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Open access Sep 2026

Dynamic and distinct physiological responses by a soil bacterium promote survival along a desiccation continuum

ABSTRACT Soil bacteria play a central role in global biogeochemical cycles and are critical for soil health and agricultural productivity. The dynamic nature of soil hydration status affects bacterial habitats by changing the energy state of soil water and disrupting aqueous connections critical for nutrient diffusion. To study how soil bacteria respond to desiccation, we used the rhizobacterium Pseudomonas synxantha 2-79 as a model organism and quantified its response to co-occurring water and nutrient limitations at the single-cell level. We hypothesized that the relative importance of osmolyte synthesis and starvation responses to desiccation tolerance is context dependent, with the optimal strategy determined by the trajectory of nutrient and water deprivation. We constructed a transcriptional reporter to track P. synxantha’s expression of biosynthesis genes for the osmolyte N-acetylglutaminylglutamine amide (NAGGN) and collected extensive single-cell growth rate, cell size, and reporter expression data through experiments that mimicked different rates and extents of soil drying. Only actively growing cells responded to an osmotic shock by synthesizing NAGGN; this response was not observed for pre-starved bacteria. Despite the lack of osmolyte NAGGN synthesis, prior starvation enhanced P. synxantha’s ability to recover from osmotic stress once water and nutrients were restored. In line with our observation that prior starvation prevented cell lysis upon rewetting, starved cells had more rigid membranes. Together, our results indicate that diverse cellular properties contribute to soil bacterial desiccation tolerance, whose relative response and fitness are tuned to different challenges imposed by soil drying dynamics. IMPORTANCE Soil bacteria are critical to agriculture, but it is unclear how these organisms respond to desiccation, a common and worsening stress. Desiccation both dehydrates bacterial cells and eliminates the liquid water connections between soil pores that bacteria use to access nutrients. We studied how a model soil bacterium responds to (co)-occurring starvation and water stress at the single-cell level, focusing on osmolyte synthesis and physiological adjustments that take place under starvation. We describe the desiccation and regrowth trajectories in these conditions at single-cell resolution. We find that starvation restricts synthesis of a dipeptide osmolyte but rigidifies the membrane, enabling bacteria to withstand more severe water stress. Distinct cellular factors thus contribute differentially to desiccation tolerance along a drying trajectory. Soil bacteria are critical to agriculture, but it is unclear how these organisms respond to desiccation, a common and worsening stress. Desiccation both dehydrates bacterial cells and eliminates the liquid water connections between soil pores that bacteria use to access nutrients. We studied how a model soil bacterium responds to (co)-occurring starvation and water stress at the single-cell level, focusing on osmolyte synthesis and physiological adjustments that take place under starvation. We describe the desiccation and regrowth trajectories in these conditions at single-cell resolution. We find that starvation restricts synthesis of a dipeptide osmolyte but rigidifies the membrane, enabling bacteria to withstand more severe water stress. Distinct cellular factors thus contribute differentially to desiccation tolerance along a drying trajectory.

Jarek V. Kwiecinski, Georgia R. Squyres, Dani Or et al. · 0 citations
Review Open access Jul 2026

Modeling soil solution electrical conductivity across Europe.

Soil salinization, referring to the excessive accumulation of soluble salts in soils, adversely influences nutrient cycling, biodiversity, soil structure, crop production, soil health, and ecosystem functioning. Accurately assessing soil salinity via electrical conductivity (EC) is key to mitigating its impacts. Thus, developing predictive tools for soil EC at regional and continental scales is essential for sustainable soil management. Here, we apply machine learning models to predict soil EC in the European Union (EU) and United Kingdom (UK) soils using different environmental factors like soil, climate, topography, and satellite data as predictors. The model is trained by ≈40,000 soil EC data points from the 2015 and 2018 Land Use/Cover Area Frame Survey data (LUCAS) surveys, complemented by the EC observations from World Soil Information Services (WoSIS) dataset. To improve the model performance, a forward feature selection technique was used resulting in selection of 17 covariates out of initially 34 predictors. The final selected XGBoost model achieved R2 values of 0.68, 0.6, and 0.63 for the training, internal testing, and independent validation datasets, respectively. For the year 2018, we estimate ≈21.7 Mha of EU + UK land exceeds an EC of 0.6 dS/m (at a 1:5 soil to water ratio, the so-called EC1:5). This estimate should be interpreted as elevated predicted EC1:5, rather than a direct estimate of soils meeting protosalic diagnostic criteria. The output of the predictive model consists of a gridded dataset that illustrates the spatial distribution of EC1:5 throughout the study area for the year 2018, along with an associated uncertainty map with a spatial resolution of 1 km.

Mohammad Aziz Zarif, Amirhossein Hassani, Mehdi H. Afshar et al. · 0 citations

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