Exploring the interaction patterns among taxa and environments from marine metagenomic data

Abstract

The sequencing revolution driven by high-throughput technologies has generated a huge amount of marine microbial sequences which hide the interaction patterns among microbial species and environment factors. Exploring these patterns is helpful for exploiting the marine resources. In this paper, we use the complex network approach to mine and analyze the interaction patterns of marine taxa and environments in spring, summer, fall and winter seasons. With the 16S rRNA pyrosequencing data of 76 time point taken monthly over 6 years, we first use our MtHc clustering algorithm to generate the operational taxonomic units (OTUs). Then, employ the k-means method to divide 76 time point samples into four seasonal groups, and utilize mutual information (MI) to construct the four correlation networks among microbial species and environment factors. Finally, we adopt the symmetrical non-negative matrix factorization method to detect the interaction patterns, and analysis the relationship between marine species and environment factors. The results show that the four seasonal microbial interaction networks have the characters of complex networks, and interaction patterns are related with the seasonal variability; the same environmental factor influences different species in the four seasons; the four environmental factors of day length, photosynthetically active radiation, NO₂+NO₃ and silicate may have stronger influences on microbes than other environment factors.

Graphical abstract

Keywords

marine microbe / operational taxonomic unit / interaction pattern / network / clustering

References

[1] Sogin, M. L., Morrison, H. G., Huber, J. A., Mark Welch, D., Huse, S. M., Neal, P. R., Arrieta, J. M. and Herndl, G. J. (2006) Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc. Natl. Acad. Sci. USA, 103, 12115–12120

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

[3] Gilbert, J. A., Field, D., Swift, P., Thomas, S., Cummings, D., Temperton, B., Weynberg, K., Huse, S., Hughes, M., Joint, I., (2010) The taxonomic and functional diversity of microbes at a temperate coastal site: a “multi-omic” study of seasonal and diel temporal variation. PLoS One, 5, e15545

[4] Kirchman, D. L., Cottrell, M. T. and Lovejoy, C. (2010) The structure of bacterial communities in the western Arctic Ocean as revealed by pyrosequencing of 16S rRNA genes. Environ. Microbiol., 12, 1132–1143

[5] Jiang, X., Hua, X., Xu, W. and Park, E.K. (2015) Predicting microbial interactions using vector autoregressive model with graph regularization. IEEE ACM T COMPUT. BI., 12, 254–261

[6] Zhou, J., Deng, Y., Luo, F., He, Z. and Yang, Y. (2011) Phylogenetic molecular ecological network of soil microbial communities in response to elevated CO₂. MBio, 2, e00122–e11

[7] Gilbert, J. A., Steele, J. A., Caporaso, J. G., Steinbrück, L., Reeder, J., Temperton, B., Huse, S., McHardy, A. C., Knight, R., Joint, I., (2012) Defining seasonal marine microbial community dynamics. ISME J., 6, 298–308

[8] Eiler, A., Heinrich, F. and Bertilsson, S. (2012) Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J., 6, 330–342

[9] Faust, K. and Raes, J. (2012) Microbial interactions: from networks to models. Nat. Rev. Microbiol., 10, 538–550

[10] Wei, Z. G. and Zhang, S. W. (2015) MtHc: a motif-based hierarchical method for clustering massive 16S rRNA sequences into OTUs. Mol. Biosyst., 11, 1907–1913

[11] Chen, W., Cheng, Y.M., Zhang, C., Zhang, S.W., Zhao, H. (2013) MSClust: A multi-seeds based clustering algorithm for microbiome profiling using 16S rRNA sequence. J. Micro. methods, 94, 347–355

[12] Cai, Y. and Sun, Y. (2011) ESPRIT-Tree: hierarchical clustering analysis of millions of 16S rRNA pyrosequences in quasilinear computational time. Nucleic Acids Res., 39, e95

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

[14] Cheng, L., Walker, A. W. and Corander, J. (2012) Bayesian estimation of bacterial community composition from 454 sequencing data. Nucleic Acids Res., 40, 5240–5249

[15] Wang, J., Chen, B., Wang, Y., Wang, N., Garbey, M., Tran-Son-Tay, R., Berceli, S. A. and Wu, R. (2013) Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information. Nucleic Acids Res., 41, e97

[16] DeSantis, T. Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E. L., Keller, K., Huber, T., Dalevi, D., Hu, P. and Andersen, G. L. (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol., 72, 5069–5072

[17] Pruesse, E., Quast, C., Knittel, K., Fuchs, B. M., Ludwig, W., Peplies, J. and Glöckner, F. O. (2007) SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res., 35, 7188–7196

[18] Cole, J. R., Chai, B., Farris, R. J., Wang, Q., Kulam, S. A., McGarrell, D. M., Garrity, G. M. and Tiedje, J. M. (2005) The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA analysis. Nucleic Acids Res., 33, D294–D296

[19] Saraph, V. and Milenković, T. (2014) MAGNA, maximizing accuracy in global network alignment. Bioinformatics, 30, 2931–2940

[20] Liu, F., Zhang, S. W., Wei, Z. G., Chen, W. and Zhou, C. (2014) Mining seasonal marine microbial pattern with greedy heuristic clustering and symmetrical nonnegative matrix factorization. Biomed. Res. Int., 2014, 189590

[21] Lee, D. D. and Seung, H. S. (1999) Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788–791

[22] Nepusz, T., Petróczi, A., Négyessy, L. and Bazsó, F. (2008) Fuzzy communities and the concept of bridgeness in complex networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 77, 016107

ACKNOWLEDGEMENTS

This paper was supported by the National Natural Science Foundation of China (Nos. 91430111, 61473232 and 61170134).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Ze-Gang Wei, Shao-Wu Zhang and Fang Jing declare 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

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg