First comparative genomics analysis of Corynebacterium auriscanis

Abstract

BACKGROUND  Corynebacterium auriscanis is a bacterial species frequently isolated from dogs with external otitis or dermatitis and a zoonotic pathogen transmitted by dog bite. It is considered an opportunistic pathogen, but its pathogenicity mechanisms are poorly studied. Comparative genomics can identify virulence and niche factors that could contribute to understanding its lifestyle.

OBJECTIVES  The objectives of this project was to compare genomes of C. auriscanis to identify genes related to its virulence and lifestyle.

METHODS  The genome of strain 32 was sequenced using Illumina HiSeq 2500 (Illumina, CA, USA) and assembled using Unicycler. The two other non-redundant genomes from the same species available in GenBank were included in the analysis. All genomes were annotated and checked for taxonomy, assembly quality, mobile elements, CRISPR-Cas systems, and virulence and antimicrobial resistance genes. The virulence genes in the three genomes were compared to the ones from other pathogens commonly isolated with C. auriscanis.

FINDINGS  The species has 42 virulence factors that can be classified as niche factors, due to the absence of true virulence factors found in primary pathogens. The gene rbpA could confer basal levels of resistance to rifampin.

MAIN CONCLUSIONS  The absence of true virulence factors in the three genomes suggests C. auriscanis has an opportunistic pathogen lifestyle.

Key words:  Corynebacterium ; comparative genomics; zoonosis

Corynebacterium auriscanis is a Gram-positive bacterium species first isolated from dogs with external otitis.¹ It is frequently isolated in canine otitis with other bacteria as Staphylococcus intermedius and Streptococcus canis, and the yeast Malassezia pachydermatis.² Similarly, it can be found with other known pathogens in cases of canine dermatitis.³ This bacterium also exhibits zoonotic potential, having been isolated from infected wounds caused by dog bites.^3,4 Due to co-isolation with other species, C. auriscanis is considered an opportunistic pathogen, although it was also isolated alone from lesions in dogs³ and in a human.⁴

Genomic data could reveal the pathogenic potential of the species, as well as other characteristics of veterinary and medical relevance, such as antimicrobial resistance genes and sequence type.⁵ As of January 2024 only four genomes of the species were publicly available in GenBank (https://www.ncbi.nlm.nih.gov/datasets/genome/?taxon=99807), including the one sequenced in this study.

To better understand the molecular mechanisms involved in the virulence and lifestyle of the species, we sequenced the genome of strain 32, isolated from a dog ear with otitis in Mexico. We then compared this genome to the other publicly available genomes. This represents the first comparative genomic analysis of C. auriscanis.

MATERIALS AND METHODS

Genome sequencing, assembly and annotation - The strain 32 was grown overnight in brain heart infusion (BHI) broth supplement with Tween 80 under aeration at 37ºC. Genomic DNA was extracted using the Wizard Genomic DNA Purification Kit (Promega) following the standard protocol. DNA was sequenced using ThruPLEX DNA-Seq Kit (Takara) and Illumina HiSeq 2500 (Illumina, San Diego, CA, USA) with 150b paired-end reads. Sequencing read quality was assessed with FastQC v. 0.11.9.⁶ A de novo assembly was performed using Unicycler v. 0.4.8.⁷ Assembly quality was evaluated using CheckM2 v. 1.0.2⁸ for completeness and contamination, GUNC v. 1.0.6⁹ for chimeric contigs, and QUAST v. 5.1.0rc1¹⁰ for fragmentation. Taxonomic classification was performed using GTDB-Tk v. 2.2.6.¹¹ and Type Strain Genome Server (TYGS).¹² The presence of plasmids was investigated using MOB-suite v. 3.1.8.¹³ Contigs with less than 200 bp were removed using Seqkit v. 2.1.0.¹⁴ The genome was annotated using NCBI Prokaryotic Genome Annotation Pipeline (NCBI’s PGAP).¹⁵

