Fungi stabilize connectivity in the lung and skin microbial ecosystems

Microbiome - Tập 6 - Trang 1-14 - 2018
Laura Tipton1,2, Christian L. Müller3, Zachary D. Kurtz4, Laurence Huang5, Eric Kleerup6, Alison Morris7, Richard Bonneau2,3, Elodie Ghedin2,8
1Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, USA
2Center for Genomics and Systems Biology, New York University, New York, USA
3Flatiron Institute, Center for Computational Biology, Simons Foundation, New York, USA
4Department of Microbiology, New York University School of Medicine, New York, USA.
5Division of Pulmonary and Critical Care Medicine and HIV/AIDS Division, University of California San Francisco, San Francisco, USA
6Division of Pulmonary and Critical Care, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
7Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, USA
8Department of Epidemiology, College of Global Public Health, New York University, New York, USA

Tóm tắt

No microbe exists in isolation, and few live in environments with only members of their own kingdom or domain. As microbiome studies become increasingly more interested in the interactions between microbes than in cataloging which microbes are present, the variety of microbes in the community should be considered. However, the majority of ecological interaction networks for microbiomes built to date have included only bacteria. Joint association inference across multiple domains of life, e.g., fungal communities (the mycobiome) and bacterial communities, has remained largely elusive. Here, we present a novel extension of the SParse InversE Covariance estimation for Ecological ASsociation Inference (SPIEC-EASI) framework that allows statistical inference of cross-domain associations from targeted amplicon sequencing data. For human lung and skin micro- and mycobiomes, we show that cross-domain networks exhibit higher connectivity, increased network stability, and similar topological re-organization patterns compared to single-domain networks. We also validate in vitro a small number of cross-domain interactions predicted by the skin association network. For the human lung and skin micro- and mycobiomes, our findings suggest that fungi play a stabilizing role in ecological network organization. Our study suggests that computational efforts to infer association networks that include all forms of microbial life, paired with large-scale culture-based association validation experiments, will help formulate concrete hypotheses about the underlying biological mechanisms of species interactions and, ultimately, help understand microbial communities as a whole.

Tài liệu tham khảo

Deng Y, Jiang Y-H, Yang Y, He Z, Luo F, Zhou J. Molecular ecological network analyses. BMC Bioinformatics. 2012;13:113. Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015;11:e1004226. Friedman J, Alm EJ. Inferring correlation networks from genomic survey data. von Mering C, editor. PLoS Comput Biol. 2012;8:e1002687. He X, McLean JS, Edlund A, Yooseph S, Hall AP, Liu S-Y, et al. Cultivation of a human-associated TM7 phylotype reveals a reduced genome and epibiotic parasitic lifestyle. Proc Natl Acad Sci. 2015;112:244–9. Ruiz VE, Battaglia T, Kurtz ZD, Bijnens L, Ou A, Engstrand I, et al. A single early-in-life macrolide course has lasting effects on murine microbial network topology and immunity. Nat Commun. 2017;8:518. Mukherjee PK, Chandra J, Retuerto M, Sikaroodi M, Brown RE, Jurevic R, et al. Oral Mycobiome analysis of HIV-infected patients: identification of Pichia as an antagonist of opportunistic fungi. PLoS Pathog. 2014;10:e1003996. Seed PC. The human mycobiome. Cold Spring Harb Perspect Med United States. 2015;5:a019810. Hoffmann C, Dollive S, Grunberg S, Chen J, Li H, Wu GD, et al. Archaea and fungi of the human gut microbiome: correlations with diet and bacterial residents. PLoS One. 2013;8:e66019. Tarkka M, Deveau AL. An emerging interdisciplinary field: fungal–bacterial interactions. In: Irina S. Druzhinina, Christian P Kubicek, editors. Mycota IV. 3rd ed. Springer, Switzerland; 2016. p. 162–178. Morris A, Beck JM, Schloss PD, Campbell TB, Crothers K, Curtis JL, et al. Comparison of the respiratory microbiome in healthy nonsmokers and smokers. Am J Respir Crit Care Med. 2013;187:1067–75. Cui L, Lucht L, Tipton L, Rogers MB, Fitch A, Kessinger C, et al. Topographic diversity of the respiratory tract mycobiome and alteration in HIV and lung disease. Am J Respir Crit Care Med. 2015;191:932–42. Grice EA, Kong HH, Conlan S, Deming CB, Davis J, Young AC, et al. Topographical and temporal diversity of the human skin microbiome. Science. 2009;324:1190–2. Findley K, Oh J, Yang J, Conlan S, Deming C, Meyer JA, et al. Human skin fungal diversity. Nature. 2013;498:367–70. Newman MEJ. Fast algorithm for detecting community structure in networks. Phys Rev E. 2004;69:66133. Poisot T. An a posteriori measure of network modularity. F1000Research. 2013;2:130. https://doi.org/10.12688/f1000research.2-130.v3. Estrada E. Topological structural classes of complex networks. Phys Rev E. 2007;75:16103. Ghosh A, Boyd S, Saberi A. Minimizing effective resistance of a graph. SIAM Rev. 2008;50:37–66. McRae BH, Dickson BG, Keitt TH, Shah VB. Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology. 2008;89:2712–24. Iyer S, Killingback T, Sundaram B, Wang Z. Attack robustness and centrality of complex networks. Hayasaka S, editor. PLoS One. 2013;8:e59613. Trofa D, Gacser A, Nosanchuk JD. Candida parapsilosis, an emerging fungal pathogen. Clin Microbiol Rev. 2008;21:606–25. Faust K, Lima-Mendez G, Lerat J-S, Sathirapongsasuti JF, Knight R, Huttenhower C, et al. Cross-biome comparison of microbial association networks. Front Microbiol. 2015;6:1200. Schroeckh V, Scherlach K, Nutzmann H-W, Shelest E, Schmidt-Heck W, Schuemann J, et al. Intimate bacterial-fungal interaction triggers biosynthesis of archetypal polyketides in Aspergillus nidulans. Proc Natl Acad Sci. 2009;106:14558–63. Cao Y, Lin W, Li H. Large Covariance Estimation for Compositional Data via Composition-Adjusted Thresholding. 2016;arXiv:1601.04397. Dollive S, Peterfreund GL, Sherrill-Mix S, Bittinger K, Sinha R, Hoffmann C, et al. A tool kit for quantifying eukaryotic rRNA gene sequences from human microbiome samples. Genome Biol. 2012;13:R60. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods. 2010;7:335–6. Dannemiller KC, Reeves D, Bibby K, Yamamoto N, Peccia J. Fungal High-throughput Taxonomic Identification tool for use with Next-Generation Sequencing (FHiTINGS). J Basic Microbiol. 2014;54:315–21. DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72. Aitchison J. A new approach to null correlations of proportions. J Int Assoc Math Geol. 1981;13:175–89. Liu H, Roeder K, Wasserman L. Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. Adv Neural Inf Process Syst. 2010;24:1432–40. Csárdi G, Nepusz T. The igraph software package for complex network research. InterJournal. 2006; Complex Systems:1695. [http://igraph.org]. Welch BL. The generalisation of student’s problems when several different population variances are involved. Biometrika. 1947;34:28–35.
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Microbiome

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