Transcriptional profiling of male mouse muscle across aging stages: a gene ontology analysis of the muscle matreotype.

Transcriptional profiling of male mouse muscle across aging stages: a gene ontology analysis of the muscle matreotype - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice BMC Genomics . 2026 Mar 9;27:380. doi: 10.1186/s12864-026-12677-z Search in PMC Search in PubMed View in NLM Catalog Add to search Transcriptional profiling of male mouse muscle across aging stages: a gene ontology analysis of the muscle matreotype A Listrat A Listrat 1 UMR1213, UMR Herbivores (UMRH), Université de Clermont Auvergne, INRAe, VetAgro Sup, Saint-Genès-Champanelle, 63122 France Find articles by A Listrat 1, ✉ , C Jousse C Jousse 2 UMR1019 Unité de Nutrition Humaine (UNH), INRAE, Université Clermont Auvergne, Saint-Genès-Champanelle, 63122 France Find articles by C Jousse 2 , J Tournayre J Tournayre 1 UMR1213, UMR Herbivores (UMRH), Université de Clermont Auvergne, INRAe, VetAgro Sup, Saint-Genès-Champanelle, 63122 France Find articles by J Tournayre 1 , C Boby C Boby 1 UMR1213, UMR Herbivores (UMRH), Université de Clermont Auvergne, INRAe, VetAgro Sup, Saint-Genès-Champanelle, 63122 France Find articles by C Boby 1 , K Lee K Lee 3 Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology, 333 Techno Jungang-daero, Daegu, 42988 Korea Find articles by K Lee 3 , D Béchet D Béchet 2 UMR1019 Unité de Nutrition Humaine (UNH), INRAE, Université Clermont Auvergne, Saint-Genès-Champanelle, 63122 France Find articles by D Béchet 2 , H Wang H Wang 4 Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798 Singapore Find articles by H Wang 4 , K L Goh K L Goh 5 Newcastle University in Singapore, 172A Ang Mo Kio Avenue 8 #05-01, Singapore, 567739 Singapore Find articles by K L Goh 5 Author information Article notes Copyright and License information 1 UMR1213, UMR Herbivores (UMRH), Université de Clermont Auvergne, INRAe, VetAgro Sup, Saint-Genès-Champanelle, 63122 France 2 UMR1019 Unité de Nutrition Humaine (UNH), INRAE, Université Clermont Auvergne, Saint-Genès-Champanelle, 63122 France 3 Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology, 333 Techno Jungang-daero, Daegu, 42988 Korea 4 Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798 Singapore 5 Newcastle University in Singapore, 172A Ang Mo Kio Avenue 8 #05-01, Singapore, 567739 Singapore ✉ Corresponding author. Received 2025 Sep 30; Accepted 2026 Feb 17; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13085706  PMID: 41803688 Abstract Background In humans, skeletal muscle mass and function progressively decline from the age of 30 years, which corresponds to around 6 months in mice, with a markedly increased rate of decline after the age of 65 years, or 20 months in mice. This process is known as sarcopenia. The mechanisms underlying the transition from adulthood to old age remain unclear. This is partly due to the selection of age groups designated as both control and aging cohorts. Only recently has the extracellular matrix (ECM) been proposed as a hallmark of aging, despite remaining relatively understudied, but the 'aging matreotype,' defined as the composition of the matrisome associated with aging, has yet to be defined. This study aims to identify the optimal age range for comparison with elderly subjects to better understand aging-related biological processes and define the muscle aging matreotype. Results Over-representation analysis (ORA) and functional class scoring (FCS) were used to compare gene profiling data obtained by microarrays of mouse muscle at 2 (young adult), 11 (mature adult), and 25 (aged) months. The analysis revealed that 82% of gene ontology (GO) terms and differentially expressed genes (DEGs) between 2 and 25 months were also present during maturation (2 to 11 months), but not during aging (11 to 25 months). Aging was characterized by distinct biological processes (BPs), including differentially expressed genes (DEGs) shared with maturation but exhibiting opposite directions of expression. Muscle aging was characterized by structural alterations, synaptic transmission changes, and a decline in ECM and angiogenesis-related BPs, alongside increased apoptotic processes linked to ECM. The study introduced the first transcriptomic 'matreotype' of muscle aging, consisting of 58 genes, 95% of which were downregulated. Conclusion This study emphasized that to capture aging-specific changes, comparisons should focus on aged (20–22 months) vs . mature adult (around 11 months) male mice. This result should be validated in female mice; and the same applies to the identified stages for studying aging in mice (11 and 25 months). They should be validated in humans, where they correspond approximately to 50 and 70 years of age, respectively. This study introduces the first transcriptional matreotype of mammalian aging and advances our understanding of the matrisome's role. Comparison with proteomics studies highlights the need for integrated transcriptomic and proteomic approaches, with protocols adapted to matrisome analysis. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-026-12677-z. Keywords: Matrisome, Matreotype, Aging, Muscle Microarrays, In silico Background Skeletal muscle is a heterogeneous tissue composed of, approximately, 90% muscle fibers and 10% connective and fat tissues. Skeletal muscles undergo profound changes from young to old age. In humans, muscle mass begins to decline by 0.5–1% per year from age 30, with a more rapid decline after age 65 [ 1 ]. According to Harrison Laboratory’s report, these ages correspond, respectively, to 6 and 20 months in mice ( https://www.jax.org/news-and-insights/jax-blog/2017/November/when-are-mice-considered-old ). This progressive loss of skeletal muscle mass and function is referred to as sarcopenia. Many studies have shown that sarcopenia is a multifactorial process [ 2 ] characterized by major structural and biological modifications [ 3 , 4 ]. In mammals, sarcopenia is characterised by a decline in regenerative capacity, driven by reduced number and function of muscle satellite cells, decreased muscle strength and mass (largely due to loss and atrophy of type IIB muscle fibres), accumulation of