Abstract

BackgroundWhile a few studies on the variations in mRNA expression and half-lives measured under different growth conditions have been used to predict patterns of regulation in bacterial organisms, the extent to which this information can also play a role in defining metabolic phenotypes has yet to be examined systematically. Here we present the first comprehensive study for a model methanogen.ResultsWe use expression and half-life data for the methanogen Methanosarcina acetivorans growing on fast- and slow-growth substrates to examine the regulation of its genes. Unlike Escherichia coli where only small shifts in half-lives were observed, we found that most mRNA have significantly longer half-lives for slow growth on acetate compared to fast growth on methanol or trimethylamine. Interestingly, half-life shifts are not uniform across functional classes of enzymes, suggesting the existence of a selective stabilization mechanism for mRNAs. Using the transcriptomics data we determined whether transcription or degradation rate controls the change in transcript abundance. Degradation was found to control abundance for about half of the metabolic genes underscoring its role in regulating metabolism. Genes involved in half of the metabolic reactions were found to be differentially expressed among the substrates suggesting the existence of drastically different metabolic phenotypes that extend beyond just the methanogenesis pathways. By integrating expression data with an updated metabolic model of the organism (iST807) significant differences in pathway flux and production of metabolites were predicted for the three growth substrates.ConclusionsThis study provides the first global picture of differential expression and half-lives for a class II methanogen, as well as provides the first evidence in a single organism that drastic genome-wide shifts in RNA half-lives can be modulated by growth substrate. We determined which genes in each metabolic pathway control the flux and classified them as regulated by transcription (e.g. transcription factor) or degradation (e.g. post-transcriptional modification). We found that more than half of genes in metabolism were controlled by degradation. Our results suggest that M. acetivorans employs extensive post-transcriptional regulation to optimize key metabolic steps, and more generally that degradation could play a much greater role in optimizing an organism’s metabolism than previously thought.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3219-8) contains supplementary material, which is available to authorized users.

Highlights

  • While a few studies on the variations in Messenger RNA (mRNA) expression and half-lives measured under different growth conditions have been used to predict patterns of regulation in bacterial organisms, the extent to which this information can play a role in defining metabolic phenotypes has yet to be examined systematically

  • This study aims to extend our knowledge of RNA stability in archaea by characterizing it in Methanosarcina acetivorans, a versatile organism capable of growth and methanogenesis using many substrates and of great importance in the global carbon cycle [22, 23]

  • This study provides the first global picture of differential expression and half-lives for a class II methanogen, as well as provides the first evidence in a single organism that drastic genome-wide shifts in RNA half-lives can be modulated by growth substrate

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Summary

Introduction

While a few studies on the variations in mRNA expression and half-lives measured under different growth conditions have been used to predict patterns of regulation in bacterial organisms, the extent to which this information can play a role in defining metabolic phenotypes has yet to be examined systematically. On longer timescales degradation finely tunes abundances of critical RNAs [2], controls slow shifts in RNA levels during adaptation between different growth states [3], and contributes significantly to the noise in the steady-state distribution observed in populations of cells [4]. These observations and those of many other studies demonstrate that post-transcriptional control of RNA dynamics is critical to understanding the cellular state; the factors defining stability over an organism’s entire transcriptome have yet to be fully defined. The consequences of changing RNA stability on metabolic state remains unknown

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