Disrupted hypothalamic transcriptomics and proteomics in a mouse model of type 2 diabetes exposed to recurrent hypoglycaemia.

AIMS/HYPOTHESIS: Repeated exposures to insulin-induced hypoglycaemia in people with diabetes progressively impairs the counterregulatory response (CRR) that restores normoglycaemia. This defect is characterised by reduced secretion of glucagon and other counterregulatory hormones. Evidence indicates that glucose-responsive neurons located in the hypothalamus orchestrate the CRR. Here, we aimed to identify the changes in hypothalamic gene and protein expression that underlie impaired CRR in a mouse model of defective CRR. METHODS: High-fat-diet fed and low-dose streptozocin-treated C57BL/6N mice were exposed to one (acute hypoglycaemia [AH]) or multiple (recurrent hypoglycaemia [RH]) insulin-induced hypoglycaemic episodes and plasma glucagon levels were measured. Single-nuclei RNA-seq (snRNA-seq) data were obtained from the hypothalamus and cortex of mice exposed to AH and RH. Proteomic data were obtained from hypothalamic synaptosomal fractions. RESULTS: The final insulin injection resulted in similar plasma glucose levels in the RH group and AH groups, but glucagon secretion was significantly lower in the RH group (AH: 94.5±9.2 ng/l [n=33]; RH: 59.0±4.8 ng/l [n=37]; p<0.001). Analysis of snRNA-seq data revealed similar proportions of hypothalamic cell subpopulations in the AH- and RH-exposed mice. Changes in transcriptional profiles were found in all cell types analysed. In neurons from RH-exposed mice, we observed a significant decrease in expression of Avp, Pmch and Pcsk1n, and the most overexpressed gene was Kcnq1ot1, as compared with AH-exposed mice. Gene ontology analysis of differentially expressed genes (DEGs) indicated a coordinated decrease in many oxidative phosphorylation genes and reduced expression of vacuolar H+- and Na+/K+-ATPases; these observations were in large part confirmed in the proteomic analysis of synaptosomal fractions. Compared with AH-exposed mice, oligodendrocytes from RH-exposed mice had major changes in gene expression that suggested reduced myelin formation. In astrocytes from RH-exposed mice, DEGs indicated reduced capacity for neurotransmitters scavenging in tripartite synapses as compared with astrocytes from AH-exposed mice. In addition, in neurons and astrocytes, multiple changes in gene expression suggested increased amyloid beta (Aß) production and stability. The snRNA-seq analysis of the cortex showed that the adaptation to RH involved different biological processes from those seen in the hypothalamus. CONCLUSIONS/INTERPRETATION: The present study provides a model of defective counterregulation in a mouse model of type 2 diabetes. It shows that repeated hypoglycaemic episodes induce multiple defects affecting all hypothalamic cell types and their interactions, indicative of impaired neuronal network signalling and dysegulated hypoglycaemia sensing, and displaying features of neurodegenerative diseases. It also shows that repeated hypoglycaemia leads to specific molecular adaptation in the hypothalamus when compared with the cortex. DATA AVAILABILITY: The transcriptomic dataset is available via the GEO ( http://www.ncbi.nlm.nih.gov/geo/ ), using the accession no. GSE226277. The proteomic dataset is available via the ProteomeXchange data repository ( http://www.proteomexchange.org ), using the accession no. PXD040183.

Risk factors and prediction of hypoglycaemia using the Hypo-RESOLVE cohort: a secondary analysis of pooled data from insulin clinical trials.