adipocytes within intramuscular connective tissue (IMCT), and reduced capillarisation [ 4 ]. Sarcopenia is also characterized by myofiber atrophy, grouping of oxidative muscle fibers, myonuclei delocalization, and increased apoptosis in capillary endothelial cells. Alterations in myofiber size and morphology are accompanied by various modifications in both the IMCT and its extracellular matrix (ECM) [ 3 ]. However, the precise biological processes (BPs) involved remain unclear. To achieve this, key life stages must first be defined to ensure that aging-related changes are correctly identified and not confounded with those from other life periods. Sarcopenia studies often use 3-, 4-, or 6-month-old mice or rats as controls [ 5 – 7 ]. However, our experience with mice, rats, and humans [ 3 , 4 , 8 ] suggests that using animals that are too young may confound mechanisms of young-to-adult transition with those of adult-to-aged transition. The hypothesis is that to accurately identify biological processes specific to aging, comparisons should be made between old mice (18–24 months) and mature adult mice (10–14 months), rather than with young adults or growing mice (3–6 months). To test this hypothesis, we used muscles from young adult (2 Mo), mature adult (11 Mo), and aged (25 Mo) mice through microarray-based transcriptomic analysis. This method provides a comprehensive, unbiased view of the transcriptional profile during physiological states. The data were analyzed using gene ontology (GO). Accordingly, our first aim was to define biologically appropriate life stages in mice for the study of skeletal muscle aging and to identify aging-specific BPs using transcriptomic analysis. Previously considered amorphous, the ECM is now recognised as a dynamic structure and as a hallmark of aging and longevity that remains relatively understudied [ 9 ]. The ECM is composed of hundreds of molecules, including collagens [ 10 ], collectively termed the matrisome. Throughout life and in particular during aging, IMCT and its ECM undergo several major changes, both structurally and biochemically [ 4 , 11 ]. The aging-associated composition of the matrisome, or 'matreotype,' and the BPs associated remain unknown in mammals [ 12 ]. Our second aim was to define this matreotype as a foundation for understanding the matrisome's role in aging. Materials and methods Ethical statements All animal procedures were conducted in compliance with the guidelines of the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) and were approved by the Institutional Animal Care and Use Committee of Nanyang Technological University (IACUC ARF SBS/NIE-A 00 36). Animals and samples Animals were obtained from the Laboratory Animal Centre of the National University of Singapore. We used six biological replicates per group being aware that this could reduce statistical power, increase the risk of false negatives and individual-level variability. To minimize the effect of potential covariables, we chose to work with a line of inbred mice (considered genetically very homogeneous, i.e. eighteen C57BL/6 mice) and only males, all bred in control conditions. They were raised and killed in the veterinarian-staffed Laboratory Animal Facility at Nanyang Technological University (NTU) in accordance with the Institutional Animal Care-and-Use Committee guidelines. Mice were housed in a temperature (22 ± 1 °C) and humidity (50–70%) -controlled facility, with a 12:12 h light–dark cycle, and food (SAFE, Singapore, Singapore) and water were provided ad libitum. Mice were either 2, 11, or 25 months of age, corresponding to young adult, mature adult, or aged mice, respectively. Mice were rendered unconscious by CO₂ inhalation prior to cervical dislocation, which was used as a secondary physical method to ensure death [ 4 ]. Body weight was recorded immediately after euthanasia, and gastrocnemius muscles (GM) were removed from both hind limbs, weighed, and snap-frozen in liquid nitrogen for total RNA extraction. Transcriptomic analysis For each mouse, RNA from GM of both limbs was isolated using a TissueRuptor and RNeasy Fibrous Tissue Mini Kit (Qiagen). Integrity of RNA samples was verified using an Agilent Bioanalyzer with the RNA 6000 Nano labchip® kit. Concentration and purity were determined with a Nanodrop ND-1000 spectrophotometer. Ribosomal fractions were depleted using 2 µg of total RNA and Ribominus Kit magnetic beads (Invitrogen). Biotinylated single stranded cDNAs were prepared from 400 ng of depleted total RNA according to the Affymetrix whole transcript protocol. After fragmentation and terminal labeling, 5.5 µg of single stranded cDNA were hybridized on the GeneChip® Mouse Exon 1.0 ST Array for 16 h at 45 °C and 60 rpm in the Affymetrix Oven 645. Genechips were washed and stained in the Affymetrix Fluidics Station 450 with Hybridization Wash and Stain Affymetrix kit. Genechips were scanned using the Affymetrix Genechip Scanner 3000 7G, and data were generated with Affymetrix Expression Console v 1.2.1 software using robust multiarray average (RMA) algorithm. Parameter setting and data summarization were performed using RMA algorithm. Affymetrix data files were processed using GeneSpring GX 14.9 (Agilent) and analyzed by one-way ANOVA followed by pairwise comparisons using Tukey HSD post-hoc test. Benjamini–Hochberg multiple testing correction was performed to identify differentially expressed entities with a significance threshold set at p < 0.05. The data are deposited in NCBI’s Gene Expression Omnibus under accession number GSE136266 . The full experimental protocol is summarized in Listrat et al. (2022) [ 13 ]. Bioinformatics analyses Analysis of overall data Functional class scoring (FCS) of pathway-based gene sets was performed using GeneTrail 3.0 [ 14 ] with a KolMogorov–Smirnov test and Benjamini–Yekutieli correction (p < 0.01) for multiple testing. QuickGO was used to obtain Gene Ontology (GO) identifying number (GO-ID) from the GO names provided by GeneTrail 3.0. Redundant GO terms were removed using REViGO [ 15 ] ( http://revigo.irb.hr/ ) allowing low similarity (C = 0.5) and selecting terms with the lowest dispensability (< C). Over-representation analysis (ORA) of up- and