Elucidation of complexity and prediction of interactions in microbial communities

Microbial Biotechnology

Volumn 10, Issue 6, 2017, Pages 1500-1522

  Zuñiga, Cristal ^a   Zaramela, Livia ^a   Zengler, Karsten ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HUMAN; MICROBIAL COMMUNITY; MICROBIOME; NONHUMAN; PREDICTION; ANIMAL; BACTERIAL PHENOMENA AND FUNCTIONS; BACTERIUM; BIOLOGY; CHEMISTRY; GENETICS; METAGENOMICS; ORGANISMAL INTERACTION;

ANIMALS; BACTERIA; BACTERIAL PHYSIOLOGICAL PHENOMENA; COMPUTATIONAL BIOLOGY; HUMANS; METAGENOMICS; MICROBIAL INTERACTIONS;

EID: 85030117495     PISSN: 17517907     EISSN: 17517915     Source Type: Journal    
DOI: 10.1111/1751-7915.12855     Document Type: Review

References (202)

1. Abraham, N.M., Liu, L., Jutras, B.L., Yadav, A.K., Narasimhan, S., Gopalakrishnan, V., et al. (2017) Pathogen-mediated manipulation of arthropod microbiota to promote infection. Proc Natl Acad Sci USA 114: E781–E790.
2. Abubucker, S., Segata, N., Goll, J., Schubert, A.M., Izard, J., Cantarel, B.L., et al. (2012) Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput Biol 8: e1002358.
3. Alhumazi, K., and Ajbar, A. (2005) Dynamics of predator-prey interactions in continuous cultures. Eng Life Sci 5: 139–147.
4. Allison, S.M., Small, J.R., Kacser, H., and Prosser, J.I. (1993) Control analysis of microbial interactions in continuous culture: a simulation study. J Gen Microbiol 139: 2309–2317.
5. Andreote, F.D. and Pereira e Silva, M.C. (2017) Microbial communities associated with plants: learning from nature to apply it in agriculture. Curr Opin Microbiol 37: 29–34.
6. Backhed, F., Ley, R., Sonnenburg, J., Peterson, D. and Gordon, J. (2005) Host-bacterial mutualism in the human intestine. Science 307: 1915–1920.
7. Bartell, J.A., Blazier, A.S., Yen, P., Thøgersen, J.C., Jelsbak, L., Goldberg, J.B., and Papin, J.A. (2017) Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis. Nat Commun 8: 14631.
8. Beijerinck, M.W. (1895) Über Spirillum desulfuricans als ursache von sulfatreduktion. Zentralblatt Bakteriol 1: 1–9.
9. Beneden, M. (1876) Mutualists. In Animal Parasites and Messmates. HS King & Company (eds). New York, NY Second edition: pp. 1–68.
10. Berendsen, R.L., Pieterse, C.M.J., and Bakker, P.A.H.M. (2012) The rhizosphere microbiome and plant health. Trends Plant Sci 17: 478–486.
11. Berleman, J., and Auer, M. (2013) The role of bacterial outer membrane vesicles for intra- and interspecies delivery. Environ Microbiol 15: 347–354.
12. Bersanelli, M., Mosca, E., Remondini, D., Giampieri, E., Sala, C., Castellani, G., and Milanesi, L. (2016) Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics 17: S15.
13. Biggs, M.B., Medlock, G.L., Kolling, G.L., and Papin, J.A. (2015) Metabolic network modeling of microbial communities. Wiley Interdiscip Rev Syst Biol Med 7: 317–334.
14. Blurock, E.S. (2003) Characterizing complex reaction mechanisms using machine learning clustering techniques. Int J Chem Kinet 36: 107–118.
15. Boguniewicz, M., and Leung, D.Y.M. (2011) Atopic dermatitis: a disease of altered skin barrier and immune dysregulation. Immunol Rev 242: 233–246.
16. Bordbar, A., Lewis, N.E., Schellenberger, J., Palsson, B.Ø. and Jamshidi, N. (2010) Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol 6: 422–435.
17. Bordbar, A., Monk, J.M., King, Z.A., and Palsson, B.O. (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15: 107–120.
18. Bordbar, A., Yurkovich, J.T., Paglia, G., Rolfsson, O., and Palsson, B.O. (2017) Elucidating dynamic metabolic physiology through network integration of quantitative time-course metabolomics. Sci Rep 7: 46249.
19. Bosi, E., Monk, J.M., Aziz, R.K., Fondi, M., Nizet, V., and Palsson, B.Ø. (2016) Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity. Proc Natl Acad Sci USA 113: E3801–E3809.
20. Brooks, J.P., Edwards, D.J., Harwich, M.D., Rivera, M.C., Fettweis, J.M., Serrano, M.G., et al. (2015) The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies. BMC Microbiol 15: 66.
21. Brown, C.T., Olm, M.R., Thomas, B.C., and Banfield, J.F. (2016) Measurement of bacterial replication rates in microbial communities. Nat Biotechnol 34: 1256–1263.
22. Brulc, J.M., Antonopoulos, D.A., Berg Miller, M.E., Wilson, M.K., Yannarell, A.C., Dinsdale, E.A., et al. (2009) Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc Natl Acad Sci USA 106: 1948–1953.
