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ICEberg 2.0 (http://db-mml.sjtu.edu.cn/ICEberg/) is an updated database that provides comprehensive information about bacterial integrative and conjugative elements (ICEs). Compared with the previous version, three major improvements were made. First, with the aid of text mining and manual curation, it now recorded the details of 1032 ICEs, including 270 with experimental supports and 762 from bioinformatics prediction. Second, as increasing evidence has shown that ICEs frequently mobilize the so-called 'hitchhikers', such as integrative and mobilizable elements (IMEs) and cis-mobilizable elements (CIMEs), 83 known transfer interactions between 49 IMEs and 7 CIMEs with 19 ICEs taken from the literature were included and illustrated with visually intuitive directed graphs. An expanded collection of 260 chromosome-borne IMEs and 235 CIMEs was also added. At last, ICEberg 2.0 provides an online tool ICEfinder to predict ICEs or IMEs in bacterial genome sequences. It combines a similarity search for the integrase, relaxase and/or type IV secretion system and the co-localization of these corresponding homologous genes. With the recent updates, ICEberg 2.0 might provide better support for understanding the biological traits of ICEs, especially as their interaction with cognate mobilizable elements may further promote horizontal gene flow.

The interspaced short palindromic repeats (CRISPR) system is an immune system widely distributed in prokaryotes, resisting the invasion of the foreign mobile genetic elements like phages or plasmids. In this study, we present the comparative analysis of 182 CRISPR loci found in 46 publicly available complete genome sequences of Streptomyces. Overall, nine direct repeats (DRs) groups are identified while all the 2104 spacers are divided into three main groups according to the multiple sequence alignment. Only 11 spacers are identical with parts of 10 plasmid sequences, which indicates a possible origin. The cas gene clusters near the CRISPR arrays are found to mainly belong to the I-E subtype. These CRISPR loci might play an important role in the genome evolution of Streptomyces.

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.

Hypervirulent K. pneumoniae variants (hvKP) have been increasingly reported worldwide, causing metastasis of severe infections such as liver abscesses and bacteremia. The capsular serotype K2 hvKP strains show diverse multi-locus sequence types (MLSTs), but with limited genetics and virulence information. In this study, we report a hypermucoviscous K. pneumoniae strain, RJF293, isolated from a human bloodstream sample in a Chinese hospital. It caused a metastatic infection and fatal septic shock in a critical patient. The microbiological features and genetic background were investigated with multiple approaches. The Strain RJF293 was determined to be multilocis sequence type (ST) 374 and serotype K2, displayed a median lethal dose (LD50) of 1.5 × 10 2 CFU in BALB/c mice and was as virulent as the ST23 K1 serotype hvKP strain NTUH-K2044 in a mouse lethality assay. Whole genome sequencing revealed that the RJF293 genome codes for 32 putative virulence factors and exhibits a unique presence/absence pattern in comparison to the other 105 completely sequenced K. pneumoniae genomes. Whole genome SNP-based phylogenetic analysis revealed that strain RJF293 formed a single clade, distant from those containing either ST66 or ST86 hvKP. Compared to the other sequenced hvKP chromosomes, RJF293 contains several strain-variable regions, including one prophage, one ICEKp1 family integrative and conjugative element and six large genomic islands. The sequencing of the first complete genome of an ST374 K2 hvKP clinical strain should reinforce our understanding of the epidemiology and virulence mechanisms of this bloodstream infection-causing hvKP with clinical significance.

Klebsiella pneumoniae is an increasingly important bacterial pathogen to human. This Gram-negative bacterium species has become a serious concern due to its dramatic increase in the levels of multiple antibiotic resistances, particularly to carbapenems. The toxin-antitoxin (TA) system has recently been reported to be involved in the formation of drug-tolerant persister cells. The type II TA system is composed of a stable toxin protein and a relatively unstable antitoxin protein that is able to inhibit the toxin. Here, we examine the type II TA locus distribution and compare the TA diversity throughout ten completely sequenced K. pneumoniae genomes by using bioinformatics approaches. Two hundred and twelve putative type II TA loci were identified in 30 replicons of these K. pneumoniae strains. The amino acid sequence similarity-based grouping shows that these loci distribute differently not only among different K. pneumoniae strains isolated from diverse sources, but also between their chromosomes and plasmids.

