Meta-Analysis of RNA-seq Identifies a Common Transcriptomic Signature Across Multiple Mouse Models of Skeletal Muscle Atrophy

Tominaga, Takaki

doi:10.51094/jxiv.6470

##article.authors##

Tominaga, Takaki Independent Researcher

DOI:

https://doi.org/10.51094/jxiv.6470

キーワード:

Muscle Atrophy、 lncRNA、 RNA-seq

抄録

Background: It is not fully understood how the whole transcriptome, including long non-coding RNAs (lncRNAs), responds to skeletal muscle atrophy. This study aimed to identify a common transcriptomic signature across multiple atrophy models via a robust meta-analysis of RNA-seq. Methods: Mouse RNA-seq datasets for four acute atrophy models (cachexia, disuse, denervation, sepsis) were analyzed using a modified comparative meta-profiling method to identify common differentially expressed mRNAs (cDEmRNAs) and lncRNAs (cDElncRNAs) with consistent directionality. Results: Meta-analysis identified 716 cDEmRNAs and 160 cDElncRNAs. Among these, ranking by consistency across muscle atrophy models identified robustly upregulated mRNAs (Nsun2, Nploc4, and Hectd1) and lncRNAs (Gm56555, Gm74406, and 2410006H16Rik), alongside downregulated mRNAs (Mtfp1, Taco1, and Kbtbd13) and lncRNAs (2310016D23Rik, Gm30794, and Gm12002). Conclusions: This study identifies a common transcriptomic signature across multiple acute muscle atrophy models, providing potential targets and biomarkers independent of specific atrophy models.

利益相反に関する開示

The author declares no conflict of interest.

ダウンロード *前日までの集計結果を表示します

ダウンロード実績データは、公開の翌日以降に作成されます。

引用文献

Baskin, K.K.; Winders, B.R.; Olson, E.N. Muscle as a “Mediator” of Systemic Metabolism. Cell Metab. 2015, 21, 237–248, doi:10.1016/j.cmet.2014.12.021.

Bonaldo, P.; Sandri, M. Cellular and Molecular Mechanisms of Muscle Atrophy. Dis. Model. Mech. 2013, 6, 25–39, doi:10.1242/dmm.010389.

Sartori, R.; Romanello, V.; Sandri, M. Mechanisms of Muscle Atrophy and Hypertrophy: Implications in Health and Disease. Nat. Commun. 2021, 12, 330, doi:10.1038/s41467-020-20123-1.

Zhang, Y.; Wang, T.; Wang, Z.; Shi, X.; Jin, J. Functions and Therapeutic Potentials of Long Noncoding RNA in Skeletal Muscle Atrophy and Dystrophy. J. Cachexia Sarcopenia Muscle 2025, 16, e13747, doi:10.1002/jcsm.13747.

Hitachi, K.; Honda, M.; Tsuchida, K. The Functional Role of Long Non-Coding RNA in Myogenesis and Skeletal Muscle Atrophy. Cells 2022, 11, doi:10.3390/cells11152291.

Pillon, N.J.; Gabriel, B.M.; Dollet, L.; Smith, J.A.B.; Sardón Puig, L.; Botella, J.; Bishop, D.J.; Krook, A.; Zierath, J.R. Transcriptomic Profiling of Skeletal Muscle Adaptations to Exercise and Inactivity. Nat. Commun. 2020, 11, 470, doi:10.1038/s41467-019-13869-w.

Rhodes, D.R.; Yu, J.; Shanker, K.; Deshpande, N.; Varambally, R.; Ghosh, D.; Barrette, T.; Pandey, A.; Chinnaiyan, A.M. Large-Scale Meta-Analysis of Cancer Microarray Data Identifies Common Transcriptional Profiles of Neoplastic Transformation and Progression. Proc. Natl. Acad. Sci. 2004, 101, 9309–9314, doi:10.1073/pnas.0401994101.

Gallot, Y.S.; Bohnert, K.R. Confounding Roles of ER Stress and the Unfolded Protein Response in Skeletal Muscle Atrophy. Int. J. Mol. Sci. 2021, 22, doi:10.3390/ijms22052567.

Rath, S.; Sharma, R.; Gupta, R.; Ast, T.; Chan, C.; Durham, T.J.; Goodman, R.P.; Grabarek, Z.; Haas, M.E.; Hung, W.H.W.; et al. MitoCarta3.0: An Updated Mitochondrial Proteome Now with Sub-Organelle Localization and Pathway Annotations. Nucleic Acids Res. 2021, 49, D1541–D1547, doi:10.1093/nar/gkaa1011.

Chellamuthu, A.; Gray, S.G. The RNA Methyltransferase NSUN2 and Its Potential Roles in Cancer. Cells 2020, 9, doi:10.3390/cells9081758.

Ruas, J.; Cervenka, I.; Leuchtmann, A.; Jannig, P.; Ducommun, S.; Naddaf, E.; Kokesh, E.; Correia, J.; Murgia, M.; Sereiva, T.; et al. The E3 Ligase HECTD1 Controls Skeletal Muscle Sarcomere and Mitochondrial Integrity. Research Square 2026, doi:10.21203/rs.3.rs-8663318/v1.

Boom, J. van den; Meyer, H. VCP/P97-Mediated Unfolding as a Principle in Protein Homeostasis and Signaling. Mol. Cell 2018, 69, 182–194, doi:10.1016/j.molcel.2017.10.028.

