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Dynamics of time-lagged gene-to-metabolite networks of Escherichia coli elucidated by integrative omics approach.

ABSTRACT: In the postgenomics era, integrative analysis of several "omics" data is absolutely required for understanding the cell as a system. Integrative analysis of transcriptomics and metabolomics can lead to elucidation of gene-to-metabolite networks. When integrating different time series "omics" data, it is necessary to take into consideration a time lag between those data. In the present study, we conducted an integrative analysis of time series transcriptomics and metabolomics data of Escherichia coli generated by cDNA microarray and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR/MS), respectively. We identified a 60-min time lag between transition points of transcriptomics and metabolomics data by using a Linear Dynamical System. Furthermore, we investigated gene-to-metabolite correlations in the context of time lag, obtained the maximum number of correlated pairs at transcripts leading 60-min time lag, and finally revealed gene-to-metabolite relations in the phospholipid biosynthesis pathway. Taking into consideration the time lag between transcriptomics and metabolomics data in time series analysis could unravel novel gene-to-metabolite relations. According to gene-to-metabolite correlations, phosphatidylglycerol plays a more critical role for membrane balance than phosphatidylethanolamine in E. coli.

SUBMITTER: Takahashi H

PROVIDER: S-EPMC3125544 | biostudies-literature | 2011 Jan-Feb

REPOSITORIES: biostudies-literature

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Dynamics of time-lagged gene-to-metabolite networks of Escherichia coli elucidated by integrative omics approach.

Takahashi Hiroki H Morioka Ryoko R Ito Ryosuke R Oshima Taku T Altaf-Ul-Amin Md M Ogasawara Naotake N Kanaya Shigehiko S

Omics : a journal of integrative biology 20100923 1-2

In the postgenomics era, integrative analysis of several "omics" data is absolutely required for understanding the cell as a system. Integrative analysis of transcriptomics and metabolomics can lead to elucidation of gene-to-metabolite networks. When integrating different time series "omics" data, it is necessary to take into consideration a time lag between those data. In the present study, we conducted an integrative analysis of time series transcriptomics and metabolomics data of Escherichia ...[more]

PMID: 20863252

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Project description:BackgroundAlthough Escherichia coli is one of the best studied model organisms, a comprehensive understanding of its gene regulation is not yet achieved. There exist many approaches to reconstruct regulatory interaction networks from gene expression experiments. Mutual information based approaches are most useful for large-scale network inference.ResultsWe used a three-step approach in which we combined gene regulatory network inference based on directed information (DTI) and sequence analysis. DTI values were calculated on a set of gene expression profiles from 19 time course experiments extracted from the Many Microbes Microarray Database. Focusing on influences between pairs of genes in which one partner encodes a transcription factor (TF) we derived a network which contains 878 TF - gene interactions of which 166 are known according to RegulonDB. Afterward, we selected a subset of 109 interactions that could be confirmed by the presence of a phylogenetically conserved binding site of the respective regulator. By this second step, the fraction of known interactions increased from 19% to 60%. In the last step, we checked the 44 of the 109 interactions not yet included in RegulonDB for functional relationships between the regulator and the target and, thus, obtained ten TF - target gene interactions. Five of them concern the regulator LexA and have already been reported in the literature. The remaining five influences describe regulations by Fis (with two novel targets), PhdR, PhoP, and KdgR. For the validation of our approach, one of them, the regulation of lipoate synthase (LipA) by the pyruvate-sensing pyruvate dehydrogenate repressor (PdhR), was experimentally checked and confirmed.ConclusionsWe predicted a set of five novel TF - target gene interactions in E. coli. One of them, the regulation of lipA by the transcriptional regulator PdhR was validated experimentally. Furthermore, we developed DTInfer, a new R-package for the inference of gene-regulatory networks from microarrays using directed information.

Project description:Emerging evidence indicates that gene products implicated in human cancers often cluster together in "hot spots" in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a "proteomics-first" approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to "seed" a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks.

Project description:The goal of bacterial engineering is to rewire metabolic pathways to generate high-value molecules for various applications. However, the production of recombinant proteins is constrained by the complexity of the connections between cellular physiology and recombinant protein synthesis. Here, we used a rational and highly efficient approach to improve bacterial engineering. Based on the complete genome and annotation information of the Escherichia coli ER2566 strain, we compared the transcriptomic profiles of the strain under leaky expression and low temperature-induced stress. Combining the gene ontology (GO) enrichment terms and differentially expressed genes (DEGs) with higher expression, we selected and knocked out 36 genes to determine the potential impact of these genes on protein production. Deletion of bluF, cydA, mngR, and udp led to a significant decrease in soluble recombinant protein production. Moreover, at low-temperature induction, 4 DEGs (gntK, flgH, flgK, flgL) were associated with enhanced expression of the recombinant protein. Knocking out several motility-related DEGs (ER2666-ΔflgH-ΔflgL-ΔflgK) simultaneously improved the protein yield by 1.5-fold at 24 °C induction, and the recombinant strain had the potential to be applied in the expression studies of different exogenous proteins, aiming to improve the yields of soluble form to varying degrees in comparison to the ER2566 strain. Totally, this study focused on the anabolic and stress-responsive hub genes of the adaptation of E. coli to recombinant protein overexpression on the transcriptome level and constructs a series of engineering strains increasing the soluble protein yield of recombinant proteins which lays a solid foundation for the engineering of bacterial strains for recombinant technological advances. KEY POINTS: • Comparative transcriptome analysis shows host responses with altered induction stress. • Deletion of bluF, cydA, mngR, and udp genes was identified to significantly decrease the soluble recombinant protein productions. • Synchronal knockout of flagellar genes in E. coli can enhance recombinant protein yield up to ~ 1.5-fold at 24 °C induction. • Non-model bacterial strains can be re-engineered for recombinant protein expression.

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Dynamics of time-lagged gene-to-metabolite networks of Escherichia coli elucidated by integrative omics approach.

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Dynamics of time-lagged gene-to-metabolite networks of Escherichia coli elucidated by integrative omics approach.

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