Genome characterisation - For further comparison and characterisation, two additional strains were retrieved from GenBank, using the RefSeq database¹⁶ to ensure consistent genomic annotation. The Type strain, represented by DSM44609 (GCF_030408435.1) and CIP106629 (GCF_000767255.1), was isolated from a dog with an ear infection in the United Kingdom (SAMN13404511). bin-22 (GCF_029984515.1) is a metagenome-assembled genome isolated from a cat’s anal gland secretion from USA (SAMN34353469). CIP106629 was not used, as it is same strain as DSM44609. Completeness and contamination of all C. auriscanis genomes were verified. Virulence or niche factors^17,18 were predicted using VFanalyzer from VFDB website (http://www.mgc.ac.cn/cgi-bin/VFs/v5/main.cgi?func=VFanalyzer)¹⁹ and PanViTa v1.1.5²⁰ with the Virulence Factor Database (VFDB).²¹ The PanViTa script (panvita.py) was modified to utilise the full VFDB dataset before installation using the command line: “sed -i ‘s/VFDB_setA_pro/VFDB_setB_pro/’ panvita.py”. Antimicrobial resistance genes were predicted using PanViTa with the Comprehensive Antibiotic Resistance Database (CARD).²² Prophages and CRISPR-Cas systems were predicted using PHASTER²³ and CRISPRCasFinder v. 1.1.2,²⁴ respectively. Genomic islands (GIs) were predicted in the type strain DSM44609 using GIPSy v1.1.3²⁵ with C. glutamicum ATCC13032 (GCF_002847405.1) as the non-pathogenic reference strain. In GIPSy, identified GIs are classified as Metabolic Islands, Pathogenicity Islands, Resistance Islands, or Symbiotic Islands based on the proportion of BLASTp hits against its databases of virulence, resistance, metabolism, and symbiotic genes. Hits with identity values ≥ 70% were considered.

Comparative genomics - A circular map comparing DSM44609 to strains 32 and bin-22 was built using BRIG v. 0.95.²⁶ Average Nucleotide Identity (ANI) values were calculated using pyani v. 0.2.12²⁷ with the BLASTn algorithm. The gene repertoire of the strains was identified using PPanGGOLiN v. 2.0.5.²⁸ Functional annotation using eggNOG-mapper v. 2.1.11 with eggNOG database v. 5.0.2.²⁹ The investigation of virulence factors and mechanisms was performed using VFanalyzer and PanViTa. The three C. auriscanis genomes were compared to: (i) the pathogenic and zoonotic species of the same genus C. ulcerans, (ii) the non-pathogenic strain from the same genus C. glutamicum, and (iii) the species that are isolated alongside C. auriscanis from otitis S. canis and S. pseudintermedius.

RESULTS

The genome of C. auriscanis 32 was assembled as a draft with 46 contigs. Completeness and contamination were estimated at 99.81 and 0.16%, respectively. The genome was deposited in GenBank under BioProject PRJNA764731 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA764731/) with assembly accession numbers GCA_026236545.1 (GenBank) and GCF_026236545.1 (RefSeq) (Table).

TABLE
Metadata and genomic features of Corynebacterium auriscanis strains

Strain	32	bin_22	DSM44609
Host	Dog	Cat	Dog
Country	Mexico	USA	United Kingdom
Isolation source	Ear infection	Anal gland secretions	Ear infection
Accession	GCF_026236545.1	GCF_029984515.1	GCF_030408435.1
Contigs	46	52	1
Size	2,507,534	2,372,762	2,599,172
GC (%)	58.8	58.64	58.49
N50	226,683	74,239	2,599,172
Completeness (%)	99.81	94.83	99.91
Contamination (%)	0.16	0.08	0.01
Taxonomy (GTDB-Tk)	C. auriscanis	C. auriscanis	C. auriscanis
Taxonomy (TYGS)	C. auriscanis	C. auriscanis	C. auriscanis
Plasmid	None	None	None
Complete prophages	0	0	0
Incomplete prophages	0	3	1
CDS	2,096	1,975	2,140
Pseudogenes	47	40	46
tRNA	50	30	50
rRNA	3	0	9
CRISPR arrays	0	0	1
CRISPR-Cas system	-	-	Type I-E
Antimicrobial resistance genes	2	2	2
Virulence factors	38	38	40

• CDS: coding sequence; CRISPR: clustered regularly interspaced short palindromic repeats; GC: guanine-cytosine; GTDB-Tk: genome taxonomy database toolkit; TYGS: type strain genome server.

