Cardiac Actions of a Small Molecule Inhibitor Targeting GATA4-NKX2-5 Interaction

Transcription factors are fundamental regulators of gene transcription, and many diseases, such as heart diseases, are associated with deregulation of transcriptional networks. In the adult heart, zinc-finger transcription factor GATA4 is a critical regulator of cardiac repair and remodelling. Previous studies also suggest that NKX2-5 plays function role as a cofactor of GATA4. We have recently reported the identification of small molecules that either inhibit or enhance the GATA4-NKX2-5 transcriptional synergy. Here, we examined the cardiac actions of a potent inhibitor (3i-1000) of GATA4-NKX2-5 interaction in experimental models of myocardial ischemic injury and pressure overload. In mice after myocardial infarction, 3i-1000 significantly improved left ventricular ejection fraction and fractional shortening, and attenuated myocardial structural changes. The compound also improved cardiac function in an experimental model of angiotensin II-mediated hypertension in rats. Furthermore, the up-regulation of cardiac gene expression induced by myocardial infarction and ischemia reduced with treatment of 3i-1000 or when micro-and nanoparticles loaded with 3i-1000 were injected intramyocardially or intravenously, respectively. The compound inhibited stretch- and phenylephrine-induced hypertrophic response in neonatal rat cardiomyocytes. These results indicate significant potential for small molecules targeting GATA4-NKX2-5 interaction to promote myocardial repair after myocardial infarction and other cardiac injuries

Lipoprotein signatures of cholesteryl ester transfer protein and HMG-CoA reductase inhibition

Cholesteryl ester transfer protein (CETP) inhibition reduces vascular event risk, but confusion surrounds its effects on low-density lipoprotein (LDL) cholesterol. Here, we clarify associations of genetic inhibition of CETP on detailed lipoprotein measures and compare those to genetic inhibition of 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR). We used an allele associated with lower CETP expression (rs247617) to mimic CETP inhibition and an allele associated with lower HMGCR expression (rs12916) to mimic the well-known effects of statins for comparison. The study consists of 65,427 participants of European ancestries with detailed lipoprotein subclass profiling from nuclear magnetic resonance spectroscopy. Genetic associations were scaled to 10% reduction in relative risk of coronary heart disease (CHD). We also examined observational associations of the lipoprotein subclass measures with risk of incident CHD in 3 population-based cohorts totalling 616 incident cases and 13,564 controls during 8-year follow-up. Genetic inhibition of CETP and HMGCR resulted in near-identical associations with LDL cholesterol concentration estimated by the Friedewald equation. Inhibition of HMGCR had relatively consistent associations on lower cholesterol concentrations across all apolipoprotein B-containing lipoproteins. In contrast, the associations of the inhibition of CETP were stronger on lower remnant and very-low-density lipoprotein (VLDL) cholesterol, but there were no associations on cholesterol concentrations in LDL defined by particle size (diameter 18-26 nm) (-0.02 SD LDL defined by particle size; 95% CI: -0.10 to 0.05 for CETP versus -0.24 SD, 95% CI -0.30 to -0.18 for HMGCR). Inhibition of CETP was strongly associated with lower proportion of triglycerides in all high-density lipoprotein (HDL) particles. In observational analyses, a higher triglyceride composition within HDL subclasses was associated with higher risk of CHD, independently of total cholesterol and triglycerides (strongest hazard ratio per 1 SD higher triglyceride composition in very large HDL 1.35; 95% CI: 1.18-1.54). In conclusion, CETP inhibition does not appear to affect size-specific LDL cholesterol but is likely to lower CHD risk by lowering concentrations of other atherogenic, apolipoprotein B-containing lipoproteins (such as remnant and VLDLs). Inhibition of CETP also lowers triglyceride composition in HDL particles, a phenomenon reflecting combined effects of circulating HDL, triglycerides, and apolipoprotein B-containing particles and is associated with a lower CHD risk in observational analyses. Our results reveal that conventional composite lipid assays may mask heterogeneous effects of emerging lipid-altering therapies.</p

Lipoprotein Signatures of Cholesteryl Ester Transfer Protein and HMG-CoA Reductase Inhibition

BACKGROUND: CETP inhibition reduces vascular event rates but confusion surrounds its low-density lipoprotein (LDL)-cholesterol effects. We sought to clarify associations of genetic inhibition of CETP on detailed lipoproteins. METHODS AND RESULTS: We used variants associated with CETP (rs247617) and HMGCR (rs12916) expression in 62,400 Europeans with detailed lipoprotein profiling from nuclear magnetic resonance spectroscopy. Genetic associations were scaled to 10% lower risk of coronary heart disease (CHD). Associations of lipoprotein measures with risk of incident CHD in three population-based cohorts (770 cases) were examined. CETP and HMGCR had near-identical associations with LDL-cholesterol concentration estimated by Friedewald-equation. HMGCR had a relatively consistent effect on cholesterol concentrations across all apolipoprotein B-containing lipoproteins. CETP had stronger effects on remnant and very-low-density lipoprotein cholesterol but no effect on cholesterol concentrations in LDL defined by particle size (diameter 18-26 nm) (-0.02SD 95%CI: -0.10, 0.05 for CETP versus -0.24SD, 95%CI -0.30, -0.18 for HMGCR). CETP had profound effects on lipid compositions of lipoproteins, with strong reductions in the triglyceride content of all high-density lipoprotein (HDL) particles. These alterations in triglyceride composition within HDL subclasses were observationally associated with risk of CHD, independently of total cholesterol and triglycerides (strongest HR per 1-SD higher triglyceride composition in very-large HDL 1.35; 95%CI: 1.18, 1.54). CONCLUSION: CETP inhibition does not affect size-specific LDL cholesterol but may lower CHD risk by lowering cholesterol in other apolipoprotein-B containing lipoproteins and lowering triglyceride content of HDL particles. Conventional composite lipid assays may mask heterogeneous effects of lipid-altering therapies

Molecular profiling of calcific aortic valve disease

Abstract Calcific aortic valve disease (CAVD) is the most common valvular heart disease in the Western world. Although it shares mainly the same risk factors as coronary heart disease (CHD), i.e. similar initial events in both diseases but with time, they lead to different clinical outcomes. Thus, when it affects the coronary arteries, the disease leads to an obstructive or rupture-prone plaque whereas in the aortic valve, it causes massive calcification and ossification. This obstructs the blood flow from the left cardiac ventricle, causing myocardial hypertrophy, and if left untreated, heart failure and death. Many of the pathobiological differences between CAVD and CHD remain unknown. Currently, there are no effective lifestyle- or pharmacologic treatments for CAVD and the only therapy is a valve replacement operation. In this thesis, several studies utilizing large-scale methods were undertaken to profile the molecular events leading to CAVD. Surgically removed valves from patients in different stages of the disease were obtained and gene transcripts, microRNA-molecules and several proteins were identified as being differentially expressed. Several of these were investigated further, including two pro-inflammatory CC-type chemokine ligands 3 and 4 (CCL3 and CCL4), microRNA-125b, several granzyme-proteins and heat-shock protein 90. The results of this thesis provide a large dataset of hundreds of molecular changes associated with CAVD. It is proposed that they can be used as a basis for the generation of new hypotheses and assist in the design of experiments to clarify the mechanisms driving CAVD.Tiivistelmä Aorttaläpän kalkkeutuva ahtauma on länsimaiden yleisin sydänläppäsairaus. Riskitekijät ovat pääosin samat kuin sepelvaltimotaudissa, ja molemmat saavat alkunsa samalla tavalla. Ajan myötä ne kuitenkin johtavat varsin erilaisiin kliinisiin ilmenemismuotoihin: sepelvaltimoihin kasvaa ahtauttavia ja repeytymisherkkiä plakkeja, kun taas aorttaläppään muodostuu runsaasti kalkkia ja luuta. Se haittaa verenvirtausta sydämen vasemmasta kammiosta aorttaan, mikä aiheuttaa sydänlihaksen paksuuntumista. Hoitamattomana tauti johtaa lopulta sydämen vajaatoimintaan ja kuolemaan. Monet syyt eroihin sepelvaltimotaudin ja aorttaläpän ahtauman välillä ovat edelleen tuntemattomia. Tällä hetkellä aorttaläpän ahtaumaan ei ole olemassa tehokasta elintapa- tai lääkehoitoa, ja ainoa hoitomuoto onkin vioittuneen aorttaläpän korvaaminen proteesilla. Tässä väitöskirjatyössä tehtiin useita laaja-alaisia molekyylitason profilointitutkimuksia, joilla selvitettiin aorttaläpän ahtaumaan mahdollisesti johtavia mekanismeja. Aineistona oli leikkauksessa potilailta poistettuja, erilaisissa taudin vaiheissa olevia aorttaläppiä. Niistä kerättiin tietoja kaikkien geenien ilmentymisestä, mikroRNA-molekyyleistä sekä koko proteomitason muutoksista. Useat tunnistetuista molekyyleistä valittiin jatkotutkimuksiin niiden tarkempien ominaisuuksien selvittämiseksi. Näitä olivat tulehdusta välittävät kemokiinit CCL3 ja CCL4, mikroRNA-125b, useat grantsyymiproteiinit sekä lämpöshokkiproteiini 90. Väitöskirjatyön tuloksista voidaan muodostaa ainutlaatuinen aineisto sadoista erilaisista aorttaläpän ahtaumaan johtavista molekyylitason muutoksista. Sitä voidaan hyödyntää uusien tutkimushypoteesien muodostamisessa sekä aorttaläpän ahtauman tarkempien mekanismien selvittämiseen tähtäävien kokeellisten tutkimusten suunnittelussa