23. Busby, P.E., Soman, C., Wagner, M.R., Friesen, M.L., Kremer, J., Bennett, A., et al. (2017) Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol 15: e2001793.
24. Byrd, A.L. and Segre, J.A. (2016) Adapting Koch's postulates. Science 351: 224–226.
25. Cabello, J., Morales, M., and Revah, S. (2014) Dynamic photosynthetic response of the microalga Scenedesmus obtusiusculus to light intensity perturbations. Chem Eng J 252: 104–111.
26. Cadwell, K. (2015) The virome in host health and disease. Immunity 42: 805–813.
27. Canale, R.P. (1970) An analysis of models describing predator-prey interaction. Biotechnol Bioeng 12: 353–378.
28. Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., et al. (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7: 335–336.
29. Carr, R., and Borenstein, E. (2014) Comparative analysis of functional metagenomic annotation and the mappability of short reads. PLoS One 9: e105776.
30. Caspi, R., Altman, T., Billington, R., Dreher, K., Foerster, H., Fulcher, C.A., et al. (2014) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res 42: 459–471.
31. Chaparro, J.M., Badri, D.V., and Vivanco, J.M. (2014) Rhizosphere microbiome assemblage is affected by plant development. ISME J 8: 790–803.
32. Chen, H., Athar, R., Zheng, G., and Williams, H.N. (2011) Prey bacteria shape the community structure of their predators. ISME J 5: 1314–1322.
33. Cohn, F. (1875) Untersuchungen über bakterien. Beitr Biol Pflanzen 1: 141–207.
34. Cortassa, S., O'Rourke, B., Winslow, R.L., and Aon, M.A. (2009) Control and regulation of mitochondrial energetics in an integrated model of cardiomyocyte function. Biophys J 96: 2466–2478.
35. Council, N.R. (2015) Critical Role of Animal Science Research in Food Security and Sustainability. Washington, DC: National Academies Press.
36. Cremer, J., Segota, I., Yang, C., Arnoldini, M., Sauls, J.T., Zhang, Z., et al. (2016) Effect of flow and peristaltic mixing on bacterial growth in a gut-like channel. Proc Natl Acad Sci USA 113: 11414–11419.
37. Degnan, P.H., Taga, M.E., and Goodman, A.L. (2014) Vitamin B12 as a modulator of gut microbial ecology. Cell Metab 20: 769–778.
38. Delong, E.F. (1992) Archaea in coastal marine environments. Proc Natl Acad Sci USA 89: 5685–5689.
39. Dhurjati, P., Ramkrishna, D., Flickinger, M.C., and Tsao, G.T. (1985) A cybernetic view of microbial growth: modeling of cells as optimal strategists. Biotechnol Bioeng 27: 1–9.
40. Dobell, C. (1923) A protozoological bicentenary: Antony van Leeuwenhoek (1632–1723) and Louis Joblot (1645–1723). Parasitology 15: 308.
41. Du, B., Zielinski, D.C., Kavvas, E.S., Dräger, A., Tan, J., Zhang, Z., et al. (2016) Evaluation of rate law approximations in bottom-up kinetic models of metabolism. BMC Syst Biol 10: 40.
42. Dunn, I.J., Heinzle, E., Ingham, J., Pfenosil, J.E., (2003) Biological reaction engineering. Principles, applications and modeling with PC simulation. Wiley-VCH GmbH & Co. KGaA. New York: Second, Completely Revised Edition, pp. 400–445.
43. Dutilh, B.E., Backus, L., Van Hijum, S.A.F.T., and Tjalsma, H. (2013) Screening metatranscriptomes for toxin genes as functional drivers of human colorectal cancer. Best Pract Res Clin Gastroenterol 27: 85–99.
44. Edwards, J.S., and Palsson, B.Ø. (2000) The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc Natl Acad Sci USA 97: 5528–5533.
45. Embree, M., Nagarajan, H., Movahedi, N., Chitsaz, H., and Zengler, K. (2014) Single-cell genome and metatranscriptome sequencing reveal metabolic interactions of an alkane-degrading methanogenic community. ISME J 8: 757–767.
46. Embree, M., Liu, J.K., Al-Bassam, M.M., and Zengler, K. (2015) Networks of energetic and metabolic interactions define dynamics in microbial communities. Proc Natl Acad Sci USA 112: 15450–15455.
47. Erickson, A.R., Cantarel, B.L., Lamendella, R., Darzi, Y., Mongodin, E.F., Pan, C., et al. (2012) Integrated metagenomics/metaproteomics reveals human host-microbiota signatures of Crohn's disease. PLoS One 7: e49138.
48. Faust, K., and Raes, J. (2012) Microbial interactions: from networks to models. Nat Rev Microbiol 10: 538–550.
49. Fischbach, M.A., and Segre, J.A. (2016) Signaling in host-associated microbial communities. Cell 164: 1288–1300.
50. Fleischmann, R.D., Adams, M.D., White, O., Clayton, R.A., Kirkness, E.F., Kerlavage, A.R., et al. (1995) Whole-genome random sequencing and assembly of Haemophilus influenzae Rd. Science 269: 496–512.
51. Flint, H.J., Scott, K.P., Louis, P., and Duncan, S.H. (2012) The role of the gut microbiota in nutrition and health. Nat Rev Gastroenterol Hepatol 9: 577–589.
