Bone mass declines with age, and the anabolic effects of skeletal loading decrease. While much research has focused on gene transcription, how bone ages and loses its mechanoresponsiveness at the protein level remains unclear.
Researchers Christopher J. Chermside-Scabbo, John T. Shuster, Petra Erdmann-Gilmore, Eric Tycksen, Qiang Zhang, R. Reid Townsend, Matthew J. Silva from Washington University School of Medicine and Washington University in St. Louis, MO, share their findings which underscore the need for complementary protein-level assays in skeletal biology research.
On October 12, 2024, their research paper was published as the cover of Aging (listed by MEDLINE/PubMed as “Aging (Albany NY)” and “Aging-US” by Web of Science), Volume 16, Issue 19, entitled, “A proteomics approach to study mouse long bones: examining baseline differences and mechanical loading-induced bone formation in young-adult and old mice.”
THE STUDY
In this study, the tibias of young-adult and old mice were analyzed using proteomics and RNA-seq techniques, while the femurs were examined for age-related changes in bone structure. A total of 1,903 proteins and 16,273 genes were detected through these analyses. Multidimensional scaling demonstrated a clear separation between the young-adult and old samples at both the protein and RNA levels. Furthermore, 93% of the detected proteins were also identifiable by RNA-seq, and the abundance of these shared targets showed a moderately positive correlation. Additionally, differential expression analysis revealed 183 age-related differentially expressed proteins and 2,290 differentially expressed genes between young-adult and old bone samples.
Proteomic and RNA-seq analyses were conducted on paired tibias from young-adult and old mice to study age-related differences and the effects of mechanical loading on bone formation. The results showed distinct differences in protein and gene expression between the two age groups. Many of the significantly upregulated and downregulated proteins and genes in old bone have been associated with bone phenotypes in genome-wide association studies (GWAS). The study also identified age-related differentially expressed proteins and genes involved in bone phenotypes and aging processes. Integrated analysis with GWAS data revealed eight targets that may be relevant to human disease, including Asrgl1 and Timp2. Furthermore, co-expression analysis identified an age-related module indicating baseline differences in TGF-beta and Wnt signaling. Baseline age-related differences in ECM/MMPs and TGF-beta signaling were detected in both the proteome and transcriptome. Following mechanical loading, the proteome showed distinct pathway, protein class, and process enrichments, with temporal differences observed between young-adult and old mice.
Overall, the findings provide valuable insights into the molecular mechanisms underlying age-related changes and the response to mechanical loading in mouse long bones.
DISCUSSION
This study aimed to compare the proteome and transcriptome of tibias from young-adult and old mice under baseline conditions and analyze changes in the bone proteome in response to mechanical loading. The researchers successfully developed a proteomics method to detect protein-level changes in cortical bone and used it to perform proteomic and RNA-seq analyses on tibias from both young-adult and old mice. They observed a moderately positive correlation between the proteome and transcriptome in bone tissue. Age-related differences were detected at both the protein and RNA levels, with altered TGF-beta signaling and changes in extracellular matrix (ECM) and matrix metalloproteinases (MMPs) protein and transcript levels in old bones. The researchers identified Tgfb2 as the most reduced Tgfb transcript in old bone, predominantly expressed by osteocytes. Proteomic analysis of the loading response showed modest changes compared to age-related differences, with fewer protein-level changes in old bones. The findings suggest that proteomics is a valuable tool for studying bone biology and can provide insights into protein-specific changes in aging.
The data obtained from the analysis were subjected to various statistical and data exploration techniques. Differential expression analysis was performed to compare protein abundance between different groups. Total RNA was extracted from the bones using TRIzol, and its integrity and concentration were measured. The bones were also processed for paraffin sectioning and RNA in situ hybridization.
Overall, the study involved the collection and analysis of bone samples from female mice to investigate age-related changes and loading responses in the skeletal system.
Click here to read the full research paper in Aging.
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Aging is indexed by PubMed/Medline (abbreviated as “Aging (Albany NY)”), PubMed Central, Web of Science: Science Citation Index Expanded (abbreviated as “Aging‐US” and listed in the Cell Biology and Geriatrics & Gerontology categories), Scopus (abbreviated as “Aging” and listed in the Cell Biology and Aging categories), Biological Abstracts, BIOSIS Previews, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science).
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