Constructing a Boolean implication network to study the interactions between environmental factors and OTUs

Abstract

Mining relationships between microbes and the environment they live in are crucial to understand the intrinsic mechanisms that govern cycles of carbon, nitrogen and energy in a microbial community. Building upon next-generation sequencing technology, the selective capture of 16S rRNA genes has enabled the study of co-occurrence patterns of microbial species from the viewpoint of complex networks, yielding successful descriptions of phenomena exhibited in a microbial community. However, since the effects of such environmental factors as temperature or soil conditions on microbes are complex, reliance on the analysis of co-occurrence networks alone cannot elucidate such complicated effects underlying microbial communities. In this study, we apply a statistical method, which is called Boolean implications for metagenomic studies (BIMS) for extracting Boolean implications (IF-THEN relationships) to capture the effects of environmental factors on microbial species based on 16S rRNA sequencing data. We first demonstrate the power and effectiveness of BIMS through comprehensive simulation studies and then apply it to a 16S rRNA sequencing dataset of real marine microbes. Based on a total of 6,514 pairwise relationships identified at a low false discovery rate (FDR) of 0.01, we construct a Boolean implication network between operational taxonomic units (OTUs) and environmental factors. Relationships in this network are supported by literature, and, most importantly, they bring biological insights into the effects of environmental factors on microbes. We next apply BIMS to detect three-way relationships and show the possibility of using this strategy to explain more complex relationships within a microbial community.

Graphical abstract

Keywords

Boolean implication / metagenome / marine OTUs / environmental factors

References

[1] Wooley, J. C., Godzik, A. and Friedberg, I. (2010) A primer on metagenomics. PLOS Comput. Biol., 6, e1000667

[2] Chaffron, S., Rehrauer, H., Pernthaler, J. and von Mering, C. (2010) A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Res., 20, 947-959

[3] Amann, R. I., Ludwig, W. and Schleifer, K. H. (1995) Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol. Rev., 59, 143-169

[4] Pace, N. R. (1997) A molecular view of microbial diversity and the biosphere. Science, 276, 734-740

[5] Rappé, M. S. and Giovannoni, S. J. (2003) The uncultured microbial majority. Annu. Rev. Microbiol., 57, 369-394

[6] King, A. J., Farrer, E. C., Suding, K. N. and Schmidt, S. K. (2012) Co-occurrence patterns of plants and soil bacteria in the high-alpine subnival zone track environmental harshness. Front. Microbiol, 3, 347

[7] Rondon, M. R., August, P. R., Bettermann, A. D., Brady, S. F., Grossman, T. H., Liles, M. R., Loiacono, K. A., Lynch, B. A., MacNeil, I. A., Minor, C., (2000) Cloning the soil metagenome: a strategy for accessing the genetic and functional diversity of uncultured microorganisms. Appl. Environ. Microbiol., 66, 2541-2547

[8] Voget, S., Leggewie, C., Uesbeck, A., Raasch, C., Jaeger, K. E. and Streit, W. R. (2003) Prospecting for novel biocatalysts in a soil metagenome. Appl. Environ. Microbiol., 69, 6235-6242

[9] Nautiyal, C. S., Chauhan, P. S. and Nene, Y. L. (2007) Medicinal smoke reduces airborne bacteria. J. Ethnopharmacol., 114, 446-451

[10] Ortiz, G., Yagüe, G., Segovia, M. and Catalán, V. (2009) A study of air microbe levels in different areas of a hospital. Curr. Microbiol., 59, 53-58

[11] Martinez, A., Tyson, G. W. and Delong, E. F. (2010) Widespread known and novel phosphonate utilization pathways in marine bacteria revealed by functional screening and metagenomic analyses. Environ. Microbiol., 12, 222-238

[12] Steele, J. A., Countway, P. D., Xia, L., Vigil, P. D., Beman, J. M., Kim, D. Y., Chow, C. E., Sachdeva, R., Jones, A. C., Schwalbach, M. S., (2011) Marine bacterial, archaeal and protistan association networks reveal ecological linkages. ISME J., 5, 1414-1425

[13] Stein, J. L., Marsh, T. L., Wu, K. Y., Shizuya, H. and DeLong, E. F. (1996) Characterization of uncultivated prokaryotes: isolation and analysis of a 40-kilobase-pair genome fragment from a planktonic marine archaeon. J. Bacteriol., 178, 591-599

[14] Tseng, C. H. and Tang, S. L. (2014) Marine microbial metagenomics: from individual to the environment. Int. J. Mol. Sci., 15, 8878-8892

