Transcriptome meta-analysis of peripheral lymphomononuclear cells indicates that gestational diabetes is closer to type 1 diabetes than to type 2 diabetes mellitus

Genome-wide association study suggests genetic candidate loci of insulin dysregulation in Finnhorses

Equine metabolic syndrome (EMS) is a common welfare problem in horses worldwide. It is characterized by insulin dysregulation (ID), predisposition to laminitis and often obesity. EMS is multifactorial by nature, with both the environment and genetics contributing to the phenotype. Environmental factors, such as feeding and exercise, can be controlled, thus forming the basis for treatment and prevention. Genetic factors, by contrast, are less well-known and not easily controllable. The aim of this study was to identify potential genetic loci influencing ID/EMS in Finnhorses. A single-breed (Finnhorse) case-control genome-wide association study (GWAS) of ID was conducted with controls that included age-appropriate non-ID horses. ID status was determined with an oral sugar test (OST) for fasted horses. Seventy-one Finnhorses participated (n = 34 ID, n = 37 control). DNA samples (hair roots) were genotyped for 65 157 single-nucleotide polymorphisms (SNPs) with the Illumina Equine SNP70 BeadChip, and these data were analysed for association and FST outliers with genomic tools. P-values that exceeded the suggestive threshold (P = 1.00 ×10-5) were found in SNP BIEC2_383954 (P = 3.45 ×10-6) in chromosome 17 and SNP BIEC2_312374 (P = 1.89 ×10-5) in chromosome 15. Hierarchical and Bayesian FST outlier tests also detected these SNPs. Potential candidate genes associated with the ID close to SNP BIEC2_383954, with functions in carbohydrate metabolism, were Arginine and Glutamate Rich 1 (ARGLU1) and Ephrin-B2 (EFNB2).

Overview of Transcriptomic Research on Type 2 Diabetes: Challenges and Perspectives

Type 2 diabetes (T2D) is a common chronic disease whose etiology is known to have a strong genetic component. Standard genetic approaches, although allowing for the detection of a number of gene variants associated with the disease as well as differentially expressed genes, cannot fully explain the hereditary factor in T2D. The explosive growth in the genomic sequencing technologies over the last decades provided an exceptional impetus for transcriptomic studies and new approaches to gene expression measurement, such as RNA-sequencing (RNA-seq) and single-cell technologies. The transcriptomic analysis has the potential to find new biomarkers to identify risk groups for developing T2D and its microvascular and macrovascular complications, which will significantly affect the strategies for early diagnosis, treatment, and preventing the development of complications. In this article, we focused on transcriptomic studies conducted using expression arrays, RNA-seq, and single-cell sequencing to highlight recent findings related to T2D and challenges associated with transcriptome experiments.

First trimester secreted Frizzled-Related Protein 4 and other adipokine serum concentrations in women developing gestational diabetes mellitus

Background

The aim of this study was to evaluate whether soluble frizzled-related protein 4 (sFRP4) concentration in the first trimester of pregnancy is individually, or in combination with Leptin, Chemerin and/or Adiponectin, associated with the development of gestational diabetes (GDM).

Methods

In a nested case-control study, 50 women with GDM who spontaneously conceived and delivered a live-born infant were matched with a total of 100 uncomplicated singleton control pregnancies based on body mass index (± 2 kg/m²), gestational age at sampling (exact day) and maternal age (± 2 years). In serum samples, obtained between 70–90 days gestational age, sFRP4, Chemerin, Leptin and Adiponectin concentrations were determined by ELISA. Statistical comparisons were performed using univariate and multi-variate logistic regression analysis after logarithmic transformation of the concentrations. Discrimination of the models was assessed by the area under the curve (AUC).

Results

First trimester sFRP4 concentrations were significantly increased in GDM cases (2.04 vs 1.93 ng/ml; p<0.05), just as Chemerin (3.19 vs 3.15 ng/ml; p<0.05) and Leptin (1.44 vs 1.32 ng/ml; p<0.01). Adiponectin concentrations were significantly decreased (2.83 vs 2.94 ng/ml; p<0.01) in GDM cases. Further analysis only showed a weak, though significant, correlation of sFRP4 with Chemerin (R² = 0.124; p<0.001) and Leptin (R² = 0.145; p<0.001), and Chemerin with Leptin (R² = 0.282; p<0.001) in the control group. In a multivariate logistic regression model of these four markers, only Adiponectin showed to be significantly associated with GDM (odds ratio 0.12, 95%CI 0.02–0.68). The AUC of this model was 0.699 (95%CI 0.605–0.793).

