Artificial neural network inference analysis identified novel genes and gene interactions associated with skeletal muscle aging

IF 9.4 1区 医学 Q1 GERIATRICS & GERONTOLOGY Journal of Cachexia Sarcopenia and Muscle Pub Date : 2024-08-29 DOI:10.1002/jcsm.13562
Janelle Tarum, Graham Ball, Thomas Gustafsson, Mikael Altun, Lívia Santos
{"title":"Artificial neural network inference analysis identified novel genes and gene interactions associated with skeletal muscle aging","authors":"Janelle Tarum,&nbsp;Graham Ball,&nbsp;Thomas Gustafsson,&nbsp;Mikael Altun,&nbsp;Lívia Santos","doi":"10.1002/jcsm.13562","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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).</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p><i>CHAD</i>, <i>ZDBF2</i>, <i>USP54</i>, and <i>JAK2</i> were identified as the genes with the strongest interactions predicting aging, while <i>SCFD1</i>, <i>KDM5D</i>, <i>EIF4A2</i>, and <i>NIPAL3</i> were the main interacting genes associated with long-term exercise in older adults. RT-qPCR confirmed significant upregulation of <i>USP54</i> (<i>P</i> = 0.005), <i>CHAD</i> (<i>P</i> = 0.03), and <i>ZDBF2</i> (<i>P</i> = 0.008) in the aging muscle, while exercise-related genes were not differentially expressed (<i>EIF4A2 P</i> = 0.99, <i>NIPAL3 P</i> = 0.94, <i>SCFD1 P</i> = 0.94, and <i>KDM5D P</i> = 0.64). GO analysis related to skeletal muscle aging suggests enrichment of pathways linked to bone development (adj <i>P</i>-value 0.006), immune response (adj <i>P</i>-value &lt;0.001), and apoptosis (adj <i>P</i>-value 0.01). In older exercising adults, these were ECM remodelling (adj <i>P</i>-value &lt;0.001), protein folding (adj <i>P</i>-value &lt;0.001), and proteolysis (adj <i>P</i>-value &lt;0.001).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Using ANNi and RT-qPCR, we identified three strongly interacting genes predicting muscle aging, <i>ZDBF2</i>, <i>USP54</i>, and <i>CHAD</i>. These findings can help to inform the design of nonpharmacological and pharmacological interventions that prevent or mitigate sarcopenia.</p>\n </section>\n </div>","PeriodicalId":48911,"journal":{"name":"Journal of Cachexia Sarcopenia and Muscle","volume":null,"pages":null},"PeriodicalIF":9.4000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcsm.13562","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cachexia Sarcopenia and Muscle","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jcsm.13562","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GERIATRICS & GERONTOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

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.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
人工神经网络推理分析确定了与骨骼肌衰老相关的新基因和基因相互作用
背景肌肉疏松症是一种与年龄有关的肌肉疾病,会增加跌倒、残疾和死亡的风险。它与包括 Akt 和 FOXO1 在内的分子信号通路驱动的肌肉蛋白质降解增加有关。本研究旨在利用一种名为人工神经网络推断(ANNi)的人工智能方法,找出迄今为止尚未发现的与老年人肌肉衰老和运动相关的基因、基因相互作用、分子通路和过程。方法从基因表达总库(GEO)数据存储库中选择并检索了四个数据集,报告了通过 RNA-seq 获得的年轻人(21-43 岁)和老年人(63-79 岁)肌肉转录组的概况。两个数据集包含与肌肉衰老相关的转录组特征,两个数据集包含与老年人耐受性锻炼相关的转录组特征,后者在锻炼训练 6 个月之前和之后。每个数据集都由基于集成到深度学习模型(Intelligent Omics)中的蜂群神经网络方法的 ANNi 进行单独分析。这使我们能够识别出受衰老或运动影响(驱动因素)或被影响(目标)的前 200 个基因,以及这些基因之间最强的相互作用。我们使用 Metacore (Clarivate™) 和开源软件 Metascape 对这 200 个基因进行了下游基因本体 (GO) 分析。为了确认相互作用最强的基因的差异表达,对 8 名年轻男性(25 ± 4 岁)和 8 名老年男性(78 ± 7.结果CHAD、ZDBF2、USP54 和 JAK2 被确定为预测衰老的相互作用最强的基因,而 SCFD1、KDM5D、EIF4A2 和 NIPAL3 则是与老年人长期运动相关的主要相互作用基因。RT-qPCR 证实了 USP54(P = 0.005)、CHAD(P = 0.03)和 ZDBF2(P = 0.008)在衰老肌肉中的显著上调,而与运动相关的基因没有差异表达(EIF4A2 P = 0.99、NIPAL3 P = 0.94、SCFD1 P = 0.94 和 KDM5D P = 0.64)。与骨骼肌衰老有关的 GO 分析表明,与骨骼发育(adj P-value 0.006)、免疫反应(adj P-value<0.001)和细胞凋亡(adj P-value 0.01)相关的通路得到了丰富。结论利用 ANNi 和 RT-qPCR,我们发现了三个预测肌肉衰老的强相互作用基因:ZDBF2、USP54 和 CHAD。这些发现有助于为设计预防或减轻肌肉疏松症的非药物和药物干预措施提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Cachexia Sarcopenia and Muscle
Journal of Cachexia Sarcopenia and Muscle MEDICINE, GENERAL & INTERNAL-
CiteScore
13.30
自引率
12.40%
发文量
234
审稿时长
16 weeks
期刊介绍: The Journal of Cachexia, Sarcopenia and Muscle is a peer-reviewed international journal dedicated to publishing materials related to cachexia and sarcopenia, as well as body composition and its physiological and pathophysiological changes across the lifespan and in response to various illnesses from all fields of life sciences. The journal aims to provide a reliable resource for professionals interested in related research or involved in the clinical care of affected patients, such as those suffering from AIDS, cancer, chronic heart failure, chronic lung disease, liver cirrhosis, chronic kidney failure, rheumatoid arthritis, or sepsis.
期刊最新文献
Issue Information Neuromuscular impairment at different stages of human sarcopenia The impact of mobility limitations on geriatric rehabilitation outcomes: Positive effects of resistance exercise training (RESORT) Artificial neural network inference analysis identified novel genes and gene interactions associated with skeletal muscle aging Hydrogen sulfide inhibits skeletal muscle ageing by up-regulating autophagy through promoting deubiquitination of adenosine 5’-monophosphate (AMP)-activated protein kinase α1 via ubiquitin specific peptidase 5
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1