Strains 32 and bin-22 were classified as C. auriscanis due to ANI > 95% (GTDB-Tk) and digital DNA-DNA Hybridisation > 70% (TYGS) when compared to the type strain CIP106629. DSM44609 is the same strain as CIP106629 (https://www.dsmz.de/collection/catalogue/details/culture/DSM-44609) [Supplementary data (Tables I-II)]. All three strains possess the same two predicted resistance genes to rifampin (rpoB2 and rbpA) [Table, Supplementary data (Table III)]. Three incomplete prophages were predicted in strain bin-22, with one incomplete prophage in strain DSM44609. All classified as different prophages, none were identified in strain 32. Only DSM44609 had a CRISPR-Cas system, classified as Type I-E [Supplementary data (Tables IV-V)]. A total of 27 GIs harbouring 617 genes were predicted in strain DSM44609. Using a 70% cut-off (consistent with PanViTa) on the BLASTp results of GIPSy, we identified one gene across the GIs involved in virulence (glnA1), 28 genes in metabolism, and none associated with antimicrobial resistance or symbiotic lifestyle (Figure) [Supplementary data (Tables VI-VII)].

ANI between the genomes ranged from 97.18 to 97.64%. A total of 2,266 protein-coding genes were identified across the three genomes, categorised into 2,013 persistent (88.83%), 142 shell (6.27%) and 111 cloud (4.90%) genes. Regarding exclusively present genes, strain 32 has 111, bin-22 has 62 (21 from a prophage), and strain DSM44609 has 142 (48 from a prophage) genes [Supplementary data (Tables VIII-X)].

In C. auriscanis a total of 42 niche or virulence factors were predicted, with 10 exclusively predicted by PanViTa, 19 by VFanalyzer, and 11 by both tools. Five of these genes are involved in adhesion (groEL2, sapD, spaD, srtC and tufa). In the metabolism category, the genes are associated with leucine and glutamine synthesis (leuD and glnA1); uptake of heme (Rv0207c), iron (ciuABCD, fagAB, GBAA_RS25985, hmuTUV, mbtI), and copper (ctpV); lipid and fatty acid metabolism (icl, panC, panD); trehalose transport (sugC); amino acids and purine metabolism (lysA and proC); and protein degradation (mpa, pafA). Other genes are involved in oxidative stress and low oxygen response (katG, regX3) and acidic stress (ureB, ureG). Genes involved in gene regulation are two alternative sigma factors (sigA and sigH), a transcription activator of sigA (whiB3), a stress-responsive two-component system (mprA) and the diphtheria toxin repressor (dtxR). Two genes are part of secretion systems (secA2 and espI), one is involved in biosynthesis of glycopeptidolipids (rmlA), other in immune modulation (nkd) and other in immune evasion (pgi). Fifteen of the 42 genes are in GIs [Supplementary data (Tables XI-XII)].

Among the three C. auriscanis genomes, strain DSM44609 has 40 niche or virulence factors, with pgi and espI being exclusive genes. Strains 32 and bin-22 each have 38 genes. Strain 32 is distinguished by the absence of sapD and fagA and the presence of ureB and ureG. No toxin-encoding gene was found in any of the three genomes. Out of the total 42 niche or virulence factors, 18 are not present in the free-living C. glutamicum ATCC13032. From these 18, nine are in GIs and 17 are present in the pathogen C. ulcerans 4290 (Figure) [Supplementary data (Table XI)]. Toxins (C. ulcerans, S. canis and S. pseudointermedius) and an invasion protein (S. pseudointermedius) we identified only in pathogenic species [Supplementary data (Table XI)].

DISCUSSION

Corynebacterium auriscanis is suggested to be an opportunistic pathogen due to its frequent isolation alongside other species causing external otitis or dermatitis in dogs, and occasionally as the sole agent in dogs and humans with infected wounds caused by dog bites.^2,3 Our objective was to identify virulence factors that could explain its lifestyle and conduct the first comparative genomic study of this species.

Strain bin-22 exhibits a smaller genome size and fewer protein coding sequences (CDS) and tRNA (Table), probably due to errors in the binning process, during contig attribution from the original metagenome sample. Within the three genomes, ANI values of approximately 97% and the classification of 88.83% of genes as persistent [Supplementary data (Tables VIII-X)] indicate that the genomes are not clonal. The different prophages across the genomes might contribute to the difference (Figure) [Supplementary data (Table XI)]. A more comprehensive analysis of additional genomes would be beneficial for a better understanding of the species’ genetic diversity.