Conclusion

In the first trimester of pregnancy, a multi-marker model with sFRP4, Leptin, Chemerin and Adiponectin is associated with the development of GDM. Therefore, this panel seems to be an interesting candidate to further evaluate for prediction of GDM in a prospective study.

Bayesian variable selection for high dimensional predictors and self-reported outcomes

Background

The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to error. Diagnosis of silent events may also occur through the use of imperfect laboratory-based diagnostic tests. In this paper, we describe an approach for variable selection in high dimensional datasets for settings in which the outcome is observed with error.

Methods

We adapt the spike and slab Bayesian Variable Selection approach in the context of error-prone, self-reported outcomes. The performance of the proposed approach is studied through simulation studies. An illustrative application is included using data from the Women’s Health Initiative SNP Health Association Resource, which includes extensive genotypic (>900,000 SNPs) and phenotypic data on 9,873 African American and Hispanic American women.

Results

Simulation studies show improved sensitivity of our proposed method when compared to a naive approach that ignores error in the self-reported outcomes. Application of the proposed method resulted in discovery of several single nucleotide polymorphisms (SNPs) that are associated with risk of type 2 diabetes in a dataset of 9,873 African American and Hispanic participants in the Women’s Health Initiative. There was little overlap among the top ranking SNPs associated with type 2 diabetes risk between the racial groups, adding support to previous observations in the literature of disease associated genetic loci that are often not generalizable across race/ethnicity populations. The adapted Bayesian variable selection algorithm is implemented in R. The source code for the simulations are available in the Supplement.

Conclusions

Variable selection accuracy is reduced when the outcome is ascertained by error-prone self-reports. For this setting, our proposed algorithm has improved variable selection performance when compared to approaches that neglect to account for the error-prone nature of self-reports.

Bioinformatics enrichment analysis of genes and pathways related to maternal type 1 diabetes associated with adverse fetal outcomes

Maternal type 1 diabetes mellitus (T1DM) may affect fetal development by altering the gene expression profile of the umbilical cord. The present study aimed to explore the T1DM-induced gene expression changes in the fetal umbilical cord. The raw gene expression profiles (ID: GSE51546) of umbilical cord tissue obtained from six normal mothers (non-diabetic) and six type 1 diabetic mothers were used to identify the differentially expressed genes. Genes that correspond to official gene symbols were selected for protein-protein interaction (PPI) and sub-network construction (combined score > 0.4). Functional annotation for Gene Ontology (GO) and pathway enrichment analysis were performed for genes involved in networking. A total of 110 differentially expressed genes were identified of which 38 were up-regulated while 72 were down-regulated. Only 37 genes were identified to significantly interact with each other. Hub genes including HSPA4, KCTD6, UBE2G1, FBXL19, and EHMT1 were up-regulated while KBTBD7, TRIM32, and NUP were down-regulated. T1DM had a major effect on the expression of genes involved in cellular death and differentiation, cell signaling and communication, protein modification and regulation of GTPase activity. Total 27 pathways were enriched and genes related to Wnt signaling, VEGF signaling, inflammation mediated by chemokine and cytokine signaling pathways, FGF signaling pathways and GnRH receptor pathways were found significantly affected by T1DM. Our results suggest that the T1DM environment seems to alter umbilical cord gene expression involved in the regulation of pathophysiology of the diabetic mother which in turn may lead to long-term consequences in various tissues in infants. This study provides insight into the molecular mechanism underlying the adverse pregnancy outcomes of maternal T1DM.

Circulating miR-330-3p in Late Pregnancy is Associated with Pregnancy Outcomes Among Lean Women with GDM

Gestational Diabetes Mellitus (GDM) is characterised by insulin resistance accompanied by reduced beta-cell compensation to increased insulin demand, typically observed in the second and third trimester and associated with adverse pregnancy outcomes. There is a need for a biomarker that can accurately monitor status and predict outcome in GDM, reducing foetal-maternal morbidity and mortality risks. To this end, circulating microRNAs (miRNAs) present themselves as promising candidates, stably expressed in serum and known to play crucial roles in regulation of glucose metabolism. We analysed circulating miRNA profiles in a cohort of GDM patients (n = 31) and nondiabetic controls (n = 29) during the third trimester for miRNA associated with insulin-secretory defects and glucose homeostasis. We identified miR-330-3p as being significantly upregulated in lean women with GDM compared to nondiabetic controls. Furthermore, increased levels of miR-330-3p were associated with better response to treatment (diet vs. insulin), with lower levels associated with exogenous insulin requirement. We observed miR-330-3p to be significantly related to the percentage of caesarean deliveries, with miR-330-3p expression significantly higher in spontaneously delivered GDM patients. We report this strong novel association of circulating miR-330-3p with risk of primary caesarean delivery as a pregnancy outcome linked with poor maternal glycaemic control, strengthening the growing body of evidence for roles of diabetes-associated miRNAs in glucose homeostasis and adaptation to the complex changes related to pregnancy.

