Zisland Explorer: detect genomic islands by combining homogeneity and heterogeneity properties

Leveraging comparative genomics to uncover alien genes in bacterial genomes

A significant challenge in bacterial genomics is to catalogue genes acquired through the evolutionary process of horizontal gene transfer (HGT). Both comparative genomics and sequence composition-based methods have often been invoked to quantify horizontally acquired genes in bacterial genomes. Comparative genomics methods rely on completely sequenced genomes and therefore the confidence in their predictions increases as the databases become more enriched in completely sequenced genomes. Recent developments including in microbial genome sequencing call for reassessment of alien genes based on information-rich resources currently available. We revisited the comparative genomics approach and developed a new algorithm for alien gene detection. Our algorithm compared favourably with the existing comparative genomics-based methods and is capable of detecting both recent and ancient transfers. It can be used as a standalone tool or in concert with other complementary algorithms for comprehensively cataloguing alien genes in bacterial genomes.

Reconstructing horizontal gene flow network to understand prokaryotic evolution

Horizontal gene transfer (HGT) is a major source of phenotypic innovation and a mechanism of niche adaptation in prokaryotes. Quantification of HGT is critical to decipher its myriad roles in microbial evolution and adaptation. Advances in genome sequencing and bioinformatics have augmented our ability to understand the microbial world, particularly the direct or indirect influence of HGT on diverse life forms. Methods for detecting HGT can be classified into phylogenetic-based and parametric or composition-based approaches. Here, we exploited the complementary strengths of both the approaches to construct a high confidence horizontal gene flow network. Our network is unique in its ability to detect the transfer of native genes of a genome to genomes from other taxa, thus establishing donor and recipient organisms (taxa), rather than through a post hoc analysis as is the practice with several other approaches. The scale-free horizontal gene flow network presented here provides new insights into modes of transfer for the exchange of genetic information and also illuminates differential gene flow across phyla.

Reconstructing horizontal gene flow network to understand prokaryotic evolution

Horizontal gene transfer (HGT) is a major source of phenotypic innovation and a mechanism of niche adaptation in prokaryotes. Quantification of HGT is critical to decipher its myriad roles in microbial evolution and adaptation. Advances in genome sequencing and bioinformatics have augmented our ability to understand the microbial world, particularly the direct or indirect influence of HGT on diverse life forms. Methods for detecting HGT can be classified into phylogenetic-based and parametric or composition-based approaches. Here, we exploited the complementary strengths of both the approaches to construct a high confidence horizontal gene flow network. Our network is unique in its ability to detect the transfer of native genes of a genome to genomes from other taxa, thus establishing donor and recipient organisms (taxa), rather than through a post hoc analysis as is the practice with several other approaches. The scale-free horizontal gene flow network presented here provides new insights into modes of transfer for the exchange of genetic information and also illuminates differential gene flow across phyla.

Anti-CRISPRdb v2.2: an online repository of anti-CRISPR proteins including information on inhibitory mechanisms, activities and neighbors of curated anti-CRISPR proteins

We previously released the Anti-CRISPRdb database hosting anti-CRISPR proteins (Acrs) and associated information. Since then, the number of known Acr families, types, structures and inhibitory activities has accumulated over time, and Acr neighbors can be used as a candidate pool for screening Acrs in further studies. Therefore, we here updated the database to include the new available information. Our newly updated database shows several improvements: (i) it comprises more entries and families because it includes both Acrs reported in the most recent literatures and Acrs obtained via performing homologous alignment; (ii) the prediction of Acr neighbors is integrated into Anti-CRISPRdb v2.2, and users can identify novel Acrs from these candidates; and (iii) this version includes experimental information on the inhibitory strength and stage for Acr-Cas/Acr-CRISPR pairs, motivating the development of tools for predicting specific inhibitory abilities. Additionally, a parameter, the rank of codon usage bias (CUBRank), was proposed and provided in the new version, which showed a positive relationship with predicted result from AcRanker; hence, it can be used as an indicator for proteins to be Acrs. CUBRank can be used to estimate the possibility of genes occurring within genome island-a hotspot hosting potential genes encoding Acrs. Based on CUBRank and Anti-CRISPRdb, we also gave the first glimpse for the emergence of Acr genes (acrs).

DATABASE URL

http://guolab.whu.edu.cn/anti-CRISPRdb.

