DOOR: a prokaryotic operon database for genome analyses and functional inference

Improved BioCyc Operon Prediction: Revisiting the Operon Prediction Problem

Introduction

Operon prediction is a valuable component of microbial-genome annotation because operon organization can yield inferences about gene function, and because knowledge of operon structure can aid the interpretation of gene expression data.

Methods

We present a number of improvements to the existing Pathway Tools operon predictor based mostly on 7 new features that we hypothesized would increase its performance. The new features include shared Gene Ontology biological process terms, similarity of codon usage and GC content, correlated gene expression, and shared protein complex.

Results

We evaluated the proposed 7 new features and found that the addition of 6 of them improved the performance of the operon predictor from 79.55% to 83.49%, a decrease in error rate of 19.3%. When gene expression data was not included, the accuracy decreased to 82.547, still an improvement of 14.7%. One of the proposed features as well as a previously used feature had no effect and were removed.

Discussion

Although some of the new features had strong predictive value individually, when combined with the other features they did not have a large impact on predictive accuracy, suggesting that they were not independent from the other features.

Compendium-Wide Analysis of Pseudomonas aeruginosa Core and Accessory Genes Reveals Transcriptional Patterns across Strains PAO1 and PA14

Pseudomonas aeruginosa is an opportunistic pathogen that causes difficult-to-treat infections. Two well-studied divergent P. aeruginosa strain types, PAO1 and PA14, have significant genomic heterogeneity, including diverse accessory genes present in only some strains. Genome content comparisons find core genes that are conserved across both PAO1 and PA14 strains and accessory genes that are present in only a subset of PAO1 and PA14 strains. Here, we use recently assembled transcriptome compendia of publicly available P. aeruginosa RNA sequencing (RNA-seq) samples to create two smaller compendia consisting of only strain PAO1 or strain PA14 samples with each aligned to their cognate reference genome. We confirmed strain annotations and identified other samples for inclusion by assessing each sample's median expression of PAO1-only or PA14-only accessory genes. We then compared the patterns of core gene expression in each strain. To do so, we developed a method by which we analyzed genes in terms of which genes showed similar expression patterns across strain types. We found that some core genes had consistent correlated expression patterns across both compendia, while others were less stable in an interstrain comparison. For each accessory gene, we also determined core genes with correlated expression patterns. We found that stable core genes had fewer coexpressed neighbors that were accessory genes. Overall, this approach for analyzing expression patterns across strain types can be extended to other groups of genes, like phage genes, or applied for analyzing patterns beyond groups of strains, such as samples with different traits, to reveal a deeper understanding of regulation. IMPORTANCE Pseudomonas aeruginosa is a ubiquitous pathogen. There is much diversity among P. aeruginosa strains, including two divergent but well-studied strains, PAO1 and PA14. Understanding how these different strain-level traits manifest is important for identifying targets that regulate different traits of interest. With the availability of thousands of PAO1 and PA14 samples, we created two strain-specific RNA-seq compendia where each one contains hundreds of samples from PAO1 or PA14 strains and used them to compare the expression patterns of core genes that are conserved in both strain types and to determine which core genes have expression patterns that are similar to those of accessory genes that are unique to one strain or the other using an approach that we developed. We found a subset of core genes with different transcriptional patterns across PAO1 and PA14 strains and identified those core genes with expression patterns similar to those of strain-specific accessory genes.

Mr.Vc v2: An updated version of database with increased data of transcriptome and experimental validated interactions

Mr.Vc is a database of curated Vibrio cholerae transcriptome data and annotated information. The main objective is to facilitate the accessibility and reusability of the rapidly growing Vibrio cholerae omics data and relevant annotation. To achieve these goals, we performed manual curation on the transcriptome data and organized the datasets in an experiment-centric manner. We collected unknown operons annotated through text-mining analysis that would provide more clues about how Vibrio cholerae modulates gene regulation. Meanwhile, to understand the relationship between genes or experiments, we performed gene co-expression analysis and experiment-experiment correlation analysis. In additional, functional module named “Interactions” which dedicates to collecting experimentally validated interactions about Vibrio cholerae from public databases, MEDLINE documents and literature in life science journals. To date, Mr.Vc v2, which is significantly increased from the previous version, contains 107 microarray experiments, 106 RNA-seq experiments, and 3 Tn-seq projects, covering 56,839 entries of DEGs (Differentially Expressed Genes) from transcriptomes and 7,463 related genes from Tn-seq, respectively. and a total of 270,129 gene co-expression entries and 11,990 entries of experiment-experiment correlation was obtained, in total 1,316 entries of interactions were collected, including 496 protein-chemical signaling molecule interactions, 472 protein–protein interactions, 306 TF (Transcription Factor)-gene interactions and 42 Vibrio cholerae-virus interactions, most of which obtained from 402 literature through text-mining analysis. To make the information easier to access, Mr.Vc v2 is equipped with a search widget, enabling users to query what they are interested in. Mr.Vc v2 is freely available at http://mrvcv2.biownmc.info.

