Genome Analysis of Moraxella catarrhalis Strain RH4, a Human Respiratory Tract Pathogen

M. catarrhalis strain RH4 was isolated from the blood of an infected patient (14). RH4 was grown on Bacto brain heart infusion (BHI) agar plates (Difco) in an atmosphere containing 5% CO₂ at 37°C or in broth at 37°C with agitation (200 rpm).

Whole-genome sequencing was performed using a hybrid strategy consisting of Roche 454 and Illumina Solexa sequencing by Agowa Genomics (Berlin, Germany). For Roche 454 sequencing, genomic DNA was extracted using a Wizard genome purification kit (Promega), after which a fragment library was prepared and sequenced using Roche standard procedures. This resulted in a total of 591,043 sequences with an average read length of 224 bp covering approximately 134 Mb, which is more than 70-fold coverage of the total genome. De novo assembly using the Roche 454 software Newbler resulted in 44 contigs consisting of over 500 bp, which were aligned with the 41 contigs of the ATCC 43617 strain (deposited in the NCBI database under patent WO0078968; GenBank accession numbers AX067426 to AX067466) using the gap4 assembler program (Staden Package; Roger Staden, Cambridge, United Kingdom). Gap closure by PCR and Sanger sequencing resulted in a contiguous sequence consisting of 1,863,286 bp, which was verified using Solexa sequencing as follows. Genomic DNA was isolated with a genomic DNA kit (Qiagen), a fragment library (150 to 200 bp) was prepared using Illumina's standard genomic DNA library preparation procedure, and the library was sequenced with an Illumina Genome Analyzer II. The data were analyzed using the standard Genome Analyzer pipeline, which yielded a total of 7,057,256 raw 36-bp reads. The 454-assembled genome was corrected with Solexa short read sequence data using ROAST, a tool developed in house (S. A. F. T. van Hijum, V. C. L. de Jager, B. Renckens, and R. J. Siezen, unpublished data). Briefly, Solexa reads were aligned with the assembled genome sequence by using BLAT (38). Alignment of reads with the reference sequence was allowed provided that nucleotide substitutions (single nucleotide polymorphisms [SNPs]) or gaps (small insertions or deletions [indels]) were at least 4 bp from the end of the read. Only SNPs and indels were allowed, with a sequence depth of at least six reads unanimously calling a genotype and with a maximum of one read indicating a different genotype. Altogether, three SNPs, one insertion, and one deletion were corrected in the reference genome sequence.

Open reading frames (ORFs) and initial automated annotation were obtained using both Pedant-Pro and the Institute for Genomics Sciences (IGS) annotation engine (http://ae.igs.umaryland.edu/cgi/ae_pipeline_outline.cgi ), and the minimal ORF length was 90 nucleotides (nt). The IGS-derived annotation was subjected to manual curation using the Pedant-Pro data, the DBGET database (http://www.genome.jp/dbget/ ), and data described previously. The putative origin of replication was identified using Ori-Finder (27), and bp 1 was assigned to the extreme of the CG disparity curve. Functional classification of the predicted ORFs was based on the IGS functional classification, which was manually improved with data from Clusters of Orthologous Groups (COGs) of proteins and Nonsupervised Orthologous Groups (NOGs), both obtained by searches using Signature (18).

A comparative genomic analysis of the RH4 and ATCC 43617 strains was performed by alignment of the RH4 coding sequences (CDSs) with the ATCC 43617 contigs using BLAT (38). CDSs potentially unique to ATCC 43617 were identified by alignment of the ATCC 43617 contigs with the RH4 sequence.

