Khác biệt giữa bản sửa đổi của “Metagenomics”

{{good article}}

[[File:Iron hydroxide precipitate in stream.jpg|thumb|right|Metagenomics allows the study of microbial communities like those present in this stream receiving acid drainage from surface coal mining.]]

 

The term "metagenomics" was first used by [[Jo Handelsman]], [[Jon Clardy]], [[Robert M. Goodman]], and others, and first appeared in publication in 1998.<ref name="Handelsman1998"/> The term metagenome referenced the idea that a collection of genes sequenced from the environment could be analyzed in a way analogous to the study of a single [[genome]]. Recently, Kevin Chen and [[Lior Pachter]] (researchers at the [[University of California, Berkeley]]) defined metagenomics as "the application of modern genomics techniques to the study of communities of microbial organisms directly in their natural environments, bypassing the need for isolation and lab cultivation of individual species".<ref name="Chen2005"/>

 

Conventional [[sequencing]] begins with a culture of identical cells as a source of [[DNA]]. However, early metagenomic studies revealed that there are probably large groups of microorganisms in many environments that cannot be [[Microbiological culture|cultured]] and thus cannot be sequenced. These early studies focused on 16S [[ribosomal]] [[RNA]] sequences which are relatively short, often [[Conserved sequence|conserved]] within a species, and generally different between species. Many 16S [[rRNA]] sequences have been found which do not belong to any known cultured [[species]], indicating that there are numerous non-isolated organisms. These surveys of ribosomal RNA (rRNA) genes taken directly from the environment revealed that [[Microbiological culture|cultivation]] based methods find less than 1% of the bacterial and [[archaea]]l species in a sample.<ref name="Hugenholz1998" /> Much of the interest in metagenomics comes from these discoveries that showed that the vast majority of microorganisms had previously gone unnoticed.

 

In 2005 Stephan C. Schuster at [[Penn State University]] and colleagues published the first sequences of an environmental sample generated with [[DNA Sequencing#High-throughput sequencing|high-throughput sequencing]], in this case massively parallel [[pyrosequencing]] developed by [[454 Life Sciences]].<ref name="Poinar2005"/> Another early paper in this area appeared in 2006 by Robert Edwards, [[Forest Rohwer]], and colleagues at [[San Diego State University]].<ref name="Edwards2006"/>

 

{{Main|DNA sequencing}}

 

The data generated by metagenomics experiments are both enormous and inherently noisy, containing fragmented data representing as many as 10,000 species.<ref name="wooley2010"/> The sequencing of the cow [[rumen]] metagenome generated 279 [[gigabase]]s, or 279 billion base pairs of nucleotide sequence data,<ref name="hess2011"/> while the human gut [[microbiome]] gene catalog identified 3.3 million genes assembled from 567.7 gigabases of sequence data.<ref name="qin2011"/> Collecting, curating, and extracting useful biological information from datasets of this size represent significant computational challenges for researchers.<ref name="segata2013"/><ref name="committee2007" />

 

The first step of metagenomic data analysis requires the execution of certain pre-filtering steps, including the removal of redundant, low-quality sequences and sequences of probable [[eukaryotic]] origin (especially in metagenomes of human origin).<ref name="mende">{{Cite journal

| doi = 10.1371/journal.pone.0031386

}}</ref>

 

{{Main|Sequence assembly}}

 

There are several assembly programs, most of which can use information from [[paired-end tag]]s in order to improve the accuracy of assemblies. Some programs, such as [[Phrap]] or [[Celera]] Assembler, were designed to be used to assemble single [[genome]]s but nevertheless produce good results when assembling metagenomic data sets.<ref name='wooley2010' /> Other programs, such as [[Velvet assembler]], have been optimized for the shorter reads produced by second-generation sequencing through the use of [[de Bruijn graph]]s. The use of reference genomes allows researchers to improve the assembly of the most abundant microbial species, but this approach is limited by the small subset of microbial phyla for which sequenced genomes are available.<ref name='koonin2008' />