Comparative Genome Analysis Reveals Phylogenetic Identity of Bacillus velezensis HNA3 and Genomic Insights into Its Plant Growth Promotion and Biocontrol Effects

Bacillus velezensis HNA3, a potential plant growth promoter and biocontrol rhizobacterium, was isolated from plant rhizosphere soils in our previous work. Here, we sequenced the entire genome of the HNA3 strain and performed a comparative genome analysis. We found that HNA3 has a 3,929-kb chromosome with 46.5% GC content and 4,080 CDSs. We reclassified HNA3 as a Bacillus velezensis strain by core genome analysis between HNA3 and 74 previously defined Bacillus strains in the evolutionary tree. A comparative genomic analysis among Bacillus velezensis HNA3, Bacillus velezensis FZB42, Bacillus amyloliquefaciens DSM7, and Bacillus subtilis 168 showed that only HNA3 has one predicated secretory protein feruloyl esterase that catalyzes the hydrolysis of plant cell wall polysaccharides. The analysis of gene clusters revealed that whole biosynthetic gene clusters type Lanthipeptide was exclusively identified in HNA3 and might lead to the synthesis of new bioactive compounds. Twelve gene clusters were detected in HNA3 responsible for the synthesis of 14 secondary metabolites including Bacillaene, Fengycin, Bacillomycin D, Surfactin, Plipastatin, Mycosubtilin, Paenilarvins, Macrolactin, Difficidin, Amylocyclicin, Bacilysin, Iturin, Bacillibactin, Paenibactin, and others. HNA3 has 77 genes encoding for possible antifungal and antibacterial secreting carbohydrate active enzymes. It also contains genes involved in plant growth promotion, such as 11 putative indole acetic acid (IAA)-producing genes, spermidine and polyamine synthase genes, volatile compound producing genes, and multiple biofilm related genes. HNA3 also has 19 phosphatase genes involved in phosphorus solubilization. Our results provide insights into the genetic characteristics responsible for the bioactivities and potential application of HNA3 as plant growth-promoting strain in ecological agriculture.

IMPORTANCE This study is the primary initiative to identify Bacillus velezensis HNA3 whole genome sequence and reveal its genomic properties as an effective biocontrol agent against plant pathogens and a plant growth stimulator. HNA3 genetic profile can be used as a reference for future studies that can be applied as a highly effective biofertilizer and biofungicide inoculum to improve agriculture productivity. HNA3 reclassified in the phylogenetic tree which may be helpful for highly effective strain engineering and taxonomy. The genetic comparison among HNA3 and closely similar species B. velezensis FZB42, B. amyloliquefaciens DSM7, and B. subtilis 168 demonstrates some distinctive genetic properties of HNA3 and provides a basis for the genetic diversity of the Bacillus genus, which allows developing more effective eco-friendly resources for agriculture and separation of Bacillus velezensis as distinct species in the phylogenetic tree.

Prediction and Analysis in silico of Genomic Islands in Aeromonas hydrophila

Aeromonas are Gram-negative rods widely distributed in the environment. They can cause severe infections in fish related to financial losses in the fish industry, and are considered opportunistic pathogens of humans causing infections ranging from diarrhea to septicemia. The objective of this study was to determine in silico the contribution of genomic islands to A. hydrophila. The complete genomes of 17 A. hydrophila isolates, which were separated into two phylogenetic groups, were analyzed using a genomic island (GI) predictor. The number of predicted GIs and their characteristics varied among strains. Strains from group 1, which contains mainly fish pathogens, generally have a higher number of predicted GIs, and with larger size, than strains from group 2 constituted by strains recovered from distinct sources. Only a few predicted GIs were shared among them and contained mostly genes from the core genome. Features related to virulence, metabolism, and resistance were found in the predicted GIs, but strains varied in relation to their gene content. In strains from group 1, O Ag biosynthesis clusters OX1 and OX6 were identified, while strains from group 2 each had unique clusters. Metabolic pathways for myo-inositol, L-fucose, sialic acid, and a cluster encoding QueDEC, tgtA5, and proteins related to DNA metabolism were identified in strains of group 1, which share a high number of predicted GIs. No distinctive features of group 2 strains were identified in their predicted GIs, which are more diverse and possibly better represent GIs in this species. However, some strains have several resistance attributes encoded by their predicted GIs. Several predicted GIs encode hypothetical proteins and phage proteins whose functions have not been identified but may contribute to Aeromonas fitness. In summary, features with functions identified on predicted GIs may confer advantages to host colonization and competitiveness in the environment.

