Approaches and developments in studying the human microbiome network
In: Israel Journal of Ecology and EvolutionDepartment of Microbiology and Molecular Genetics, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem
Search for other papers by Mor Nitzan in
Current site
Google Scholar
Search for other papers by Sefi Mintzer in
Current site
Google Scholar
Search for other papers by Hanah Margalit in
Current site
Google Scholar
The human microbiome is dynamic and unique to each individual, and its role is being increasingly recognized in healthy physiology and in disease, including gastrointestinal and neuropsychiatric disorders. Therefore, characterizing the human microbiome and the factors that shape its bacterial population, how they are related to host-specific attributes, and understanding the ways in which it can be manipulated and the phenotypic consequences of such manipulations are of great importance. Characterization of the microbiome so far has been mostly based on compositional studies alone, where relative abundances of different species are compared in different conditions, such as health and disease. However, inter-relationships among the bacterial species, such as competition and cooperation over metabolic resources, may be an important factor that affects the structure and function of the microbiome. Here we review the network-based approaches in answering such questions and explore the first attempts that focus on the interactions facet, complementing compositional studies, towards understanding the microbiome structure and its complex relationship with the human host.
Purchase
Buy instant access (PDF download and unlimited online access):
Institutional Login
Log in with Open Athens, Shibboleth, or your institutional credentials
Personal login
Log in with your brill.com account
-
Aas J, , Gessert CE, , Bakken JS. 2003. Recurrent Clostridium difficile colitis: case series involving 18 patients treated with donor stool administered via a nasogastric tube. Clin Infect Dis. 36(5):580–585.
-
Achard S, , Salvador R, , Whitcher B, , Suckling J, , Bullmore E. 2006. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci. 26(1):63–72.
-
Albert R, , Jeong H, , Barabasi AL. 2000. Error and attack tolerance of complex networks. Nature. 406(6794):378–382.
-
Backhed F, , Ding H, , Wang T, , Hooper LV, , Koh GY, , Nagy A, , Semenkovich CF, , Gordon JI. 2004. The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci U S A. 101(44):15718–15723.
-
Backhed F, , Ley RE, , Sonnenburg JL, , Peterson DA, , Gordon JI. 2005. Host-bacterial mutualism in the human intestine. Science. 307(5717):1915–1920.
-
Barabasi AL, , Albert R. 1999. Emergence of scaling in random networks. Science. 286(5439):509–512.
-
Barabasi AL, , Oltvai ZN. 2004. Network biology: understanding the cell's functional organization. Nat Rev Genet. 5(2):101–113.
-
Bassett DS, , Bullmore ET. 2009. Human brain networks in health and disease. Curr Opin Neurol. 22(4):340–347.
-
Borenstein E, , Kupiec M, , Feldman MW, , Ruppin E. 2008. Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proc Natl Acad Sci USA. 105(38):14482–14487.
-
Borody TJ, , Warren EF, , Leis SM, , Surace R, , Ashman O, , Siarakas S. 2004. Bacteriotherapy using fecal flora: toying with human motions. J Clin Gastroenterol. 38(6):475–483.
-
Cho I, , Yamanishi S, , Cox L, , Methe BA, , Zavadil J, , Li K, , Gao Z, , Mahana D, , Raju K, , Teitler I, et al et al.. 2012. Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature. 488(7413):621–626.
-
Chung H, , Pap SJ, , Hill JA, , Surana NK, , Edelman SM, , Troy EB, , Reading NC, , Villablanca EJ, , Wang S, , Mora JR, et al et al.. 2012. Gut immune maturation depends on colonization with a host-specific microbiota. Cell. 149(7):1578–1593.
-
Cornwell WK, , Schwilk LD, , Ackerly DD. 2006. A trait-based test for habitat filtering: convex hull volume. Ecology. 87(6):1465–1471.
-
De Angelis M, , Picollo M, , Vannini L, , Siragusa S, , Giacomo AD, , Serrazzanetti DI, , Cristofori F, , Guerzoni ME, , Gobbetti M, , Ruggiero F. 2013. Fecal microbiota and metabolome of children with autism and pervasive developmental disorder not otherwise specified. PLoS One. 8(10):e76993.
-
Foster JA, , McVey Neufeld KA. 2013. Gut–brain axis: how the microbiome influences anxiety and depression. Trends Neurosci. 36(5):305–312.
-
Freilich S, , Kreimer A, , Melijson I, , Gophna U, , Sharan R, , Ruppin E. 2010. The large-scale organization of the bacterial network of ecological co-occurrence interactions. Nucleic Acids Res. 38(12):3857–3868.