Pathway analysis was performed using the KEGG Pathway database (36) with KEGG orthology (KO) identifiers and was complemented by pathway reconstructions based on previously described data. KO identifiers were assigned with the web-based KEGG automatic annotation server (KAAS) (http://www.genome.jp/kegg/kaas/ ) using the bidirectional best-hit methods for a set of prokaryotic reference genomes (organism codes prw, par, pcr, abm, aby, aci, ngo, nmc, hso, nma, acb, apl, cvi, hin, hit, hip, and pmu), manually selected based on high abundance in Pedant-Pro BlastP hits. The list acquired was complemented with KO identifiers assigned by searching against the prokaryotic reference set. Finally, the RH4 genome was analyzed for the presence of clustered regularly interspaced short palindromic repeat (CRISPR) regions using CRISPRFinder (29).

The subcellular localization of the RH4 proteins was predicted by using an extended version of the LocateP software (75) and was validated with a highly curated list of Escherichia coli proteins. Protein localization prediction was tailored for the Gram-negative organism M. catarrhalis by replacement of specific Gram-positive components with tools suitable for Gram-negative bacteria (M. Zhou, K. Mezger, S. A. F. T. van Hijum, and R. J. Siezen, unpublished data), such as BOMP (7), CELLO (73), LipoP (35), and SecretomeP (6).

A detailed annotation, including localization prediction and comparative genomics data, is shown in File S1 in the supplemental material.

The 16S rRNA type was determined as described by Bootsma et al. (8). Multilocus sequence typing (MLST) of sequence fragments of eight housekeeping genes was performed as described by Wirth et al. (71). Allelic sequences were analyzed (http://mlst.ucc.ie/mlst/dbs/Mcatarrhalis ) and compared to the reference database containing 282 M. catarrhalis strains. The lipooligosaccharide (LOS) serotype was determined by using the method described by Edwards et al. (19), and copB typing was performed as described by Verhaegh et al. (66).

Bacteria were grown in BHI medium at 37°C with agitation (200 rpm), harvested by centrifugation at lag phase (optical density at 620 nm [OD₆₂₀], 0.2 to 0.3), exponential phase (OD₆₂₀, 1.2 to 1.4), and stationary phase (OD₆₂₀, 2.0 to 2.2), and treated with 2 volumes of RNAprotect bacterial reagent (Qiagen). Total RNA was extracted using an RNeasy minikit (Qiagen), after which contaminating genomic DNA was removed by treatment with DNase (DNAfree; Ambion). Total RNA was labeled essentially as described by Ouellet et al. (51). Briefly, 10 μg of RNA was incubated for 3 h with 7 μg of 5′ Cy3-labeled random nonamers (TriLink Biotechnologies) and Superscript III reverse transcriptase (800 U; Invitrogen) under appropriate reaction conditions (1× first-strand buffer, 5 mM dithiothreitol [DTT], 0.33 mM deoxynucleoside triphosphates [dNTPs], 21 mM actinomycin D [Sigma Aldrich], 40 U RNaseOut). After first-strand synthesis, RNA was degraded by incubation with sodium hydroxide, which was followed by neutralization of the reaction mixture with hydrochloric acid. Labeled cDNA was purified and concentrated with CyScribe columns (GE Healthcare Life Sciences), followed by Micron-30 columns (Millipore). Two micrograms of labeled cDNA was applied in duplicate to 4x72K custom-designed NimbleGen arrays. Overnight hybridization at 42°C and subsequent washing of arrays were performed according to the manufacturer's instructions. The Nimblegen array contained 1 to 118 probes per predicted CDS with an average coverage of 15 probes per CDS, probes for both strands in the CRISPR1 and CRISPR2 regions (no specific probes could be designed for the putative CRISPR3 region), and 1,103 negative-control probes with a length distribution and G+C content similar to those of the experimental probes. Array images were acquired with a NimbleGen MS200 scanner, and images were processed with NimbleScan software using the RMA algorithm. Normalized and background-corrected probe signal intensities obtained in this way were used to calculate the median level of expression of the CDSs. An expression threshold was defined by the median of the log₂ signal intensity of the random control probes plus four times the standard deviation. The levels of expression for CDSs were classified as follows: low (log₂ signal intensity, <10), moderate (log₂ signal intensity, 10 to 12.5), high (log₂ signal intensity, >12.5), or none (4/6 replicates [biological triplicate and technical duplicate] less than the threshold value). Expression data for the RH4 predicted CDSs are shown in File S1 in the supplemental material.

