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Sequencing and beyond: integrating molecular 'omics' for microbial community profiling

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Franzosa, Eric A., Hsu, Tiffany, Sirota-Madi, Alexandra, Shafquat, Afrah, Abu-Ali, Galeb, Morgan, Xochitl C., Huttenhower, Curtis·2015·Nature Reviews Microbiology
DOI 10.1038/nrmicro3451
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Abstract

High-throughput DNA sequencing has proven invaluable for investigating diverse environmental and host-associated microbial communities. In this Review, we discuss emerging strategies for microbial community analysis that complement and expand traditional metagenomic profiling. These include novel DNA sequencing strategies for identifying strain-level microbial variation and community temporal dynamics; measuring multiple 'omic' data types that better capture community functional activity, such as transcriptomics, proteomics and metabolomics; and combining multiple forms of omic data in an integrated framework. We highlight studies in which the 'multi-omics' approach has led to improved mechanistic models of microbial community structure and function.

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  1. Handelsman, J., Rondon, M. R., Brady, S. F., Clardy, J. & Goodman, R. M. Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products. Chem. Biol. 5, R245–R249 (1998). · DOI · Paper page
  2. Rondon, M. R. et al. Cloning the soil metagenome: a strategy for accessing the genetic and functional diversity of uncultured microorganisms. Appl. Environ. Microbiol. 66, 2541–2547 (2000). · DOI · Paper page
  3. Kembel, S. W. et al. Architectural design influences the diversity and structure of the built environment microbiome. ISME J. 6, 1469–1479 (2012). · DOI · Paper page
  4. Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009). · DOI · Paper page
  5. Ley, R. E. Obesity and the human microbiome. Curr. Opin. Gastroenterol. 26, 5–11 (2010). · DOI · Paper page
  6. Morgan, X. C. et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 13, R79 (2012). A metagenomic assessment of perturbations of the human gut microbiome in inflammatory bowel disease; it reveals that changes in functional composition are more pronounced than changes in community membership. · DOI · Paper page
  7. Berry, D. & Reinisch, W. Intestinal microbiota: a source of novel biomarkers in inflammatory bowel diseases? Best Pract. Res. Clin. Gastroenterol. 27, 47–58 (2013). · DOI
  8. Kostic, A. D. et al. Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome Res. 22, 292–298 (2012). · DOI · Paper page
  9. Eckburg, P. B. et al. Diversity of the human intestinal microbial flora. Science 308, 1635–1638 (2005). An early metagenomic survey of the human gut microbiome that considers both stool and mucosal samples; it reveals many previously uncultured taxa along with strong inter-subject and inter-site differences. · DOI · Paper page
  10. Karch, H., Tarr, P. I. & Bielaszewska, M. Enterohaemorrhagic Escherichia coli in human medicine. Int. J. Med. Microbiol. 295, 405–418 (2005). · DOI · Paper page
  11. Eren, A. M. et al. Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol. 4, 1111–1119 (2013) This paper presents a computational approach for improving taxonomic resolution in surveys of microbial communities based on 16S rRNA sequencing. · DOI · Paper page
  12. Eren, A. M. et al. Exploring the diversity of Gardnerella vaginalis in the genitourinary tract microbiota of monogamous couples through subtle nucleotide variation. PLoS ONE 6, e26732 (2011). · DOI
  13. McLellan, S. L. et al. Sewage reflects the distribution of human faecal Lachnospiraceae. Environ. Microbiol. 15, 2213–2227 (2013). · DOI
  14. Scher, J. U. et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. eLife 2, e01202 (2013). · DOI · Paper page
  15. Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014). · DOI · Paper page
  16. Schloissnig, S. et al. Genomic variation landscape of the human gut microbiome. Nature 493, 45–50 (2013). · DOI · Paper page
  17. Wrighton, K. C. et al. Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science 337, 1661–1665 (2012). · DOI · Paper page
