Genomic Island Prediction Research Articles

The complete genomic sequence of the type strain Brochothrix thermosphacta DSM 20171 highlights a diversity of prophages in this species.

The bacterium Brochothrix thermosphacta is a known muscle food spoiler. Here, the complete genome sequence of the B. thermosphacta type strain, DSM 20171, is reported. Prediction of prophages and genomic islands reveals an unsuspected diversity in this bacterial species that deserves further investigation.

Genomic Characterization of a Plasmid-Free and Highly Drug-Resistant Salmonella enterica Serovar Indiana Isolate in China.

The emergence of multi-drug resistant (MDR) Salmonella enterica serovar Indiana (S. Indiana) strains in China is commonly associated with the presence of one or more resistance plasmids harboring integrons pivotal in acquiring antimicrobial resistance (AMR). This study aims to elucidate the genetic makeup of this plasmid-free, highly drug-resistant S. Indiana S1467 strain. Genomic sequencing was performed using Illumina HiSeq 2500 sequencer and PacBio RS II System. Prodigal software predicted putative protein-coding sequences while BLASTP analysis was conducted. The S1467 genome comprises a circular 4,998,300 bp chromosome with an average GC content of 51.81%, encompassing 4709 open reading frames (ORFs). Fifty-four AMR genes were identified, conferring resistance across 16 AMR categories, aligning closely with the strain's antibiotic susceptibility profile. Genomic island prediction unveiled an approximately 51 kb genomic island housing a unique YeeVU toxin-antitoxin system (TAS), a rarity in Salmonella species. This suggests that the AMR gene cluster on the S1467 genomic island may stem from the integration of plasmids originating from other Enterobacteriaceae. This study contributes not only to the understanding of the genomic characteristics of a plasmid-free, highly drug-resistant S. Indiana strain but also sheds light on the intricate mechanisms underlying antimicrobial resistance. The implications of our findings extend to the broader context of horizontal gene transfer between bacterial species, emphasizing the need for continued surveillance and research to address the evolving challenges posed by drug-resistant pathogens.

Genome characterization and taxonomy of Actinomyces acetigenes sp. nov., and Actinomyces stomatis sp. nov., previously isolated from the human oral cavity

BackgroundActinomyces strains are commonly found as part of the normal microflora on human tissue surfaces, including the oropharynx, gastrointestinal tract, and female genital tract. Understanding the diversity and characterization of Actinomyces species is crucial for human health, as they play an important role in dental plaque formation and biofilm-related infections. Two Actinomyces strains ATCC 49340 T and ATCC 51655 T have been utilized in various studies, but their accurate species classification and description remain unresolved.ResultsTo investigate the genomic properties and taxonomic status of these strains, we employed both 16S rRNA Sanger sequencing and whole-genome sequencing using the Illumina HiSeq X Ten platform with PE151 (paired-end) sequencing. Our analyses revealed that the draft genome of Actinomyces acetigenes ATCC 49340 T was 3.27 Mbp with a 68.0% GC content, and Actinomyces stomatis ATCC 51655 T has a genome size of 3.08 Mbp with a 68.1% GC content. Multi-locus (atpA, rpoB, pgi, metG, gltA, gyrA, and core genome SNPs) sequence analysis supported the phylogenetic placement of strains ATCC 51655 T and ATCC 49340 T as independent lineages. Digital DNA-DNA hybridization (dDDH), average nucleotide identity (ANI), and average amino acid identity (AAI) analyses indicated that both strains represented novel Actinomyces species, with values below the threshold for species demarcation (70% dDDH, 95% ANI and AAI). Pangenome analysis identified 5,731 gene clusters with strains ATCC 49340 T and ATCC 51655 T possessing 1,515 and 1,518 unique gene clusters, respectively. Additionally, genomic islands (GIs) prediction uncovered 24 putative GIs in strain ATCC 49340 T and 16 in strain ATCC 51655 T, contributing to their genetic diversity and potential adaptive capabilities. Pathogenicity analysis highlighted the potential human pathogenicity risk associated with both strains, with several virulence-associated factors identified. CRISPR-Cas analysis exposed the presence of CRISPR and Cas genes in both strains, indicating these strains might evolve a robust defense mechanism against them.ConclusionThis study supports the classification of strains ATCC 49340 T and ATCC 51655 T as novel species within the Actinomyces, in which the name Actinomyces acetigenes sp. nov. (type strain ATCC 49340 T = VPI D163E-3 T = CCUG 34286 T = CCUG 35339 T) and Actinomyces stomatis sp. nov. (type strain ATCC 51655 T = PK606T = CCUG 33930 T) are proposed.

