Research on Potential Network Markers and Signaling Pathways in Type 2 Diabetes Based on Conditional Cell-Specific Network.
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ABSTRACT:
SUBMITTER: Xie Y
PROVIDER: S-EPMC9320152 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers.We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period. Multivariate logistic regression was used to assess statistical associations, and regularised least-squares modelling was used to perform machine learning-based risk classification and marker selection. The predictive performance of the machine learning models and marker panels was evaluated using repeated nested cross-validation, and replicated in an independent French cohort of 1044 individuals including 231 participants who progressed to type 2 diabetes during a 9 year follow-up period in the DESIR (Data from an Epidemiological Study on the Insulin Resistance Syndrome) study.Nine metabolites were negatively associated (potentially protective) and 25 were positively associated with progression to type 2 diabetes. Machine learning models based on the entire metabolome predicted progression to type 2 diabetes (area under the receiver operating characteristic curve, AUC = 0.77) significantly better than the reference model based on clinical risk factors alone (AUC = 0.68; DeLong's p = 0.0009). The panel of metabolic markers selected by the machine learning-based feature selection also significantly improved the predictive performance over the reference model (AUC = 0.78; p = 0.00019; integrated discrimination improvement, IDI = 66.7%). This approach identified novel predictive biomarkers, such as ?-tocopherol, bradykinin hydroxyproline, X-12063 and X-13435, which showed added value in predicting progression to type 2 diabetes when combined with known biomarkers such as glucose, mannose and ?-hydroxybutyrate and routinely used clinical risk factors.This study provides a panel of novel metabolic markers for future efforts aimed at the prevention of type 2 diabetes.
| S-EPMC5552834 | biostudies-other
Project description:Type 2 diabetes mellitus (T2DM) is a multifactorial and multigenetic disease, and its pathogenesis is complex and largely unknown. In the present study, microarray data (GSE201966) of β‑cell enriched tissue obtained by laser capture microdissection were downloaded, including 10 control and 10 type 2 diabetic subjects. A comprehensive bioinformatics analysis of microarray data in the context of protein‑protein interaction (PPI) networks was employed, combined with subcellular location information to mine the potential candidate genes for T2DM and provide further insight on the possible mechanisms involved. First, differential analysis screened 108 differentially expressed genes. Then, 83 candidate genes were identified in the layered network in the context of PPI via network analysis, which were either directly or indirectly linked to T2DM. Of those genes obtained through literature retrieval analysis, 27 of 83 were involved with the development of T2DM; however, the rest of the 56 genes need to be verified by experiments. The functional analysis of candidate genes involved in a number of biological activities, demonstrated that 46 upregulated candidate genes were involved in 'inflammatory response' and 'lipid metabolic process', and 37 downregulated candidate genes were involved in 'positive regulation of cell death' and 'positive regulation of cell proliferation'. These candidate genes were also involved in different signaling pathways associated with 'PI3K/Akt signaling pathway', 'Rap1 signaling pathway', 'Ras signaling pathway' and 'MAPK signaling pathway', which are highly associated with the development of T2DM. Furthermore, a microRNA (miR)‑target gene regulatory network and a transcription factor‑target gene regulatory network were constructed based on miRNet and NetworkAnalyst databases, respectively. Notably, hsa‑miR‑192‑5p, hsa‑miR‑124‑5p and hsa‑miR‑335‑5p appeared to be involved in T2DM by potentially regulating the expression of various candidate genes, including procollagen C‑endopeptidase enhancer 2, connective tissue growth factor and family with sequence similarity 105, member A, protein phosphatase 1 regulatory inhibitor subunit 1 A and C‑C motif chemokine receptor 4. Smad5 and Bcl6, as transcription factors, are regulated by ankyrin repeat domain 23 and transmembrane protein 37, respectively, which might also be used in the molecular diagnosis and targeted therapy of T2DM. Taken together, the results of the present study may offer insight for future genomic‑based individualized treatment of T2DM and help determine the underlying molecular mechanisms that lead to T2DM.
| S-EPMC7411335 | biostudies-literature
Project description:In type 1 diabetes (T1D), pancreatic beta-cells are attacked and destroyed by the immune system, which leads to a loss of endogenous insulin secretion. The desirable outcome of therapeutic intervention in autoimmune diseases is the restoration of immune tolerance to prevent organ damage. Past trials with immune suppressive drugs highlight the fact that T1D is in principle a curable condition. However, the barrier in T1D therapy in terms of drug safety is set particularly high because of the predominantly young population and the good prognosis associated with modern exogenous insulin therapy. Thus, there is a general consensus that chronic immune suppression is associated with unacceptable long-term safety risks. On the other hand, immune-modulatory biologicals have recently failed to confer significant protection in phase 3 clinical trials. However, the concept of antigen-specific tolerization may offer a unique strategy to safely induce long-term protection against T1D. In this review, we analyze the potential reasons for the failure of the different tolerization therapies, and describe how the concept of antigen-specific toleraization may overcome the obstacles associated with clinical therapy in T1D.
