions encoding the E1, E2, NS2, and NS5A proteins. No recombination events were detected, suggesting that in analogy to HCV (31), recombination is not frequent in cattle HVs. However, the limited data set prevents definite assertions on presence or absence of recombination in cattle HVs.
As noted above, a hallmark of HCV and other HVs is a high degree of GORS. Similar to HCV, cattle HVs contained high levels of GORS, as suggested by comparisons of minimum folding energy differences (MFED) between cattle HV polyprotein sequences and shuffled control sequences (Fig. 3C). The mean MFED of cattle HVs was 9.2%, which was slightly lower than the 13% observed previously in equine HVs (9) and almost identical to the 8.5% observed in HCV (30).
Finally, the viral genome ends were determined for two viruses, termed GHC25 and GHC52, that represented the distinct cattle HV lineages. The full-length genomes encompassed 8,879 and 8,891 nt, respectively. As shown in Fig. 4A, the viral 5′-untranslated region (UTR) of both viruses comprised 294 nt, forming a typical HV/Pestivirus-like type 4 internal ribosomal entry site (IRES). Only five polymorphic sites were observed between the two analyzed viruses. The IRES included a completely conserved and openly accessible microRNA122 (miR122)-binding site. No evidence for the existence of a second miR122-binding site and for a homologue of stem-loop I (SLI), commonly found in HVs, was found in either of the two virus lineages despite repeated attempts. Whether these 5′-UTR elements indeed are absent from cattle HVs or whether the 5′-UTR is incomplete remains to be determined. As shown in Fig. 4B, the viral 3′-UTRs comprised 235 to 250 nt, which formed three highly ordered SL structures, resembling the structure of the HCV 3′-UTR (8). Several polymorphic sites and small insertions were observed in the 3′-UTR sequences of the two viruses. A major difference between the 3′-UTR of HCV and that of cattle HVs was a kissing-like pairing observed within SLI of the cattle HV 3′-UTR (32). However, experimental validation will be required to consolidate this novel HV 3′-UTR pairing and to assess whether it is as relevant for cattle HV replication as the long-distance kissing-loop interaction described between SLII of the HCV 3′-UTR and an SL element located upstream in the HCV NS5B gene (33). Similar to HCV and contrary to equine HVs (9), no evidence was found for a poly(A) tail. Contrary to HCV, no poly(U) stretch separated the viral polyprotein gene and 3′-UTR.
FIG 4 Cattle hepacivirus genome ends. (A) 5′-End secondary structure of cattle HVs (represented by virus GHC25) with stem-loops numbered next to structures. The microRNA122-binding site is given in blue. PK, pseudoknot. (B) 3′-End secondary structure of cattle HVs. Kissing loops are highlighted. Differences between the primary sequences of the 5′ and 3′ genome ends are depicted in black next to structures; covariant mutations are in green.


We identified highly divergent HVs in African cattle, analyzed their evolutionary relationships, and conducted preliminary investigations into their infection patterns.

Infection patterns.

A hallmark of the infection caused by HCV and rodent and equine HVs is replication in the liver (8, 27, 34). In HCV, a major reason for liver tropism is interaction of the viral genome with a liver-specific miR122 (35). In analogy to HCV, the presence of a completely conserved miR122-binding site in cattle HVs is suggestive of liver tropism. Quantitative RT-PCR and histopathological analyses of tissue from solid organs will be required to provide further confirmation for the putative liver tropism of cattle HVs (8). Our data suggested frequent infection of cattle with HVs but could not elucidate whether this occurs preferentially through vertical or horizontal transmission routes.
A second hallmark of HCV infection is delayed virus clearance (34). Data from experimental infections and field investigations suggested that delayed clearance also occurs in animal HVs, albeit less frequently than in HCV (8, 9, 27). For cattle, the high prevalence and the high level of GORS commonly found in persisting viruses (30) were compatible with prolonged courses of infection. Longitudinal studies and experimental inoculation of cattle will provide further data on the infection patterns of cattle HVs.
Because of the high economic and alimentary relevance of cattle, particularly for small farmers in sub-Saharan Africa (36), it may be highly relevant to investigate the pathogenicity of HVs in cattle. In analogy to several other zoonotic pathogens originating from cattle (37), the potential of cattle HVs to cause zoonotic infections should be investigated. Human risk groups potentially exposed to cattle HV include butchers, abattoir workers, and farmers.

Animal model.

