Metaphenomic Responses of a Native Prairie Soil Microbiome to Moisture Perturbations

Climate change is predicted to result in increased drought extent and intensity in the highly productive, former tallgrass prairie region of the continental United States. These soils store large reserves of carbon. The decrease in soil moisture due to drought has largely unknown consequences on soil carbon cycling and other key biogeochemical cycles carried out by soil microbiomes. In this study, we found that soil drying had a significant impact on the structure and function of soil microbial communities, including shifts in expression of specific metabolic pathways, such as those leading toward production of osmoprotectant compounds. This study demonstrates the application of an untargeted multi-omics approach to decipher details of the soil microbial community’s metaphenotypic response to environmental perturbations and should be applicable to studies of other complex microbial systems as well. ABSTRACT Climate change is causing shifts in precipitation patterns in the central grasslands of the United States, with largely unknown consequences on the collective physiological responses of the soil microbial community, i.e., the metaphenome. Here, we used an untargeted omics approach to determine the soil microbial community’s metaphenomic response to soil moisture and to define specific metabolic signatures of the response. Specifically, we aimed to develop the technical approaches and metabolic mapping framework necessary for future systematic ecological studies. We collected soil from three locations at the Konza Long-Term Ecological Research (LTER) field station in Kansas, and the soils were incubated for 15 days under dry or wet conditions and compared to field-moist controls. The microbiome response to wetting or drying was determined by 16S rRNA amplicon sequencing, metatranscriptomics, and metabolomics, and the resulting shifts in taxa, gene expression, and metabolites were assessed. Soil drying resulted in significant shifts in both the composition and function of the soil microbiome. In contrast, there were few changes following wetting. The combined metabolic and metatranscriptomic data were used to generate reaction networks to determine the metaphenomic response to soil moisture transitions. Site location was a strong determinant of the response of the soil microbiome to moisture perturbations. However, some specific metabolic pathways changed consistently across sites, including an increase in pathways and metabolites for production of sugars and other osmolytes as a response to drying. Using this approach, we demonstrate that despite the high complexity of the soil habitat, it is possible to generate insight into the effect of environmental change on the soil microbiome and its physiology and functions, thus laying the groundwork for future, targeted studies. IMPORTANCE Climate change is predicted to result in increased drought extent and intensity in the highly productive, former tallgrass prairie region of the continental United States. These soils store large reserves of carbon. The decrease in soil moisture due to drought has largely unknown consequences on soil carbon cycling and other key biogeochemical cycles carried out by soil microbiomes. In this study, we found that soil drying had a significant impact on the structure and function of soil microbial communities, including shifts in expression of specific metabolic pathways, such as those leading toward production of osmoprotectant compounds. This study demonstrates the application of an untargeted multi-omics approach to decipher details of the soil microbial community’s metaphenotypic response to environmental perturbations and should be applicable to studies of other complex microbial systems as well.

[1]  Liwei Zhang,et al.  Metabolic Responses of Poplar to Apripona germari (Hope) as Revealed by Metabolite Profiling , 2016, International journal of molecular sciences.

[2]  A. Knapp,et al.  The immediate and prolonged effects of climate extremes on soil respiration in a mesic grassland , 2016 .

[3]  Susan Holmes,et al.  phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data , 2013, PloS one.

[4]  Bruce K. Wylie,et al.  Climate-Driven Interannual Variability in Net Ecosystem Exchange in the Northern Great Plains Grasslands , 2010 .

[5]  J. Blair,et al.  Increased rainfall variability and reduced rainfall amount decreases soil CO 2 flux in a grassland ecosystem , 2005 .

[6]  Peter J. Woolf,et al.  GAGE: generally applicable gene set enrichment for pathway analysis , 2009, BMC Bioinformatics.

[7]  M. Firestone,et al.  Responses of soil bacterial and fungal communities to extreme desiccation and rewetting , 2013, The ISME Journal.

