Inferring interactions in complex microbial communities from nucleotide sequence data and environmental parameters

Interactions occur between two or more organisms affecting each other. Interactions are decisive for the ecology of the organisms. Without direct experimental evidence the analysis of interactions is difficult. Correlation analyses that are based on co-occurrences are often used to approximate interaction. Here, we present a new mathematical model to estimate the interaction strengths between taxa, based on changes in their relative abundances across environmental gradients.

[1]  N. Stenseth,et al.  Population regulation in snowshoe hare and Canadian lynx: asymmetric food web configurations between hare and lynx. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[2]  K. Foster,et al.  The ecology of the microbiome: Networks, competition, and stability , 2015, Science.

[3]  Joao B Xavier,et al.  The Evolution of Quorum Sensing in Bacterial Biofilms , 2008, PLoS biology.

[4]  T. Wubet,et al.  Metacommunity analysis of amoeboid protists in grassland soils , 2016, Scientific Reports.

[5]  R. Amann,et al.  The species concept for prokaryotes. , 2013, FEMS microbiology reviews.

[6]  K. Pearson Mathematical contributions to the theory of evolution.—On a form of spurious correlation which may arise when indices are used in the measurement of organs , 1897, Proceedings of the Royal Society of London.

[7]  T. Crowther,et al.  Predicting the responsiveness of soil biodiversity to deforestation: a cross‐biome study , 2014, Global change biology.

[8]  A. Griffin,et al.  Social evolution theory for microorganisms , 2006, Nature Reviews Microbiology.

[9]  M. Meima‐Franke,et al.  Microbial minorities modulate methane consumption through niche partitioning , 2013, The ISME Journal.

[10]  Tracy K. Teal,et al.  Agriculture's impact on microbial diversity and associated fluxes of carbon dioxide and methane , 2011, The ISME Journal.

[11]  D. Curran‐Everett,et al.  The fickle P value generates irreproducible results , 2015, Nature Methods.

[12]  B. Everitt The Cambridge Dictionary of Statistics , 1998 .

[13]  W. Boer,et al.  Mycophagous growth of Collimonas bacteria in natural soils, impact on fungal biomass turnover and interactions with mycophagous Trichoderma fungi , 2009, The ISME Journal.

[14]  S. Carpenter,et al.  ESTIMATING COMMUNITY STABILITY AND ECOLOGICAL INTERACTIONS FROM TIME‐SERIES DATA , 2003 .

[15]  Hubert Rehrauer,et al.  A global network of coexisting microbes from environmental and whole-genome sequence data. , 2010, Genome research.

[16]  James D. Murray Mathematical Biology: I. An Introduction , 2007 .

[17]  S. Scheu,et al.  Secondary metabolite production facilitates establishment of rhizobacteria by reducing both protozoan predation and the competitive effects of indigenous bacteria , 2008 .

[18]  Alan Clewer,et al.  Cambridge Dictionary of Statistics , 1999 .

[19]  Sophie J. Weiss,et al.  Correlation detection strategies in microbial data sets vary widely in sensitivity and precision , 2016, The ISME Journal.

[20]  I. Schöning,et al.  Forest Management Type Influences Diversity and Community Composition of Soil Fungi across Temperate Forest Ecosystems , 2015, Front. Microbiol..

[21]  Charles K. Fisher,et al.  Identifying Keystone Species in the Human Gut Microbiome from Metagenomic Timeseries Using Sparse Linear Regression , 2014, PloS one.

[22]  Eörs Szathmáry,et al.  What can ecosystems learn? Expanding evolutionary ecology with learning theory , 2015, Biology Direct.

[23]  J A Asenjo,et al.  The Monod equation and mass transfer. , 1995, Biotechnology and bioengineering.

[24]  K. Lewis,et al.  A new antibiotic kills pathogens without detectable resistance , 2015, Nature.

[25]  Jens Timmer,et al.  Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models , 2015, PLoS Comput. Biol..

[26]  M. Lange,et al.  Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment , 2010, Nature.

[27]  F. Martin,et al.  Pyrosequencing reveals a contrasted bacterial diversity between oak rhizosphere and surrounding soil. , 2010, Environmental microbiology reports.

[28]  Richard F. Gunst,et al.  Applied Regression Analysis , 1999, Technometrics.

[29]  G. Casella,et al.  Pyrosequencing enumerates and contrasts soil microbial diversity , 2007, The ISME Journal.

