A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility

Single Nucleus Multiome Analysis Reveals Early Inflammatory Response to High-Fat Diet in Mouse Pancreatic Islets

In periods of sustained hyper-nutrition, pancreatic β-cells undergo functional compensation through transcriptional upregulation of gene programs driving insulin secretion. This adaptation is essential for maintaining systemic glucose homeostasis and metabolic health. Using single nuclei multiomics, we have mapped the early transcriptional compensation mechanisms in murine islets of Langerhans exposed to high-fat diet (HFD) for one and three weeks. We show that β-cells exhibit the largest transcriptional response to HFD, characterized by early activation of proinflammatory eRegulons and downregulation of β-cell identity genes, particularly in a distinct subset of β-cells. Our observations translate to humans, as we observe an increase in the inflammatory gene signatures in human β-cells in pre-diabetes and diabetes. Collectively, these observations point to cellular cross-talk through proinflammatory signaling as a central and early driver of β-cell dysfunction that limits the compensatory capacity of β-cells, which is closely linked to the development of diabetes.

Enhanced dynorphin expression and secretion in pancreatic beta-cells under hyperglycemic conditions

OBJECTIVE

Dynorphin, an endogenous opioid peptide predominantly expressed in the central nervous system and involved in stress response, pain, and addiction, has intrigued researchers due to its expression in pancreatic β-cells. In this study, we aimed to characterize dynorphin expression in mouse and human islets and explore the mechanisms regulating its expression.

METHODS

We used primary mouse and human islets with unbiased published datasets to examine how glucose and other nutrients regulate dynorphin expression and secretion in islets.

RESULTS

The prodynorphin gene is significantly upregulated in β-cells under hyperglycemic conditions. In vitro studies revealed that increased glucose concentrations correlate with increased dynorphin expression, indicating a critical interplay involving Ca2+, CamKII, and CREB pathways in β-cells. Perifusion studies allowed us to measure the dynamic secretion of dynorphin in response to glucose from mouse and human islets for the first time. Furthermore, we confirmed that increased dynorphin content within the β-cells directly correlates with enhanced dynorphin secretion. Finally, our findings demonstrate a synergistic effect of palmitate in conjunction with high glucose, further amplifying dynorphin levels and secretion in pancreatic islets.

CONCLUSIONS

This study demonstrates that the opioid peptide prodynorphin is expressed in mouse and human β-cells. Prodynorphin levels are regulated in parallel with insulin in response to glucose, palmitate, and amino acids. Our findings elucidate the signaling pathways involved, with CamKII playing a key role in regulating prodynorphin levels in β-cells. Finally, our findings are the first to demonstrate active dynorphin secretion from mouse and human islets in response to glucose.

Beta cell-specific PAK1 enrichment ameliorates diet-induced glucose intolerance in mice by promoting insulin biogenesis and minimising beta cell apoptosis

AIMS/HYPOTHESIS

p21 (CDC42/RAC1) activated kinase 1 (PAK1) is depleted in type 2 diabetic human islets compared with non-diabetic human islets, and acute PAK1 restoration in the islets can restore insulin secretory function ex vivo. We hypothesised that beta cell-specific PAK1 enrichment in vivo can mitigate high-fat-diet (HFD)-induced glucose intolerance by increasing the functional beta cell mass.

METHODS

Human islets expressing exogenous PAK1 specifically in beta cells were used for bulk RNA-seq. Human EndoC-βH1 cells overexpressing myc-tagged PAK1 were used for chromatin immunoprecipitation (ChIP) and ChIP-sequencing (ChIP-seq). Novel doxycycline-inducible beta cell-specific PAK1-expressing (iβPAK1-Tg) mice were fed a 45% HFD pre-induction for 3 weeks and for a further 3 weeks with or without doxycycline induction. These HFD-fed mice were evaluated for GTT, ITT, 6 h fasting plasma insulin and blood glucose, body composition, islet insulin content and apoptosis.

RESULTS

Beta cell-specific PAK1 enrichment in type 2 diabetes human islets resulted in decreased beta cell apoptosis and increased insulin content. RNA-seq showed an upregulation of INS gene transcription by PAK1. Using clonal human beta cells, we found that PAK1 protein was localised in the cytoplasm and the nucleus. ChIP studies revealed that nuclear PAK1 enhanced pancreatic and duodenal homeobox1 (PDX1) and neuronal differentiation 1 (NEUROD1) binding to the INS promoter in a glucose-responsive manner. Importantly, the iβPAK1-Tg mice, when challenged with HFD and doxycycline induction displayed enhanced glucose tolerance, increased islet insulin content and reduced beta cell apoptosis when compared with iβPAK1-Tg mice without doxycycline induction.

CONCLUSIONS/INTERPRETATION

PAK1 plays an unforeseen and beneficial role in beta cells by promoting insulin biogenesis via enhancing the expression of PDX1, NEUROD1 and INS, along with anti-apoptotic effects, that culminate in increased insulin content and beta cell mass in vivo and ameliorate diet-induced glucose intolerance.

DATA AVAILABILITY

The raw and processed RNA-seq data and ChIP-seq data, which has been made publicly available at Gene Expression Omnibus (GEO) at https://www.ncbi.nlm.nih.gov/geo/ , can be accessed in GSE239382.

Multi-omic human pancreatic islet endoplasmic reticulum and cytokine stress response mapping provides type 2 diabetes genetic insights

Endoplasmic reticulum (ER) and inflammatory stress responses contribute to islet dysfunction in type 2 diabetes (T2D). Comprehensive genomic understanding of these human islet stress responses and whether T2D-associated genetic variants modulate them is lacking. Here, comparative transcriptome and epigenome analyses of human islets exposed ex vivo to these stressors revealed 30% of expressed genes and 14% of islet cis-regulatory elements (CREs) as stress responsive, modulated largely in an ER- or cytokine-specific fashion. T2D variants overlapped 86 stress-responsive CREs, including 21 induced by ER stress. We linked the rs6917676-T T2D risk allele to increased islet ER-stress-responsive CRE accessibility and allele-specific β cell nuclear factor binding. MAP3K5, the ER-stress-responsive putative rs6917676 T2D effector gene, promoted stress-induced β cell apoptosis. Supporting its pro-diabetogenic role, MAP3K5 expression correlated inversely with human islet β cell abundance and was elevated in T2D β cells. This study provides genome-wide insights into human islet stress responses and context-specific T2D variant effects.

The identification of a novel shared therapeutic target and drug across all insulin-sensitive tissues under insulin resistance

Background

To identify key and shared insulin resistance (IR) molecular signatures across all insulin-sensitive tissues (ISTs), and their potential targeted drugs.

Methods

Three datasets from Gene Expression Omnibus (GEO) were acquired, in which the ISTs (fat, muscle, and liver) were from the same individual with obese mice. Integrated bioinformatics analysis was performed to obtain the differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was carried out to determine the "most significant trait-related genes" (MSTRGs). Enrichment analysis and PPI network were performed to find common features and novel hub genes in ISTs. The shared genes of DEGs and genes between DEGs and MSTRGs across four ISTs were identified as key IR therapeutic target. The Attie Lab diabetes database and obese rats were used to verify candidate genes. A medical drug-gene interaction network was conducted by using the Comparative Toxicogenomics Database (CTD) to find potential targeted drugs. The candidate drug was validated in Hepa1-6 cells.

Results

Lipid metabolic process, mitochondrion, and oxidoreductase activity as common features were enriched from ISTs under an obese context. Thirteen shared genes (Ubd, Lbp, Hp, Arntl, Cfd, Npas2, Thrsp., Tpx2, Pkp1, Sftpd, Mthfd2, Tnfaip2, and Vnn3) of DEGs across ISTs were obtained and confirmed. Among them, Ubd was the only shared gene between DEGs and MSTRGs across four ISTs. The expression of Ubd was significantly upregulated across four ISTs in obese rats, especially in the liver. The IR Hepa1-6 cell models treated with dexamethasone (Dex), palmitic acid (PA), and 2-deoxy-D-ribose (dRib) had elevated expression of Ubd. Knockdown of Ubd increased the level of p-Akt. A lowing Ubd expression drug, promethazine (PMZ) from CTD analysis rescued the decreased p-Akt level in IR Hepa1-6 cells.

Conclusion

This study revealed Ubd, a novel and shared IR molecular signature across four ISTs, as an effective biomarker and provided new insight into the mechanisms of IR. PMZ was a candidate drug for IR which increased p-Akt level and thus improved IR by targeting Ubd and downregulation of Ubd expression. Both Ubd and PMZ merit further clinical translational investigation to improve IR.