52. Fouts, D.E., Szpakowski, S., Purushe, J., Torralba, M., Waterman, R.C., MacNeil, M.D., et al. (2012) Next generation sequencing to define prokaryotic and fungal diversity in the bovine rumen. PLoS One 7: e48289.
53. Franzosa, E.A., Morgan, X.C., Segata, N., Waldron, L., Reyes, J., Earl, A.M., et al. (2014) Relating the metatranscriptome and metagenome of the human gut. Proc Natl Acad Sci USA 111: E2329–E2338.
54. Franzosa, E.A., Hsu, T., Sirota-Madi, A., Shafquat, A., Abu-Ali, G., Morgan, X.C., and Huttenhower, C. (2015) Sequencing and beyond: integrating molecular “omics” for microbial community profiling. Nat Rev Microbiol 13: 360–372.
55. Freilich, S., Zarecki, R., Eilam, O., Segal, E.S., Henry, C.S., Kupiec, M., et al. (2011) Competitive and cooperative metabolic interactions in bacterial communities. Nat Commun 2: 589.
56. Fuhrman, J.A., McCallum, K., and Davis, A.A. (1992) Novel major archaebacterial group from marine plankton. Nature 356: 148–149.
57. Gause, G.F. (1934) The struggle for existence.The Williams & Wilkins company 1934, (ed.). Baltimore, pp. 163–170.
58. Geng, J., Song, H.-S., Yuan, J., and Ramkrishna, D. (2012) On enhancing productivity of bioethanol with multiple species. Biotechnol Bioeng 109: 1508–1517.
59. Germerodt, S., Bohl, K., Lück, A., Pande, S., Schröter, A., Kaleta, C., et al. (2016) Pervasive selection for cooperative cross-feeding in bacterial communities. PLoS Comput Biol 12: e1004986.
60. Gibson, G.R., Hutkins, R., Sanders, M.E., Prescott, S.L., Reimer, R.A., Salminen, S.J., et al. (2017) Expert consensus document: The International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of prebiotics. Nat Rev Gastroenterol Hepatol 14: 491–502.
61. Gilbert, J.A., Meyer, F., Antonopoulos, D., Balaji, P., Brown, C.T., Brown, C.T., et al. (2010) Meeting report: the terabase metagenomics workshop and the vision of an earth microbiome project. Stand Genomic Sci 3: 243–248.
62. Giovannoni, S.J., DeLong, E.F., Schmidt, T.M., and Pace, N.R. (1990) Tangential flow filtration and preliminary phylogenetic analysis of marine picoplankton. Appl Environ Microbiol 56: 2572–2575.
63. Gonzalez, D.J., Haste, N.M., Hollands, A., Fleming, T.C., Hamby, M., Pogliano, K., et al. (2011) Microbial competition between Bacillus subtilis and Staphylococcus aureus monitored by imaging mass spectrometry. Microbiology 157: 2485–2492.
64. Gopal, M., Gupta, A. and Thomas, G.V. (2013) Bespoke microbiome therapy to manage plant diseases. Front Microbiol 4: 355–359.
65. Gowda, H., Ivanisevic, J., Johnson, C.H., Kurczy, M.E., Benton, H.P., Rinehart, D., et al. (2014) Interactive XCMS online: simplifying advanced metabolomic data processing and subsequent statistical analyses. Anal Chem 86: 6931–6939.
66. Greenblum, S., Turnbaugh, P.J., and Borenstein, E. (2012) Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proc Natl Acad Sci USA 109: 594–599.
67. Guidi, L., Chaffron, S., Bittner, L., Eveillard, D., Larhlimi, A., Roux, S., et al. (2016) Plankton networks driving carbon export in the oligotrophic ocean. Nature 532: 465–470.
68. Guimaraes, J.C., Rocha, M., and Arkin, A.P. (2014) Transcript level and sequence determinants of protein abundance and noise in Escherichia coli. Nucleic Acids Res 42: 4791–4799.
69. Gupta, S., Allen-Vercoe, E., and Petrof, E.O. (2016) Fecal microbiota transplantation: in perspective. Therap Adv Gastroenterol 9: 229–239.
70. Halfvarson, J., Brislawn, C.J., Lamendella, R., Vázquez-Baeza, Y., Walters, W.A., Bramer, L.M., et al. (2017) Dynamics of the human gut microbiome in inflammatory bowel disease. Nat Microbiol 2: 17004.
71. 2 transfer in a methanogenic, syntrophic community. PLoS Comput Biol 11: e1004364.
72. Heinken, A., and Thiele, I. (2015) Systems biology of host-microbe metabolomics. Wiley Interdiscip Rev Syst Biol Med 7: 195–219.
73. Heintz, C., and Mair, W. (2014) You are what you host: microbiome modulation of the aging process. Cell 156: 408–411.
74. Henry, C.S., DeJongh, M., Best, A.A., Frybarger, P.M., Linsay, B., and Stevens, R.L. (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28: 977–982.
75. Hibberd, M.C., Wu, M., Rodionov, D.A., Li, X., Cheng, J., Griffin, N.W., et al. (2017) The effects of micronutrient deficiencies on bacterial species from the human gut microbiota. Sci Transl Med 9: eaal4069.
76. Hocher, B., and Adamski, J. (2017) Metabolomics for clinical use and research in chronic kidney disease. Nat Rev Nephrol 13: 269–284.