Systematic analysis of the underlying genomic architecture for transcriptional-translational coupling in prokaryotes

Transcriptional-translational coupling is accepted to be a fundamental mechanism of gene expression in prokaryotes and therefore has been analyzed in detail. However, the underlying genomic architecture of the expression machinery has not been well investigated so far. In this study, we established a bioinformatics pipeline to systematically investigated >1800 bacterial genomes for the abundance of transcriptional and translational associated genes clustered in distinct gene cassettes. We identified three highly frequent cassettes containing transcriptional and translational genes, i.e. rplk-nusG (gene cassette 1; in 553 genomes), rpoA-rplQ-rpsD-rpsK-rpsM (gene cassette 2; in 656 genomes) and nusA-infB (gene cassette 3; in 877 genomes). Interestingly, each of the three cassettes harbors a gene (nusG, rpsD and nusA) encoding a protein which links transcription and translation in bacteria. The analyses suggest an enrichment of these cassettes in pathogenic bacterial phyla with >70% for cassette 3 (i.e. Neisseria, Salmonella and Escherichia) and >50% for cassette 1 (i.e. Treponema, Prevotella, Leptospira and Fusobacterium) and cassette 2 (i.e. Helicobacter, Campylobacter, Treponema and Prevotella). These insights form the basis to analyze the transcriptional regulatory mechanisms orchestrating transcriptional-translational coupling and might open novel avenues for future biotechnological approaches.

Polar mutagenesis of polycistronic bacterial transcriptional units using Cas12a

Background

Functionally related genes in bacteria are often organized and transcribed as polycistronic transcriptional units. Examples are the fim operon, which codes for biogenesis of type 1 fimbriae in Escherichia coli, and the atp operon, which codes for the FoF1 ATP synthase. We tested the hypothesis that markerless polar mutations could be efficiently engineered using CRISPR/Cas12a in these loci.

Results

Cas12a-mediated engineering of a terminator sequence inside the fimA gene occurred with efficiencies between 10 and 80% and depended on the terminator’s sequence, whilst other types of mutations, such as a 97 bp deletion, occurred with 100% efficiency. Polar mutations using a terminator sequence were also engineered in the atp locus, which induced its transcriptional shutdown and produced identical phenotypes as a deletion of the whole atp locus (ΔatpIBEFHAGDC). Measuring the expression levels in the fim and atp loci showed that many supposedly non-polar mutants induced a significant polar effect on downstream genes. Finally, we also showed that transcriptional shutdown or deletion of the atp locus induces elevated levels of intracellular ATP during the exponential growth phase.

Conclusions

We conclude that Cas12a-mediated mutagenesis is an efficient simple system to generate polar mutants in E. coli. Different mutations were induced with varying degrees of efficiency, and we confirmed that all these mutations abolished the functions encoded in the fim and atp loci. We also conclude that it is difficult to predict which mutagenesis strategy will induce a polar effect in genes downstream of the mutation site. Furthermore the strategies described here can be used to manipulate the metabolism of E. coli as showcased by the increase in intracellular ATP in the markerless ΔatpIBEFHAGDC mutant.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01844-y.

Density-based binning of gene clusters to infer function or evolutionary history using GeneGrouper

MOTIVATION

Identifying variant forms of gene clusters of interest in phylogenetically proximate and distant taxa can help to infer their evolutionary histories and functions. Conserved gene clusters may differ by only a few genes, but these small differences can in turn induce substantial phenotypes, such as by the formation of pseudogenes or insertions interrupting regulation. Particularly as microbial genomes and metagenomic assemblies become increasingly abundant, unsupervised grouping of similar, but not necessarily identical, gene clusters into consistent bins can provide a population-level understanding of their gene content variation and functional homology.