downregulated differentially expressed genes (DEGs) was performed using Metascape ( https://metascape.org/ ) for pathway and process enrichment. Terms with p < 0.01, a minimum count of 5, and an enrichment factor > 1.5 were grouped into clusters, with the most significant term summarizing each cluster, following Zhou et al. (2019) [ 16 ]. Protein–protein interaction analysis was conducted on 461 aging genes using Metascape, Cytoscape, BioGrid, and STRING (physical score > 0.132). The Molecular Complex Detection (MCODE) algorithm identified densely connected network components [ 17 ]. The TRRUST v2 database ( www.grnpedia.org/trrust ) [ 18 ] was used to identify transcription factors regulating gene expression in aging mouse muscle. Analysis of matrisome genes Matrisome genes among the DEGs were identified by comparing the overall DEG list with the Mus musculus matrisome [ 19 ] using the web tool 'Calculate and Draw Venn Diagrams' ( https://bioinformatics.psb.ugent.be/webtools/Venn/ ). The protein–protein interaction network of matrisome DEGs was analyzed using the STRING database (version 11.0, https://string-db.org , accessed 15 April 2021) [ 20 ]. For this study, the analysis was performed in 'evidence' mode with high confidence (0.7), applying MCL clustering with an inflation parameter of 1.8 to identify sub-groups within the network. Ligand–receptor interactions for the DEGs were retrieved using the Omnipath R package, which provides programmatic access to the OmniPath database of literature-curated signaling interactions ( https://pubmed.ncbi.nlm.nih.gov/33749993/ ). Receptors were retained only if they showed gene-level mean expression values above 1 TPM across GM muscle samples, based on the ARCHS4 mouse RNA-seq dataset ( https://www.nature.com/articles/s41467-018-03751-6 ) [ 21 ]. Receptor membrane localization was then assessed using UniProt annotations ( https://academic.oup.com/nar/article/53/D1/D609/7902999?login=false ) based on two successive queries. First, a broad membrane query retained proteins manually annotated as localized to the cell or plasma membrane, using UniProt keywords and subcellular location terms supported by manual evidence. Second, a stricter membrane filter was applied by intersecting this list with proteins additionally annotated with membrane-associated structural features, including transmembrane helices, beta strands, GPI-LIKE, and GPI-anchor annotations. For this second query, both manually curated and electronically inferred UniProt annotations were accepted in order to avoid excessive data loss. Results and discussion Transcriptomic analysis reveals age-dependent gene expression patterns associated with maturation and sarcopenia in skeletal muscle Aging leads to a decline in skeletal muscle mass, accompanied by structural and compositional changes in myofibers and IMCT, contributing to sarcopenia. These aspects have been the focus of many studies [ 22 ] but the mechanisms involved remain to clarified. To investigate sarcopenia mechanisms and identify aging-related genes, we conducted a transcriptomic study by microarrays on GM muscles from mice aged of 2, 11 to 25 month old (6 animals per stage). Currently, the use of RNA-seq approach is more common than microarray analysis. Microarray analyses demonstrate good reproducibility, particularly for well-characterised genes, and enable rapid analysis of results. The protocols are well established, and data-processing pipelines are standardised, making analysis straightforward. They therefore allow relatively large numbers of samples to be processed. This technology is adapted for model organisms (humans, mice, etc.) whose genes are well annotated, its cost is lower than this one of RNA-seq. However, there may be a saturation effect for highly expressed genes or on the contrary a reduced sensitivity for weakly expressed genes ( https://rna.cd-genomics.com/resource/microarray-vs-rna-seq-gene-expression-profiling.html ). Several authors have compared the two methods and have showed that each had its advantages and disadvantages and that, according the experimental conditions, the two methods were equivalent [ 23 , 24 ]. Regarding the animals in this study, we previously observed [ 4 ] that GM mass increased between 2 and 11 months, remained stable until 20 months, and then sharply declined from 22 months. This sharp decline indicates sarcopenia. This weight loss was accompanied by significant structural changes in both muscle fibers and IMCT. Between 2 and 11 months, myofibre shape and cross-sectional area stabilised, with a chessboard-like distribution in which different fibre types were evenly interspersed in cross-section. From 11 months, the area, the form and the metabolism of myofibers started to change and a delocalization of myonuclei appeared. Atrophy developed gradually with the onset of ECM fibrosis and oxidative muscle fiber grouping [ 4 ]. Based on these results and on our prior studies in rats (unpublished) and mice [ 4 ], which showed that muscle senescence began at 18 months and established by 24 months, we selected 25 months as an aging time point and 11 months as mature adults. Because mice reach sexual maturity between 2 and 3 months [ 25 ], the 2-month stage, representing early adulthood, was chosen as the starting point for this study. These data are in agreement with those of Borsch et al. (2021) [ 26 ] in both mouse and rat models. Together, these structural changes mark the transition from mature adulthood to muscle aging and provide context for the transcriptomic analyses presented below. Among the 15,300 genes present on the arrays, 2,719 differentially expressed genes (DEGs) were identified across all age stages. Whole genome expression profiling revealed 2,299 DEGs between 2 and 11 months, with 1,470 upregulated and 829 downregulated, 461 DEGs between11 and 25 months, with 159 upregulated and 302 downregulated and 2,376 DEGs between 2 and 25 months, including 1,444 upregulated and 932 downregulated (Table 1 ). There is no consensus on the age that defines old in rodents. This lack of agreement makes comparisons with other studies challenging. In mice, Kang et al. (2022) [ 27 ] have highlighted 1395 DEGs, 292 upregulated