AIMS/HYPOTHESIS: The objective of the Hypoglycaemia REdefining SOLutions for better liVES (Hypo-RESOLVE) project is to use a dataset of pooled clinical trials across pharmaceutical and device companies in people with type 1 or type 2 diabetes to examine factors associated with incident hypoglycaemia events and to quantify the prediction of these events. METHODS: Data from 90 trials with 46,254 participants were pooled. Analyses were done for type 1 and type 2 diabetes separately. Poisson mixed models, adjusted for age, sex, diabetes duration and trial identifier were fitted to assess the association of clinical variables with hypoglycaemia event counts. Tree-based gradient-boosting algorithms (XGBoost) were fitted using training data and their predictive performance in terms of area under the receiver operating characteristic curve (AUC) evaluated on test data. Baseline models including age, sex and diabetes duration were compared with models that further included a score of hypoglycaemia in the first 6 weeks from study entry, and full models that included further clinical variables. The relative predictive importance of each covariate was assessed using XGBoost's importance procedure. Prediction across the entire trial duration for each trial (mean of 34.8 weeks for type 1 diabetes and 25.3 weeks for type 2 diabetes) was assessed. RESULTS: For both type 1 and type 2 diabetes, variables associated with more frequent hypoglycaemia included female sex, white ethnicity, longer diabetes duration, treatment with human as opposed to analogue-only insulin, higher glucose variability, higher score for hypoglycaemia across the 6 week baseline period, lower BP, lower lipid levels and treatment with psychoactive drugs. Prediction of any hypoglycaemia event of any severity was greater than prediction of hypoglycaemia requiring assistance (level 3 hypoglycaemia), for which events were sparser. For prediction of level 1 or worse hypoglycaemia during the whole follow-up period, the AUC was 0.835 (95% CI 0.826, 0.844) in type 1 diabetes and 0.840 (95% CI 0.831, 0.848) in type 2 diabetes. For level 3 hypoglycaemia, the AUC was lower at 0.689 (95% CI 0.667, 0.712) for type 1 diabetes and 0.705 (95% CI 0.662, 0.748) for type 2 diabetes. Compared with the baseline models, almost all the improvement in prediction could be captured by the individual's hypoglycaemia history, glucose variability and blood glucose over a 6 week baseline period. CONCLUSIONS/INTERPRETATION: Although hypoglycaemia rates show large variation according to sociodemographic and clinical characteristics and treatment history, looking at a 6 week period of hypoglycaemia events and glucose measurements predicts future hypoglycaemia risk.

An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study.

AIMS/HYPOTHESIS: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. METHODS: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel's C statistic. RESULTS: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0-11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3-11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. CONCLUSIONS/INTERPRETATION: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. DATA AVAILABILITY: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch .

BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus.

Novel biomarkers are key to addressing the ongoing pandemic of type 2 diabetes mellitus. While new technologies have improved the potential of identifying such biomarkers, at the same time there is an increasing need for informed prioritization to ensure efficient downstream verification. We have built BALDR, an automated pipeline for biomarker comparison and prioritization in the context of diabetes. BALDR includes protein, gene, and disease data from major public repositories, text-mining data, and human and mouse experimental data from the IMI2 RHAPSODY consortium. These data are provided as easy-to-read figures and tables enabling direct comparison of up to 20 biomarker candidates for diabetes through the public website https://baldr.cpr.ku.dk.

Type I interferons protect T cells against NK cell attack mediated by the activating receptor NCR1.

Direct type I interferon (IFN) signaling on T cells is necessary for the proper expansion, differentiation, and survival of responding T cells following infection with viruses prominently inducing type I IFN. The reasons for the abortive response of T cells lacking the type I IFN receptor (Ifnar1(-/-)) remain unclear. We report here that Ifnar1(-/-) T cells were highly susceptible to natural killer (NK) cell-mediated killing in a perforin-dependent manner. Depletion of NK cells prior to lymphocytic choriomeningitis virus (LCMV) infection completely restored the early expansion of Ifnar1(-/-) T cells. Ifnar1(-/-) T cells had elevated expression of natural cytotoxicity triggering receptor 1 (NCR1) ligands upon infection, rendering them targets for NCR1 mediated NK cell attack. Thus, direct sensing of type I IFNs by T cells protects them from NK cell killing by regulating the expression of NCR1 ligands, thereby revealing a mechanism by which T cells can evade the potent cytotoxic activity of NK cells.

MetaNetX/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models.

MetaNetX/MNXref is a reconciliation of metabolites and biochemical reactions providing cross-links between major public biochemistry and Genome-Scale Metabolic Network (GSMN) databases. The new release brings several improvements with respect to the quality of the reconciliation, with particular attention dedicated to preserving the intrinsic properties of GSMN models. The MetaNetX website (https://www.metanetx.org/) provides access to the full database and online services. A major improvement is for mapping of user-provided GSMNs to MXNref, which now provides diagnostic messages about model content. In addition to the website and flat files, the resource can now be accessed through a SPARQL endpoint (https://rdf.metanetx.org).