Systematic comparison of genome information processing and boundary recognition tools used for genomic island detection

Genomic islands are fragments of foreign DNA that are found in bacterial and archaeal genomes, and are typically associated with symbiosis or pathogenesis. While numerous genomic island detection methods have been proposed, there has been limited evaluation of the efficiency of the genome information processing and boundary recognition tools. In this study, we conducted a review of the statistical methods involved in genomic signatures, host signature extraction, informative signature selection, divergence measures, and boundary detection steps in genomic island prediction. We compared the performances of these methods on simulated experiments using alien fragments obtained from both artificial and real genomes. Our results indicate that among the nine genomic signatures evaluated, genomic signature frequency and full probability performed the best. However, their performance declined when normalized to their expectations and variances, such as Z-score and composition vector. Based on our experiments of the E. coli genome, we found that the confidence intervals of the window variances achieved the best performance in the signature extraction of the host, with the best confidence interval being 1.5–2 times the standard error. Ordered kurtosis was most effective in selecting informative signatures from a single genome, without requiring prior knowledge from other datasets. Among the three divergence measures evaluated, the two-sample t-test was the most successful, and a non-overlapping window with a small eye window (size 2) was best suited for identifying compositionally distinct regions. Finally, the maximum of the Markovian Jensen-Shannon divergence score, in terms of GC-content bias, was found to make boundary detection faster while maintaining a similar error rate.

Genomic Characteristics and Functional Analysis of Brucella sp. Strain WY7 Isolated from Antarctic Krill.

Antarctic krill (Euphausia superba) is a key species of the Antarctic ecosystem whose unique ecological status and great development potential have attracted extensive attention. However, the genomic characteristics and potential biological functions of the symbiotic microorganisms of Antarctic krill remain unknown. In this study, we cultured and identified a strain of Brucella sp. WY7 from Antarctic krill using whole-genome sequencing and assembly, functional annotation, and comparative genomics analysis. First, based on 16S rDNA sequence alignment and phylogenetic tree analysis, we identified strain WY7 as Brucella. The assembled genome of strain WY7 revealed that it has two chromosomes and a plasmid, with a total genome length of 4,698,850 bp and an average G + C content of 57.18%. The DNA-DNA hybridization value and average nucleotide identity value of strain WY7 and Brucella anthropi ATCC® 49188TM, a type strain isolated from human clinical specimens, were 94.8% and 99.07%, respectively, indicating that strain WY7 is closely related to Brucella anthropi. Genomic island prediction showed that the strain has 60 genomic islands, which may produce HigB and VapC toxins. AntiSMASH analysis results showed that strain WY7 might produce many secondary metabolites, such as terpenes, siderophores and ectoine. Moreover, the genome contains genes involved in the degradation of aromatic compounds, suggesting that strain WY7 can use aromatic compounds in its metabolism. Our work will help to understand the genomic characteristics and metabolic potential of bacterial strains isolated from Antarctic krill, thereby revealing their roles in Antarctic krill and marine ecosystems.

Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks.

Outbreaks of virulent and/or drug-resistant bacteria have a significant impact on human health and major economic consequences. Genomic islands (GIs; defined as clusters of genes of probable horizontal origin) are of high interest because they disproportionately encode virulence factors, some antimicrobial-resistance (AMR) genes, and other adaptations of medical or environmental interest. While microbial genome sequencing has become rapid and inexpensive, current computational methods for GI analysis are not amenable for rapid, accurate, user-friendly and scalable comparative analysis of sets of related genomes. To help fill this gap, we have developed IslandCompare, an open-source computational pipeline for GI prediction and comparison across several to hundreds of bacterial genomes. A dynamic and interactive visualization strategy displays a bacterial core-genome phylogeny, with bacterial genomes linearly displayed at the phylogenetic tree leaves. Genomes are overlaid with GI predictions and AMR determinants from the Comprehensive Antibiotic Resistance Database (CARD), and regions of similarity between the genomes are also displayed. GI predictions are performed using Sigi-HMM and IslandPath-DIMOB, the two most precise GI prediction tools based on nucleotide composition biases, as well as a novel blast-based consistency step to improve cross-genome prediction consistency. GIs across genomes sharing sequence similarity are grouped into clusters, further aiding comparative analysis and visualization of acquisition and loss of mobile GIs in specific sub-clades. IslandCompare is an open-source software that is containerized for local use, plus available via a user-friendly, web-based interface to allow direct use by bioinformaticians, biologists and clinicians (at https://islandcompare.ca).