77. Holz, C., Benning, J., Schaudt, M., Heilmann, A., Schultchen, J., Goelling, D., and Lang, C. (2017) Novel bioactive from Lactobacillus brevis DSM17250 to stimulate the growth of Staphylococcus epidermidis: a pilot study. Benef Microbes 8: 121–131.
78. Huan, T., Forsberg, E.M., Rinehart, D., Johnson, C.H., Ivanisevic, J., Benton, H.P., et al. (2017) Systems biology guided by XCMS Online metabolomics. Nat Methods 14: 461–462.
79. Hug, L.A., Baker, B.J., Anantharaman, K., Brown, C.T., Probst, A.J., Castelle, C.J., et al. (2016) A new view of the tree of life. Nat Microbiol 1: 16048.
80. Huston, M., and Smith, T. (1987) Plant succession: life history and competition. Am Nat 130: 168–198.
81. Ingolia, N.T. (2016) Ribosome footprint profiling of translation throughout the genome. Cell 165: 22–33.
82. Isaacson, R., and Kim, H.B. (2012) The intestinal microbiome of the pig. Anim Health Res Rev 13: 100–109.
83. Islam, M.A., Waller, A.S., Hug, L.A., Provart, N.J., Edwards, E.A., and Mahadevan, R. (2014) New insights into Dehalococcoides mccartyi metabolism from a reconstructed metabolic network-based systems-level analysis of D. mccartyi transcriptomes. PLoS One 9: e94808.
84. Ivey, M., Massel, M., and Phister, T.G. (2013) Microbial interactions in food fermentations. Annu Rev Food Sci Technol 4: 141–162.
85. Jansson, J.K., and Baker, E.S. (2016) A multi-omic future for microbiome studies. Nat Microbiol 1: 16049.
86. Javan, G.T., Finley, S.J., Abidin, Z. and Mulle, J.G. (2016a) The thanatomicrobiome: a missing piece of the microbial puzzle of death. Front Microbiol 7: 225–232.
87. Javan, G.T., Finley, S.J., Can, I., Wilkinson, J.E., Hanson, J.D., and Tarone, A.M. (2016b) Human thanatomicrobiome succession and time since death. Sci Rep 6: 29598.
88. Ji, B. and Nielsen, J. (2015) From next-generation sequencing to systematic modeling of the gut microbiome. Front Genet 6: 219–228.
89. Jorth, P., Turner, K.H., Gumus, P., Nizam, N., Buduneli, N. and Whiteley, M. (2014) Metatranscriptomics of the human oral microbiome during health and disease. MBio 5: e01012–e01014.
90. von Kamp, A., and Klamt, S. (2017) Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms. Nat Commun 8: 15956.
91. Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M. (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res 42: 199–205.
92. Kang, D., Shi, B., Erfe, M.C., Craft, N. and Li, H. (2015) Vitamin B12 modulates the transcriptome of the skin microbiota in acne pathogenesis. Sci Transl Med 7: 293ra103.
93. Karr, J.R., Sanghvi, J.C., Macklin, D.N., Gutschow, M.V., Jacobs, J.M., Bolival, B., et al. (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150: 389–401.
94. Kayala, M.A., Azencott, C.-A., Chen, J.H., and Baldi, P. (2011) Learning to predict chemical reactions. J Chem Inf Model 51: 2209–2222.
95. Kerkhoven, E.J., Pomraning, K.R., Baker, S.E. and Nielsen, J. (2016) Regulation of amino-acid metabolism controls flux to lipid accumulation in Yarrowia lipolytica. NPJ Syst Biol Appl 2: 16005.
96. King, Z.A., Dräger, A., Ebrahim, A., Sonnenschein, N., Lewis, N.E., and Palsson, B.O. (2015) Escher: a web application for building, sharing, and embedding data-rich visualizations of biological pathways. PLoS Comput Biol 11: e1004321.
97. Klemashevich, C., Wu, C., Howsmon, D., Alaniz, R.C., Lee, K., and Jayaraman, A. (2014) Rational identification of diet-derived postbiotics for improving intestinal microbiota function. Curr Opin Biotechnol 26: 85–90.
98. Knight, R., Callewaert, C., Marotz, C., Hyde, E.R., Debelius, J.W., McDonald, D. and Sogin, M.L. (2017) The microbiome and human biology. Annu Rev Genomics Hum Genet 18: 1–22.
99. Koleva, P., Bridgman, S., and Kozyrskyj, A. (2015) The infant gut microbiome: evidence for obesity risk and dietary intervention. Nutrients 7: 2237–2260.
100. Kumar, R., Yi, N., Zhi, D., Eipers, P., Goldsmith, K.T., Dixon, P., et al. (2017) Identification of donor microbe species that colonize and persist long term in the recipient after fecal transplant for recurrent Clostridium difficile. NPJ Biofilms Microbiomes 3: 12.
101. Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., et al. (2001) Initial sequencing and analysis of the human genome. Nature 409: 860–921.
102. Lane, D.J., Pace, B., Olsen, G.J., Stahl, D.A., Sogin, M.L., and Pace, N.R. (1985) Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc Natl Acad Sci USA 82: 6955–6959.