Circular map of Corynebacterium auriscanis DSM44609. The 18 niche or virulence factors shown are not shared with C. glutamicum ATCC13032. GC: guanine-cytosine; GI: genomic island.

CRISPR-Cas systems are adaptive immune systems of bacteria that protect against exogenous DNA, such as phages and plasmids.³⁰ We identified a CRISPR-Cas system Type I-E in C. auriscanis strain DSM44609 (cas3, cse1, cse2, cas7, cas5, cas6, cas1, cas2) [Supplementary data (Table V)]. This type is well known in Corynebacterium species such as C. diphtheria,³¹C. pseudotuberculosis,³²C. rouxii³³and C. striatum.³⁴

PanViTa identified two genes from the CARD database within the three C. auriscanis genomes that could potentially confer rifampin resistance: rpoB2 and rbpA [Supplementary data (Table III)]. The gene rpoB2 in the CARD database is a rifampin-resistant paralog of the rpoB gene, which encodes the β subunit of bacterial RNA polymerase (RNAP), as described in Nocardia farcinica.³⁵ The identification of rpoB2 in the three genomes of C. auriscanis is a false positive. Given that these genomes only contain a single copy of rpoB, the rpoB2 sequence from the CARD database, with 71.8% identity, was likely the best alignment hit (PPanGGoLiN results in Supplementary data). The rbpA gene encodes an RNA polymerase-binding protein commonly found in actinomycetes. It is part of the σ^R regulon and confers basal levels of resistance to rifampin by protecting the product of rpoB.³⁶

Genomic islands are horizontally acquired regions that can contribute to adaptations to different environments and bacterial evolution.³⁷ Using C. glutamicum ATCC13032 as a non-pathogenic reference genome, we identified 27 GIs within the complete genome of C. auriscanis DSM44609. Many of these GIs are shared with strains 32 and bin-22 (Figure) [Supplementary data (Table VI)]. Since we compared C. auriscanis to the free-living bacterium C. glutamicum, the identified GIs may contain exclusive genes related to its commensal lifestyle.³⁸

The discrepancies in the prediction of virulence factors by PanViTa and VFanalyzer, despite their reliance on the same VFDB database, are likely attributable to the distinct algorithms they employ. PanViTa aligns protein sequences from query and database using DIAMOND-BLASTp and applies a filter of 70% identity and 70% coverage.²⁰ VFanalyzer identifies orthologous genes within the query genome and the reference genomes from the database to mitigate false positives due to paralogs. It subsequently conducts iterative sequence similarity searches within hierarchical datasets encompassing other genera to identify atypical or strain-specific genes, and ultimately performs a refinement process that incorporates the genome context.¹⁹

Within the three C. auriscanis genomes, we identified 42 virulence or niche factors. None of these factors qualify as true virulence factors. However, we did uncover toxins and an invasin in the three primary pathogens C. ulcerans, S. canis and S. pseudointermedius [Supplementary data (Table XI)]. The term “niche factor” was coined to describe components involved in the colonisation of distinct ecological niches, rather than specific interactions between pathogens and hosts. These factors are prevalent in bacteria with diverse lifestyles. Their role in pathogenicity depends on their combination with true virulence factors and specific environments. Examples of niche factors include adhesins, surface-anchored enzymes, cell envelope components, and antimicrobial resistance.^17,18 True virulence factors enable pathogens to gain access or survive in body sites not typically colonised, cause damage to the body, and lead to disease symptoms due to the dysregulation of the immune system or neurological responses. Examples of such factors include internalins, invasins, cytolytic or hemolytic toxins, superantigens, and neurotoxins.¹⁷ In Corynebacterium known true virulence factors are the toxin encoding genes tox (diphtheriae toxin) and pld (phospholipase D).¹⁸ Given these distinctions, the 42 virulence or niche factors identified in the three C. auriscanis genomes can be classified as niche factors rather than true virulence factors.