  18. Albertsen, M. et al. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nature Biotech. 31, 533–538 (2013). · DOI · Paper page
  19. Castelle, C. J. et al. Extraordinary phylogenetic diversity and metabolic versatility in aquifer sediment. Nature Commun. 4, 2120 (2013). · DOI · Paper page
  20. Di Rienzi, S. C. et al. The human gut and groundwater harbor non-photosynthetic bacteria belonging to a new candidate phylum sibling to Cyanobacteria. eLife 2, e01102 (2013). · DOI · Paper page
  21. Lax, S. et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 345, 1048–1052 (2014). · DOI · Paper page
  22. Gronlund, M. M., Lehtonen, O. P., Eerola, E. & Kero, P. Fecal microflora in healthy infants born by different methods of delivery: permanent changes in intestinal flora after cesarean delivery. J. Pediatr. Gastroenterol. Nutr. 28, 19–25 (1999). · doi:10.1097/00005176-199901000-00007 · Paper page
  23. Sharon, I. et al. Time series community genomics analysis reveals rapid shifts in bacterial species, strains, and phage during infant gut colonization. Genome Res. 23, 111–120 (2013). · DOI · Paper page
  24. Spencer, M. D. et al. Association between composition of the human gastrointestinal microbiome and development of fatty liver with choline deficiency. Gastroenterology 140, 976–986 (2011). · DOI · Paper page
  25. Rodriguez-Brito, B. et al. Viral and microbial community dynamics in four aquatic environments. ISME J. 4, 739–751 (2010). · DOI · Paper page
  26. Giannoukos, G. et al. Efficient and robust RNA-seq process for cultured bacteria and complex community transcriptomes. Genome Biol. 13, R23 (2012). · DOI · Paper page
  27. Poretsky, R. S. et al. Analysis of microbial gene transcripts in environmental samples. Appl. Environ. Microbiol. 71, 4121–4126 (2005). · DOI
  28. Gilbert, J. A. et al. Detection of large numbers of novel sequences in the metatranscriptomes of complex marine microbial communities. PLoS ONE 3, e3042 (2008). · DOI · Paper page
  29. Frias-Lopez, J. et al. Microbial community gene expression in ocean surface waters. Proc. Natl Acad. Sci. USA 105, 3805–3810 (2008). · DOI · Paper page
  30. Zhang, T. et al. RNA viral community in human feces: prevalence of plant pathogenic viruses. PLoS Biol. 4, e3 (2006). · DOI · Paper page
  31. Culley, A. I., Lang, A. S. & Suttle, C. A. Metagenomic analysis of coastal RNA virus communities. Science 312, 1795–1798 (2006). · DOI · Paper page
  32. Mason, O. U. et al. Metagenome, metatranscriptome and single-cell sequencing reveal microbial response to Deepwater Horizon oil spill. ISME J. 6, 1715–1727 (2012). · DOI · Paper page
  33. McNulty, N. P. et al. The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins. Sci. Transl. Med. 3, 106ra106 (2011). · DOI · Paper page
  34. Gilbert, J. A. et al. The taxonomic and functional diversity of microbes at a temperate coastal site: a 'multi-omic' study of seasonal and diel temporal variation. PLoS ONE 5, e15545 (2010). · DOI
  35. Altelaar, A. F., Munoz, J. & Heck, A. J. Next-generation proteomics: towards an integrative view of proteome dynamics. Nature Rev. Genet. 14, 35–48 (2013). · DOI · Paper page
  36. Gygi, S. P., Rochon, Y., Franza, B. R. & Aebersold, R. Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19, 1720–1730 (1999). · DOI · Paper page
  37. Verberkmoes, N. C. et al. Shotgun metaproteomics of the human distal gut microbiota. ISME J. 3, 179–189 (2009). · DOI · Paper page
  38. Mosier, A. C. et al. Elevated temperature alters proteomic responses of individual organisms within a biofilm community. ISME J. 9, 180–194 (2015). · DOI
  39. Sowell, S. M. et al. Transport functions dominate the SAR11 metaproteome at low-nutrient extremes in the Sargasso Sea. ISME J. 3, 93–105 (2009). · DOI · Paper page
  40. Morris, R. M. et al. Comparative metaproteomics reveals ocean-scale shifts in microbial nutrient utilization and energy transduction. ISME J. 4, 673–685 (2010). · DOI · Paper page
  41. Lacerda, C. M., Choe, L. H. & Reardon, K. F. Metaproteomic analysis of a bacterial community response to cadmium exposure. J. Proteome Res. 6, 1145–1152 (2007). · DOI
  42. Turnbaugh, P. J. & Gordon, J. I. An invitation to the marriage of metagenomics and metabolomics. Cell 134, 708–713 (2008). · DOI · Paper page