RESULTS

We developed GeneGrouper, a command-line tool that uses a density-based clustering method to group gene clusters into bins. GeneGrouper demonstrated high recall and precision in benchmarks for the detection of the 23-gene Salmonella enterica LT2 Pdu gene cluster and four-gene Pseudomonas aeruginosa PAO1 Mex gene cluster among 435 genomes spanning mixed taxa. In a subsequent application investigating the diversity and impact of gene-complete and -incomplete LT2 Pdu gene clusters in 1130 S.enterica genomes, GeneGrouper identified a novel, frequently occurring pduN pseudogene. When investigated in vivo, introduction of the pduN pseudogene negatively impacted microcompartment formation. We next demonstrated the versatility of GeneGrouper by clustering distant homologous gene clusters and variable gene clusters found in integrative and conjugative elements.

AVAILABILITY AND IMPLEMENTATION

GeneGrouper software and code are publicly available at https://pypi.org/project/GeneGrouper/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

Bacteria and archaea, single-cell organisms collectively known as prokaryotes, live in all imaginable environments and comprise the majority of living organisms on this planet. Prokaryotes play a critical role in the homeostasis of multicellular organisms (such as animals and plants) and ecosystems. In addition, bacteria can be pathogenic and cause a variety of diseases in these same hosts and ecosystems. In short, understanding the biology and molecular functions of bacteria and archaea and devising mechanisms to engineer and optimize their properties are critical scientific endeavors with significant implications in healthcare, agriculture, manufacturing, and climate science among others. One major molecular difference between unicellular and multicellular organisms is the way they express genes–multicellular organisms make individual RNA molecules for each gene while, prokaryotes express operons (i.e., a group of genes coding functionally related proteins) in contiguous polycistronic RNA molecules. Understanding which genes exist within operons is critical for elucidating basic biology and for engineering organisms. In this work, we use a combination of statistical and machine learning-based methods to use next-generation sequencing data to predict operon structure across a range of prokaryotes. Our method provides an easily implemented, robust, accurate, and flexible way to determine operon structure in an organism-agnostic manner using readily available data.

RNA landscape of the emerging cancer-associated microbe Fusobacterium nucleatum

Fusobacterium nucleatum, long known as a constituent of the oral microflora, has recently garnered renewed attention for its association with several different human cancers. The growing interest in this emerging cancer-associated bacterium contrasts with a paucity of knowledge about its basic gene expression features and physiological responses. As fusobacteria lack all established small RNA-associated proteins, post-transcriptional networks in these bacteria are also unknown. In the present study, using differential RNA-sequencing, we generate high-resolution global RNA maps for five clinically relevant fusobacterial strains-F. nucleatum subspecies nucleatum, animalis, polymorphum and vincentii, as well as F. periodonticum-for early, mid-exponential growth and early stationary phase. These data are made available in an online browser, and we use these to uncover fundamental aspects of fusobacterial gene expression architecture and a suite of non-coding RNAs. Developing a vector for functional analysis of fusobacterial genes, we discover a conserved fusobacterial oxygen-induced small RNA, FoxI, which serves as a post-transcriptional repressor of the major outer membrane porin FomA. Our findings provide a crucial step towards delineating the regulatory networks enabling F. nucleatum adaptation to different environments, which may elucidate how these bacteria colonize different compartments of the human body.

A novel computational framework for genome-scale alternative transcription units prediction

Alternative transcription units (ATUs) are dynamically encoded under different conditions and display overlapping patterns (sharing one or more genes) under a specific condition in bacterial genomes. Genome-scale identification of ATUs is essential for studying the emergence of human diseases caused by bacterial organisms. However, it is unrealistic to identify all ATUs using experimental techniques because of the complexity and dynamic nature of ATUs. Here, we present the first-of-its-kind computational framework, named SeqATU, for genome-scale ATU prediction based on next-generation RNA-Seq data. The framework utilizes a convex quadratic programming model to seek an optimum expression combination of all of the to-be-identified ATUs. The predicted ATUs in Escherichia coli reached a precision of 0.77/0.74 and a recall of 0.75/0.76 in the two RNA-Sequencing datasets compared with the benchmarked ATUs from third-generation RNA-Seq data. In addition, the proportion of 5'- or 3'-end genes of the predicted ATUs, having documented transcription factor binding sites and transcription termination sites, was three times greater than that of no 5'- or 3'-end genes. We further evaluated the predicted ATUs by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses. The results suggested that gene pairs frequently encoded in the same ATUs are more functionally related than those that can belong to two distinct ATUs. Overall, these results demonstrated the high reliability of predicted ATUs. We expect that the new insights derived by SeqATU will not only improve the understanding of the transcription mechanism of bacteria but also guide the reconstruction of a genome-scale transcriptional regulatory network.