Genomic Island Prediction via Chi-Square Test and Random Forest Algorithm

Genomic islands are related to microbial adaptation and carry different genomic characteristics from the host. Therefore, many methods have been proposed to detect genomic islands from the rest of the genome by evaluating its sequence composition. Many sequence features have been proposed, but many of them have not been applied to the identification of genomic islands. In this paper, we present a scheme to predict genomic islands using the chi-square test and random forest algorithm. We extract seven kinds of sequence features and select the important features with the chi-square test. All the selected features are then input into the random forest to predict the genome islands. Three experiments and comparison show that the proposed method achieves the best performance. This understanding can be useful to design more powerful method for the genomic island prediction.

A Type VI Secretion System Facilitates Fitness, Homeostasis, and Competitive Advantages for Environmental Adaptability and Efficient Nicotine Biodegradation

Gram-negative bacteria employ secretion systems to translocate proteinaceous effectors from the cytoplasm to the extracellular milieu, thus interacting with the surrounding environment or microniche. It is known that bacteria can benefit from the type VI secretion system (T6SS) by transporting ions to combat reactive oxygen species (ROS). Here, we report that T6SS activities conferred tolerance to nicotine-induced oxidative stress in Pseudomonas sp. strain JY-Q, a highly active nicotine degradation strain isolated from tobacco waste extract. AA098_13375 was identified to encode a dual-functional effector with antimicrobial and anti-ROS activities. Wild-type strain JY-Q grew better than the AA098_13375 deletion mutant in nicotine-containing medium by antagonizing increased intracellular ROS levels. It was, therefore, tentatively designated TseN (type VI secretion system effector for nicotine tolerance), homologs of which were observed to be broadly ubiquitous in Pseudomonas species. TseN was identified as a Tse6-like bacteriostatic toxin via monitoring intracellular NAD+ TseN presented potential antagonism against ROS to fine tune the heavy traffic of nicotine metabolism in strain JY-Q. It is feasible that the dynamic tuning of NAD+ driven by TseN could satisfy demands from nicotine degradation with less cytotoxicity. In this scenario, T6SS involves a fascinating accommodation cascade that prompts constitutive biotransformation of N-heterocyclic aromatics by improving bacterial robustness/growth. In summary, the T6SS in JY-Q mediated resistance to oxidative stress and promoted bacterial fitness via a contact-independent growth competitive advantage, in addition to the well-studied T6SS-dependent antimicrobial activities.IMPORTANCE Mixtures of various pollutants and the coexistence of numerous species of organisms are usually found in adverse environments. Concerning biodegradation of nitrogen-heterocyclic contaminants, the scientific community has commonly focused on screening functional enzymes that transform pollutants into intermediates of attenuated toxicity or for primary metabolism. Here, we identified dual roles of the T6SS effector TseN in Pseudomonas sp. strain JY-Q, which is capable of degrading nicotine. The T6SS in strain JY-Q is able to deliver TseN to kill competitors and provide a growth advantage by a contact-independent pattern. TseN could monitor the intracellular NAD+ level by its hydrolase activity, causing cytotoxicity in competitive rivals but metabolic homeostasis on JY-Q. Moreover, JY-Q could be protected from TseN toxicity by the immunity protein TsiN. In conclusion, we found that TseN with cytotoxicity to bacterial competitors facilitated the nicotine tolerance of JY-Q. We therefore reveal a working model between T6SS and nicotine metabolism. This finding indicates that multiple diversified weapons have been evolved by bacteria for their growth and robustness.

Experimental approaches to tracking mobile genetic elements in microbial communities

Horizontal gene transfer is an important mechanism of microbial evolution and is often driven by the movement of mobile genetic elements between cells. Due to the fact that microbes live within communities, various mechanisms of horizontal gene transfer and types of mobile elements can co-occur. However, the ways in which horizontal gene transfer impacts and is impacted by communities containing diverse mobile elements has been challenging to address. Thus, the field would benefit from incorporating community-level information and novel approaches alongside existing methods. Emerging technologies for tracking mobile elements and assigning them to host organisms provide promise for understanding the web of potential DNA transfers in diverse microbial communities more comprehensively. Compared to existing experimental approaches, chromosome conformation capture and methylome analyses have the potential to simultaneously study various types of mobile elements and their associated hosts. We also briefly discuss how fermented food microbiomes, given their experimental tractability and moderate species complexity, make ideal models to which to apply the techniques discussed herein and how they can be used to address outstanding questions in the field of horizontal gene transfer in microbial communities.