Performance assessment of genomic island prediction tools with an improved version of Design-Island

Genomic Islands (GIs) play an important role in the evolution and adaptation of prokaryotes. The origin and extent of ecological diversity of prokaryotes can be analyzed by comparing GIs across closely or distantly related prokaryotes. Understanding the importance of GI and to study the bacterial evolution, several GI prediction tools have been generated. An unsupervised method, Design-Island, was developed to identify GIs using Monte-Carlo statistical test on randomly selected segments of a chromosome. Here, in the present study Design-Island was modified with the incorporation of majority voting, multiple hypothesis testing correction. The performance of the modified version, Design-Island-II was tested and compared with the existing GI prediction tools. The performance assessment and benchmarking of the GI prediction tools require experimentally validated dataset, which is lacking. So, different datasets, generated or taken from literature were utilized to compare the sensitivity (SN), specificity (SP), precision (PPV) and accuracy (AC) of Design-Island-II. It showed substantial enhancement in term of SN, SP, PPV and AC, and significantly reduced the computation time of the algorithm. The performance of Design-Island-II has also been compared with several GI prediction tools using curated dataset of putative horizontally transferred genes. Design-Island-II showed the highest sensitivity and F1 score, comparable specificity, precision and accuracy in comparison to the other available methods. IslandViewer4 and Islander outperformed all the available methods in terms of AC and PPV respectively. Our study suggested Design-Island-II, IslandViewer4 and GIHunter among the top performing GI prediction tools considering both sensitivity and specificity of the methods.

Genomic Insights into Drug Resistance Determinants in Cedecea neteri, A Rare Opportunistic Pathogen.

Cedecea, a genus in the Enterobacteriaceae family, includes several opportunistic pathogens reported to cause an array of sporadic acute infections, most notably of the lung and bloodstream. One species, Cedecea neteri, is associated with cases of bacteremia in immunocompromised hosts and has documented resistance to different antibiotics, including β-lactams and colistin. Despite the potential to inflict serious infections, knowledge about drug resistance determinants in Cedecea is limited. In this study, we utilized whole-genome sequence data available for three environmental strains (SSMD04, M006, ND14a) of C. neteri and various bioinformatics tools to analyze drug resistance genes in this bacterium. All three genomes harbor multiple chromosome-encoded β-lactamase genes. A deeper analysis of β-lactamase genes in SSMD04 revealed four metallo-β-lactamases, a novel variant, and a CMY/ACT-type AmpC putatively regulated by a divergently transcribed AmpR. Homologs of known resistance-nodulation-cell division (RND)-type multidrug efflux pumps such as OqxB, AcrB, AcrD, and MdtBC were also identified. Genomic island prediction for SSMD04 indicated that tolC, involved in drug and toxin export across the outer membrane of Gram-negative bacteria, was acquired by a transposase-mediated genetic transfer mechanism. Our study provides new insights into drug resistance mechanisms of an environmental microorganism capable of behaving as a clinically relevant opportunistic pathogen.

SSG-LUGIA: Single Sequence based Genome Level Unsupervised Genomic Island Prediction Algorithm.