103. Latif, H., Szubin, R., Tan, J., Brunk, E., Lechner, A., Zengler, K. and Palsson, B.O. (2015) A streamlined ribosome profiling protocol for the characterization of microorganisms. Biotechniques 58: 329–332.
104. Lerman, J.A., Hyduke, D.R., Latif, H., Portnoy, V.A., Lewis, N.E., Orth, J.D., et al. (2012) In silico method for modelling metabolism and gene product expression at genome scale. Nat Commun 3: 929.
105. Letten, A.D., Ke, P.-J. and Fukami, T. (2017) Linking modern coexistence theory and contemporary niche theory. Ecol Monogr 87: 161–177.
106. Levering, J., Broddrick, J., Dupont, C.L., Peers, G., Beeri, K., Mayers, J., et al. (2016) Genome-scale model reveals metabolic basis of biomass partitioning in a model diatom. PLoS One 11: 1–22.
107. Levy, R., and Borenstein, E. (2013) Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc Natl Acad Sci USA 110: 12804–12809.
108. Lewin, A., Johansen, J., Wentzel, A., Kotlar, H.K., Drabløs, F., and Valla, S. (2014) The microbial communities in two apparently physically separated deep subsurface oil reservoirs show extensive DNA sequence similarities. Environ Microbiol 16: 545–558.
109. LeWitt, P.A., Li, J., Lu, M., Guo, L., and Auinger, P. (2017) Metabolomic biomarkers as strong correlates of Parkinson disease progression. Neurology 88: 862–869.
110. Liu, J.K., O'Brien, E.J., Lerman, J.A., Zengler, K., Palsson, B.O., and Feist, A.M. (2014) Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale. BMC Syst Biol 8: 110.
111. Lotka, A.J. (1925) Elements of physical biology reissued as elements of mathematical biology. Williams & Wilkins Company (Eds) Baltimore: MD, pp. 161-184.
112. Louca, S., Parfrey, L.W. and Doebeli, M. (2016) Decoupling function and taxonomy in the global ocean microbiome. Science 353: 1272–1277.
113. Lyon, M., Wood, S., Pelletier, X., Donazzolo, Y., Gahler, R., and Bellisle, F. (2011) Effects of a 3-month supplementation with a novel soluble highly viscous polysaccharide on anthropometry and blood lipids in nondieting overweight or obese adults. J Hum Nutr Diet 24: 351–359.
114. Magnúsdóttir, S., Heinken, A., Kutt, L., Ravcheev, D.A., Bauer, E., Noronha, A., et al. (2016) Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat Biotechnol 35: 81–89.
115. Mahowald, M.A., Rey, F.E., Seedorf, H., Turnbaugh, P.J., Fulton, R.S., Wollam, A., et al. (2009) Characterizing a model human gut microbiota composed of members of its two dominant bacterial phyla. Proc Natl Acad Sci USA 106: 5859–5864.
116. Maier, T., Güell, M., and Serrano, L. (2009) Correlation of mRNA and protein in complex biological samples. FEBS Lett 583: 3966–3973.
117. Marco, M.B., Moineau, S., and Quiberoni, A. (2012) Bacteriophages and dairy fermentations. Bacteriophage 2: 149–158.
118. Mardinoglu, A., Shoaie, S., Bergentall, M., Ghaffari, P., Zhang, C., Larsson, E., et al. (2015) The gut microbiota modulates host amino acid and glutathione metabolism in mice. Mol Syst Biol 11: 834.
119. Martinez-Garcia, M., Swan, B.K., Poulton, N.J., Gomez, M.L., Masland, D., Sieracki, M.E., and Stepanauskas, R. (2012) High-throughput single-cell sequencing identifies photoheterotrophs and chemoautotrophs in freshwater bacterioplankton. ISME J 6: 113–123.
120. Maynard, N.D., Gutschow, M.V., Birch, E.W., and Covert, M.W. (2010) The virus as metabolic engineer. Biotechnol J 5: 686–694.
121. McCalley, C.K. and Sparks, J.P. (2009) Abiotic gas formation drives nitrogen loss from a desert ecosystem. Science 326: 837–840.
122. McDonald, D., Birmingham, A., and Knight, R. (2015) Context and the human microbiome. Microbiome 3: 52.
123. McDonald, D., Glusman, G., and Price, N.D. (2016) Personalized nutrition through big data. Nat Biotechnol 34: 152–154.
124. McGibbon, R.T., and Pande, V.S. (2013) Learning kinetic distance metrics for markov state models of protein conformational dynamics. J Chem Theory Comput 9: 2900–2906.
125. Mee, M.T., Collins, J.J., Church, G.M., and Wang, H.H. (2014) Syntrophic exchange in synthetic microbial communities. Proc Natl Acad Sci USA 111: E2149–E2156.
126. Meeuwse, P., Akbari, P., Tramper, J., and Rinzema, A. (2012) Modeling growth, lipid accumulation and lipid turnover in submerged batch cultures of Umbelopsis isabellina. Bioprocess Biosyst Eng 35: 591–603.
127. Meng, C., Zeleznik, O.A., Thallinger, G.G., Kuster, B., Gholami, A.M., and Culhane, A.C. (2016) Dimension reduction techniques for the integrative analysis of multi-omics data. Brief Bioinform 17: 628–641.