Evolution of Diverse Strategies for Promoter Regulation

DNA is fundamentally important for all cellular organisms due to its role as a store of hereditary genetic information. The precise and accurate regulation of gene transcription depends primarily on promoters, which vary significantly within and between genomes. Some promoters are rich in specific types of bases, while others have more varied, complex sequence characteristics. However, it is not only base sequence but also epigenetic modifications and altered DNA structure that regulate promoter activity. Significantly, many promoters across all organisms contain sequences that can form intrastrand hairpins (cruciforms) or four-stranded structures (G-quadruplex or i-motif). In this review we integrate recent studies on promoter regulation that highlight the importance of DNA structure in the evolutionary adaptation of promoter sequences.

The Exploration of Novel Regulatory Relationships Drives Haloarchaeal Operon-Like Structural Dynamics over Short Evolutionary Distances

Operons are a dominant feature of bacterial and archaeal genome organization. Numerous investigations have related aspects of operon structure to operon function, making operons exemplars for studies aimed at deciphering Nature's design principles for genomic organization at a local scale. We consider this understanding to be both fundamentally important and ultimately useful in the de novo design of increasingly complex synthetic circuits. Here we analyze the evolution of the genomic context of operon-like structures in a set of 76 sequenced and annotated species of halophilic archaea. The phylogenetic depth and breadth of this dataset allows insight into changes in operon-like structures over shorter evolutionary time scales than have been studied in previous cross-species analysis of operon evolution. Our analysis, implemented in the updated software package JContextExplorer finds that operon-like context as measured by changes in structure frequently differs from a sequence divergence model of whole-species phylogeny and that changes seem to be dominated by the exploration of novel regulatory relationships.

The functional determinants in the organization of bacterial genomes

Bacterial genomes are now recognized as interacting intimately with cellular processes. Uncovering organizational mechanisms of bacterial genomes has been a primary focus of researchers to reveal the potential cellular activities. The advances in both experimental techniques and computational models provide a tremendous opportunity for understanding these mechanisms, and various studies have been proposed to explore the organization rules of bacterial genomes associated with functions recently. This review focuses mainly on the principles that shape the organization of bacterial genomes, both locally and globally. We first illustrate local structures as operons/transcription units for facilitating co-transcription and horizontal transfer of genes. We then clarify the constraints that globally shape bacterial genomes, such as metabolism, transcription and replication. Finally, we highlight challenges and opportunities to advance bacterial genomic studies and provide application perspectives of genome organization, including pathway hole assignment and genome assembly and understanding disease mechanisms.

Genome-scale exploration of transcriptional regulation in the nisin Z producer Lactococcus lactis subsp. lactis IO-1

Transcription is of the most crucial steps of gene expression in bacteria, whose regulation guarantees the bacteria's ability to adapt to varying environmental conditions. Discovering the molecular basis and genomic principles of the transcriptional regulation is thus one of the most important tasks in cellular and molecular biology. Here, a comprehensive phylogenetic footprinting framework was implemented to predict maximal regulons of Lactococcus lactis subsp. lactis IO-1, a lactic acid bacterium known for its high potentials in nisin Z production as well as efficient xylose consumption which have made it a promising biotechnological strain. A total set of 321 regulons covering more than 90% of all the bacterium's operons have been elucidated and validated according to available data. Multiple novel biologically-relevant members were introduced amongst which arsC, mtlA and mtl operon for BusR, MtlR and XylR regulons can be named, respectively. Moreover, the effect of riboflavin on nisin biosynthesis was assessed in vitro and a negative correlation was observed. It is believed that understandings from such networks not only can be useful for studying transcriptional regulatory potentials of the target organism but also can be implemented in biotechnology to rationally design favorable production conditions.