Ensemble machine learning reveals key features for diabetes duration from electronic health records

Diabetes is a metabolic disorder that affects more than 420 million of people worldwide, and it is caused by the presence of a high level of sugar in blood for a long period. Diabetes can have serious long-term health consequences, such as cardiovascular diseases, strokes, chronic kidney diseases, foot ulcers, retinopathy, and others. Even if common, this disease is uneasy to spot, because it often comes with no symptoms. Especially for diabetes type 2, that happens mainly in the adults, knowing how long the diabetes has been present for a patient can have a strong impact on the treatment they can receive. This information, although pivotal, might be absent: for some patients, in fact, the year when they received the diabetes diagnosis might be well-known, but the year of the disease unset might be unknown. In this context, machine learning applied to electronic health records can be an effective tool to predict the past duration of diabetes for a patient. In this study, we applied a regression analysis based on several computational intelligence methods to a dataset of electronic health records of 73 patients with diabetes type 1 with 20 variables and another dataset of records of 400 patients of diabetes type 2 with 49 variables. Among the algorithms applied, Random Forests was able to outperform the other ones and to efficiently predict diabetes duration for both the cohorts, with the regression performances measured through the coefficient of determination R2. Afterwards, we applied the same method for feature ranking, and we detected the most relevant factors of the clinical records correlated with past diabetes duration: age, insulin intake, and body-mass index. Our study discoveries can have profound impact on clinical practice: when the information about the duration of diabetes of patient is missing, medical doctors can use our tool and focus on age, insulin intake, and body-mass index to infer this important aspect. Regarding limitations, unfortunately we were unable to find additional dataset of EHRs of patients with diabetes having the same variables of the two analyzed here, so we could not verify our findings on a validation cohort.

Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks

Background

Gene co-expression networks (GCNs) describe relationships among expressed genes key to maintaining cellular identity and homeostasis. However, the small sample size of typical RNA-seq experiments which is several orders of magnitude fewer than the number of genes is too low to infer GCNs reliably. recount3, a publicly available dataset comprised of 316,443 uniformly processed human RNA-seq samples, provides an opportunity to improve power for accurate network reconstruction and obtain biological insight from the resulting networks.

Results

We compared alternate aggregation strategies to identify an optimal workflow for GCN inference by data aggregation and inferred three consensus networks: a universal network, a non-cancer network, and a cancer network in addition to 27 tissue context-specific networks. Central network genes from our consensus networks were enriched for evolutionarily constrained genes and ubiquitous biological pathways, whereas central context-specific network genes included tissue-specific transcription factors and factorization based on the hubs led to clustering of related tissue contexts. We discovered that annotations corresponding to context-specific networks inferred from aggregated data were enriched for trait heritability beyond known functional genomic annotations and were significantly more enriched when we aggregated over a larger number of samples.

Conclusion

This study outlines best practices for network GCN inference and evaluation by data aggregation. We recommend estimating and regressing confounders in each data set before aggregation and prioritizing large sample size studies for GCN reconstruction. Increased statistical power in inferring context-specific networks enabled the derivation of variant annotations that were enriched for concordant trait heritability independent of functional genomic annotations that are context-agnostic. While we observed strictly increasing held-out log-likelihood with data aggregation, we noted diminishing marginal improvements. Future directions aimed at alternate methods for estimating confounders and integrating orthogonal information from modalities such as Hi-C and ChIP-seq can further improve GCN inference.

Endpoints in NASH Clinical Trials: Are We Blind in One Eye?

This narrative review aims to illustrate the notion that nonalcoholic steatohepatitis (NASH), recently renamed metabolic dysfunction-associated steatohepatitis (MASH), is a systemic metabolic disorder featuring both adverse hepatic and extrahepatic outcomes. In recent years, several NASH trials have failed to identify effective pharmacological treatments and, therefore, lifestyle changes are the cornerstone of therapy for NASH. with this context, we analyze the epidemiological burden of NASH and the possible pathogenetic factors involved. These include genetic factors, insulin resistance, lipotoxicity, immuno-thrombosis, oxidative stress, reprogramming of hepatic metabolism, and hypoxia, all of which eventually culminate in low-grade chronic inflammation and increased risk of fibrosis progression. The possible explanations underlying the failure of NASH trials are also accurately examined. We conclude that the high heterogeneity of NASH, resulting from variable genetic backgrounds, exposure, and responses to different metabolic stresses, susceptibility to hepatocyte lipotoxicity, and differences in repair-response, calls for personalized medicine approaches involving research on noninvasive biomarkers. Future NASH trials should aim at achieving a complete assessment of systemic determinants, modifiers, and correlates of NASH, thus adopting a more holistic and unbiased approach, notably including cardiovascular-kidney-metabolic outcomes, without restricting therapeutic perspectives to histological surrogates of liver-related outcomes alone.

Cell cycle regulation of the psoriasis associated gene CCHCR1 by transcription factor E2F1

The coiled-coil alpha-helical rod protein 1 (CCHCR1) was first identified as a candidate gene in psoriasis and has lately been found to be associated with a wide range of clinical conditions including COVID-19. CCHCR1 is located within P-bodies and centrosomes, but its exact role in these two subcellular structures and its transcriptional control remain largely unknown. Here, we showed that CCHCR1 shares a bidirectional promoter with its neighboring gene, TCF19. This bidirectional promoter is activated by the G1/S-regulatory transcription factor E2F1, and both genes are co-induced during the G1/S transition of the cell cycle. A luciferase reporter assay suggests that the short intergenic sequence, only 287 bp in length, is sufficient for the G1/S induction of both genes, but the expression of CCHCR1 is further enhanced by the presence of exon 1 from both TCF19 and CCHCR1. This research uncovers the transcriptional regulation of the CCHCR1 gene, offering new perspectives on its function. These findings contribute to the broader understanding of diseases associated with CCHCR1 and may serve as a foundational benchmark for future research in these vital medical fields.

Plasma microRNA Profiling in Type 2 Diabetes Mellitus: A Pilot Study

Type 2 diabetes mellitus (T2D) is a chronic metabolic disease characterized by insulin resistance and β-cell dysfunction and leading to many micro- and macrovascular complications. In this study we analyzed the circulating miRNA expression profiles in plasma samples from 44 patients with T2D and 22 healthy individuals using next generation sequencing and detected 229 differentially expressed miRNAs. An increased level of miR-5588-5p, miR-125b-2-3p, miR-1284, and a reduced level of miR-496 in T2D patients was verified. We also compared the expression landscapes in the same group of patients depending on body mass index and identified differential expression of miR-144-3p and miR-99a-5p in obese individuals. Identification and functional analysis of putative target genes was performed for miR-5588-5p, miR-125b-2-3p, miR-1284, and miR-496, showing chromatin modifying enzymes and apoptotic genes being among the significantly enriched pathways.

D2H2: diabetes data and hypothesis hub

Motivation

There is a rapid growth in the production of omics datasets collected by the diabetes research community. However, such published data are underutilized for knowledge discovery. To make bioinformatics tools and published omics datasets from the diabetes field more accessible to biomedical researchers, we developed the Diabetes Data and Hypothesis Hub (D2H2).

Results

D2H2 contains hundreds of high-quality curated transcriptomics datasets relevant to diabetes, accessible via a user-friendly web-based portal. The collected and processed datasets are curated from the Gene Expression Omnibus (GEO). Each curated study has a dedicated page that provides data visualization, differential gene expression analysis, and single-gene queries. To enable the investigation of these curated datasets and to provide easy access to bioinformatics tools that serve gene and gene set-related knowledge, we developed the D2H2 chatbot. Utilizing GPT, we prompt users to enter free text about their data analysis needs. Parsing the user prompt, together with specifying information about all D2H2 available tools and workflows, we answer user queries by invoking the most relevant tools via the tools' API. D2H2 also has a hypotheses generation module where gene sets are randomly selected from the bulk RNA-seq precomputed signatures. We then find highly overlapping gene sets extracted from publications listed in PubMed Central with abstract dissimilarity. With the help of GPT, we speculate about a possible explanation of the high overlap between the gene sets. Overall, D2H2 is a platform that provides a suite of bioinformatics tools and curated transcriptomics datasets for hypothesis generation.

Availability and implementation

D2H2 is available at: https://d2h2.maayanlab.cloud/ and the source code is available from GitHub at https://github.com/MaayanLab/D2H2-site under the CC BY-NC 4.0 license.

