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BACKGROUND: Sequencing the expressed genetic information of an ecosystem (metatranscriptome) can provide information about the response of organisms to varying environmental conditions. Until recently, metatranscriptomics has been limited to microarray technology and random cloning methodologies. The application of high-throughput sequencing technology is now enabling access to both known and previously unknown transcripts in natural communities. METHODOLOGY/PRINCIPAL FINDINGS: We present a study of a complex marine metatranscriptome obtained from random whole-community mRNA using the GS-FLX Pyrosequencing technology. Eight samples, four DNA and four mRNA, were processed from two time points in a controlled coastal ocean mesocosm study (Bergen, Norway) involving an induced phytoplankton bloom producing a total of 323,161,989 base pairs. Our study confirms the finding of the first published metatranscriptomic studies of marine and soil environments that metatranscriptomics targets highly expressed sequences which are frequently novel. Our alternative methodology increases the range of experimental options available for conducting such studies and is characterized by an exceptional enrichment of mRNA (99.92%) versus ribosomal RNA. Analysis of corresponding metagenomes confirms much higher levels of assembly in the metatranscriptomic samples and a far higher yield of large gene families with >100 members, approximately 91% of which were novel. CONCLUSIONS/SIGNIFICANCE: This study provides further evidence that metatranscriptomic studies of natural microbial communities are not only feasible, but when paired with metagenomic data sets, offer an unprecedented opportunity to explore both structure and function of microbial communities--if we can overcome the challenges of elucidating the functions of so many never-seen-before gene families.

Authors: J. A. Gilbert, D. Field, Y. Huang, R. Edwards, W. Li, P. Gilna, I. Joint

Date Published: 22nd Aug 2008

Publication Type: Journal Article

Abstract (Expand)

Abstract Background Cell growth underlies many key cellular and developmental processes, yet a limited number of studies have been carried out on cell-growth regulation. Comprehensive studies at theout on cell-growth regulation. Comprehensive studies at the transcriptional, proteomic and metabolic levels under defined controlled conditions are currently lacking. Results Metabolic control analysis is being exploited in a systems biology study of the eukaryotic cell. Using chemostat culture, we have measured the impact of changes in flux (growth rate) on the transcriptome, proteome, endometabolome and exometabolome of the yeast Saccharomyces cerevisiae . Each functional genomic level shows clear growth-rate-associated trends and discriminates between carbon-sufficient and carbon-limited conditions. Genes consistently and significantly upregulated with increasing growth rate are frequently essential and encode evolutionarily conserved proteins of known function that participate in many protein-protein interactions. In contrast, more unknown, and fewer essential, genes are downregulated with increasing growth rate; their protein products rarely interact with one another. A large proportion of yeast genes under positive growth-rate control share orthologs with other eukaryotes, including humans. Significantly, transcription of genes encoding components of the TOR complex (a major controller of eukaryotic cell growth) is not subject to growth-rate regulation. Moreover, integrative studies reveal the extent and importance of post-transcriptional control, patterns of control of metabolic fluxes at the level of enzyme synthesis, and the relevance of specific enzymatic reactions in the control of metabolic fluxes during cell growth. Conclusion This work constitutes a first comprehensive systems biology study on growth-rate control in the eukaryotic cell. The results have direct implications for advanced studies on cell growth, in vivo regulation of metabolic fluxes for comprehensive metabolic engineering, and for the design of genome-scale systems biology models of the eukaryotic cell.

Authors: Juan I Castrillo, Leo A Zeef, David C Hoyle, Nianshu Zhang, Andrew Hayes, David CJ Gardner, Michael J Cornell, June Petty, Luke Hakes, Leanne Wardleworth, Bharat Rash, Marie Brown, Warwick B Dunn, David Broadhurst, Kerry O'Donoghue, Svenja S Hester, Tom PJ Dunkley, Sarah R Hart, Neil Swainston, Peter Li, Simon J Gaskell, Norman W Paton, Kathryn S Lilley, Douglas B Kell, Stephen G Oliver

Date Published: 30th Apr 2007

Publication Type: Journal Article

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