Genomic Islands (GIs) are clusters of genes that are mobilized through horizontal gene transfer. GIs play a pivotal role in bacterial evolution as a mechanism of diversification and adaptation to different niches. Therefore, identification and characterization of GIs in bacterial genomes is important for understanding bacterial evolution. However, quantifying GIs is inherently difficult, and the existing methods suffer from low prediction accuracy and precision-recall trade-off. Moreover, several of them are supervised in nature, and thus, their applications to newly sequenced genomes are riddled with their dependency on the functional annotation of existing genomes. We present SSG-LUGIA, a completely automated and unsupervised approach for identifying GIs and horizontally transferred genes. SSG-LUGIA is a novel method based on unsupervised anomaly detection technique, accompanied by further refinement using cues from signal processing literature. SSG-LUGIA leverages the atypical compositional biases of the alien genes to localize GIs in prokaryotic genomes. SSG-LUGIA was assessed on a large benchmark dataset `IslandPick' and on a set of 15 well-studied genomes in the literature and followed by a thorough analysis on the well-understood Salmonella typhi CT18 genome. Furthermore, the efficacy of SSG-LUGIA in identifying horizontally transferred genes was evaluated on two additional bacterial genomes, namely, those of Corynebacterium diphtheria NCTC13129 and Pseudomonas aeruginosa LESB58. SSG-LUGIA was examined on draft genomes and was demonstrated to be efficient as an ensemble method. Our results indicate that SSG-LUGIA achieved superior performance in comparison to frequently used existing methods. Importantly, it yielded a better trade-off between precision and recall than the existing methods. Its nondependency on the functional annotation of genomes makes it suitable for analyzing newly sequenced, yet uncharacterized genomes. Thus, our study is a significant advance in identification of GIs and horizontally transferred genes. SSG-LUGIA is available as an open source software at https://nibtehaz.github.io/SSG-LUGIA/.

Whole-genome comparative analysis of Malaysian Burkholderia pseudomallei clinical isolates.

Burkholderia pseudomallei , a soil-dwelling Gram-negative bacterium, is the causative agent of the endemic tropical disease melioidosis. Clinical manifestations of B. pseudomallei infection range from acute or chronic localized infection in a single organ to fulminant septicaemia in multiple organs. The diverse clinical manifestations are attributed to various factors, including the genome plasticity across B. pseudomallei strains. We previously characterized B. pseudomallei strains isolated in Malaysia and noted different levels of virulence in model hosts. We hypothesized that the difference in virulence might be a result of variance at the genome level. In this study, we sequenced and assembled four Malaysian clinical B. pseudomallei isolates, UKMR15, UKMPMC2000, UKMD286 and UKMH10. Phylogenomic analysis showed that Malaysian subclades emerged from the Asian subclade, suggesting that the Malaysian strains originated from the Asian region. Interestingly, the low-virulence strain, UKMH10, was the most distantly related compared to the other Malaysian isolates. Genomic island (GI) prediction analysis identified a new island of 23 kb, GI9c, which is present in B. pseudomallei and Burkholderia mallei , but not Burkholderia thailandensis . Genes encoding known B. pseudomallei virulence factors were present across all four genomes, but comparative analysis of the total gene content across the Malaysian strains identified 104 genes that are absent in UKMH10. We propose that these genes may encode novel virulence factors, which may explain the reduced virulence of this strain. Further investigation on the identity and role of these 104 proteins may aid in understanding B. pseudomallei pathogenicity to guide the design of new therapeutics for treating melioidosis.

Estimation of pathogenic potential of an environmental Pseudomonas aeruginosa isolate using comparative genomics

The isolation and sequencing of new strains of Pseudomonas aeruginosa created an extensive dataset of closed genomes. Many of the publicly available genomes are only used in their original publication while additional in silico information, based on comparison to previously published genomes, is not being explored. In this study, we defined and investigated the genome of the environmental isolate P. aeruginosa KRP1 and compared it to more than 100 publicly available closed P. aeruginosa genomes. By using different genomic island prediction programs, we could identify a total of 17 genomic islands and 8 genomic islets, marking the majority of the accessory genome that covers ~ 12% of the total genome. Based on intra-strain comparisons, we are able to predict the pathogenic potential of this environmental isolate. It shares a substantial amount of genomic information with the highly virulent PSE9 and LESB58 strains. For both of these, the increased virulence has been directly linked to their accessory genome before. Hence, the integrated use of previously published data can help to minimize expensive and time consuming wetlab work to determine the pathogenetic potential.

VCGIDB: A Database and Web Resource for the Genomic Islands from Vibrio Cholerae.

Vibrio cholerae is the causative agent of cholera, which is a severe, life-threatening diarrheal disease. The current seventh pandemic has not been eradicated and the outbreak is still ongoing around the world. The evolution of the pandemic-causing strain has been greatly influenced by lateral gene transfer, and the mechanisms of acquisition of pathogenicity in V. cholerae are mainly involved with genomic islands (GIs). Thus, detecting GIs and their comprehensive information is necessary to understand the continuing resurgence and newly emerging pathogenic V. cholerae strains. In this study, 798 V. cholerae strains were tested using the GI-Scanner algorithm, which was developed to detect candidate GIs and identify them in a comparative genomics approach. The algorithm predicted 435 highly possible genomic islands, and we built a database, called Vibrio cholerae Genomic Island Database (VCGIDB). This database shows advanced results that were acquired from a large genome set using phylogeny-based predictions. Moreover, VCGIDB is a highly expendable database that does not require intensive computation, which enables us to update it with a greater number of genomes using a novel genomic island prediction method. The VCGIDB website allows the user to browse the data and presents the results in a visual manner.