-
Freilich S, , Zarecki R, , Eilam O, , Segal ES, , Henry CS, , Kupiec M, , Gophna U, , Sharan R, , Ruppin E. 2011. Competitive and cooperative metabolic interactions in bacterial communities. Nat Commun. 2:589.
-
Gough E, , Shaikh H, , Manges AR. 2011. Systematic review of intestinal microbiota transplantation (fecal bacteriotherapy) for recurrent Clostridium difficile infection. Clin Infect Dis. 53(10):994–1002.
-
Group, NIH HMP Working, Peterson J, , Garges S, , Giovanni M, , Mclnnes P, , Wang L, , Schloss JA, , Bonazzi V, , McEwen JE, , Wetterstrand KA, et al et al.. 2009. The NIH human microbiome project. Genome Res. 19(12):2317–2323.
-
Henry CS, , DeJongh M, , Best AA, , Frybarger PM, , Linsay B, , Stevens RL. 2010. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol. 28(9):977–982.
-
Human Microbiome Project, Consortium. 2012. Structure, function and diversity of the healthy human microbiome. Nature. 486(7402):207–214.
-
Jeong H, , Tombor B, , Albert R, , Oltvai ZN, , Barabasi AL. 2000. The large-scale organization of metabolic networks. Nature. 407(6804):651–654.
-
Joossens M, , Huys G, , Cnockaert M, , Preter VD, , Verbeke K, , Rutgeerts P, , Vandamme P, , Vermeire S. 2011. Dysbiosis of the faecal microbiota in patients with Crohn's disease and their unaffected relatives. Gut. 60(5):631–637.
-
Kinross JM, , Darzi AW, , Nicholson JK. 2011. Gut microbiome-host interactions in health and disease. Genome Med. 3(3):14.
-
Klitgord N, , Segre D. 2010. Environments that induce synthetic microbial ecosystems. PLoS Comput Biol. 6(11):e1001002.
-
Klitgord N, , Segre D. 2011. Ecosystems biology of microbial metabolism. Curr Opin Biotechnol. 22(4):541–546.
-
Koren O, , Goodrich JK, , Cullender TC, , Spor A, , Laitinen K, , Backhed HK, , Gonzalez A, , Werner JJ, , Angenent LT, , Knight R, et al et al.. 2012. Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell. 150(3):470–480.
-
Levy R, , Borenstein E. 2013. Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc Natl Acad Sci USA. 110(31):12804–12809.
-
Ley RE, , Backhed F, , Turnbaugh P, , Lozupone CA, , Knight RD, , Gordon JI. 2005. Obesity alters gut microbial ecology. Proc Natl Acad Sci USA. 102(31):11070–11075.
-
Manichanh C, , Rigottier-Gois L, , Bonnaud E, , Gloux K, , Pelletier E, , Frangeul L, , Nalin R, , Jarrin C, , Chardon P, , Marteauy P, et al et al.. 2006. Reduced diversity of faecal microbiota in Crohn's disease revealed by a metagenomic approach. Gut. 55(2):205–211.
-
Milo R, , Shen-Orr S, , Itzkovitz S, , Kashtan N, , Chklovskii D, , Alon U. 2002. Network motifs: simple building blocks of complex networks. Science. 298(5594):824–827.
-
Newman ME. 2001. The structure of scientific collaboration networks. Proc Natl Acad Sci USA. 98(2):404–409.
-
Newman ME. 2002. Assortative mixing in networks. Phys Rev Lett. 89(20):208701.
-
Olszak T, , An D, , Zeissig S, , Vera MP, , Richter J, , Franke A, , Glickman JN, , Siebert R, , Baron RM, , Kasper DL, , Blumberg RS. 2012. Microbial exposure during early life has persistent effects on natural killer T cell function. Science. 336(6080):489–493.
-
Qin J, , Li R, , Raes J, , Arumugam M, , Burgdorf KS, , Manichanh C, , Nielsen T, , Pons N, , Levenez F, , Yamada T, et al et al.. 2010. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 464(7285):59–65.
-
Qin J, , Li Y, , Cai Z, , Li S, , Zhu J, , Zhang F, , Liang S, , Zhang W, , Guan Y, , Shen D, et al et al.. 2012. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 490(7418):55–60.
-
Rubinov M, , Sporns O. 2010. Complex network measures of brain connectivity: uses and interpretations. Neuroimage. 52(3):1059–1069.