The Role of Cdc42 in the Insulin and Leptin Pathways Contributing to the Development of Age-Related Obesity

Age-related obesity significantly increases the risk of chronic diseases such as type 2 diabetes, cardiovascular diseases, hypertension, and certain cancers. The insulin-leptin axis is crucial in understanding metabolic disturbances associated with age-related obesity. Rho GTPase Cdc42 is a member of the Rho family of GTPases that participates in many cellular processes including, but not limited to, regulation of actin cytoskeleton, vesicle trafficking, cell polarity, morphology, proliferation, motility, and migration. Cdc42 functions as an integral part of regulating insulin secretion and aging. Some novel roles for Cdc42 have also been recently identified in maintaining glucose metabolism, where Cdc42 is involved in controlling blood glucose levels in metabolically active tissues, including skeletal muscle, adipose tissue, pancreas, etc., which puts this protein in line with other critical regulators of glucose metabolism. Importantly, Cdc42 plays a vital role in cellular processes associated with the insulin and leptin signaling pathways, which are integral elements involved in obesity development if misregulated. Additionally, a change in Cdc42 activity may affect senescence, thus contributing to disorders associated with aging. This review explores the complex relationships among age-associated obesity, the insulin-leptin axis, and the Cdc42 signaling pathway. This article sheds light on the vast molecular web that supports metabolic dysregulation in aging people. In addition, it also discusses the potential therapeutic implications of the Cdc42 pathway to mitigate obesity since some new data suggest that inhibition of Cdc42 using antidiabetic drugs or antioxidants may promote weight loss in overweight or obese patients.

Systems genetics approaches for understanding complex traits with relevance for human disease

Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.

Novel regulators of islet function identified from genetic variation in mouse islet Ca2+ oscillations

Insufficient insulin secretion to meet metabolic demand results in diabetes. The intracellular flux of Ca2+ into β-cells triggers insulin release. Since genetics strongly influences variation in islet secretory responses, we surveyed islet Ca2+ dynamics in eight genetically diverse mouse strains. We found high strain variation in response to four conditions: (1) 8 mM glucose; (2) 8 mM glucose plus amino acids; (3) 8 mM glucose, amino acids, plus 10 nM glucose-dependent insulinotropic polypeptide (GIP); and (4) 2 mM glucose. These stimuli interrogate β-cell function, α- to β-cell signaling, and incretin responses. We then correlated components of the Ca2+ waveforms to islet protein abundances in the same strains used for the Ca2+ measurements. To focus on proteins relevant to human islet function, we identified human orthologues of correlated mouse proteins that are proximal to glycemic-associated single-nucleotide polymorphisms in human genome-wide association studies. Several orthologues have previously been shown to regulate insulin secretion (e.g. ABCC8, PCSK1, and GCK), supporting our mouse-to-human integration as a discovery platform. By integrating these data, we nominate novel regulators of islet Ca2+ oscillations and insulin secretion with potential relevance for human islet function. We also provide a resource for identifying appropriate mouse strains in which to study these regulators.

Microvesicle-associated and circulating microRNAs in diabetic dyslipidemia: miR-218, miR-132, miR-143, and miR-21, miR-122, miR-155 have biomarker potential

BACKGROUND

Circulating MicroRNAs (miRNAs) carried by microvesicles (MVs) have various physiological and pathological functions by post-transcriptional regulation of gene expression being considered markers for many diseases including diabetes and dyslipidemia. We aimed to identify new common miRNAs both in MVs and plasma that could be predictive biomarkers for diabetic dyslipidemia evolution.

METHODS

For this purpose, plasma from 63 participants in the study (17 type 2 diabetic patients, 17 patients with type 2 diabetes and dyslipidemia, 14 patients with dyslipidemia alone and 15 clinically healthy persons without diabetes or dyslipidemia) was used for the analysis of circulating cytokines, MVs, miRNAs and MV-associated miRNAs.

RESULTS

The results uncovered three miRNAs, miR-218, miR-132 and miR-143, whose expression was found to be significantly up-regulated in both circulating MVs and plasma from diabetic patients with dyslipidemia. These miRNAs showed significant correlations with important plasma markers, representative of this pathology. Thus, MV/plasma miR-218 was negatively correlated with the levels of erythrocyte MVs, plasma miR-132 was positively connected with MV miR-132 and negatively with uric acid and erythrocyte plasma levels, and plasma miR-143 was negatively related with creatinine levels and diastolic blood pressure. Also, three miRNAs common to MV and plasma, namely miR-21, miR-122, and miR-155, were identified to be down-regulated and up-regulated, respectively, in diabetic dyslipidemia. In addition, MV miR-21 was positively linked with cholesterol plasma levels and plasma miR-21 with TNFα plasma levels, MV miR-122 was negatively correlated with LDL-c levels and plasma miR-122 with creatinine and diastolic blood pressure and positively with MV miR-126 levels, MV miR-155 was positively associated with cholesterol and total MV levels and negatively with HDL-c levels, whereas plasma miR-155 was positively correlated with Il-1β plasma levels and total MV levels and negatively with MV miR-223 levels.

CONCLUSIONS

In conclusion, miR-218, miR-132, miR-143, and miR-21, miR-122, miR-155 show potential as biomarkers for diabetic dyslipidemia, but there is a need for more in-depth studies. These findings bring new information regarding the molecular biomarkers specific to diabetic dyslipidemia and could have important implications for the treatment of patients affected by this pathology.

FoxM1 coordinates cell division, protein synthesis, and mitochondrial activity in a subset of β cells during acute metabolic stress

Pancreatic β cells display functional and transcriptional heterogeneity in health and disease. The sequence of events leading to β cell heterogeneity during metabolic stress is poorly understood. Here, we characterize β cell responses to early metabolic stress in vivo by employing RNA sequencing (RNA-seq), assay for transposase-accessible chromatin with sequencing (ATAC-seq), single-cell RNA-seq (scRNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), and real-time imaging to decipher temporal events of chromatin remodeling and gene expression regulating the unfolded protein response (UPR), protein synthesis, mitochondrial function, and cell-cycle progression. We demonstrate that a subpopulation of β cells with active UPR, decreased protein synthesis, and insulin secretary capacities is more susceptible to proliferation after insulin depletion. Alleviation of endoplasmic reticulum (ER) stress precedes the progression of the cell cycle and mitosis and ensures appropriate insulin synthesis. Furthermore, metabolic stress rapidly activates key transcription factors including FoxM1, which impacts on proliferative and quiescent β cells by regulating protein synthesis, ER stress, and mitochondrial activity via direct repression of mitochondrial-encoded genes.

What the BTBR/J mouse has taught us about diabetes and diabetic complications

Human and mouse genetics have delivered numerous diabetogenic loci, but it is mainly through the use of animal models that the pathophysiological basis for their contribution to diabetes has been investigated. More than 20 years ago, we serendipidously identified a mouse strain that could serve as a model of obesity-prone type 2 diabetes, the BTBR (Black and Tan Brachyury) mouse (BTBR T+ Itpr3tf/J, 2018) carrying the Lepob mutation. We went on to discover that the BTBR-Lepob mouse is an excellent model of diabetic nephropathy and is now widely used by nephrologists in academia and the pharmaceutical industry. In this review, we describe the motivation for developing this animal model, the many genes identified and the insights about diabetes and diabetes complications derived from >100 studies conducted in this remarkable animal model.

Panax notoginseng alleviates oxidative stress through miRNA regulations based on systems biology approach

BACKGROUND

Herbal medicine Sanqi (SQ), the dried root or stem of Panax notoginseng (PNS), has been reported to have anti-diabetic and anti-obesity effects and is usually administered as a decoction for Chinese medicine. Alternative to utilizing PNS pure compound for treatment, we are motivated to propose an unconventional scheme to investigate the functions of PNS mixture. However, studies providing a detailed overview of the transcriptomics-based signaling network in response to PNS are seldom available.

METHODS

To explore the reasoning of PNS in treating metabolic disorders such as insulin resistance, we implemented a systems biology-based approach with RNA sequencing (RNA-seq) and miRNA sequencing data to elucidate key pathways, genes and miRNAs involved.

RESULTS

Functional enrichment analysis revealed PNS up-regulating oxidative stress-related pathways and down-regulating insulin and fatty acid metabolism. Superoxide dismutase 1 (SOD1), peroxiredoxin 1 (PRDX1), heme oxygenase-1 (Hmox1) and glutamate cysteine ligase (GCLc) mRNA and protein levels, as well as related miRNA levels, were measured in PNS treated rat pancreatic β cells (INS-1). PNS treatment up-regulated Hmox1, SOD1 and GCLc expression while down-regulating miR-24-3p and miR-139-5p to suppress oxidative stress. Furthermore, we verified the novel interactions between miR-139-5p and miR-24-3p with GCLc and SOD1.

