Molecular ecological network analyses

BMC Bioinformatics

Ye Deng⁵  Yi-Huei Jiang³  Yunfeng Yang²  Zhili He³  Feng Luo⁴  Jizhong Zhou¹ 

[1] Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA

[2] State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China

[3] Institute for Environmental Genomics and Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019, USA

[4] School of Computing, Clemson University, Clemson, SC 29634, USA

[5] Glomics Inc, Norman, OK 73072, USA

关键词: Environmental changes;    Network interaction;    Microbiological ecology;    Microbial community;    Random Matrix Theory;    Ecological network;   

Others  :  1088257
DOI  :  10.1186/1471-2105-13-113

 received in 2011-12-29, accepted in 2012-05-30,  发布年份 2012

【 摘 要 】

Background

Understanding the interaction among different species within a community and their responses to environmental changes is a central goal in ecology. However, defining the network structure in a microbial community is very challenging due to their extremely high diversity and as-yet uncultivated status. Although recent advance of metagenomic technologies, such as high throughout sequencing and functional gene arrays, provide revolutionary tools for analyzing microbial community structure, it is still difficult to examine network interactions in a microbial community based on high-throughput metagenomics data.

Results

Here, we describe a novel mathematical and bioinformatics framework to construct ecological association networks named molecular ecological networks (MENs) through Random Matrix Theory (RMT)-based methods. Compared to other network construction methods, this approach is remarkable in that the network is automatically defined and robust to noise, thus providing excellent solutions to several common issues associated with high-throughput metagenomics data. We applied it to determine the network structure of microbial communities subjected to long-term experimental warming based on pyrosequencing data of 16 S rRNA genes. We showed that the constructed MENs under both warming and unwarming conditions exhibited topological features of scale free, small world and modularity, which were consistent with previously described molecular ecological networks. Eigengene analysis indicated that the eigengenes represented the module profiles relatively well. In consistency with many other studies, several major environmental traits including temperature and soil pH were found to be important in determining network interactions in the microbial communities examined. To facilitate its application by the scientific community, all these methods and statistical tools have been integrated into a comprehensive Molecular Ecological Network Analysis Pipeline (MENAP), which is open-accessible now (http://ieg2.ou.edu/MENA webcite).

Conclusions

The RMT-based molecular ecological network analysis provides powerful tools to elucidate network interactions in microbial communities and their responses to environmental changes, which are fundamentally important for research in microbial ecology and environmental microbiology.