and 787 downregulated in a 2 vs. 24 month comparison. The analysis of these DEGs with Metascape revealed that the downregulated genes, whether in Kang's study [ 27 ] or our own, were involved in the same BPs, at least for the top 10 clusters. There were few processes that were shared across the lists of the two studies for the upregulated genes. This result can perhaps be explained by the age difference between the animals in the two studies (2 vs. 24 Mo for Kang et al. (2022) [ 27 ] and 2 vs. 25 Mo for us. As a matter of fact, Kang et al. (2022) [ 27 ] have highlighted that there was a substantial change in the gene expression profile of skeletal muscle between 24 and 28 Mo. Table 1. Description of the overall (O) and matrisome (M) datasets. Number of differentially expressed genes significantly upregulated or downregulated among the 15,300 genes present on the microarray, between 2 and 11 months old (Mo), 11 and 25 Mo, and 2 and 25 Mo Age [Mo] 2 vs. 11 11 vs. 25 2 vs. 25 Up-regulated O M 1,470 45 159 4 1,444 35 Down-regulated O M 829 106 302 54 932 133 Total O M 2,299 151 461 58 2,376 168 Open in a new tab We calculated the fold change (FC) of intensity of expression of each gene between pairs of stages (2 vs. 11 Mo, 11 vs. 25 Mo, and 2 vs. 25 Mo). When differences in gene expression between two stages were statistically significant (p < 0.05), genes were considered up- or down-regulated. This analysis revealed 17 distinct patterns of gene expression evolution (Fig. 1 , Supplementary Table 1 A and B, sheet 1), which were grouped qualitatively into four subgroups. The 'aging' group included DEGs that changed direction between 11 and 25 months compared to 2 and 11 months. The ‘maturation and aging’ group comprised DEGs whose expression changed in the same direction (increased or decreased) between 2 and 11 months, 11 and 25 months and 2 and 25 months. The ‘maturation’ group included DEGs that changed between 2 and 11 months but remained unchanged between 11 and 25 months. The 'life-long' group comprised only genes that showed expression changes between 2 and 25 months, with no expression changes at any other intervals (2 to 11 months or 11 to 25 months) (Fig. 1 ). Fig. 1. Open in a new tab Seventeen patterns of mRNA expression changes between studied stages (2 vs. 11 months (Mo), 11 vs 25 Mo, 2 vs 25 Mo; D, blue: DEGs downregulated; I, orange: DEGs upregulated, _: no significant changes of expression) grouped in four groups: « aging”, “maturation”, “maturation and aging” and “life-long” (throughout life) (for more details, see supplementary Table 1 A, sheet 1) Aged versus mature adult mice for identifying aging-specific mechanisms To capture all changes between stages, we used the functional class scoring (FCS) method and over-representation analysis (ORA). The FCS method takes in account the entire gene set, grouping genes by pathways or processes to identify weaker but coordinated changes. This approach allowed us to summarize shared gene ontology (GO) terms and to identify key similarities and differences among biological processes across the 2 vs. 11, 11 vs. 25, and 2 vs. 25 month comparisons (Fig. 2 A,B; Supplementary Table 2). The majority of GO terms (80%) were shared between the stages 2 vs. 11 Mo and 2 vs. 25 Mo, and strikingly, all changed in the same direction, against only 13% shared between the stages 2 vs. 25 Mo and 11 vs. 25 Mo and 27% shared between the stages 11 vs. 25 Mo and 2 vs. 11 Mo, most of them changing in opposite direction (Fig. 2 A and B). Interestingly, 73% (113 out of 154) of the GO terms were specific of stages 11 vs. 25. ORA operates under the hypothesis that large changes in DEGs have major physiological implications. ORA analysis revealed that most DEGs identified between 2 and 25 months were shared with those identified between 2 and 11 Mo or those identified between 11 and 25 Mo (83 and 71%), (Fig. 3 A, groups 1, 2, 3, 4 and a small part of group 5) and Fig. 3 A, groups 3, 4, 7, 8 and a small part of groups 5 and 6). Among the 461 DEGs, only 22% (103) were shared between 11 vs. 25 Mo, 2 vs. 11 Mo and 2 vs. 25 Mo stages (Fig. 3 A, groups 3, 4). Aging studies often compare only two ages: young or very young animals and older ones, with little consideration for intermediate stages. For example, Soriano-Arroquia et al. (2016) [ 28 ] used 6 and 24 Mo mice, Myers et al. (2021) [ 29 ], 5 and 20 Mo, Sleima et al. (2020) [ 30 ], 3 and 23 Mo, Rodriguez et al. (2016) [ 31 ], 4 and 18 Mo. Based on the results of the present study and on those of Wang et al. (2013) [ 4 ] and Börsch et al. (2021) [ 21 ], we conclude that studies focusing on aging should avoid comparisons between aged and very young animals, as such designs risk conflating young-to-adult transitions with adult-to-aged mechanisms and obscuring aging-specific information. Instead, comparisons should be made between aged animals and mature adults at the end of their adult life or early aging, corresponding to approximately 11-month-old mice. Fig. 2. Open in a new tab Functional Class Scoring of muscle transcriptome. Differential expression of pathway-based sets of genes was analyzed using GeneTrail2 to compare the variations of gene expression between different life stages (2 vs. 11 Mo, 11 vs. 25 Mo, and 2 vs. 25 Mo). A . The height of each box is proportional to the number (indicated inside) of gene ontology (GO) terms (Biological Processes, BPs) identified. Aligned boxes indicate identical BPs shared between stages. Blue and orange boxes correspond respectively to depleted and enriched BPs. B . Table summarizing the number of GO terms identified Fig. 3. Open in a new tab Differentially expressed genes (DEGs) and over-representation analysis (ORA). DEGs were identified using one-way Anova, Tukey’s post-hoc and Benjamini–Hochberg correction for multiple testing. A Number of DEGs that changed between 2 vs . 