There is no easily accessible animal model for HCV (4). Chimpanzees support HCV infection, but their usage is limited by ethical and infrastructural constraints (4, 8). Usage of the novel rodent HVs as candidate models for HCV is a promising approach but remains to be established (8). Initial data suggest horses can be used as animal models to study HV infection (27, 38). Cattle could constitute another important large-animal model. A major benefit of cattle and horses as animal models could be the longevity and hepatic mass of these animals compared to those of rodents (38). Thus, long-term studies may allow more insights into carcinogenesis and long-term inflammatory tissue damage than in available and future small-mammal models (4).

Virus evolution.

Cattle HVs clustered phylogenetically in a basal sister relationship to other HVs from primates, rodents, and bats, suggesting these viruses are ancient members of the genus Hepacivirus. At the same time, cattle HVs and equine HVs infecting hosts from the genetically related orders Artiodactyla and Perissodactyla were not monophyletic. Thus, our data confirmed previous observations on cross-order host switches involved in the evolution of HVs (6, 12).
The novel cattle HVs were genetically less diversified than HCV genotypes. On the one hand, this may represent a distinct geographical pattern of cattle HV types, implying that more diversified cattle HVs remain to be found. This would be analogous to HCV genotypes evolved in historically more isolated human populations (6). Thus, it would be highly relevant to investigate cattle from other geographic areas for HVs. Because Africa harbors evolutionarily old taurine cattle breeds (39), HV evolution in Africa may have preceded HV evolution in other geographical areas. The long branches leading to cattle HVs in phylogenetic reconstructions and the existence of diversified subtypes in one geographic area may result from long evolution of HVs in African cattle. Alternatively, cattle HVs may have been introduced into Africa through importation of European or Asian cattle, similar to bovine tuberculosis and rinderpest virus (40, 41).
On the other hand, the narrower genetic diversity of cattle HVs may result from human manipulation of animals. The domestication of cattle took place about 10,000 years ago through several independent events that involved cross-breeding of different (sub)species and establishment of large cattle herds (39, 42). This may have favored transmission of specific HV strains or recombination events preventing virus divergence. Of note, the limited degree of genetic variability between the two cocirculating cattle HV subtypes is unlikely to imply the existence of different serotypes against which animals do not develop cross-protective immune responses. For comparison, lack of cross-protection from infection with heterologous HCV genotypes has been described (43, 44) and bank voles were shown to harbor different HV serotypes (8). However, the level of variability between immunogenic epitopes of different HCV genotypes and bank vole HV types is likely severalfold higher than that between the Ghanaian cattle HV subtypes.
Finally, the lower genetic diversity of cattle HVs compared to that of HCV may be due to a relatively recent viral host switch from an unknown source. Larger samples, longitudinal studies, or experimental infections will be necessary to investigate the genetic and antigenic variability of cattle HVs in more detail. The investigation of other bovine species will allow further analyses of the evolutionary associations between HVs and bovids potentially preceding the formation of the taurine cattle stem lineage.


We thank Monika Eschbach-Bludau, Tobias Bleicker, and Sebastian Brünink for technical assistance.
This study was funded by the German Federal Ministry of Education and Research (01KIO16D), the German Research Foundation (DR771/12-1), and the Russian Scientific Foundation (14-15-00619).