| S-EPMC3740700 | biostudies-literature
Project description:Defects in pancreatic islets and the progression of multi-tissue insulin resistance in combination with environmental factors are the main causes of type 2 diabetes (T2D). Mass spectrometry-based proteomics of five key-metabolic tissues on a cohort of 42 multi-organ donors provided deep coverage of the proteomes of pancreatic islets, visceral adipose tissue (VAT), liver, skeletal muscle and serum. Enrichment analysis of gene ontology (GO) terms built a tissue-specific map of the chronological order of altered biological processes across healthy controls (CTRL), pre-diabetes (PD) and T2D subjects. This unique dataset allowed us to explore alterations of entire biological pathways and individual proteins in multiple tissues. We confirmed the significant decrease of the citric acid cycle and the respiratory electron transport in VAT and muscle of T2D and we provided a thorough visual representation of the complete set of downregulated proteins. Importantly, we found widespread novel alterations in relevant biological pathways including the increase in hemostasis in pancreatic islets of PD, the increase in the complement cascade in liver and pancreatic islets of PD and the elevation in cholesterol biosynthesis in liver of T2D. Overall, our findings suggest inflammatory, immune and vascular impairments in pancreatic islets as potentially causal factors of insufficient insulin production and increased glucagon levels in the early stages of T2D. In contrast alterations in lipid metabolism and mitochondrial function in the liver and VAT/muscle, respectively, became evident later in manifest T2D. This first multi-tissue proteomic map indicates the temporal order of tissue-specific metabolic dysregulation in T2D development.
2022-10-12 | PXD027597 | Pride
Project description:BackgroundAlzheimer's disease (AD) is a pervasive age-related and highly heritable neurodegenerative disorder but has no effective therapy. The complex cellular microenvironment in the AD brain impedes our understanding of pathogenesis. Thus, a comprehensive investigation of cell type-specific responses in AD is crucial to provide precise molecular and cellular targets for therapeutic development.MethodsHere, we integrated analyzed 4,441 differentially expressed genes (DEGs) that were identified from 263,370 single-cells in cortex samples by single-nucleus RNA sequencing (snRNA-seq) between 42 AD-pathology subjects and 39 normal controls within 3 studies. DEGs were analyzed in microglia, astrocytes, oligodendrocytes, excitatory neurons, inhibitory neurons, and endothelial cells, respectively. In each cell type, we identified both common DEGs which were observed in all 3 studies, and overlapping DEGs which have been seen in at least 2 studies. Firstly, we showed the common DEGs expression and explained the biological functions by comparing with existing literature or multil-omics signaling pathways knowledgebase. We then determined the significant modules and hub genes, and explored the biological processes using the overlapping DEGs. Finally, we identified the common and distinct dysregulated pathways using overall DEGs and overlapping DEGs in a cell type-specific manner.ResultsUp-regulated LINGO1 has been seen in both oligodendrocytes and excitatory neurons across 3 studies. Interestingly, genes enriched in the mitochondrial module were up-regulated across all cell types, which indicates mitochondrial dysfunction in the AD brain. The estrogen signaling pathway seems to be the most common pathway that is disrupted in AD.ConclusionTogether, these analyses provide detailed information of cell type-specific and overall transcriptional changes and pathways underlying the human AD-pathology. These findings may provide important insights for drug development to tackle this disease.
| S-EPMC8487122 | biostudies-literature
Project description:The formation of multicellular organisms requires genetically predefined signaling pathways in various cell types. Besides differences in size, energy balance and life time, cell types should be enable to modulate appropriate developmental and adaptive responses in ever-changing surrounding environment. One of the most important environmental cues is light which regulates a variety of physiological and cellular processes. During evolution, diverse light-sensitive proteins, so-called photoreceptors, and corresponding signaling pathways have evolved, in almost all kingdoms of life, to monitor light continuously and adjust their growth and development accordingly. However, considering the fact that different cell types should be enable to trigger distinct light signaling pathways according to their needs, cell-type specific light signaling pathways are required to guarantee cell type-matched modulation of cellular and developmental processes in response to different light signals. The multicellular green alga Volvox carteri, which has only 2 cell types with clear division of labor, possesses cell-type specific photoreceptors and light signaling pathways which allow differential regulation of genes involved in various cellular and metabolic pathways in response to environmental light. The existence of cell-type specific light signaling pathways in multicellular organism like Volvox reflects an early development of cell-type specific signaling mechanisms during evolution to ensure maintenance of differentiation.