[8]  James R. Cole,et al.  Ribosomal Database Project: data and tools for high throughput rRNA analysis , 2013, Nucleic Acids Res..

[9]  Bruce K. Wylie,et al.  Upscaling carbon fluxes over the Great Plains grasslands: Sinks and sources , 2011 .

[10]  Kishori M. Konwar,et al.  MetaPathways v2.5: quantitative functional, taxonomic and usability improvements , 2015, Bioinform..

[11]  U. Karaoz,et al.  Linking soil biology and chemistry in biological soil crust using isolate exometabolomics , 2018, Nature Communications.

[12]  J. Blair,et al.  Regional grassland productivity responses to precipitation during multiyear above‐ and below‐average rainfall periods , 2018, Global change biology.

[13]  Alan K. Knapp,et al.  Altering Rainfall Timing and Quantity in a Mesic Grassland Ecosystem: Design and Performance of Rainfall Manipulation Shelters , 2000, Ecosystems.

[14]  J. Peñuelas,et al.  Strong functional stability of soil microbial communities under semiarid Mediterranean conditions and subjected to long-term shifts in baseline precipitation , 2014 .

[15]  E. Purdom,et al.  Drought and host selection influence bacterial community dynamics in the grass root microbiome , 2017, The ISME Journal.

[16]  C. Taylor,et al.  Afternoon rain more likely over drier soils , 2012, Nature.

[17]  P. Ciais,et al.  Reconciling inconsistencies in precipitation-productivity relationships: implications for climate change. , 2017, The New phytologist.

[18]  Sarah J. Fansler,et al.  Moleculo Long-Read Sequencing Facilitates Assembly and Genomic Binning from Complex Soil Metagenomes , 2016, mSystems.

[19]  R. Christen,et al.  Stimulation of Different Functional Groups of Bacteria by Various Plant Residues as a Driver of Soil Priming Effect , 2013, Ecosystems.

[20]  Sam T. Roweis,et al.  EM Algorithms for PCA and SPCA , 1997, NIPS.

[21]  Robert C. Edgar,et al.  BIOINFORMATICS APPLICATIONS NOTE , 2001 .

[22]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[23]  Brian Bushnell,et al.  BBMap: A Fast, Accurate, Splice-Aware Aligner , 2014 .

[24]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[25]  D. Welsh,et al.  Ecological significance of compatible solute accumulation by micro-organisms: from single cells to global climate. , 2000, FEMS microbiology reviews.

[26]  Alan K. Knapp,et al.  Controls of Aboveground Net Primary Production in Mesic Savanna Grasslands: An Inter-Hemispheric Comparison , 2009, Ecosystems.

[27]  C. Owensby,et al.  Fluxes of CO2 From Grazed and Ungrazed Tallgrass Prairie , 2006 .

[28]  S. Hedges,et al.  A major clade of prokaryotes with ancient adaptations to life on land. , 2009, Molecular biology and evolution.

[29]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[30]  Eoin L. Brodie,et al.  Pre-exposure to drought increases the resistance of tropical forest soil bacterial communities to extended drought , 2012, The ISME Journal.

[31]  S. Tringe,et al.  Microbial Community Structure and Functional Potential in Cultivated and Native Tallgrass Prairie Soils of the Midwestern United States , 2018, Front. Microbiol..

[32]  Kristin E. Burnum-Johnson,et al.  MPLEx: a Robust and Universal Protocol for Single-Sample Integrative Proteomic, Metabolomic, and Lipidomic Analyses , 2016, mSystems.

[33]  Janet K Jansson,et al.  The soil microbiome-from metagenomics to metaphenomics. , 2018, Current opinion in microbiology.

[34]  Aaron M. Newman,et al.  The genome sequence of the colonial chordate, Botryllus schlosseri , 2013, eLife.

[35]  Alan K. Knapp,et al.  Altered Rainfall Patterns, Gas Exchange, and Growth in Grasses and Forbs , 2002, International Journal of Plant Sciences.