Sexually dimorphic roles for the type 2 diabetes-associated C2cd4b gene in murine glucose homeostasis.

AIMS/HYPOTHESIS: Variants close to the VPS13C/C2CD4A/C2CD4B locus are associated with altered risk of type 2 diabetes in genome-wide association studies. While previous functional work has suggested roles for VPS13C and C2CD4A in disease development, none has explored the role of C2CD4B. METHODS: CRISPR/Cas9-induced global C2cd4b-knockout mice and zebrafish larvae with c2cd4a deletion were used to study the role of this gene in glucose homeostasis. C2 calcium dependent domain containing protein (C2CD)4A and C2CD4B constructs tagged with FLAG or green fluorescent protein were generated to investigate subcellular dynamics using confocal or near-field microscopy and to identify interacting partners by mass spectrometry. RESULTS: Systemic inactivation of C2cd4b in mice led to marked, but highly sexually dimorphic changes in body weight and glucose homeostasis. Female C2cd4b mice displayed unchanged body weight compared with control littermates, but abnormal glucose tolerance (AUC, p = 0.01) and defective in vivo, but not in vitro, insulin secretion (p = 0.02). This was associated with a marked decrease in follicle-stimulating hormone levels as compared with wild-type (WT) littermates (p = 0.003). In sharp contrast, male C2cd4b null mice displayed essentially normal glucose tolerance but an increase in body weight (p < 0.001) and fasting blood glucose (p = 0.003) after maintenance on a high-fat and -sucrose diet vs WT littermates. No metabolic disturbances were observed after global inactivation of C2cd4a in mice, or in pancreatic beta cell function at larval stages in C2cd4a null zebrafish. Fasting blood glucose levels were also unaltered in adult C2cd4a-null fish. C2CD4B and C2CD4A were partially localised to the plasma membrane, with the latter under the control of intracellular Ca2+. Binding partners for both included secretory-granule-localised PTPRN2/phogrin. CONCLUSIONS/INTERPRETATION: Our studies suggest that C2cd4b may act centrally in the pituitary to influence sex-dependent circuits that control pancreatic beta cell function and glucose tolerance in rodents. However, the absence of sexual dimorphism in the impact of diabetes risk variants argues for additional roles for C2CD4A or VPS13C in the control of glucose homeostasis in humans. DATA AVAILABILITY: The datasets generated and/or analysed during the current study are available in the Biorxiv repository ( www.biorxiv.org/content/10.1101/2020.05.18.099200v1 ). RNA-Seq (GSE152576) and proteomics (PXD021597) data have been deposited to GEO ( www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152576 ) and ProteomeXchange ( www.ebi.ac.uk/pride/archive/projects/PXD021597 ) repositories, respectively.

Replication and cross-validation of type 2 diabetes subtypes based on clinical variables: an IMI-RHAPSODY study.

AIMS/HYPOTHESIS: Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic. METHODS: In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort's cluster centres. Finally, we compared the time to insulin requirement for each cluster. RESULTS: Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6-90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression. CONCLUSIONS/INTERPRETATION: Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA1c, HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration.

A systems genetics resource and analysis of sleep regulation in the mouse.

Sleep is essential for optimal brain functioning and health, but the biological substrates through which sleep delivers these beneficial effects remain largely unknown. We used a systems genetics approach in the BXD genetic reference population (GRP) of mice and assembled a comprehensive experimental knowledge base comprising a deep "sleep-wake" phenome, central and peripheral transcriptomes, and plasma metabolome data, collected under undisturbed baseline conditions and after sleep deprivation (SD). We present analytical tools to interactively interrogate the database, visualize the molecular networks altered by sleep loss, and prioritize candidate genes. We found that a one-time, short disruption of sleep already extensively reshaped the systems genetics landscape by altering 60%-78% of the transcriptomes and the metabolome, with numerous genetic loci affecting the magnitude and direction of change. Systems genetics integrative analyses drawing on all levels of organization imply α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor trafficking and fatty acid turnover as substrates of the negative effects of insufficient sleep. Our analyses demonstrate that genetic heterogeneity and the effects of insufficient sleep itself on the transcriptome and metabolome are far more widespread than previously reported.

