Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome

By Sahar Abubucker, Nicola Segata, Johannes Goll, Alyxandria M. Schubert, Jacques Izard, Brandi L. Cantarel, Beltran Rodriguez-Mueller, Jeremy D. Zucker, M. Thiagarajan, Bernard Henrissat and 7 more. PLoS Comput. Biol., 2012

1,001 citations recorded by the source corpus.

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/humann. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7cf9aaeb0a1d99c767d6205af72b82fc854e6c52

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Citations in the corpus

DNA Sequencing as a Tool to Monitor Marine Ecological Status (2017, background): “…tools and platforms are now tailored to gene sequences derived from environmental samples, particularly for prokaryotic assemblages (Kanehisa et al., 2008; Meyer et al., 2008; Caporaso et al., 2010; Abubucker et al., 2012; Thomas et al., 2012; Kozich et al., 2013; Hunter et al., 2014).”
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Chapter 12: Human Microbiome Analysis (2012, background): “For example, the Human Microbiome Project began to investigate what typical human microbial community members are doing [60], how they are affecting their human hosts [2], what impact they have on health or disease, and these help to suggest how pro- or antibiotics can be used to change community be”
Species-level functional profiling of metagenomes and metatranscriptomes (2018, positive): “…for computing pathway abundance (copy number) and coverage (detection confidence) are computed largely as described and benchmarked in HUMAnN1 14 , with modifications added to account for i) the move from KEGG-to MetaCyc-based pathway definitions and ii) the need to compute the values in a…”
Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights (2016, methodology): “While in this manuscript we focused on taxonomic information, metagenomic functional data such as gene or gene-family abundance data [53] can be exploited in a similar way to conduct a more advanced function-based analysis.”
The healthy human microbiome (2016, background): “cores of body-wide and niche-specific pathways that are maintained in health [6, 7, 9, 98].”
Variation in Microbiome LPS Immunogenicity Contributes to Autoimmunity in Humans. (2016, positive): “Differential Microbial Functions between Populations To survey the functional and metabolic consequences of the taxonomic differences between countries, we next analyzed the metagenomic sequences for their genomic functional potential using HUMAnN2 (Abubucker et al., 2012) and linked quantified gene”
Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis (2013, methodology): “…potential of the NORA metagenome To determine if the NORA metagenome encodes unique functions compared to healthy subjects, we applied HUMAnN (Abubucker et al., 2012) to quantitate the coverage and abundances of KEGG (Kanehisa and Goto, 2000) modules (small sets of genes in well-defined…”
U.S. immigration westernizes the human gut microbiome (2018, methodology): “US Immigrants Lose Enzymes Associated with Plant Fiber Degradation We identified differences in functional pathways (Abubucker et al., 2012) between HmongThai and long-term US-resident Hmong1st (>30 years residence) using shotgun metagenomics data (ANOVA, FDR-corrected q < 0.”
SqueezeMeta, A Highly Portable, Fully Automatic Metagenomic Analysis Pipeline (2018, methodology): “Several pipelines have been created to automate most of these analyses (Li, 2009; Arumugam et al., 2010; Glass and Meyer, 2011; Abubucker et al., 2012; Eren et al., 2015; Kim et al., 2016).”
A novel Ruminococcus gnavus clade enriched in inflammatory bowel disease patients (2017, methodology): “Functional and pathway composition was calculated with HUMANn2 [17] using the UniRef90 database with default settings.”
Computational meta'omics for microbial community studies (2013, methodology): “MetaPhlAn (Segata et al , 2012) is one of many alternatives to detect and quantify microbial clades with species-level resolution (see Section 3), whereas HUMAnN (Abubucker et al , 2012) quantitatively characterizes genes, pathways, and metabolic modules from each community (see Section 4).”
Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment (2012, methodology): “For each of these three types of pathway, HUMAnN input the inferred relative abundances of all genes in each sample, and output the relative abundances of pathways within the sample.”