Summary: Scientists have identified new genes linked to muscle aging, offering potential targets for therapies to slow muscle loss in older adults. The study used artificial intelligence to analyze gene expression, identifying the gene USP54 as a key player in muscle aging and degradation.
The findings could inform drug development and exercise-based interventions to preserve muscle mass and reduce the risk of falls and disabilities. Further research may lead to new treatments for muscle aging and conditions like sarcopenia, which affects older adults.
Key Facts:
- The gene USP54 was found to play a significant role in muscle aging.
- AI analysis identified 200 genes linked to aging and exercise in muscle tissue.
- The study offers potential for new therapies targeting muscle aging and sarcopenia.
Source: Nottingham Trent University
Scientists have identified previously unreported genes which appear to play a key role in the muscle aging process. It is hoped that the findings from a Nottingham Trent University study could be used to help delay the impact of the aging process.
The study, which also involved Sweden’s Karolinska Institute, Karolinska University Hospital, and Anglia Ruskin University, is reported in the Journal of Cachexia, Sarcopenia and Muscle.
Muscle aging is a natural process that occurs in everyone, causing people to lose muscle mass, strength and endurance as they get older—and is linked to increasing falls and physical disabilities.
The work provides new insight and understanding into the genes and mechanisms that drive muscle aging. The researchers argue that they may have found new targets for drug discovery, which could spark therapies for muscle aging and for older people living with the disease sarcopenia, enhanced muscle loss linked to this process.
Physical exercise is currently the only recommended treatment for muscle aging and sarcopenia, showing benefits in improving life expectancy and delaying the onset of age-associated disorders.
The new study involved analyzing gene expression datasets of both younger (aged 21-43) and older (63-79) adults related to both muscle aging and resistance exercise.
Using artificial intelligence, the researchers were able to identify the top 200 genes influencing—or being influenced by—aging or exercise, along with the strongest interactions between them.
The team found that one gene in particular—USP54 –appears to play a key role in the advancement of muscle aging and muscle degradation in older people. The significance of the findings was then further confirmed via muscle biopsy in older adults, where the gene was found to be highly expressed.
The researchers also discovered several potential resistance exercise-associated genes. While further research is required, the team argues these could aid development of more informed exercise-based interventions targeting the preservation of muscle mass in older people, which would be key to mitigating against falls and disabilities.
“We want to identify genes that we can utilize to delay the impacts of the aging process and extend the healthspan,” said Dr. Lívia Santos, an expert in musculoskeletal biology at Nottingham Trent University.
“We have used AI to identify the genes, gene interactions and molecular pathways and processes associated with muscle aging that until now have remained undiscovered. The data was analyzed in 20 different ways and every time the significant genes were found to be the same.
“Muscle aging is a huge challenge. As people lose muscle mass and strength, we see changes in their gait, which makes them more prone to falls, but they are also at increased risk of developing a range of physical disabilities, making it a major public health concern.
“We urgently need to understand the mechanisms regulating muscle aging. This is crucial in helping to prevent and treat sarcopenia and enable a greater level of dependency among older people.”
Researcher Dr. Janelle Tarum said, “This study suggests that AI has a 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 motivated us to apply it to discover new genes to better understand and predict sarcopenia, or be used as targets for therapies that could benefit research on sarcopenia.”
About this genetics and aging research news
Author: Lívia Santos
Source: Nottingham Trent University
Contact: Lívia Santos – Nottingham Trent University
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Artificial neural network inference analysis identified 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 identified novel genes and gene interactions associated with skeletal muscle aging
Background
Sarcopenia is an age-related muscle disease that increases the risk of falls, disabilities, and death. It is associated with increased muscle protein degradation driven by molecular signalling pathways including Akt and FOXO1.
This study aims to identify genes, gene interactions, and molecular pathways and processes associated with muscle aging and exercise in older adults that remained undiscovered until now leveraging on an artificial intelligence approach called artificial neural network inference (ANNi).
Methods
Four datasets reporting the profile of muscle transcriptome obtained by RNA-seq of young (21–43 years) and older adults (63–79 years) were selected and retrieved from the Gene Expression Omnibus (GEO) data repository.
Two datasets contained the transcriptome profiles associated to muscle aging and two the transcriptome linked to resistant exercise in older adults, the latter before and after 6 months of exercise training. Each dataset was individually analysed by ANNi based on a swarm neural network approach integrated into a deep learning model (Intelligent Omics).
This allowed us to identify top 200 genes influencing (drivers) or being influenced (targets) by aging or exercise 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 confirm the differential expression of the genes showing the strongest interactions, real-time quantitative PCR (RT-qPCR) was employed on human muscle biopsies obtained from eight young (25 ± 4 years) and eight older men (78 ± 7.6 years), partaking in a 6-month resistance exercise training programme.
Results
CHAD, ZDBF2, USP54, and JAK2 were identified as the genes with the strongest interactions predicting aging, while SCFD1, KDM5D, EIF4A2, and NIPAL3 were the main interacting genes associated with long-term exercise in older adults. RT-qPCR confirmed significant upregulation of USP54 (P = 0.005), CHAD (P = 0.03), and ZDBF2 (P = 0.008) in the aging muscle, while exercise-related genes were not differentially expressed (EIF4A2 P = 0.99, NIPAL3 P = 0.94, SCFD1 P = 0.94, and KDM5D P = 0.64).
GO analysis related to skeletal muscle aging suggests enrichment of pathways linked to bone development (adj P-value 0.006), immune response (adj P-value <0.001), and apoptosis (adj P-value 0.01). In older exercising adults, these were ECM remodelling (adj P-value <0.001), protein folding (adj P-value <0.001), and proteolysis (adj P-value <0.001).
Conclusions
Using ANNi and RT-qPCR, we identified three strongly interacting genes predicting muscle aging, ZDBF2, USP54, and CHAD. These findings can help to inform the design of nonpharmacological and pharmacological interventions that prevent or mitigate sarcopenia.