High-Throughput Screen for Cell Wall Synthesis Network Module in Mycobacterium tuberculosis Based on Integrated Bioinformatics Strategy

BACKGROUND

Mycobacterium tuberculosis is one of the deadliest pathogens in humans. Co-infection of M. tuberculosis with HIV and the emergence of multi-drug-resistant tuberculosis (TB) constitute a serious global threat. However, no effective anti-TB drugs are available, with the exception of first-line drugs such as isoniazid. The cell wall of M. tuberculosis, which is primarily responsible for the lack of effective anti-TB drugs and the escape of the bacteria from host immunity, is an important drug target. The core components of the cell wall of M. tuberculosis are peptidoglycan, arabinogalactan, and mycotic acid. However, the functional genome and metabolic regulation pathways for the M. tuberculosis cell wall are still unknown. In this study, we used the biclustering algorithm integrated into cMonkey, sequence alignment, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and other bioinformatics methods to scan the whole genome of M. tuberculosis as well as to identify and statistically analyze the genes related to the synthesis of the M. tuberculosis cell wall.

METHOD

We performed high-throughput genome-wide screening for M. tuberculosis using Biocarta, KEGG, National Cancer Institute Pathway Interaction Database (NCI-PID), HumanCyc, and Reactome. We then used the Database of Origin and Registration (DOOR) established in our laboratory to classify the collection of operons for M. tuberculosis cell wall synthetic genes. We used the cMonkey double clustering algorithm to perform clustering analysis on the gene expression profile of M. tuberculosis for cell wall synthesis. Finally, we visualized the results using Cytoscape.

RESULT AND CONCLUSION

Through bioinformatics and statistical analyses, we identified 893 M. tuberculosis H37Rv cell wall synthesis genes, distributed in 20 pathways, involved in 46 different functions related to cell wall synthesis, and clustered in 386 modules. We identified important pivotal genes and proteins in the cell wall synthesis pathway such as murA, a class of operons containing genes involved in cell wall synthesis such as ID6951, and a class of operons indispensable for the survival of the bacteria. In addition, we found 41 co-regulatory modules for cell wall synthesis and five co-expression networks of molecular complexes involved in peptidoglycan biosynthesis, membrane transporter synthesis, and other cell wall processes.

The actinobacterium Tsukamurella paurometabola has a functionally divergent arylamine N-acetyltransferase (NAT) homolog

Actinobacteria in the Tsukamurella genus are aerobic, high-GC, Gram-positive mycolata, considered as opportunistic pathogens and isolated from various environmental sources, including sites contaminated with oil, urban or industrial waste and pesticides. Although studies look into xenobiotic biotransformation by Tsukamurella isolates, the relevant enzymes remain uncharacterized. We investigated the arylamine N-acetyltransferase (NAT) enzyme family, known for its role in the xenobiotic metabolism of prokaryotes and eukaryotes. Xenobiotic sensitivity of Tsukamurella paurometabola type strain DSM 20162^T was assessed, followed by cloning, recombinant expression and functional characterization of its single NAT homolog (TSUPD)NAT1. The bacterium appeared quite robust against chloroanilines, but more sensitive to 4-anisidine and 2-aminophenol. However, metabolic activity was not evident towards those compounds, presumably due to mechanisms protecting cells from xenobiotic entry. Of the pharmaceutical arylhydrazines tested, hydralazine was toxic, but the bacterium was less sensitive to isoniazid, a drug targeting mycolic acid biosynthesis in mycobacteria. Although (TSUPD)NAT1 protein has an atypical Cys-His-Glu (instead of the expected Cys-His-Asp) catalytic triad, it is enzymatically active, suggesting that this deviation is likely due to evolutionary adaptation potentially serving a different function. The protein was indeed found to use malonyl-CoA, instead of the archetypal acetyl-CoA, as its preferred donor substrate. Malonyl-CoA is important for microbial biosynthesis of fatty acids (including mycolic acids) and polyketide chains, and the corresponding enzymatic systems have common evolutionary histories, also linked to xenobiotic metabolism. This study adds to accummulating evidence suggesting broad phylogenetic and functional divergence of microbial NAT enzymes that goes beyond xenobiotic metabolism and merits investigation.