[36]  W. Chapman,et al.  Biogeography and organic matter removal shape long-term effects of timber harvesting on forest soil microbial communities , 2017, The ISME Journal.

[37]  Erik Aronesty,et al.  Comparison of Sequencing Utility Programs , 2013 .

[38]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[39]  Joseph J. Heijnen,et al.  Leakage-free rapid quenching technique for yeast metabolomics , 2008, Metabolomics.

[40]  C. Rice,et al.  Altered precipitation regime affects the function and composition of soil microbial communities on multiple time scales. , 2013, Ecology.

[41]  Peer Bork,et al.  iPath3.0: interactive pathways explorer v3 , 2018, Nucleic Acids Res..

[42]  J. Blair,et al.  Vertical distribution of fungal communities in tallgrass prairie soil , 2010, Mycologia.

[43]  B. Wylie,et al.  Correction to “Upscaling carbon fluxes over the Great Plains grasslands: Sinks and sources” , 2011 .

[44]  Weijun Luo,et al.  Pathview: an R/Bioconductor package for pathway-based data integration and visualization , 2013, Bioinform..

[45]  M. Wallenstein,et al.  A trait-based framework for predicting when and where microbial adaptation to climate change will affect ecosystem functioning , 2012, Biogeochemistry.

[46]  Joel G. Pounds,et al.  Improved quality control processing of peptide-centric LC-MS proteomics data , 2011, Bioinform..

[47]  J. Wink,et al.  Actinobacteria from Arid and Desert Habitats: Diversity and Biological Activity , 2016, Front. Microbiol..

[48]  V. Sundaresan,et al.  Drought Stress Results in a Compartment-Specific Restructuring of the Rice Root-Associated Microbiomes , 2017, mBio.

[49]  M. Wallenstein,et al.  Soil microbial community response to drying and rewetting stress: does historical precipitation regime matter? , 2012, Biogeochemistry.

[50]  J. Doran,et al.  Effect of water-filled pore space on carbon dioxide and nitrous oxide production in tilled and nontilled soils [Maize; Illinois; Kentucky; Minnesota; Nebraska] , 1984 .

[51]  Rick L. Stevens,et al.  A communal catalogue reveals Earth’s multiscale microbial diversity , 2017, Nature.

[52]  A. Porporato,et al.  Superstatistics of hydro‐climatic fluctuations and interannual ecosystem productivity , 2006 .

[53]  S. Cox,et al.  Predominant bacterial and fungal assemblages in agricultural soils during a record drought/heat wave and linkages to enzyme activities of biogeochemical cycling , 2014 .

[54]  P. Gong,et al.  Analysis of Factors Controlling Soil Carbon in the Conterminous United States , 2006 .

[55]  Markus Reichstein,et al.  Consequences of More Extreme Precipitation Regimes for Terrestrial Ecosystems , 2008 .

[56]  B. Rogers,et al.  Not all droughts are created equal: the impacts of interannual drought pattern and magnitude on grassland carbon cycling , 2016, Global change biology.

[57]  O. Fiehn,et al.  FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. , 2009, Analytical chemistry.

[58]  Jason M. Wood,et al.  Diel metabolomics analysis of a hot spring chlorophototrophic microbial mat leads to new hypotheses of community member metabolisms , 2015, Front. Microbiol..

[59]  William A. Walters,et al.  Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms , 2012, The ISME Journal.

[60]  R. Seager,et al.  Atmospheric circulation anomalies during two persistent north american droughts: 1932–1939 and 1948–1957 , 2011 .

[61]  Dietmar Schomburg,et al.  MetaboliteDetector: comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis. , 2009, Analytical chemistry.

[62]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[63]  R. Knight,et al.  Impact of fire on active layer and permafrost microbial communities and metagenomes in an upland Alaskan boreal forest , 2014, The ISME Journal.

[64]  S. Collins,et al.  Differential sensitivity to regional-scale drought in six central US grasslands , 2015, Oecologia.