  43. Tang, J. Microbial metabolomics. Curr. Genomics 12, 391–403 (2011). · DOI · Paper page
  44. Williams, E. A., Coxhead, J. M. & Mathers, J. C. Anti-cancer effects of butyrate: use of micro-array technology to investigate mechanisms. Proc. Nutr. Soc. 62, 107–115 (2003). · DOI
  45. Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011). Using a combination of integrated multi-omic analysis and experimental work in mice, these authors demonstrate a functional link between the metabolism of dietary compounds by the gut microbiome and the development of CVD. · DOI · Paper page
  46. Koeth, R. A. et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nature Med. 19, 576–585 (2013). · DOI · Paper page
  47. Kaddurah-Daouk, R. et al. Enteric microbiome metabolites correlate with response to simvastatin treatment. PLoS ONE 6, e25482 (2011). · DOI · Paper page
  48. Mosier, A. C. et al. Metabolites associated with adaptation of microorganisms to an acidophilic, metal-rich environment identified by stable-isotope-enabled metabolomics. mBio 4, e00484-12 (2013). · DOI
  49. Karaoz, U. et al. Whole-genome annotation by using evidence integration in functional-linkage networks. Proc. Natl Acad. Sci. USA 101, 2888–2893 (2004). · DOI · Paper page
  50. Troyanskaya, O. G., Dolinski, K., Owen, A. B., Altman, R. B. & Botstein, D. A. Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc. Natl Acad. Sci. USA 100, 8348–8353 (2003). · DOI · Paper page
  51. Lee, I., Date, S. V., Adai, A. T. & Marcotte, E. M. A probabilistic functional network of yeast genes. Science 306, 1555–1558 (2004). · DOI · Paper page
  52. Myers, C. L. et al. Discovery of biological networks from diverse functional genomic data. Genome Biol. 6, R114 (2005). · DOI · Paper page
  53. Jansen, R. et al. A Bayesian networks approach for predicting protein–protein interactions from genomic data. Science 302, 449–453 (2003). · DOI · Paper page
  54. Park, C. Y., Hess, D. C., Huttenhower, C. & Troyanskaya, O. G. Simultaneous genome-wide inference of physical, genetic, regulatory, and functional pathway components. PLoS Comput. Biol. 6, e1001009 (2010). · doi:10.1371/journal.pcbi.1001009
  55. Durot, M., Bourguignon, P. Y. & Schachter, V. Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol. Rev. 33, 164–190 (2009). · DOI · Paper page
  56. Stolyar, S. et al. Metabolic modeling of a mutualistic microbial community. Mol. Syst. Biol. 3, 92 (2007). · DOI · Paper page
  57. Klitgord, N. & Segre, D. Environments that induce synthetic microbial ecosystems. PLoS Comput. Biol. 6, e1001002 (2010). · DOI · Paper page
  58. Heinken, A., Sahoo, S., Fleming, R. M. & Thiele, I. Systems-level characterization of a host–microbe metabolic symbiosis in the mammalian gut. Gut Microbes 4, 28–40 (2013). · DOI · Paper page
  59. Mazumdar, V., Amar, S. & Segre, D. Metabolic proximity in the order of colonization of a microbial community. PLoS ONE 8, e77617 (2013). · DOI
  60. Howe, A. C. et al. Tackling soil diversity with the assembly of large, complex metagenomes. Proc. Natl Acad. Sci. USA 111, 4904–4909 (2014). · DOI · Paper page
  61. Roberts, R. J. et al. COMBREX: a project to accelerate the functional annotation of prokaryotic genomes. Nucleic Acids Res. 39, D11–D14 (2011). · DOI
  62. Harrington, E. D. et al. Quantitative assessment of protein function prediction from metagenomics shotgun sequences. Proc. Natl Acad. Sci. USA 104, 13913–13918 (2007). · DOI
  63. Gough, E., Shaikh, H. & Manges, A. R. Systematic review of intestinal microbiota transplantation (fecal bacteriotherapy) for recurrent Clostridium difficile infection. Clin. Infect. Dis. 53, 994–1002 (2011). · DOI · Paper page
  64. Bhavsar, A. P., Guttman, J. A. & Finlay, B. B. Manipulation of host-cell pathways by bacterial pathogens. Nature 449, 827–834 (2007). · DOI · Paper page
  65. Riesenfeld, C. S., Schloss, P. D. & Handelsman, J. Metagenomics: genomic analysis of microbial communities. Annu. Rev. Genet. 38, 525–552 (2004). · DOI · Paper page
  66. Hamady, M. & Knight, R. Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res. 19, 1141–1152 (2009). · DOI · Paper page
