Integrated network modeling approach defines key metabolic responses of soil microbiomes to perturbations.

The soil environment is constantly changing due to shifts in soil moisture, nutrient availability and other conditions. To contend with these changes, soil microorganisms have evolved a variety of ways to adapt to environmental perturbations, including regulation of gene expression. However, it is challenging to untangle the complex phenotypic response of the soil to environmental change, partly due to the absence of predictive modeling frameworks that can mechanistically link molecular-level changes in soil microorganisms to a community's functional phenotypes (or metaphenome). Towards filling this gap, we performed a combined analysis of metabolic and gene co-expression networks to explore how the soil microbiome responded to changes in soil moisture and nutrient conditions and to determine which genes were expressed under a given condition. Our integrated modeling approach revealed previously unknown, but critically important aspects of the soil microbiomes' response to environmental perturbations. Incorporation of metabolomic and transcriptomic data into metabolic reaction networks identified condition-specific signature genes that are uniquely associated with dry, wet, and glycine-amended conditions. A subsequent gene co-expression network analysis revealed that drought-associated genes occupied more central positions in a network model of the soil community, compared to the genes associated with wet, and glycine-amended conditions. These results indicate the occurrence of system-wide metabolic coordination when soil microbiomes cope with moisture or nutrient perturbations. Importantly, the approach that we demonstrate here to analyze large-scale multi-omics data from a natural soil environment is applicable to other microbiome systems for which multi-omics data are available.

Spatial distribution of prokaryotic communities in hypersaline soils.

Increasing salinization in wetland systems is a major threat to ecosystem services carried out by microbial communities. Thus, it is paramount to understand how salinity drives both microbial community structures and their diversity. Here we evaluated the structure and diversity of the prokaryotic communities from a range of highly saline soils (EC1:5 from 5.96 to 61.02 dS/m) from the Odiel Saltmarshes and determined their association with salinity and other soil physicochemical features by analyzing 16S rRNA gene amplicon data through minimum entropy decomposition (MED). We found that these soils harbored unique communities mainly composed of halophilic and halotolerant taxa from the phyla Euryarchaeota, Proteobacteria, Balneolaeota, Bacteroidetes and Rhodothermaeota. In the studied soils, several site-specific properties were correlated with community structure and individual abundances of particular sequence variants. Salinity had a secondary role in shaping prokaryotic communities in these highly saline samples since the dominant organisms residing in them were already well-adapted to a wide range of salinities. We also compared ESV-based results with OTU-clustering derived ones, showing that, in this dataset, no major differences in ecological outcomes were obtained by the employment of one or the other method.

Standardizing methylation method during phospholipid fatty acid analysis to profile soil microbial communities.

Phospholipid fatty acid (PLFA) as biomarkers, is widely used to profile microbial communities in environmental samples. However, PLFA extraction and derivatization protocols are not standardized and have widely varied among published studies. Specifically investigators have used either HCl/MeOH or KOH/MeOH or both for the methylation step of PLFA analysis, without justification or research to support either one. It seems likely that each method could have very different outcomes and conclusions for PLFA based studies. Therefore, the objective of this study was to determine the effect of catalyst type for methylation on detecting PLFAs and implications for interpreting microbial profiling in soil. Fatty acid samples extracted from soils obtained from a wetland, an intermittently flooded site, and an adjacent upland site were subjected to HCl/MeOH or KOH/MeOH catalyzed methylation procedures during PLFA analyses. The methylation method using HCl/MeOH resulted in significantly higher concentrations of most PLFAs than the KOH/MeOH method. Another important outcome was that fatty acids with a methyl group (18:1ω,7c 11Me, TBSA 10Me 18:0, 10Me 18:0, 17:0 10Me and 16:0 10Me being an actinomycetes biomarker) could not be detected by HCl/MeOH catalyzed methylation but were found in appreciable concentrations with KOH/MeOH method. From our results, because the HCl/MeOH method did not detect the fatty acids containing methyl groups that could strongly influence the microbial community profile, we recommend that the KOH/MeOH catalyzed transesterification method should become the standard procedure for PLFA profiling of soil microbial communities.