Network Analysis of Local Gene Regulators in Arabidopsis thaliana under Spaceflight Stress

Abstract

Spaceflight microgravity affects normal plant growth in several ways. The transcriptional dataset of the plant model organism Arabidopsis thaliana grown in the international space station is mined using graph-theoretic network analysis approaches to identify significant gene transcriptions in microgravity essential for the plant’s survival and growth in altered environments. The photosynthesis process is critical for the survival of the plants in spaceflight under different environmentally stressful conditions such as lower levels of gravity, lesser oxygen availability, low atmospheric pressure, and the presence of cosmic radiation. Lasso regression method is used for gene regulatory network inferencing from gene expressions of four different ecotypes of Arabidopsis in spaceflight microgravity related to the photosynthetic process. The individual behavior of hub-genes and stress response genes in the photosynthetic process and their impact on the whole network is analyzed. Logistic regression on centrality measures computed from the networks, including average shortest path, betweenness centrality, closeness centrality, and eccentricity, and the HITS algorithm is used to rank genes and identify interactor or target genes from the networks. Through the hub and authority gene interactions, several biological processes associated with photosynthesis and carbon fixation genes are identified. The altered conditions in spaceflight have made all the ecotypes of Arabidopsis sensitive to dehydration-and-salt stress. The oxidative and heat-shock stress-response genes regulate the photosynthesis genes that are involved in the oxidation-reduction process in spaceflight microgravity, enabling the plant to adapt successfully to the spaceflight environment.

Detection of Genes in Arabidopsis thaliana L. Responding to DNA Damage from Radiation and Other Stressors in Spaceflight

“…We have also shown how topological network measures of degree distribution are useful for the extraction of subnetwork modules from larger GRNs. The subnetworks sampled from the original GRNs also have a scale-free topology with a degree distribution that follows an exponential power law [ 54 ]. Centrality measures are useful for distinguishing protein complexes [ 55 ].…”

Detection of Target Genes for Drug Repurposing to Treat Skeletal Muscle Atrophy in Mice Flown in Spaceflight

“…Graph-based Gene Regulatory Network (GRN) inferencing methods of Pearson correlation and Markov Blanket (MB) are utilized to identify the most regulated genes in the seven GeneLab datasets [ 17 , 18 ]. The gene expression values of pairs of genes are used to compute the Pearson correlation value.…”

Combining Proteomics and Metabolomics to Analyze the Effects of Spaceflight on Rice Progeny

“…It has been proved by Vaulina et al (1984) that spaceflight can cause chromosome aberration, gene deletion and recombination of crops, thus causing genetic material variation. Moreover, crops adapted to the effects of spaceflight by adjusting their own metabolic network ( Paul et al, 2017 ), which was induced by oxidative stress and heat stress ( Manian et al, 2021 ). The current research conclusions showed that spaceflight has caused changes in plant lipid peroxidation, antioxidant enzymes, energy metabolism, signal transduction, protein synthesis, cell wall biosynthesis and other processes ( Matía et al, 2010 ; Ferl et al, 2015 ; Choi et al, 2019 ).…”