Ventricular Zone Expressed PH Domain Containing 1 (VEPH1): an adaptor protein capable of modulating multiple signaling transduction pathways during normal and pathological development

Ventricular Zone Expressed PH Domain-Containing 1 (VEPH1) is an 833-amino acid protein encoded by an evolutionarily conserved single-copy gene that emerged with pseudocoelomates. This gene has no paralog in any species identified to date and few studies have investigated the function of its encoded protein. Loss of expression of its ortholog, melted, in Drosophila results in a severe neural phenotype and impacts TOR, FoxO, and Hippo signaling. Studies in mammals indicate a role for VEPH1 in modulating TGFβ signaling and AKT activation, while numerous studies indicate VEPH1 expression is altered in several pathological conditions, including cancer. Although often referred to as an uncharacterized protein, available evidence supports VEPH1 as an adaptor protein capable of modulating multiple signal transduction networks. Further studies are required to define these adaptor functions and the role of VEPH1 in development and disease progression.

Transcriptomic hallmarks of bone remodelling revealed by RNA-Seq profiling in blood of Arabian horses during racing training regime

The impact of exercises on young developing organisms is still of interest to researchers. Similarly like Thoroughbreds, Arabian horses competing at the race track. The high percent of lameness and loss of days in training are often the result of weakness in the condition of the musculoskeletal system. The objective of the presented study was to identify by RNA-Seq method, the possible skeletal system originating transcriptomic profile in peripheral blood of Arabian horses undergoing race training. Obtained results showed that one of the most significantly deregulated pathway involved in bone homeostasis was those involved in osteoclast differentiation. Among the significantly expressed molecules, we recognized twelve genes potentially involved in the metabolism of the skeletal system: BGLAP, CTSK, TYROBP, PDLIM7, SLC9B2, TWSG1, NOTCH2, IL6ST, VAV3, NFATc1, CLEC5A, TXLNG. The panel of identified genes should be evaluated as candidate biomarkers for bone homeostasis indicators of Arabians performing on race tracks to assess bone remodelling states during training for race track competitions.

Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells

Human peripheral blood mononuclear cells (PBMCs) are the key drivers of the immune responses. These cells undergo activation, proliferation and differentiation into various subsets. During these processes they initiate metabolic reprogramming, which is coordinated by specific gene and protein activities. PBMCs as a model system have been widely used to study metabolic and autoimmune diseases. Herein we review various omics and systems-based approaches such as transcriptomics, epigenomics, proteomics, and metabolomics as applied to PBMCs, particularly T helper subsets, that unveiled disease markers and the underlying mechanisms. We also discuss and emphasize several aspects of T cell metabolic modeling in healthy and disease states using genome-scale metabolic models.

Clinical Recommendations for the Use of Islet Cell Autoantibodies to Distinguish Autoimmune and Non-Autoimmune Gestational Diabetes

Gestational diabetes mellitus (GDM) is defined as carbohydrate intolerance that begins or is first recognized during pregnancy. The prevalence of GDM is highly variable, depending on the population studied, and reflects the underlying pattern of diabetes in the population. GDM manifests by the second half of pregnancy and disappears following delivery in most cases, but is associated with the risk of subsequent diabetes development. Normal pregnancy induces carbohydrate intolerance to favor the availability of nutrients for the fetus, which is compensated by increased insulin secretion from the maternal pancreas. Pregnancy shares similarities with adiposity in metabolism to save energy, and both conditions favor the development of insulin resistance (IR) and low-grade inflammation. A highly complicated network of modified regulatory mechanisms may primarily affect carbohydrate metabolism by promoting autoimmune reactions to pancreatic β cells and affecting insulin function. As a result, diabetes development during pregnancy is facilitated. Depending on a pregnant woman's genetic susceptibility to diabetes, autoimmune mechanisms or IR are fundamental to the development autoimmune or non-autoimmune GDM, respectively. Pregnancy may facilitate the identification of women at risk of developing diabetes later in life; autoimmune and non-autoimmune GDM may be early markers of the risk of future type 1 and type 2 diabetes, respectively. The most convenient and efficient way to discriminate GDM types is to assess pancreatic β-cell autoantibodies along with diagnosing diabetes in pregnancy.