  67. Segata, N. et al. Computational meta'omics for microbial community studies. Mol. Syst. Biol. 9, 666 (2013). An in-depth review of computational methods in microbial community analysis. · DOI · Paper page
  68. McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012). · DOI · Paper page
  69. Yilmaz, P. et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res. 42, D643–D648 (2014). · DOI · Paper page
  70. Knights, D., Parfrey, L. W., Zaneveld, J., Lozupone, C. & Knight, R. Human-associated microbial signatures: examining their predictive value. Cell Host Microbe 10, 292–296 (2011). · DOI
  71. McHardy, A. C., Martin, H. G., Tsirigos, A., Hugenholtz, P. & Rigoutsos, I. Accurate phylogenetic classification of variable-length DNA fragments. Nature Methods 4, 63–72 (2007). · DOI · Paper page
  72. Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nature Methods 10, 1196–1199 (2013). · DOI · Paper page
  73. Brady, A. & Salzberg, S. PhymmBL expanded: confidence scores, custom databases, parallelization and more. Nature Methods 8, 367 (2011). · DOI
  74. Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014). · DOI · Paper page
  75. Tatusov, R. L., Koonin, E. V. & Lipman, D. J. A genomic perspective on protein families. Science 278, 631–637 (1997). · DOI · Paper page
  76. Powell, S. et al. eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res. 40, D284–289 (2012). · DOI · Paper page
  77. Punta, M. et al. The Pfam protein families database. Nucleic Acids Res. 40, D290–D301 (2012). · DOI · Paper page
  78. Overbeek, R. et al. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res. 33, 5691–5702 (2005). · DOI · Paper page
  79. Markowitz, V. M. et al. IMG/M: the integrated metagenome data management and comparative analysis system. Nucleic Acids Res. 40, D123–D129 (2012). · DOI · Paper page
  80. Meyer, F. et al. The metagenomics RAST server — a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9, 386 (2008). · DOI · Paper page
  81. Konwar, K. M., Hanson, N. W., Page, A. P. & Hallam, S. J. MetaPathways: a modular pipeline for constructing pathway/genome databases from environmental sequence information. BMC Bioinformatics 14, 202 (2013). · DOI
  82. Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358 (2012). · DOI · Paper page
  83. Ramette, A. Multivariate analyses in microbial ecology. FEMS Microbiol. Ecol. 62, 142–160 (2007). · DOI · Paper page
  84. Gianoulis, T. A. et al. Quantifying environmental adaptation of metabolic pathways in metagenomics. Proc. Natl Acad. Sci. USA 106, 1374–1379 (2009). An in-depth review of statistical procedures for identifying patterns in high-dimensional microbial community data. · DOI
  85. McHardy, I. H. et al. Integrative analysis of the microbiome and metabolome of the human intestinal mucosal surface reveals exquisite inter-relationships. Microbiome 1, 17 (2013). · DOI · Paper page
  86. Knights, D., Costello, E. K. & Knight, R. Supervised classification of human microbiota. FEMS Microbiol. Rev. 35, 343–359 (2011). · DOI · Paper page
  87. White, J. R., Nagarajan, N. & Pop, M. Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput. Biol. 5, e1000352 (2009). · DOI · Paper page
  88. Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011). · DOI · Paper page
  89. Tickle, T. L., Segata, N., Waldron, L., Weingart, U. & Huttenhower, C. Two-stage microbial community experimental design. ISME J. 7, 2330–2339 (2013). · DOI

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Metadata

Authors
Franzosa, E. A., Hsu, T., Sirota-Madi, A., Shafquat, A., Abu-Ali, G., Morgan, X. C., & Huttenhower, C.
Venue
Nature Reviews Microbiology
Publisher
Springer Science and Business Media LLC
Published
2015-04-27
Volume
13
Issue
6
Pages
360-372
ISSN
  • 1740-1526 (ISSN-L)
  • 1740-1534
DOI
10.1038/nrmicro3451
OpenAlex
W1979966476
Cited by
719
References
30
FWCI
4.98
Type
journal article
Language
en
Open access
green
Licence
www.springer.com/tdm (tdm)

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Franzosa, E. A., Hsu, T., Sirota-Madi, A., Shafquat, A., Abu-Ali, G., Morgan, X. C., & Huttenhower, C. (2015). Sequencing and beyond: integrating molecular 'omics' for microbial community profiling. Nature Reviews Microbiology. https://doi.org/10.1038/nrmicro3451

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