Type 2 diabetes and obesity induce similar transcriptional reprogramming in human myocytes

Skeletal muscle TET3 promotes insulin resistance through destabilisation of PGC-1α

AIM/HYPOTHESIS

The peroxisome proliferator-activated receptor-γ coactivator α (PGC-1α) plays a critical role in the maintenance of glucose, lipid and energy homeostasis by orchestrating metabolic programs in multiple tissues in response to environmental cues. In skeletal muscles, PGC-1α dysregulation has been associated with insulin resistance and type 2 diabetes but the underlying mechanisms have remained elusive. This research aims to understand the role of TET3, a member of the ten-eleven translocation (TET) family dioxygenases, in PGC-1α dysregulation in skeletal muscles in obesity and diabetes.

METHODS

TET expression levels in skeletal muscles were analysed in humans with or without type 2 diabetes, as well as in mouse models of high-fat diet (HFD)-induced or genetically induced (ob/ob) obesity/diabetes. Muscle-specific Tet3 knockout (mKD) mice were generated to study TET3's role in muscle insulin sensitivity. Genome-wide expression profiling (RNA-seq) of muscle tissues from wild-type (WT) and mKD mice was performed to mine deeper insights into TET3-mediated regulation of muscle insulin sensitivity. The correlation between PGC-1α and TET3 expression levels was investigated using muscle tissues and in vitro-derived myotubes. PGC-1α phosphorylation and degradation were analysed using in vitro assays.

RESULTS

TET3 expression was elevated in skeletal muscles of humans with type 2 diabetes and in HFD-fed and ob/ob mice compared with healthy controls. mKD mice exhibited enhanced glucose tolerance, insulin sensitivity and resilience to HFD-induced insulin resistance. Pathway analysis of RNA-seq identified 'Mitochondrial Function' and 'PPARα Pathway' to be among the top biological processes regulated by TET3. We observed higher PGC-1α levels (~25%) in muscles of mKD mice vs WT mice, and lower PGC-1α protein levels (~25-60%) in HFD-fed or ob/ob mice compared with their control counterparts. In human and murine myotubes, increased PGC-1α levels following TET3 knockdown contributed to improved mitochondrial respiration and insulin sensitivity. TET3 formed a complex with PGC-1α and interfered with its phosphorylation, leading to its destabilisation.

CONCLUSIONS/INTERPRETATION

Our results demonstrate an essential role for TET3 in the regulation of skeletal muscle insulin sensitivity and suggest that TET3 may be used as a potential therapeutic target for the metabolic syndrome.

DATA AVAILABILITY

Sequences are available from the Gene Expression Omnibus ( https://www.ncbi.nlm.nih.gov/geo/ ) with accession number of GSE224042.

Fat infiltration in skeletal muscle: Influential triggers and regulatory mechanism

Fat infiltration in skeletal muscle (also known as myosteatosis) is now recognized as a distinct disease from sarcopenia and is directly related to declining muscle capacity. Hence, understanding the origins and regulatory mechanisms of fat infiltration is vital for maintaining skeletal muscle development and improving human health. In this article, we summarized the triggering factors such as aging, metabolic diseases and metabolic syndromes, nonmetabolic diseases, and muscle injury that all induce fat infiltration in skeletal muscle. We discussed recent advances on the cellular origins of fat infiltration and found several cell types including myogenic cells and non-myogenic cells that contribute to myosteatosis. Furthermore, we reviewed the molecular regulatory mechanism, detection methods, and intervention strategies of fat infiltration in skeletal muscle. Based on the current findings, our review will provide new insight into regulating function and lipid metabolism of skeletal muscle and treating muscle-related diseases.

Hyperglycemia effect of Pinctada martensii hydrolysate in diabetic db/db mice

ETHNOPHARMACOLOGICAL RELEVANCE

Pinctada martensii (Dunker) and other marine shellfish flesh have been traditionally used in China as folk remedies regulate blood sugar.

AIM OF THE STUDY

To investigate the main active constituents and the pharmacological mechanism of Pinctada martensii flesh enzymatic hydrolysate (PMH) against T2DM.

MATERIALS AND METHODS

The hypoglycemic activity of enzymolysis peptides from Pinctada martensii was evaluated by using db/db mice, through the influence of glycemic index, blood lipid and key protein expression of PI3K-Akt pathway. In addition, label-free quantitative proteomics was used to screen the key proteins for Pinctada martensii hydrolysate (PMH) to improve T2DM, and Western blot and qRT-PCR were used to verify the expression difference of differential proteins at protein and mRNA levels between different groups.

RESULTS

PMH were prepared and characterized. In vivo investigations revealed that the PMH could regulate blood glucose and improve glucose tolerance and insulin tolerance, reduced serum total cholesterol, triglyceride, low-density lipoprotein cholesterol levels and increase high-density lipoprotein cholesterol levels in db/db mice. Western blot results showed that PMH could up-regulate IRS-1, P-PI3K/PI3K and P-Akt/Akt levels in db/db mice. Label-free quantitative proteomic approach was used to analyze the proteome in db/db mouse liver, 231 proteins were reversed significantly (p < 0.05), and these proteins were involved in oxidative phosphorylation, glycolysis/gluconeogenesis and other pathways. Further screened 15 proteins with FC > 1.2 could be enriched in the retinol metabolic pathway, and the proteins in this pathway were also verified.

