Generation and Analysis of a Mouse Intestinal Metatranscriptome through Illumina Based RNA-Sequencing

Program in Molecular Structure and Function, The Hospital for Sick Children, Toronto, Canada.
PLoS ONE (Impact Factor: 3.23). 04/2012; 7(4):e36009. DOI: 10.1371/journal.pone.0036009
Source: PubMed

ABSTRACT With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics.

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Available from: John Parkinson, Sep 28, 2015
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    • "Metatranscriptomic data analysis can be considerably facilitated when performed in tandem with metagenomics. Xiong et al. (2012) developed an experimental and analytical pipeline for the analysis of metatranscriptomes in the absence of extended sets of reference genomes [50]. Their workflow employs a peptide-centric search strategy by performing in silico translation of detected transcripts. "
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    ABSTRACT: Some of the most transformative discoveries promising to enable the resolution of this century's grand societal challenges will most likely arise from environmental science and particularly environmental microbiology and biotechnology. Understanding how microbes interact in situ, and how microbial communities respond to environmental changes remains an enormous challenge for science. Systems biology offers a powerful experimental strategy to tackle the exciting task of deciphering microbial interactions. In this framework, entire microbial communities are considered as metaorganisms and each level of biological information (DNA, RNA, proteins and metabolites) is investigated along with in situ environmental characteristics. In this way, systems biology can help unravel the interactions between the different parts of an ecosystem ultimately responsible for its emergent properties. Indeed each level of biological information provides a different level of characterisation of the microbial communities. Metagenomics, metatranscriptomics, metaproteomics, metabolomics and SIP-omics can be employed to investigate collectively microbial community structure, potential, function, activity and interactions. Omics approaches are enabled by high-throughput 21st century technologies and this review will discuss how their implementation has revolutionised our understanding of microbial communities.
    Computational and Structural Biotechnology Journal 12/2015; 13:24–32. DOI:10.1016/j.csbj.2014.11.009
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    • "In contrast, close to nothing is known about how sRNAs shape the interaction of pathogens with commensals and we are yet to see if such sRNAs would also impact virulence. Again, NGS-based metatranscriptomics of multi-species intestinal communities could provide a valuable starting point to address the relevance of regulatory RNAs and metabolic genes in the context of the host microbiota (Xiong et al., 2012). These new exciting venues at the interface of microbiology and host-microbe interaction might become relevant for the design of alternative anti-microbial compounds which consider both, the pathogen and the host microbiota. "
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    ABSTRACT: Enteric pathogens often cycle between virulent and saprophytic lifestyles. To endure these frequent changes in nutrient availability and composition bacteria possess an arsenal of regulatory and metabolic genes allowing rapid adaptation and high flexibility. While numerous proteins have been characterized with regard to metabolic control in pathogenic bacteria, small non-coding RNAs have emerged as additional regulators of metabolism. Recent advances in sequencing technology have vastly increased the number of candidate regulatory RNAs and several of them have been found to act at the interface of bacterial metabolism and virulence factor expression. Importantly, studying these riboregulators has not only provided insight into their metabolic control functions but also revealed new mechanisms of post-transcriptional gene control. This review will focus on the recent advances in this area of host-microbe interaction and discuss how regulatory small RNAs may help coordinate metabolism and virulence of enteric pathogens.
    Frontiers in Cellular and Infection Microbiology 07/2014; 4:91. DOI:10.3389/fcimb.2014.00091 · 3.72 Impact Factor
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    • "This option will be available in future considering the decreasing sequencing costs offering new perspectives in metatranscriptome analysis of phytoplasma colonizing hosts and vectors. First studies such as the profiling of the mouse intestinal metatranscriptome highlight this emerging research field [59]. "
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    ABSTRACT: 'Candidatus Phytoplasma mali' is a phytopathogenic bacterium of the family Acholeplasmataceae assigned to the class Mollicutes. This causative agent of the apple proliferation colonizes in Malus domestica the sieve tubes of the plant phloem resulting in a range of symptoms such as witches'- broom formation, reduced vigor and affecting size and quality of the crop. The disease is responsible for strong economical losses in Europe. Although the genome sequence of the pathogen is available, there is only limited information on expression of selected genes and metabolic key features that have not been examined on the transcriptomic or proteomic level so far. This situation is similar to many other phytoplasmas. In the work presented here, RNA-Seq and mass spectrometry shotgun techniques were applied on tissue samples from Nicotiana occidentalis infected by 'Ca. P. mali' strain AT providing insights into transcriptome and proteome of the pathogen. Data analysis highlights expression of 208 genes including 14 proteins located in the terminal inverted repeats of the linear chromosome. Beside a high portion of house keeping genes, the recently discussed chaperone GroES/GroEL is expressed. Furthermore, gene expression involved in formation of a type IVB and of the Sec-dependent secretion system was identified as well as the highly expressed putative pathogenicity-related SAP11-like effector protein. Metabolism of phytoplasmas depends on the uptake of spermidine/putescine, amino acids, co-factors, carbohydrates and in particular malate/citrate. The expression of these transporters was confirmed and the analysis of the carbohydrate cycle supports the suggested alternative energy-providing pathway for phytoplasmas releasing acetate and providing ATP. The phylogenetic analyses of malate dehydrogenase and acetate kinase in phytoplasmas show a closer relatedness to the Firmicutes in comparison to Mycoplasma species indicating an early divergence of the Acholeplasmataceae from the Mollicutes.
    PLoS ONE 04/2014; 9(4):e94391. DOI:10.1371/journal.pone.0094391 · 3.23 Impact Factor
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