Comparative analysis of genome sequences covering the seven cronobacter species.

BACKGROUND: Species of Cronobacter are widespread in the environment and are occasional food-borne pathogens associated with serious neonatal diseases, including bacteraemia, meningitis, and necrotising enterocolitis. The genus is composed of seven species: C. sakazakii, C. malonaticus, C. turicensis, C. dublinensis, C. muytjensii, C. universalis, and C. condimenti. Clinical cases are associated with three species, C. malonaticus, C. turicensis and, in particular, with C. sakazakii multilocus sequence type 4. Thus, it is plausible that virulence determinants have evolved in certain lineages. METHODOLOGY/PRINCIPAL FINDINGS: We generated high quality sequence drafts for eleven Cronobacter genomes representing the seven Cronobacter species, including an ST4 strain of C. sakazakii. Comparative analysis of these genomes together with the two publicly available genomes revealed Cronobacter has over 6,000 genes in one or more strains and over 2,000 genes shared by all Cronobacter. Considerable variation in the presence of traits such as type six secretion systems, metal resistance (tellurite, copper and silver), and adhesins were found. C. sakazakii is unique in the Cronobacter genus in encoding genes enabling the utilization of exogenous sialic acid which may have clinical significance. The C. sakazakii ST4 strain 701 contained additional genes as compared to other C. sakazakii but none of them were known specific virulence-related genes. CONCLUSIONS/SIGNIFICANCE: Genome comparison revealed that pair-wise DNA sequence identity varies between 89 and 97% in the seven Cronobacter species, and also suggested various degrees of divergence. Sets of universal core genes and accessory genes unique to each strain were identified. These gene sequences can be used for designing genus/species specific detection assays. Genes encoding adhesins, T6SS, and metal resistance genes as well as prophages are found in only subsets of genomes and have contributed considerably to the variation of genomic content. Differences in gene content likely contribute to differences in the clinical and environmental distribution of species and sequence types.

Transcriptomic analysis of the woodchuck model of chronic hepatitis B.

UNLABELLED: The Eastern woodchuck (Marmota monax) is naturally infected with woodchuck hepatitis virus (WHV), a hepadnavirus closely related to the human hepatitis B virus (HBV). The woodchuck is used as an animal model for studying chronic hepatitis B (CHB) and HBV-associated hepatocellular carcinoma (HCC) in humans, but the lack of sequence information has hitherto precluded functional genomics analysis. To address this major limitation of the model, we report here the sequencing, assembly, and annotation of the woodchuck transcriptome, together with the generation of custom woodchuck microarrays. Using this new platform, we characterized the transcriptional response to persistent WHV infection and WHV-induced HCC. This revealed that chronic WHV infection, like HBV, is associated with (1) a limited intrahepatic type I interferon response; (2) intrahepatic induction of markers associated with T cell exhaustion; (3) elevated levels of suppressor of cytokine signaling 3 (SOCS3) in the liver; and (4) intrahepatic accumulation of neutrophils. Underscoring the translational value of the woodchuck model, this study also determined that WHV-induced HCC shares molecular characteristics with a subtype of human HCC with poor prognosis. CONCLUSION: Our data establish the translational value of the woodchuck model and provide new insight into immune pathways which may play a role either in the persistence of HBV infection or the sequelae of CHB.

Prospective genomic characterization of the German enterohemorrhagic Escherichia coli O104:H4 outbreak by rapid next generation sequencing technology.

An ongoing outbreak of exceptionally virulent Shiga toxin (Stx)-producing Escherichia coli O104:H4 centered in Germany, has caused over 830 cases of hemolytic uremic syndrome (HUS) and 46 deaths since May 2011. Serotype O104:H4, which has not been detected in animals, has rarely been associated with HUS in the past. To prospectively elucidate the unique characteristics of this strain in the early stages of this outbreak, we applied whole genome sequencing on the Life Technologies Ion Torrent PGM™ sequencer and Optical Mapping to characterize one outbreak isolate (LB226692) and a historic O104:H4 HUS isolate from 2001 (01-09591). Reference guided draft assemblies of both strains were completed with the newly introduced PGM™ within 62 hours. The HUS-associated strains both carried genes typically found in two types of pathogenic E. coli, enteroaggregative E. coli (EAEC) and enterohemorrhagic E. coli (EHEC). Phylogenetic analyses of 1,144 core E. coli genes indicate that the HUS-causing O104:H4 strains and the previously published sequence of the EAEC strain 55989 show a close relationship but are only distantly related to common EHEC serotypes. Though closely related, the outbreak strain differs from the 2001 strain in plasmid content and fimbrial genes. We propose a model in which EAEC 55989 and EHEC O104:H4 strains evolved from a common EHEC O104:H4 progenitor, and suggest that by stepwise gain and loss of chromosomal and plasmid-encoded virulence factors, a highly pathogenic hybrid of EAEC and EHEC emerged as the current outbreak clone. In conclusion, rapid next-generation technologies facilitated prospective whole genome characterization in the early stages of an outbreak.

