Metabolomic predictors of insulin resistance and diabetes
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ABSTRACT:
PROVIDER: PRJNA706476 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Global transcript profiling to identify differentially expressed skeletal muscle genes in insulin resistance, a major risk factor for Type II (non-insulin-dependent) diabetes mellitus. Compared gene expression profiles of skeletal muscle tissues from 18 insulin-sensitive versus 17 insulin-resistant equally obese, non-diabetic Pima Indians. Keywords: other
2002-12-17 | GSE121 | GEO
Project description:We aim to identify a novel pathway to regulate insulin resistance from transcriptional profiles of skeletal muscles from patients with diabetes and to demonstrate its role in experimental models of insulin resistance. We performed transcriptional profiling of skeletal muscles from subjects with or without diabetes. Through an integrative analysis of our dataset with four previous datasets, we identified the core gene sets associated with insulin resistance.
2015-09-01 | E-MTAB-3420 | biostudies-arrayexpress
Project description:Recent discovery reveals HFD insult can cause insulin resistance very rapidly, but the underlying mechanism is still not well understood. We performed a short term experiment in a Diet Induced Insulin resistance mouse model. 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. Comparison of gene expression in muscle tissue during High Fat Diet (HFD) time course (day 5 and day 42). Chow diet will serve as control for HFD. 4 samples per group serve as experimental replicates.
2015-12-07 | E-GEOD-73036 | biostudies-arrayexpress
Project description:There is evidence indicating the involvement of DNA methylation memory in maintaining gene expression patterns associated with insulin resistance. Although the exact mechanism remains unknown, it has been proved that insulin resistance is correlated to low heat shock protein (HSP) expression. We reveal that intranuclear insulin can reduce HSP DNA methylation level to up-regulate HSP protein expression and result in long term cure of hyperglycemia. Type 2 diabetes KKAy mouse were selected in our experiments.Three conditions were compared with three replicates each. These are:(1) Untreated KKAy mouse (2) Insulin treated KKAy mouse(Insulin); (3) Biomineralized insulin treated KKAy mouse(BI).
2014-05-21 | E-GEOD-57817 | biostudies-arrayexpress
Project description:Postoperative insulin resistance refers to the phenomenon that the body’s glucose uptake stimulated by insulin is reduced due to stress effects such as trauma or the inhibitory effect of insulin on liver glucose output is weakened after surgery.
There is a clear link between postoperative insulin resistance and poor perioperative prognosis. Therefore, exploring interventions to reduce postoperative stress insulin resistance, stabilize postoperative blood glucose, and reduce postoperative complications are clinical problems that need to be solved urgently. In recent years, research on branched-chain amino acids and metabolic diseases has become a hot spot. Studies have found that in the rat model, preoperatively given a high branched-chain amino acid diet can inhibit postoperative insulin resistance and stabilize blood glucose levels. This research plan is to try to add branched-chain amino acids before surgery to observe the occurrence of postoperative insulin resistance in patients.
| 2744698 | ecrin-mdr-crc
Project description:Insulin resistance is a heterogeneous disorder caused by a range of genetic and environmental factors, and we hypothesise that its aetiology varies considerably between individuals. This heterogeneity provides significant challenges to the development of effective therapeutic regimes for long-term management of type 2 diabetes. We describe a novel strategy, using large-scale gene expression profiling, to develop a gene expression signature (GES) that reflects the overall state of insulin resistance in cells and patients. The GES was developed from 3T3-L1 adipocytes that were made M-bM-^@M-^\insulin resistantM-bM-^@M-^] by treatment with tumour necrosis factor (TNF-alpha and then reversed with aspirin and troglitazone (M-bM-^@M-^\re-sensitizedM-bM-^@M-^]). The GES consisted of 5 genes whose expression levels best discriminated between the insulin resistant and insulin re-sensitized states. We then used this GES to screen a compound library for agents that affected the GES genes in 3T3-L1 adipocytes in a way that most closely resembled the changes seen when insulin resistance was successfully reversed using aspirin and troglitazone. This screen identified both known and new insulin sensitising compounds including non-steroidal anti-inflammatory agents, beta-adrenergic antagonists, beta-lactams and sodium channel blockers. We tested the biological relevance of this GES in participants in the San Antonio Family Heart Study (n = 1,240) and showed that patients with the lowest GES scores were more insulin resistant (according to HOMA_IR and fasting plasma insulin levels, P < 0.001). These findings show that GES technology can be used for both discovery of insulin sensitising compounds and characterisation of patients into subtypes of insulin resistance according to GES scores, opening the possibility of developing a personalised medicine approach to type 2 diabetes. Three-condition experiment, Vehicle, TNF and TNF+ASA+TGZ with biological replicates: 22 Vehicle, 21 TNF and 21 TNF+ASA+TGZ , independently grown and harvested. One replicate per array.