128. Metcalf, J.L., Xu, Z.Z., Bouslimani, A., Dorrestein, P., Carter, D.O. and Knight, R. (2017) Microbiome tools for forensic science. Trends Biotechnol 35: 814–823.
129. Methé, B.A., Nelson, K.E., Pop, M., Creasy, H.H., Giglio, M.G., Huttenhower, C., et al. (2012) A framework for human microbiome research. Nature 486: 215–221.
130. Meyer, F., Paarmann, D., D'Souza, M., Olson, R., Glass, E., Kubal, M., et al. (2008) The metagenomics RAST server – a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9: 386.
131. Monk, J.M., Charusanti, P., Aziz, R.K., Lerman, J.A., Premyodhin, N., Orth, J.D., et al. (2013) Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments. Proc Natl Acad Sci USA 110: 20338–20343.
132. Morris, B.E.L., Henneberger, R., Huber, H., and Moissl-Eichinger, C. (2013) Microbial syntrophy: interaction for the common good. FEMS Microbiol Rev 37: 384–406.
133. Myles, I.A., Williams, K.W., Reckhow, J.D., Jammeh, M.L., Pincus, N.B., Sastalla, I., et al. (2016) Transplantation of human skin microbiota in models of atopic dermatitis. JCI Insight 1: 1–10.
134. Nagarajan, H., Embree, M., Rotaru, A.-E., Shrestha, P.M., Feist, A.M., Palsson, B.Ø., et al. (2013) Characterization and modelling of interspecies electron transfer mechanisms and microbial community dynamics of a syntrophic association. Nat Commun 4: 1–10.
135. Nakatsuji, T., Chen, T.H., Narala, S., Chun, K.A., Two, A.M., Yun, T., et al. (2017) Antimicrobials from human skin commensal bacteria protect against Staphylococcus aureus and are deficient in atopic dermatitis. Sci Transl Med 9: 1–12.
136. Nature-Biotechnology (2017) Recent patents in microbiomes and microbial genomics. Nat Biotechnol 35: 517.
137. Nemergut, D.R., Schmidt, S.K., Fukami, T., O'Neill, S.P., Bilinski, T.M., Stanish, L.F., et al. (2013) Patterns and processes of microbial community assembly. Microbiol Mol Biol Rev 77: 342–356.
138. Ng, C., DeMaere, M.Z., Williams, T.J., Lauro, F.M., Raftery, M., Gibson, J.A.E., et al. (2010) Metaproteogenomic analysis of a dominant green sulfur bacterium from Ace Lake, Antarctica. ISME J 4: 1002–1019.
139. Nielsen, J., and Olson, L. (2002) An expanded role for microbial physiology in metabolic engineering and functional genomics: moving towards systems biology. FEMS Yeast Res 2: 175–181.
140. Nothias, L.-F., Knight, R., and Dorrestein, P.C. (2016) Antibiotic discovery is a walk in the park. Proc Natl Acad Sci USA 113: 201618221.
141. O'Brien, E.J., Lerman, J.A., Chang, R.L., Hyduke, D.R., and Palsson, B.O. (2014) Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol 9: 693.
142. O'Callaghan, M. (2016) Microbial inoculation of seed for improved crop performance: issues and opportunities. Appl Microbiol Biotechnol 100: 5729–5746.
143. Olle, B. (2013) Medicines from microbiota. Nat Biotechnol 31: 309–315.
144. Paddon, C.J., and Keasling, J.D. (2014) Semi-synthetic artemisinin: a model for the use of synthetic biology in pharmaceutical development. Nat Rev Microbiol 12: 355–367.
145. Palsson, B., and Zengler, K. (2010) The challenges of integrating multi-omic data sets. Nat Chem Biol 6: 787–789.
146. Perez-Garcia, O., Lear, G. and Singhal, N. (2016) Metabolic network modeling of microbial interactions in natural and engineered environmental systems. Front Microbiol 7: 673–703.
147. Petschow, B., Dore, J., Hibberd, P., Dinan, T., Reid, G., Blaser, M., et al. (2013) Probiotics, prebiotics, and the host microbiome: the science of translation. Ann N Y Acad Sci 1306: 1–17.
148. Plichta, D.R., Juncker, A.S., Bertalan, M., Rettedal, E., Gautier, L., Varela, E., et al. (2016) Transcriptional interactions suggest niche segregation among microorganisms in the human gut. Nat Microbiol 1: 16152.
149. Ponomarova, O., and Patil, K.R. (2015) Metabolic interactions in microbial communities: untangling the Gordian knot. Curr Opin Microbiol 27: 37–44.
150. Rascovan, N., Carbonetto, B., Perrig, D., Díaz, M., Canciani, W., Abalo, M., et al. (2016) Integrated analysis of root microbiomes of soybean and wheat from agricultural fields. Sci Rep 6: 28084.
151. Resat, H., Costa, M.N., and Shankaran, H. (2011) Spatial aspects in biological system simulations. Methods Enzymol 487: 485–511.
152. Roling, W.F.M. and van Bodegom, P.M. (2014) Toward quantitative understanding on microbial community structure and functioning: a modeling-centered approach using degradation of marine oil spills as example. Front Microbiol 5: 125–137.
153. Röling, W.F., Ferrer, M., and Golyshin, P.N. (2010) Systems approaches to microbial communities and their functioning. Curr Opin Biotechnol 21: 532–538.