The genomic sequence of M. catarrhalis RH4 has been deposited in the GenBank database under accession number CP002005. Microarray data have been deposited in the NCBI Gene Expression Omnibus (GEO) database (www.ncbi.nlm.nih.gov/geo/ ) under GEO Series accession number GSE21632.

The RH4 genome (Fig. 1) consists of 1,863,286 nucleotides (nt), and the overall G+C content is 41.7%. Both the length and the G+C content are comparable to those of the unassembled, partial ATCC 43617 genome (∼1.9 Mb), which is represented by 41 contigs (59, 68). The RH4 genome is predicted to include 1,964 genes, 1,886 of which are protein-encoding genes (Table 1), similar to the 1,761 to 1,849 ORFs predicted for ATCC 43617 (59, 68). Of the 1,886 protein-encoding genes predicted for RH4, 63.6% could be assigned to a functional category based on similarity of their products to proteins in public databases, while the functions of 172 predicted proteins are unknown. The products of the remaining 515 ORFs are classified as either conserved hypothetical proteins (102 ORFs) or hypothetical proteins (413 ORFs) (Table 2). The majority (∼62%) of the best BlastP hit proteins were found to be proteins of Psychrobacter species (data not shown).

Comparative genome analysis of the RH4 genome and the ATCC 43617 contigs revealed that for 416 RH4 coding sequences (CDSs) there was an exact match in the ATCC 43617 genome (Table 3). When a maximum of 20% mismatches was allowed, 1,252 full-length RH4 CDSs were identified in the ATCC 46317 contigs. A total of 1,793 (∼95%) of the predicted CDSs were found to be conserved in RH4 and ATCC 43617, and a minimum of 80% of the CDSs were covered. As expected, RH4 genes encoding highly variable virulence factors, such as members of the ubiquitous surface protein A (uspA) family (10), showed lower levels of homology with ATCC 43617 sequences. Further, 68 CDSs were found to be unique to RH4, 41 of which are annotated as genes encoding (conserved) hypothetical proteins. Conversely, using the preliminary annotation of the partial ATCC 43617 genome (68), 14 CDSs were found to be unique to ATCC 43617, 12 of which encode hypothetical proteins.

The RH4 genome contains genes encoding 50 tRNAs for all 20 amino acids. There are four identical rRNA operons (16S, 23S, and 5S rRNA genes) in which the 16S and 23S rRNA genes are interspersed with genes encoding tRNAs for isoleucine and alanine (Table 1). Genes encoding the RNA polymerase core subunits (α, β, β′, and ω), the σ⁷⁰ sigma factor, and the alternative σ³² sigma factor were identified, as were genes encoding the transcription elongation factor GreA and the transcription termination-antitermination factors NusA, NusB, Rho, and NusG. Interestingly, the RH4 genome is predicted to encode only 32 transcription factors and 4 two-component regulatory systems and to contain 2 orphan two-component system genes. This raises the possibility that M. catarrhalis has other, alternative mechanisms for adapting its gene expression to changing environmental conditions, such as phase-variable expression or noncoding RNA-based regulation. Phase-variable expression has been described previously for two M. catarrhalis virulence factors, ubiquitous surface protein A1 (UspA1) (40) and M. catarrhalis immunoglobulin D binding protein (MID) (45), but a preliminary search for additional homopolymeric tracts known to be involved in phase variation did not result in discovery of novel candidates in the RH4 genome. Clearly, further studies to elucidate the main mechanism of transcriptional regulation in M. catarrhalis are required.