 

{{Main|Gene prediction}}

 

Gene annotations provide the "what", while measurements of [[Biodiversity|species diversity]] provide the "who".<ref name="konopka2008"/> In order to connect community composition and function in metagenomes, sequences must be binned. [[Binning (Metagenomics)|Binning]] is the process of associating a particular sequence with an organism.<ref name='koonin2008'/> In similarity-based binning, methods such as [[BLAST]] are used to rapidly search for phylogenetic markers or otherwise similar sequences in existing public databases. This approach is implemented in [[MEGAN]].<ref name="MEGAN2007"/> Another tool, PhymmBL, uses [[Markov model|interpolated Markov model]]s to assign reads.<ref name='wooley2010'/> [http://huttenhower.sph.harvard.edu/metaphlan MetaPhlAn] and [[AMPHORA]] are methods based on unique clade-specific markers for estimating organismal relative abundances with improved computational performances.<ref name="segata2012"/> In composition based binning, methods use intrinsic features of the sequence, such as oligonucleotide frequencies or [[codon usage bias]].<ref name='wooley2010'/> Once sequences are binned, it is possible to carry out comparative analysis of diversity and richness utilising tools such as [[UniFrac|Unifrac]].

 

 

<!-- Deleted image removed: [[File:F2.large.jpg|thumb|Metagenome data analysis in [[Integrated Microbial Genomes System|IMG]]/M 3.4]] -->

One of the first standalone tools for analysing high-throughput metagenome shotgun data was [[MEGAN]] (MEta Genome ANalyzer).<ref name="MEGAN2011"/><ref name="MEGAN2007"/> A first version of the program was used in 2005 to analyse the metagenomic context of DNA sequences obtained from a mammoth bone.<ref name="Poinar2005" /> Based on a BLAST comparison against a reference database, this tool performs both taxonomic and functional binning, by placing the reads onto the nodes of the NCBI taxonomy using a simple lowest common ancestor (LCA) algorithm or onto the nodes of the [http://www.theseed.org/wiki/Main_Page SEED] or [[KEGG]] classifications, respectively.<ref name="mitra2011"/>

 

 

Comparative analyses between metagenomes can provide additional insight into the function of complex microbial communities and their role in host health.<ref name="kurokawa2007"/> Pairwise or multiple comparisons between metagenomes can be made at the level of sequence composition (comparing [[GC-content]] or genome size), taxonomic diversity, or functional complement. Comparisons of population structure and phylogenetic diversity can be made on the basis of 16S and other phylogenetic marker genes, or—in the case of low-diversity communities—by genome reconstruction from the metagenomic dataset.<ref name="simon2011"/> Functional comparisons between metagenomes may be made by comparing sequences against reference databases such as [[Gene cluster|COG]] or [[KEGG]], and tabulating the abundance by category and evaluating any differences for statistical significance.<ref name="mitra2011"/> This gene-centric approach emphasizes the functional complement of the ''community'' as a whole rather than taxonomic groups, and shows that the functional complements are analogous under similar environmental conditions.<ref name="simon2011"/> Consequently, metadata on the environmental context of the metagenomic sample is especially important in comparative analyses, as it provides researchers with the ability to study the effect of habitat upon community structure and function.<ref name="wooley2010" />

}}</ref> Ghosh et al. (2011) <ref name="ghosh2011"/> also indicated that differences in tetranucleotide usage patterns can be used to identify genes (or metagenomic reads) originating from specific habitats.

