Pamela Weisenhorn

The Soil Microbiome Influences Grapevine-Associated Microbiota

ABSTRACT Grapevine is a well-studied, economically relevant crop, whose associated bacteria could influence its organoleptic properties. In this study, the spatial and temporal dynamics of the bacterial communities associated with grapevine organs (leaves, flowers, grapes, and roots) and soils were characterized over two growing seasons to determine the influence of vine cultivar, edaphic parameters, vine developmental stage (dormancy, flowering, preharvest), and vineyard. Belowground bacterial communities differed significantly from those aboveground, and yet the communities associated with leaves, flowers, and grapes shared a greater proportion of taxa with soil communities than with each other, suggesting that soil may serve as a bacterial reservoir. A subset of soil microorganisms, including root colonizers significantly enriched in plant growth-promoting bacteria and related functional genes, were selected by the grapevine. In addition to plant selective pressure, the structure of soil and root microbiota was significantly influenced by soil pH and C:N ratio, and changes in leaf- and grape-associated microbiota were correlated with soil carbon and showed interannual variation even at small spatial scales. Diazotrophic bacteria, e.g., Rhizobiaceae and Bradyrhizobium spp., were significantly more abundant in soil samples and root samples of specific vineyards. Vine-associated microbial assemblages were influenced by myriad factors that shape their composition and structure, but the majority of organ-associated taxa originated in the soil, and their distribution reflected the influence of highly localized biogeographic factors and vineyard management. IMPORTANCE Vine-associated bacterial communities may play specific roles in the productivity and disease resistance of their host plant. Also, the bacterial communities on grapes have the potential to influence the organoleptic properties of the wine, contributing to a regional terroir. Understanding that factors that influence these bacteria may provide insights into management practices to shape and craft individual wine properties. We show that soil serves as a key source of vine-associated bacteria and that edaphic factors and vineyard-specific properties can influence the native grapevine microbiome preharvest. Vine-associated bacterial communities may play specific roles in the productivity and disease resistance of their host plant. Also, the bacterial communities on grapes have the potential to influence the organoleptic properties of the wine, contributing to a regional terroir. Understanding that factors that influence these bacteria may provide insights into management practices to shape and craft individual wine properties. We show that soil serves as a key source of vine-associated bacteria and that edaphic factors and vineyard-specific properties can influence the native grapevine microbiome preharvest.

Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction

Metabolic network modeling of microbial communities provides an in‐depth understanding of community‐wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high‐quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community‐level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph–heterotroph consortium that was used to provide data needed for a community‐level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources. J. Cell. Physiol. 231: 2339–2345, 2016.

Network-based metabolic analysis and microbial community modeling.

Network inference is being applied to studies of microbial ecology to visualize and characterize microbial communities. Network representations can allow examination of the underlying organizational structure of a microbial community, and identification of key players or environmental conditions that influence community assembly and stability. Microbial co-association networks provide information on the dynamics of community structure as a function of time or other external variables. Community metabolic networks can provide a mechanistic link between species through identification of metabolite exchanges and species specific resource requirements. When used together, co-association networks and metabolic networks can provide a more in-depth view of the hidden rules that govern the stability and dynamics of microbial communities.

Metabolic Reconstruction and Modeling Microbial Electrosynthesis

Microbial electrosynthesis is a renewable energy and chemical production platform that relies on microbial cells to capture electrons from a cathode and fix carbon. Yet despite the promise of this technology, the metabolic capacity of the microbes that inhabit the electrode surface and catalyze electron transfer in these systems remains largely unknown. We assembled thirteen draft genomes from a microbial electrosynthesis system producing primarily acetate from carbon dioxide, and their transcriptional activity was mapped to genomes from cells on the electrode surface and in the supernatant. This allowed us to create a metabolic model of the predominant community members belonging to Acetobacterium, Sulfurospirillum, and Desulfovibrio. According to the model, the Acetobacterium was the primary carbon fixer, and a keystone member of the community. Transcripts of soluble hydrogenases and ferredoxins from Acetobacterium and hydrogenases, formate dehydrogenase, and cytochromes of Desulfovibrio were found in high abundance near the electrode surface. Cytochrome c oxidases of facultative members of the community were highly expressed in the supernatant despite completely sealed reactors and constant flushing with anaerobic gases. These molecular discoveries and metabolic modeling now serve as a foundation for future examination and development of electrosynthetic microbial communities.

Carbon constrains fungal endophyte assemblages along the timberline.

The alpha diversity of foliar fungal endophytes (FEs) in leaves of Betula ermanii in a subalpine timberline ecotone on Changbai Mountain, China increased with elevation. There were also significant differences in beta diversity along the elevation gradient. Among the environmental variables analysed, leaf carbon significantly increased with elevation, and was the most significant environmental factor that constrained the alpha and beta diversity in the FE communities. Tree height and the cellulose, lignin, and carbon/nitrogen ratio of the leaves also affected the FE assemblages. When controlled for the effects of elevation, leaf carbon was still the main driver of changes in evenness, Shannon diversity and FE community composition. The results offered clues of the carbon acquisition strategy of the foliar FEs across this cold terrain. There was strong multicollinearity between both annual precipitation and temperature, with elevation (|Pearson r| > 0.986), so the effects of these climatic variables were impossible to separate; however, they may play key roles, and the direct effects of both warrant further investigation. As pioneer decomposers of leaf litter, variations in diversity and community composition of FE measured here may feedback and influence carbon cycling and dynamics in these forest ecosystems.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain.

Soil Bacterial Diversity Is Associated with Human Population Density in Urban Greenspaces

Urban greenspaces provide extensive ecosystem services, including pollutant remediation, water management, carbon maintenance, and nutrient cycling. However, while the urban soil microbiota underpin these services, we still have limited understanding of the factors that influence their distribution. We characterized soil bacterial communities from turf-grasses associated with urban parks, streets, and residential sites across a major urban environment, including a gradient of human population density. Bacterial diversity was significantly positively correlated with the population density; and species diversity was greater in park and street soils, compared to residential soils. Population density and greenspace type also led to significant differences in the microbial community composition that was also significantly correlated with soil pH, moisture, and texture. Co-occurrence network analysis revealed that microbial guilds in urban soils were well correlated. Abundant soil microbes in high density population areas had fewer interactions, while abundant bacteria in high moisture soils had more interactions. These results indicate the significant influence of changes in urban demographics and land-use on soil microbial communities. As urbanization is rapidly growing across the planet, it is important to improve our understanding of the consequences of urban zoning on the soil microbiota.