Temporal metabolic and transcriptomic characteristics crossing islets and liver reveal dynamic pathophysiology in diet-induced diabetes.
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ABSTRACT:
SUBMITTER: Gao R
PROVIDER: S-EPMC8008187 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of resting-state functional MRI studies. However, accumulating evidence points to the presence of temporal fluctuations in FC, leading to increasing interest in estimating FC as a dynamic quantity. One central issue that has arisen in this new view of connectivity is the dramatic increase in complexity caused by dynamic functional connectivity (dFC) estimation. To computationally handle this increased complexity, a limited set of dFC properties, primarily the mean and variance, have generally been considered. Additionally, it remains unclear how to integrate the increased information from dFC into pattern recognition techniques for subject-level prediction. In this study, we propose an approach to address these two issues based on a large number of previously unexplored temporal and spectral features of dynamic functional connectivity. A Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to estimate time-varying patterns of functional connectivity between resting-state networks. Time-frequency analysis is then performed on dFC estimates, and a large number of previously unexplored temporal and spectral features drawn from signal processing literature are extracted for dFC estimates. We apply the investigated features to two neurologic populations of interest, healthy controls and patients with temporal lobe epilepsy, and show that the proposed approach leads to substantial increases in predictive performance compared to both traditional estimates of static connectivity as well as current approaches to dFC. Variable importance is assessed and shows that there are several quantities that can be extracted from dFC signal which are more informative than the traditional mean or variance of dFC. This work illuminates many previously unexplored facets of the dynamic properties of functional connectivity between resting-state networks, and provides a platform for dynamic functional connectivity analysis that facilitates its usage as an investigative measure for healthy as well as abnormal brain function.
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Project description:Purpose: Hyperinsulinemia and insulin resistance are co-existing characteristics of type 2 diabetes, whereas the forerunner initiating the deleterious cycle remains elusive. The temporal transcriptomic landscape of islets (responsible for hyperinsulinemia) and liver (involved in insulin resistance) could provide new insights. Methods: C57BL/6N mice were fed a 60% high-fat diet (HFD) or control diet (CD) for 24 weeks. RNA-sequencing of islet and liver samples were respectively performed in quadruplicates at six consecutive time points of diet treatments (week 4, 8, 12, 16, 20 and 24), using BGISEQ-500 sequencing platform by the Beijing Genomics Institute (Shenzhen, China).The sequencing raw reads were filtered for low-quality, adaptor-polluted, high content of unknown base reads by SOAPnuke (v1.5.2). We used Trinity (v2.0.6) to perform de novo assembly, and Tgicl (v2.0.6) on cluster transcripts to remove redundancy and get unigenes. The high-quality clean reads were then mapped to the mouse reference genome (GRCm38) via HISAT2 (v2.0.4) and full-length transcriptome database via Bowtie2 (v2.2.5). The gene expression levels were then quantified by RSEM (v1.1.12) and were normalized by the method of fragments per kilobase of exon model per million reads mapped (FPKM). To interpret the functional significance of differentially expressed genes (DEGs), pathway analyses was conducted to determine enriched canonical pathways. Results: Combined analyses of all 96 samples yielded the identification of 21990 annotated genes. Differentially expressed genes (DEGs) between the two groups (HFD vs. CD) at each time point were identified using the criteria of fold change ≥2 and adjusted P-value ≤0.05. In total, 3844 DEGs were found in islets, of which 33 were shared among all six time points. With regard to liver, 4101 DEGs were discovered throughout 24 weeks of feeding, of which 39 were overlapped. Our islet and liver RNA-sequencing datasets outlined the impact of HFD on dynamics of molecular network at different stages. Correlation analyses of islet and liver modules with metabolic phenotypes illustrated that these two tissues jointly program β-cell adaption to irreversible impairment. Top scored networks combining islet and liver transcriptomes showed potential interactions of genes implicated in cell cycle during week 4, organismal development around week 12, and immune cell trafficking at week 24. Conclusions: Our data provide a comprehensive landscape of crosstalk between islets and liver in diet-induced diabetes, linking to the development of islet dysfunction and insulin resistance.
2020-06-26 | GSE153222 | GEO
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Project description:Background: The purpose of the study was to find out the molecular and metabolic characteristics of human superficial (SSAT) and deep subcutaneous adipose tissue (DSAT) by performing transcriptomics and metabolomics analysis Methods: We conducted RNA-sequencing and metabolome analysis of SSAT and DSAT in a 48-year-old female patient with a BMI of 27.6 kg/m2. Six samples for each group were collected during a deep inferior epigastric perforator flap breast reconstruction. The calculated transcripts per million values were processed for unsupervised hierarchically clustered heat map generation, and Gene set enrichment analysis. For metabolomics analysis, the samples were analyzed in two modes for cationic and anionic metabolites by Capillary Electrophoresis Time-of-Flight Mass Spectrometry (CE-TOFMS). The metabolome was processed for principal component analysis (PCA) and heat map generation. For indirect metabolic flux analyses, primary adipocytes obtained from SSAT and DSAT were analyzed with Seahorse® extracellular flux analyzer. Results: PCA and heat map data revealed global differences in the transcriptome and metabolome of SSAT and DSAT. The top up-regulated gene sets in the SSAT were related to adipogenesis (FDR q < 0.0001), oxidative phosphorylation (FDR q < 0.0001), fatty acid metabolism (FDR q < 0.0001) and glycolysis (FDR q = 0.001), while inflammatory response (FDR q < 0.05) was the top up-regulated gene set in DSAT. Consequently, the metabolites related to glycolysis were abundant in SSAT, while the metabolites related to fatty acid metabolism and oxidative phosphorylation were abundant in DSAT. In cellular flux analysis, SSAT showed higher level of glycolysis and spare oxidative phosphorylation capacities. Conclusion: The global transcriptome and metabolome difference suggest that human superficial and deep subcutaneous adipose tissue are metabolically distinguishable subcompartments.