CONCLUSION

This work has demonstrated the mechanism of how PNS regulates cellular molecules in metabolic disorders. Therefore, combining omics data with a systems biology strategy could be a practical means to explore the potential function and molecular mechanisms of Chinese herbal medicine in the treatment of metabolic disorders.

Endothelial specific reduction in Arf6 impairs insulin-stimulated vasodilation and skeletal muscle blood flow resulting in systemic insulin resistance

Background

Much of what we know about insulin resistance is based on studies from metabolically active tissues such as liver, adipose tissue, and skeletal muscle. Emerging evidence suggests that the vascular endothelium plays a crucial role in systemic insulin resistance, however, the underlying mechanisms remain incompletely understood. ADP ribosylation factor 6 (Arf6) is a small GTPase that plays a critical role in endothelial cell (EC) function. Here, we tested the hypothesis that the deletion of endothelial Arf6 will result in systemic insulin resistance.

Methods

We used mouse models of constitutive EC-specific Arf6 deletion (Arf6 f/- Tie2Cre) and tamoxifen inducible Arf6 knockout (Arf6 f/f Cdh5Cre). Endothelium-dependent vasodilation was assessed using pressure myography. Metabolic function was assessed using a battery of metabolic assessments including glucose- and insulin-tolerance tests and hyperinsulinemic-euglycemic clamps. A fluorescence microsphere-based technique was used to measure tissue blood flow. Intravital microscopy was used to assess skeletal muscle capillary density.

Results

Endothelial Arf6 deletion impaired insulin-stimulated vasodilation in white adipose tissue (WAT) and skeletal muscle feed arteries. The impairment in vasodilation was primarily due to attenuated insulin-stimulated nitric oxide (NO) bioavailability but independent of altered acetylcholine- or sodium nitroprusside-mediated vasodilation. In vitro Arf6 inhibition resulted in suppressed insulin stimulated phosphorylation of Akt and endothelial NO synthase. Endothelial cell-specific deletion of Arf6 also resulted in systematic insulin resistance in normal chow fed mice and glucose intolerance in high fat diet fed obese mice. The underlying mechanisms of glucose intolerance were reductions in insulin-stimulated blood flow and glucose uptake in the skeletal muscle and were independent of changes in capillary density or vascular permeability.

Conclusion

Results from this study support the conclusion that endothelial Arf6 signaling is essential for maintaining insulin sensitivity. Reduced expression of endothelial Arf6 impairs insulin-mediated vasodilation and results in systemic insulin resistance. These results have therapeutic implications for diseases that are associated with endothelial cell dysfunction and insulin resistance such as diabetes.

Identification of potential feature genes in non-alcoholic fatty liver disease using bioinformatics analysis and machine learning strategies

The prevalence of non-alcoholic fatty liver disease (NAFLD) and NAFLD-associated hepatocellular carcinoma (HCC) has continuously increased in recent years. Machine learning is an effective method for screening the feature genes of a disease for prediction, prevention and personalized treatment. Here, we used the "limma" package and weighted gene co-expression network analysis (WGCNA) to screen 219 NAFLD-related genes and found that they were mainly enriched in inflammation-related pathways. Four feature genes (AXUD1, FOSB, GADD45B, and SOCS2) were screened by LASSO regression and support vector machine-recursive feature elimination (SVM-RFE) machine learning algorithms. Therefore, a clinical diagnostic model with an area under the curve (AUC) value of 0.994 was constructed, which was superior to other indicators of NAFLD. Significant correlations existed between feature genes expression and steatohepatitis histology or clinical variables. These findings were also validated in external datasets and a mouse model. Finally, we found that feature genes expression was significantly decreased in NAFLD-associated HCC and that SOCS2 may be a prognostic biomarker. Our findings may provide new insights into the diagnosis, prevention and treatment targets of NAFLD and NAFLD-associated HCC.

Analysis of a genetic region affecting mouse body weight

Genetic factors affect an individual's risk of developing obesity, but in most cases each genetic variant has a small effect. Discovery of genes that regulate obesity may provide clues about its underlying biological processes and point to new ways the disease can be treated. Preclinical animal models facilitate genetic discovery in obesity because environmental factors can be better controlled compared with the human population. We studied inbred mouse strains to identify novel genes affecting obesity and glucose metabolism. BTBR T+ Itpr3^tf/J (BTBR) mice are fatter and more glucose intolerant than C57BL/6J (B6) mice. Prior genetic studies of these strains identified an obesity locus on chromosome 2. Using congenic mice, we found that obesity was affected by a ∼316 kb region, with only two known genes, pyruvate dehydrogenase kinase 1 (Pdk1) and integrin α 6 (Itga6). Both genes had mutations affecting their amino acid sequence and reducing mRNA levels. Both genes have known functions that could modulate obesity, lipid metabolism, insulin secretion, and/or glucose homeostasis. We hypothesized that genetic variation in or near Pdk1 or Itga6 causing reduced Pdk1 and Itga6 expression would promote obesity and impaired glucose tolerance. We used knockout mice lacking Pdk1 or Itga6 fed an obesigenic diet to test this hypothesis. Under the conditions we studied, we were unable to detect an individual contribution of either Pdk1 or Itga6 to body weight. During our studies, with conditions outside our control, we were unable to reproduce some of our previous body weight data. However, we identified a previously unknown role for Pdk1 in cardiac cholesterol metabolism providing the basis for future investigations. The studies described in this paper highlight the importance and the challenge using physiological outcomes to study obesity genes in mice.

Differential co-expression network analysis with DCoNA reveals isomiR targeting aberrations in prostate cancer

MOTIVATION

One of the standard methods of high-throughput RNA sequencing analysis is differential expression. However, it does not detect changes in molecular regulation. In contrast to the standard differential expression analysis, differential co-expression one aims to detect pairs or clusters whose mutual expression changes between two conditions.

RESULTS

We developed Differential Co-expression Network Analysis (DCoNA)-an open-source statistical tool that allows one to identify pair interactions, which correlation significantly changes between two conditions. Comparing DCoNA with the state-of-the-art analog, we showed that DCoNA is a faster, more accurate and less memory-consuming tool. We applied DCoNA to prostate mRNA/miRNA-seq data collected from The Cancer Genome Atlas (TCGA) and compared predicted regulatory interactions of miRNA isoforms (isomiRs) and their target mRNAs between normal and cancer samples. As a result, almost all highly expressed isomiRs lost negative correlation with their targets in prostate cancer samples compared to ones without the pathology. One exception to this trend was the canonical isomiR of hsa-miR-93-5p acquiring cancer-specific targets. Further analysis showed that cancer aggressiveness simultaneously increased with the expression level of this isomiR in both TCGA primary tumor samples and 153 blood plasma samples of P. Hertsen Moscow Oncology Research Institute patients' cohort analyzed by miRNA microarrays.

AVAILABILITY AND IMPLEMENTATION

Source code and documentation of DCoNA are available at https://github.com/zhiyanov/DCoNA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Exploring the shared molecular mechanism of microvascular and macrovascular complications in diabetes: Seeking the hub of circulatory system injury

BACKGROUND

Microvascular complications, such as diabetic retinopathy (DR) and diabetic nephropathy (DN), and macrovascular complications, referring to atherosclerosis (AS), are the main complications of diabetes. Blindness or fatal microvascular diseases are considered to be identified earlier than fatal macrovascular complications. Exploring the intrinsic relationship between microvascular and macrovascular complications and the hub of pathogenesis is of vital importance for prolonging the life span of patients with diabetes and improving the quality of life.

MATERIALS AND METHODS

The expression profiles of GSE28829, GSE30529, GSE146615 and GSE134998 were downloaded from the Gene Expression Omnibus database, which contained 29 atherosclerotic plaque samples, including 16 AS samples and 13 normal controls; 22 renal glomeruli and tubules samples from diabetes nephropathy including 12 DN samples and 10 normal controls; 73 lymphoblastoid cell line samples, including 52 DR samples and 21 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques including GSEA analysis, GO-KEGG functional clustering by R (version 4.0.5), PPI analysis by Cytoscape (version 3.8.2) and String database, miRNA analysis by Diana database, and hub genes analysis by Metascape database. The drug sensitivity of characteristic DEGs was analyzed.