-
Sartor RB. 2008. Microbial influences in inflammatory bowel diseases. Gastroenterology. 134(2):577–594.
-
Saulnier DM, , Riehle K, , Mistretta T-A, , Diaz M-A, , Mandal D, , Raza S, , Weilder EM, , Qin X, , Coarfa C, , Milosavljevic A, et al et al.. 2011. Gastrointestinal microbiome signatures of pediatric patients with irritable bowel syndrome. Gastroenterology. 141(5):1782–1791.
-
Sharan R, , Ideker T. 2006. Modeling cellular machinery through biological network comparison. Nat Biotechnol. 24(4):427–433.
-
Shen-Orr SS, , Milo R, , Mangan S, , Alon U. 2002. Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet. 31(1):64–68.
-
Stolyar S, , Dien SV, , Hillesland KL, , Pinel N, , Lie TJ, , Leigh JA, , Stahl DA. 2007. Metabolic modeling of a mutualistic microbial community. Mol Syst Biol. 3:92.
-
Suweis S, , Simini F, , Banavar JR, , Maritan A. 2013. Emergence of structural and dynamical properties of ecological mutualistic networks. Nature. 500(7463):449–452.
-
Turnbaugh PJ, , Ley RE, , Mahowald MA, , Magrini V, , Mardis ER, Gordon JI. 2006. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 444(7122):1027–1031.
-
Turnbaugh PJ, , Hamady M, , Yatsunenko T, , Cantarel BL, , Duncan A, , Ley RE, , Sogin ML, , Jones WJ, , Roe BA, , Affourtit JP, et al et al.. 2009. A core gut microbiome in obese and lean twins. Nature. 457(7228):480–484.
-
van Nood E, , Vrieze A, , Nieuwdorp M, , Fuentes S, , Zoetendal EG, , de Vos WM, , Visser CE, , Kuijper EJ, , Bartelsman JFWM, , Tijssen JGP, et al et al.. 2013. Duodenal infusion of donor feces for recurrent Clostridium difficile. N Engl J Med. 368(5):407–415.
-
Vijay-Kumar M, , Aitken JD, , Carvalho FA, , Cullender TC, , Mwangi S, , Srinivasan S, , Sitaraman SV, , Knight R, , Ley RE, , Gewirtz AT. 2010. Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science. 328(5975):228–231.
-
Weiher E, , Clarke PGD, , Keddy PA. 1998. Community assembly rules, morphological dispersion, and the coexistence of plant species. Oikos. 81:309–322.
-
Wintermute EH, , Silver PA. 2010. Emergent cooperation in microbial metabolism. Mol Syst Biol. 6:407.
-
Yook SH, , Jeong H, , Barabasi AL. 2002. Modeling the Internet's large-scale topology. Proc Natl Acad Sci USA. 99(21):13382–13386.
| All Time | Past 365 days | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 326 | 81 | 20 |
| Full Text Views | 25 | 1 | 0 |
| PDF Views & Downloads | 24 | 2 | 0 |
Approaches and developments in studying the human microbiome network
In: Israel Journal of Ecology and EvolutionDepartment of Microbiology and Molecular Genetics, IMRIC, Faculty of Medicine, Hebrew University of Jerusalem
Search for other papers by Mor Nitzan in
Current site
Google Scholar
PubMed
Search for other papers by Sefi Mintzer in
Current site
Google Scholar
PubMed
Search for other papers by Hanah Margalit in
Current site
Google Scholar
PubMed
The human microbiome is dynamic and unique to each individual, and its role is being increasingly recognized in healthy physiology and in disease, including gastrointestinal and neuropsychiatric disorders. Therefore, characterizing the human microbiome and the factors that shape its bacterial population, how they are related to host-specific attributes, and understanding the ways in which it can be manipulated and the phenotypic consequences of such manipulations are of great importance. Characterization of the microbiome so far has been mostly based on compositional studies alone, where relative abundances of different species are compared in different conditions, such as health and disease. However, inter-relationships among the bacterial species, such as competition and cooperation over metabolic resources, may be an important factor that affects the structure and function of the microbiome. Here we review the network-based approaches in answering such questions and explore the first attempts that focus on the interactions facet, complementing compositional studies, towards understanding the microbiome structure and its complex relationship with the human host.
| All Time | Past 365 days | Past 30 Days | |
|---|---|---|---|
| Abstract Views | 326 | 81 | 20 |
| Full Text Views | 25 | 1 | 0 |
| PDF Views & Downloads | 24 | 2 | 0 |
