Inferring microbial interactions with their environment from genomic and metagenomic data.

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics.

Microbial community composition controls carbon flux across litter types in early phase of litter decomposition.

Leaf litter decomposition is a major carbon input to soil, making it a target for increasing soil carbon storage through microbiome engineering. We expand upon previous findings to show with multiple leaf litter types that microbial composition can drive variation in carbon flow from litter decomposition and specific microbial community features are associated with synonymous patterns of carbon flow among litter types. Although plant litter type selects for different decomposer communities, within a litter type, microbial composition drives variation in the quantity of dissolved organic carbon (DOC) measured at the end of the decomposition period.

Soil Bacterial and Fungal Richness Forecast Patterns of Early Pine Litter Decomposition.

Discovering widespread microbial processes that drive unexpected variation in carbon cycling may improve modeling and management of soil carbon (Prescott, 2010; Wieder et al., 2015a, 2018). A first step is to identify community features linked to carbon cycle variation.

Rainforest-to-pasture conversion stimulates soil methanogenesis across the Brazilian Amazon.

The Amazon rainforest is a biodiversity hotspot and large terrestrial carbon sink threatened by agricultural conversion. Rainforest-to-pasture conversion stimulates the release of methane, a potent greenhouse gas. The biotic methane cycle is driven by microorganisms; therefore, this study focused on active methane-cycling microorganisms and their functions across land-use types.

Belowground changes to community structure alter methane-cycling dynamics in Amazonia.

Amazonian rainforest is undergoing increasing rates of deforestation, driven primarily by cattle pasture expansion. Forest-to-pasture conversion has been associated with increases in soil methane (CH) emission. To better understand the drivers of this change, we measured soil CH flux, environmental conditions, and belowground microbial community structure across primary forests, cattle pastures, and secondary forests in two Amazonian regions.

Differences in substrate use linked to divergent carbon flow during litter decomposition.

Discovering widespread microbial processes that create variation in soil carbon (C) cycling within ecosystems may improve soil C modeling. Toward this end, we screened 206 soil communities decomposing plant litter in a common garden microcosm environment and examined features linked to divergent patterns of C flow. C flow was measured as carbon dioxide (CO2) and dissolved organic carbon (DOC) from 44-days of litter decomposition.

New Biological Insights Into How Deforestation in Amazonia Affects Soil Microbial Communities Using Metagenomics and Metagenome-Assembled Genomes.

Deforestation in the Brazilian Amazon occurs at an alarming rate, which has broad effects on global greenhouse gas emissions, carbon storage, and biogeochemical cycles. In this study, soil metagenomes and metagenome-assembled genomes (MAGs) were analyzed for alterations to microbial community composition, functional groups, and putative physiology as it related to land-use change and tropical soil. A total of 28 MAGs were assembled encompassing 10 phyla, including both dominant and rare biosphere lineages.

Atomic force microscopy of red-light photoreceptors using peakforce quantitative nanomechanical property mapping.

Atomic force microscopy (AFM) uses a pyramidal tip attached to a cantilever to probe the force response of a surface. The deflections of the tip can be measured to ~10 pN by a laser and sectored detector, which can be converted to image topography. Amplitude modulation or "tapping mode" AFM involves the probe making intermittent contact with the surface while oscillating at its resonant frequency to produce an image.