RESULT

A total of 3709, 4185 and 8086 DEGs were recognized in AS, DN, DR, respectively, with 1820, 1666, 888 upregulated and 1889, 2519, 7198 downregulated. GO and KEGG pathway analyses of DEGs and GSEA analysis of common differential genes demonstrated that these significant sites focused primarily on inflammation-oxidative stress and immune regulation pathways. PPI networks show the connection and regulation on top-250 significant sites of AS, DN, DR. MiRNA analysis explored the non-coding RNA upstream regulation network and significant pathway in AS, DN, DR. The joint analysis of multiple diseases shows the common influenced pathways of AS, DN, DR and explored the interaction between top-1000 DEGs at the same time.

CONCLUSION

In the microvascular and macrovascular complications of diabetes, immune-mediated inflammatory response, chronic inflammation caused by endothelial cell activation and oxidative stress are the three links linking atherosclerosis, diabetes retinopathy and diabetes nephropathy together. Our study has clarified the intrinsic relationship and common tissue damage mechanism of microcirculation and circulatory system complications in diabetes, and explored the mechanism center of these two vascular complications. It has far-reaching clinical and social value for reducing the incidence of fatal events and early controlling the progress of disabling and fatal circulatory complications in diabetes.

CISD1 Is a Breast Cancer Prognostic Biomarker Associated with Diabetes Mellitus

Women with diabetes mellitus are believed to have increased risk of developing breast cancer and lower life expectancies. This study aims to depict the association between the CISD1, the co-expressed genes, and diabetes mellitus to offer potential therapeutic targets for further mechanical research. The TCGA-BRCA RNAseq data is acquired. All the data and analyzed using R packages and web-based bioinformatics tools. CISD1 gene expression was evaluated between tumor bulk and adjacent tissue. Immune cell infiltration evaluation was performed. CISD1 expressed significantly higher in tumor tissue than that of the normal tissue, indicating poor overall survival rates. High expression level of CISD1 in tumor shows less pDC and NK cells penetration. There are 138 genes shared between CISD1 co-expressed gene pool in BRCA and diabetes mellitus related genes using "diabetes" as the term for text mining. These shared genes enrich in "cell cycle" and other pathways. MCODE analysis demonstrates that p53-independent G1/S DNA damage checkpoint, p53-independent DNA damage response, and ubiquitin mediated degradation of phosphorylated cdc25A are top-ranked than other terms. CISD1 and co-expressed genes, especially shared ones with diabetes mellitus, can be the focused genes considered when addressing clinical problems in breast cancer with a diabetes mellitus background.

Identification of FOXM1 and CXCR4 as key genes in breast cancer prevention and prognosis after intermittent energy restriction through bioinformatics and functional analyses

We explored potential biomarkers and molecular mechanisms regarding breast cancer (BC) risk reduction after intermittent energy restriction (IER) and further explored the association between IER and BC prognosis. We identified differentially expressed genes (DEGs) in breast tissues before and after IER by analyzing the expression profile from GEO. Then, enrichment analysis was used to identify important pathways of DEGs and hub genes were selected from PPI network. After that, GEPIA, ROC, and KM plotter were used to explore the preventive and prognostic value of hub genes. It was found that FOXM1 and CXCR4 were highly expressed in BC tissues and associated with the worse prognosis. FOXM1 and CXCR4 were down-regulated after IER , which meant that FOXM1 and CXCR4 might be the most important key genes for reducing the risk and improving prognosis of BC after IER . ROC curve indicated that FOXM1 and CXCR4 also had the predictive value for BC. Our study contributed to a better understanding of the specific mechanisms in protective effects of IER on BC and provided a new approach to improve the prognosis of BC, which might provide partial guidance for the subsequent development of more effective treatments and prevention strategies.

GeneNetTools: tests for Gaussian graphical models with shrinkage

MOTIVATION

Gaussian graphical models (GGMs) are network representations of random variables (as nodes) and their partial correlations (as edges). GGMs overcome the challenges of high-dimensional data analysis by using shrinkage methodologies. Therefore, they have become useful to reconstruct gene regulatory networks from gene-expression profiles. However, it is often ignored that the partial correlations are 'shrunk' and that they cannot be compared/assessed directly. Therefore, accurate (differential) network analyses need to account for the number of variables, the sample size, and also the shrinkage value, otherwise, the analysis and its biological interpretation would turn biased. To date, there are no appropriate methods to account for these factors and address these issues.

RESULTS

We derive the statistical properties of the partial correlation obtained with the Ledoit-Wolf shrinkage. Our result provides a toolbox for (differential) network analyses as (i) confidence intervals, (ii) a test for zero partial correlation (null-effects) and (iii) a test to compare partial correlations. Our novel (parametric) methods account for the number of variables, the sample size and the shrinkage values. Additionally, they are computationally fast, simple to implement and require only basic statistical knowledge. Our simulations show that the novel tests perform better than DiffNetFDR-a recently published alternative-in terms of the trade-off between true and false positives. The methods are demonstrated on synthetic data and two gene-expression datasets from Escherichia coli and Mus musculus.

AVAILABILITY AND IMPLEMENTATION

The R package with the methods and the R script with the analysis are available in https://github.com/V-Bernal/GeneNetTools.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Investigating the Expression and Function of the Glucose Transporter GLUT6 in Obesity

Obesity-related insulin resistance is a highly prevalent and growing health concern, which places stress on the pancreatic islets of Langerhans by increasing insulin secretion to lower blood glucose levels. The glucose transporters GLUT1 and GLUT3 play a key role in glucose-stimulated insulin secretion in human islets, while GLUT2 is the key isoform in rodent islets. However, it is unclear whether other glucose transporters also contribute to insulin secretion by pancreatic islets. Herein, we show that SLC2A6 (GLUT6) is markedly upregulated in pancreatic islets from genetically obese leptin-mutant (ob/ob) and leptin receptor-mutant (db/db) mice, compared to lean controls. Furthermore, we observe that islet SLC2A6 expression positively correlates with body mass index in human patients with type 2 diabetes. To investigate whether GLUT6 plays a functional role in islets, we crossed GLUT6 knockout mice with C57BL/6 ob/ob mice. Pancreatic islets isolated from ob/ob mice lacking GLUT6 secreted more insulin in response to high-dose glucose, compared to ob/ob mice that were wild type for GLUT6. The loss of GLUT6 in ob/ob mice had no adverse impact on body mass, body composition, or glucose tolerance at a whole-body level. This study demonstrates that GLUT6 plays a role in pancreatic islet insulin secretion in vitro but is not a dominant glucose transporter that alters whole-body metabolic physiology in ob/ob mice.

DOC2b Enhances β-Cell Function via a Novel Tyrosine Phosphorylation-Dependent Mechanism

Double C2 domain Β (DOC2b) protein is required for glucose-stimulated insulin secretion (GSIS) in β-cells, the underlying mechanism of which remains unresolved. Our biochemical analysis using primary human islets and human and rodent clonal β-cells revealed that DOC2b is tyrosine phosphorylated within 2 min of glucose stimulation, and Src family kinase member YES is required for this process. Biochemical and functional analysis using DOC2bY301 mutants revealed the requirement of Y301 phosphorylation for the interaction of DOC2b with YES kinase and increased content of VAMP2, a protein on insulin secretory granules, at the plasma membrane (PM), concomitant with DOC2b-mediated enhancement of GSIS in β-cells. Coimmunoprecipitation studies demonstrated an increased association of DOC2b with ERM family proteins in β-cells following glucose stimulation or pervanadate treatment. Y301 phosphorylation-competent DOC2b was required to increase ERM protein activation, and ERM protein knockdown impaired DOC2b-mediated boosting of GSIS, suggesting that tyrosine-phosphorylated DOC2b regulates GSIS via ERM-mediated granule localization to the PM. Taken together, these results demonstrate the glucose-induced posttranslational modification of DOC2b in β-cells, pinpointing the kinase, site of action, and downstream signaling events and revealing a regulatory role of YES kinase at various steps in GSIS. This work will enhance the development of novel therapeutic strategies to restore glucose homeostasis in diabetes.