An efficient genomic signature ranking method for genomic island prediction from a single genome

Genomic islands that are associated with microbial adaptations and carry genomic signatures different from that of the host, and thus many methods have been proposed to select the informative genomic signatures from a range of organisms and discriminate genomic islands from the rest of the genome in terms of these signature biases. However, they are of limited use when closely related genomes are unavailable. In the present work, we proposed a kurtosis-based ranking method to select the informative genomic signatures from a single genome. In simulations with alien fragments from artificial and real genomes, the proposed kurtosis-based ranking method efficiently selected the informative genomic signatures from a single genome, without annotated information of genomes or prior knowledge from other datasets. This understanding can be useful to design more powerful method for genomic island detection.

Comparative Analysis of Genomic Island Prediction Tools.

Tools for genomic island prediction use strategies for genomic comparison analysis and sequence composition analysis. The goal of comparative analysis is to identify unique regions in the genomes of related organisms, whereas sequence composition analysis evaluates and relates the composition of specific regions with other regions in the genome. The goal of this study was to qualitatively and quantitatively evaluate extant genomic island predictors. We chose tools reported to produce significant results using sequence composition prediction, comparative genomics, and hybrid genomics methods. To maintain diversity, the tools were applied to eight complete genomes of organisms with distinct characteristics and belonging to different families. Escherichia coli CFT073 was used as a control and considered as the gold standard because its islands were previously curated in vitro. The results of predictions with the gold standard were manually curated, and the content and characteristics of each predicted island were analyzed. For other organisms, we created GenBank (GBK) files using Artemis software for each predicted island. We copied only the amino acid sequences from the coding sequence and constructed a multi-FASTA file for each predictor. We used BLASTp to compare all results and generate hits to evaluate similarities and differences among the predictions. Comparison of the results with the gold standard revealed that GIPSy produced the best results, covering ~91% of the composition and regions of the islands, followed by Alien Hunter (81%), IslandViewer (47.8%), Predict Bias (31%), GI Hunter (17%), and Zisland Explorer (16%). The tools with the best results in the analyzes of the set of organisms were the same ones that presented better performance in the tests with the gold standard.

Genomic analysis of the Phalaenopsis pathogen Dickeya sp. PA1, representing the emerging species Dickeya fangzhongdai

BackgroundDickeya sp. strain PA1 is the causal agent of bacterial soft rot in Phalaenopsis, an important indoor orchid in China. PA1 and a few other strains were grouped into a novel species, Dickeya fangzhongdai, and only the orchid-associated strains have been shown to cause soft rot symptoms.MethodsWe constructed the complete PA1 genome sequence and used comparative genomics to explore the differences in genomic features between D. fangzhongdai and other Dickeya species.ResultsPA1 has a 4,979,223-bp circular genome with 4269 predicted protein-coding genes. D. fangzhongdai was phylogenetically similar to Dickeya solani and Dickeya dadantii. The type I to type VI secretion systems (T1SS–T6SS), except for the stt-type T2SS, were identified in D. fangzhongdai. The three phylogenetically similar species varied significantly in terms of their T5SSs and T6SSs, as did the different D. fangzhongdai strains. Genomic island (GI) prediction and synteny analysis (compared to D. fangzhongdai strains) of PA1 also indicated the presence of T5SSs and T6SSs in strain-specific regions. Two typical CRISPR arrays were identified in D. fangzhongdai and in most other Dickeya species, except for D. solani. CRISPR-1 was present in all of these Dickeya species, while the presence of CRISPR-2 varied due to species differentiation. A large polyketide/nonribosomal peptide (PK/NRP) cluster, similar to the zeamine biosynthetic gene cluster in Dickeya zeae rice strains, was discovered in D. fangzhongdai and D. solani. The D. fangzhongdai and D. solani strains might recently have acquired this gene cluster by horizontal gene transfer (HGT).ConclusionsOrchid-associated strains are the typical members of D. fangzhongdai. Genomic analysis of PA1 suggested that this strain presents the genomic characteristics of this novel species. Considering the absence of the stt-type T2SS, the presence of CRISPR loci and the zeamine biosynthetic gene cluster, D. fangzhongdai is likely a transitional form between D. dadantii and D. solani. This is supported by the later acquisition of the zeamine cluster and the loss of CRISPR arrays by D. solani. Comparisons of phylogenetic positions and virulence determinants could be helpful for the effective quarantine and control of this emerging species.