New candidates for regulated gene integrity revealed through precise mapping of integrative genetic elements

Integrative genetic elements (IGEs) are mobile multigene DNA units that integrate into and excise from host bacterial genomes. Each IGE usually targets a specific site within a conserved host gene, integrating in a manner that preserves target gene function. However, a small number of bacterial genes are known to be inactivated upon IGE integration and reactivated upon excision, regulating phenotypes of virulence, mutation rate, and terminal differentiation in multicellular bacteria. The list of regulated gene integrity (RGI) cases has been slow-growing because IGEs have been challenging to precisely and comprehensively locate in genomes. We present software (TIGER) that maps IGEs with unprecedented precision and without attB site bias. TIGER uses a comparative genomic, ping-pong BLAST approach, based on the principle that the IGE integration module (i.e. its int-attP region) is cohesive. The resultant IGEs from 2168 genomes, along with integrase phylogenetic analysis and gene inactivation tests, revealed 19 new cases of genes whose integrity is regulated by IGEs (including dut, eccCa1, gntT, hrpB, merA, ompN, prkA, tqsA, traG, yifB, yfaT and ynfE), as well as recovering previously known cases (in sigK, spsM, comK, mlrA and hlb genes). It also recovered known clades of site-promiscuous integrases and identified possible new ones.

Microbial genomic island discovery, visualization and analysis

Horizontal gene transfer (also called lateral gene transfer) is a major mechanism for microbial genome evolution, enabling rapid adaptation and survival in specific niches. Genomic islands (GIs), commonly defined as clusters of bacterial or archaeal genes of probable horizontal origin, are of particular medical, environmental and/or industrial interest, as they disproportionately encode virulence factors and some antimicrobial resistance genes and may harbor entire metabolic pathways that confer a specific adaptation (solvent resistance, symbiosis properties, etc). As large-scale analyses of microbial genomes increases, such as for genomic epidemiology investigations of infectious disease outbreaks in public health, there is increased appreciation of the need to accurately predict and track GIs. Over the past decade, numerous computational tools have been developed to tackle the challenges inherent in accurate GI prediction. We review here the main types of GI prediction methods and discuss their advantages and limitations for a routine analysis of microbial genomes in this era of rapid whole-genome sequencing. An assessment is provided of 20 GI prediction software methods that use sequence-composition bias to identify the GIs, using a reference GI data set from 104 genomes obtained using an independent comparative genomics approach. Finally, we present guidelines to assist researchers in effectively identifying these key genomic regions.

IslandCafe: Compositional Anomaly and Feature Enrichment Assessment for Delineation of Genomic Islands

One of the evolutionary forces driving bacterial genome evolution is the acquisition of clusters of genes through horizontal gene transfer (HGT). These genomic islands may confer adaptive advantages to the recipient bacteria, such as, the ability to thwart antibiotics, become virulent or hypervirulent, or acquire novel metabolic traits. Methods for detecting genomic islands either search for markers or features typical of islands or examine anomaly in oligonucleotide composition against the genome background. The former tends to underestimate, missing islands that have the markers either lost or degraded, while the latter tends to overestimate, due to their inability to discriminate compositional atypicality arising because of HGT from those that are a consequence of other biological factors. We propose here a framework that exploits the strengths of both these approaches while bypassing the pitfalls of either. Genomic islands lacking markers are identified by their association with genomic islands with markers. This was made possible by performing marker enrichment and phyletic pattern analyses within an integrated framework of recursive segmentation and clustering. The proposed method, IslandCafe, compared favorably with frequently used methods for genomic island detection on synthetic test datasets and on a test-set of known islands from 15 well-characterized bacterial species. Furthermore, IslandCafe identified novel islands with imprints of likely horizontal acquisition.

Improved genomic island predictions with IslandPath-DIMOB

Motivation

Genomic islands (GIs) are clusters of genes of probable horizontal origin that play a major role in bacterial and archaeal genome evolution and microbial adaptability. They are of high medical and industrial interest, due to their enrichment in virulence factors, some antimicrobial resistance genes and adaptive metabolic pathways. The development of more sensitive but precise prediction tools, using either sequence composition-based methods or comparative genomics, is needed as large-scale analyses of microbial genomes increase.

Results

IslandPath-DIMOB, a leading GI prediction tool in the IslandViewer webserver, has now been significantly improved by modifying both the decision algorithm to determine sequence composition biases, and the underlying database of HMM profiles for associated mobility genes. The accuracy of IslandPath-DIMOB and other major software has been assessed using a reference GI dataset predicted by comparative genomics, plus a manually curated dataset from literature review. Compared to the previous version (v0.2.0), this IslandPath-DIMOB v1.0.0 achieves 11.7% and 5.3% increase in recall and precision, respectively. IslandPath-DIMOB has the highest Matthews correlation coefficient among individual prediction methods tested, combining one of the highest recall measures (46.9%) at high precision (87.4%). The only method with higher recall had notably lower precision (55.1%). This new IslandPath-DIMOB v1.0.0 will facilitate more accurate studies of GIs, including their key roles in microbial adaptability of medical, environmental and industrial interest.