【 授权许可】

   
2012 Deng et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Fig. 1.

【 参考文献 】

• [1]Montoya JM, Pimm SL, Sole RV: Ecological networks and their fragility. Nature 2006, 442(7100):259-264.
• [2]May RM: Stability and complexity in model ecosystems. Princeton University Press, Princeton, New Jersey; 1973.
• [3]Pim S: Food webs. Chapman & Hall, London; 1982.
• [4]Dunne JA, Williams RJ, Martinez ND: Food-web structure and network theory: The role of connectance and size. Proc Natl Acad Sci USA 2002, 99(20):12917-12922.
• [5]Montoya JM, Sole RV: Small world patterns in food webs. J Theor Biol 2002, 214(3):405-412.
• [6]Rezende EL, Lavabre JE, Guimaraes PR, Jordano P, Bascompte J: Non-random coextinctions in phylogenetically structured mutualistic networks. Nature 2007, 448(7156):925-U926.
• [7]Raes J, Bork P: Molecular eco-systems biology: towards an understanding of community function. Nat Rev Miobiol 2008, 6(9):693-699.
• [8]Barabasi AL, Oltvai ZN: Network biology: Understanding the cell's functional organization. Nat Rev Genet 2004, 5(2):101-U115.
• [9]Akutsu T, Miyano S, Kuhara S: Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Pac Symp Biocomput 1999,  :17-28.
• [10]Gardner TS, di Bernardo D, Lorenz D, Collins JJ: Inferring genetic networks and identifying compound mode of action via expression profiling. Science 2003, 301(5629):102-105.
• [11]Liang S, Fuhrman S, Somogyi R: Reveal, a general reverse engineering algorithm for inference of genetic network architectures. Pac Symp Biocomput 1998, 3:18-29.
• [12]Yeung MKS, Tegner J, Collins JJ: Reverse engineering gene networks using singular value decomposition and robust regression. Proc Natl Acad Sci USA 2002, 99(9):6163-6168.
• [13]Friedman N, Linial M, Nachman I: Pe'er D: Using Bayesian networks to analyze expression data. Journal of Computational Biology 2000, 7(3–4):601-620.
• [14]Gerstung M, Baudis M, Moch H, Beerenwinkel N: Quantifying cancer progression with conjunctive Bayesian networks. Bioinformatics 2009, 25(21):2809-2815.
• [15]Butte AJ, Tamayo P, Slonim D, Golub TR, Kohane IS: Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci USA 2000, 97(22):12182-12186.
• [16]Chen Y, Zhu J, Lum PY, Yang X, Pinto S, MacNeil DJ, Zhang C, Lamb J, Edwards S, Sieberts SK, et al.: Variations in DNA elucidate molecular networks that cause disease. Nature 2008, 452(7186):429-435.
• [17]Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Qi S, Chen Z, et al.: Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci USA 2006, 103(46):17402-17407.
• [18]Oldham MC, Horvath S, Geschwind DH: Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci USA 2006, 103(47):17973-17978.
• [19]Schmitt WA: Raab RM, Stephanopoulos G: Elucidation of gene interaction networks through time-lagged correlation analysis of transiptional data. Genome Res 2004, 14(8):1654-1663.
• [20]Zhang B, Horvath S: A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Bio 2005, 4:17.
• [21]Gardner TS, Faith JJ: Reverse-engineering transiption control networks. Phys Life Rev 2005, 2(1):65-88.
• [22]Luo F, Yang Y, Zhong J, Gao H, Khan L, Thompson DK, Zhou J: Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory. BMC Bioinformatics 2007, 8:299. BioMed Central Full Text
• [23]Luo F, Zhong JX, Yang YF, Scheuermann RH, Zhou JZ: Application of random matrix theory to biological networks. Phys Lett A 2006, 357(6):420-423.
• [24]Yang Y, Harris DP, Luo F, Xiong W, Joachimiak M, Wu L, Dehal P, Jacobsen J, Yang Z, Palumbo AV, et al.: Snapshot of iron response in Shewanella oneidensis by gene network reconstruction. BMC Genomics 2009, 10(1):131. BioMed Central Full Text
• [25]Handelsman J, Tiedje JM, Alvarez-Cohen L, Ashburner M, Cann IKO, DeLong EF, Doolittle WF, Fraser-Liggett CM, Godzik A, Gordon JI, et al.: Committee on Metagenomics: Challenges and Functional Applications. In . National Academy of Sciences, Washington; 2007:1-158.
• [26]He Z, Gentry TJ, Schadt CW, Wu L, Liebich J, Chong SC, Huang Z, Wu W, Gu B, Jardine P, et al.: GeoChip: a comprehensive mioarray for investigating biogeochemical, ecological and environmental processes. ISME J 2007, 1(1):67-77.
• [27]Zhou J, Deng Y, Luo F, He Z: Tu Q. Zhi X: Functional molecular ecological networks. mBio 2010, 1(4):e00169-00110.
• [28]Zhou J, Deng Y, Luo F, He Z, Yang Y: Phylogenetic molecular ecological network of soil miobial communities in response to elevated CO2. mBio 2011, 2(4):e00122–11.
• [29]Bascompte J: Networks in ecology. Basic Appl Ecol 2007, 8(6):485-490.
• [30]Dunne JA, Williams RJ, Martinez ND, Wood RA, Erwin DH: Compilation and network analyses of cambrian food webs. PLoS Biol 2008, 6(4):e102.
• [31]Dunne JA: The network structure of food webs. In: Ecological Networks: Linking Structure to Dynamics in Food Webs. Edited by M. P, Dunne JA. Oxford: Oxford University Press 2006,  :27-86.