Decreased STARD10 Expression Is Associated with Defective Insulin Secretion in Humans and Mice.

Genetic variants near ARAP1 (CENTD2) and STARD10 influence type 2 diabetes (T2D) risk. The risk alleles impair glucose-induced insulin secretion and, paradoxically but characteristically, are associated with decreased proinsulin:insulin ratios, indicating improved proinsulin conversion. Neither the identity of the causal variants nor the gene(s) through which risk is conferred have been firmly established. Whereas ARAP1 encodes a GTPase activating protein, STARD10 is a member of the steroidogenic acute regulatory protein (StAR)-related lipid transfer protein family. By integrating genetic fine-mapping and epigenomic annotation data and performing promoter-reporter and chromatin conformational capture (3C) studies in ß cell lines, we localize the causal variant(s) at this locus to a 5 kb region that overlaps a stretch-enhancer active in islets. This region contains several highly correlated T2D-risk variants, including the rs140130268 indel. Expression QTL analysis of islet transcriptomes from three independent subject groups demonstrated that T2D-risk allele carriers displayed reduced levels of STARD10 mRNA, with no concomitant change in ARAP1 mRNA levels. Correspondingly, ß-cell-selective deletion of StarD10 in mice led to impaired glucose-stimulated Ca2+ dynamics and insulin secretion and recapitulated the pattern of improved proinsulin processing observed at the human GWAS signal. Conversely, overexpression of StarD10 in the adult ß cell improved glucose tolerance in high fat-fed animals. In contrast, manipulation of Arap1 in ß cells had no impact on insulin secretion or proinsulin conversion in mice. This convergence of human and murine data provides compelling evidence that the T2D risk associated with variation at this locus is mediated through reduction in STARD10 expression in the ß cell.

Inferring gene expression networks with hubs using a degree weighted Lasso approach.

MOTIVATION: Genome-scale gene networks contain regulatory genes called hubs that have many interaction partners. These genes usually play an essential role in gene regulation and cellular processes. Despite recent advancements in high-throughput technology, inferring gene networks with hub genes from high-dimensional data still remains a challenging problem. Novel statistical network inference methods are needed for efficient and accurate reconstruction of hub networks from high-dimensional data. RESULTS: To address this challenge we propose DW-Lasso, a degree weighted Lasso (least absolute shrinkage and selection operator) method which infers gene networks with hubs efficiently under the low sample size setting. Our network reconstruction approach is formulated as a two stage procedure: first, the degree of networks is estimated iteratively, and second, the gene regulatory network is reconstructed using degree information. A useful property of the proposed method is that it naturally favors the accumulation of neighbors around hub genes and thereby helps in accurate modeling of the high-throughput data under the assumption that the underlying network exhibits hub structure. In a simulation study, we demonstrate good predictive performance of the proposed method in comparison to traditional Lasso type methods in inferring hub and scale-free graphs. We show the effectiveness of our method in an application to microarray data of Escherichia coli and RNA sequencing data of Kidney Clear Cell Carcinoma from The Cancer Genome Atlas datasets. AVAILABILITY AND IMPLEMENTATION: Under the GNU General Public Licence at https://cran.r-project.org/package=DWLasso. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prognostic imaging biomarkers for diabetic kidney disease (iBEAt): study protocol.