Smith DB, Bukh J, Kuiken C, Muerhoff AS, Rice CM, Stapleton JT, Simmonds P. 2014. Expanded classification of hepatitis C virus into 7 genotypes and 67 subtypes: updated criteria and genotype assignment web resource. Hepatology 59:318–327.
Mohd Hanafiah K, Groeger J, Flaxman AD, Wiersma ST. 2013. Global epidemiology of hepatitis C virus infection: new estimates of age-specific antibody to HCV seroprevalence. Hepatology 57:1333–1342.
Gellad ZF, Reed SD, Muir AJ. 2012. Economic evaluation of direct-acting antiviral therapy in chronic hepatitis C. Antiviral Ther 17:1189–1199.
Bukh J. 2012. Animal models for the study of hepatitis C virus infection and related liver disease. Gastroenterology 142:1279–1287 e1273.
Hill A, Khoo S, Fortunak J, Simmons B, Ford N. 2014. Minimum costs for producing hepatitis C direct-acting antivirals for use in large-scale treatment access programs in developing countries. Clin Infect Dis 58:928–936.
Simmonds P. 2013. The origin of hepatitis C virus. Curr Top Microbiol Immunol 369:1–15.
Keele BF, Van Heuverswyn F, Li Y, Bailes E, Takehisa J, Santiago ML, Bibollet-Ruche F, Chen Y, Wain LV, Liegeois F, Loul S, Ngole EM, Bienvenue Y, Delaporte E, Brookfield JF, Sharp PM, Shaw GM, Peeters M, Hahn BH. 2006. Chimpanzee reservoirs of pandemic and nonpandemic HIV-1. Science 313:523–526.
Drexler JF, Corman VM, Muller MA, Lukashev AN, Gmyl A, Coutard B, Adam A, Ritz D, Leijten LM, van Riel D, Kallies R, Klose SM, Gloza-Rausch F, Binger T, Annan A, Adu-Sarkodie Y, Oppong S, Bourgarel M, Rupp D, Hoffmann B, Schlegel M, Kummerer BM, Kruger DH, Schmidt-Chanasit J, Setien AA, Cottontail VM, Hemachudha T, Wacharapluesadee S, Osterrieder K, Bartenschlager R, Matthee S, Beer M, Kuiken T, Reusken C, Leroy EM, Ulrich RG, Drosten C. 2013. Evidence for novel hepaciviruses in rodents. PLoS Pathog 9:e1003438.
Burbelo PD, Dubovi EJ, Simmonds P, Medina JL, Henriquez JA, Mishra N, Wagner J, Tokarz R, Cullen JM, Iadarola MJ, Rice CM, Lipkin WI, Kapoor A. 2012. Serology-enabled discovery of genetically diverse hepaciviruses in a new host. J Virol 86:6171–6178.
Quan PL, Firth C, Conte JM, Williams SH, Zambrana-Torrelio CM, Anthony SJ, Ellison JA, Gilbert AT, Kuzmin IV, Niezgoda M, Osinubi MO, Recuenco S, Markotter W, Breiman RF, Kalemba L, Malekani J, Lindblade KA, Rostal MK, Ojeda-Flores R, Suzan G, Davis LB, Blau DM, Ogunkoya AB, Alvarez Castillo DA, Moran D, Ngam S, Akaibe D, Agwanda B, Briese T, Epstein JH, Daszak P, Rupprecht CE, Holmes EC, Lipkin WI. 2013. Bats are a major natural reservoir for hepaciviruses and pegiviruses. Proc Natl Acad Sci U S A 110:8194–8199.
Kapoor A, Simmonds P, Gerold G, Qaisar N, Jain K, Henriquez JA, Firth C, Hirschberg DL, Rice CM, Shields S, Lipkin WI. 2011. Characterization of a canine homolog of hepatitis C virus. Proc Natl Acad Sci U S A 108:11608–11613.
Pybus OG, Gray RR. 2013. Virology: the virus whose family expanded. Nature 498:310–311.
Hassanin A, Ropiquet A, Couloux A, Cruaud C. 2009. Evolution of the mitochondrial genome in mammals living at high altitude: new insights from a study of the tribe Caprini (Bovidae, Antilopinae). J Mol Evol 68:293–310.
Woolhouse ME, Gowtage-Sequeria S. 2005. Host range and emerging and reemerging pathogens. Emerg Infect Dis 11:1842–1847.
Fischer N, Rohde H, Indenbirken D, Gunther T, Reumann K, Lutgehetmann M, Meyer T, Kluge S, Aepfelbacher M, Alawi M, Grundhoff A. 2014. Rapid metagenomic diagnostics for suspected outbreak of severe pneumonia. Emerg Infect Dis 20:1072–1075.
Zuker M. 2003. Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31:3406–3415.
Drexler JF, Kupfer B, Petersen N, Grotto RM, Rodrigues SM, Grywna K, Panning M, Annan A, Silva GF, Douglas J, Koay ES, Smuts H, Netto EM, Simmonds P, Pardini MI, Roth WK, Drosten C. 2009. A novel diagnostic target in the hepatitis C virus genome. PLoS Med 6:e31.
Ronquist F, Huelsenbeck JP. 2003. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19:1572–1574.
Simmonds P. 2012. SSE: a nucleotide and amino acid sequence analysis platform. BMC Res Notes 5:50.
Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. 2011. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28:2731–2739.
Petersen TN, Brunak S, von Heijne G, Nielsen H. 2011. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8:785–786.
