Dataset Information

Robust detection of hierarchical communities from Escherichia coli gene expression data.

ABSTRACT: Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect co-regulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.

SUBMITTER: Trevino S

PROVIDER: S-EPMC3285575 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Robust detection of hierarchical communities from Escherichia coli gene expression data.

Treviño Santiago S Sun Yudong Y Cooper Tim F TF Bassler Kevin E KE

PLoS computational biology 20120223 2

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily ...[more]

PMID: 22383870

Similar Datasets

Project description:Post-transcriptional regulation of gene expression plays a crucial role in many bacterial pathways. In particular, the translation of mRNA can be regulated by trans-acting, small, non-coding RNAs (sRNAs) or mRNA-binding proteins, each of which has been successfully treated theoretically using two-component models. An important system that includes a combination of these modes of post-transcriptional regulation is the Colicin E2 system. DNA damage, by triggering the SOS response, leads to the heterogeneous expression of the Colicin E2 operon including the cea gene encoding the toxin colicin E2, and the cel gene that codes for the induction of cell lysis and release of colicin. Although previous studies have uncovered the system's basic regulatory interactions, its dynamical behavior is still unknown. Here, we develop a simple, yet comprehensive, mathematical model of the colicin E2 regulatory network, and study its dynamics. Its post-transcriptional regulation can be reduced to three hierarchically ordered components: the mRNA including the cel gene, the mRNA-binding protein CsrA, and an effective sRNA that regulates CsrA. We demonstrate that the stationary state of this system exhibits a pronounced threshold in the abundance of free mRNA. As post-transcriptional regulation is known to be noisy, we performed a detailed stochastic analysis, and found fluctuations to be largest at production rates close to the threshold. The magnitude of fluctuations can be tuned by the rate of production of the sRNA. To study the dynamics in response to an SOS signal, we incorporated the LexA-RecA SOS response network into our model. We found that CsrA regulation filtered out short-lived activation peaks and caused a delay in lysis gene expression for prolonged SOS signals, which is also seen in experiments. Moreover, we showed that a stochastic SOS signal creates a broad lysis time distribution. Our model thus theoretically describes Colicin E2 expression dynamics in detail and reveals the importance of the specific regulatory components for the timing of toxin release.

Project description:Shiga toxin-producing Escherichia coli (STEC) strain RM13514 is a clinical isolate linked to the 2010 romaine lettuce-associated outbreak in the U.S. The genes encoding a type II restriction and modification system, PstI R-M, are located in a prophage genome that is also encoding Shiga toxin. In-frame deletion of the PstI R-M genes or dam, encoding a DNA adenine methylase, in strain RM13514 were generated, resulting in two mutant strains MQC599 and MQC602, respectively. The mutant strain MQC599 exhibited a similar growth rate as the wild-type (WT) strain RM13514 when grown in Luria-Bertani (LB) broth at 37 °C. In contrast, the growth of mutant strain MQC602 was significantly slower than either RM13514 or MQC599. Genes transcriptionally regulated by the PstI R-M system or by Dam were examined by the RNA-Seq based comparative transcriptomics. The total RNA was extracted from cells of each strain grown in LB broth at exponential and stationary phases. Three biological replicates were collected for each strain. After removal of ribosomal RNA, the mRNAs were converted to cDNAs followed by Illumina sequence library construction. For strains RM13514 and MQC599, six libraires were generated for each, three from the cells in the exponential growth phase and three from the cells in the stationary phase. For strain MQC602, three additional libraries were constructed from the cells in the early exponential growth phase. The resulting 21 libraries were combined in equal amounts and sequenced on an Illumina HighSeq 2000 instrument with the Paired End 100 bp (PE100) read format, generating a total of 45.83 Gbp sequence reads. This set of sequence data is available in the NCBI SRA database under the BioProject accession number PRJNA684587. This set of transcriptomic data provides information on methylation-mediated epigenetic regulation in STEC, an important foodborne pathogen that is frequently associated with large epidemic outbreaks and can cause life-threatening disease in humans [1]. This set of data will be useful for researchers who are interested in physiology and pathogenicity of foodborne pathogens or in the fundamental mechanisms of epigenetic regulation in bacteria.

Dataset Information

Robust detection of hierarchical communities from Escherichia coli gene expression data.

Publications

Robust detection of hierarchical communities from Escherichia coli gene expression data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets