Summary: Scientists have identified a new gene associated with muscle aging, providing a potential target for treatments to slow muscle loss in older adults. The study used artificial intelligence to analyze gene expression and identified the gene USP54 as a key player in muscle aging and breakdown.
The findings could inform drug development and exercise-based interventions to preserve muscle mass and reduce the risk of falls and disability. Further research could lead to new treatments for muscle aging and conditions such as sarcopenia that affect older people.
Important facts:
The gene USP54 was found to play an important role in muscle aging. AI analysis identified 200 genes associated with muscle tissue aging and exercise. This study offers the potential for new treatments targeting muscle aging and sarcopenia.
Source: Nottingham Trent University
Scientists have identified a previously unreported gene that appears to play an important role in the muscle aging process. It is hoped that research results from Nottingham Trent University will help slow down the effects of the aging process.
Sweden’s Karolinska Institute, Karolinska University Hospital and Anglia Ruskin University also participated in the study, which is reported in the Journal of Cachexia, Sarcopenia and Muscle.
Muscle aging is a natural process that occurs in everyone, and as we age, we lose muscle mass, strength, and endurance, leading to an increase in falls and disability.
This study provides new insights and understanding of the genes and mechanisms that cause muscle aging. Researchers have discovered a new drug target that could lead to treatments for older adults suffering from muscle aging and the disease associated with this process called sarcopenia. claims that it is possible.
Currently, the only recommended treatment for muscle aging and sarcopenia is exercise, which has been shown to increase life expectancy and delay the onset of age-related disorders.
The new study included analysis of gene expression datasets from both young (21-43 years) and older adults (63-79 years) related to both muscle aging and strength exercise.
Using artificial intelligence, researchers were able to identify the top 200 genes that influence or are affected by aging and exercise, as well as the strongest interactions between them.
The researchers found that one gene in particular, USP54, appears to play an important role in the progression of muscle aging and muscle breakdown in older adults. The significance of this finding was subsequently further confirmed by muscle biopsies from older adults, where the gene was found to be highly expressed.
Researchers also discovered several genes that may be associated with resistance exercise. Although more research is needed, the research team believes these studies could help develop more informed exercise-based interventions aimed at preserving muscle mass in older adults, which is key to reducing falls and disability. claims to be sexual.
Dr Livia Santos, an expert in musculoskeletal biology at Nottingham Trent University, said: ‘We want to identify genes that can be exploited to slow down the effects of the aging process and extend healthy lifespans.’
“We used AI to identify previously unexplored genes, gene interactions, and molecular pathways and processes associated with muscle aging. The data was analyzed in 20 different ways, identifying key findings each time. It turns out that the genes are the same.
“Muscle aging is a major challenge. As people lose muscle mass and strength, they change the way they walk and are more likely to fall, but they also increase their risk of developing a range of physical disorders, making it a major public health issue. This is a matter of concern.
“There is an urgent need to understand the mechanisms that control muscle aging. This is critical to helping prevent and treat sarcopenia and enabling higher levels of dependence in older adults.”
Researcher Dr. Janelle Talm said: “This study suggests that AI may have the potential to benefit the field of muscle aging and sarcopenia.
“AI has not previously been used in the field of skeletal muscle mass regulation. This could benefit sarcopenia research or discover new genes to better understand and predict sarcopenia. This motivated us to apply it to use it as a potential therapeutic target.”
About this genetics and aging research news
Author: Livia Santos
Source: Nottingham Trent University
Contact: Lívia Santos – Nottingham Trent University
Image: Image credited to Neuroscience News
Original research: Open access.
“Artificial Neural Network Inference Analysis Identifies Novel Genes and Gene Interactions Associated with Skeletal Muscle Aging” by Lívia Santos et al., Journal of Cachexia, Sarcopenia and Muscle
abstract
Artificial neural network inference analysis identifies novel genes and gene interactions associated with skeletal muscle aging
background
Sarcopenia is an age-related muscle disease that increases the risk of falls, disability, and death. This is associated with increased muscle proteolysis caused by molecular signaling pathways such as Akt and FOXO1.
This study leverages an artificial intelligence approach called artificial neural network inference (ANNi) to identify previously undiscovered genes, gene interactions, and molecular pathways and processes associated with muscle aging and exercise in older adults. It is intended to.
method
Four datasets reporting muscle transcriptome profiles obtained by RNA-seq in young (21–43 years) and older adults (63–79 years) were selected and analyzed using the Gene Expression Omnibus (GEO ) retrieved from the data repository.
Two datasets include transcriptome profiles associated with muscle aging, and two datasets include transcriptomes associated with resistance exercise in older adults, the latter after 6 months of exercise training. It was before and after. Each dataset was independently analyzed by ANNi based on a swarm neural network approach integrated with deep learning models (Intelligent Omics).
This allowed us to identify the top 200 genes that influence (drivers) or affected genes (targets) and the strongest interactions between such genes. Downstream gene ontology (GO) analysis of these 200 genes was performed using Metacore (Clarivate™) and the open source software Metascape.
To identify the differential expression of genes showing the strongest interactions, we compared human muscle biopsies obtained from eight young men (25 ± 4 years) and eight older men (78 ± 7.6 years). Real-time quantitative PCR (RT-qPCR) was employed. Participate in a 6-month resistance exercise training program.
result
CHAD, ZDBF2, USP54, and JAK2 were identified as the strongest interacting genes predicting aging, while SCFD1, KDM5D, EIF4A2, and NIPAL3 were the main interacting genes associated with long-term exercise in older adults. It was. RT-qPCR confirmed significant upregulation of USP54 (P = 0.005), CHAD (P = 0.03), and ZDBF2 (P = 0.008) in aging muscles, but no differences in the expression of exercise-related genes. There were no (EIF4A2 P = 0.99, NIPAL3 P = 0.94, SCFD1 P = 0.94, and KDM5D P = 0.64).
Skeletal muscle aging-related GO analysis suggests enrichment of pathways related to bone development (adjusted P value 0.006), immune response (adjusted P value <0.001), and apoptosis (adjusted P value 0.01) . In active older adults, ECM remodeling (adj P value <0.001), protein folding (adj P value <0.001), and protein degradation (adj P value <0.001).
conclusion
Using ANNi and RT-qPCR, we identified three strongly interacting genes, ZDBF2, USP54, and CHAD, that predict muscle aging. These findings may aid in the design of non-pharmacological and pharmacological interventions to prevent or reduce sarcopenia.