In silico phylogenetic analysis classified RH4 as a 16S rRNA type 1 strain (see Fig. S1A in the supplemental material); such strains are predominantly members of the seroresistant lineage (8). MLST analysis assigned RH4 to sequence type 128 (see Fig. S1B in the supplemental material), like M. catarrhalis GRJ 11, which was isolated from a diseased individual in Salamanca (Spain) and also belongs to the seroresistant lineage (71). In line with this, experimental evidence also demonstrated that RH4 has a seroresistant phenotype (data not shown).

Microarray expression profiling for three phases of in vitro growth in BHI medium (lag, exponential, and stationary) showed that 88.1% of the predicted CDSs were expressed during at least one growth phase and that 84.6% of the predicted CDSs were expressed in all three growth phases. Of the predicted CDSs for which no transcripts were detected, ∼81% were annotated as (conserved) hypothetical. Further, we observed expression for 30 of the 41 (conserved) hypothetical CDSs that were not present in the ATCC 43617 strain. Expression of specific genes is discussed in detail below.

Various proteins have been described as proteins that play pivotal roles in M. catarrhalis pathogenesis (for recent reviews, see references 15 and 53), and all but one of these proteins were found to be encoded by genes in the RH4 genome (Table 4). The ubiquitous surface proteins (UspAs) are among the major virulence factors; UspA1 mediates binding to epithelial cells and extracellular matrix (ECM) components, and the mutually exclusive UspA2 and UspA2H proteins have a role predominantly in immune evasion (3, 39). Determination of the modular structure of the predicted UspA1 and UspA2H proteins (the strain did not possess a uspA2 gene) revealed the presence of the VEEG-NINNY-VEEG amino acid sequence motif involved in binding to Chang conjunctival cells or fibronectin (10) in UspA1, whereas this motif was not present in UspA2H (Fig. 2). RH4 contains several other adhesins, namely, M. catarrhalis immunoglobulin D (IgD) binding protein (MID) (also referred to as hemagglutinin [Hag]) (22), the M. catarrhalis adherence protein (McaP) (63), and outer membrane protein (OMP) CD (33). Resistance to the action of the human complement system is an important aspect of M. catarrhalis virulence. Previous studies have shown that, in addition to the UspA proteins mentioned above, the M. catarrhalis proteins CopB (32), OMP CD (33), and OMP E (48) play a role in serum resistance, and the corresponding genes are all present in RH4. All of the virulence factors described above were found to be expressed at intermediate or high levels during all three phases of in vitro growth examined. Interestingly, the only known virulence locus not present in RH4 is the mha locus encoding filamentous hemagglutinin-like proteins involved in adhesion (4, 54). We did identify three ORFs (MCR_0770, MCR_0777, and MCR_0778) with a small region of homology (37 to 71 amino acids) to mhaB1, but transcriptional profiling indicated that there was either no expression (MCR_0778) or low levels of expression (MCR_0770 and MCR_0777) of these loci.

A prominent surface component of M. catarrhalis that is generally considered to be an important virulence factor is lipooligosaccharide (LOS). Genes encoding LOS glycosyl transferases (Lgt), which are enzymes that catalyze the formation of core or branched oligosaccharide chains (43), as well as genes required for biogenesis of the deoxy-d-manno-2-octulosonic acid (KDO)-lipid A moiety (for a review, see reference 55), were all identified in the RH4 genome (see Table S2 in the supplemental material) Expression was detected for all components of the pathways involved except lgt5, whose product catalyzes addition of the terminal α-(1→4)-linked terminal galactose of the core oligosaccharide chain (70) (see Table S2 in the supplemental material). Analysis of the RH4 lgt locus (lgt5, lgt1, lgt2b/c, and lgt3) revealed that RH4 is a LOS type B strain (see Fig. S1C in the supplemental material) (19); LOS type B strains are exclusively 16S rRNA type 1 isolates (66).