CONCLUSIONS

PMH has hypoglycemic effect and can be used as a potential natural T2DM intervener. The hypoglycemic activity of PMH is related to its regulation of the PI3K/AKT pathway. The PI3K/AKT pathway and the retinol pathway are considered as another potential pathway for PMH to exert hypoglycemic effects.

Interactions among insulin resistance, epigenetics, and donor sex in gene expression regulation of iPSC-derived myoblasts

About 25% of people in the general population are insulin resistant, increasing the risk for type 2 diabetes (T2D) and metabolic disease. Transcriptomic analysis of induced pluripotent stem cells differentiated into myoblasts (iMyos) from insulin-resistant (I-Res) versus insulin-sensitive (I-Sen) nondiabetic individuals revealed that 306 genes increased and 271 genes decreased in expression in iMyos from I-Res donors with differences of 2-fold or more. Over 30 of the genes changed in I-Res iMyos were associated with T2D by SNPs and were functionally linked to insulin action and control of metabolism. Interestingly, we also identified more than 1,500 differences in gene expression that were dependent on the sex of the cell donor, some of which modified the insulin resistance effects. Many of these sex differences were associated with increased DNA methylation in cells from female donors and were reversed by 5-azacytidine. By contrast, the insulin sensitivity differences were not reversed and thus appear to reflect genetic or methylation-independent epigenetic effects.

A transcriptomic and proteomic atlas of obesity and type 2 diabetes in cynomolgus monkeys

Obesity and type 2 diabetes (T2D) remain major global healthcare challenges, and developing therapeutics necessitates using nonhuman primate models. Here, we present a transcriptomic and proteomic atlas of all the major organs of cynomolgus monkeys with spontaneous obesity or T2D in comparison to healthy controls. Molecular changes occur predominantly in the adipose tissues of individuals with obesity, while extensive expression perturbations among T2D individuals are observed in many tissues such as the liver and kidney. Immune-response-related pathways are upregulated in obesity and T2D, whereas metabolism and mitochondrial pathways are downregulated. Moreover, we highlight some potential therapeutic targets, including SLC2A1 and PCSK1 in obesity as well as SLC30A8 and SLC2A2 in T2D. Our study provides a resource for exploring the complex molecular mechanism of obesity and T2D and developing therapies for these diseases, with limitations including lack of hypothalamus, isolated islets of Langerhans, longitudinal data, and body fat percentage.

A holistic view of muscle metabolic reprogramming through personalized metabolic modeling in newly diagnosed diabetic patients

Type 2 diabetes mellitus (T2DM) is a challenging and progressive metabolic disease caused by insulin resistance. Skeletal muscle is the major insulin-sensitive tissue that plays a pivotal role in blood sugar homeostasis. Dysfunction of muscle metabolism is implicated in the disturbance of glucose homeostasis, the development of insulin resistance, and T2DM. Understanding metabolism reprogramming in newly diagnosed patients provides opportunities for early diagnosis and treatment of T2DM as a challenging disease to manage. Here, we applied a system biology approach to investigate metabolic dysregulations associated with the early stage of T2DM. We first reconstructed a human muscle-specific metabolic model. The model was applied for personalized metabolic modeling and analyses in newly diagnosed patients. We found that several pathways and metabolites, mainly implicating in amino acids and lipids metabolisms, were dysregulated. Our results indicated the significance of perturbation of pathways implicated in building membrane and extracellular matrix (ECM). Dysfunctional metabolism in these pathways possibly interrupts the signaling process and develops insulin resistance. We also applied a machine learning method to predict potential metabolite markers of insulin resistance in skeletal muscle. 13 exchange metabolites were predicted as the potential markers. The efficiency of these markers in discriminating insulin-resistant muscle was successfully validated.

Multidimensional conservation analysis decodes the expression of conserved long noncoding RNAs

Although long noncoding RNAs (lncRNAs) experience weaker evolutionary constraints and exhibit lower sequence conservation than coding genes, they can still conserve their features in various aspects. Here, we used multiple approaches to systemically evaluate the conservation between human and mouse lncRNAs from various dimensions including sequences, promoter, global synteny, and local synteny, which led to the identification of 1,731 conserved lncRNAs with 427 high-confidence ones meeting multiple criteria. Conserved lncRNAs, compared with non-conserved ones, generally have longer gene bodies, more exons and transcripts, stronger connections with human diseases, and are more abundant and widespread across different tissues. Transcription factor (TF) profile analysis revealed a significant enrichment of TF types and numbers in the promoters of conserved lncRNAs. We further identified a set of TFs that preferentially bind to conserved lncRNAs and exert stronger regulation on conserved than non-conserved lncRNAs. Our study has reconciled some discrepant interpretations of lncRNA conservation and revealed a new set of transcriptional factors ruling the expression of conserved lncRNAs.