[15] Woyke, T., Xie, G., Copeland, A., González, J. M., Han, C., Kiss, H., Saw, J. H., Senin, P., Yang, C., Chatterji, S., (2009) Assembling the marine metagenome, one cell at a time. PLoS One, 4, e5299

[16] Manichanh, C., Rigottier-Gois, L., Bonnaud, E., Gloux, K., Pelletier, E., Frangeul, L., Nalin, R., Jarrin, C., Chardon, P., Marteau, P., (2006) Reduced diversity of faecal microbiota in Crohn’s disease revealed by a metagenomic approach. Gut, 55, 205-211

[17] Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., Liang, S., Zhang, W., Guan, Y., Shen,D., (2012) A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature, 490, 55-60

[18] Cho, I. and Blaser, M. J. (2012) The human microbiome: at the interface of health and disease. Nat. Rev. Genet., 13, 260-270

[19] Rasheed, Z., Rangwala, H. and Barbará, D. (2013) 16S rRNA metagenome clustering and diversity estimation using locality sensitive hashing. BMC Syst. Biol., 7, S11

[20] Eren, A. M., Maignien, L., Sul, W. J., Murphy, L. G. and Grim, S. L. (2013) Oligotyping: Differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol., 4

[21] Chen, W., Zhang, C. K., Cheng, Y., Zhang, S. and Zhao, H. (2013) A comparison of methods for clustering 16S rRNA sequences into OTUs. PLoS One, 8, e70837

[22] Hao, X., Jiang, R. and Chen, T. (2011) Clustering 16S rRNA for OTU prediction: a method of unsupervised Bayesian clustering. Bioinformatics, 27, 611-618

[23] Schloss, P. D. and Handelsman, J. (2005) Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl. Environ. Microbiol., 71, 1501-1506

[24] Huse, S. M., Welch, D. M., Morrison, H. G. and Sogin, M. L. (2010) Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ. Microbiol., 12, 1889-1898

[25] Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., Lesniewski, R. A., Oakley, B. B., Parks, D. H., Robinson, C. J., (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol., 75, 7537-7541

[26] Assenov, Y., Ramírez, F., Schelhorn, S. E., Lengauer, T. and Albrecht, M. (2008) Computing topological parameters of biological networks. Bioinformatics, 24, 282-284

[27] Li, W. and Godzik, A. (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics, 22, 1658-1659

[28] Li, W., Jaroszewski, L. and Godzik, A. (2001) Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics, 17, 282-283

[29] Sahoo, D., Dill, D. L., Gentles, A. J., Tibshirani, R. and Plevritis, S. K. (2008) Boolean implication networks derived from large scale, whole genome microarray datasets. Genome Biol., 9, R157

[30] Watts, D. J. and Strogatz, S. H. (1998) Collective dynamics of ‘small-world’ networks. Nature, 393, 440-442

[31] Montoya, J. M., Pimm, S. L. and Solé, R. V. (2006) Ecological networks and their fragility. Nature, 442, 259-264

[32] Timothy Pennington, J. and Chavez, F. P. (2000) Seasonal fluctuations of temperature, salinity, nitrate, chlorophyll and primary production at station H3/M1 over 1989-1996 in Monterey Bay, California. Deep Sea Res. Part II Top. Stud. Oceanogr., 47, 947-973

[33] Edgar, R. C. (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26, 2460-2461

[34] Sahoo, D., Dill, D. L., Tibshirani, R. and Plevritis, S. K. (2007) Extracting binary signals from microarray time-course data. Nucleic Acids Res., 35, 3705-3712

[35] Sinha, S., Tsang, E. K., Zeng, H., Meister, M. and Dill, D. L. (2014) Mining TCGA data using Boolean implications. PLoS One, 9, e102119

[36] Fuhrman, J. A., Hewson, I., Schwalbach, M. S., Steele, J. A., Brown, M. V. and Naeem, S. (2006) Annually reoccurring bacterial communities are predictable from ocean conditions. Proc. Natl. Acad. Sci. USA, 103, 13104-13109

[37] Vigil, P., Countway, P. D., Rose, J., Lonsdale, D. J. and Gobler, C. J. (2009) Rapid shifts in dominant taxa among microbial eukaryotes in estuarine ecosystems. Aquat. Microb. Ecol., 54, 83-100

ACKNOWLEDGEMENTS

This research was partially supported by the National Basic Research Program of China (2012CB316504), the National High Technology Research and Development Program of China (2012AA020401), the National Natural Science Foundation of China (61175002), the Recruitment Program of Global Experts of China, and Tsinghua National Laboratory for Information Science and Technology (TNList). The authors declare that they have no competing interests.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Congmin Zhu, Rui Jiang and Ting Chen declare that they have no conflict of interests.

This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg