RESUMEN
Microgravity, as a unique hazardous factor encountered in space, can induce a series of harmful effects on living organisms. The impact of microgravity on the pivotal functional gene modules stemming from gene enrichment analysis via the regulation of miRNAs is not fully illustrated. To explore the microgravity-induced alterations in critical functional gene modules via the regulation of miRNAs, in the present study, we proposed a novel bioinformatics algorithm for the integrated analysis of miRNAome and transcriptome from short-term space-flown C. elegans. The samples of C. elegans were exposed to two space conditions, namely spaceflight (SF) and spaceflight control (SC) onboard the International Space Station for 4 days. Additionally, the samples of ground control (GC) were included for comparative analysis. Using the present algorithm, we constructed regulatory networks of functional gene modules annotated from differentially expressed genes (DEGs) and their associated regulatory differentially expressed miRNAs (DEmiRNAs). The results showed that functional gene modules of molting cycle, defense response, fatty acid metabolism, lysosome, and longevity regulating pathway were facilitated by 25 down-regulated DEmiRNAs (e.g., cel-miR-792, cel-miR-65, cel-miR-70, cel-lsy-6, cel-miR-796, etc.) in the SC vs. GC groups, whereas these modules were inhibited by 13 up-regulated DEmiRNAs (e.g., cel-miR-74, cel-miR-229, cel-miR-70, cel-miR-249, cel-miR-85, etc.) in the SF vs. GC groups. These findings indicated that microgravity could significantly alter gene expression patterns and their associated functional gene modules in short-term space-flown C. elegans. Additionally, we identified 34 miRNAs as post-transcriptional regulators that modulated these functional gene modules under microgravity conditions. Through the experimental verification, our results demonstrated that microgravity could induce the down-regulation of five critical functional gene modules (i.e., molting cycle, defense response, fatty acid metabolism, lysosome, and longevity regulating pathways) via the regulation of miRNAs in short-term space-flown C. elegans.
Asunto(s)
Caenorhabditis elegans , Redes Reguladoras de Genes , MicroARNs , Vuelo Espacial , Transcriptoma , Ingravidez , Animales , Caenorhabditis elegans/genética , MicroARNs/genética , Perfilación de la Expresión Génica , Regulación de la Expresión GénicaRESUMEN
Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.