Unraveling the Mechanism of Immunity and Inflammation Related to Molecular Signatures Crosstalk Among Obesity, T2D, and AD: Insights From Bioinformatics Approaches

Individuals with type 2 diabetes (T2D) and obesity have a higher risk of developing Alzheimer disease (AD), and increasing evidence indicates a link between impaired immune signaling pathways and the development of AD. However, the shared cellular mechanisms and molecular signatures among these 3 diseases remain unknown. The purpose of this study was to uncover similar molecular markers and pathways involved in obesity, T2D, and AD using bioinformatics and a network biology approach. First, we investigated the 3 RNA sequencing (RNA-seq) gene expression data sets and determined 224 commonly shared differentially expressed genes (DEGs) from obesity, T2D, and AD diseases. Gene ontology and pathway enrichment analyses revealed that mutual DEGs were mainly enriched with immune and inflammatory signaling pathways. In addition, we constructed a protein-protein interactions network for finding hub genes, which have not previously been identified as playing a critical role in these 3 diseases. Furthermore, the transcriptional factors and protein kinases regulating commonly shared DEGs among obesity, T2D, and AD were also identified. Finally, we suggested potential drug candidates as possible therapeutic interventions for 3 diseases. The results of this bioinformatics analysis provided a new understanding of the potential links between obesity, T2D, and AD pathologies.

Comparing the long non-coding RNA expression profiles of skeletal muscle and kidney tissues from patients with diabetes

Background

Diabetes causes the dysregulation of several organs, and these effects are often closely associated with changes in the expression of long non-coding RNAs (lncRNAs), a group of non-coding RNAs, within these tissues. Previous studies have described a variety of changes in the expression profile of several lncRNAs from different organs in response to the pathogenesis of diabetes. However, none of these studies compared the expression profiles of these lncRNAs between these organs. This study was designed to identify common and specific lncRNAs involved in the progression of diabetes in the skeletal muscles and kidneys.

Methods

Publicly available RNA sequencing data of diabetic patients was obtained from the Gene Expression Omnibus database. By analyzing the expression of lncRNAs in these datasets, differentially expressed lncRNAs in each tissue between non-diabetic and diabetic patients were identified. To identify any lncRNAs changed in common in both kidney and muscle tissues, those lncRNAs that are significantly dysregulated in both datasets were selected.

Results

These evaluations identified a series of novel lncRNAs unique to each organ and several transcripts that were common to both skeletal muscle and kidney tissues in these patients. Interestingly, the genomic location of these lncRNAs suggests that they reside in close proximity to several protein-coding genes known to be related to diabetes suggesting that these lncRNAs may have a regulatory relationship with their neighboring genes.

Conclusion

These results offer valuable insights into the role of lncRNAs during the pathogenesis of diabetes.

In vitro skeletal muscle models for type 2 diabetes

Type 2 diabetes mellitus, a metabolic disorder characterized by abnormally elevated blood sugar, poses a growing social, economic, and medical burden worldwide. The skeletal muscle is the largest metabolic organ responsible for glucose homeostasis in the body, and its inability to properly uptake sugar often precedes type 2 diabetes. Although exercise is known to have preventative and therapeutic effects on type 2 diabetes, the underlying mechanism of these beneficial effects is largely unknown. Animal studies have been conducted to better understand the pathophysiology of type 2 diabetes and the positive effects of exercise on type 2 diabetes. However, the complexity of in vivo systems and the inability of animal models to fully capture human type 2 diabetes genetics and pathophysiology are two major limitations in these animal studies. Fortunately, in vitro models capable of recapitulating human genetics and physiology provide promising avenues to overcome these obstacles. This review summarizes current in vitro type 2 diabetes models with focuses on the skeletal muscle, interorgan crosstalk, and exercise. We discuss diabetes, its pathophysiology, common in vitro type 2 diabetes skeletal muscle models, interorgan crosstalk type 2 diabetes models, exercise benefits on type 2 diabetes, and in vitro type 2 diabetes models with exercise.

Signaling and Gene Expression in Skeletal Muscles in Type 2 Diabetes: Current Results and OMICS Perspectives

Skeletal muscles mainly contribute to the emergence of insulin resistance, impaired glucose tolerance and the development of type 2 diabetes. Molecular mechanisms that regulate glucose uptake are diverse, including the insulin-dependent as most important, and others as also significant. They involve a wide range of proteins that control intracellular traffic and exposure of glucose transporters on the cell surface to create an extensive regulatory network. Here, we highlight advantages of the omics approaches to explore the insulin-regulated proteins and genes in human skeletal muscle with varying degrees of metabolic disorders. We discuss methodological aspects of the assessment of metabolic dysregulation and molecular responses of human skeletal muscle to insulin. The known molecular mechanisms of glucose uptake regulation and the first results of phosphoproteomic and transcriptomic studies are reviewed, which unveiled a large-scale array of insulin targets in muscle cells. They demonstrate that a clear depiction of changes that occur during metabolic dysfunction requires systemic and combined analysis at different levels of regulation, including signaling pathways, transcription factors, and gene expression. Such analysis seems promising to explore yet undescribed regulatory mechanisms of glucose uptake by skeletal muscle and identify the key regulators as potential therapeutic targets.