Hyperglycemia-induced changes in miRNA expression patterns in epicardial adipose tissue of piglets

MicroRNAs (miRNAs) are a class of molecular posttranscriptional regulators found to participate in numerous biological mechanisms, such as adipogenesis, fat deposition, or glucose metabolism. Additionally, a detailed analysis on the molecular and cellular mechanisms of miRNA-related effects on metabolism leads to developing novel diagnostic markers and therapeutic approaches. To identify miRNA whose activity changed in epicardial adipose tissue in piglets during hyperglycemia, we analyzed the different miRNA expression patterns between control and hyperglycemia groups. The microarray analysis selected three differentially expressed microRNAs as potential biomarkers: hsa-miR-675-5p, ssc-miR-193a-3p, and hsa-miR-144-3p. The validation of miRNA expression with real-time PCR indicated an increased expression levels of ssc-miR-193a-3p and miR-675-5p, whereas the expression level of hsa-miR-144-3p was lower in epicardial adipose tissue in response to hyperglycemia (P<0.01). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses suggested that these miRNAs differentially expressed between hyperglycemic and control piglets are involved in insulin, adipocytokine, and phosphatidylinositol 3-kinase-Akt signaling pathways, and development of type 2 diabetes as well. The results suggested that hyperglycemia can significantly affect the expression patterns of miRNA in porcine adipose tissue.

Buckwheat as a Functional Food and Its Effects on Health

Buckwheat (BW) is a gluten-free pseudocereal that belongs to the Polygonaceae family. BW grain is a highly nutritional food component that has been shown to provide a wide range of beneficial effects. Health benefits attributed to BW include plasma cholesterol level reduction, neuroprotection, anticancer, anti-inflammatory, antidiabetic effects, and improvement of hypertension conditions. In addition, BW has been reported to possess prebiotic and antioxidant activities. In vitro and animal studies suggest that BW's bioactive compounds, such as D-chiro-inositol (DCI), BW proteins (BWP), and BW flavonoids (mainly rutin and quercetin) may be partially responsible for the observed effects. The purpose of this paper is to review the recent research regarding the health benefits of BW, in vitro and in vivo, focusing on the specific role of its bioactive compounds and on the mechanisms by which these effects are exerted.

A genomewide association mapping study using ultrasound-scanned information identifies potential genomic regions and candidate genes affecting carcass traits in Nellore cattle

The aim of this study was to identify candidate genes and genomic regions associated with ultrasound-derived measurements of the rib-eye area (REA), backfat thickness (BFT) and rumpfat thickness (RFT) in Nellore cattle. Data from 640 Nellore steers and young bulls with genotypes for 290 863 single nucleotide polymorphisms (SNPs) were used for genomewide association mapping. Significant SNP associations were explored to find possible candidate genes related to physiological processes. Several of the significant markers detected were mapped onto functional candidate genes including ARFGAP3, CLSTN2 and DPYD for REA; OSBPL3 and SUDS3 for BFT; and RARRES1 and VEPH1 for RFT. The physiological pathway related to lipid metabolism (CLSTN2, OSBPL3, RARRES1 and VEPH1) was identified. The significant markers within previously reported QTLs reinforce the importance of the genomic regions, and the other loci offer candidate genes that have not been related to carcass traits in previous investigations.

Assessment of immune status using blood transcriptomics and potential implications for global health

The immune system plays a key role in health maintenance and pathogenesis of a wide range of diseases. Leukocytes that are present in the blood convey valuable information about the status of the immune system. Blood transcriptomics, which consists in profiling blood transcript abundance on genome-wide scales, has gained in popularity over the past several years. Indeed, practicality and simplicity largely makes up for what this approach may lack in terms of cell population-level resolution. An extensive survey of the literature reveals increasingly widespread use across virtually all fields of medicine as well as across a number of different animal species, including model organisms but also animals of economical importance. Dissemination across such a wide range of disciplines holds the promise of adding a new perspective, breadth or context, to the considerable depth afforded by whole genome profiling of blood transcript abundance. Indeed, it is only through such contextualization that a truly global perspective will be gained from the use of systems approaches. Also discussed are opportunities that may arise for the fields of immunology and medicine from using blood transcriptomics as a common denominator for developing interactions and cooperation across fields of research that have traditionally been and largely remain compartmentalized. Finally, an argument is made for building immunology research capacity using blood transcriptomics platforms in low-resource and high-disease burden settings.

