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Plants are expected to play a critical role in the biological life support systems of crewed spaceflight missions, including in the context of upcoming missions targeting the Moon and Mars. Therefore, understanding the response of plants to spaceflight is essential for improving the selection and engineering of plants and spaceflight conditions. In particular, understanding the root-tip’s response to spaceflight is of importance as it is the center of orchestrating the development of the root, the primary organ for the absorption of nutrients and anchorage. GLDS-120 is a pioneering study by Paul et al. that used transcriptomics to evaluate the spaceflight response of the root-tip of the model plant Arabidopsis thaliana in dark and light through separate analyses of three genotype groups (Wassilewskija, Columbia-0, and Columbia-0 PhyD) and comparison of genotype responses. Here, we provide a complementary analysis of this dataset through a combined analysis of all samples while controlling for the genotypes in a paired analysis. We identified a robust transcriptional response to spaceflight with 622 DEGs in light and 200 DEGs in dark conditions. Gene enrichment analysis identified 37 and 13 significantly enriched terms from biological processes in light and dark conditions, respectively. Prominent enrichment for hypoxia-related terms in both conditions suggests hypoxia is a key stressor for root development during spaceflight. Additional enriched terms in light conditions include the circadian cycle, light response, and terms for the metabolism of flavonoid and indole-containing compounds. These results further our understanding of plants’ responses to the spaceflight environment.Key message: We report a complementary analysis of GLDS-120 dataset, identifying a robust transcriptional response and prominent enrichment for hypoxia-related pathways by Arabidopsis thaliana’s root-tip during spaceflight. [ABSTRACT FROM AUTHOR]

To identify sensitive genes for space radiation, we integrated the transcriptomic samples of spaceflight mice from GeneLab and predicted the radiation doses absorbed by individuals in space. A single-sample network (SSN) for each individual sample was constructed. Then, using machine learning and genetic algorithms, we built the regression models to predict the absorbed dose equivalent based on the topological structure of SSNs. Moreover, we analyzed the SSNs from each tissue and compared the similarities and differences among them. Our model exhibited excellent performance with the following metrics: R 2 = 0.980 , MSE = 6.74 e − 04 , and the Pearson correlation coefficient of 0.990 (p value <.0001) between predicted and actual values. We identified 20 key genes, the majority of which had been proven to be associated with radiation. However, we uniquely established them as space radiation sensitive genes for the first time. Through further analysis of the SSNs, we discovered that the different tissues exhibited distinct mechanisms in response to space stressors. The topology structures of SSNs effectively predicted radiation doses under spaceflight conditions, and the SSNs revealed the gene regulatory patterns within the organisms under space stressors. [ABSTRACT FROM AUTHOR]