 

 

===Community metabolism===

Metagenomics allows researchers to access the functional and metabolic diversity of microbial communities, but it cannot show which of these processes are active.<ref name="simon2011"/> The extraction and analysis of metagenomic [[mRNA]] (the '''metatranscriptome''') provides information on the [[Gene regulation|regulation]] and [[Gene expression|expression]] profiles of complex communities. Because of the technical difficulties (the [[MRNA#Degradation|short half-life]] of mRNA, for example) in the collection of environmental RNA there have been relatively few ''[[In situ#Biology|in situ]]'' metatranscriptomic studies of microbial communities to date.<ref name="simon2011"/> While originally limited to [[DNA microarray|microarray]] technology, metatranscriptomcs studies have made use of direct high-throughput [[cDNA]] sequencing to provide whole-genome expression and quantification of a microbial community,<ref name="simon2011"/> as first employed by Leininger ''et al.'' (2006) in their analysis of ammonia oxidation in soils.<ref name="leininger2006"/>

 

{{Main|Viral metagenomics}}

 

Metagenomic sequencing is particularly useful in the study of viral communities. As viruses lack a shared universal phylogenetic marker (as [[16S ribosomal RNA|16S RNA]] for bacteria and archaea, and [[18S ribosomal RNA|18S RNA]] for eukarya), the only way to access the genetic diversity of the viral community from an environmental sample is through metagenomics. Viral metagenomes (also called viromes) should thus provide more and more information about viral diversity and evolution.<ref name="Kristensen"/>

 

 

Metagenomics has the potential to advance knowledge in a wide variety of fields. It can also be applied to solve practical challenges in [[medicine]], [[engineering]], [[agriculture]], [[sustainability]] and [[ecology]].<ref name="committee2007"/>

 

[[Microbial communities]] play a key role in preserving human [[health]], but their composition and the mechanism by which they do so remains mysterious.<ref name="zimmer2010"/> Metagenomic sequencing is being used to characterize the microbial communities from 15-18 body sites from at least 250 individuals. This is part of the Human [[Microbiome]] initiative with primary goals to determine if there is a core human microbiome, to understand the changes in the human microbiome that can be correlated with human health, and to develop new technological and [[bioinformatics]] tools to support these goals.<ref name="NelsonWhite"/>

 

While these study highlights some potentially valuable medical applications, only 31-48.8% of the reads could be aligned to 194 public human gut bacterial genomes and 7.6-21.2% to bacterial genomes available in GenBank which indicates that there is still far more research necessary to capture novel bacterial genomes.<ref>{{cite journal|last=Qin|first=Junjie|coauthors=Ruiqiang Li, Jeroen Raes, Manimozhiyan Arumugam, Kristoffer Solvesten Burgdorf|title=A human gut microbial gene catalogue established by metagenomic sequencing|journal=Nature|date=March 2010|volume=464|pages=59–65|accessdate=7 October 2013|doi=10.1038/nature08821|pmid=20203603|issue=7285}}</ref>

 

{{main|Biofuel}}

[[File:Pg166 bioreactor.jpg|thumb|[[w:Bioreactor|Bioreactors]] allow the observation of microbial communities as they convert [[biomass]] into [[w:Cellulosic ethanol|cellulosic ethanol]].]]

The [[Issues relating to biofuels#Technical issues|efficient industrial-scale deconstruction]] of biomass requires novel [[enzymes]] with higher productivity and lower cost.<ref name="hess2011"/> Metagenomic approaches to the analysis of complex microbial communities allow the targeted [[Genetic screen|screening]] of [[enzymes]] with industrial applications in biofuel production, such as [[glycoside hydrolase]]s.<ref name="li2009"/> Furthermore, knowledge of how these microbial communities function is required to control them, and metagenomics is a key tool in their understanding. Metagenomic approaches allow comparative analyses between [[convergent evolution|convergent]] microbial systems like [[biogas]] fermenters<ref name="jaenicke2011"/> or [[insect]] [[herbivore]]s such as the [[ant-fungus mutualism|fungus garden]] of the [[leafcutter ant]]s.<ref name="suen2010"/>

 

{{Main|Bioremediation}}

Metagenomics can improve strategies for monitoring the impact of [[pollutant]]s on [[ecosystem]]s and for cleaning up contaminated environments. Increased understanding of how microbial communities cope with pollutants improves assessments of the potential of contaminated sites to recover from pollution and increases the chances of [[bioaugmentation]] or [[biostimulation]] trials to succeed.<ref name="george2010"/>