2024-10-28 | GSE216803 | GEO
Project description:Chronic low-grade inflammation is a subclinical condition directly and indirectly linked to the development of a wide range of diseases responsible for the vast majority of morbidity. To examine mechanisms coupled to chronic disease, a group of overweight and obese human subjects without known inflammatory diseases participated in a high-fat meal challenge as an acute inflammation stimulus. Analysis of serum metabolites grouped by baseline cytokine levels revealed that single samples had little power in differentiating groups. However, an analysis that incorporated temporal response separated inflammatory response phenotypes and allowed us to create a metabolic signature of inflammation which revealed metabolic components that are crucial to a cytokine-mediated inflammation response. The use of temporal response, rather than a single time point, improved metabolomic prediction of high postprandial inflammation responses and led to the development of a dynamic biosignature as a potential tool for stratifying risk to a wide range of diseases.
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Project description:BackgroundPre-exposing plants to abiotic stresses can induce stress memory, which is crucial for adapting to subsequent stress exposure. Although numerous genes involved in salt stress response have been identified, the understanding of memory responses to salt stress remains limited.ResultsIn this study, we conducted physiological and transcriptional assays on maize plants subjected to recurrent salt stress to characterize salt stress memory. During the second exposure to salt stress, the plants exhibited enhanced salt resistance, as evidenced by increased proline content and higher POD and SOD activity, along with decreased MDA content, indicative of physiological memory behavior. Transcriptional analysis revealed fewer differentially expressed genes and variations in response processes during the second exposure compared to the first, indicative of transcriptional memory behavior. A total of 2,213 salt stress memory genes (SMGs) were identified and categorized into four response patterns. The most prominent group of SMGs consisted of genes with elevated expression during the first exposure to salt stress but reduced expression after recurrent exposure to salt stress, or vice versa ([+ / -] or [- / +]), indicating that a revised response is a crucial process in plant stress memory. Furthermore, nine transcription factors (TFs) (WRKY40, WRKY46, WRKY53, WRKY18, WRKY33, WRKY70, MYB15, KNAT7, and WRKY54) were identified as crucial factors related to salt stress memory. These TFs regulate over 53% of SMGs, underscoring their potential significance in salt stress memory.ConclusionsOur study demonstrates that maize can develop salt stress memory, and the genes identified here will aid in the genetic improvement of maize and other crops.
| S-EPMC10690987 | biostudies-literature
Project description:Aims/hypothesisNumerous new genes have recently been identified in genome-wide association studies for type 2 diabetes. Most are highly expressed in beta cells and presumably play important roles in their function. However, these genes account for only a small proportion of total risk and there are likely to be additional candidate genes not detected by current methodology. We therefore investigated islets from the polygenic New Zealand mouse (NZL) model of diet-induced beta cell dysfunction to identify novel genes and pathways that may play a role in the pathogenesis of diabetes.MethodsNZL mice were fed a diabetogenic high-fat diet (HF) or a diabetes-protective carbohydrate-free HF diet (CHF). Pancreatic islets were isolated by laser capture microdissection (LCM) and subjected to genome-wide transcriptome analyses.ResultsIn the prediabetic state, 2,109 islet transcripts were differentially regulated (>1.5-fold) between HF and CHF diets. Of the genes identified, 39 (e.g. Cacna1d, Chd2, Clip2, Igf2bp2, Dach1, Tspan8) correlated with data from the Diabetes Genetics Initiative and Wellcome Trust Case Control Consortium genome-wide scans for type 2 diabetes, thus validating our approach. HF diet induced early changes in gene expression associated with increased cell-cycle progression, proliferation and differentiation of islet cells, and oxidative stress (e.g. Cdkn1b, Tmem27, Pax6, Cat, Prdx4 and Txnip). In addition, pathway analysis identified oxidative phosphorylation as the predominant gene-set that was significantly upregulated in response to the diabetogenic HF diet.Conclusions/interpretationWe demonstrated that LCM of pancreatic islet cells in combination with transcriptional profiling can be successfully used to identify novel candidate genes for diabetes. Our data strongly implicate glucose-induced oxidative stress in disease progression.
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Project description:The response of vital organs to different types of nutrition or diet is a fundamental question in physiology. We examined the cardiac response to 4 weeks of high-fat diet in mice, measuring cardiac metabolites and mRNA. Metabolomics showed dramatic differences after a high-fat diet, including increases in several acyl-carnitine species. The RNA-seq data showed changes consistent with adaptations to use more fatty acid as substrate and an increase in the antioxidant protein catalase. Changes in mRNA were correlated with changes in protein level for several highly responsive genes. We also found significant sex differences in both metabolomics and RNA-seq datasets, both at baseline and after high fat diet. This work reveals the response of a vital organ to dietary intervention at both metabolomic and transcriptomic levels, which is a fundamental question in physiology. This work also reveals significant sex differences in cardiac metabolites and gene expression.
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