Nampt activator P7C3 ameliorates diabetes and improves skeletal muscle function modulating cell metabolism and lipid mediators

BACKGROUND

Nicotinamide phosphoribosyltransferase (Nampt), a key enzyme in NAD salvage pathway is decreased in metabolic diseases, and its precise role in skeletal muscle function is not known. We tested the hypothesis, Nampt activation by P7C3 (3,6-dibromo-α-[(phenylamino)methyl]-9H-carbazol-9-ethanol) ameliorates diabetes and muscle function.

METHODS

We assessed the functional, morphometric, biochemical, and molecular effects of P7C3 treatment in skeletal muscle of type 2 diabetic (db/db) mice. Nampt^+/- mice were utilized to test the specificity of P7C3.

RESULTS

Insulin resistance increased 1.6-fold in diabetic mice compared with wild-type mice and after 4 weeks treatment with P7C3 rescued diabetes (P < 0.05). In the db-P7C3 mice fasting blood glucose levels decreased to 0.96-fold compared with C57Bl/6J wild-type naïve control mice. The insulin and glucose tolerance tests blood glucose levels were decreased to 0.6-fold and 0.54-folds, respectively, at 120 min along with an increase in insulin secretion (1.76-fold) and pancreatic β-cells (3.92-fold) in db-P7C3 mice. The fore-limb and hind-limb grip strengths were increased to 1.13-fold and 1.17-fold, respectively, together with a 14.2-fold increase in voluntary running wheel distance in db-P7C3 mice. P7C3 treatment resulted in a 1.4-fold and 7.1-fold increase in medium-sized and larger-sized myofibres cross-sectional area, with a concomitant 0.5-fold decrease in smaller-sized myofibres of tibialis anterior (TA) muscle. The transmission electron microscopy images also displayed a 1.67-fold increase in myofibre diameter of extensor digitorum longus muscle along with 2.9-fold decrease in mitochondrial area in db-P7C3 mice compared with db-Veh mice. The number of SDH positive myofibres were increased to 1.74-fold in db-P7C3 TA muscles. The gastrocnemius and TA muscles displayed a decrease in slow oxidative myosin heavy chain type1 (MyHC1) myofibres expression (0.46-fold) and immunostaining (6.4-fold), respectively. qPCR analysis displayed a 2.9-fold and 1.3-fold increase in Pdk4 and Cpt1, and 0.55-fold and 0.59-fold decrease in Fgf21 and 16S in db-P7C3 mice. There was also a 3.3-fold and 1.9-fold increase in Fabp1 and CD36 in db-Veh mice. RNA-seq differential gene expression volcano plot displayed 1415 genes to be up-regulated and 1726 genes down-regulated (P < 0.05) in db-P7C3 mice. There was 1.02-fold increase in serum HDL, and 0.9-fold decrease in low-density lipoprotein/very low-density lipoprotein ratio in db-P7C3 mice. Lipid profiling of gastrocnemius muscle displayed a decrease in inflammatory lipid mediators n-6; AA (0.83-fold), and n-3; DHA (0.69-fold) and EPA (0.81-fold), and a 0.66-fold decrease in endocannabinoid 2-AG and 2.0-fold increase in AEA in db-P7C3 mice.

CONCLUSIONS

Overall, we demonstrate that P7C3 activates Nampt, improves type 2 diabetes and skeletal muscle function in db/db mice.

Epigenetic interaction between UTX and DNMT1 regulates diet-induced myogenic remodeling in brown fat

Brown adipocytes share the same developmental origin with skeletal muscle. Here we find that a brown adipocyte-to-myocyte remodeling also exists in mature brown adipocytes, and is induced by prolonged high fat diet (HFD) feeding, leading to brown fat dysfunction. This process is regulated by the interaction of epigenetic pathways involving histone and DNA methylation. In mature brown adipocytes, the histone demethylase UTX maintains persistent demethylation of the repressive mark H3K27me3 at Prdm16 promoter, leading to high Prdm16 expression. PRDM16 then recruits DNA methyltransferase DNMT1 to Myod1 promoter, causing Myod1 promoter hypermethylation and suppressing its expression. The interaction between PRDM16 and DNMT1 coordinately serves to maintain brown adipocyte identity while repressing myogenic remodeling in mature brown adipocytes, thus promoting their active brown adipocyte thermogenic function. Suppressing this interaction by HFD feeding induces brown adipocyte-to-myocyte remodeling, which limits brown adipocyte thermogenic capacity and compromises diet-induced thermogenesis, leading to the development of obesity.