Eighteen of the 42 niche genes of C. auriscanis are absent in the free-living C. glutamicum, and 17 of these 18 are shared with the pathogen C. ulcerans [Supplementary data (Table XI)]. These 17 genes likely represent adaptations to a host-associated lifestyle. Some of these adaptations may directly involve bacterial-host interactions, such as proteins involved in adhesion (sapD, spaD and srtC), immune system evasion (ndk and pgi), secretion systems (espI and secA2) and oxidative stress resistance (katG), while others are involved in nutrient uptake or metabolism.

In conclusion, the absence of true virulence factors in the three genomes of C. auriscanis suggests that the species is not a primary pathogen but rather an opportunistic one, thriving only when associated with pathogenic bacteria or in damaged tissues. However, further analysis of additional genomes is necessary to evaluate the genetic diversity of the species, which could include genomic islands or virulence factors that may confer strain-specific pathogenic potential.

REFERENCES

• ¹ Collins MD, Hoyles L, Lawson PA, Falsen E, Robson RL, Foster G. Phenotypic and phylogenetic characterization of a new Corynebacterium species from dogs: description of Corynebacterium auriscanis sp. nov. J Clin Microbiol. 1999; 37: 3443-7.
• ² Aalbæk B, Bemis DA, Schjærff M, Kania SA, Frank LA, Guardabassi L. Coryneform bacteria associated with canine otitis externa. Vet Microbiol. 2010; 145: 292-8.
• ³ Boynosky NA, Stokking LB. Retrospective evaluation of canine dermatitis secondary to Corynebacterium spp. J Am Anim Hosp Assoc. 2015; 51: 372-9.
• ⁴ Bygott JM, Malnick H, Shah JJ, Chattaway MA, Karas JA. First clinical case of Corynebacterium auriscanis isolated from localized dog bite infection. J Med Microbiol. 2008; 57: 899-900.
• ⁵ Atxaerandio-Landa A, Arrieta-Gisasola A, Laorden L, Bikandi J, Garaizar J, Martinez-Malaxetxebarria I, et al. A practical bioinformatics workflow for routine analysis of bacterial WGS data. Microorganisms. 2022; 10: 2364.
• ⁶ Andrews S. FASTQC a quality control tool for high throughput sequence data. Babraham Institute. 2015. Available from: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3 Analysis Modules/
  » http://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/3 Analysis Modules/
• ⁷ Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol. 2017; 13: e1005595.
• ⁸ Chklovski A, Parks DH, Woodcroft BJ, Tyson GW. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat Methods. 2023; 20: 1203-12.
• ⁹ Orakov A, Fullam A, Coelho LP, Khedkar S, Szklarczyk D, Mende DR, et al. GUNC: detection of chimerism and contamination in prokaryotic genomes. Genome Biol. 2021; 22: 178.
• ¹⁰ Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013; 29: 1072-5.
• ¹¹ Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics. 2022; 38: 5315-6.
• ¹² Meier-Kolthoff JP, Carbasse JS, Peinado-Olarte RL, Göker M. TYGS and LPSN: a database tandem for fast and reliable genome-based classification and nomenclature of prokaryotes. Nucleic Acids Res. 2022; 50(D1): D801-7.
• ¹³ Robertson J, Nash JHE. MOB-suite: software tools for clustering, reconstruction and typing of plasmids from draft assemblies. Microb Genom. 2018; 4(8): e000206.
• ¹⁴ Shen W, Le S, Li Y, Hu F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS One. 2016; 11: e0163962.
• ¹⁵ Tatusova T, DiCuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016; 44: 6614-24.
• ¹⁶ Li W, O'Neill KR, Haft DH, DiCuccio M, Chetvernin V, Badretdin A, et al. RefSeq: expanding the Prokaryotic Genome Annotation Pipeline reach with protein family model curation. Nucleic Acids Res. 2021; 49: D1020-8.
• ¹⁷ Hill C. Virulence or niche factors: what's in a name? J Bacteriol. 2012; 194: 5725-7.
