title: Probabilistic inference of biochemical reactions in microbial communities from metagenomic sequences.
abstract: Shotgun metagenomics has been applied to the studies of the functionality of various microbial communities. As a critical analysis step in these studies, biological pathways are reconstructed based on the genes predicted from metagenomic shotgun sequences. Pathway reconstruction provides insights into the functionality of a microbial community and can be used for comparing multiple microbial communities. The utilization of pathway reconstruction, however, can be jeopardized because of imperfect functional annotation of genes, and ambiguity in the assignment of predicted enzymes to biochemical reactions (e.g., some enzymes are involved in multiple biochemical reactions). Considering that metabolic functions in a microbial community are carried out by many enzymes in a collaborative manner, we present a probabilistic sampling approach to profiling functional content in a metagenomic dataset, by sampling functions of catalytically promiscuous enzymes within the context of the entire metabolic network defined by the annotated metagenome. We test our approach on metagenomic datasets from environmental and human-associated microbial communities. The results show that our approach provides a more accurate representation of the metabolic activities encoded in a metagenome, and thus improves the comparative analysis of multiple microbial communities. In addition, our approach reports likelihood scores of putative reactions, which can be used to identify important reactions and metabolic pathways that reflect the environmental adaptation of the microbial communities. Source code for sampling metabolic networks is available online at http://omics.informatics.indiana.edu/mg/MetaNetSam/.
authors.affiliation: School of Informatics and Computing, Indiana University, Bloomington, Indiana, United States of America.
authors.forename: Dazhi
authors.initials: D
authors.lastname: Jiao
authors.forename: Yuzhen
authors.initials: Y
authors.lastname: Ye
authors.forename: Haixu
authors.initials: H
authors.lastname: Tang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D030541:Databases, Genetic; D004783:Environmental Microbiology; D006801:Humans; D008390:Markov Chains; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D054892:Metagenome / Q000235:genetics*; D056186:Metagenomics / Q000379:methods*
references.citation: Proc Natl Acad Sci U S A. 2012 Jan 10;109(2):594-9
references.pmid: 22184244
references.citation: Biochim Biophys Acta. 1987;895(2):63-79
references.pmid: 3332774
references.citation: J Comput Biol. 2011 Mar;18(3):495-505
references.pmid: 21385050
references.citation: ISME J. 2010 Jan;4(1):78-87
references.pmid: 19693100
references.citation: Nat Rev Microbiol. 2006 Aug;4(8):608-20
references.pmid: 16845431
references.citation: BMC Proc. 2011 May 28;5 Suppl 2:S9
references.pmid: 21554767
references.citation: J Biol Chem. 1975 Feb 10;250(3):905-11
references.pmid: 1112796
references.citation: Proc Natl Acad Sci U S A. 2011 Mar 15;108 Suppl 1:4513-5
references.pmid: 21406595
references.citation: Genome Res. 2002 Apr;12(4):656-64
references.pmid: 11932250
references.citation: Appl Environ Microbiol. 2005 Dec;71(12):8228-35
references.pmid: 16332807
references.citation: ISME J. 2011 Feb;5(2):169-72
references.pmid: 20827291
references.citation: Genome Biol. 2007;8(8):R171
references.pmid: 17708774
references.citation: Nucleic Acids Res. 2005 Oct 07;33(17):5691-702
references.pmid: 16214803
references.citation: Nat Rev Microbiol. 2009 Feb;7(2):129-43
references.pmid: 19116616
references.citation: Environ Microbiol. 2010 Oct;12(10):2677-87
references.pmid: 20482738
references.citation: PLoS Comput Biol. 2012;8(6):e1002358
references.pmid: 22719234
references.citation: Nucleic Acids Res. 2006 Jan 1;34(Database issue):D511-6
references.pmid: 16381923
references.citation: Nucleic Acids Res. 2012 Jan;40(Database issue):D123-9
references.pmid: 22086953
references.citation: BMC Bioinformatics. 2008 Sep 19;9:386
references.pmid: 18803844
references.citation: Appl Environ Microbiol. 2006 Jul;72(7):5069-72
references.pmid: 16820507
references.citation: Science. 2006 Jan 27;311(5760):496-503
references.pmid: 16439655
references.citation: Proc Natl Acad Sci U S A. 2009 Feb 3;106(5):1374-9
references.pmid: 19164758
references.citation: ISME J. 2010 Jan;4(1):17-27
references.pmid: 19710709
references.citation: FEMS Microbiol Ecol. 2010 Oct;74(1):103-13
references.pmid: 20695892
references.citation: PLoS One. 2011;6(7):e22295
references.pmid: 21779409
references.citation: Nature. 2011 Nov 06;480(7377):368-71
references.pmid: 22056985
references.citation: Brief Bioinform. 2012 Nov;13(6):769-80
references.pmid: 22589385
references.citation: Nat Biotechnol. 2009 Feb;27(2):157-67
references.pmid: 19204698
references.citation: ISME J. 2011 May;5(5):879-95
references.pmid: 21124488
references.citation: Nucleic Acids Res. 2000 Jan 1;28(1):27-30
references.pmid: 10592173
references.citation: Genome Res. 2009 Dec;19(12):2317-23
references.pmid: 19819907
references.citation: BMC Bioinformatics. 2011 May 15;12:159
references.pmid: 21575167
references.citation: ISME J. 2012 Sep;6(9):1629-39
references.pmid: 22378536
references.citation: J Mol Biol. 1990 Oct 5;215(3):403-10
references.pmid: 2231712
references.citation: PLoS Comput Biol. 2009 Aug;5(8):e1000465
references.pmid: 19680427