Funchhildebrandt0059

Community rescue occurs when ecological or evolutionary processes restore positive growth in a highly stressful environment that was lethal to the community in its ancestral form, thus averting biomass collapse in a deteriorating environment. Laboratory evidence suggests that community rescue is most likely in high-biomass communities that have previously experienced moderate doses of sublethal stress. We assessed this result under more natural conditions, in a mesocosm experiment with phytoplankton communities exposed to the ubiquitous herbicide glyphosate. We tested whether community biomass and prior herbicide exposure would facilitate community rescue after severe contamination. We found that prior exposure to glyphosate was a very strong predictor of the rescue outcome, while high community biomass was not. Furthermore, although glyphosate had negative effects on diversity, it did not influence community composition significantly, suggesting a modest role for genus sorting in this rescue process. Our results expand the scope of community rescue theory to complex ecosystems and confirm that prior stress exposure is a key predictor of rescue.Unprecedented species loss in diverse forests indicates the urgent need to test its consequences for ecosystem functioning. However, experimental evaluation based on realistic extinction scenarios is lacking. Using species interaction networks we introduce an approach to separate effects of node loss (reduced species number) from effects of link loss or compensation (reduced or increased interspecific interactions) on ecosystem functioning along directed extinction scenarios. By simulating random and non-random extinction scenarios in an experimental subtropical Chinese forest, we find that species loss is detrimental for stand volume in all scenarios, and that these effects strengthen with age. However, the magnitude of these effects depends on the type of attribute on which the directed species loss is based, with preferential loss of evolutionarily distinct species and those from small families having stronger effects than those that are regionally rare or have high specific leaf area. These impacts were due to both node loss and link loss or compensation. At high species richness (reductions from 16 to 8 species), strong stand-volume reduction only occurred in directed but not random extinction. Our results imply that directed species loss can severely hamper productivity in already diverse young forests.Temperature is one of the fundamental environmental variables that determine the composition and function of microbial communities. However, a predictive understanding of how microbial communities respond to changes in temperature is lacking, partly because it is not obvious which aspects of microbial physiology determine whether a species could benefit from a change in the temperature. Here we incorporate how microbial growth rates change with temperature into a modified Lotka-Volterra competition model and predict that higher temperatures should-in general-favour the slower-growing species in a bacterial community. We experimentally confirm this prediction in pairwise cocultures assembled from a diverse set of species and show that these changes to pairwise outcomes with temperature are also predictive of changing outcomes in three-species communities, suggesting that our theory may be applicable to more-complex assemblages. Our results demonstrate that it is possible to predict how bacterial communities will shift with temperature knowing only the growth rates of the community members. These results provide a testable hypothesis for future studies of more-complex natural communities and we hope that this work will help to bridge the gap between ecological theory and the complex dynamics observed in metagenomic surveys.Ferroptosis, a form of regulated cell death, is characterized by an excessive degree of iron accumulation and lipid peroxidation. Although it was originally identified only in cells expressing a mutant RAS oncogene, ferroptosis has also been found in normal cells following treatment by small molecules (e.g., erastin and RSL3) or drugs (e.g., sulfasalazine, sorafenib, and artesunate), which target antioxidant enzyme systems, especially the amino acid antiporter system xc- and the glutathione peroxidase GPX4. Dysfunctional ferroptosis is implicated in various physiological and pathological processes (e.g., metabolism, differentiation, and immunity). Targeting the ferroptotic network appears to a new treatment option for diseases or pathological conditions (e.g., cancer, neurodegeneration, and ischemia reperfusion injury). While the molecular machinery of ferroptosis remains largely unknown, several transcription factors (e.g., TP53, NFE2L2/NRF2, ATF3, ATF4, YAP1, TAZ, TFAP2C, SP1, HIF1A, EPAS1/HIF2A, BACH1, TFEB, JUN, HIC1, and HNF4A) play multiple roles in shaping ferroptosis sensitivity through either transcription-dependent or transcription-independent mechanisms. In this review, we summarize recent progress in understanding the transcriptional regulation underlying ferroptotic cell death, and discuss how it has provided new insights into cancer therapy.PURPOSE Diagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools commonly used in diagnostic laboratories. METHODS Two hundred fifty-seven variants (coding and noncoding) were referred for analysis across three laboratories. Blood RNA samples underwent targeted reverse transcription polymerase chain reaction (RT-PCR) analysis with Sanger sequencing of PCR products and agarose gel electrophoresis. Seventeen samples also underwent transcriptome-wide RNA sequencing with targeted splicing analysis based on Sashimi plot visualization. Bioinformatic splicing predictions were obtained using Alamut, HSF 3.1, and SpliceAI software. RESULTS Eighty-five variants (33%) were associated with abnormal splicing. Metabolism inhibitor The most frequent abnormality was upstream exon skipping (39/85 variants), which was most often associated with splice donor region variants.