11 months (Mo), 11 vs. 25 Mo and 2 vs. 25 Mo. The eight of each box is proportional to the number of DEGs (indicated inside), and aligned boxes indicate identical DEGs shared between the stages. B Heat map displaying the relative expression for DEGs at 2, 11 and 25 Mo. The color intensity of each block, either orange (higher expression) or blue (lower expression), represents the magnitude of difference from the mean. ( C , D , E ) For ORA, the gene ontology (GO) biological processes (BPs) were summarized using Metascape for the different groups of genes. The groups 1 and 2 illustrate the number of genes and the related GOs that are depleted (blue) or enriched (orange) between 2 vs 11 Mo and 2 vs 25 Mo (identified as D–, D-D or I–, I-I in Fig. 1 and in Supplementary Table 1), the groups 3, 4, 5, 6, 7 and 8 illustrate the number of genes and the related GOs either changing similarly between 11 vs. 25 Mo and 2 vs. 11 Mo (group 3 and 4, identified as DDD or III), inversely 11 vs. 25 Mo and 2 vs. 11 Mo (groups 5 and 6, identified as ID-, IDD, IDI, DI-, DID), or exclusively between 11 vs. 25 Mo (groups 7 and 8, identified as -DD, -D-, -II, -I-), the groups 9 and 10 (identified as –I or –D) illustrate the number of genes and the related GOs that increase or decrease exclusively between 2 vs. 25 Mo. Enr: fold enrichment Key biological processes during maturation and aging Maturation and life-long ORA analyses highlighted that the under-represented BPs between 2 vs. 11 Mo and between 2 vs. 25 Mo (Fig. 3 A, B, C, D, groups 1 and 3) were related to the ECM. They also included under-represented BPs in relationship with actin-filament-based processes (Fig. 3 A, D, group 1), blood vessel development and response to growth factors (Fig. 3 A, group 3). Conversely, there was an over-representation of BPs in relationships with protein catabolic processes and of autophagy (Fig. 3 A, D, group 2) and of apoptotic processes (Fig. 3 A, group 4). These results align with Wang et al. (2013) [ 4 ] which demonstrated that sarcopenia is associated with increased apoptosis of capillary endothelial cells located in ECM, along with modifications of structure and composition in both the ECM and muscle fibers, linked to their innervation. This relationship between muscle fiber innervation and sarcopenia has been extensively documented in the literature [ 22 ]. Aging ORA analysis also revealed that the BPs that were under-represented between 2 vs. 11 Mo then over-represented between 11 vs. 25 Mo (Fig. 3 A, D, group 5) or conversely that were over-represented between 2 vs. 11 Mo then decreased between 11 vs. 25 Mo (Fig. 3 A, D, group 6) were related to the regulation of ion transport, the hormone secretion and MAPK cascade. Lastly, the BPs that were under-represented exclusively between 11 vs. 25 Mo and 2 vs. 25 Mo (Fig. 3 A, C, group 7) were related with ECM organization and energy metabolism, and those that were over-represented (Fig. 3 A, C, group 8) mainly with muscle and neuronal system processes. The data that illustrated these results were listed in Supplementary Table 1 A, sheet 1. Seven transcription factors associated with muscle aging Transcription factors (TFs) regulate cellular functions by interacting with proximal or distal cis-regulatory DNA elements of target genes. We therefore undertook an analysis to identify which TFs were potentially involved in the aging of muscle ECM. These analyses identified 37 key TFs. Among these 37 TFs, 7 TFs (Sp1, Trp53, Nfkb1, Ep300, Rela, Srebf1, Stat3) regulated at least five target DEGs. Among these seven TFs at least 4 are well known to directly regulate many ECM genes [ 32 ]. The potential roles of the seven TFs in aging are as follows. Trp53 encodes the protein p53, which plays a dual role in aging, acting both as a protector against cancer and a potential driver of longevity and aging, in several species such as mice and humans. It can or extend or shorten the lifespan in a context-dependent-manner. The net effect depends on how strongly and in which contexts p53 is activated [ 33 ]. Ep300 encodes the protein p300, essential for VEGF-dependent enhancers and the regulation of the expression of many angiogenic genes [ 34 ]. Rela encodes the transcription factor p65 (NF-kappa-B p65 subunit), which participates in oxidative pathways and other processes. It is also a central pro-inflammatory transcription factor whose activity increases with age and contributes causally to several hallmarks of aging. Experimental reduction or inhibition of p65 can partially reverse age-related phenotypes in mice [ 35 ].​ With respect to its relationship with the ECM, increased p65 activity during aging may suppress type I collagen production while promoting pro-inflammatory signalling, and may thereby contribute to fibrotic remodelling [ 36 ]. Sp1 is a ubiquitous transcription factor that can activate or repress transcription in response to physiological and pathological stimuli. Sp1 is involved in the basal expression of ECM genes [ 37 ]. STAT3 (Signal transducer and activator of transcription 3) plays a dual role in muscle aging and fibrosis, often impairing regeneration while promoting fibrotic responses [ 38 ]. The 37 TFs identified regulated 33 target genes (Supplementary Table 3 A and Table 2 ), including 17 matrisomal genes (Supplementary Table 3B), out of 461 aging-DEGs (q-value < 0.05. These 33 DEGs were mainly involved in the ECM organization (GO:0030198) and blood vessel development (GO: 0001568). These results indicated that aging-related ECM remodeling is not merely a secondary consequence of muscle degeneration but is actively driven by coordinated transcriptional programs. Table 2. Transcription factors (TFs) regulating five or more genes differentially expressed (DEGs) during aging and Gene Ontology Biological Processes associated. DEGs upregulated during aging are in red and DEG downregulated are in blue Open in a new tab Taken together, these data demonstrate that ECM is a central and dynamic target of aging-associated molecular changes driven by specific transcriptional regulators, associated to structural remodeling and tightly linked to inflammation, apoptosis and impaired vascular system. Transcriptomic aging matreotype in mouse skeletal muscle The ECM consists of core structural components, such as collagens, proteoglycans, and glycoproteins, which form its structural framework. Additionally, it contains other molecules, including growth factors, cytokines and ECM-remodeling enzymes, that influence its structure, function, and interactions with cells. These