Streptomyces xiamenensis 318, a moderate halophile isolated from a mangrove sediment, produces the anti-fibrotic compound xiamenmycin. The whole genome sequence of strain 318 was obtained through long-read single-molecule real-time (SMRT) sequencing, high-throughput Illumina HiSeq and 454 pyrosequencing technologies. The assembled genome comprises a linear chromosome as a single contig of 5,961,401-bp, which is considerably smaller than other reported complete genomes of the genus Streptomyces. Based on the antiSMASH pipeline, a total of 21 gene clusters were predicted to be involved in secondary metabolism. The gene cluster responsible for the biosynthesis of xiamenmycin resides in a strain-specific 61,387-bp genomic island belonging to the left-arm region. A core metabolic network consisting of 104 reactions that supports xiamenmycin biosynthesis was constructed to illustrate the necessary precursors derived from the central metabolic pathway. In accordance with the finding of a putative ikarugamycin gene cluster in the genome, the targeted chemical profiling of polycyclic tetramate macrolactams (PTMs) resulted in the identification of ikarugamycin. A successful genome mining for bioactive molecules with different skeletons suggests that the naturally minimized genome of S. xiamenensis 318 could be used as a blueprint for constructing a chassis cell with versatile biosynthetic capabilities for the production of secondary metabolites.

Acinetobacter baumannii is an important human pathogen due to its multi-drug resistance. In this study, the genome of an ST10 outbreak A. baumannii isolate LAC-4 was completely sequenced to better understand its epidemiology, antibiotic resistance genetic determinants and potential virulence factors. Compared with 20 other complete genomes of A. baumannii, LAC-4 genome harbors at least 12 copies of five distinct insertion sequences. It contains 12 and 14 copies of two novel IS elements, ISAba25 and ISAba26, respectively. Additionally, three novel composite transposons were identified: Tn6250, Tn6251 and Tn6252, two of which contain resistance genes. The antibiotic resistance genetic determinants on the LAC-4 genome correlate well with observed antimicrobial susceptibility patterns. Moreover, twelve genomic islands (GI) were identified in LAC-4 genome. Among them, the 33.4-kb GI12 contains a large number of genes which constitute the K (capsule) locus. LAC-4 harbors several unique putative virulence factor loci. Furthermore, LAC-4 and all 19 other outbreak isolates were found to harbor a heme oxygenase gene (hemO)-containing gene cluster. The sequencing of the first complete genome of an ST10 A. baumannii clinical strain should accelerate our understanding of the epidemiology, mechanisms of resistance and virulence of A. baumannii.

Klebsiella pneumoniae is an important pathogen commonly associated with opportunistic infections. Here we report the genome sequence of a strain, HS11286, isolated from human sputum in 2011 in Shanghai, China. It contains one chromosome (5.3 Mb), three multidrug resistance plasmids (∼110 kb), including a carbapenemase producer, and three small plasmids (∼3 kb).

Bacillus coagulans 2-6 is an efficient producer of lactic acid. The genome of B. coagulans 2-6 has the smallest genome among the members of the genus Bacillus known to date. The frameshift mutation at the start of the d-lactate dehydrogenase sequence might be responsible for the production of high-optical-purity l-lactic acid.

Klebsiella pneumoniae is an important bacterial pathogen of man that is commonly associated with opportunistic and hospital-associated infections. Increasing levels of multiple-antibiotic resistance associated with this species pose a major emerging clinical problem. This organism also occurs naturally in other diverse environments, including the soil. Consistent with its varied lifestyle and membership of the Enterobacteriaceae family, K. pneumoniae genomes exhibit highly plastic architecture comprising a core genome backbone interspersed with numerous and varied alien genomic islands. In this study the size of the presently known K. pneumoniae pan-genome gene pool was estimated through analysis of complete sequences of three chromosomes and 31 plasmids belonging to K. pneumoniae strains. In addition, using a PCR-based strategy the genomic content of eight tRNA/tmRNA gene sites that serve as DNA insertion hotspots were investigated in 28 diverse environmental and clinical strains of K. pneumoniae. Sequencing and characterization of five newly identified horizontally-acquired tmRNA-associated islands further expanded the archived K. pneumoniae gene pool to a total of 7648 unique gene members. Large-scale investigation of the content of tRNA/tmRNA hotspots will be useful to identify and/or survey accessory sequences dispersed amongst hundreds to thousands of members of many key bacterial species., (Copyright © 2010 Elsevier B.V. All rights reserved.)