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Project description:The rising prevalence of diabetes worldwide has increased interest in the cost of diabetes. Inpatient costs for all people with diabetes in Scotland were investigated.The Scottish Care Information-Diabetes Collaboration (SCI-DC), a real-time clinical information system of almost all diagnosed cases of diabetes in Scotland, UK, was linked to data on all hospital admissions for people with diabetes. Inpatient stay costs were estimated using the 2007-2008 Scottish National Tariff. The probability of hospital admission and total annual cost of admissions were estimated in relation to age, sex, type of diabetes, history of vascular admission, HbA(1c), creatinine, body mass index and diabetes duration.In Scotland during 2005-2007, 24,750 people with type 1 and 195,433 people with type 2 diabetes were identified, accounting for approximately 4.3% of the total Scottish population (5.1 million). The estimated total annual cost of admissions for all people diagnosed with type 1 and type 2 diabetes was £26 million and £275 million, respectively, approximately 12% of the total Scottish inpatient expenditure (£2.4 billion). Sex, increasing age, serum creatinine, previous vascular history and HbA(1c) (the latter differentially in type 1 and type 2) were all associated with likelihood and total annual cost of admission.Diabetes inpatient expenditure accounted for 12% of the total Scottish inpatient expenditure, whilst people with diabetes account for 4.3% of the population. Of the modifiable risk factors, HbA(1c) was the most important driver of cost in type 1 diabetes.
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Project description:The do-calculus is a well-known deductive system for deriving connections between interventional and observed distributions, and has been proven complete for a number of important identifiability problems in causal inference [1, 8, 18]. Nevertheless, as it is currently defined, the do-calculus is inapplicable to causal problems that involve complex nested counterfactuals which cannot be expressed in terms of the "do" operator. Such problems include analyses of path-specific effects and dynamic treatment regimes. In this paper we present the potential outcome calculus (po-calculus), a natural generalization of do-calculus for arbitrary potential outcomes. We thereby provide a bridge between identification approaches which have their origins in artificial intelligence and statistics, respectively. We use po-calculus to give a complete identification algorithm for conditional path-specific effects with applications to problems in mediation analysis and algorithmic fairness.
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Project description:The burden of type 2 diabetes and its associated premature morbidity and mortality is rapidly growing, and the need for novel efficacious treatments is pressing. We report here that serotonin 2C receptor (5-HT(2C)R) agonists, typically investigated for their anorectic properties, significantly improve glucose tolerance and reduce plasma insulin in murine models of obesity and type 2 diabetes. Importantly, 5-HT(2C)R agonist-induced improvements in glucose homeostasis occurred at concentrations of agonist that had no effect on ingestive behavior, energy expenditure, locomotor activity, body weight, or fat mass. We determined that this primary effect on glucose homeostasis requires downstream activation of melanocortin-4 receptors (MC4Rs), but not MC3Rs. These findings suggest that pharmacological targeting of 5-HT(2C)Rs may enhance glucose tolerance independently of alterations in body weight and that this may prove an effective and mechanistically novel strategy in the treatment of type 2 diabetes.
| S-EPMC2075535 | biostudies-literature
Project description:The mechanisms by which oligodendroglia modulate CNS angiogenesis remain elusive. Previous in vitro data suggest that oligodendroglia regulate CNS endothelial cell proliferation and blood vessel formation through hypoxia inducible factor alpha (HIFα)-activated Wnt (but not VEGF) signaling. Using in vivo genetic models, we show that HIFα in oligodendroglia is necessary and sufficient for angiogenesis independent of CNS regions. At the molecular level, HIFα stabilization in oligodendroglia does not perturb Wnt signaling but rather activates VEGF. At the functional level, genetically blocking oligodendroglia-derived VEGF but not Wnt significantly decreases oligodendroglial HIFα-regulated CNS angiogenesis. Blocking astroglia-derived Wnt signaling reduces astroglial HIFα-regulated CNS angiogenesis. Together, our in vivo data demonstrate that oligodendroglial HIFα regulates CNS angiogenesis through Wnt-independent and VEGF-dependent signaling. These findings suggest an alternative mechanistic understanding of CNS angiogenesis by postnatal glial cells and unveil a glial cell type-dependent HIFα-Wnt axis in regulating CNS vessel formation.
| S-EPMC7181614 | biostudies-literature