The peptidoglycan layer is the main target for β-lactam antibiotics that can be degraded by the BRO β-lactamases produced by M. catarrhalis (RH4 expresses the bro-2 gene [Table 4]) (9). The complete set of genes required for biosynthesis of peptidoglycan (61) was identified in RH4 and was found to be expressed in all growth phases (see Table S3 in the supplemental material).

Type IV pili (TFP) have a wide variety of functions, including adhesion to epithelial cells, biofilm formation, and motility (44). Biogenesis of TFP is a complex process requiring a large set of proteins (52), and the genes encoding these proteins are present in the RH4 genome (see Table S4 in the supplemental material). Overall, low levels of expression were detected for most genes encoding components of the TFP pathway, except for pilA, which encodes the major pilin subunit, which on average was found to be highly expressed (see Table S4 in the supplemental material).

Gram-negative bacteria transport proteins from the cytosol across the inner membrane (IM) to the periplasm via one of two protein secretion systems, either the Sec system or the Tat system (16, 41). RH4 was found to contain complete Sec machinery, as well as the main components of the Tat system (see Table S5 in the supplemental material). After deposition in the periplasmic space, proteins reach their final destination by other means. For instance, outer membrane lipoproteins are inserted into the outer membrane by the components of the Lol system (49) (see Table S5 in the supplemental material). Furthermore, several ORFs are predicted to encode components of resistance-nodulation-division (RND) efflux systems, such as components of the Acr and Mtr systems (50); components of both of these systems share homology with type I secretion system components (see Table S5 in the supplemental material). Strikingly, no components of the general secretory pathway, a type II secretion system, were found, although all of the evolutionarily related TFP assembly machinery (62) is present, as mentioned above. Even though a homolog of the type III secretion effector (HopJ) was identified (MCR_0582), other components of type III secretion systems were not found, nor did we identify components of type IV, V, and VI secretion systems. An overview of protein secretion components and the expression levels detected is shown in Table S5 in the supplemental material.

A complete genome sequence provides an opportunity to discover novel vaccine targets, and surface-exposed and secreted proteins are of special interest for vaccine development. Ruckdeschel et al. used a genome mining approach on the partial genome sequence of M. catarrhalis ATCC 43617, which led to discovery and animal model testing of novel vaccine targets (58, 59). Extensive subcellular localization prediction revealed that 134 (7.1%) of the predicted RH4 proteins localize to the outer membrane or are secreted into the extracellular environment. The surface-exposed proteins include vaccine candidates, such as the lipid-anchored outer membrane protein Msp22 (59) and OMP E (48). In addition, we identified 35 (1.9%) proteins that are predicted to localize to the cytoplasm but may be secreted via nonclassical secretion pathways (i.e., not via the Sec or Tat pathway), including, for example, the autotransporter McaP (42). The subcellular compartment distribution of the predicted RH4 proteome is summarized in Table 5.