154. Ryu, J.Y., Kim, H.U., and Lee, S.Y. (2015) Reconstruction of genome-scale human metabolic models using omics data. Integr Biol 7: 859–868.
155. Sangwan, N., Xia, F., and Gilbert, J.A. (2016) Recovering complete and draft population genomes from metagenome datasets. Microbiome 4: 8.
156. Schellenberger, J., Que, R., Fleming, R.M.T., Thiele, I., Orth, J.D., Feist, A.M., et al. (2011) Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat Protoc 6: 1290–1307.
157. Schultz, A., and Qutub, A.A. (2016) Reconstruction of tissue-specific metabolic networks using CORDA. PLoS Comput Biol 12: e1004808.
158. Sekirov, I., Russell, S.L., Antunes, L.C.M., and Finlay, B.B. (2010) Gut microbiota in health and disease. Physiol Rev 90: 859–904.
159. Shoaie, S., Karlsson, F., Mardinoglu, A., Nookaew, I., Bordel, S. and Nielsen, J. (2013) Understanding the interactions between bacteria in the human gut through metabolic modeling. Sci Rep 3: 1–10.
160. Shoaie, S., Ghaffari, P., Kovatcheva-Datchary, P., Mardinoglu, A., Sen, P., Pujos-Guillot, E., et al. (2015) Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab 22: 320–331.
161. Sierra-Garcia, I.N., Dellagnezze, B.M., Santos, V.P., Chaves B, M.R., Capilla, R., Santos Neto, E.V., et al. (2016) Microbial diversity in degraded and non-degraded petroleum samples and comparison across oil reservoirs at local and global scales. Extremophiles 21: 211–229.
162. Silva, R.R., Jourdan, F., Salvanha, D.M., Letisse, F., Jamin, E.L., Guidetti-Gonzalez, S., et al. (2014) ProbMetab: an R package for Bayesian probabilistic annotation of LC-MS-based metabolomics. Bioinformatics 30: 1336–1337.
163. Singer, E., Wagner, M. and Woyke, T. (2017) Capturing the genetic makeup of the active microbiome in situ. ISME J 11: 1949–1963.
164. Steffani-Vallejo, J.L., Zuniga, C., Cruz-Morales, P., Lozano, L., Morales, M., Licona-Cassani, C., et al. (2017) Draft genome sequence of Sphingobacterium sp. CZ-UAM, isolated from a methanotrophic consortium. Genome Announc 5: 1–2.
165. Stolyar, S., Van Dien, S., Hillesland, K.L., Pinel, N., Lie, T.J., Leigh, J.A. and Stahl, D.A. (2007) Metabolic modeling of a mutualistic microbial community. Mol Syst Biol 3: 1–14.
166. Swainston, N., Smallbone, K., Hefzi, H., Dobson, P.D., Brewer, J., Hanscho, M., et al. (2016) Recon 2.2: from reconstruction to model of human metabolism. Metabolomics 12: 109.
167. Tabiś, B., Skoneczny, S. and Stryjewski, W.S. (2014) Nonlinear dynamics of a controlled stirred tank bioreactor with predator-prey relationship. Chem Process Eng 35: 349–360.
168. Tan, J., Zuniga, C., and Zengler, K. (2015) Unraveling interactions in microbial communities - from co-cultures to microbiomes. J Microbiol 53: 295–305.
169. Thibodeau, A., Letellier, A., Yergeau, E., Larriviere-Gauthier, G. and Fravalo, P. (2017) Lack of evidence that selenium-yeast improves chicken health and modulates the caecal microbiota in the context of colonization by Campylobacter jejuni. Front Microbiol 8: 451–460.
170. Thomaz, A., Fukumori, C., and Ferreira, C.M. (2016) New insights into therapeutic strategies for gut microbiota modulation in inflammatory diseases. Clin Transl Immunology 5: e87.
171. Truong, D.T., Franzosa, E.A., Tickle, T.L., Scholz, M., Weingart, G., Pasolli, E., et al. (2015) MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods 12: 902–903.
172. Tsuno, H., Hidaka, T., and Nishimura, F. (2002) A simple biofilm model of bacterial competition for attached surface. Water Res 36: 996–1006.
173. Turnbaugh, P.J., Ley, R.E., Hamady, M., Fraser-Liggett, C.M., Knight, R., and Gordon, J.I. (2007) The human microbiome project. Nature 449: 804–810.
174. Vincent, K.M. and Postovit, L.-M. (2017) Investigating the utility of human melanoma cell lines as tumour models. Oncotarget 8: 10498–10509.
175. Vollmers, J., Wiegand, S., and Kaster, A.-K. (2017) Comparing and evaluating metagenome assembly tools from a microbiologist's perspective - not only size matters!. PLoS One 12: e0169662.
176. Volova, T., Kiselev, E., Vinogradova, O., Nikolaeva, E., Chistyakov, A., Sukovatiy, A., and Shishatskaya, E. (2014) A glucose-utilizing strain, Cupriavidus euthrophus B-10646: growth kinetics, characterization and synthesis of multicomponent PHAs. PLoS One 9: e87551.
177. Volterra, V. (1926) Fluctuations in the abundance of a species considered mathematically. Nature 118: 558–560.
178. Wade, M.J., Harmand, J., Benyahia, B., Bouchez, T., Chaillou, S., Cloez, B., et al. (2016) Perspectives in mathematical modelling for microbial ecology. Ecol Modell 321: 64–74.
