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Kaesler, J.

Publications and source records attributed to Kaesler, J..

2 recordsLinked to original sources

Microbial carbon metabolism is linked to organic matter chemistry across soil systems

Soil microbial growth and respiration play a critical role for soil organic carbon dynamics. Yet, we lack understanding of the main controls of soil microbial carbon metabolism at large scales. Here, we investigated whether and how the chemical composition of microbially available organic matter affects soil microbial carbon metabolism across soil systems. We linked soil microbial growth and respiration rates as well as carbon use efficiency (quantified with 18O stable isotope probing) to the chemical composition of extractable organic matter (characterized with reversed-phase liquid chromatography coupled to Fourier-transform ion cyclotron resonance mass spectrometry) along a geoclimatic gradient of 33 Chilean temperate grassland soils. We found that biomass-normalized rates of growth and respiration were primarily positively linked to aliphatics such as carbohydrate-, proteinaceous- and amino sugar-like compounds, and secondarily to unsaturated lignin-like compounds. Respiration was positively linked to compounds with carbon in a reduced oxidation state, suggesting carbon-conserving catabolism, while growth appeared unrelated to the oxidation state of carbon. This suggests that other mechanisms than mere energetic constraints control microbial growth rates in aerated soils. Our findings demonstrate that information on the chemical composition of bioavailable organic matter can provide insights into the processes that govern the fate of carbon across different ecosystems. Key pointsO_LIWe investigated if bulk soil microbial growth (18O stable isotope probing) and respiration is linked to the chemical composition of extractable organic matter (LC-FT-ICR MS) along a geoclimatic gradient of temperate grasslands. C_LIO_LIHigher rates of microbial carbon turnover were positively linked to aliphatic and unsaturated compounds. C_LIO_LISpecific (i.e., biomass-normalized) respiration was positively linked to compounds with carbon in a reduced oxidation state, suggesting carbon-conserving catabolism. C_LIO_LISpecific growth was unrelated to the oxidation states of substrate carbon, suggesting that soil microbial substrate use for anabolism may not be determined by direct energetic constraints. C_LI

microbiology↗

ROS induction as a strategy to target persister cancer cells with low metabolic activity in NRAS mutated melanoma

Metabolic reprogramming is an emerging hallmark of resistance to cancer therapy but may generate vulnerabilities that can be targeted with small molecules. Multi-omics analysis revealed that NRAS-mutated melanoma cells with a mesenchymal transcriptional profile adopt a quiescent metabolic program to resist cellular stress response induced by MEK-inhibitor resistance. However, as a result of elevated baseline ROS levels, these cells become highly sensitive to ROS induction. In vivo xenograft experiments and single-cell RNA sequencing demonstrated that intra-tumor heterogeneity requires the combination of a ROS-inducer and a MEK-inhibitor to target both tumor growth and metastasis. By ex vivo pharmacoscopy of 62 human metastatic melanomas, we found that MEK-inhibitor resistant tumors significantly benefitted from the combination therapy. Finally, we profiled 486 cancer cell lines and revealed that oxidative stress responses and translational suppression are biomarkers of ROS-inducer sensitivity, independent of cancer indication. These findings link transcriptional plasticity to a metabolic phenotype that can be inhibited by ROS-inducers in melanoma and other cancers. Statement of SignificanceTargeted-therapy resistance in cancer arises from genetic selection and both transcriptional and metabolic adaptation. We show that metabolic reprogramming sensitizes resistant cells to ROS-induction in combination with pathway inhibitors. Predictive biomarkers of metabolic sensitivity to ROS-inducing agents were identified in many cancer entities, highlighting the generalizability of this treatment approach. Graphical summary O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

cancer biology↗