Functional expansion of a TCA cycle operon mRNA by a 3' end-derived small RNA

Global RNA profiling studies in bacteria have predicted the existence of many of small noncoding RNAs (sRNAs) that are processed off mRNA 3′ ends to regulate other mRNAs via the RNA chaperones Hfq and ProQ. Here, we present targets of SdhX (RybD), an Hfq-dependent sRNA that is generated by RNase E mediated 3′ processing of the ∼10 000-nt mRNA of the TCA cycle operon sdhCDAB-sucABCD in enteric bacteria. An in silico search predicted ackA mRNA, which encodes acetate kinase, as a conserved primary target of SdhX. Through base pairing, SdhX represses AckA synthesis during growth of Salmonella on acetate. Repression can be achieved by a naturally occurring 38-nucleotide SdhX variant, revealing the shortest functional Hfq-associated sRNA yet. Salmonella SdhX also targets the mRNAs of fumB (anaerobic fumarase) and yfbV, a gene of unknown function adjacent to ackA. Instead, through a slightly different seed sequence, SdhX can repress other targets in Escherichia coli, namely katG (catalase) and fdoG (aerobic formate dehydrogenase). This study illustrates how a key operon from central metabolism is functionally connected to other metabolic pathways through a 3′ appended sRNA, and supports the notion that mRNA 3′UTRs are a playground for the evolution of regulatory RNA networks in bacteria.

SLING: a tool to search for linked genes in bacterial datasets

Gene arrays and operons that encode functionally linked proteins form the most basic unit of transcriptional regulation in bacteria. Rules that govern the order and orientation of genes in these systems have been defined; however, these were based on a small set of genomes that may not be representative. The growing availability of large genomic datasets presents an opportunity to test these rules, to define the full range and diversity of these systems, and to understand their evolution. Here we present SLING, a tool to Search for LINked Genes by searching for a single functionally essential gene, along with its neighbours in a rule-defined proximity (https://github.com/ghoresh11/sling/wiki). Examining this subset of genes enables us to understand the basic diversity of these genetic systems in large datasets. We demonstrate the utility of SLING on a clinical collection of enteropathogenic Escherichia coli for two relevant operons: toxin antitoxin (TA) systems and RND efflux pumps. By examining the diversity of these systems, we gain insight on distinct classes of operons which present variable levels of prevalence and ability to be lost or gained. The importance of this analysis is not limited to TA systems and RND pumps, and can be expanded to understand the diversity of many other relevant gene arrays.

Mr.Vc: a database of microarray and RNA-seq of Vibrio cholerae

Gram-negative bacterium Vibrio cholerae is the causative agent of cholera, a life-threatening diarrheal disease. During its infectious cycle, V. cholerae routinely switches niches between aquatic environment and host gastrointestinal tract, in which V. cholerae modulates its transcriptome pattern accordingly for better survival and proliferation. A comprehensive resource for V. cholerae transcriptome will be helpful for cholera research, including prevention, diagnosis and intervention strategies. In this study, we constructed a microarray and RNA-seq database of V. cholerae (Mr.Vc), containing gene transcriptional expression data of 145 experimental conditions of V. cholerae from various sources, covering 25 937 entries of differentially expressed genes. In addition, we collected relevant information including gene annotation, operons they may belong to and possible interaction partners of their protein products. With Mr.Vc, users can easily find transcriptome data they are interested in, such as the experimental conditions in which a gene of interest was differentially expressed in, or all genes that were differentially expressed in an experimental condition. We believe that Mr.Vc database is a comprehensive data repository dedicated to V. cholerae and could be a useful resource for all researchers in related fields. Mr.Vc is available for free at http://bioinfo.life.hust.edu.cn/mrvc.

RECTA: Regulon Identification Based on Comparative Genomics and Transcriptomics Analysis

Regulons, which serve as co-regulated gene groups contributing to the transcriptional regulation of microbial genomes, have the potential to aid in understanding of underlying regulatory mechanisms. In this study, we designed a novel computational pipeline, regulon identification based on comparative genomics and transcriptomics analysis (RECTA), for regulon prediction related to the gene regulatory network under certain conditions. To demonstrate the effectiveness of this tool, we implemented RECTA on Lactococcus lactis MG1363 data to elucidate acid-response regulons. A total of 51 regulons were identified, 14 of which have computational-verified significance. Among these 14 regulons, five of them were computationally predicted to be connected with acid stress response. Validated by literature, 33 genes in Lactococcus lactis MG1363 were found to have orthologous genes which were associated with six regulons. An acid response related regulatory network was constructed, involving two trans-membrane proteins, eight regulons (llrA, llrC, hllA, ccpA, NHP6A, rcfB, regulons #8 and #39), nine functional modules, and 33 genes with orthologous genes known to be associated with acid stress. The predicted response pathways could serve as promising candidates for better acid tolerance engineering in Lactococcus lactis. Our RECTA pipeline provides an effective way to construct a reliable gene regulatory network through regulon elucidation, and has strong application power and can be effectively applied to other bacterial genomes where the elucidation of the transcriptional regulation network is needed.