TF2Network : predicting transcription factor regulators and gene regulatory networks in Arabidopsis using publicly available binding site information

A gene regulatory network (GRN) is a collection of regulatory interactions between transcription factors (TFs) and their target genes. GRNs control different biological processes and have been instrumental to understand the organization and complexity of gene regulation. Although various experimental methods have been used to map GRNs in Arabidop-sis thaliana, their limited throughput combined with the large number of TFs makes that for many genes our knowledge about regulating TFs is incomplete. We introduce TF2Network, a tool that exploits the vast amount of TF binding site information and enables the delineation of GRNs by detecting potential regulators for a set of co-expressed or functionally related genes. Validation using two experimental benchmarks reveals that TF2Network predicts the correct regulator in 75-92% of the test sets. Furthermore, our tool is robust to noise in the input gene sets, has a low false discovery rate, and shows a better performance to recover correct regulators compared to other plant tools. TF2Network is accessible through a web interface where GRNs are interactively visualized and annotated with various types of experimental functional information. TF2Network was used to perform systematic functional and regulatory gene annotations, identifying new TFs involved in circadian rhythm and stress response

Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana

BACKGROUND: The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation. RESULTS: In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF - target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes. CONCLUSION: CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks

Inferring a Transcriptional Regulatory Network from Gene Expression Data Using Nonlinear Manifold Embedding

Transcriptional networks consist of multiple regulatory layers corresponding to the activity of global regulators, specialized repressors and activators of transcription as well as proteins and enzymes shaping the DNA template. Such intrinsic multi-dimensionality makes uncovering connectivity patterns difficult and unreliable and it calls for adoption of methodologies commensurate with the underlying organization of the data source. Here we present a new computational method that predicts interactions between transcription factors and target genes using a compendium of microarray gene expression data and the knowledge of known interactions between genes and transcription factors. The proposed method called Kernel Embedding of REgulatory Networks (KEREN) is based on the concept of gene-regulon association and it captures hidden geometric patterns of the network via manifold embedding. We applied KEREN to reconstruct gene regulatory interactions in the model bacteria E.coli on a genome-wide scale. Our method not only yields accurate prediction of verifiable interactions, which outperforms on certain metrics comparable methodologies, but also demonstrates the utility of a geometric approach to the analysis of high-dimensional biological data. We also describe the general application of kernel embedding techniques to some other function and network discovery algorithms

Functional genomics with a comprehensive library of transposon mutants for the sulfate-reducing bacterium Desulfovibrio alaskensis G20.

UnlabelledThe genomes of sulfate-reducing bacteria remain poorly characterized, largely due to a paucity of experimental data and genetic tools. To meet this challenge, we generated an archived library of 15,477 mapped transposon insertion mutants in the sulfate-reducing bacterium Desulfovibrio alaskensis G20. To demonstrate the utility of the individual mutants, we profiled gene expression in mutants of six regulatory genes and used these data, together with 1,313 high-confidence transcription start sites identified by tiling microarrays and transcriptome sequencing (5' RNA-Seq), to update the regulons of Fur and Rex and to confirm the predicted regulons of LysX, PhnF, PerR, and Dde_3000, a histidine kinase. In addition to enabling single mutant investigations, the D. alaskensis G20 transposon mutants also contain DNA bar codes, which enables the pooling and analysis of mutant fitness for thousands of strains simultaneously. Using two pools of mutants that represent insertions in 2,369 unique protein-coding genes, we demonstrate that the hypothetical gene Dde_3007 is required for methionine biosynthesis. Using comparative genomics, we propose that Dde_3007 performs a missing step in methionine biosynthesis by transferring a sulfur group to O-phosphohomoserine to form homocysteine. Additionally, we show that the entire choline utilization cluster is important for fitness in choline sulfate medium, which confirms that a functional microcompartment is required for choline oxidation. Finally, we demonstrate that Dde_3291, a MerR-like transcription factor, is a choline-dependent activator of the choline utilization cluster. Taken together, our data set and genetic resources provide a foundation for systems-level investigation of a poorly studied group of bacteria of environmental and industrial importance.ImportanceSulfate-reducing bacteria contribute to global nutrient cycles and are a nuisance for the petroleum industry. Despite their environmental and industrial significance, the genomes of sulfate-reducing bacteria remain poorly characterized. Here, we describe a genetic approach to fill gaps in our knowledge of sulfate-reducing bacteria. We generated a large collection of archived, transposon mutants in Desulfovibrio alaskensis G20 and used the phenotypes of these mutant strains to infer the function of genes involved in gene regulation, methionine biosynthesis, and choline utilization. Our findings and mutant resources will enable systematic investigations into gene function, energy generation, stress response, and metabolism for this important group of bacteria

