ArticleOmics : a journal of integrative biology
Dynamics of time-lagged gene-to-metabolite networks of Escherichia coli elucidated by integrative omics approach.
Article in Omics : a journal of integrative biology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 46 citations in OpenAlex.
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- Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains.BMC bioinformatics · 2015Article
- Transcriptomic landscape of Pueraria lobata demonstrates potential for phytochemical study.Frontiers in plant science · 2015Article
- Systems biology in the context of big data and networks.BioMed research international · 2014Review
- Deciphering herbivory-induced gene-to-metabolite dynamics in Nicotiana attenuata tissues using a multifactorial approach.Plant physiology · 2013Article
- Comprehensive analysis of gene-environmental interactions with temporal gene expression profiles in Pseudomonas aeruginosa.PloS one · 2012Article
- Detection and interpretation of metabolite-transcript coresponses using combined profiling data.Bioinformatics (Oxford, England) · 2011Article
- AMDORAP: non-targeted metabolic profiling based on high-resolution LC-MS.BMC bioinformatics · 2011Article
- MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data.Metabolomics : Official journal of the Metabolomic SocietyArticle
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Authors and funding
7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
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.
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