Availability and implementation

IslandPath-DIMOB v1.0.0 is freely available through the IslandViewer webserver {{http://www.pathogenomics.sfu.ca/islandviewer/}} and as standalone software {{https://github.com/brinkmanlab/islandpath/}} under the GNU-GPLv3.

Supplementary information

Supplementary data are available at Bioinformatics online.

MTGIpick allows robust identification of genomic islands from a single genome

Genomic islands (GIs) that are associated with microbial adaptations and carry sequence patterns different from that of the host are sporadically distributed among closely related species. This bias can dominate the signal of interest in GI detection. However, variations still exist among the segments of the host, although no uniform standard exists regarding the best methods of discriminating GIs from the rest of the genome in terms of compositional bias. In the present work, we proposed a robust software, MTGIpick, which used regions with pattern bias showing multiscale difference levels to identify GIs from the host. MTGIpick can identify GIs from a single genome without annotated information of genomes or prior knowledge from other data sets. When real biological data were used, MTGIpick demonstrated better performance than existing methods, as well as revealed potential GIs with accurate sizes missed by existing methods because of a uniform standard. Software and supplementary are freely available at http://bioinfo.zstu.edu.cn/MTGI or https://github.com/bioinfo0706/MTGIpick.

VRprofile: gene-cluster-detection-based profiling of virulence and antibiotic resistance traits encoded within genome sequences of pathogenic bacteria

VRprofile is a Web server that facilitates rapid investigation of virulence and antibiotic resistance genes, as well as extends these trait transfer-related genetic contexts, in newly sequenced pathogenic bacterial genomes. The used backend database MobilomeDB was firstly built on sets of known gene cluster loci of bacterial type III/IV/VI/VII secretion systems and mobile genetic elements, including integrative and conjugative elements, prophages, class I integrons, IS elements and pathogenicity/antibiotic resistance islands. VRprofile is thus able to co-localize the homologs of these conserved gene clusters using HMMer or BLASTp searches. With the integration of the homologous gene cluster search module with a sequence composition module, VRprofile has exhibited better performance for island-like region predictions than the other widely used methods. In addition, VRprofile also provides an integrated Web interface for aligning and visualizing identified gene clusters with MobilomeDB-archived gene clusters, or a variety set of bacterial genomes. VRprofile might contribute to meet the increasing demands of re-annotations of bacterial variable regions, and aid in the real-time definitions of disease-relevant gene clusters in pathogenic bacteria of interest. VRprofile is freely available at http://bioinfo-mml.sjtu.edu.cn/VRprofile.

Virulence factor landscape of a Staphylococcus aureus sequence type 45 strain, MCRF184

Background

We describe the virulence factors of a methicillin-sensitive Staphylococcus aureus sequence type (ST) 45 strain, MCRF184, (spa type t917), that caused severe necrotizing fasciitis in a 72-year-old diabetic male. The genome of MCRF184 possesses three genomic islands: a relatively large type III νSaα with 42 open reading frames (ORFs) that includes superantigen- and lipoprotein-like genes, a truncated νSaβ that consists mostly of the enterotoxin gene cluster (egc), and a νSaγ island with 18 ORFs including α-toxin. Additionally, the genome has two phage-related regions: phage φSa3 with three genes of the immune evasion cluster (IEC), and an incomplete phage that is distinct from other S. aureus phages. Finally, the region between orfX and orfY harbors a putative efflux pump, acetyltransferase, regulators, and mobilization genes instead of genes of SCCmec.

Results

Virulence factors included phenol soluble modulins (PSMs) α1 through α4 and PSMs β1 and β2. Ten ORFs identified in MCRF184 had not been reported in previously sequenced S. aureus strains.

Conclusion

The dire clinical outcome in the patient and the described virulence factors all suggest that MCRF184, a ST45 strain is a highly virulent strain of S. aureus.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5394-2) contains supplementary material, which is available to authorized users.

