GCN5 Maintains Muscle Integrity by Acetylating YY1 to Promote Dystrophin Expression

Addicks, Gregory C.; Zhang, Hongbo; Ryu, Dongryeol; Vasam, Goutham; Pl, Marshall; Green, Alex E.; Patel, Sonia; Be, Kang; Kim, Doyoun; Katsyuba, Elena; Eg, Williams; Renaud, Jean‐Marc; Auwerx, Johan; Menzies, Keir J.

doi:10.1101/2021.03.29.436986

Cited by 1 publication

(1 citation statement)

References 70 publications

(76 reference statements)

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“… 24 KAT2A is one of the well‐defined count partners of Sirtuins 25 and a very recent study proposed that KAT2A regulates muscle integrity. 26 In addition, the gene‐regulating glycolysis displayed altered gene expression. An additional intensive study of these 27 genes featured by our AI algorithm will expand our understanding of muscle ageing and sarcopenia.…”

Section: Discussionmentioning

confidence: 99%

Artificial‐intelligence‐driven discovery of prognostic biomarker for sarcopenia

Chung

Ryu

et al. 2021

J cachexia sarcopenia muscle

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Background Sarcopenia is defined as muscle wasting, characterized by a progressive loss of muscle mass and function due to ageing. Diagnosis of sarcopenia typically involves both muscle imaging and the physical performance of people exhibiting signs of muscle weakness. Despite its worldwide prevalence, a molecular method for accurately diagnosing sarcopenia has not been established. Methods We develop an artificial intelligence (AI) diagnosis model of sarcopenia using a published transcriptome dataset comprising patients from multiple ethnicities. For the AI model for sarcopenia diagnosis, we use a transcriptome database comprising 17 339 genes from 118 subjects. Among the 17 339 genes, we select 27 features as the model inputs. For feature selection, we use a random forest, extreme gradient boosting and adaptive boosting. Using the top 27 features, we propose a four-layer deep neural network, named DSnet-v1, for sarcopenia diagnosis. Results Among isolated testing datasets, DSnet-v1 provides high sensitivity (100%), specificity (94.12%), accuracy (95.83%), balanced accuracy (97.06%) and area under receiver operating characteristics (0.99). To extend the number of patient data, we develop a web application (http://sarcopeniaAI.ml/), where the model can be accessed unrestrictedly to diagnose sarcopenia if the transcriptome is available. A focused analysis of the top 27 genes for their differential or co-expression with other genes implied the potential existence of race-specific factors for sarcopenia, suggesting the possibility of identifying causal factors of sarcopenia when a more extended dataset is provided. Conclusions Our new AI model, DSnet-v1, accurately diagnoses sarcopenia and is currently available publicly to assist healthcare providers in diagnosing and treating sarcopenia.

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Section: Discussionmentioning

confidence: 99%