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Transcriptomic profile in lymphomonocytes of healthy subjects identifies an early signature of insulin resistance and related diseases

ABSTRACT: Insulin resistance is considered to be a pathogenetic mechanism in several and diverse diseases (e.g. type 2 diabetes, atherosclerosis) often antedating them in apparently healthy subjects. The aim of this study was to investigate whether IR per se is characterized by a specific pattern of gene expression. We analyzed the transcriptomic profile of peripheral blood mononuclear cells in two groups (10 subjects each) of healthy individuals, with extreme insulin resistance or sensitivity, matched for BMI, age and gender, selected within the MultiKnowledge Study cohort (n=148). Data were analyzed with an ad-hoc rank-based classification method. 321 genes composed the gene set distinguishing the insulin resistant and sensitive groups, within which the “Adrenergic signaling in cardiomyocytes” Kegg pathway was significantly represented, suggesting a pattern of increased intracellular cAMP and Ca2+, and apoptosis in the IR group. The same pathway allow to discriminate between insulin resistance and insulin sensitive subjects with BMI >25, supporting his role as a biomarker of IR. Moreover, ASCM pathway harbored biomarkers able to distinguish healthy and diseased subjects (from publicly available data sets) in IR-related diseases involving excitable cells: type 2 diabetes, chronic heart failure, and Alzheimer’s disease. Altered gene expression profile of the ASCM pathway is an early molecular signature of IR and could provide a common molecular pathogenetic platform for IR-related disorders, possibly representing an important aid in the efforts aiming at preventing, early detecting and optimally treating IR-related diseases.

ORGANISM(S): Homo sapiens

PROVIDER: GSE87005 | GEO | 2017/08/18

SECONDARY ACCESSION(S): PRJNA343204

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.

Project description:Metabolic alterations relevant to postprandial dyslipidemia were previously identified in the intestine of obese subjects with systemic insulin resistance. These dysregulations were closely associated with an amplification of intestinal lipogenesis and lipoprotein output, which was triggered by insulin resistance likely sustained by oxidative stress and inflammation. The aim of the study was to identify the genes deregulated by the presence of systemic insulin resistance in the intestine of severely obese subjects. Transcripts from duodenal samples of insulin-sensitive (HOMA-IR<3, n=9) and insulin-resistant (HOMA-IR>7, n=9) obese subjects were assayed by microarray (Illumina HumanHT-12). The small intestine exhibits a highly specific mRNA expression profile that was similar between insulin-sensitive and insulin-resistant individuals. A total of 195 annotated genes were identified as differentially expressed between these two groups with a fold change higher than 1.2. Metabolic pathway analysis revealed that 36 differently expressed genes were directly involved in known intestinal functions, including digestion, extracellular matrix, endocrine system, immunity, inflammation/oxidative stress and cholesterol metabolism. Several signaling pathways involved in immunity and inflammation were significantly enriched in differently expressed genes and were predicted to be activated in the intestine of insulin-resistant subjects. Using stringent criteria (Fold change>1.5; FDR<0.05), only three genes were found to be significantly and differently expressed in the intestine of insulin-resistant compared to insulin-sensitive subjects: the transcripts of the insulinotropic glucose-dependant peptide (GIP) and of the β-microseminoprotein (MSMB) were significantly reduced, but that of the humanin like-1 (HNL1) protein was significantly increased. These results underline that systemic insulin resistance is associated with an amplification of local inflammatory process and remodeling of key intestinal functions. Genes identified in this study are potentially triggered throughout the development of intestinal metabolic alterations, which could contribute to dyslipidemia, a component of metabolic syndrome and diabetes.

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Transcriptomic profile in lymphomonocytes of healthy subjects identifies an early signature of insulin resistance and related diseases

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