2010-09-10 | E-GEOD-18103 | biostudies-arrayexpress
Project description:Insulin resistance is a major risk factor for human metabolic diseases including type 2 diabetes, cardiovascular diseases and some cancers. We analysed the proteome analyses across in vitro and in vivo models of insulin resistance to find common features of insulin resistance and identified lower expression of enzymes within the mevalonate pathway. mitochondrial CoQ was lower in all models of insulin resistance. Specifically, the coenzyme Q (CoQ) biosynthetic proteins Coq 7 and 9 were decreased in adipose tissue from a mouse model of diet-induced obesity, and mitochondrial CoQ was lower in all models of insulin resistance. Moreover, the mitochondrial content of CoQ in adipose tissue correlated positively with insulin sensitivity in obese humans. Inhibition of CoQ biosynthesis induced insulin resistance, while replenishment of CoQ restored insulin sensitivity in cell culture models and in animals, demonstrating that loss of CoQ is both necessary and sufficient for adipocyte insulin resistance. Mechanistically, loss of CoQ increased mitochondrial peroxides. These findings place defective mitochondrial CoQ homeostasis upstream of increased mitochondrial oxidants in the induction of insulin resistance in adipocytes and highlight the CoQ biosynthetic pathway as an appealing therapeutic target to combat insulin resistance.
2018-02-07 | PXD005128 | Pride
Project description:Skeletal muscle insulin resistance is the earliest defect in type 2 diabetes (T2D), preceding and predicting disease development. Whether this represents the underlying primary defect in T2D or effects of changes in hormones or circulating metabolites is unknown. To address this question, we have developed a “disease-in-a-dish” model by differentiating iPS cells from T2D patients and controls into myoblasts (iMyo) and studied their function in vitro. We find that T2D iMyos exhibit multiple defects mirroring human disease including altered insulin signaling through the IRS/AKT pathway, decreased insulin-stimulated glucose uptake, and reduced mitochondrial oxidation. In addition, using global phosphoproteomics we find that T2D alters phosphorylation of a large network of targets of mTOR, S6K, PKC and other kinases including proteins involved in regulation of Rho-GTPases, mRNA splicing/processing, vesicular trafficking, gene transcription and chromatin remodeling. This cell-autonomous dysregulated phosphorylation network reveals a new dimension in the mechanism underlying insulin resistance in T2D.
2020-08-27 | PXD015430 | Pride
Project description:Insulin resistance in skeletal muscle is a key phenotype associated with type 2 diabetes (T2D) and is even present in offspring of diabetic parents. However, molecular mediators of insulin resistance remain unclear. We find that the top-ranking gene set in expression analysis of muscle from humans with T2D and normoglycemic insulin resistant subjects with parental family history (FH+) of T2D is increased expression of actin cytoskeleton genes regulated by serum response factor (SRF) and its coactivator MKL1. Furthermore, the SRF activator STARS is upregulated in FH+ and T2D and inversely correlated with insulin sensitivity. These patterns are recapitulated in insulin resistant mice, and linked to alterations in two other regulators of this pathway: reduced G-actin and increased nuclear localization of MKL1. Both genetic and pharmacologic manipulation of STARS/MKL1/SRF pathway significantly alter insulin action: 1) Overexpression of MKL1 or reduction in G-actin decreased insulin-stimulated Akt phosphorylation; 2) reduced STARS expression increased insulin signalling and glucose uptake, and 3) SRF inhibition by CCG-1423 reduced nuclear MKL1, improved glucose uptake, and improved glucose tolerance in insulin resistant mice in vivo. Thus, SRF pathway alterations are a signature of insulin resistance which may also contribute to T2D pathogenesis and be a novel therapeutic target. Skeletal muscle samples were obtained from 10 subjects with type 2 diabetes, 25 subjects with a family history of type 2 diabetes (one or both parents), and 15 subjects with no family history of type 2 diabetes. In this analysis RNA was isolated for cRNA preparation and hybridized to Affymetrix Human Genome U133 Plus 2.0 microarrays.
2011-03-01 | E-GEOD-25462 | biostudies-arrayexpress
Project description:Recent discovery reveals HFD insult can cause insulin resistance very rapidly, but the underlying mechanism is still not well understood. We performed a short term experiment in a Diet Induced Insulin resistance mouse model. 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.
2015-12-07 | GSE73036 | GEO