Julenius K, Molgaard A, Gupta R, Brunak S. 2005. Prediction, conservation analysis, and structural characterization of mammalian mucin-type O-glycosylation sites. Glycobiology 15:153–164.
Kosakovsky Pond SL, Posada D, Gravenor MB, Woelk CH, Frost SD. 2006. Automated phylogenetic detection of recombination using a genetic algorithm. Mol Biol Evol 23:1891–1901.
Scheffler K, Martin DP, Seoighe C. 2006. Robust inference of positive selection from recombining coding sequences. Bioinformatics 22:2493–2499.
Delport W, Poon AF, Frost SD, Kosakovsky Pond SL. 2010. Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology. Bioinformatics 26:2455–2457.
Pfaender S, Brown RJP, Pietschmann T, Steinmann E. 2014. Natural reservoirs for homologs of hepatitis C virus. Emerg Microbes Infect 3:e21.
Pfaender S, Cavalleri JM, Walter S, Doerrbecker J, Campana B, Brown RJ, Burbelo PD, Postel A, Hahn K, Kusuma A, Riebesehl N, Baumgartner W, Becher P, Heim MH, Pietschmann T, Feige K, Steinmann E. 2014. Clinical course of infection and viral tissue tropism of hepatitis C virus-like non-primate hepaciviruses. Hepatology 61:447–459.
Ticehurst JR, Hamzeh FM, Thomas DL. 2007. Factors affecting serum concentrations of hepatitis C virus (HCV) RNA in HCV genotype 1-infected patients with chronic hepatitis. J Clin Microbiol 45:2426–2433.
Gray RR, Parker J, Lemey P, Salemi M, Katzourakis A, Pybus OG. 2011. The mode and tempo of hepatitis C virus evolution within and among hosts. BMC Evol Biol 11:131.
Simmonds P, Tuplin A, Evans DJ. 2004. Detection of genome-scale ordered RNA structure (GORS) in genomes of positive-stranded RNA viruses: Implications for virus evolution and host persistence. RNA 10:1337–1351.
Gonzalez-Candelas F, Lopez-Labrador FX, Bracho MA. 2011. Recombination in hepatitis C virus. Viruses 3:2006–2024.
Kolykhalov AA, Feinstone SM, Rice CM. 1996. Identification of a highly conserved sequence element at the 3′ terminus of hepatitis C virus genome RNA. J Virol 70:3363–3371.
Friebe P, Boudet J, Simorre JP, Bartenschlager R. 2005. Kissing-loop interaction in the 3′ end of the hepatitis C virus genome essential for RNA replication. J Virol 79:380–392.
Haydon GH, Jarvis LM, Blair CS, Simmonds P, Harrison DJ, Simpson KJ, Hayes PC. 1998. Clinical significance of intrahepatic hepatitis C virus levels in patients with chronic HCV infection. Gut 42:570–575.
Jopling CL, Yi M, Lancaster AM, Lemon SM, Sarnow P. 2005. Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science 309:1577–1581.
de Leeuw PN, McDermott JJ, Lebbie SHB. 1995. Monitoring of livestock health and production in sub-Saharan Africa. Preventive Vet Med 25:195–212.
McDaniel CJ, Cardwell DM, Moeller RB Jr, Gray GC. 2014. Humans and cattle: a review of bovine zoonoses. Vector Borne Zoonotic Dis 14:1–19.
Ramsay JD, Evanoff R, Wilkinson TE, Divers TJ, Knowles DP, Mealey RH. 10 January 2015. Experimental transmission of equine hepacivirus in horses as a model for hepatitis C virus. Hepatology.
Decker JE, Pires JC, Conant GC, McKay SD, Heaton MP, Chen K, Cooper A, Vilkki J, Seabury CM, Caetano AR, Johnson GS, Brenneman RA, Hanotte O, Eggert LS, Wiener P, Kim JJ, Kim KS, Sonstegard TS, Van Tassell CP, Neibergs HL, McEwan JC, Brauning R, Coutinho LL, Babar ME, Wilson GA, McClure MC, Rolf MM, Kim J, Schnabel RD, Taylor JF. 2009. Resolving the evolution of extant and extinct ruminants with high-throughput phylogenomics. Proc Natl Acad Sci U S A 106:18644–18649.
Smith NH. 2012. The global distribution and phylogeography of Mycobacterium bovis clonal complexes. Infect Genet Evol 12:857–865.
Mariner JC, House JA, Mebus CA, Sollod AE, Chibeu D, Jones BA, Roeder PL, Admassu B, van't Klooster GG. 2012. Rinderpest eradication: appropriate technology and social innovations. Science 337:1309–1312.
McTavish EJ, Decker JE, Schnabel RD, Taylor JF, Hillis DM. 2013. New World cattle show ancestry from multiple independent domestication events. Proc Natl Acad Sci U S A 110:E1398–E1406.
Prince AM, Brotman B, Lee DH, Pfahler W, Tricoche N, Andrus L, Shata MT. 2005. Protection against chronic hepatitis C virus infection after rechallenge with homologous, but not heterologous, genotypes in a chimpanzee model. J Infect Dis 192:1701–1709.
Giugliano S, Oezkan F, Bedrejowski M, Kudla M, Reiser M, Viazov S, Scherbaum N, Roggendorf M, Timm J. 2009. Degree of cross-genotype reactivity of hepatitis C virus-specific CD8+ T cells directed against NS3. Hepatology 50:707–716.