As a respiratory tract pathogen, M. catarrhalis is at least partially dependent on nutrients available inside its human host to fill its needs for energy and intermediates for biosynthesis of essential macromolecules. M. catarrhalis is reported to be incapable of utilizing exogenous carbohydrates and consequently does not produce acid from carbohydrates (12). In line with this, no genes encoding carbohydrate transport or catabolism components were found in the RH4 genome. Reconstruction of the central metabolism (Fig. 3; see Table S6 in the supplemental material) showed that while RH4 possesses an incomplete glycolytic pathway, all of the enzymes of the gluconeogenic pathway are present, indicating that carbohydrate intermediates can be synthesized. Gluconeogenesis uses phosphoenolpyruvate (PEP) as a starting substrate, which can be generated from tricarboxylic acid (TCA) cycle intermediates. The TCA cycle is supplied with acetyl coenzyme A (acetyl-CoA) via degradation of fatty acids and acetate assimilation, and the genes required for these processes were identified in RH4. The incomplete TCA cycle, which lacks both subunits of succinyl-CoA synthetase, can be bypassed by the glyoxylate pathway, which is complete in RH4. The glyoxylate pathway has an anaplerotic function and supplies the TCA cycle with oxaloacetate (acetyl-CoA acceptor molecule). Furthermore, the enzymes required for oxidative stages of the pentose phosphate pathway are not present, but transaldolase and the enzymes of the nonoxidative branch are present. As mentioned above, RH4 possesses all of the genes required for beta-oxidation of long-chain fatty acids (see Table S6 in the supplemental material) (23). Long-chain fatty acids are transported across the cell membrane by the combined action of the outer membrane protein FadL and the inner membrane-associated FadD protein, an acyl-CoA synthase. We predict that the highly conserved OMP E (48) is the FadL homolog in RH4. Two adjacent ORFs (67% identical) appear to encode homologs of FadD (aerobic) or its anaerobic counterpart, FadK (13). Interestingly, we did not detect obvious homologs of known fatty acid metabolism regulators, such as FadR, although we did identify members of the family containing such proteins, such as GntR and DeoR family regulators (23). Unfortunately, no obvious function could be assigned to these ORFs. Further, we identified two ORFs encoding proteins with homology to the 3-oxoacid CoA transferase involved in degradation of short-chain fatty acids (34). Finally, we identified all of the genes encoding the enzymes required for completion of fatty acid biosynthesis (23) (see Table S6 in the supplemental material).

Expression profiling showed that all of the gluconeogenesis, TCA cycle, fatty acid degradation, acetate assimilation, and pentose phosphate pathway genes are expressed during in vitro growth (see Table S6 in the supplemental material). However, expression of malate synthase, the second enzyme of the glyoxylate pathway, was not detectable in the lag and exponential phases of growth, and the levels during the stationary growth phase were low. This suggests that glyoxylate is further processed via other enzymes, e.g., glycerate dehydrogenase (MCR_1529) and phosphoglycolate phosphatase (MCR_0365), both of which were expressed at intermediate levels in all growth phases.

It has been suggested that in M. catarrhalis a truncated denitrification pathway (reduction of nitrite to nitrous oxide) described by Wang et al. provides an alternative mechanism to generate energy under low-oxygen-tension conditions and contributes to biofilm formation and in vivo virulence. In diagnostic microbiological laboratories, reduction of nitrate is one of the differential tests used to confirm the identity of M. catarrhalis (12). The genes encoding the nitrate reductase complex (narGHJI), nitrite reductase (aniA), nitric oxide reductase (norB), and the narX/narL two-component system were identified in RH4 (see Table S7 in the supplemental material). In addition, a putative regulator of fumarate and nitrate reduction was identified; however, the true function of this fumarate and nitrate regulator (FNR)/cyclic AMP receptor protein (CRP) family member remains to be elucidated (69). In addition to the nitrate ABC transport system, encoded by nrtABCD, we identified two other candidates that could play a role in nitrate and nitrite transport, namely, NarK1 and a putative nitrate-nitrite transporter designated NarK2 (see Table S7 in the supplemental material). The levels of expression of the denitrification pathway components were not uniform; e.g., the level of nitrate reductase expression ranged from undetectable to low, whereas the level of nitrite reductase expression was high during the lag and exponential phases (see Table S7 in the supplemental material).