Discovering cis-regulatory RNAs in Shewanella genomes by Support Vector Machines.

An increasing number of cis-regulatory RNA elements have been found to regulate gene expression post-transcriptionally in various biological processes in bacterial systems. Effective computational tools for large-scale identification of novel regulatory RNAs are strongly desired to facilitate our exploration of gene regulation mechanisms and regulatory networks. We present a new computational program named RSSVM (RNA Sampler+Support Vector Machine), which employs Support Vector Machines (SVMs) for efficient identification of functional RNA motifs from random RNA secondary structures. RSSVM uses a set of distinctive features to represent the common RNA secondary structure and structural alignment predicted by RNA Sampler, a tool for accurate common RNA secondary structure prediction, and is trained with functional RNAs from a variety of bacterial RNA motif/gene families covering a wide range of sequence identities. When tested on a large number of known and random RNA motifs, RSSVM shows a significantly higher sensitivity than other leading RNA identification programs while maintaining the same false positive rate. RSSVM performs particularly well on sets with low sequence identities. The combination of RNA Sampler and RSSVM provides a new, fast, and efficient pipeline for large-scale discovery of regulatory RNA motifs. We applied RSSVM to multiple Shewanella genomes and identified putative regulatory RNA motifs in the 5' untranslated regions (UTRs) in S. oneidensis, an important bacterial organism with extraordinary respiratory and metal reducing abilities and great potential for bioremediation and alternative energy generation. From 1002 sets of 5'-UTRs of orthologous operons, we identified 166 putative regulatory RNA motifs, including 17 of the 19 known RNA motifs from Rfam, an additional 21 RNA motifs that are supported by literature evidence, 72 RNA motifs overlapping predicted transcription terminators or attenuators, and other candidate regulatory RNA motifs. Our study provides a list of promising novel regulatory RNA motifs potentially involved in post-transcriptional gene regulation. Combined with the previous cis-regulatory DNA motif study in S. oneidensis, this genome-wide discovery of cis-regulatory RNA motifs may offer more comprehensive views of gene regulation at a different level in this organism. The RSSVM software, predictions, and analysis results on Shewanella genomes are available at http://ural.wustl.edu/resources.html#RSSVM.

RNA Sampler: a new sampling based algorithm for common RNA secondary structure prediction and structural alignment.

MOTIVATION: Non-coding RNA genes and RNA structural regulatory motifs play important roles in gene regulation and other cellular functions. They are often characterized by specific secondary structures that are critical to their functions and are often conserved in phylogenetically or functionally related sequences. Predicting common RNA secondary structures in multiple unaligned sequences remains a challenge in bioinformatics research. METHODS AND RESULTS: We present a new sampling based algorithm to predict common RNA secondary structures in multiple unaligned sequences. Our algorithm finds the common structure between two sequences by probabilistically sampling aligned stems based on stem conservation calculated from intrasequence base pairing probabilities and intersequence base alignment probabilities. It iteratively updates these probabilities based on sampled structures and subsequently recalculates stem conservation using the updated probabilities. The iterative process terminates upon convergence of the sampled structures. We extend the algorithm to multiple sequences by a consistency-based method, which iteratively incorporates and reinforces consistent structure information from pairwise comparisons into consensus structures. The algorithm has no limitation on predicting pseudoknots. In extensive testing on real sequence data, our algorithm outperformed other leading RNA structure prediction methods in both sensitivity and specificity with a reasonably fast speed. It also generated better structural alignments than other programs in sequences of a wide range of identities, which more accurately represent the RNA secondary structure conservations. AVAILABILITY: The algorithm is implemented in a C program, RNA Sampler, which is available at http://ural.wustl.edu/software.html

A graph theoretical approach for predicting common RNA secondary structure motifs including pseudoknots in unaligned sequences.

MOTIVATION: RNA structure motifs contained in mRNAs have been found to play important roles in regulating gene expression. However, identification of novel RNA regulatory motifs using computational methods has not been widely explored. Effective tools for predicting novel RNA regulatory motifs based on genomic sequences are needed. RESULTS: We present a new method for predicting common RNA secondary structure motifs in a set of functionally or evolutionarily related RNA sequences. This method is based on comparison of stems (palindromic helices) between sequences and is implemented by applying graph-theoretical approaches. It first finds all possible stable stems in each sequence and compares stems pairwise between sequences by some defined features to find stems conserved across any two sequences. Then by applying a maximum clique finding algorithm, it finds all significant stems conserved across at least k sequences. Finally, it assembles in topological order all possible compatible conserved stems shared by at least k sequences and reports a number of the best assembled stem sets as the best candidate common structure motifs. This method does not require prior structural alignment of the sequences and is able to detect pseudoknot structures. We have tested this approach on some RNA sequences with known secondary structures, in which it is capable of detecting the real structures completely or partially correctly and outperforms other existing programs for similar purposes. AVAILABILITY: The algorithm has been implemented in C++ in a program called comRNA, which is available at http://ural.wustl.edu/softwares.html