An inhibitory compound produced by a soil isolate of Rhodococcus has strong activity against the veterinary pathogen R. equi

Complete genome sequencing of dozens of strains of the soil bacterium Rhodococcus has revealed the presence of many cryptic biosynthetic gene clusters, presumably dedicated to the production of small molecules. This has sparked a renewed interest in this underexplored member of the Actinobacteria as a potential source of new bioactive compounds. Reported here is the discovery of a potent inhibitory molecule produced by a newly isolated strain of Rhodococcus, strain MTM3W5.2. This small inhibitory molecule shows strong activity against all Rhodococcus species tested, including the veterinary pathogen R. equi, and some closely related genera. It is not active against other Gram positive or Gram negative bacteria. A screen of random transposon mutants identified a gene required to produce this inhibitory compound. This gene is a large multi-domain, type I polyketide synthase that is part of a very large multi-gene biosynthetic gene cluster in the chromosome of strain MTM3W5.2. The high resolution mass spectrum of a major chromatogram peak from a broth culture extract of MTM3W5.2 shows the presence of a compound at m/z 911.5490 atomic mass units. This compound is not detected in the culture extracts from a non-producing mutant strain of MTM3W5.2. A large gene cluster containing at least 14 different type I polyketide synthase genes is proposed to be required to synthesize this antibiotic-like compound.

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.

Whole-genome sequencing and comparative analysis of two plant-associated strains of Rhodopseudomonas palustris (PS3 and YSC3)

Rhodopseudomonas palustris strains PS3 and YSC3 are purple non-sulfur phototrophic bacteria isolated from Taiwanese paddy soils. PS3 has beneficial effects on plant growth and enhances the uptake efficiency of applied fertilizer nutrients. In contrast, YSC3 has no significant effect on plant growth. The genomic structures of PS3 and YSC3 are similar; each contains one circular chromosome that is 5,269,926 or 5,371,816 bp in size, with 4,799 or 4,907 protein-coding genes, respectively. In this study, a large class of genes involved in chemotaxis and motility was identified in both strains, and genes associated with plant growth promotion, such as nitrogen fixation-, IAA synthesis- and ACC deamination-associated genes, were also identified. We noticed that the growth rate, the amount of biofilm formation, and the relative expression levels of several chemotaxis-associated genes were significantly higher for PS3 than for YSC3 upon treatment with root exudates. These results indicate that PS3 responds better to the presence of plant hosts, which may contribute to the successful interactions of PS3 with plant hosts. Moreover, these findings indicate that the existence of gene clusters associated with plant growth promotion is required but not sufficient for a bacterium to exhibit phenotypes associated with plant growth promotion.

Mutation Landscape of Base Substitutions, Duplications, and Deletions in the Representative Current Cholera Pandemic Strain

Pandemic cholera is a major concern for public health because of its high mortality and morbidity. Mutation accumulation (MA) experiments were performed on a representative strain of the current cholera pandemic. Although the base-pair substitution mutation rates in Vibrio cholerae (1.24 × 10-10 per site per generation for wild-type lines and 3.29 × 10-8 for mismatch repair deficient lines) are lower than that previously reported in other bacteria using MA analysis, we discovered specific high rates (8.31 × 10-8 site/generation for wild-type lines and 1.82 × 10-6 for mismatch repair deficient lines) of base duplication or deletion driven by large-scale copy number variations (CNVs). These duplication-deletions are located in two pathogenic islands, IMEX and the large integron island. Each element of these islands has discrepant rate in rapid integration and excision, which provides clues to the pandemicity evolution of V. cholerae. These results also suggest that large-scale structural variants such as CNVs can accumulate rapidly during short-term evolution. Mismatch repair deficient lines exhibit a significantly increased mutation rate in the larger chromosome (Chr1) at specific regions, and this pattern is not observed in wild-type lines. We propose that the high frequency of GATC sites in Chr1 improves the efficiency of MMR, resulting in similar rates of mutation in the wild-type condition. In addition, different mutation rates and spectra were observed in the MA lines under distinct growth conditions, including minimal media, rich media and antibiotic treatments.