non-structural components, which play roles in signaling and ECM regulation, are collectively referred to as ECM-associated proteins [ 19 ]. Together, the structural components and ECM-associated proteins comprise the matrisome [ 19 , 39 ]. Functional decline in skeletal muscle is closely linked to progressive remodelling of the ECM, characterised by increased area and length of intramuscular connective tissue [ 4 ]. Total collagen content increases with a shift toward increased fibrillar collagens (especially type I and VI) and reduced components such as elastin and collagen III, making the matrix denser [ 40 ]. These compositional changes increase cross-linking and reduce collagen fiber elasticity. This leads to fibrosis, stiffness, and microvascular rarefaction that together impair force generation by muscle. These ECM changes alter both mechanical properties and cell signaling, driving satellite cell dysfunction, mitochondrial stress, and ultimately sarcopenia Over time, both of these changes lead to reduced myofibre cross-sectional area, diminished regenerative capacity, and decreased muscle mass, strength, and elasticity. [ 3 , 4 ]. In this field of research, the majority of studies focus on individual molecules using immunology-based biochemical methods. Consequently, comprehensive analyses of the matrisome and its role in muscle aging are still relatively scarce. Lofaro et al. (2021) [ 41 ] investigated the matrisome of aged muscle using proteomics and mass spectrometry (MS). However, there are challenges in using MS-based proteomics for the characterization of the matrisome composition due to the physico-chemical properties of its proteins. Core matrisomal proteins are large, with high molecular weight, and extensively cross-linked, making them insoluble, unlike ECM-associated proteins [ 42 ]. As a result, most ECM-associated proteins are lost during extractions together with cell debris. While transcriptomics offers an alternative approach for studying the ECM, few studies have applied it to the matrisome. Due to the technical limitations of proteomics in accurately defining the matreotype of aged muscle, we chose transcriptomics for this purpose. To our knowledge, this study provides the first transcriptomic definition of the aging matreotype in mammals. In this study, the matrisome accounted for a large proportion (~ 27%; 28 matrisome DEGs among 103) of DEGs during maturation and aging, compared with only 8.4% (30 matrisome DEGs among 358) if we consider aging-specific DEGs. The matrisome accounted for 12.6% if we considered both maturation and aging and aging-specific DEGs (Supplementary Table 1B, sheet 1). Building on this observation, we performed further analysis to better characterize the muscle matreotype during maturation and aging. Among the 2,689 DEGs during maturation (2–11 Mo), we identified 151 differentially expressed matrisome genes, with 45 upregulated and 106 downregulated. Between young (2 Mo) and old (25 Mo) muscles (life-long variations), 168 matrisome genes were differentially expressed, including 65 genes upregulated and 133 genes downregulated (Table 1 ). The transcriptomic aging matreotype of the mouse consisted of 58 genes, including maturation and aging and aging-specific genes. Of these, 54 genes were downregulated while only 4 upregulated: EGF, PPBP, FIGF (secreted factors) and ADAM22 (an ECM regulator). The 58 genes of aging matreotype represented 0.5% of Mus musculus matrisome [ 19 ] (Supplementary Table 1 C, sheet 2). Among them, 20.9% of Mus musculus matrisome collagens were present, followed by proteoglycans and glycoproteins (~ 8%), of ECM regulators (~ 5%), ECM affiliated (~ 4%) and secreted factors (~ 2%) (Fig. 4 ). The 58 genes of aging matreotype were composed of 27.6% and 25.9% of glycoproteins and ECM-regulators, followed by 15.5% of collagens, 13.8%, of secreted factors and 5.2% of proteoglycans (Fig. 4 ). The expression of all collagen isoforms (a total of 16 of the 43 described in the Mus musculus matrisome [ 19 ]) decreased during both maturation and aging, but no collagens showed a specific decrease during aging (11 to 25 Mo) (Fig. 5 A, B and C). This reduction in collagen isoform expression was accompanied by decreased expression of most ECM regulators; of the 43 identified, 34 were differentially downregulated during maturation and aging. All of these ECM regulators were mainly involved in collagen metabolic process and ECM organization (GO: 0032963 and GO: 0030198). Among other matrisomal protein categories, a small proportion increased during maturation. Of the proteins common to both aging and maturation, or specific to aging, only a very small proportion (0.2 to 1%, corresponding to 4 genes) of secreted factor and ECM regulators showed increased expression (Fig. 5 A). Fig. 4. Open in a new tab Percentage of each category of protein of matrisome differentially expressed during aging relatively to their counterpart of the Mus musculus matrisome (black bars) and relatively to the total number of genes of matrisome differentially expressed during aging (grey bars). Category of protein of matrisome: Cols: Collagens; PGs: ProteoGlycans; GPs: GlycoProteins; ECM-Affs: ExtraCellular Matrix Affiliated; ECM-Regs: ExtraCellular Regulators; SFs: Secreted Factors Fig. 5. Open in a new tab Characterization of the muscle matrisome during maturation and aging. Matrisome proteins are classified into core matrisome components (collagens, ECM glycoproteins and proteoglycans) and matrisome-associated components (ECM regulators, ECM-affiliated proteins and secreted factors). A Proportion of matrisome components among genes differentially expressed during maturation only (left), aging only (right), or during both processes (center). B , C Protein–protein interaction networks of differentially expressed matrisome genes associated with both maturation and aging ( B ), or with aging only ( C ). The dotted square highlights the absence of collagens in the network. Interaction networks were generated using STRING (version 11.0, https://string-db.org , accessed 15 April 2021) with a confidence score of 0.7 and all linkage criteria were fulfilled. The genes used in the networks are listed in