RNA sequencing reveals potential interacting networks between the altered transcriptome and ncRNome in the skeletal muscle of diabetic mice

For a global epidemic like Type 2 diabetes mellitus (T2DM), while impaired gene regulation is identified as a primary cause of aberrant cellular physiology; in the past few years, non-coding RNAs (ncRNAs) have emerged as important regulators of cellular metabolism. However, there are no reports of comprehensive in-depth cross-talk between these regulatory elements and the potential consequences in the skeletal muscle during diabetes. Here, using RNA sequencing, we identified 465 mRNAs and 12 long non-coding RNAs (lncRNAs), to be differentially regulated in the skeletal muscle of diabetic mice and pathway enrichment analysis of these altered transcripts revealed pathways of insulin, FOXO and AMP-activated protein kinase (AMPK) signaling to be majorly over-represented. Construction of networks showed that these pathways significantly interact with each other that might underlie aberrant skeletal muscle metabolism during diabetes. Gene-gene interaction network depicted strong interactions among several differentially expressed genes (DEGs) namely, Prkab2, Irs1, Pfkfb3, Socs2 etc. Seven altered lncRNAs depicted multiple interactions with the altered transcripts, suggesting possible regulatory roles of these lncRNAs. Inverse patterns of expression were observed between several of the deregulated microRNAs (miRNAs) and the differentially expressed transcripts in the tissues. Towards validation, overexpression of miR-381-3p and miR-539-5p in skeletal muscle C2C12 cells significantly decreased the transcript levels of their targets, Nfkbia, Pik3r1 and Pi3kr1, Cdkn2d, respectively. Collectively, the findings provide a comprehensive understanding of the interactions and cross-talk between the ncRNome and transcriptome in the skeletal muscle during diabetes and put forth potential therapeutic options for improving insulin sensitivity.

Electrical Stimulation of Cultured Myotubes in vitro as a Model of Skeletal Muscle Activity: Current State and Future Prospects

Skeletal muscles comprise more than a third of human body mass and critically contribute to regulation of body metabolism. Chronic inactivity reduces metabolic activity and functional capacity of muscles, leading to metabolic and other disorders, reduced life quality and duration. Cellular models based on progenitor cells isolated from human muscle biopsies and then differentiated into mature fibers in vitro can be used to solve a wide range of experimental tasks. The review discusses the aspects of myogenesis dynamics and regulation, which might be important in the development of an adequate cell model. The main function of skeletal muscle is contraction; therefore, electrical stimulation is important for both successful completion of myogenesis and in vitro modeling of major processes induced in the skeletal muscle by acute or regular physical exercise. The review analyzes the drawbacks of such cellular model and possibilities for its optimization, as well as the prospects for its further application to address fundamental aspects of muscle physiology and biochemistry and explore cellular and molecular mechanisms of metabolic diseases.

Human type 2 diabetes mellitus-associated transcriptional disturbances in a high-sugar diet long-term exposed Drosophila melanogaster

Type 2 Diabetes mellitus (T2DM) is a multifactorial and polygenic disorder with the molecular bases still idiopathic. Experimental analyses and tests are quite limited upon human samples due to the access, variability of patient's conditions, and the size and complexity of the genome. Therefore, high-sugar diet exposure is commonly used for modeling T2DM in non-human animals, which includes invertebrate organisms like the fruit fly Drosophila melanogaster. Interestingly, high-sugar diet (HSD) induces delayed time for pupation and reduced viability in fruit fly larvae hatched from a 30% sucrose-containing medium (HSD-30%). Here we carried out an mRNA-deep sequencing study to identify differentially transcribed genes in adult fruit fly hatched and reared from an HSD-30%. Seven days after hatching, flies reared on control and HSD-30% were used to glucose and triglyceride level measurements and RNA extraction for sequencing. Remarkably, glucose levels were about 2-fold higher than the control group in fruit flies exposed to HSD-30%, whereas triglycerides levels increased 1.7-fold. After RNA-sequencing, we found that 13.5% of the genes were differentially transcribed in the dyslipidemic and hyperglycaemic insects. HSD-30% up-regulated genes involved in ribosomal biogenesis (e.g. dTOR, ERK and dS6K) and down-regulated genes involved in energetic process (e.g. Pfk, Gapdh1, and Pyk from pyruvate metabolism; kdn, Idh and Mdh2 from the citric acid cycle; ATPsynC and ATPsynẞ from ATP synthesis) and insect development. We found a remarkable down-regulation for Actin (Act88F) that likely impairs muscle development. Moreover, HSD-30% up-regulated both the insulin-like peptides 7 and 8 and down-regulated the insulin receptor substrate p53, isoform A and insulin-like peptide 6 genes, whose functional products are insulin signaling markers. All these features pointed together to a tightly correlation of the T2DM-like phenotype modeled by the D. melanogaster and an intricate array of phenomena, which includes energetic processes, muscle development, and ribosomal synthesis as that observed for the human pathology.