BACKGROUND: Diabetic kidney disease (DKD) remains one of the leading causes of premature death in diabetes. DKD is classified on albuminuria and reduced kidney function (estimated glomerular filtration rate (eGFR)) but these have modest value for predicting future renal status. There is an unmet need for biomarkers that can be used in clinical settings which also improve prediction of renal decline on top of routinely available data, particularly in the early stages. The iBEAt study of the BEAt-DKD project aims to determine whether renal imaging biomarkers (magnetic resonance imaging (MRI) and ultrasound (US)) provide insight into the pathogenesis and heterogeneity of DKD (primary aim) and whether they have potential as prognostic biomarkers in DKD (secondary aim). METHODS: iBEAt is a prospective multi-centre observational cohort study recruiting 500 patients with type 2 diabetes (T2D) and eGFR ≥30 ml/min/1.73m2. At baseline, blood and urine will be collected, clinical examinations will be performed, and medical history will be obtained. These assessments will be repeated annually for 3 years. At baseline each participant will also undergo quantitative renal MRI and US with central processing of MRI images. Biological samples will be stored in a central laboratory for biomarker and validation studies, and data in a central data depository. Data analysis will explore the potential associations between imaging biomarkers and renal function, and whether the imaging biomarkers improve the prediction of DKD progression. Ancillary substudies will: (1) validate imaging biomarkers against renal histopathology; (2) validate MRI based renal blood flow measurements against H2O15 positron-emission tomography (PET); (3) validate methods for (semi-)automated processing of renal MRI; (4) examine longitudinal changes in imaging biomarkers; (5) examine whether glycocalyx and microvascular measures are associated with imaging biomarkers and eGFR decline; (6) explore whether the findings in T2D can be extrapolated to type 1 diabetes. DISCUSSION: iBEAt is the largest DKD imaging study to date and will provide valuable insights into the progression and heterogeneity of DKD. The results may contribute to a more personalised approach to DKD management in patients with T2D. TRIAL REGISTRATION: Clinicaltrials.gov ( NCT03716401 ).

Protective role of the ELOVL2/docosahexaenoic acid axis in glucolipotoxicity-induced apoptosis in rodent beta cells and human islets.

AIMS/HYPOTHESIS: Dietary n-3 polyunsaturated fatty acids, especially docosahexaenoic acid (DHA), are known to influence glucose homeostasis. We recently showed that Elovl2 expression in beta cells, which regulates synthesis of endogenous DHA, was associated with glucose tolerance and played a key role in insulin secretion. The present study aimed to examine the role of the very long chain fatty acid elongase 2 (ELOVL2)/DHA axis on the adverse effects of palmitate with high glucose, a condition defined as glucolipotoxicity, on beta cells. METHODS: We detected ELOVL2 in INS-1 beta cells and mouse and human islets using quantitative PCR and western blotting. Downregulation and adenoviral overexpression of Elovl2 was carried out in beta cells. Ceramide and diacylglycerol levels were determined by radio-enzymatic assay and lipidomics. Apoptosis was quantified using caspase-3 assays and poly (ADP-ribose) polymerase cleavage. Palmitate oxidation and esterification were determined by [U-14C]palmitate labelling. RESULTS: We found that glucolipotoxicity decreased ELOVL2 content in rodent and human beta cells. Downregulation of ELOVL2 drastically potentiated beta cell apoptosis induced by glucolipotoxicity, whereas adenoviral Elovl2 overexpression and supplementation with DHA partially inhibited glucolipotoxicity-induced cell death in rodent and human beta cells. Inhibition of beta cell apoptosis by the ELOVL2/DHA axis was associated with a decrease in ceramide accumulation. However, the ELOVL2/DHA axis was unable to directly alter ceramide synthesis or metabolism. By contrast, DHA increased palmitate oxidation but did not affect its esterification. Pharmacological inhibition of AMP-activated protein kinase and etomoxir, an inhibitor of carnitine palmitoyltransferase 1 (CPT1), the rate-limiting enzyme in fatty acid ß-oxidation, attenuated the protective effect of the ELOVL2/DHA axis during glucolipotoxicity. Downregulation of CPT1 also counteracted the anti-apoptotic action of the ELOVL2/DHA axis. By contrast, a mutated active form of Cpt1 inhibited glucolipotoxicity-induced beta cell apoptosis when ELOVL2 was downregulated. CONCLUSIONS/INTERPRETATION: Our results identify ELOVL2 as a critical pro-survival enzyme for preventing beta cell death and dysfunction induced by glucolipotoxicity, notably by favouring palmitate oxidation in mitochondria through a CPT1-dependent mechanism.

Systems biology of the IMIDIA biobank from organ donors and pancreatectomised patients defines a novel transcriptomic signature of islets from individuals with type 2 diabetes.