Background: Bacterial non-coding small RNAs (sRNAs) have attracted considerable attention due to their ubiquitous nature and contribution to numerous cellular processes including survival, adaptation and pathogenesis. Existing computational approaches for identifying bacterial sRNAs demonstrate varying levels of success and there remains considerable room for improvement., Methodology/principal Findings: Here we have proposed a transcriptional signal-based computational method to identify intergenic sRNA transcriptional units (TUs) in completely sequenced bacterial genomes. Our sRNAscanner tool uses position weight matrices derived from experimentally defined E. coli K-12 MG1655 sRNA promoter and rho-independent terminator signals to identify intergenic sRNA TUs through sliding window based genome scans. Analysis of genomes representative of twelve species suggested that sRNAscanner demonstrated equivalent sensitivity to sRNAPredict2, the best performing bioinformatics tool available presently. However, each algorithm yielded substantial numbers of known and uncharacterized hits that were unique to one or the other tool only. sRNAscanner identified 118 novel putative intergenic sRNA genes in Salmonella enterica Typhimurium LT2, none of which were flagged by sRNAPredict2. Candidate sRNA locations were compared with available deep sequencing libraries derived from Hfq-co-immunoprecipitated RNA purified from a second Typhimurium strain (Sittka et al. (2008) PLoS Genetics 4: e1000163). Sixteen potential novel sRNAs computationally predicted and detected in deep sequencing libraries were selected for experimental validation by Northern analysis using total RNA isolated from bacteria grown under eleven different growth conditions. RNA bands of expected sizes were detected in Northern blots for six of the examined candidates. Furthermore, the 5'-ends of these six Northern-supported sRNA candidates were successfully mapped using 5'-RACE analysis., Conclusions/significance: We have developed, computationally examined and experimentally validated the sRNAscanner algorithm. Data derived from this study has successfully identified six novel S. Typhimurium sRNA genes. In addition, the computational specificity analysis we have undertaken suggests that approximately 40% of sRNAscanner hits with high cumulative sum of scores represent genuine, undiscovered sRNA genes. Collectively, these data strongly support the utility of sRNAscanner and offer a glimpse of its potential to reveal large numbers of sRNA genes that have to date defied identification. sRNAscanner is available from: http://bicmku.in:8081/sRNAscanner or http://cluster.physics.iisc.ernet.in/sRNAscanner/.

mGenomeSubtractor performs an mpiBLAST-based comparison of reference bacterial genomes against multiple user-selected genomes for investigation of strain variable accessory regions. With parallel computing architecture, mGenomeSubtractor is able to run rapid BLAST searches of the segmented reference genome against multiple subject genomes at the DNA or amino acid level within a minute. In addition to comparison of protein coding sequences, the highly flexible sliding window-based genome fragmentation approach offered can be used to identify short unique sequences within or between genes. mGenomeSubtractor provides powerful schematic outputs for exploration of identified core and accessory regions, including searches against databases of mobile genetic elements, virulence factors or bacterial essential genes, examination of G+C content and binucleotide distribution bias, and integrated primer design tools. mGenomeSubtractor also allows for the ready definition of species-specific gene pools based on available genomes. Pan-genomic arrays can be easily developed using the efficient oligonucleotide design tool. This simple high-throughput in silico 'subtractive hybridization' analytical tool will support the rapidly escalating number of comparative bacterial genomics studies aimed at defining genomic biomarkers of evolutionary lineage, phenotype, pathotype, environmental adaptation and/or disease-association of diverse bacterial species. mGenomeSubtractor is freely available to all users without any login requirement at: http://bioinfo-mml.sjtu.edu.cn/mGS/.

MobilomeFINDER (http://mml.sjtu.edu.cn/MobilomeFINDER) is an interactive online tool that facilitates bacterial genomic island or 'mobile genome' (mobilome) discovery; it integrates the ArrayOme and tRNAcc software packages. ArrayOme utilizes a microarray-derived comparative genomic hybridization input data set to generate 'inferred contigs' produced by merging adjacent genes classified as 'present'. Collectively these 'fragments' represent a hypothetical 'microarray-visualized genome (MVG)'. ArrayOme permits recognition of discordances between physical genome and MVG sizes, thereby enabling identification of strains rich in microarray-elusive novel genes. Individual tRNAcc tools facilitate automated identification of genomic islands by comparative analysis of the contents and contexts of tRNA sites and other integration hotspots in closely related sequenced genomes. Accessory tools facilitate design of hotspot-flanking primers for in silico and/or wet-science-based interrogation of cognate loci in unsequenced strains and analysis of islands for features suggestive of foreign origins; island-specific and genome-contextual features are tabulated and represented in schematic and graphical forms. To date we have used MobilomeFINDER to analyse several Enterobacteriaceae, Pseudomonas aeruginosa and Streptococcus suis genomes. MobilomeFINDER enables high-throughput island identification and characterization through increased exploitation of emerging sequence data and PCR-based profiling of unsequenced test strains; subsequent targeted yeast recombination-based capture permits full-length sequencing and detailed functional studies of novel genomic islands.

