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Segata et al. (2013): Computational meta'omics for microbial community studies
This article reviews "the technological and computational meta’omics approaches that are already available, those that are under active development, their success in biological discovery, and several outstanding challenges". As the abstract says, the technologies that are already available allow to "comprehensively and accurately characterize microbial communities and their interactions with their environments and hosts".
What kinds of approaches is this review about? The authors write:
Although the ubiquity and complexity of microbial communities have been well studied for decades, advances in high-throughput sequencing have provided new tools that supplement culture-based approaches both in their molecular detail and in their accessibility to a broad scientific community. [...] More recently, genome-wide sequencing approaches, such as metagenomics and metatranscriptomics, have further expanded the experimental tools available for studying the microbiome. Such ‘meta’omic’ approaches expose the genes, transcripts, and eventually proteins and metabolites from thousands of microbes to analysis of biochemical function and systems-level microbial interactions. [...] Metagenomic, metatranscriptomic, and other wholecommunity functional assays provide new ways to study complex ecosystems involving host organisms, biogeochemical environments, pathogens, biochemistry and metabolism, and the interactions among them. Interaction modeling is particularly relevant for human health, and current host–microbe–microbiome systems most often rely on mouse models of the interplay of commensal microbes, pathogens, and hosts. [...] [I]ntegrative meta’omic approaches and advanced computational tools are key for a system-level understanding of relevant biomedical and environmental processes[.]
What is the aim of a meta’omic study and how is it done? Quoting the authors of this paper:
A meta’omic study typically aims to identify a panel of microbial organisms, genes, variants, pathways, or metabolic functions characterizing the microbial community populating an uncultured sample. [...] Metagenomic sequencing, if performed at a sufficiently high coverage, can in some cases allow reconstruction of complete genomes of organisms in a community. [...] [R]ecent years have seen an explosion of metagenome-specific assemblers, which use strategies to tease apart sequencing artifacts from true biological ambiguity within communities. [...] Whole-genome assembly from metagenomes is impossible in most cases, and such assemblers instead aim to provide the largest reliable and useful contigs achievable from their input sequence reads.
These approaches "rely on reference genome catalogs" such as the Human Microbiome Project and the Genomic Encyclopedia of Bacteria and Archaea, which "are systematically filling the gaps in the sequenced portion of the phylogeny".
Another purpose of this is "gene function annotation and metabolic reconstruction":
Microbial communities can be seen not only as groups of individual microbes, but also as collections of biochemical functions affecting and responding to an environment or host organism. Metagenomics can thus also identify the genes and pathways carried by a microbial community, and metatranscriptomics can profile their expressed function. [...] Functional profiling using reference information can be based either on reference genome read mapping (at the nucleotide level) or on translated protein database searches.
Meta’omics can also be used to investigate "microbial ecosystem interaction and association networks", but:
All of these current approaches, however, identify only the descriptive covariation of multiple microbes; they characterize neither the mechanisms of nor the regulatory ramifications of such variation. There is thus a pressing need for multiorganism metabolic models to explain such interactions and for a systems-level understanding of their effect on microbial signaling and growth.
Metatranscriptomics in particular can be used to unravel community expression patterns:
Most current meta’omic tools and studies focus on metagenomic DNA sequencing, but metatranscriptomics is becoming increasingly practical as a window into the regulation and dynamics of microbial community transcription. [...] The major challenge faced in metatranscriptomics is the isolation of microbial mRNA, which usually makes up only a small percentage of total microbial RNA and an even smaller proportion of total RNA if host nucleotides are present.
Single-cell sequencing provides an alternative approach to accessing novel information about uncultured microbes. [...] Current single-cell approaches first isolate single microbial cells by sorting them, lyse them separately, amplify and label them separately, and sequence the resulting pool. The subsequent analysis of single-cell sequence data thus relies much more heavily than do meta’omics on assembly, but fortunately in a less-challenging setting. Recently, elegant combinations of both single-cell genomics and metagenomics have begun to emerge, e.g., in the sequencing of a novel, low-salinity ammonia-oxidizing archaeon from an enrichment culture. Such a combinatorial approach may continue to prove very useful, as the single-cell perspective on novel organism-specific sequences tends to complement whole-metagenome and metatranscriptome overviews of diverse communities.
Meta’omics provides an important tool for studying evolution within microbial communities, which can occur on two very different time scales. Over the course of days, weeks, or the years of a host’s lifetime, microbial genome plasticity allows remarkably rapid acquisitions of novel mutations and laterally transferred genes. Over the course of millennia, however, the overall structure of host-associated communities, their phylogenetic composition, and their microbial pan-genomes can evolve more slowly in tandem with their hosts’ physiology and immune systems. [...] Characterizing the coevolution of quickly evolving complex microbial communities with relatively slowly evolving eukaryotic hosts remains a challenging and largely unexplored field.
One of the ultimate goals of microbial community systems biology is to develop predictive models of the whole-community response to changing stimuli, be it their temperature or pH in the environment, or dietary components in a host gut. Such models may be mechanistic, relying on joint metabolic networks as discussed above, or a descriptive systems biology of microbial physiological ‘rules’ may emerge as a simpler alternative. No unifying approach yet exists, although meta’omic data have provided training input for several first attempts. [...] Given the complexity of most ‘wild’ microbial communities, one of the most promising approaches for such validation has been in the construction of model microbial communities. These have been successful both entirely in vitro, by scaling up the ex vivo coculture of multiple organisms, and when associated with hosts in vivo.
The authors conclude:
In combination with innovative computational models, meta’omics in such environments and in vivo will continue to improve our understanding of microbial community systems biology.
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