Genome-wide discovery of hidden genes mediating known drug-disease association using KDDANet

Many of genes mediating Known Drug-Disease Association (KDDA) are escaped from experimental detection. Identifying of these genes (hidden genes) is of great significance for understanding disease pathogenesis and guiding drug repurposing. Here, we presented a novel computational tool, called KDDANet, for systematic and accurate uncovering the hidden genes mediating KDDA from the perspective of genome-wide functional gene interaction network. KDDANet demonstrated the competitive performances in both sensitivity and specificity of identifying genes in mediating KDDA in comparison to the existing state-of-the-art methods. Case studies on Alzheimer's disease (AD) and obesity uncovered the mechanistic relevance of KDDANet predictions. Furthermore, when applied with multiple types of cancer-omics datasets, KDDANet not only recapitulated known genes mediating KDDAs related to cancer, but also revealed novel candidates that offer new biological insights. Importantly, KDDANet can be used to discover the shared genes mediating multiple KDDAs. KDDANet can be accessed at http://www.kddanet.cn and the code can be freely downloaded at https://github.com/huayu1111/KDDANet .

Molecular pathways behind acquired obesity: Adipose tissue and skeletal muscle multiomics in monozygotic twin pairs discordant for BMI

Tissue-specific mechanisms prompting obesity-related development complications in humans remain unclear. We apply multiomics analyses of subcutaneous adipose tissue and skeletal muscle to examine the effects of acquired obesity among 49 BMI-discordant monozygotic twin pairs. Overall, adipose tissue appears to be more affected by excess body weight than skeletal muscle. In heavier co-twins, we observe a transcriptional pattern of downregulated mitochondrial pathways in both tissues and upregulated inflammatory pathways in adipose tissue. In adipose tissue, heavier co-twins exhibit lower creatine levels; in skeletal muscle, glycolysis- and redox stress-related protein and metabolite levels remain higher. Furthermore, metabolomics analyses in both tissues reveal that several proinflammatory lipids are higher and six of the same lipid derivatives are lower in acquired obesity. Finally, in adipose tissue, but not in skeletal muscle, mitochondrial downregulation and upregulated inflammation are associated with a fatty liver, insulin resistance, and dyslipidemia, suggesting that adipose tissue dominates in acquired obesity.

Multiomics analyses of adipose tissue and skeletal muscle in BMI-discordant twins

Excess body weight downregulates mitochondrial pathways in both tissues

Excess body weight upregulates proinflammatory pathways in both tissues

Adipose tissue alterations are associated with metabolic health in acquired obesity

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Kdm6a suppresses the alternative activation of macrophages and impairs energy expenditure in obesity

Histone lysine demethylase 6a (Kdm6a) mediates the removal of repressive trimethylation from histone H3 lysine 27 (H3K27me3) to activate target gene expression. Obesity is associated with metabolic inflammation, and adipose tissue macrophages (ATMs) are key players orchestrating metabolic inflammation. However, it is still unclear whether the Kdm6a pathway in ATMs regulates energy homeostasis. Here, we identified Kdm6a as a critical epigenetic switch that modulates macrophage polarisation and further disrupts energy balance. Myeloid-specific Kdm6a knockout in Kdm6a^F/Y;Lyz2-Cre mice significantly reversed the high-fat diet (HFD)-induced M1–M2 imbalance in white adipose tissue (WAT) and blocked HFD-induced obesity. The brown adipose tissue (BAT) activity, WAT browning and energy expenditure were significantly increased in Kdm6a^F/Y;Lyz2-Cre mice. Furthermore, Kdm6a regulated the Ire1α expression in a demethylase activity-dependent manner and augmented the M2 polarisation of macrophages. Macrophage with higher Kdm6a significantly promotes adipogenesis in white adipocyte and inhibits thermogenesis in beige adipocytes. These results suggest that the Kdm6a in macrophages drives obesity and metabolic syndrome by impairing BAT activity and WAT differentiation.

Mitochondrial Function in Muscle Stem Cell Fates

Mitochondria are crucial organelles that control cellular metabolism through an integrated mechanism of energy generation via oxidative phosphorylation. Apart from this canonical role, it is also integral for ROS production, fatty acid metabolism and epigenetic remodeling. Recently, a role for the mitochondria in effecting stem cell fate decisions has gained considerable interest. This is important for skeletal muscle, which exhibits a remarkable property for regeneration following injury, owing to satellite cells (SCs), the adult myogenic stem cells. Mitochondrial function is associated with maintaining and dictating SC fates, linked to metabolic programming during quiescence, activation, self-renewal, proliferation and differentiation. Notably, mitochondrial adaptation might take place to alter SC fates and function in the presence of different environmental cues. This review dissects the contribution of mitochondria to SC operational outcomes, focusing on how their content, function, dynamics and adaptability work to influence SC fate decisions.

RNA-Seq analysis of the pathogenesis of STZ-induced male diabetic mouse liver

OBJECTIVE

Diabetes mellitus (DM) is a chronic disease characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. The liver is a key organ involved in glucose metabolism, and the major target proteins' changes in the pathogenesis are still unknown.

METHODS

A diabetic mouse model was induced by intraperitoneal injection of streptozotocin (STZ) solution and the RNA-Seq analysis was used to evaluate the transcription differences in the livers of diabetic mice of this study. And then, the differentially expressed genes were validated between a normal mouse group (n = 6) and a diabetic mouse group (n = 6) using quantitative real-time PCR (qRT-PCR) and Western blotting analysis. In addition, we also constructed protein-protein interacting (PPI) networks of up-regulated and down-regulated genes.