Deciphering pathogenicity and antibiotic resistance islands in methicillin-resistant Staphylococcus aureus genomes

Staphylococcus aureus is a versatile pathogen that is capable of causing infections in both humans and animals. It can cause furuncles, septicaemia, pneumonia and endocarditis. Adaptation of S. aureus to the modern hospital environment has been facilitated, in part, by the horizontal acquisition of drug resistance genes, such as mecA gene that imparts resistance to methicillin. Horizontal acquisitions of islands of genes harbouring virulence and antibiotic resistance genes have made S. aureus resistant to commonly used antibiotics. To decipher genomic islands (GIs) in 22 hospital- and 9 community-associated methicillin-resistant S. aureus strains and classify a subset of GIs carrying virulence and resistance genes as pathogenicity and resistance islands respectively, we applied a host of methods for localizing genomic islands in prokaryotic genomes. Surprisingly, none of the frequently used GI prediction methods could perform well in delineating the resistance islands in the S. aureus genomes. Rather, a gene clustering procedure exploiting biases in codon usage for identifying horizontally transferred genes outperformed the current methods for GI detection, in particular in identifying the known islands in S. aureus including the SCCmec island that harbours the mecA resistance gene. The gene clustering approach also identified novel, as yet unreported islands, with many of these found to harbour virulence and/or resistance genes. These as yet unexplored islands may provide valuable information on the evolution of drug resistance in S. aureus.

xenoGI: reconstructing the history of genomic island insertions in clades of closely related bacteria

Background

Genomic islands play an important role in microbial genome evolution, providing a mechanism for strains to adapt to new ecological conditions. A variety of computational methods, both genome-composition based and comparative, have been developed to identify them. Some of these methods are explicitly designed to work in single strains, while others make use of multiple strains. In general, existing methods do not identify islands in the context of the phylogeny in which they evolved. Even multiple strain approaches are best suited to identifying genomic islands that are present in one strain but absent in others. They do not automatically recognize islands which are shared between some strains in the clade or determine the branch on which these islands inserted within the phylogenetic tree.

Results

We have developed a software package, xenoGI, that identifies genomic islands and maps their origin within a clade of closely related bacteria, determining which branch they inserted on. It takes as input a set of sequenced genomes and a tree specifying their phylogenetic relationships. Making heavy use of synteny information, the package builds gene families in a species-tree-aware way, and then attempts to combine into islands those families whose members are adjacent and whose most recent common ancestor is shared. The package provides a variety of text-based analysis functions, as well as the ability to export genomic islands into formats suitable for viewing in a genome browser. We demonstrate the capabilities of the package with several examples from enteric bacteria, including an examination of the evolution of the acid fitness island in the genus Escherichia. In addition we use output from simulations and a set of known genomic islands from the literature to show that xenoGI can accurately identify genomic islands and place them on a phylogenetic tree.

Conclusions

xenoGI is an effective tool for studying the history of genomic island insertions in a clade of microbes. It identifies genomic islands, and determines which branch they inserted on within the phylogenetic tree for the clade. Such information is valuable because it helps us understand the adaptive path that has produced living species.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2038-0) contains supplementary material, which is available to authorized users.

The Transcriptional Regulators of the CRP Family Regulate Different Essential Bacterial Functions and Can Be Inherited Vertically and Horizontally

One of the best-studied transcriptional regulatory proteins in bacteria is the Escherichia coli catabolite repressor protein (CRP) that when complexed with 3′-5′-cyclic AMP (cAMP) changes its conformation and interacts with specific DNA-sequences. CRP DNA-binding can result in positive or negative regulation of gene expression depending on the position of its interaction with respect to RNA polymerase binding site. The aim of this work is to review the biological role and phylogenetic relations that some members of the CRP family of transcriptional regulators (also known as cAMP receptor protein family) have in different bacterial species. This work is not intended to give an exhaustive revision of bacterial CRP-orthologs, but to provide examples of the role that these proteins play in the expression of genes that are fundamental for the life style of some bacterial species. We highlight the conservation of their structural characteristics and of their binding to conserved-DNA sequences, in contrast to their very diverse repertoire of gene activation. CRP activates a wide variety of fundamental genes for the biological characteristic of each bacterial species, which in several instances form part of their core-genome (defined as the gene sequences present in all members of a bacterial species). We present evidence that support the fact that some of the transcriptional regulators that belong to the CRP family in different bacterial species, and some of the genes that are regulated by them, can be inherited by horizontal gene transfer. These data are discussed in the framework of bacterial evolution models.

Identification and analysis of genomic islands in Burkholderia cenocepacia AU 1054 with emphasis on pathogenicity islands

BACKGROUND

Genomic islands (GIs) are genomic regions that reveal evidence of horizontal DNA transfer. They can code for many functions and may augment a bacterium's adaptation to its host or environment. GIs have been identified in strain J2315 of Burkholderia cenocepacia, whereas in strain AU 1054 there has been no published works on such regions according to our text mining and keyword search in Medline.

