Mining of Multi-omics Molecular Interaction Patterns and Identification of Key Genes in Multiple Mouse Tissues under Spaceflight Conditions

ZHANG Yan; YANG Qing; DU Xiaohui; ZHAO Lei; SUN Yeqing

doi:10.11728/cjss2025.02.2024-0137

Volume 45 Issue 2

Apr. 2025

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ZHANG Yan, YANG Qing, DU Xiaohui, ZHAO Lei, SUN Yeqing. Mining of Multi-omics Molecular Interaction Patterns and Identification of Key Genes in Multiple Mouse Tissues under Spaceflight Conditions (in Chinese). Chinese Journal of Space Science, 2025, 45(2): 529-555 doi: 10.11728/cjss2025.02.2024-0137

Citation:

ZHANG Yan, YANG Qing, DU Xiaohui, ZHAO Lei, SUN Yeqing. Mining of Multi-omics Molecular Interaction Patterns and Identification of Key Genes in Multiple Mouse Tissues under Spaceflight Conditions (in Chinese). Chinese Journal of Space Science, 2025, 45(2): 529-555 doi: 10.11728/cjss2025.02.2024-0137

Citation:

ZHANG Yan, YANG Qing, DU Xiaohui, ZHAO Lei, SUN Yeqing. Mining of Multi-omics Molecular Interaction Patterns and Identification of Key Genes in Multiple Mouse Tissues under Spaceflight Conditions (in Chinese). Chinese Journal of Space Science, 2025, 45(2): 529-555 doi: 10.11728/cjss2025.02.2024-0137

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Mining of Multi-omics Molecular Interaction Patterns and Identification of Key Genes in Multiple Mouse Tissues under Spaceflight Conditions

doi: 10.11728/cjss2025.02.2024-0137 cstr: 32142.14.cjss.2024-0137

Institute of Environmental Systems Biology, College of Environmental Science and Engineering, Dalian Maritime University, Dalian 116026

Received Date: 2024-10-26
Rev Recd Date: 2025-02-20

Available Online: 2025-03-19

Abstract

Abstract

To explore the space biological effects from a systems biology perspective, a bioinformatics analysis pipeline was developed based on Single-Sample Network (SSN) and was used to mine multi-omics molecular interaction patterns and key genes in multiple mouse tissues under spaceflight conditions. First, we collected four spaceflight mouse datasets from the GeneLab platform, which included transcriptome, DNA methylation, and proteome sequencing results from the adrenal gland, kidney, liver, and quadriceps. For each sample, four SSNs (mRNA-SSN, meth-promoter-SSN, meth-body-SSN, protein-SSN) were constructed. Next, the topological features of the nodes in each SSN were extracted, and a T-test was performed to identify the molecules with altered interaction patterns across different omics levels under spaceflight conditions. The results indicated that, although the overlap of molecules with altered interaction patterns across different omics levels was limited, the biological processes and pathways they regulated exhibited similarities, which primarily included metabolic processes, DNA damage response, cell cycle, oxidative stress, and circadian rhythms. Notably, the protein/amino acid metabolic process and nucleic acid (DNA/RNA) metabolic process appeared in multiple tissues and showed high significance. The key genes involved in multi-omics regulation were identified, including Park7, Tmed3, Rbbp7, Hunk, Rad23a, Cd36, etc. Furthermore, we constructed co-interaction networks for the transcriptome and DNA methylation, transcriptome and proteome, as well as for all three omics layers, and identified the Hub genes (such as Rbbp7, Egfr, Rpl4, Srms, Cabp4) from them. Functional analysis revealed that several key/Hub genes were involved in regulating the aforementioned biological processes. To gain further insight into the expression patterns of key/Hub genes, additional datasets from the same tissue in the GeneLab database were analyzed using DESeq2. The results revealed that several genes were also differentially expressed under spaceflight conditions in other datasets. The molecular interaction patterns altered by spaceflight might be associated with the occurrence of various diseases (such as cancer, neurological disorders, neurodegenerative diseases, cardiovascular diseases, and diabetes) and the reactivation of viruses (such as Epstein-Barr virus, herpes simplex virus, and human cytomegalovirus).
- Space environment,
- Single-sample network,
- Molecular interaction pattern,
- Multi-omics

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