 

 

Microbial communities produce a vast array of biologically active chemicals that are used in competition and communication.<ref name="booklet2007"/> Many of the drugs in use today were originally uncovered in microbes; recent progress in mining the rich genetic resource of non-culturable microbes has led to the discovery of new genes, enzymes, and natural products.<ref name="simon2011"/><ref name="simon2009a"/> The application of metagenomics has allowed the development of [[Commodity chemicals|commodity]] and [[fine chemicals]], [[agrochemical]]s and [[Pharmaceutical drug|pharmaceuticals]] where the benefit of [[Enzyme catalysis|enzyme-catalyzed]] [[chiral synthesis]] is increasingly recognized.<ref name="wong2010"/>

Two types of analysis are used in the [[bioprospecting]] of metagenomic data: function-driven screening for an expressed trait, and sequence-driven screening for DNA sequences of interest.<ref name="schloss2003"/> Function-driven analysis seeks to identify clones expressing a desired trait or useful activity, followed by biochemical characterization and sequence analysis. This approach is limited by availability of a suitable screen and the requirement that the desired trait be expressed in the host cell. Moreover, the low rate of discovery (less than one per 1,000 clones screened) and its labor-intensive nature further limit this approach.<ref name="kakirde2010"/> In contrast, sequence-driven analysis uses [[Conserved sequence|conserved DNA sequences]] to [[Primer (molecular biology)#PCR primer design|design PCR primers]] to screen clones for the sequence of interest.<ref name="schloss2003" /> In comparison to cloning-based approaches, using a sequence-only approach further reduces the amount of bench work required. The application of massively parallel sequencing also greatly increases the amount of sequence data generated, which require high-throughput bioinformatic analysis pipelines.<ref name="kakirde2010" /> The sequence-driven approach to screening is limited by the breadth and accuracy of gene functions present in public sequence databases. In practice, experiments make use of a combination of both functional and sequence-based approaches based upon the function of interest, the complexity of the sample to be screened, and other factors.<ref name="kakirde2010"/><ref name="parachin2011"/>

 

 

The [[soil]]s in which plants grow are inhabited by microbial communities, with one gram of soil containing around 10⁹-10¹⁰ microbial cells which comprise about one gigabase of sequence information.<ref name="jansson2011"/><ref name="vogel2009"/> The microbial communities which inhabit soils are some of the most complex known to science, and remain poorly understood despite their economic importance.<ref name="terra"/> Microbial consortia perform a wide variety of [[ecosystem service]]s necessary for plant growth, including fixing atmospheric nitrogen, [[nutrient cycling]], disease suppression, and [[Siderophore|sequester]] [[iron]] and other [[metal]]s.<ref name="booklet2007"/> Functional metagenomics strategies are being used to explore the interactions between plants and microbes through cultivation-independent study of these microbial communities.<ref name="charles2010"/> By allowing insights into the role of previously uncultivated or rare community members in nutrient cycling and the promotion of plant growth, metagenomic approaches can contribute to improved disease detection in [[crop]]s and [[livestock]] and the adaptation of enhanced [[Agriculture|farming]] practices which improve crop health by harnessing the relationship between microbes and plants.<ref name="committee2007" />

 

 

Metagenomics can provide valuable insights into the functional ecology of environmental communities.<ref name="raes2011">{{cite pmid|21407210}}</ref> Metagenomic analysis of the bacterial consortia found in the defecations of Australian sea lions suggests that nutrient-rich sea lion faeces may be an important nutrient source for coastal ecosystems. This is because the bacteria that are expelled simultaneously with the defecations are adept at breaking down the nutrients in the faeces into a bioavailable form that can be taken up into the food chain.<ref name="lavery2012">{{cite doi|10.1371/journal.pone.0036478}}</ref>

*[[Pathogenomics]]

 

{{Reflist|2|refs=

<!-- Converted to LDR format