• [32]Chaffron S, Rehrauer H, Pernthaler J, von Mering C: A global network of coexisting miobes from environmental and whole-genome sequence data. Genome Res 2010, 20(7):947-959.
• [33]Guimera R, Sales-Pardo M, Amaral LA: Classes of complex networks defined by role-to-role connectivity profiles. Nat Phys 2007, 3(1):63-69.
• [34]Brandes U, Erlebach T: Network analysis: methodological foundations. Springer, Berlin; 2005.
• [35]Bonacich P: Power and Centrality - a Family of Measures. Am J Sociol 1987, 92(5):1170-1182.
• [36]Watts DJ, Strogatz SH: Collective dynamics of 'small-world' networks. Nature 1998, 393(6684):440-442.
• [37]Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabasi AL: Hierarchical organization of modularity in metabolic networks. Science 2002, 297(5586):1551-1555.
• [38]Costa LD, Rodrigues FA, Travieso G, Boas PRV: Characterization of complex networks: A survey of measurements. Adv Phys 2007, 56(1):167-242.
• [39]West DB: Introduction to Graph Theory. Prentice Hall, Upper Saddle River, N.J.; 1996.
• [40]Latora V, Marchiori M: Efficient behavior of small-world networks. Phys Rev Lett 2001, 87(19):198701.
• [41]Wasserman S, Faust K: Social Network Analysis: Methods and applications. Cambridge Univerisity Press, Cambridge; 1994.
• [42]Krackhardt D: Graph Theoretical Dimensions of Informal Organizations. Lawrence Erlbaum and Associates, Hillsdale, NJ; 1994.
• [43]Amaral LA, Scala A, Barthelemy M, Stanley HE: Classes of small-world networks. Proc Natl Acad Sci USA 2000, 97(21):11149-11152.
• [44]Girvan M, Newman ME: Community structure in social and biological networks. Proc Natl Acad Sci USA 2002, 99(12):7821-7826.
• [45]Newman MEJ: Modularity and community structure in networks. Proc Natl Acad Sci USA 2006, 103(23):8577-8582.
• [46]Luo F, Zhong J, Yang Y, Zhou J: Application of random matrix theory to mioarray data for discovering functional gene modules. Phys Rev E 2006, 73(3 Pt 1):031924.
• [47]Ravasz E, Barabasi AL: Hierarchical organization in complex networks. Phys Rev E 2003, 67(2):026112.
• [48]Zhou J, Wu L, Deng Y, Zhi X, Jiang YH, Tu Q, Xie J, Van Nostrand JD, He Z, Yang Y: Reproducibility and quantitation of amplicon sequencing-based detection. ISME J 2011, 5:1303-1313.
• [49]He Z, Xu M, Deng Y, Kang S, Kellogg L, Wu L, van Nostrand JD, Hobbie SE, Reich P, Zhou J: Metagenomic analysis reveals a marked divergence in the structure of belowground miobial communities at elevated CO2. Ecol Lett 2010, 13(5):564-575.
• [50]Nacke H, Thurmer A, Wollherr A, Will C, Hodac L, Herold N, Schoning I, Schrumpf M, Daniel R: Pyrosequencing-Based Assessment of Bacterial Community Structure Along Different Management Types in German Forest and Grassland Soils. PLoS ONE 201, 6(2):e17000.
• [51]Luo YQ, Hui DF, Zhang DQ: Elevated CO2 stimulates net accumulations of carbon and nitrogen in land ecosystems: A meta-analysis. Ecology 2006, 87(1):53-63.
• [52]Dethlefsen L, Huse S, Sogin ML, Relman DA: The Pervasive Effects of an Antibiotic on the Human Gut Miobiota, as Revealed by Deep 16 S rRNA Sequencing. PLoS Biol 2008, 6(11):2383-2400.
• [53]Alon U: Biological networks: The tinkerer as an engineer. Science 2003, 301(5641):1866-1867.
• [54]Clauset A, Moore C, Newman ME: Hierarchical structure and the prediction of missing links in networks. Nature 2008, 453(7191):98-101.
• [55]Olesen JM, Bascompte J, Dupont YL, Jordano P: The modularity of pollination networks. Proc Natl Acad Sci USA 2007, 104(50):19891-19896.
• [56]Pons P, Latapy M: Computing communities in large networks using random walks. Computer and Information Sciences - Iscis 2005, Proceedings 2005, 3733:284-293.
• [57]Newman MEJ: Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 2006, 74(3):036104.
• [58]Guimera R, Amaral LAN: Cartography of complex networks: modules and universal roles. J Stat Mech-Theory Exp 2005, 2005:P02001.
• [59]Guimera R, Amaral LAN: Functional cartography of complex metabolic networks. Nature 2005, 433(7028):895-900.
• [60]Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, Geschwind DH: Functional organization of the transiptome in human brain. Nat Neurosci 2008, 11(11):1271-1282.
• [61]Horvath S, Dong J: Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol 2008, 4(8):e1000117.
• [62]Langfelder P, Horvath S: Eigengene networks for studying the relationships between co-expression modules. BMC Syst Biol 2007, 1:54. BioMed Central Full Text
• [63]Butts CT: Social Network Analysis with sna. J Stat Softw 2008, 24(6):1-51.
• [64]The igraph library [http://cneurocvs.rmki.kfki.hu/igraph/ webcite]
• [65]Langfelder P, Horvath S: WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559. BioMed Central Full Text
• [66]Clauset A, Newman ME, Moore C: Finding community structure in very large networks. Phys Rev E 2004, 70(6 Pt 2):066111.
