Mapping the microbial interactome: Statistical and experimental approaches for microbiome network inference

Experimental Biology and Medicine - Tập 244 Số 6 - Trang 445-458 - 2019
Anders B. Dohlman1, Xiling Shen1
1Department of Biomedical Engineering, Duke University, Durham, NC 27710 USA

Tóm tắt

Advances in high-throughput sequencing have ushered in a new era of research into the gut microbiome and its role in human health and disease. However, due to the unique characteristics of microbiome survey data, their use for the detection of ecological interaction networks remains a considerable challenge, and a field of active methodological development. In this review, we discuss the landscape of existing statistical and experimental methods for detecting and characterizing microbial interactions, as well as the role that host and environmental metabolic signals play in mediating the behavior of these networks. Numerous statistical tools for microbiome network inference have been developed. Yet due to tool-specific biases, the networks identified by these methods are often discordant, motivating a need for the development of more general tools, the use of ensemble approaches, and the incorporation of prior knowledge into prediction. By elucidating the complex dynamics of the microbial interactome, we will enhance our understanding of the microbiome’s role in disease, more precisely predict the microbiome’s response to perturbation, and inform the development of future therapeutic strategies for microbiome-related disease.Impact statementThis review provides a comprehensive description of experimental and statistical tools used for network analyses of the human gut microbiome. Understanding the system dynamics of microbial interactions may lead to the improvement of therapeutic approaches for managing microbiome-associated diseases. Microbiome network inference tools have been developed and applied to both cross-sectional and longitudinal experimental designs, as well as to multi-omic datasets, with the goal of untangling the complex web of microbe-host, microbe-environmental, and metabolism-mediated microbial interactions. The characterization of these interaction networks may lead to a better understanding of the systems dynamics of the human gut microbiome, augmenting our knowledge of the microbiome’s role in human health, and guiding the optimization of effective, precise, and rational therapeutic strategies for managing microbiome-associated disease.

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Tài liệu tham khảo

10.1111/j.1469-0691.2012.03916.x

10.1038/sj.embor.7400731

10.1093/dnares/dsn033

10.1038/nature18309

10.1038/nature07540

10.1186/gb-2014-15-6-r76

10.1016/j.clim.2015.05.013

10.1136/gutjnl-2013-305994

10.1186/gb-2012-13-9-r79

10.1136/gutjnl-2016-313235

10.1159/000380892

10.1007/s11920-012-0337-0

10.3389/fncel.2017.00120

10.1016/j.bbi.2016.12.010

10.1016/j.cell.2016.11.018

10.1515/revneuro-2017-0072

10.3748/wjg.v21.i1.102

10.1186/s40168-016-0225-7

10.9758/cpn.2016.14.3.231

10.1136/archdischild-2013-305799

10.1016/S0140-6736(13)61218-0

10.1136/bmjopen-2017-018115

10.1186/1745-6215-15-170

10.1038/nrmicro2419

10.1039/B916989J

Lovell D, 2011, Composit Data Anal, 193

10.1016/j.annepidem.2016.03.002

Aitchison J., 1982, J R Stat Soc B Met, 44, 139, 10.1111/j.2517-6161.1982.tb01195.x

10.1146/annurev-statistics-010814-020351

10.1371/journal.pcbi.1002687

10.1128/mSphere.00047-17

10.1038/nrmicro2832

10.1038/s41467-017-02090-2

10.1038/ismej.2015.235

Pawlowsky-Glahn V, 2015, Stat Pract, 1

10.1371/journal.pcbi.1004226

10.1186/s40168-017-0393-0

10.1093/bioinformatics/btv349

10.1093/bioinformatics/btv364

10.1371/journal.pcbi.1005852

10.1371/journal.pcbi.1005915

10.1126/science.1205438

10.1093/bioinformatics/btl417

10.1080/10934529.2018.1439856

10.1093/bioinformatics/bts668

10.1186/1471-2105-13-113

10.1371/journal.pcbi.1002606

10.1186/s40168-017-0355-6

10.1038/s41598-018-30569-5

10.1186/gb-2011-12-5-r50

10.1371/journal.pone.0102451

10.1186/s12859-016-1359-0

10.1371/journal.pcbi.1002624

10.1186/s13059-016-0980-6

10.3389/fmicb.2018.00036

10.1007/s00422-006-0098-0

10.1371/journal.pcbi.1005364

10.1126/science.1227079

10.2307/1912791

10.1098/rsta.2011.0613

10.1021/j150111a004

10.1038/118558a0

10.1128/AEM.01663-07

10.1371/journal.pcbi.1003388

10.1016/j.mib.2018.07.004

10.1073/pnas.1206855109

10.7554/eLife.25051

10.1038/nrg3244

10.1186/gb-2006-7-5-r36

10.1093/bioinformatics/btu274

10.1093/bioinformatics/btx549

10.1214/12-AOAS592

West M, Harrison J. Bayesian forecasting and dynamic models. 2nd ed. New York: Springer, 1997, pp.xiv–680

10.1371/journal.pcbi.1003388

10.1073/pnas.1311322111

10.1038/nature13828

10.1016/j.jmb.2014.03.017

10.1016/j.febslet.2014.02.037

10.1038/ncomms11965

10.1098/rsif.2016.0966

10.1073/pnas.1501305112

10.1038/ismej.2008.36

Trosvik P, 2010, Environ Microbiol, 12, 2677

10.1038/nature11234

Blaut M., 2013, Curr Top Microbiol Immunol, 358, 247

Mackie RI, 1997, Gastrointestinal microbiology

10.1038/nchembio.739

10.3389/fcimb.2017.00396

10.3390/metabo7040062

10.1093/nar/gkx295

Larsen PE, 2011, Bioinformatics, 337

10.1371/journal.pcbi.1002358

10.1038/nbt.2676

10.1093/bioinformatics/btv287

10.1371/journal.pone.0166104

10.1073/pnas.1319284111

10.1038/ismej.2008.108

10.1136/gutjnl-2012-303184

10.1128/mSystems.00013-15

10.1002/cem.775

10.1093/biomet/28.3-4.321

10.1111/j.1365-2427.1994.tb01741.x

10.3390/metabo7040062

10.1038/ncomms15393

10.1016/j.cmet.2011.02.018

10.1038/nature12721

10.1038/nrmicro3344

10.1038/nbt.2488

10.1038/nprot.2009.203

10.1038/msb.2009.77

Knight JM, 2014, Sci Rep, 4, 10.1038/srep05453

10.1038/ncomms11535

10.1101/gad.212522.112

10.1038/nature11053

10.1038/s41591-018-0210-8

10.1038/nature12820

10.7554/eLife.05224

10.1038/ismej.2009.144

10.1371/journal.pone.0050267

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Experimental Biology and Medicine

Tập 244 Số 6

445-458

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