Re Cecconi, A.D.; Barone, M.; Gaspari, S.; Tortarolo, M.; Bendotti, C.; Porcu, L.; Terribile, G.; Piccirillo, R. The P97-Nploc4 ATPase Complex Plays a Role in Muscle Atrophy during Cancer and Amyotrophic Lateral Sclerosis. J. Cachexia Sarcopenia Muscle 2022, 13, 2225–2241, doi:10.1002/jcsm.13011.

Huang, R.; Tang, X.; Liu, S.; Sun, L. Decoding CKD-Induced Muscle Atrophy through the Critical Role of lncRNA GAS5 and Pyroptosis. Mol. Ther. Nucleic Acids 2025, 36, doi:10.1016/j.omtn.2025.102451.

Schutt, C.; Hallmann, A.; Hachim, S.; Klockner, I.; Valussi, M.; Atzberger, A.; Graumann, J.; Braun, T.; Boettger, T. Linc‐MYH Configures INO80 to Regulate Muscle Stem Cell Numbers and Skeletal Muscle Hypertrophy. EMBO J. 2020, 39, EMBJ2020105098, doi:10.15252/embj.2020105098.

Furlow, J.D.; Watson, M.L.; Waddell, D.S.; Neff, E.S.; Baehr, L.M.; Ross, A.P.; Bodine, S.C. Altered Gene Expression Patterns in Muscle Ring Finger 1 Null Mice during Denervation- and Dexamethasone-Induced Muscle Atrophy. Physiol. Genomics 2013, 45, 1168–1185, doi:10.1152/physiolgenomics.00022.2013.

Hitachi, K.; Nakatani, M.; Kiyofuji, Y.; Inagaki, H.; Kurahashi, H.; Tsuchida, K. An Analysis of Differentially Expressed Coding and Long Non-Coding RNAs in Multiple Models of Skeletal Muscle Atrophy. Int. J. Mol. Sci. 2021, 22, doi:10.3390/ijms22052558.

Ballarino, M.; Cipriano, A.; Tita, R.; Santini, T.; Desideri, F.; Morlando, M.; Colantoni, A.; Carrieri, C.; Nicoletti, C.; Musarò, A.; et al. Deficiency in the Nuclear Long Noncoding RNA Charme Causes Myogenic Defects and Heart Remodeling in Mice. EMBO J. 2018, 37, EMBJ201899697, doi:10.15252/embj.201899697.

Yao, Y.; Yan, C.; Huang, H.; Wang, S.; Li, J.; Chen, Y.; Qu, X.; Bao, Q.; Xu, L.; Zhang, Y.; et al. LncRNA-MEG3 Regulates Muscle Mass and Metabolic Homeostasis by Facilitating SUZ12 Liquid–Liquid Phase Separation. Adv. Sci. 2025, 12, 2417715, doi:10.1002/advs.202417715.

Hitachi, K.; Nakatani, M.; Funasaki, S.; Hijikata, I.; Maekawa, M.; Honda, M.; Tsuchida, K. Expression Levels of Long Non-Coding RNAs Change in Models of Altered Muscle Activity and Muscle Mass. Int. J. Mol. Sci. 2020, 21, doi:10.3390/ijms21051628.

Mattick, J.S.; Amaral, P.P.; Carninci, P.; Carpenter, S.; Chang, H.Y.; Chen, L.-L.; Chen, R.; Dean, C.; Dinger, M.E.; Fitzgerald, K.A.; et al. Long Non-Coding RNAs: Definitions, Functions, Challenges and Recommendations. Nat. Rev. Mol. Cell Biol. 2023, 24, 430–447, doi:10.1038/s41580-022-00566-8.

Tominaga, T. ttominaga7713/RNsequant: RNsequant v1.0.0. 2026, doi:10.5281/zenodo.21450707.

Download SRA Sequences from Entrez Search Results Available online: https://www.ncbi.nlm.nih.gov/sra/docs/sradownload/ (accessed on 20 July 2026).

Babraham Bioinformatics - Trim Galore! Available online: https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ (accessed on 20 July 2026).

Patro, R.; Duggal, G.; Love, M.I.; Irizarry, R.A.; Kingsford, C. Salmon Provides Fast and Bias-Aware Quantification of Transcript Expression. Nat. Methods 2017, 14, 417–419, doi:10.1038/nmeth.4197.

Soneson, C.; Love, M.I.; Robinson, M.D. Differential Analyses for RNA-Seq: Transcript-Level Estimates Improve Gene-Level Inferences. F1000Research 2016, 4, 1521, doi:10.12688/f1000research.7563.2.

Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data. Bioinformatics 2010, 26, 139–140, doi:10.1093/bioinformatics/btp616.

Tominaga, T. ttominaga7713/RNsequant-Addons: RNsequant-Addon v1.0.0. 2026, doi:10.5281/zenodo.21839589.

Elizarraras, J.M.; Liao, Y.; Shi, Z.; Zhu, Q.; Pico, A.R.; Zhang, B. WebGestalt 2024: Faster Gene Set Analysis and New Support for Metabolomics and Multi-Omics. Nucleic Acids Res. 2024, 52, W415–W421, doi:10.1093/nar/gkae456.