Data-driven multivariate population subgrouping via lipoprotein phenotypes versus apolipoprotein B in the risk assessment of coronary heart disease

Background and aims Population subgrouping has been suggested as means to improve coronary heart disease (CHD) risk assessment. We explored here how unsupervised data-driven metabolic subgrouping, based on comprehensive lipoprotein subclass data, would work in large-scale population cohorts. Methods We applied a self-organizing map (SOM) artificial intelligence methodology to define subgroups based on detailed lipoprotein profiles in a population-based cohort (n = 5789) and utilised the trained SOM in an independent cohort (n = 7607). We identified four SOM-based subgroups of individuals with distinct lipoprotein profiles and CHD risk and compared those to univariate subgrouping by apolipoprotein B quartiles. Results The SOM-based subgroup with highest concentrations for non-HDL measures had the highest, and the subgroup with lowest concentrations, the lowest risk for CHD. However, apolipoprotein B quartiles produced better resolution of risk than the SOM-based subgroups and also striking dose-response behaviour. Conclusions These results suggest that the majority of lipoprotein-mediated CHD risk is explained by apolipoprotein B-containing lipoprotein particles. Therefore, even advanced multivariate subgrouping, with comprehensive data on lipoprotein metabolism, may not advance CHD risk assessment

Apt interpretation of comprehensive lipoprotein data in large-scale epidemiology:disclosure of fundamental structural and metabolic relationships

Abstract Background: Quantitative lipoprotein analytics using nuclear magnetic resonance (NMR) spectroscopy is currently commonplace in large-scale studies. One methodology has become widespread and is currently being utilized also in large biobanks. It allows the comprehensive characterization of 14 lipoprotein subclasses, clinical lipids, apolipoprotein A-I and B. The details of these data are conceptualized here in relation to lipoprotein metabolism with particular attention on the fundamental characteristics of subclass particle numbers, lipid concentrations and compositional measures. Methods and results: The NMR methodology was applied to fasting serum samples from Northern Finland Birth Cohorts 1966 and 1986 with 5651 and 5605 participants, respectively. All results were highly consistent between the cohorts. Circulating lipid concentrations in a particular lipoprotein subclass arise predominantly as the result of the circulating number of those subclass particles. The spherical lipoprotein particle shape, with a radially oriented surface monolayer, imposes size-dependent biophysical constraints for the lipid composition of individual subclass particles and inherently restricts the accommodation of metabolic changes via compositional modifications. The new finding that the relationship between lipoprotein subclass particle concentrations and the particle size is log-linear reveals that circulating lipoprotein particles are also under rather strict metabolic constraints for both their absolute and relative concentrations. Conclusions: The fundamental structural and metabolic relationships between lipoprotein subclasses elucidated in this study empower detailed interpretation of lipoprotein metabolism. Understanding the intricate details of these extensive data is important for the precise interpretation of novel therapeutic opportunities and for fully utilizing the potential of forthcoming analyses of genetic and metabolic data in large biobanks

Increased mesenchymal podoplanin expression is associated with calcification in aortic valves