Insights into the genome diversity and virulence of two clinical isolates of Burkholderia cenocepacia.

Burkholderia cenocepacia is among the most common members of the Burkholderia cepacia complex (Bcc) isolated from patients with cystic fibrosis (CF). The factors triggering the high rates of morbidity and mortality in CF patients are not well elucidated. In this study, we aim to highlight the genome diversity of two clinical isolates of B. cenocepacia through comparative genome analysis. The repertoire of virulence factors and resistance genes compared to reference strains J2315 and K56-2 was elucidated. The isolates were screened for the presence of phages and insertion sequences. Two methods were combined to obtain an accurate prediction of genomic islands (GIs): the cumulative GC profile and the IslandViewer web tool. To study evolutionary relatedness, whole genome-based single-nucleotide polymorphism (wgSNP) analysis was also performed with 43 publically available strains of the Bcc of various sequence types.Results/Key findings. Genome-based species identification of the two isolates BC-AUH and BC-BMEH confirmed the species as B. cenocepacia. Both belonged to ST-602, a double-locus variant of ST-32 (CC31), genomovar IIIA, and carried a large number of antibiotic resistance genes. Eighteen GIs were predicted in BC-AUH and BC-BMEH, occupying 9.3 and 6.1 % of the respective genomes. Comparison to J2315 revealed 89 and 85 genes unique to BC-BMEH and BC-AUH, respectively. Additionally, 1823 intergenic SNPs were detected between BC-BMEH and BC-AUH. This study mapped existing genetic variations in B. cenocepacia associated with notorious outcomes in CF patients, and the data obtained provide comprehensive, genome-inferred insights and multifactorial examination of an important human pathogen.

IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets

IslandViewer (http://www.pathogenomics.sfu.ca/islandviewer/) is a widely-used webserver for the prediction and interactive visualization of genomic islands (GIs, regions of probable horizontal origin) in bacterial and archaeal genomes. GIs disproportionately encode factors that enhance the adaptability and competitiveness of the microbe within a niche, including virulence factors and other medically or environmentally important adaptations. We report here the release of IslandViewer 4, with novel features to accommodate the needs of larger-scale microbial genomics analysis, while expanding GI predictions and improving its flexible visualization interface. A user management web interface as well as an HTTP API for batch analyses are now provided with a secured authentication to facilitate the submission of larger numbers of genomes and the retrieval of results. In addition, IslandViewer's integrated GI predictions from multiple methods have been improved and expanded by integrating the precise Islander method for pre-computed genomes, as well as an updated IslandPath-DIMOB for both pre-computed and user-supplied custom genome analysis. Finally, pre-computed predictions including virulence factors and antimicrobial resistance are now available for 6193 complete bacterial and archaeal strains publicly available in RefSeq. IslandViewer 4 provides key enhancements to facilitate the analysis of GIs and better understand their role in the evolution of successful environmental microbes and pathogens.

Assessing the Genotypic Differences between Strains of Corynebacterium pseudotuberculosis biovar equi through Comparative Genomics.

