The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches,... Show moreThe presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired 1 beta cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments. Show less
Background The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be... Show moreBackground The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D. Methods Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts. Results We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling. Conclusions Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D. Show less
Kidney fibrosis is the common pathophysiological mechanism in end-stage renal disease characterized by excessive accumulation of myofibroblast-derived extracellular matrix. Natriuretic peptides... Show moreKidney fibrosis is the common pathophysiological mechanism in end-stage renal disease characterized by excessive accumulation of myofibroblast-derived extracellular matrix. Natriuretic peptides have been demonstrated to have cyclic guanosine monophosphate (cGMP)-dependent anti-fibrotic properties likely due to interference with pro-fibrotic tissue growth factor beta (TGF-beta) signaling. However, in vivo, natriuretic peptides are rapidly degraded by neutral endopeptidases (NEP). In a unilateral ureteral obstruction (UUO) mouse model for kidney fibrosis we assessed the anti-fibrotic effects of SOL1, an orally active compound that inhibits NEP and endothelin-converting enzyme (ECE). Mice (n= 10 per group) subjected to UUO were treated for 1 week with either solvent, NEP-/ECE-inhibitor SOL1 (two doses), reference NEP-inhibitor candoxatril or the angiotensin II receptor type 1 (AT1)-antagonist losartan. While NEP-inhibitors had no significant effect on blood pressure, they did increase urinary cGMP levels as well as endothelin-1 (ET-1) levels. Immunohistochemical staining revealed a marked decrease in renal collagen (similar to 55% reduction, P< 0.05) and alpha-smooth muscle actin (alpha-SMA; similar to 40% reduction, P< 0.05). Moreover, the number of alpha-SMA positive cells in the kidneys of SOL1-treated groups inversely correlated with cGMP levels consistent with a NEP-dependent anti-fibrotic effect. To dissect the molecular mechanisms associated with the anti-fibrotic effects of NEP inhibition, we performed a 'deep serial analysis of gene expression (Deep SAGE)' transcriptome and targeted metabolomics analysis of total kidneys of all treatment groups. Pathway analyses linked increased cGMP and ET-1 levels with decreased nuclear receptor signaling (peroxisome proliferator-activated receptor [PPAR] and liver X receptor/retinoid X receptor [LXR/RXR] signaling) and actin cytoskeleton organization. Taken together, although our transcriptome and metabolome data indicate metabolic dysregulation, our data support the therapeutic potential of NEP inhibition in the treatment of kidney fibrosis via cGMP elevation and reduced myofibroblast formation. Show less
Molnos, S.; Wahl, S.; Haid, M.; Eekhoff, E.M.W.; Pool, R.; Floegel, A.; ... ; Hart, L.M. 't 2018
Introduction Metabolic changes have been frequently associated with Huntington's disease (HD). At the same time peripheral blood represents aminimally invasive sampling avenue with little distress... Show moreIntroduction Metabolic changes have been frequently associated with Huntington's disease (HD). At the same time peripheral blood represents aminimally invasive sampling avenue with little distress to Huntington's disease patients especially when brain or other tissue samples are difficult to collect.Objectives We investigated the levels of 163 metabolites in HD patient and control serum samples in order to identify disease related changes. Additionally, we integrated the metabolomics data with our previously published next generation sequencing-based gene expression data from the same patients in order to interconnect the metabolomics changes with transcriptional alterations. Methods This analysis was performed using targeted metabolomics and flow injection electrospray ionization tandem mass spectrometry in 133 serum samples from 97 Huntington's disease patients (29 pre-symptomatic and 68 symptomatic) and 36 controls.Results By comparing HD mutation carriers with controls we identified 3 metabolites significantly changed in HD (serine and threonine and one phosphatidylcholine-PC ae C36:0) and an additional 8 phosphatidylcholines (PC aa C38:6, PC aa C36:0, PC ae C38:0, PC aa C38:0, PC ae C38:6, PC ae C42:0, PC aa C36:5 and PC ae C36:0) that exhibited a significant association with disease severity. Using workflow based exploitation of pathway databases and by integrating our metabolomics data with our gene expression data from the same patients we identified 4 deregulated phosphatidylcholine metabolism related genes (ALDH1B1, MBOAT1, MTRR and PLB1) that showed significant association with the changes in metabolite concentrations.Conclusion Our results support the notion that phosphatidylcholine metabolism is deregulated in HD blood and that these metabolite alterations are associated with specific gene expression changes. Show less
Mastrokolias, A.; Pool, R.; Mina, E.; Hettne, K.M.; Duijn, E. van; Mast, R.C. van der; ... ; Roon-Mom, W. van 2016
IntroductionMetabolic changes have been frequently associated with Huntington’s disease (HD). At the same time peripheral blood represents a minimally invasive sampling avenue with little distress... Show moreIntroductionMetabolic changes have been frequently associated with Huntington’s disease (HD). At the same time peripheral blood represents a minimally invasive sampling avenue with little distress to Huntington’s disease patients especially when brain or other tissue samples are difficult to collect.ObjectivesWe investigated the levels of 163 metabolites in HD patient and control serum samples in order to identify disease related changes. Additionally, we integrated the metabolomics data with our previously published next generation sequencing-based gene expression data from the same patients in order to interconnect the metabolomics changes with transcriptional alterations.MethodsThis analysis was performed using targeted metabolomics and flow injection electrospray ionization tandem mass spectrometry in 133 serum samples from 97 Huntington’s disease patients (29 pre-symptomatic and 68 symptomatic) and 36 controls.ResultsBy comparing HD mutation carriers with controls we identified 3 metabolites significantly changed in HD (serine and threonine and one phosphatidylcholine—PC ae C36:0) and an additional 8 phosphatidylcholines (PC aa C38:6, PC aa C36:0, PC ae C38:0, PC aa C38:0, PC ae C38:6, PC ae C42:0, PC aa C36:5 and PC ae C36:0) that exhibited a significant association with disease severity. Using workflow based exploitation of pathway databases and by integrating our metabolomics data with our gene expression data from the same patients we identified 4 deregulated phosphatidylcholine metabolism related genes (ALDH1B1, MBOAT1, MTRR and PLB1) that showed significant association with the changes in metabolite concentrations.ConclusionOur results support the notion that phosphatidylcholine metabolism is deregulated in HD blood and that these metabolite alterations are associated with specific gene expression changes. Show less
Berg, R. van den; Mook-Kanamori, D.O.; Donga, E.; Dijk, M. van; Dijk, J.G. van; Lammers, G.J.; ... ; Biermasz, N.R. 2016