• ¹⁸ Tauch A, Burkovski A. Molecular armory or niche factors: virulence determinants of Corynebacterium species. FEMS Microbiol Lett. 2015; 67: fnv185.
• ¹⁹ Liu B, Zheng D, Jin Q, Chen L, Yang J. VFDB 2019: a comparative pathogenomic platform with an interactive web interface. Nucleic Acids Res. 2019; 47: D687-92.
• ²⁰ Rodrigues DLN, Ariute JC, Rodrigues da Costa FM, Benko-Iseppon AM, Barh D, Azevedo V, et al. PanViTa: pan virulence and resisTance analysis. Front Bioinform. 2023; 3: 1070406.
• ²¹ Liu B, Zheng D, Zhou S, Chen L, Yang J. VFDB 2022: a general classification scheme for bacterial virulence factors. Nucleic Acids Res. 2022; 50: D912-7.
• ²² Alcock BP, Raphenya AR, Lau TTY, Tsang KK, Bouchard M, Edalatmand A, et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 2020; 48: D517-25.
• ²³ Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016; 44: W16-21.
• ²⁴ Couvin D, Bernheim A, Toffano-Nioche C, Touchon M, Michalik J, Néron B, et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res. 2018; 46: W246-51.
• ²⁵ Soares SC, Geyik H, Ramos RTJ, de Sá PHCG, Barbosa EGV, Baumbach J, et al. GIPSy: genomic island prediction software. J Biotechnol. 2016; 232: 2-11.
• ²⁶ Alikhan N-F, Petty NK, Ben Zakour NL, Beatson SA. BLAST Ring Image Generator (BRIG): simple prokaryote genome comparisons. BMC Genomics. 2011; 12: 402.
• ²⁷ Pritchard L, Glover RH, Humphris S, Elphinstone JG, Toth IK. Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens. Anal Methods. 2016; 8: 12-24.
• ²⁸ Gautreau G, Bazin A, Gachet M, Planel R, Burlot L, Dubois M, et al. PPanGGOLiN: depicting microbial diversity via a partitioned pangenome graph. PLoS Comput Biol. 2020; 16: e1007732.
• ²⁹ Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, Von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017; 34: 2115-22.
• ³⁰ Makarova KS, Wolf YI, Iranzo J, Shmakov SA, Alkhnbashi OS, Brouns SJJ, et al. Evolutionary classification of CRISPR-Cas systems: a burst of class 2 and derived variants. Nat Rev Microbiol. 2020; 18: 67-83.
• ³¹ Ramos JN, Araújo MRB, Sant'Anna LO, Bokermann S, Camargo CH, Prates FD, et al. Molecular characterization and whole-genome sequencing of Corynebacterium diphtheriae causing skin lesion. Eur J Clin Microbiol Infect Dis. 2024; 43: 203-8.
• ³² Gimenez EGT, Viana MVC, Sousa TJ, Aburjaile F, Brenig B, Silva A, et al. Resequencing and characterization of the first Corynebacterium pseudotuberculosis genome isolated from camel. PeerJ. 2024; 12: e16513.
• ³³ Prates FD, Araújo MRB, Sousa EG, Ramos JN, Viana MVC, Soares SC, et al. First pangenome of Corynebacterium rouxii, a potentially toxigenic species of Corynebacterium diphtheriae complex. Bacteria. 2024; 3: 99-117.
• ³⁴ Ramos JN, Baio PVP, Veras JFC, Vieira ÉMD, Mattos-Guaraldi AL, Vieira VV. Novel configurations of type I-E CRISPR-Cas system in Corynebacterium striatum clinical isolates. Braz J Microbiol. 2023; 54: 69-80.
• ³⁵ Ishikawa J, Chiba K, Kurita H, Satoh H. Contribution of rpoB2 RNA polymerase ß subunit gene to rifampin resistance in Nocardia Species. Antimicrob Agents Chemother. 2006; 50: 1342-6.
• ³⁶ Newell KV, Thomas DP, Brekasis D, Paget MSB. The RNA polymerase-binding protein RbpA confers basal levels of rifampicin resistance on Streptomyces coelicolor. Mol Microbiol. 2006; 60: 687-96.
• ³⁷ Bertelli C, Tilley KE, Brinkman FSL. Microbial genomic island discovery, visualization and analysis. Brief Bioinform. 2019; 20: 1685-98.
• ³⁸ Toft C, Andersson SGE. Evolutionary microbial genomics: insights into bacterial host adaptation. Nat Rev Genet. 2010; 11: 465-75.

• Financial support: CAPES, CNPq, UFMG, FAPEMIG.

Publication Dates

• Publication in this collection
  25 Oct 2024
• Date of issue
  2024

History

• Received
  15 July 2024
• Accepted
  05 Sept 2024