RESULTS

Transcriptome sequencing analysis revealed 370 up-regulated differentially expressed genes and 281 down-regulated differentially expressed genes in the diabetes model. The gene ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results showed that the differentially expressed genes were mainly involved in immunity, enzyme activity, metabolism, and steroid synthesis. PPI analysis results indicated that the main 15 core differential proteins (Cyp51a1, Acsl4, Ugt1a1, Stat1, Gsta2, Cbr1, Aldh1a1, Fasn, Ces1, Camk2b, Tap1, Egr1, Sqle, Lpin1, Fabp5) were involved in the pathogenesis of diabetes. The qRT-PCR results showed that expression changes of four genes (Acsl4, Stat1, Gsta2, Fabp5) were in different directions from those of RNA-Seq. Western blotting results indicated that Sqle expression change at the protein level was in opposition direction from qRT-PCR, and we speculated that Sqle may be involved in the post-transcriptional modification process.

CONCLUSIONS

Our data speculated that the pathogenesis of diabetes may be mediated mainly through steroid biosynthesis, metabolic processes, and immune responses. Further researches on these pathways may provide new targets for the prevention and treatment of diabetes.

Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform

We present Knowledge Engine for Genomics (KnowEnG), a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis, and expression signature analysis. The system specializes in "knowledge-guided" data mining and machine learning algorithms, in which user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge bases and encoded in a massive "Knowledge Network." KnowEnG adheres to "FAIR" principles (findable, accessible, interoperable, and reuseable): its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution, and are interoperable with other computing platforms. The analysis tools are made available through multiple access modes, including a web portal with specialized visualization modules. We demonstrate the KnowEnG system's potential value in democratization of advanced tools for the modern genomics era through several case studies that use its tools to recreate and expand upon the published analysis of cancer data sets.

Horizontal and vertical integrative analysis methods for mental disorders omics data

In recent biomedical studies, omics profiling has been extensively conducted on various types of mental disorders. In most of the existing analyses, a single type of mental disorder and a single type of omics measurement are analyzed. In the study of other complex diseases, integrative analysis, both vertical and horizontal integration, has been conducted and shown to bring significantly new insights into disease etiology, progression, biomarkers, and treatment. In this article, we showcase the applicability of integrative analysis to mental disorders. In particular, the horizontal integration of bipolar disorder and schizophrenia and the vertical integration of gene expression and copy number variation data are conducted. The analysis is based on the sparse principal component analysis, penalization, and other advanced statistical techniques. In data analysis, integration leads to biologically sensible findings, including the disease-related gene expressions, copy number variations, and their associations, which differ from the “benchmark” analysis. Overall, this study suggests the potential of integrative analysis in mental disorder research.

Dysregulated autophagy in muscle precursor cells from humans with type 2 diabetes

Autophagy is active during cellular remodeling including muscle differentiation. Muscle differentiation is dysregulated in type 2 diabetes and we therefore hypothesize that muscle precursor cells from people with type 2 diabetes (T2DM) have a dysregulation of their autophagy leading to impaired myogenesis. Muscle precursor cells were isolated from people with T2DM or healthy controls and differentiated in vitro. Autophagy marker levels were assessed by immunoblotting. Differentially expressed autophagy-related genes between healthy and T2DM groups were identified based on a previously published RNA-sequencing data-set, which we verified by RT-qPCR. siRNA was used to assess the function of differentially expressed autophagy genes. Basal autophagy increases during human muscle differentiation, while T2DM muscle cells have reduced levels of autophagy marker ATG7 and show a blunted response to starvation. Moreover, we demonstrate that the 3 non-canonical autophagy genes DRAM1, VAMP8 and TP53INP1 as differentially expressed between healthy and T2DM groups during myoblast differentiation, and that T53INP1 knock-down alters expression of both pro-and anti-apoptotic genes. In vitro differentiated T2DM muscle cells show differential expression of autophagy-related genes. These genes do not regulate myogenic transcription factors but may rather be involved in p53-associated myoblast apoptosis during early myogenesis.

Epigenetic Markers and Microbiota/Metabolite-Induced Epigenetic Modifications in the Pathogenesis of Obesity, Metabolic Syndrome, Type 2 Diabetes, and Non-alcoholic Fatty Liver Disease

PURPOSE OF REVIEW

The metabolic syndrome is a pathological state in which one of the key components is insulin resistance. A wide spectrum of body compartments is involved in its pathophysiology. Genetic and environmental factors such as diet and physical activity are both related to its etiology. Reversible modulation of gene expression without altering the DNA sequence, known as epigenetic modifications, has been shown to drive this complex metabolic cluster of conditions. Here, we aim to examine some of the recent research of specific epigenetically mediated mechanisms and microbiota-induced epigenetic modifications on the development of adipose tissue and obesity, β-cell dysfunction and diabetes, and hepatocytes and non-alcoholic fatty disease.