Seven genomes of Corynebacterium pseudotuberculosis biovar equi were sequenced on the Ion Torrent PGM platform, generating high-quality scaffolds over 2.35 Mbp. This bacterium is the causative agent of disease known as “pigeon fever” which commonly affects horses worldwide. The pangenome of biovar equi was calculated and two phylogenomic approaches were used to identify clustering patterns within Corynebacterium genus. Furthermore, other comparative analyses were performed including the prediction of genomic islands and prophages, and SNP-based phylogeny. In the phylogenomic tree, C. pseudotuberculosis was divided into two distinct clades, one formed by nitrate non-reducing species (biovar ovis) and another formed by nitrate-reducing species (biovar equi). In the latter group, the strains isolated from California were more related to each other, while the strains CIP 52.97 and 1/06-A formed the outermost clade of the biovar equi. A total of 1,355 core genes were identified, corresponding to 42.5% of the pangenome. This pangenome has one of the smallest core genomes described in the literature, suggesting a high genetic variability of biovar equi of C. pseudotuberculosis. The analysis of the similarity between the resistance islands identified a higher proximity between the strains that caused more severe infectious conditions (infection in the internal organs). Pathogenicity islands were largely conserved between strains. Several genes that modulate the pathogenicity of C. pseudotuberculosis were described including peptidases, recombination enzymes, micoside synthesis enzymes, bacteriocins with antimicrobial activity and several others. Finally, no genotypic differences were observed between the strains that caused the three different types of infection (external abscess formation, infection with abscess formation in the internal organs, and ulcerative lymphangitis). Instead, it was noted that there is a higher phenetic correlation between strains isolated at California compared to the other strains. Additionally, high variability of resistance islands suggests gene acquisition through several events of horizontal gene transfer.

Prediction of Putative Resistance Islands in a Carbapenem-Resistant Acinetobacter baumannii Global Clone 2 Clinical Isolate.

BackgroundWe investigated the whole genome sequence (WGS) of a carbapenem-resistant Acinetobacter baumannii isolate belonging to the global clone 2 (GC2) and predicted resistance islands using a software tool.MethodsA. baumannii strain YU-R612 was isolated from the sputum of a 61-yr-old man with sepsis. The WGS of the YU-R612 strain was obtained by using the PacBio RS II Sequencing System (Pacific Biosciences Inc., USA). Antimicrobial resistance genes and resistance islands were analyzed by using ResFinder and Genomic Island Prediction software (GIPSy), respectively.ResultsThe YU-R612 genome consisted of a circular chromosome (ca. 4,075 kb) and two plasmids (ca. 74 kb and 5 kb). Its sequence type (ST) under the Oxford scheme was ST191, consistent with assignment to GC2. ResFinder analysis showed that YU-R612 possessed the following resistance genes: four β-lactamase genes blaADC-30, blaOXA-66, blaOXA-23, and blaTEM-1; armA, aadA1, and aacA4 as aminoglycoside resistance-encoding genes; aac(6')Ib-cr for fluoroquinolone resistance; msr(E) for macrolide, lincosamide, and streptogramin B resistance; catB8 for phenicol resistance; and sul1 for sulfonamide resistance. By GIPSy analysis, six putative resistant islands (PRIs) were determined on the YU-R612 chromosome. Among them, PRI1 possessed two copies of Tn2009 carrying blaOXA-23, and PRI5 carried two copies of a class I integron carrying sul1 and armA genes.ConclusionsBy prediction of resistance islands in the carbapenem-resistant A. baumannii YU-R612 GC2 strain isolated in Korea, PRIs were detected on the chromosome that possessed Tn2009 and class I integrons. The prediction of resistance islands using software tools was useful for analysis of the WGS.

A Novel Method to Predict Genomic Islands Based on Mean Shift Clustering Algorithm.

Genomic Islands (GIs) are regions of bacterial genomes that are acquired from other organisms by the phenomenon of horizontal transfer. These regions are often responsible for many important acquired adaptations of the bacteria, with great impact on their evolution and behavior. Nevertheless, these adaptations are usually associated with pathogenicity, antibiotic resistance, degradation and metabolism. Identification of such regions is of medical and industrial interest. For this reason, different approaches for genomic islands prediction have been proposed. However, none of them are capable of predicting precisely the complete repertory of GIs in a genome. The difficulties arise due to the changes in performance of different algorithms in the face of the variety of nucleotide distribution in different species. In this paper, we present a novel method to predict GIs that is built upon mean shift clustering algorithm. It does not require any information regarding the number of clusters, and the bandwidth parameter is automatically calculated based on a heuristic approach. The method was implemented in a new user-friendly tool named MSGIP—Mean Shift Genomic Island Predictor. Genomes of bacteria with GIs discussed in other papers were used to evaluate the proposed method. The application of this tool revealed the same GIs predicted by other methods and also different novel unpredicted islands. A detailed investigation of the different features related to typical GI elements inserted in these new regions confirmed its effectiveness. Stand-alone and user-friendly versions for this new methodology are available at http://msgip.integrativebioinformatics.me.