AIMS/HYPOTHESIS: Pancreatic islet beta cell failure causes type 2 diabetes in humans. To identify transcriptomic changes in type 2 diabetic islets, the Innovative Medicines Initiative for Diabetes: Improving beta-cell function and identification of diagnostic biomarkers for treatment monitoring in Diabetes (IMIDIA) consortium ( www.imidia.org ) established a comprehensive, unique multicentre biobank of human islets and pancreas tissues from organ donors and metabolically phenotyped pancreatectomised patients (PPP). METHODS: Affymetrix microarrays were used to assess the islet transcriptome of islets isolated either by enzymatic digestion from 103 organ donors (OD), including 84 non-diabetic and 19 type 2 diabetic individuals, or by laser capture microdissection (LCM) from surgical specimens of 103 PPP, including 32 non-diabetic, 36 with type 2 diabetes, 15 with impaired glucose tolerance (IGT) and 20 with recent-onset diabetes (<1 year), conceivably secondary to the pancreatic disorder leading to surgery (type 3c diabetes). Bioinformatics tools were used to (1) compare the islet transcriptome of type 2 diabetic vs non-diabetic OD and PPP as well as vs IGT and type 3c diabetes within the PPP group; and (2) identify transcription factors driving gene co-expression modules correlated with insulin secretion ex vivo and glucose tolerance in vivo. Selected genes of interest were validated for their expression and function in beta cells. RESULTS: Comparative transcriptomic analysis identified 19 genes differentially expressed (false discovery rate ≤0.05, fold change ≥1.5) in type 2 diabetic vs non-diabetic islets from OD and PPP. Nine out of these 19 dysregulated genes were not previously reported to be dysregulated in type 2 diabetic islets. Signature genes included TMEM37, which inhibited Ca2+-influx and insulin secretion in beta cells, and ARG2 and PPP1R1A, which promoted insulin secretion. Systems biology approaches identified HNF1A, PDX1 and REST as drivers of gene co-expression modules correlated with impaired insulin secretion or glucose tolerance, and 14 out of 19 differentially expressed type 2 diabetic islet signature genes were enriched in these modules. None of these signature genes was significantly dysregulated in islets of PPP with impaired glucose tolerance or type 3c diabetes. CONCLUSIONS/INTERPRETATION: These studies enabled the stringent definition of a novel transcriptomic signature of type 2 diabetic islets, regardless of islet source and isolation procedure. Lack of this signature in islets from PPP with IGT or type 3c diabetes indicates differences possibly due to peculiarities of these hyperglycaemic conditions and/or a role for duration and severity of hyperglycaemia. Alternatively, these transcriptomic changes capture, but may not precede, beta cell failure.

New molecular insights into modulation of platelet reactivity in aspirin-treated patients using a network-based approach.

Platelet reactivity (PR) is variable between individuals and modulates clinical outcome in cardiovascular (CV) patients treated with antiplatelet drugs. Although several data point to a genetic control of platelet reactivity, the genes contributing to the modulation of this phenotype are not clearly identified. Integration of data derived from high-throughput technologies may yield novel insights into the molecular mechanisms that govern platelet reactivity. The aim of this study is to identify candidate genes modulating platelet reactivity in aspirin-treated CV patients using an integrative network-based approach. Patients with extreme high (n = 6) or low PR (n = 6) were selected and data derived from quantitative proteomic of platelets and platelet sub-cellular fractions, as well as from transcriptomic analysis were integrated with a network biology approach. Two modules within the network containing 123 and 182 genes were identified. We then specifically assessed the level of miRNAs in these two groups of patients. Among the 12 miRNAs differentially expressed, 2 (miR-135a-5p and miR-204-5p) correlated with PR. The predicted targets of these miRNAs were mapped onto the network, allowing the identification of seven overlapping genes (THBS1, CDC42, CORO1C, SPTBN1, TPM3, GTPBP2, and MAPRE2), suggesting a synergistic effect of these two miRNAs on these predicted targets. Integration of several omics data sets allowed the identification of 2 candidate miRNAs and 7 candidate genes regulating platelet reactivity in aspirin-treated CV patients.

Angiogenic activity of breast cancer patients' monocytes reverted by combined use of systems modeling and experimental approaches.