RESULTS

In this study, we identified 21 GIs in AU 1054 by combining two computational tools. Feature analyses suggested that the predictions are highly reliable and hence illustrated the advantage of joint predictions by two independent methods. Based on putative virulence factors, four GIs were further identified as pathogenicity islands (PAIs). Through experiments of gene deletion mutants in live bacteria, two putative PAIs were confirmed, and the virulence factors involved were identified as lipA and copR. The importance of the genes lipA (from PAI 1) and copR (from PAI 2) for bacterial invasion and replication indicates that they are required for the invasive properties of B. cenocepacia and may function as virulence determinants for bacterial pathogenesis and host infection.

CONCLUSIONS

This approach of in silico prediction of GIs and subsequent identification of potential virulence factors in the putative island regions with final validation using wet experiments could be used as an effective strategy to rapidly discover novel virulence factors in other bacterial species and strains.

RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation

Background

RNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms.

Results

An alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package.

Conclusion

RNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA comparison tools, RNApdist and RNAdistance, showcased that RNA-TVcurve can efficiently capture subtle relationships among RNAs for mutation detection and non-coding RNA classification. All the relevant results were shown in an intuitive graphical manner, and can be freely downloaded from this server. RNA-TVcurve, along with test examples and detailed documents, are available at: http://ml.jlu.edu.cn/tvcurve/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1481-7) contains supplementary material, which is available to authorized users.

Identification of Novel Genomic Islands in Liverpool Epidemic Strain of Pseudomonas aeruginosa Using Segmentation and Clustering

Pseudomonas aeruginosa is an opportunistic pathogen implicated in a myriad of infections and a leading pathogen responsible for mortality in patients with cystic fibrosis (CF). Horizontal transfers of genes among the microorganisms living within CF patients have led to highly virulent and multi-drug resistant strains such as the Liverpool epidemic strain of P. aeruginosa, namely the LESB58 strain that has the propensity to acquire virulence and antibiotic resistance genes. Often these genes are acquired in large clusters, referred to as "genomic islands (GIs)." To decipher GIs and understand their contributions to the evolution of virulence and antibiotic resistance in P. aeruginosa LESB58, we utilized a recursive segmentation and clustering procedure, presented here as a genome-mining tool, "GEMINI." GEMINI was validated on experimentally verified islands in the LESB58 strain before examining its potential to decipher novel islands. Of the 6062 genes in P. aeruginosa LESB58, 596 genes were identified to be resident on 20 GIs of which 12 have not been previously reported. Comparative genomics provided evidence in support of our novel predictions. Furthermore, GEMINI unraveled the mosaic structure of islands that are composed of segments of likely different evolutionary origins, and demonstrated its ability to identify potential strain biomarkers. These newly found islands likely have contributed to the hyper-virulence and multidrug resistance of the Liverpool epidemic strain of P. aeruginosa.

Phylogenomic Analyses and Reclassification of Species within the Genus Tsukamurella: Insights to Species Definition in the Post-genomic Era

Owing to the highly similar phenotypic profiles, protein spectra and 16S rRNA gene sequences observed between three pairs of Tsukamurella species (Tsukamurella pulmonis/Tsukamurella spongiae, Tsukamurella tyrosinosolvens/Tsukamurella carboxy-divorans, and Tsukamurella pseudospumae/Tsukamurella sunchonensis), we hypothesize that and the six Tsukamurella species may have been misclassified and that there may only be three Tsukamurella species. In this study, we characterized the type strains of these six Tsukamurella species by tradition DNA-DNA hybridization (DDH) and "digital DDH" after genome sequencing to determine their exact taxonomic positions. Traditional DDH showed 81.2 ± 0.6% to 99.7 ± 1.0% DNA-DNA relatedness between the two Tsukamurella species in each of the three pairs, which was above the threshold for same species designation. "Digital DDH" based on Genome-To-Genome Distance Calculator and Average Nucleotide Identity for the three pairs also showed similarity results in the range of 82.3-92.9 and 98.1-99.1%, respectively, in line with results of traditional DDH. Based on these evidence and according to Rules 23a and 42 of the Bacteriological Code, we propose that T. spongiae Olson et al. 2007, should be reclassified as a later heterotypic synonym of T. pulmonis Yassin et al. 1996, T. carboxydivorans Park et al. 2009, as a later heterotypic synonym of T. tyrosinosolvens Yassin et al. 1997, and T. sunchonensis Seong et al. 2008 as a later heterotypic synonym of T. pseudospumae Nam et al. 2004. With the advancement of genome sequencing technologies, classification of bacterial species can be readily achieved by "digital DDH" than traditional DDH.