We devised software tools to systematically investigate the contents and contexts of bacterial tRNA and tmRNA genes, which are known insertion hotspots for genomic islands (GIs). The strategy, based on MAUVE-facilitated multigenome comparisons, was used to examine 87 Escherichia coli MG1655 tRNA and tmRNA genes and their orthologues in E.coli EDL933, E.coli CFT073 and Shigella flexneri Sf301. Our approach identified 49 GIs occupying approximately 1.7 Mb that mapped to 18 tRNA genes, missing 2 but identifying a further 30 GIs as compared with Islander [Y. Mantri and K. P. Williams (2004), Nucleic Acids Res., 32, D55-D58]. All these GIs had many strain-specific CDS, anomalous GC contents and/or significant dinucleotide biases, consistent with foreign origins. Our analysis demonstrated marked conservation of sequences flanking both empty tRNA sites and tRNA-associated GIs across all four genomes. Remarkably, there were only 2 upstream and 5 downstream deletions adjacent to the 328 loci investigated. In silico PCR analysis based on conserved flanking regions was also used to interrogate hotspots in another eight completely or partially sequenced E.coli and Shigella genomes. The tools developed are ideal for the analysis of other bacterial species and will lead to in silico and experimental discovery of new genomic islands.

ArrayOme is a new program that calculates the size of genomes represented by microarray-based probes and facilitates recognition of key bacterial strains carrying large numbers of novel genes. Protein-coding sequences (CDS) that are contiguous on annotated reference templates and classified as 'Present' in the test strain by hybridization to microarrays are merged into ICs (ICs). These ICs are then extended to account for flanking intergenic sequences. Finally, the lengths of all extended ICs are summated to yield the 'microarray-visualized genome (MVG)' size. We tested and validated ArrayOme using both experimental and in silico-generated genomic hybridization data. MVG sizing of five sequenced Escherichia coli and Shigella strains resulted in an accuracy of 97-99%, as compared to true genome sizes, when the comprehensive ShE.coli meta-array gene sequences (6239 CDS) were used for in silico hybridization analysis. However, the E.coli CFT073 genome size was underestimated by 14% as this meta-array lacked probes for many CFT073 CDS. ArrayOme permits rapid recognition of discordances between PFGE-measured genome and MVG sizes, thereby enabling high-throughput identification of strains rich in novel genes. Gene discovery studies focused on these strains will greatly facilitate characterization of the global gene pool accessible to individual bacterial species.

In this paper, a self-training method is proposed to recognize translation start sites in bacterial genomes without a prior knowledge of rRNA in the genomes concerned. Many features with biological meanings are incorporated, including mononucleotide distribution patterns near the start codon, the start codon itself, the coding potential and the distance from the most-left start codon to the start codon. The proposed method correctly predicts 92% of the translation start sites of 195 experimentally confirmed Escherichia coli CDSs, 96% of 58 reliable Bacillus subtilis CDSs and 82% of 140 reliable Synechocystis CDSs. Moreover, the self-training method presented might also be used to relocate the translation start sites of putative CDSs of genomes, which are predicted by gene-finding programs. After post-processing by the method presented, the improvement of gene start prediction of some gene-finding programs is remarkable, e.g., the accuracy of gene start prediction of Glimmer 2.02 increases from 63 to 91% for 832 E. coli reliable CDSs. An open source computer program to implement the method, GS-Finder, is freely available for academic purposes from http://tubic.tju.edu.cn/GS-Finder/.

The nucleotide distribution of all 33,527 open reading frames (ORFs) (> or =300 bp) in the genome of Streptomyces coelicolor A3(2) has been analyzed using the Z curve method. Each ORF is mapped onto a point in a 9-dimensional space. To visualize the distribution of mapping points, the points are projected onto the principal plane based on principal component analysis. Consequently, the distribution pattern of the 33,527 points in the principal plane shows a flower-like shape, in which there are seven distinct regions. In addition to the central region, there are six petal-like regions around the center, one of which corresponds to 7172 coding sequences. The central region and the remaining five petal-like regions correspond to the intergenic sequences and out-of-frame non-coding ORFs, respectively. It is shown that selective pressure produces a remarkable bias of the G+C content among three codon positions, resulting in the interesting phenomenon observed. A similar phenomenon is also observed for other bacterial genomes with high genomic G+C content, such as Pseudomonas aeruginosa PA01 (G+C = 66.6%). However, for the genomes of Bacillus subtilis (G+C = 43.5%) and Clostridium perfringens (G+C = 28.6%), no similar phenomenon was observed. The finding presented here may be useful to improve the gene-finding algorithms for genomes with high G+C content. A set of supplementary materials including the plots displaying the base distribution patterns of ORFs in 12 prokaryotes is provided on the website http://tubic.tju.edu.cn/highGC/., (Copyright 2003 Published by Elsevier Science B.V. on behalf of the Federation of European Biochemical Societies)