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Biomedical subjects

Jennifer Pett-Ridge

Publications and source records attributed to Jennifer Pett-Ridge.

2 recordsLinked to original sources

Reduced legacy precipitation decreases microbial community growth efficiency and alters soil organic carbon in a California grassland.

BACKGROUND: Changes in global patterns can leave a lasting legacy in semiarid grasslands by reshaping microbial growth dynamics and carbon cycling during the first wet-up in the autumn-a period known for intense microbial activity and significant carbon emissions. To study the lasting impacts of decreased winter rain, we implemented two precipitation regimes (100% vs. 50% mean annual precipitation) in California Mediterranean-climate grassland field plots. After the dry season, soils were rewetted in the laboratory with H218O and sampled at 0 h, 3 h, 24 h, 48 h, 72 h, and 168 h post rewet. We quantified CO2 efflux, measured microbial growth and mortality via quantitative 18O stable isotope probing and 16S rRNA gene amplicon sequencing, and characterized the soil organic carbon chemical composition, metagenomes, and metatranscriptomes. RESULTS: We found that reduced winter precipitation imposed a strong legacy effect on microbial turnover; despite maintaining similar respiration rates, microbial growth declined by ~1 order of magnitude, yielding decreased community growth efficiency (CGE = new biomass growth/respiration), and microbial mortality declined by ~2 orders of magnitude. Soil organic carbon also shifted from lipid-like, amino-sugar-like, and protein-like compounds (indicative of microbial necromass) to more oxidized lignin-like and tannin-like compounds (indicative of decomposing plant-derived compounds). Meta-omics revealed distinct metabolic strategies linked to CGE. At high-CGE, microbes appeared to consume more energetically favorable N-rich necromass (released via high microbial turnover); this allowed for increased amino acids and peptidoglycan biosynthesis and greater aromatic compound degradation, fueling further energy production and growth efficiency. At low CGE, communities had elevated carbohydrate metabolism and lipid turnover, consistent with increased investment in plant detritus degradation and membrane repair and maintenance rather than growth. CONCLUSIONS: Together, our findings demonstrate that reduced winter rainfall decreases microbial turnover following rewetting without a concurrent reduction in CO2 emissions. This shift results in persistently lower CGE, which has the potential to increase soil carbon loss as CO2. If such conditions are maintained over multiple years, these changes could reshape soil organic carbon stocks and alter the balance of grassland ecosystems under future climate scenarios. While our data suggest that sustained reductions in CGE may drive SOC decline, the magnitude and persistence of these effects depend on long-term environmental dynamics and warrant further investigation. Video Abstract.

Soil Microbiology

Predictions of rhizosphere microbiome dynamics with a genome-informed and trait-based energy budget model.

Soil microbiomes are highly diverse, and to improve their representation in biogeochemical models, microbial genome data can be leveraged to infer key functional traits. By integrating genome-inferred traits into a theory-based hierarchical framework, emergent behaviour arising from interactions of individual traits can be predicted. Here we combine theory-driven predictions of substrate uptake kinetics with a genome-informed trait-based dynamic energy budget model to predict emergent life-history traits and trade-offs in soil bacteria. When applied to a plant microbiome system, the model accurately predicted distinct substrate-acquisition strategies that aligned with observations, uncovering resource-dependent trade-offs between microbial growth rate and efficiency. For instance, inherently slower-growing microorganisms, favoured by organic acid exudation at later plant growth stages, exhibited enhanced carbon use efficiency (yield) without sacrificing growth rate (power). This insight has implications for retaining plant root-derived carbon in soils and highlights the power of data-driven, trait-based approaches for improving microbial representation in biogeochemical models.

Rhizosphere