Angiogenesis plays a key role in tumor growth and cancer progression. TIE-2-expressing monocytes (TEM) have been reported to critically account for tumor vascularization and growth in mouse tumor experimental models, but the molecular basis of their pro-angiogenic activity are largely unknown. Moreover, differences in the pro-angiogenic activity between blood circulating and tumor infiltrated TEM in human patients has not been established to date, hindering the identification of specific targets for therapeutic intervention. In this work, we investigated these differences and the phenotypic reversal of breast tumor pro-angiogenic TEM to a weak pro-angiogenic phenotype by combining Boolean modelling and experimental approaches. Firstly, we show that in breast cancer patients the pro-angiogenic activity of TEM increased drastically from blood to tumor, suggesting that the tumor microenvironment shapes the highly pro-angiogenic phenotype of TEM. Secondly, we predicted in silico all minimal perturbations transitioning the highly pro-angiogenic phenotype of tumor TEM to the weak pro-angiogenic phenotype of blood TEM and vice versa. In silico predicted perturbations were validated experimentally using patient TEM. In addition, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM are plastic cells and can be reverted to immunological potent monocytes. Finally, the relapse-free survival analysis showed a statistically significant difference between patients with tumors with high and low expression values for genes encoding transitioning proteins detected in silico and validated on patient TEM. In conclusion, the inferred TEM regulatory network accurately captured experimental TEM behavior and highlighted crosstalk between specific angiogenic and inflammatory signaling pathways of outstanding importance to control their pro-angiogenic activity. Results showed the successful in vitro reversion of such an activity by perturbation of in silico predicted target genes in tumor derived TEM, and indicated that targeting tumor TEM plasticity may constitute a novel valid therapeutic strategy in breast cancer.

Extensive remodeling of DC function by rapid maturation-induced transcriptional silencing.

The activation, or maturation, of dendritic cells (DCs) is crucial for the initiation of adaptive T-cell mediated immune responses. Research on the molecular mechanisms implicated in DC maturation has focused primarily on inducible gene-expression events promoting the acquisition of new functions, such as cytokine production and enhanced T-cell-stimulatory capacity. In contrast, mechanisms that modulate DC function by inducing widespread gene-silencing remain poorly understood. Yet the termination of key functions is known to be critical for the function of activated DCs. Genome-wide analysis of activation-induced histone deacetylation, combined with genome-wide quantification of activation-induced silencing of nascent transcription, led us to identify a novel inducible transcriptional-repression pathway that makes major contributions to the DC-maturation process. This silencing response is a rapid primary event distinct from repression mechanisms known to operate at later stages of DC maturation. The repressed genes function in pivotal processes--including antigen-presentation, extracellular signal detection, intracellular signal transduction and lipid-mediator biosynthesis--underscoring the central contribution of the silencing mechanism to rapid reshaping of DC function. Interestingly, promoters of the repressed genes exhibit a surprisingly high frequency of PU.1-occupied sites, suggesting a novel role for this lineage-specific transcription factor in marking genes poised for inducible repression.

Genome-wide profiling of the cardiac transcriptome after myocardial infarction identifies novel heart-specific long non-coding RNAs.

AIM: Heart disease is recognized as a consequence of dysregulation of cardiac gene regulatory networks. Previously, unappreciated components of such networks are the long non-coding RNAs (lncRNAs). Their roles in the heart remain to be elucidated. Thus, this study aimed to systematically characterize the cardiac long non-coding transcriptome post-myocardial infarction and to elucidate their potential roles in cardiac homoeostasis. METHODS AND RESULTS: We annotated the mouse transcriptome after myocardial infarction via RNA sequencing and ab initio transcript reconstruction, and integrated genome-wide approaches to associate specific lncRNAs with developmental processes and physiological parameters. Expression of specific lncRNAs strongly correlated with defined parameters of cardiac dimensions and function. Using chromatin maps to infer lncRNA function, we identified many with potential roles in cardiogenesis and pathological remodelling. The vast majority was associated with active cardiac-specific enhancers. Importantly, oligonucleotide-mediated knockdown implicated novel lncRNAs in controlling expression of key regulatory proteins involved in cardiogenesis. Finally, we identified hundreds of human orthologues and demonstrate that particular candidates were differentially modulated in human heart disease. CONCLUSION: These findings reveal hundreds of novel heart-specific lncRNAs with unique regulatory and functional characteristics relevant to maladaptive remodelling, cardiac function and possibly cardiac regeneration. This new class of molecules represents potential therapeutic targets for cardiac disease. Furthermore, their exquisite correlation with cardiac physiology renders them attractive candidate biomarkers to be used in the clinic.