IGF2BP1 Promotes Proliferation of Neuroendocrine Neoplasms by Post-Transcriptional Enhancement of EZH2

Simple Summary

Neuroendocrine neoplasms (NEN) are very heterogeneous malignancies arising at different sites of the body that show an increasing incidence in recent decades. Here, we show that IGF2 mRNA binding protein 1 (IGF2BP1) is highly expressed in NEN cell lines, leading to enhanced cell proliferation. This oncogenic function relies on post-transcriptional stimulation of EZH2 expression by IGF2BP1, resulting in epigenetic silencing of cell cycle inhibitors via tri-methylation of histone H3 at lysine 27 (H3K27me3). Combinatorial pharmacological targeting of IGF2BP1, EZH2, and the EZH2-activator Myc leads to synergistic antiproliferative and proapoptotic effects in NEN cells, representing a novel therapeutic strategy in neuroendocrine malignancies.

Abstract

Neuroendocrine neoplasms (NENs) represent a heterogenous class of highly vascularized neoplasms that are increasing in prevalence and are predominantly diagnosed at a metastatic state. The molecular mechanisms leading to tumor initiation, metastasis, and chemoresistance are still under investigation. Hence, identification of novel therapeutic targets is of great interest. Here, we demonstrate that the RNA-binding Protein IGF2BP1 is a post-transcriptional regulator of components of the Polycomb repressive complex 2 (PRC2), an epigenic modifier affecting transcriptional regulation and proliferation: Comprehensive in silico analyses along with in vitro experiments showed that IGF2BP1 promotes neuroendocrine tumor cell proliferation by stabilizing the mRNA of Enhancer of Zeste 2 (EZH2), the catalytic subunit of PRC2, which represses gene expression by tri-methylation of histone H3 at lysine 27 (H3K27me3). The IGF2BP1-driven stabilization and protection of EZH2 mRNA is m6A-dependent and enhances EZH2 protein levels which stimulates cell cycle progression by silencing cell cycle arrest genes through enhanced H3K27 tri-methylation. Therapeutic inhibition of IGF2BP1 destabilizes EZH2 mRNA and results in a reduced cell proliferation, paralleled by an increase in G1 and sub-G1 phases. Combined targeting of IGF2BP1, EZH2, and Myc, a transcriptional activator of EZH2 and well-known target of IGF2BP1 cooperatively induces tumor cell apoptosis. Our data identify IGF2BP1 as an important driver of tumor progression in NEN, and indicate that disruption of the IGF2BP1-Myc-EZH2 axis represents a promising approach for targeted therapy of neuroendocrine neoplasms.

Meta-Inflammation and De Novo Lipogenesis Markers Are Involved in Metabolic Associated Fatty Liver Disease Progression in BTBR ob/ob Mice

Metabolic associated fatty liver disease (MAFLD) is a hepatic manifestation of metabolic syndrome and usually associated with obesity and diabetes. Our aim is to characterize the pathophysiological mechanism involved in MAFLD development in Black Tan and brachyuric (BTBR) insulin-resistant mice in combination with leptin deficiency (ob/ob). We studied liver morphology and biochemistry on our diabetic and obese mice model (BTBR ob/ob) as well as a diabetic non-obese control (BTBR + streptozotocin) and non-diabetic control mice (BTBR wild type) from 4–22 weeks. Lipid composition was assessed, and lipid related pathways were studied at transcriptional and protein level. Microvesicular steatosis was evident in BTBR ob/ob from week 6, progressing to macrovesicular in the following weeks. At 12th week, inflammatory clusters, activation of STAT3 and Nrf2 signaling pathways, and hepatocellular ballooning. At 22 weeks, the histopathological features previously observed were maintained and no signs of fibrosis were detected. Lipidomic analysis showed profiles associated with de novo lipogenesis (DNL). BTBR ob/ob mice develop MAFLD profile that resemble pathological features observed in humans, with overactivation of inflammatory response, oxidative stress and DNL signaling pathways. Therefore, BTBR ob/ob mouse is an excellent model for the study of the steatosis to steatohepatitis transition.

Primary Cilia in Pancreatic β- and α-Cells: Time to Revisit the Role of Insulin-Degrading Enzyme

The primary cilium is a narrow organelle located at the surface of the cell in contact with the extracellular environment. Once underappreciated, now is thought to efficiently sense external environmental cues and mediate cell-to-cell communication, because many receptors, ion channels, and signaling molecules are highly or differentially expressed in primary cilium. Rare genetic disorders that affect cilia integrity and function, such as Bardet-Biedl syndrome and Alström syndrome, have awoken interest in studying the biology of cilium. In this review, we discuss recent evidence suggesting emerging roles of primary cilium and cilia-mediated signaling pathways in the regulation of pancreatic β- and α-cell functions, and its implications in regulating glucose homeostasis.

Plasma triacylglycerols are biomarkers of β-cell function in mice and humans

Objectives

To find plasma biomarkers prognostic of type 2 diabetes, which could also inform on pancreatic β-cell deregulations or defects in the function of insulin target tissues.

Methods

We conducted a systems biology approach to characterize the plasma lipidomes of C57Bl/6J, DBA/2J, and BALB/cJ mice under different nutritional conditions, as well as their pancreatic islet and liver transcriptomes. We searched for correlations between plasma lipids and tissue gene expression modules.

Results

We identified strong correlation between plasma triacylglycerols (TAGs) and islet gene modules that comprise key regulators of glucose- and lipid-regulated insulin secretion and of the insulin signaling pathway, the two top hits were Gck and Abhd6 for negative and positive correlations, respectively. Correlations were also found between sphingomyelins and islet gene modules that overlapped in part with the gene modules correlated with TAGs. In the liver, the gene module most strongly correlated with plasma TAGs was enriched in mRNAs encoding fatty acid and carnitine transporters as well as multiple enzymes of the β-oxidation pathway. In humans, plasma TAGs also correlated with the expression of several of the same key regulators of insulin secretion and the insulin signaling pathway identified in mice. This cross-species comparative analysis further led to the identification of PITPNC1 as a candidate regulator of glucose-stimulated insulin secretion.

Conclusion

TAGs emerge as biomarkers of a liver-to-β-cell axis that links hepatic β-oxidation to β-cell functional mass and insulin secretion.

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Plasma triacylglycerols correlated with genes controlling β-cell mass and function.

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Plasma triacylglycerols correlated with genes controlling liver β-oxidation.

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In humans, triacylglycerols also correlated with key regulators of insulin secretion.

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Mouse and human data identified PITPNC1 as a candidate regulator of insulin secretion.

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Triacylglycerols are biomarkers of the liver-to-β-cell axis and β-cell function.

Prostaglandin EP3 receptor signaling is required to prevent insulin hypersecretion and metabolic dysfunction in a non-obese mouse model of insulin resistance

When homozygous for the Leptin^Ob mutation (Ob), Black-and-Tan Brachyury (BTBR) mice become morbidly obese and severely insulin resistant, and by 10 wk of age, frankly diabetic. Previous work has shown prostaglandin EP3 receptor (EP3) expression and activity is upregulated in islets from BTBR-Ob mice as compared with lean controls, actively contributing to their β-cell dysfunction. In this work, we aimed to test the impact of β-cell-specific EP3 loss on the BTBR-Ob phenotype by crossing Ptger3 floxed mice with the rat insulin promoter (RIP)-Cre^Herr driver strain. Instead, germline recombination of the floxed allele in the founder mouse-an event whose prevalence we identified as directly associated with underlying insulin resistance of the background strain-generated a full-body knockout. Full-body EP3 loss provided no diabetes protection to BTBR-Ob mice but, unexpectedly, significantly worsened BTBR-lean insulin resistance and glucose tolerance. This in vivo phenotype was not associated with changes in β-cell fractional area or markers of β-cell replication ex vivo. Instead, EP3-null BTBR-lean islets had essentially uncontrolled insulin hypersecretion. The selective upregulation of constitutively active EP3 splice variants in islets from young, lean BTBR mice as compared with C57BL/6J, where no phenotype of EP3 loss has been observed, provides a potential explanation for the hypersecretion phenotype. In support of this, high islet EP3 expression in Balb/c females versus Balb/c males was fully consistent with their sexually dimorphic metabolic phenotype after loss of EP3-coupled Gα_z protein. Taken together, our findings provide a new dimension to the understanding of EP3 as a critical brake on insulin secretion.NEW & NOTEWORTHY Islet prostaglandin EP3 receptor (EP3) signaling is well known as upregulated in the pathophysiological conditions of type 2 diabetes, contributing to β-cell dysfunction. Unexpected findings in mouse models of non-obese insulin sensitivity and resistance provide a new dimension to our understanding of EP3 as a key modulator of insulin secretion. A previously unknown relationship between mouse insulin resistance and the penetrance of rat insulin promoter-driven germline floxed allele recombination is critical to consider when creating β-cell-specific knockouts.