179. Wagner, M.R., Lundberg, D.S., del Rio, T.G., Tringe, S.G., Dangl, J.L., and Mitchell-Olds, T. (2016) Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat Commun 7: 12151.
180. Walters, W., Hyde, E.R., Berg-Lyons, D., Ackermann, G., Humphrey, G., Parada, A., et al. (2016) Improved bacterial 16S rRNA gene (V4 and V4-5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. mSystems 1: e00009–e00015.
181. Wang, Y., and Papenguth, H.W. (2001) Kinetic modeling of microbially-driven redox chemistry of radionuclides in subsurface environments: coupling transport, microbial metabolism and geochemistry. J Contam Hydrol 47: 297–309.
182. Wang, Z., Klipfell, E., Bennett, B.J., Koeth, R., Levison, B.S., DuGar, B., et al. (2011) Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472: 57–63.
183. Wang, Y., Tian, R.M., Gao, Z.M., Bougouffa, S. and Qian, P.Y. (2014) Optimal eukaryotic 18S and universal 16S/18S ribosomal RNA primers and their application in a study of symbiosis. PLoS One 9: 1–11.
184. Wang, M., Carver, J.J., Phelan, V.V., Sanchez, L.M., Garg, N., Peng, Y., et al. (2016) Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol 34: 828–837.
185. Ward, L., Taylor, M.W., Power, J.F., Scott, B.J., McDonald, I.R. and Stott, M.B. (2017) Microbial community dynamics in Inferno Crater Lake, a thermally fluctuating geothermal spring. ISME J i: 1–10.
186. Wilmes, P., Heintz-Buschart, A., and Bond, P.L. (2015) A decade of metaproteomics: where we stand and what the future holds. Proteomics 15: 3409–3417.
187. Winogradsky, S. (1887) Ueber schwefelbacterien. Bot Zeitung 31: 489–507.
188. Xiong, W., Abraham, P.E., Li, Z., Pan, C., and Hettich, R.L. (2015) Microbial metaproteomics for characterizing the range of metabolic functions and activities of human gut microbiota. Proteomics 15: 3424–3438.
189. Xu, A., Dolfing, J., Curtis, T.P., Montague, G., and Martin, E. (2011) Maintenance affects the stability of a two-tiered microbial “food chain”? J Theor Biol 276: 35–41.
190. Yang, B., Wang, Y., and Qian, P.-Y. (2016) Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis. BMC Bioinformatics 17: 135.
191. Yao, W., Gao, Y., and Wan, Z. (2017) Serum metabolomics profiling to identify biomarkers for unstable angina. Biomed Res Int 2017: 1–8.
192. Yoshizawa, J.M., Schafer, C.A., Schafer, J.J., Farrell, J.J., Paster, B.J., and Wong, D.T.W. (2013) Salivary biomarkers: toward future clinical and diagnostic utilities. Clin Microbiol Rev 26: 781–791.
193. Young, J.C., Pan, C., Adams, R.M., Brooks, B., Banfield, J.F., Morowitz, M.J., and Hettich, R.L. (2015) Metaproteomics reveals functional shifts in microbial and human proteins during a preterm infant gut colonization case. Proteomics 15: 3463–3473.
194. Zampieri, E., Chiapello, M., Daghino, S., Bonfante, P., and Mello, A. (2016) Soil metaproteomics reveals an inter-kingdom stress response to the presence of black truffles. Sci Rep 6: 25773.
195. Zeigler Allen, L., McCrow, J.P., Ininbergs, K., Dupont, C.L., Badger, J.H., Hoffman, J.M., et al. (2017) The Baltic Sea virome: diversity and transcriptional activity of DNA and RNA viruses. mSystems 2: 1–16.
196. Zelezniak, A., Andrejev, S., Ponomarova, O., Mende, D.R., Bork, P. and Patil, K.R. (2015) Correction for Zelezniak et al., Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc Natl Acad Sci USA 112: E7156.
197. Zengler, K. (ed) (2008) Accessing uncultivated microorganisms: from the environment to organisms and genomes and back. Washington, DC: ASM Press.
198. Zengler, K., and Palsson, B.O. (2012) A road map for the development of community systems (CoSy) biology. Nat Rev Microbiol 10: 366–372.
199. Zhang, Z., Scharer, J., and Moo-Young, M. (1997) Protein production using recombinant yeast in an immobilized-cell-film airlift bioreactor. Biotechnol Bioeng 55: 241–251.
200. Zhuang, K., Izallalen, M., Mouser, P., Richter, H., Risso, C., Mahadevan, R., and Lovley, D.R. (2011) Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments. ISME J 5: 305–316.
201. Zielinski, D.C., Filipp, F.V., Bordbar, A., Jensen, K., Smith, J.W., Herrgard, M.J., et al. (2015) Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis. Nat Commun 6: 7101.
202. Zuñiga, C., Li, C.-T., Huelsman, T., Levering, J., Zielinski, D.C., McConnell, B.O., et al. (2016) Genome-scale metabolic model for the green alga Chlorella vulgaris UTEX 395 accurately predicts phenotypes under autotrophic, heterotrophic, and mixotrophic growth conditions. Plant Physiol 172: 589–602.