Supplementary Table 1. Colored network nodes represent query proteins, Proteins with the same color are predicted to participate in related biological processes; edges represent protein–protein associations supported by different lines of evidence (s olid edges = known (curated) interactions; Dotted edges = predicted or indirect interactions from computational or statistical inference) Cross-species comparison of matreotypes The only other known aging matreotype is that of Caenorabditis elegans, defined in silico by Ewald (2019) [ 12 ], who demonstrated a general decline in C. elegans matrisome gene expression during aging. This decline is comparable to the decline of matrisome gene expression observed in the present study. Although rodents are widely used as model organisms for human biology, it remains unclear which aspects of sarcopenia are conserved across species. Börsch et al. (2021) [ 26 ] showed that rodent models recapitulate many molecular changes observed in human sarcopenia; however, the timing of aging-related changes differs between species. In rodents, these changes occur gradually, whereas in humans they appear in two distinct waves: an initial metabolic remodelling in young adulthood, followed by alterations in cellularity and inflammation in the seventh decade of life. Furthermore, Borsch et al. [ 26 ] reported stronger conservation at the pathway level than at the level of individual genes. To clarify which molecular patterns are conserved across rodent models and to evaluate the suitability of rodents for studying the muscle aging matreotype in humans, we compared the aging matreotype defined in this study with published datasets. These included matrisomal DEGs reported by Shavlakadze et al. [ 43 ] for the GM muscle of male Sprague Dawley rats (aged 6 vs. 27 Mo), by Gueugneau et al. (2021)’results [ 8 ] for human skeletal muscle (young adults, 21 years, vs. elderly individuals, 73 years), and by Lagerwaard et al. (2021) [ 44 ] for muscles of men aged 20 vs. 69 years. Comparison between our results and those of Shavlakadze et al. [ 43 ] as well as with the human datasets, revealed approximately 60 common matrisome DEGs, of which around 50% changed in the same direction across species. These shared genes were predominantly involved in ECM organisation (GO:0030198) and in skeletal and vascular development (GO:0001501 and GO:0001944). This finding is not unexpected, as ECM stiffening and fibrosis extend into the perivascular space, with associated thickening of the vascular basement membrane and disruption of capillary–muscle fibre coupling [ 45 ]. Finally, we compare the aging muscle matreotype with the longevity-associated matreotype reported by Ham et al. (2022) [ 46 ] in which caloric restriction (CR) and rapamycin in 30 Mo mice were shown to modulate ECM remodeling. The CR- and rapamycin-associated matreotypes comprised 227 and 144 DEGs, respectively, compared with 192 DEGs in the aging matreotype identified in the present study. All three matreotypes were associated with the same BPs (Fig. 6 ), with the aging muscle matreotype showing greater similarity to the CR-associated matreotype than to the rapamycin-associated matreotype. This finding confirms the conclusions of Ham et al. (2022) [ 46 ], namely that rapamycin and caloric restriction exert distinct effects on aging skeletal muscle. Fig. 6. Open in a new tab Heatmap of enriched terms across input gene lists (Listrat_ aging, matreotype described in this paper, Ham_caloric restriction matreotype and rapamycine matreotype), using Metascape, colored by p-values. The matreotypes from Ham et al. (2022) [ 46 ] have been identified by comparing the lists of DEGs published by these authors with the matreotype of the mouse published by Hynes and Naba, 2012 [ 39 ] Changes in collagen expression during maturation and aging Given the extensive research on the collagen family, we chose to focus our discussion on this category of matrisome molecules. In the present study, we identified 24 DEGs encoding different collagen chains out of the 43 known genes in mice [ 19 ] (Supplementary Table 1 C, sheet 2). Their expression decreased throughout the studied period (2 vs. 25 Mo). Kang et al. (2022) [ 27 ] and Chen et al. (2021) [ 47 ] reported results identical to ours for collagen genes in mouse muscle, namely a decrease in collagen type I mRNA. However we previously showed [ 4 ], consistent with the findings of Lofaro et al. (2021) [ 41 ], an increase in collagen content in the GM muscle of mice during aging. This decrease in collagen gene expression, despite fibrosis clearly demonstrated by histology [ 4 ], suggests that collagen accumulation may be regulated by post-transcriptional and post-translational mechanisms in addition to transcriptional control. This apparent discrepancy has been addressed by Takasugi et al. [ 48 ], who mapped age-related changes in protein abundance across multiple mouse tissues. Their study showed that age-associated changes in protein levels frequently do not mirror corresponding mRNA levels, identifying post-transcriptional dysregulation as a key feature of aging. Notably, age-related post-transcriptional regulation was associated with the accumulation of core matrisome proteins and a reduction in mitochondrial membrane proteins, including components of oxidative phosphorylation, across several tissues, including skeletal muscle. With respect to the ECM, several mechanisms may account for the divergence between collagen mRNA expression and collagen accumulation: (1) reduced collagen degradation due to decreased activity of matrix metalloproteinases (MMPs), allowing collagen to accumulate despite unchanged or reduced transcription; (2) increased collagen cross-linking mediated by enhanced activity of lysyl oxidases and lysyl hydroxylases, which stabilises collagen fibres and increases resistance to degradation; (3) altered ECM turnover resulting from changes in the balance between MMPs and tissue inhibitors of metalloproteinases (TIMPs), leading to prolonged persistence of ECM components; and (4) reduced steady-state collagen mRNA levels driven by post-transcriptional regulation, including microRNA-mediated repression or the action of RNA-binding