The 'un-shrunk' partial correlation in Gaussian graphical models

BACKGROUND

In systems biology, it is important to reconstruct regulatory networks from quantitative molecular profiles. Gaussian graphical models (GGMs) are one of the most popular methods to this end. A GGM consists of nodes (representing the transcripts, metabolites or proteins) inter-connected by edges (reflecting their partial correlations). Learning the edges from quantitative molecular profiles is statistically challenging, as there are usually fewer samples than nodes ('high dimensional problem'). Shrinkage methods address this issue by learning a regularized GGM. However, it remains open to study how the shrinkage affects the final result and its interpretation.

RESULTS

We show that the shrinkage biases the partial correlation in a non-linear way. This bias does not only change the magnitudes of the partial correlations but also affects their order. Furthermore, it makes networks obtained from different experiments incomparable and hinders their biological interpretation. We propose a method, referred to as 'un-shrinking' the partial correlation, which corrects for this non-linear bias. Unlike traditional methods, which use a fixed shrinkage value, the new approach provides partial correlations that are closer to the actual (population) values and that are easier to interpret. This is demonstrated on two gene expression datasets from Escherichia coli and Mus musculus.

CONCLUSIONS

GGMs are popular undirected graphical models based on partial correlations. The application of GGMs to reconstruct regulatory networks is commonly performed using shrinkage to overcome the 'high-dimensional problem'. Besides it advantages, we have identified that the shrinkage introduces a non-linear bias in the partial correlations. Ignoring this type of effects caused by the shrinkage can obscure the interpretation of the network, and impede the validation of earlier reported results.

Decoding disease: from genomes to networks to phenotypes

Interpreting the effects of genetic variants is key to understanding individual susceptibility to disease and designing personalized therapeutic approaches. Modern experimental technologies are enabling the generation of massive compendia of human genome sequence data and associated molecular and phenotypic traits, together with genome-scale expression, epigenomics and other functional genomic data. Integrative computational models can leverage these data to understand variant impact, elucidate the effect of dysregulated genes on biological pathways in specific disease and tissue contexts, and interpret disease risk beyond what is feasible with experiments alone. In this Review, we discuss recent developments in machine learning algorithms for genome interpretation and for integrative molecular-level modelling of cells, tissues and organs relevant to disease. More specifically, we highlight existing methods and key challenges and opportunities in identifying specific disease-causing genetic variants and linking them to molecular pathways and, ultimately, to disease phenotypes.

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes

The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This research aims to determine the genetic factors and processes contributing to the synergistic severity of SARS-CoV-2 infection among T2D patients through bioinformatics approaches. We analyzed two sets of transcriptomic data of SARS-CoV-2 infection obtained from lung epithelium cells and PBMCs, and two sets of T2D data from pancreatic islet cells and PBMCs to identify the associated differentially expressed genes (DEGs) followed by their functional enrichment analyses in terms of protein-protein interaction (PPI) to detect hub-proteins and associated comorbidities, transcription factors (TFs), microRNAs (miRNAs) as well as the potential drug candidates. In PPI analysis, four potential hub-proteins (i.e., BIRC3, C3, MME, and IL1B) were identified among 25 DEGs shared between the disease pair. Enrichment analyses using the mutually overlapped DEGs revealed the most prevalent GO and cell signalling pathways, including TNF signalling, cytokine-cytokine receptor interaction, and IL-17 signalling, which are related to cytokine activities. Furthermore, as significant TFs, we identified IRF1, KLF11, FOSL1, and CREB3L1 while miRNAs including miR-1-3p, 34a-5p, 16–5p, 155–5p, 20a-5p, and let-7b-5p were found to be noteworthy. The findings illustrated the significant association between COVID-19 and T2D at the molecular level. These genetic determinants can further be explored for their specific roles in disease progression and therapeutic intervention, while significant pathways can also be studied as molecular checkpoints. Finally, the identified drug candidates may be evaluated for their potency to minimize the severity of COVID-19 patients with pre-existing T2D.

SIRT4 is an early regulator of branched-chain amino acid catabolism that promotes adipogenesis

Upon nutrient stimulation, pre-adipocytes undergo differentiation to transform into mature adipocytes capable of storing nutrients as fat. We profiled cellular metabolite consumption to identify early metabolic drivers of adipocyte differentiation. We find that adipocyte differentiation raises the uptake and consumption of numerous amino acids. In particular, branched-chain amino acid (BCAA) catabolism precedes and promotes peroxisome proliferator-activated receptor gamma (PPARγ), a key regulator of adipogenesis. In early adipogenesis, the mitochondrial sirtuin SIRT4 elevates BCAA catabolism through the activation of methylcrotonyl-coenzyme A (CoA) carboxylase (MCCC). MCCC supports leucine oxidation by catalyzing the carboxylation of 3-methylcrotonyl-CoA to 3-methylglutaconyl-CoA. Sirtuin 4 (SIRT4) expression is decreased in adipose tissue of numerous diabetic mouse models, and its expression is most correlated with BCAA enzymes, suggesting a potential role for SIRT4 in adipose pathology through the alteration of BCAA metabolism. In summary, this work provides a temporal analysis of adipocyte differentiation and uncovers early metabolic events that stimulate transcriptional reprogramming.

Novel advances in understanding fatty acid-binding G protein-coupled receptors and their roles in controlling energy balance

Diabetes, obesity, and other metabolic diseases have been recognized as the main factors that endanger human health worldwide. Most of these metabolic syndromes develop when the energy balance in the body is disrupted. Energy balance depends upon the systemic regulation of food intake, glucose homeostasis, and lipid metabolism. Fatty acid-binding G protein-coupled receptors (GPCRs) are widely expressed in various types of tissues and cells involved in energy homeostasis regulation. In this review, the distribution and biological functions of fatty acid-binding GPCRs are summarized, particularly with respect to the gut, pancreas, and adipose tissue. A systematic understanding of the physiological functions of the fatty acid-binding GPCRs involved in energy homeostasis regulation will help in identifying novel pharmacological targets for metabolic diseases.

Identification of Meat Quality Determining Marker Genes in Fibroblasts of Bovine Muscle Using Transcriptomic Profiling

In the present study, we comparatively analyzed the transcriptomic profiling of fibroblasts derived from two different muscles, biceps femoris and longissimus dorsi with significant difference in the meat quality and tenderness. EBSeq algorithm was applied to analyze the data, and genes were considered to be significantly differentially expressed if the false discovery rate value was <0.05, the P value was <0.01, and the fold change was >0.585. The results revealed that 253 genes were differentially expressed genes (DEGs) (170 genes were upregulated, and 83 were downregulated) and more than 100 DEGs were probably associated with intramuscular fat deposition, tenderness, and toughness, which are driving the meat quality and were involved in biological processes such as collagen synthesis, cell differentiation, and muscle tissue and fiber development; molecular functions such as chemokine activity and collagen activity; cellular components such as cytoplasm and myofibril; and pathways such as collagen signaling and metabolic pathways. A gene-act network and a co-expression network revealed the close relationship between intramuscular fat deposition and meat tenderness. The expressions of 20 DEGs were validated by real-time PCR, and the results suggested that the DEGs are correlated with RNA-seq data and play crucial roles in muscle growth, development processes, toughness, and tenderness of the meat. Together, the genome-wide transcriptome analysis revealed that various genes are responsible for toughness and tenderness variance in the difference muscles of beef.

Islet Regeneration: Endogenous and Exogenous Approaches

Both type 1 and type 2 diabetes are characterized by a progressive loss of beta cell mass that contributes to impaired glucose homeostasis. Although an optimal treatment option would be to simply replace the lost cells, it is now well established that unlike many other organs, the adult pancreas has limited regenerative potential. For this reason, significant research efforts are focusing on methods to induce beta cell proliferation (replication of existing beta cells), promote beta cell formation from alternative endogenous cell sources (neogenesis), and/or generate beta cells from pluripotent stem cells. In this article, we will review (i) endogenous mechanisms of beta cell regeneration during steady state, stress and disease; (ii) efforts to stimulate endogenous regeneration and transdifferentiation; and (iii) exogenous methods of beta cell generation and transplantation.

