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DNA methylation of insulin signalling pathways in human primary muscle stem cells is associated with HOMA2-IR in older adults

ABSTRACT: Context: While ageing is associated with increased insulin resistance (IR), the molecular mechanisms underlying increased IR in muscle, the primary organ for glucose clearance, have yet to be elucidated in older individuals. As epigenetic processes are suggested to contribute to development of ageing-associated diseases, in human primary muscle stem cells (myoblasts) from community dwelling older individuals we investigated whether differential DNA methylation was associated with IR. Methods: We measured DNA methylation (Infinium HumanMethylationEPIC BeadChip) in myoblast cultures from vastus lateralis biopsies (119 male/females, mean age 78.24 years) from the Hertfordshire Sarcopenia Study extension (HSSe) and examined differentially methylated Cytosine phosphate Guanine (CpG) sites (dmCpG), regions (DMRs) and gene pathways associated with HOMA2-IR (derived from fasting insulin/glucose) and HbA1c. Results: 38 dmCpGs (false discovery rate (FDR)<0.05), and 8 DMRs (Stouffer<0.05) were associated with HOMA2-IR; 6 dmCpGs and 3 DMRs were robust to adjustment for BMI, including DMRs within T-box transcription factor (TBX1) and nuclear receptor subfamily-2 group F member-2 (NR2F2). HOMA2-IR associated dmCpGs were enriched in genes linked with skeletal muscle development and insulin signalling. 49 dmCpGs were associated with HbA1c, with 48 robust to adjustment for BMI. The only dmCpG associated with both HOMA2-IR and HbA1c was cg13451048, located within Exoribonuclease Family Member 3 (ERI3). HOMA2-IR and HbA1c dmCpGs were not associated with accelerated epigenetic ageing. Conclusions: These findings suggest differential DNA methylation in muscle stem cells may contribute to age-related decline in glycaemic control. The identification of dmCpGs, associated genes and pathways may provide novel targets for pharmacological intervention.

ORGANISM(S): Homo sapiens

PROVIDER: GSE221540 | GEO | 2023/11/16

REPOSITORIES: GEO

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Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between Î²-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the Î²-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation. Gene expression from muscle biopsies of Lean, Obese insulin sensitive (OIS), Obese insulin resistant (OIR) and obese T2D patients (T2D) were compared for differential expression between the groups (n=7 in each group)

Project description:The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between β-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the β-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation.

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DNA methylation of insulin signalling pathways in human primary muscle stem cells is associated with HOMA2-IR in older adults

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