[30]  L. Øvreås,et al.  Microbial diversity and function in soil: from genes to ecosystems. , 2002, Current opinion in microbiology.

[31]  Jens Nieschulze,et al.  Implementing large-scale and long-term functional biodiversity research: The Biodiversity Exploratories , 2010 .

[32]  R. Schmidt,et al.  Volatile affairs in microbial interactions , 2015, The ISME Journal.

[33]  E. Blagodatskaya,et al.  Microbial hotspots and hot moments in soil: Concept & review , 2015 .

[34]  M. Schloter,et al.  Seasonal controls on grassland microbial biogeography: Are they governed by plants, abiotic properties or both? , 2014 .

[35]  J. Monod The Growth of Bacterial Cultures , 1949 .

[36]  I. Schöning,et al.  Factors controlling decomposition rates of fine root litter in temperate forests and grasslands , 2014, Plant and Soil.

[37]  George Sugihara,et al.  Detecting Causality in Complex Ecosystems , 2012, Science.

[38]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[39]  J. Lobo,et al.  Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity , 2015 .

[40]  M. Bradford,et al.  Testing the functional significance of microbial community composition. , 2009, Ecology.

[41]  Jonathan Friedman,et al.  Inferring Correlation Networks from Genomic Survey Data , 2012, PLoS Comput. Biol..

[42]  James Stuart Tanton,et al.  Encyclopedia of Mathematics , 2005 .

[43]  Pierre Legendre,et al.  Numerical Ecology with R , 2011 .

[44]  Vladimir Jojic,et al.  Learning Microbial Interaction Networks from Metagenomic Count Data , 2014, J. Comput. Biol..

[45]  S. Sherry,et al.  Modeling human evolution--to tree or not to tree? , 1997, Genome research.

[46]  Harris David,et al.  Estimating species interactions from observational data with Markov networks , 2015 .

[47]  N. Draper,et al.  Applied Regression Analysis: Draper/Applied Regression Analysis , 1998 .

[48]  Scott T. Bates,et al.  Cross-biome metagenomic analyses of soil microbial communities and their functional attributes , 2012, Proceedings of the National Academy of Sciences.

[49]  K. Winzer,et al.  Look who's talking: communication and quorum sensing in the bacterial world , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[50]  J. Raes,et al.  Microbial interactions: from networks to models , 2012, Nature Reviews Microbiology.

[51]  S. Marhan,et al.  Estimates of Soil Bacterial Ribosome Content and Diversity Are Significantly Affected by the Nucleic Acid Extraction Method Employed , 2016, Applied and Environmental Microbiology.

[52]  Douglas M. Hawkins,et al.  The Problem of Overfitting , 2004, J. Chem. Inf. Model..

[53]  Johan Wagemans,et al.  A New Perceptual Bias Reveals Suboptimal Population Decoding of Sensory Responses , 2012, PLoS Comput. Biol..

[54]  Jari Oksanen,et al.  How to model species responses along ecological gradients – Huisman–Olff–Fresco models revisited , 2013 .

[55]  K. Pollard,et al.  Toward Accurate and Quantitative Comparative Metagenomics , 2016, Cell.

[56]  T. Urich,et al.  Metabolic and trophic interactions modulate methane production by Arctic peat microbiota in response to warming , 2015, Proceedings of the National Academy of Sciences.

[57]  Hongyu Zhao,et al.  CCLasso: correlation inference for compositional data through Lasso , 2015, Bioinform..

[58]  Cheng-Shang Chang Calculus , 2020, Bicycle or Unicycle?.

[59]  Lingling An,et al.  Investigating microbial co-occurrence patterns based on metagenomic compositional data , 2015, Bioinform..

[60]  Vanni Bucci,et al.  Towards predictive models of the human gut microbiome. , 2014, Journal of molecular biology.

[61]  J. Overmann Principles of Enrichment, Isolation, Cultivation and Preservation of Prokaryotes , 2006 .

[62]  R. Knight,et al.  Global patterns in bacterial diversity , 2007, Proceedings of the National Academy of Sciences.

[63]  Christian L. Müller,et al.  Sparse and Compositionally Robust Inference of Microbial Ecological Networks , 2014, PLoS Comput. Biol..

[64]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[65]  A. W.,et al.  Journal of chemical information and computer sciences. , 1995, Environmental science & technology.

[66]  D. Goodin The cambridge dictionary of statistics , 1999 .