A Genome-Wide Analysis of Genetic Diversity inTrypanosoma cruzi Intergenic Regions

Background Trypanosoma cruzi is the causal agent of Chagas Disease. Recently, the genomes of representative strains from two major evolutionary lineages were sequenced, allowing the construction of a detailed genetic diversity map for this important parasite. However this map is focused on coding regions of the genome, leaving a vast space of regulatory regions uncharacterized in terms of their evolutionary conservation and/or divergence. Methodology Using data from the hybrid CL Brener and Sylvio X10 genomes (from the TcVI and TcI Discrete Typing Units, respectively), we identified intergenic regions that share a common evolutionary ancestry, and are present in both CL Brener haplotypes (TcII-like and TcIII-like) and in the TcI genome; as well as intergenic regions that were conserved in only two of the three genomes/haplotypes analyzed. The genetic diversity in these regions was characterized in terms of the accumulation of indels and nucleotide changes. Principal Findings Based on this analysis we have identified i) a core of highly conserved intergenic regions, which remained essentially unchanged in independently evolving lineages; ii) intergenic regions that show high diversity in spite of still retaining their corresponding upstream and downstream coding sequences; iii) a number of defined sequence motifs that are shared by a number of unrelated intergenic regions. A fraction of indels explains the diversification of some intergenic regions by the expansion/contraction of microsatellite-like repeats.Fil: Panunzi, Leonardo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Agüero, Fernan Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentin

Definition of the σW regulon of Bacillus subtilis in the absence of stress

Bacteria employ extracytoplasmic function (ECF) sigma factors for their responses to environmental stresses. Despite intensive research, the molecular dissection of ECF sigma factor regulons has remained a major challenge due to overlaps in the ECF sigma factor-regulated genes and the stimuli that activate the different ECF sigma factors. Here we have employed tiling arrays to single out the ECF σW regulon of the Gram-positive bacterium Bacillus subtilis from the overlapping ECF σX, σY, and σM regulons. For this purpose, we profiled the transcriptome of a B. subtilis sigW mutant under non-stress conditions to select candidate genes that are strictly σW-regulated. Under these conditions, σW exhibits a basal level of activity. Subsequently, we verified the σW-dependency of candidate genes by comparing their transcript profiles to transcriptome data obtained with the parental B. subtilis strain 168 grown under 104 different conditions, including relevant stress conditions, such as salt shock. In addition, we investigated the transcriptomes of rasP or prsW mutant strains that lack the proteases involved in the degradation of the σW anti-sigma factor RsiW and subsequent activation of the σW-regulon. Taken together, our studies identify 89 genes as being strictly σW-regulated, including several genes for non-coding RNAs. The effects of rasP or prsW mutations on the expression of σW-dependent genes were relatively mild, which implies that σW-dependent transcription under non-stress conditions is not strictly related to RasP and PrsW. Lastly, we show that the pleiotropic phenotype of rasP mutant cells, which have defects in competence development, protein secretion and membrane protein production, is not mirrored in the transcript profile of these cells. This implies that RasP is not only important for transcriptional regulation via σW, but that this membrane protease also exerts other important post-transcriptional regulatory functions

Bacterial regulon modeling and prediction based on systematic \u3ci\u3ecis\u3c/i\u3e regulatory motif analyses

Regulons are the basic units of the response system in a bacterial cell, and each consists of a set of transcriptionally co-regulated operons. Regulon elucidation is the basis for studying the bacterial global transcriptional regulation network. In this study, we designed a novel co-regulation score between a pair of operons based on accurate operon identification and cis regulatory motif analyses, which can capture their co-regulation relationship much better than other scores. Taking full advantage of this discovery, we developed a new computational framework and built a novel graph model for regulon prediction. This model integrates the motif comparison and clustering and makes the regulon prediction problem substantially more solvable and accurate. To evaluate our prediction, a regulon coverage score was designed based on the documented regulons and their overlap with our prediction; and a modified Fisher Exact test was implemented to measure how well our predictions match the co-expressed modules derived from E. coli microarray gene-expression datasets collected under 466 conditions. The results indicate that our program consistently performed better than others in terms of the prediction accuracy. This suggests that our algorithms substantially improve the state-of-the-art, leading to a computational capability to reliably predict regulons for any bacteria