proteins that influence collagen mRNA stability and transcriptional machinery [ 49 , 50 ]. In silico analysis of cell-surface receptors and ligands associated with the aging matreotype Skeletal muscle is composed of striated fibres bundled together by connective tissue, which consists of cells embedded within the ECM. This matrix communicates with muscle fibres primarily through membrane receptors that transduce mechanical and chemical signals into intracellular signals, thereby regulating contraction efficiency, growth, repair, and gene expression in myofibres. To investigate these interactions, we performed an in silico analysis to predict cell-surface receptors and their potential ligands based on differentially expressed genes (DEGs) from the aging matreotype. We identified 85 receptors (Supplementary Table 4), which clustered into five densely connected network components (Fig. 7 ). These components were mainly associated with integrin-mediated signalling (GO:0007229), cell–matrix adhesion (GO:0007160), cell–substrate adhesion (GO:0031589), regulation of the MAPK cascade (GO:0043408, GO:0043410), and semaphorin–plexin signalling (GO:0071526). Fig. 7. Open in a new tab The list of target genes of aging matreotype (Supplementary Table 4) has been used to carried out a protein–protein interaction enrichment analysis with Metascape. The resultant network contains the subset of proteins that form physical interactions with at least one other member in the list. If the network contains between from 3 to 500 proteins, the Molecular Complex Detection (MCODE) algorithm has been applied to identify densely connected network components. A . This analysis has highlighted 5 densely connected networks (a, b, c, d, e). B . Illustration of biological processes in which are involved the genes densely connected from the 5 networks. Pathway and process enrichment analysis has been applied to each MCODE component independently, and the three best-scoring terms by p-value have been retained as the functional description of the corresponding components Our analysis (Supplementary Table 4 and Supplementary Fig. 1) showed that most predicted ligand–receptor interactions were stimulatory, with the most frequent interactions occurring between collagens and integrins. These interactions establish a physical link between the ECM and the actin cytoskeleton, contributing to the maintenance of tissue architecture and enabling cells—including fibroblasts, epithelial cells, platelets, and immune cells—to withstand mechanical stress. Collagen–integrin engagement is known to activate focal adhesion kinase (FAK), Src family kinases, and downstream MAPK and PI3K–Akt signalling pathways, thereby promoting cell survival, proliferation, and migration [ 51 ]. In addition, collagen–integrin signalling regulates both collagen synthesis and degradation, contributing to ECM remodelling. Loss or inhibition of collagen-binding integrins in fibroblasts reduces force generation, collagen deposition, and fibrotic tissue formation, underscoring their importance in tissue stiffness, fibrosis, and aging [ 52 ]. Conclusion In this study, we identified specific BPs and genes in the male mouse muscle associated with the aging period (11 vs. 25 Mo), characterized by distinct patterns of variation. The life-long period (2 vs. 25 Mo) largely reflected the maturation period (2 vs. 11 Mo) and differed significantly from the aging phase. These findings suggest that to identify aging-specific changes in mice, comparisons should focus on aged animals (20 vs. 22 Mo) vs. mature animals (around 11 Mo). As this study involved only male mice, the findings should be validated in female mice and in humans, in whom the stages studied in mice correspond approximately to 50 and 70 years of age. We propose the first transcriptomic matreotype of aging in a mammal. This work complements our existing knowledge on the role of matrisome during aging. Comparisons of our findings with proteomic studies by other authors highlight the critical need to complement transcriptomic studies with proteomic analyses. To achieve accurate matrisome charaterization, the protocols of proteomic and of mass spectrometry should be adapted for matrisome studies [ 42 , 53 ]. Supplementary Information Supplementary Material 1. (697.2KB, pptx) Supplementary Material 2. (724.6KB, xlsx) Supplementary Material 3. (45.8KB, xlsx) Supplementary Material 4. (36.8KB, xlsx) Supplementary Material 5. (46.6KB, xlsx) Acknowledgements The authors thank the staffs of BIOMARQUEURS team, more specifically Geneviève Gentès for her skilled technical assistance. Authors’ contributions Conceptualization (AL, KLG, JC, JT, BC, HW, KL, DB); Data curation (AL, HW); Formal analysis (AL, HW); Funding acquisition (KLG, KL); Investigation (AL, WH, KL, DB); Methodology (AL, HW, KL, DB); Project administration (AL, HW); Resources (AL, DB); Software (AL, KLG, HW, DB); Supervision (AL, KLG, JC, KL, DB); Validation (AL, HW); Visualization (AL, HW); Roles/Writing - original draft (AL); Writing - review & editing (AL, KLG, JC, JT, BC, HW, KL, DB) Funding The authors are grateful for the financial support provided by the Merlion-French programme (N° dossier: 5.03.07) and Singapore Ministry of Education (AcRF Tier 1, RG37-07). Data availability The datasets analyzed during the current study are deposited in NCBI’s Gene Expression Omnibus under accession number GSE136266 . Declarations Ethics approval and consent to participate All methods were carried out following relevant guidelines and regulations. The protocols for all animal experiments were approved by the Animal Care and Use Ethics Committee of the Institute of Animal Sciences, Chinese Academy of Agricultural Science (CAAS) (NO. IAS2019-82), and all methods strictly obeyed the Guide for the ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines 2.0. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References 1. Nair KS. Aging muscle. Am J Clin Nutr. 2005;81:953–63. 10.1093/ajcn/81.5.953. [ DOI ] [ PubMed ] [ Google Scholar ] 2. Narici MV, Maffulli N. Sarcopenia: characteristics, mechanisms and functional significance. Br Med Bull. 2010;95:139–59. 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