Information & Contributors


Published In

cover image Journal of Virology
Journal of Virology
Volume 89Number 111 June 2015
Pages: 5876 - 5882
Editor: J.-H. J. Ou
PubMed: 25787289


Received: 12 February 2015
Accepted: 13 March 2015
Published online: 5 May 2015


Request permissions for this article.



Victor Max Corman
Institute of Virology, University of Bonn Medical Centre, Bonn, Germany
German Centre for Infection Research (DZIF), partner sites Bonn-Cologne, Hamburg-Lübeck-Borstel, and Hannover-Braunschweig, Germany
Adam Grundhoff
German Centre for Infection Research (DZIF), partner sites Bonn-Cologne, Hamburg-Lübeck-Borstel, and Hannover-Braunschweig, Germany
Heinrich-Pette Institute, Leibniz Institute for Experimental Virology, Research Group Virus Genomics, Hamburg, Germany
Christine Baechlein
German Centre for Infection Research (DZIF), partner sites Bonn-Cologne, Hamburg-Lübeck-Borstel, and Hannover-Braunschweig, Germany
Institute of Virology, University of Veterinary Medicine Hannover, Hannover, Germany
Nicole Fischer
German Centre for Infection Research (DZIF), partner sites Bonn-Cologne, Hamburg-Lübeck-Borstel, and Hannover-Braunschweig, Germany
Institute for Medical Microbiology, Virology and Hygiene, University Medical Centre Hamburg–Eppendorf, Hamburg, Germany
Anatoly Gmyl
Chumakov Institute of Poliomyelitis and Viral Encephalitides, Moscow, Russia
Lomonosov Moscow State University, Moscow, Russia
Robert Wollny
Institute of Virology, University of Bonn Medical Centre, Bonn, Germany
Dickson Dei
Ghana Veterinary Service, Kumasi, Ghana
Daniel Ritz
Institute of Virology, University of Bonn Medical Centre, Bonn, Germany
Tabea Binger
Institute of Virology, University of Bonn Medical Centre, Bonn, Germany
Ernest Adankwah
Kumasi Centre for Collaborative Research in Tropical Medicine (KCCR), Kumasi, Ghana
Kwadwo Sarfo Marfo
Kumasi Centre for Collaborative Research in Tropical Medicine (KCCR), Kumasi, Ghana
Lawrence Annison
Kumasi Centre for Collaborative Research in Tropical Medicine (KCCR), Kumasi, Ghana
Augustina Annan
Kumasi Centre for Collaborative Research in Tropical Medicine (KCCR), Kumasi, Ghana
Yaw Adu-Sarkodie
Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Samuel Oppong
Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Paul Becher
German Centre for Infection Research (DZIF), partner sites Bonn-Cologne, Hamburg-Lübeck-Borstel, and Hannover-Braunschweig, Germany
Institute of Virology, University of Veterinary Medicine Hannover, Hannover, Germany
Christian Drosten
Institute of Virology, University of Bonn Medical Centre, Bonn, Germany
German Centre for Infection Research (DZIF), partner sites Bonn-Cologne, Hamburg-Lübeck-Borstel, and Hannover-Braunschweig, Germany
Jan Felix Drexler
Institute of Virology, University of Bonn Medical Centre, Bonn, Germany
German Centre for Infection Research (DZIF), partner sites Bonn-Cologne, Hamburg-Lübeck-Borstel, and Hannover-Braunschweig, Germany


J.-H. J. Ou


Address correspondence to Jan Felix Drexler, [email protected].

Metrics & Citations


Note: There is a 3- to 4-day delay in article usage, so article usage will not appear immediately after publication.

Citation counts come from the Crossref Cited by service.


If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. For an editable text file, please select Medlars format which will download as a .txt file. Simply select your manager software from the list below and click Download.

View Options

Figures and Media






Share the article link

Share with email

Email a colleague

Share on social media

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