Iron is a key nutrient that is functionally involved as a cofactor in various metabolic processes and is essential for both M. catarrhalis and its human host (56). The RH4 genome contains genes encoding components of many iron acquisition and transport systems (see Table S8 in the supplemental material), including all of the following M. catarrhalis proteins previously described as proteins involved in this process: lactoferrin binding proteins A and B (17), transferrin binding proteins A and B (74), heme utilization protein (25), M. catarrhalis hemoglobin utilization protein (26), CopB (1), and the main regulator of iron-responsive genes, Fur (24). In addition, iron may be acquired through degradation of heme, which is catalyzed by a heme oxygenase. An iron transport system for transport of Fe³⁺ from the periplasm to the cytosol, encoded by the fbpABC gene cluster, was identified based on homology to the corresponding locus in H. influenzae (2). Further, the afeABCD gene cluster was identified in RH4; this cluster has been proposed to be involved in the acquisition of chelated iron, as described previously for Actinobacillus actinomycetemcomitans and Yersinia pestis, and is regulated by Fur in these species (5, 57). Interestingly, the levels of expression of the iron binding components of the fbpABC and afeABCD ABC transporter systems were higher than the levels of expression of the other components of these systems. Two putative NRAMP homologs, which are involved in transport of Fe²⁺ and Mn²⁺, may compete with the host divalent ion transporters of the NRAMP family (21). In addition, RH4 possesses two putative bacterioferritins, which are intracellular iron storage proteins important for preventing the presence of free iron (11), both of which were found to be expressed at high levels during all phases of growth. Overall, all of the iron acquisition and transport systems of RH4 were found to be expressed in vitro, but the importance of the individual pathways remains to be investigated.

Inherent in aerobic metabolism is oxidative stress caused by reactive oxygen species (ROS) (20). As mentioned above, acquisition of iron is essential for growth of M. catarrhalis, but it can also be harmful for the bacterium, as iron can react with hydrogen peroxide, resulting in the formation of hydroxyl radicals via the Fenton reaction (20). The well-studied superoxide dismutase-catalase system is able to counteract the effect of oxidative stress by catalyzing the conversion of superoxide to water and oxygen. RH4 contains and expresses the sodA and catalase genes, but it lacks the sodB gene (see Table S9 in the supplemental material). Catalase production is used during routine identification, but it has limited differential value because not all strains produce catalase (12). Further, RH4 was found to encode and express several putative bacterial peroxiredoxins (Prx) that catalyze the reduction of peroxide, peroxynitrite, and diverse organic hydroperoxides (ROOH) (72), as well as alkyl hydroperoxide reductase-thioredoxin family members (67). Despite the presence of a large number of antioxidant genes in the RH4 genome, we could not identify homologs of known oxidative stress regulators, such as OxyR and SoxR (67).

Clustered regularly interspaced short palindromic repeats (CRISPRs) are widespread in genomes of prokaryotic organisms and are thought to be transcribed and processed into small RNAs that confer resistance to phages (30). CRISPR direct repeats are separated by nonrepetitive spacer elements and are often located near gene clusters encoding CRISPR-associated (Cas) protein family members. Two CRISPR regions, CRISPR regions I and II, and one putative repeat region (CRISPR region III) were identified in the RH4 genome. CRISPR region II, localized at nt 30997 to 28086, is in the vicinity of six genes encoding proteins with significant homology to known Cas proteins, showing high levels of similarity to the Yersinia pestis subtype (30). RH4 CRISPR regions I and II are characterized by an average repeat length of 28.1 nt (Y. pestis, 28.0 nt) and a spacer length of 32.0 nt (Y. pestis, 32.1 nt). Microarray analysis showed that there was constitutive expression of both CRISPR region I and CRISPR region II only from the minus strand (Fig. 4). In addition, intermediate levels of expression of the six cas genes were detected during all three growth phases (data not shown). The exact role of the CRISPR system in M. catarrhalis remains to be determined.

In this paper, we describe the completely assembled and annotated genome of the clinically important bacterial pathogen M. catarrhalis strain RH4. Comparative genomics revealed high degrees of similarity and sequence conservation for the RH4 and ATCC 43617 genomes. Like RH4, the ATCC 43617 strain is classified as a 16S rRNA type 1 strain (8), and MLST analysis showed that it also belongs to the seroresistant lineage (71). The completely assembled and annotated genome of a clinical isolate of M. catarrhalis described here should facilitate identification of novel (surface-exposed) vaccine targets, should provide the basis for omics-based research, such as transcriptomics and proteomics studies, and is indispensable for a complete understanding of the biology of M. catarrhalis.