Detecting Environmental Stress In Situ Using Molecular Data: A Case Study with the Filamentous Green Alga...

The polar environment is one of the most extreme environments of our world. However, even in the cold deserts of Antarctica, life thrives, often in the form of biocrusts (biological soil crusts)-complex communities consisting of hundreds of organisms. The reaction to abiotic stress in members of these communities is often inferred from laboratory experiments on isolated species and single factors, without taking into consideration any mitigation effects by the communities or complex habitats. In this study, we aimed to infer the stress situation of the filamentous green alga Klebsormidium in Antarctic biocrusts in situ using metatranscriptomic data. Klebsormidium is ubiquitous in biocrusts and well studied with respect to abiotic factors, allowing the comparison of lab experiments with the in situ situation. In this study, we identified Klebsormidium flaccidum to be present in biocrusts from Livingston Island (Antarctica). Metatranscriptomic data for the biocrust were used to investigate the presence of cold and desiccation stress in situ. To this end, we identified consistently expressed and stress-regulated genes in published stress transcriptomes of Klebsormidium that could serve as markers for environmental stress levels. These "marker genes" were used to construct marker gene indices to assess stress states in biocrusts by comparing transcript expression ratios under different conditions-a novel framework for the assessment of microbial community responses to environmental stressors. However, many potential marker genes behaved quite differently in the laboratory and in the natural environment. In the end, rather than relying on indices based on individual marker genes, comparing the expression levels of whole stressor-regulated gene sets proved to be a more reliable approach to examining stress in situ. This study highlights the potential of marker genes for broader ecological and environmental monitoring using metatranscriptomic data.