Integrative analysis of the transcriptome profiles observed in type 1, type 2 and gestational diabetes mellitus reveals the role of inflammation

BACKGROUND

Type 1 diabetes (T1D) is an autoimmune disease, while type 2 (T2D) and gestational diabetes (GDM) are considered metabolic disturbances. In a previous study evaluating the transcript profiling of peripheral mononuclear blood cells obtained from T1D, T2D and GDM patients we showed that the gene profile of T1D patients was closer to GDM than to T2D. To understand the influence of demographical, clinical, laboratory, pathogenetic and treatment features on the diabetes transcript profiling, we performed an analysis integrating these features with the gene expression profiles of the annotated genes included in databases containing information regarding GWAS and immune cell expression signatures.

METHODS

Samples from 56 (19 T1D, 20 T2D, and 17 GDM) patients were hybridized to whole genome one-color Agilent 4x44k microarrays. Non-informative genes were filtered by partitioning, and differentially expressed genes were obtained by rank product analysis. Functional analyses were carried out using the DAVID database, and module maps were constructed using the Genomica tool.

RESULTS

The functional analyses were able to discriminate between T1D and GDM patients based on genes involved in inflammation. Module maps of differentially expressed genes revealed that modulated genes: i) exhibited transcription profiles typical of macrophage and dendritic cells; ii) had been previously associated with diabetic complications by association and by meta-analysis studies, and iii) were influenced by disease duration, obesity, number of gestations, glucose serum levels and the use of medications, such as metformin.

CONCLUSION

This is the first module map study to show the influence of epidemiological, clinical, laboratory, immunopathogenic and treatment features on the transcription profiles of T1D, T2D and GDM patients.

Identifying common and specific microRNAs expressed in peripheral blood mononuclear cell of type 1, type 2, and gestational diabetes mellitus patients

Background

Regardless the regulatory function of microRNAs (miRNA), their differential expression pattern has been used to define miRNA signatures and to disclose disease biomarkers. To address the question of whether patients presenting the different types of diabetes mellitus could be distinguished on the basis of their miRNA and mRNA expression profiling, we obtained peripheral blood mononuclear cell (PBMC) RNAs from 7 type 1 (T1D), 7 type 2 (T2D), and 6 gestational diabetes (GDM) patients, which were hybridized to Agilent miRNA and mRNA microarrays. Data quantification and quality control were obtained using the Feature Extraction software, and data distribution was normalized using quantile function implemented in the Aroma light package. Differentially expressed miRNAs/mRNAs were identified using Rank products, comparing T1DxGDM, T2DxGDM and T1DxT2D. Hierarchical clustering was performed using the average linkage criterion with Pearson uncentered distance as metrics.

Results

The use of the same microarrays platform permitted the identification of sets of shared or specific miRNAs/mRNA interaction for each type of diabetes. Nine miRNAs (hsa-miR-126, hsa-miR-1307, hsa-miR-142-3p, hsa-miR-142-5p, hsa-miR-144, hsa-miR-199a-5p, hsa-miR-27a, hsa-miR-29b, and hsa-miR-342-3p) were shared among T1D, T2D and GDM, and additional specific miRNAs were identified for T1D (20 miRNAs), T2D (14) and GDM (19) patients. ROC curves allowed the identification of specific and relevant (greater AUC values) miRNAs for each type of diabetes, including: i) hsa-miR-1274a, hsa-miR-1274b and hsa-let-7f for T1D; ii) hsa-miR-222, hsa-miR-30e and hsa-miR-140-3p for T2D, and iii) hsa-miR-181a and hsa-miR-1268 for GDM. Many of these miRNAs targeted mRNAs associated with diabetes pathogenesis.

Conclusions

These results indicate that PBMC can be used as reporter cells to characterize the miRNA expression profiling disclosed by the different diabetes mellitus manifestations. Shared miRNAs may characterize diabetes as a metabolic and inflammatory disorder, whereas specific miRNAs may represent biological markers for each type of diabetes, deserving further attention.