RECENT FINDINGS

DNA methylation patterns and histone modifications have been identified in this context; the integrated analysis of genome, epigenome, and transcriptome is likely to expand our knowledge of epigenetics in health and disease. Epigenetic modifications induced by diet-related microbiota or metabolites possibly contribute to the insulin-resistant state. The identification of epigenetic signatures on diabetes and obesity may give us the possibility of developing new interventions, prevention measures, and follow-up strategies.

Subject level clustering using a negative binomial model for small transcriptomic studies

Background

Unsupervised clustering represents one of the most widely applied methods in analysis of high-throughput ‘omics data. A variety of unsupervised model-based or parametric clustering methods and non-parametric clustering methods have been proposed for RNA-seq count data, most of which perform well for large samples, e.g. N ≥ 500. A common issue when analyzing limited samples of RNA-seq count data is that the data follows an over-dispersed distribution, and thus a Negative Binomial likelihood model is often used. Thus, we have developed a Negative Binomial model-based (NBMB) clustering approach for application to RNA-seq studies.

Results

We have developed a Negative Binomial Model-Based (NBMB) method to cluster samples using a stochastic version of the expectation-maximization algorithm. A simulation study involving various scenarios was completed to compare the performance of NBMB to Gaussian model-based or Gaussian mixture modeling (GMM). NBMB was also applied for the clustering of two RNA-seq studies; type 2 diabetes study (N = 96) and TCGA study of ovarian cancer (N = 295). Simulation results showed that NBMB outperforms GMM applied with different transformations in majority of scenarios with limited sample size. Additionally, we found that NBMB outperformed GMM for small clusters distance regardless of sample size. Increasing total number of genes with fixed proportion of differentially expressed genes does not change the outperformance of NBMB, but improves the overall performance of GMM. Analysis of type 2 diabetes and ovarian cancer tumor data with NBMB found good agreement with the reported disease subtypes and the gene expression patterns. This method is available in an R package on CRAN named NB.MClust.

Conclusion

Use of Negative Binomial model based clustering is advisable when clustering over dispersed RNA-seq count data.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2556-9) contains supplementary material, which is available to authorized users.

Obesity in Type 1 Diabetes: Pathophysiology, Clinical Impact, and Mechanisms

There has been an alarming increase in the prevalence of obesity in people with type 1 diabetes in recent years. Although obesity has long been recognized as a major risk factor for the development of type 2 diabetes and a catalyst for complications, much less is known about the role of obesity in the initiation and pathogenesis of type 1 diabetes. Emerging evidence suggests that obesity contributes to insulin resistance, dyslipidemia, and cardiometabolic complications in type 1 diabetes. Unique therapeutic strategies may be required to address these comorbidities within the context of intensive insulin therapy, which promotes weight gain. There is an urgent need for clinical guidelines for the prevention and management of obesity in type 1 diabetes. The development of these recommendations will require a transdisciplinary research strategy addressing metabolism, molecular mechanisms, lifestyle, neuropsychology, and novel therapeutics. In this review, the prevalence, clinical impact, energy balance physiology, and potential mechanisms of obesity in type 1 diabetes are described, with a special focus on the substantial gaps in knowledge in this field. Our goal is to provide a framework for the evidence base needed to develop type 1 diabetes-specific weight management recommendations that account for the competing outcomes of glycemic control and weight management.

Cysteine- and glycine-rich protein 3 regulates glucose homeostasis in skeletal muscle

Skeletal muscle is the major site of postprandial peripheral glucose uptake, but in obesity-induced insulin-resistant states insulin-stimulated glucose disposal is markedly impaired. Despite the importance of skeletal muscle in regulating glucose homeostasis, the specific transcriptional changes associated with insulin-sensitive vs. -resistant states in muscle remain to be fully elucidated. Herein, using an RNA-seq approach we identified 20 genes differentially expressed in an insulin-resistant state in skeletal muscle, including cysteine- and glycine-rich protein 3 ( Csrp3), which was highly expressed in insulin-sensitive conditions but significantly reduced in the insulin-resistant state. CSRP3 has diverse functional roles including transcriptional regulation, signal transduction, and cytoskeletal organization, but its role in glucose homeostasis has yet to be explored. Thus, we investigated the role of CSRP3 in the development of obesity-induced insulin resistance in vivo. High-fat diet-fed CSRP3 knockout (KO) mice developed impaired glucose tolerance and insulin resistance as well as increased inflammation in skeletal muscle compared with wild-type (WT) mice. CSRP3-KO mice had significantly impaired insulin signaling, decreased GLUT4 translocation to the plasma membrane, and enhanced levels of phospho-PKCα in muscle, which all contributed to reduced insulin-stimulated glucose disposal in muscle in HFD-fed KO mice compared with WT mice. CSRP3 is a highly inducible protein and its expression is acutely increased after fasting. After 24h fasting, glucose tolerance was significantly improved in WT mice, but this effect was blunted in CSRP3-KO mice. In summary, we identify a novel role for Csrp3 expression in skeletal muscle in the development of obesity-induced insulin resistance.