Rat prostaglandin EP3 receptor is highly promiscuous and is the sole prostanoid receptor family member that regulates INS-1 (832/3) cell glucose-stimulated insulin secretion

Chronic elevations in fatty acid metabolites termed prostaglandins can be found in circulation and in pancreatic islets from mice or humans with diabetes and have been suggested as contributing to the β‐cell dysfunction of the disease. Two‐series prostaglandins bind to a family of G‐protein‐coupled receptors, each with different biochemical and pharmacological properties. Prostaglandin E receptor (EP) subfamily agonists and antagonists have been shown to influence β‐cell insulin secretion, replication, and/or survival. Here, we define EP3 as the sole prostanoid receptor family member expressed in a rat β‐cell‐derived line that regulates glucose‐stimulated insulin secretion. Several other agonists classically understood as selective for other prostanoid receptor family members also reduce glucose‐stimulated insulin secretion, but these effects are only observed at relatively high concentrations, and, using a well‐characterized EP3‐specific antagonist, are mediated solely by cross‐reactivity with rat EP3. Our findings confirm the critical role of EP3 in regulating β‐cell function, but are also of general interest, as many agonists supposedly selective for other prostanoid receptor family members are also full and efficacious agonists of EP3. Therefore, care must be taken when interpreting experimental results from cells or cell lines that also express EP3.

Novel Insights into the Molecular Features and Regulatory Mechanisms of Mitochondrial Dynamic Disorder in the Pathogenesis of Cardiovascular Disease

Mitochondria maintain mitochondrial homeostasis through continuous fusion and fission, that is, mitochondrial dynamics, which is precisely mediated by mitochondrial fission and fusion proteins, including dynamin-related protein 1 (Drp1), mitofusin 1 and 2 (Mfn1/2), and optic atrophy 1 (OPA1). When the mitochondrial fission and fusion of cardiomyocytes are out of balance, they will cause their own morphology and function disorders, which damage the structure and function of the heart, are involved in the occurrence and progression of cardiovascular disease such as ischemia-reperfusion injury (IRI), septic cardiomyopathy, and diabetic cardiomyopathy. In this paper, we focus on the latest findings regarding the molecular features and regulatory mechanisms of mitochondrial dynamic disorder in cardiovascular pathologies. Finally, we will address how these findings can be applied to improve the treatment of cardiovascular disease.

Conventional and Unconventional Mechanisms by which Exocytosis Proteins Oversee β-cell Function and Protection

Type 2 diabetes (T2D) is one of the prominent causes of morbidity and mortality in the United States and beyond, reaching global pandemic proportions. One hallmark of T2D is dysfunctional glucose-stimulated insulin secretion from the pancreatic β-cell. Insulin is secreted via the recruitment of insulin secretory granules to the plasma membrane, where the soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) and SNARE regulators work together to dock the secretory granules and release insulin into the circulation. SNARE proteins and their regulators include the Syntaxins, SNAPs, Sec1/Munc18, VAMPs, and double C2-domain proteins. Recent studies using genomics, proteomics, and biochemical approaches have linked deficiencies of exocytosis proteins with the onset and progression of T2D. Promising results are also emerging wherein restoration or enhancement of certain exocytosis proteins to β-cells improves whole-body glucose homeostasis, enhances β-cell function, and surprisingly, protection of β-cell mass. Intriguingly, overexpression and knockout studies have revealed novel functions of certain exocytosis proteins, like Syntaxin 4, suggesting that exocytosis proteins can impact a variety of pathways, including inflammatory signaling and aging. In this review, we present the conventional and unconventional functions of β-cell exocytosis proteins in normal physiology and T2D and describe how these insights might improve clinical care for T2D.

Pancreatic β-Cell-Specific Deletion of VPS41 Causes Diabetes Due to Defects in Insulin Secretion

Insulin secretory granules (SGs) mediate the regulated secretion of insulin, which is essential for glucose homeostasis. The basic machinery responsible for this regulated exocytosis consists of specific proteins present both at the plasma membrane and on insulin SGs. The protein composition of insulin SGs thus dictates their release properties, yet the mechanisms controlling insulin SG formation, which determine this molecular composition, remain poorly understood. VPS41, a component of the endolysosomal tethering homotypic fusion and vacuole protein sorting (HOPS) complex, was recently identified as a cytosolic factor involved in the formation of neuroendocrine and neuronal granules. We now find that VPS41 is required for insulin SG biogenesis and regulated insulin secretion. Loss of VPS41 in pancreatic β-cells leads to a reduction in insulin SG number, changes in their transmembrane protein composition, and defects in granule-regulated exocytosis. Exploring a human point mutation, identified in patients with neurological but no endocrine defects, we show that the effect on SG formation is independent of HOPS complex formation. Finally, we report that mice with a deletion of VPS41 specifically in β-cells develop diabetes due to severe depletion of insulin SG content and a defect in insulin secretion. In sum, our data demonstrate that VPS41 contributes to glucose homeostasis and metabolism.

Debates in Pancreatic Beta Cell Biology: Proliferation Versus Progenitor Differentiation and Transdifferentiation in Restoring β Cell Mass

In all forms of diabetes, β cell mass or function is reduced and therefore the capacity of the pancreatic cells for regeneration or replenishment is a critical need. Diverse lines of research have shown the capacity of endocrine as well as acinar, ductal and centroacinar cells to generate new β cells. Several experimental approaches using injury models, pharmacological or genetic interventions, isolation and in vitro expansion of putative progenitors followed by transplantations or a combination thereof have suggested several pathways for β cell neogenesis or regeneration. The experimental results have also generated controversy related to the limitations and interpretation of the experimental approaches and ultimately their physiological relevance, particularly when considering differences between mouse, the primary animal model, and human. As a result, consensus is lacking regarding the relative importance of islet cell proliferation or progenitor differentiation and transdifferentiation of other pancreatic cell types in generating new β cells. In this review we summarize and evaluate recent experimental approaches and findings related to islet regeneration and address their relevance and potential clinical application in the fight against diabetes.

Co-expression analysis of pancreatic cancer proteome reveals biology and prognostic biomarkers

PURPOSE

Despite extensive biological and clinical studies, including comprehensive genomic and transcriptomic profiling efforts, pancreatic ductal adenocarcinoma (PDAC) remains a devastating disease, with a poor survival and limited therapeutic options. The goal of this study was to assess co-expressed PDAC proteins and their associations with biological pathways and clinical parameters.

METHODS

Correlation network analysis is emerging as a powerful approach to infer tumor biology from omics data and to prioritize candidate genes as biomarkers or drug targets. In this study, we applied a weighted gene co-expression network analysis (WGCNA) to the proteome of 20 surgically resected PDAC specimens (PXD015744) and confirmed its clinical value in 82 independent primary cases.

RESULTS

Using WGCNA, we obtained twelve co-expressed clusters with a distinct biology. Notably, we found that one module enriched for metabolic processes and epithelial-mesenchymal-transition (EMT) was significantly associated with overall survival (p = 0.01) and disease-free survival (p = 0.03). The prognostic value of three proteins (SPTBN1, KHSRP and PYGL) belonging to this module was confirmed using immunohistochemistry in a cohort of 82 independent resected patients. Risk score evaluation of the prognostic signature confirmed its association with overall survival in multivariate analyses. Finally, immunofluorescence analysis confirmed co-expression of SPTBN1 and KHSRP in Hs766t PDAC cells.

CONCLUSIONS

Our WGCNA analysis revealed a PDAC module enriched for metabolic and EMT-associated processes. In addition, we found that three of the proteins involved were associated with PDAC survival.

Integrative analyses of biomarkers and pathways for adipose tissue after bariatric surgery

We explored potential biomarkers and molecular mechanisms regarding multiple benefits after bariatric surgery. Differentially expressed genes (DEGs) for subcutaneous adipose tissue (AT) after bariatric surgery were identified by analyzing two expression profiles from the GEO. Subsequently, enrichment analysis, GSEA, PPI network, and gene-microRNAs and gene-TFs networks were interrogated to identify hub genes and associated pathways. Co-expressed DEGs included one that was up-regulated and 22 that were down-regulated genes. The enrichment analyses indicated that down-regulated DEGs were significantly involved in inflammatory responses. GSEA provided comprehensive evidence that most genes enriched in pro-inflammation pathways, while gene-sets after surgery enriched in metabolism. We identified nine hub genes in the PPI network, most of which were validated as highly expressed and hypomethylated in obesity by Attie Lab Diabetes and DiseaseMeth databases, respectively. DGIdb was also applied to predict potential therapeutic agents that might reverse abnormally high hub gene expression. Bariatric surgery induces a significant shift from an obese pro-inflammatory state to an anti-inflammatory state, with improvement in adipocyte metabolic function – representing key mechanisms whereby AT function improves after bariatric surgery. Our study deepens a mechanistic understanding of the benefits of bariatric surgery and provides potential biomarkers or treatment targets for further research.