• [67]Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, eech M, Gross B, et al.: Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2007, 2(10):2366-2382.
• [68]Maslov S, Sneppen K: Specificity and stability in topology of protein networks. Science 2002, 296(5569):910-913.
• [69]Bascompte J, Jordano P, Melian CJ, Olesen JM: The nested assembly of plant-animal mutualistic networks. Proc Natl Acad Sci USA 2003, 100(16):9383-9387.
• [70]Bastolla U, Fortuna MA, Pascual-Garcia A, Ferrera A, Luque B, Bascompte J: The architecture of mutualistic networks minimizes competition and ineases biodiversity. Nature 2009, 458(7241):1018-1020.
• [71]Bascompte J, Jordano P: Plant-animal mutualistic networks: The architecture of biodiversity. Annu Rev Ecol Evol Syst 2007, 38:567-593.
• [72]Arumugam M, Raes J, Pelletier E, Le Paslier D, Yamada T, Mende DR, Fernandes GR, Tap J, Bruls T, Batto JM, et al.: Enterotypes of the human gut miobiome. Nature 2011, 473(7346):174-180.
• [73]Fuhrman JA: Miobial community structure and its functional implications. Nature 2009, 459(7244):193-199.
• [74]Muegge BD, Kuczynski J, Knights D, Clemente JC, Gonzalez A, Fontana L, Henrissat B, Knight R, Gordon JI: Diet Drives Convergence in Gut Miobiome Functions Aoss Mammalian Phylogeny and Within Humans. Science 2011, 332(6032):970-974.
• [75]Bandyopadhyay JN, Jalan S: Universality in complex networks: Random matrix analysis. Phys Rev E 2007, 76(2):026109.
• [76]Plerou V, Gopikrishnan P, Rosenow B, Amaral LAN, Stanley HE: Universal and nonuniversal properties of oss correlations in financial time series. Phys Rev Lett 1999, 83(7):1471-1474.
• [77]Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, et al.: A human gut miobial gene catalogue established by metagenomic sequencing. Nature 2010, 464(7285):59-65.
• [78]Zhou X, Kao MC, Wong WH: Transitive functional annotation by shortest-path analysis of gene expression data. Proc Natl Acad Sci USA 2002, 99(20):12783-12788.
• [79]Epstein SS: Miobial awakenings. Nature 2009, 457(7233):1083.
• [80]May RM: Stability and complexity in model ecosystems, 1st Princeton landmarks in biology. Oxford: Princeton University Press, Princeton, N.J; 2001.
• [81]Kitano H: Biological robustness. Nat Rev Genet 2004, 5(11):826-837.
• [82]Thebault E, Fontaine C: Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 2010, 329(5993):853-856.
• [83]Wang F, Zhou H, Meng J, Peng X, Jiang L, Sun P, Zhang C, Van Nostrand JD, Deng Y, He Z, et al.: GeoChip-based analysis of metabolic diversity of miobial communities at the Juan de Fuca Ridge hydrothermal vent. Proc Natl Acad Sci USA 2009, 106(12):4840-4845.
• [84]Zhou J, Kang S, Schadt CW, Garten CT: Spatial scaling of functional gene diversity aoss various miobial taxa. Proc Natl Acad Sci USA 2008, 105(22):7768-7773.
• [85]Butte AJ, Kohane IS: Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput 2000, 5:418-429.
• [86]Carter DA: Comprehensive strategies to study neuronal function in transgenic animal models. Biol Psychiatry 2004, 55(8):785-788.
• [87]Carlson MR, Zhang B, Fang Z, Mischel PS, Horvath S, Nelson SF: Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks. BMC Genomics 2006, 7:40. BioMed Central Full Text
• [88]Wigner EP: Random Matrices in Physics. Siam Review 1967, 9(1):1.
• [89]Mehta ML: Random Matrices, 2nd edition. Academic Press 1990.
• [90]Zhong JX, Geisel T: Level fluctuations in quantum systems with multifractal eigenstates. Phys Rev E 1999, 59(4):4071.
• [91]Bohigas O, Giannoni MJ, Schmit C: Spectral Properties of the Laplacian and Random Matrix Theories. J Phys Lett-Paris 1984, 45(21):1015-1022.
• [92]Seba P: Random matrix analysis of human EEG data. Phys Rev Lett 2003, 91(19):198104.
• [93]Barabasi AL: Scale-free networks: a decade and beyond. Science 2009, 325(5939):412-413.
• [94]Barabasi AL, Albert R: Emergence of scaling in random networks. Science 1999, 286(5439):509-512.
• [95]Muller-Linow M, Hilgetag CC, Hutt MT: Organization of excitable dynamics in hierarchical biological networks. PLoS Comput Biol 2008, 4(9):e1000190.
• [96]Kirkpatrick S, Gelatt CD: Vecchi MP: Optimization by simulated annealing. Science 1983, 220(4598):671-680.
• [97]Newman ME: Fast algorithm for detecting community structure in networks. Phys Rev E 2004, 69(6 Pt 2):066133.
• [98]Olesen JM, Bascompte J, Dupont YL, Jordano P: The smallest of all worlds: pollination networks. J Theor Biol 2006, 240(2):270-276.
• [99]Alter O, Brown PO, Botstein D: Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci USA 2000, 97(18):10101-10106.
• [100]Mantel N: Detection of Disease Clustering and a Generalized Regression Approach. Cancer Research 1967, 27(2p):209.
• [101]Dixon P: VEGAN, a package of R functions for community ecology. J Veg Sci 2003, 14(6):927-930.