Abstract Background and aim of the study: Calcific aortic valve disease (CAVD) is a progressive disease starting from mild valvular sclerosis and progressing to severe aortic stenosis (AS) with calcified valves. The origin of the calcification is proposed to be mesenchymal cells which have differentiated towards an osteoblastic phenotype. Podoplanin is a glycoprotein expressed in the endothelium of lymphatic vessels and in osteoblasts and osteocytes, mesenchymal cells, as well as in many carcinomas and aortic atherosclerotic lesions. In CAVD, its expression has been evaluated only as a marker of the lymphatic vasculature. Materials and methods: We determined podoplanin expression in human aortic valves in four patient groups: control (C, n=7), aortic regurgitation (AR, n=8), aortic regurgitation and fibrosis (AR + f, n=15) and AS (n=49) by immunohistochemistry and quantitative real-time PCR (RT-PCR). Results: Immunohistochemically, podoplanin expression was significantly increased in AR + f and AS groups when compared with the control and AR groups and the level of expression positively correlated with the extent of calcification and vascularity. Podoplanin mRNA levels were 1.7-fold higher in the AS group as compared with the control group (P=.05). Podoplanin-positivity was present not only in lymphatic vessel endothelium but also in osteoblasts, osteocytes, chondrocytes, macrophages and extracellular matrix. The majority of the podoplanin-positivity was in spindle cells with a myofibroblastic phenotype, often associated with calcifications. Tricuspid valves had more calcification-associated podoplanin than bi/unicuspid valves (median 1.52 vs 1.16, P&lt;.001). Conclusions: CAVD is characterized by an increased expression of podoplanin; this is associated with the differentiation of valvular interstitial cells into calcium-producing, myofibroblast-like cells. In addition, tricuspid valves express relatively more podoplanin than bi/unicuspid valves

Data-driven multivariate population subgrouping via lipoprotein phenotypes versus apolipoprotein B in the risk assessment of coronary heart disease

Abstract Background and aims: Population subgrouping has been suggested as means to improve coronary heart disease (CHD) risk assessment. We explored here how unsupervised data-driven metabolic subgrouping, based on comprehensive lipoprotein subclass data, would work in large-scale population cohorts. Methods: We applied a self-organizing map (SOM) artificial intelligence methodology to define subgroups based on detailed lipoprotein profiles in a population-based cohort (n = 5789) and utilised the trained SOM in an independent cohort (n = 7607). We identified four SOM-based subgroups of individuals with distinct lipoprotein profiles and CHD risk and compared those to univariate subgrouping by apolipoprotein B quartiles. Results: The SOM-based subgroup with highest concentrations for non-HDL measures had the highest, and the subgroup with lowest concentrations, the lowest risk for CHD. However, apolipoprotein B quartiles produced better resolution of risk than the SOM-based subgroups and also striking dose-response behaviour. Conclusions: These results suggest that the majority of lipoprotein-mediated CHD risk is explained by apolipoprotein B-containing lipoprotein particles. Therefore, even advanced multivariate subgrouping, with comprehensive data on lipoprotein metabolism, may not advance CHD risk assessment

Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics

Background: Quantitative molecular data from urine are rare in epidemiology and genetics. NMR spectroscopy could provide these data in high throughput, and it has already been applied in epidemiological settings to analyse urine samples. However, quantitative protocols for large-scale applications are not available. Methods: We describe in detail how to prepare urine samples and perform NMR experiments to obtain quantitative metabolic information. Semi-automated quantitative line shape fitting analyses were set up for 43 metabolites and applied to data from various analytical test samples and from 1004 individuals from a population-based epidemiological cohort. Novel analyses on how urine metabolites associate with quantitative serum NMR metabolomics data (61 metabolic measures; n = 995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted (n = 578). The fully automated quantitative regression-based spectral analysis is demonstrated for creatinine and glucose (n = 4548). Results: Intra-assay metabolite variations were mostly <5%, indicating high robustness and accuracy of urine NMR spectroscopy methodology per se. Intra-individual metabolite variations were large, ranging from 6% to 194%. However, population-based inter-individual metabolite variations were even larger (from 14% to 1655%), providing a sound base for epidemiological applications. Metabolic associations between urine and serum were found to be clearly weaker than those within serum and within urine, indicating that urinary metabolomics data provide independent metabolic information. Two previous genome-wide hits for formate and 2-hydroxyisobutyrate were replicated at genome-wide significance. Conclusion: Quantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided

Proof of concept for quantitative urine NMR metabolomics pipeline for large-scale epidemiology and genetics

Background:Quantitative molecular data from urine are rare in epidemiology and genetics. NMR spectroscopy could provide these data in high throughput, and it has already been applied in epidemiological settings to analyse urine samples. However, quantitative protocols for large-scale applications are not available. Methods:We describe in detail how to prepare urine samples and perform NMR experiments to obtain quantitative metabolic information. Semi-automated quantitative line shape fitting analyses were set up for 43 metabolites and applied to data from various analytical test samples and from 1004 individuals from a population-based epidemiological cohort. Novel analyses on how urine metabolites associate with quantitative serum NMR metabolomics data (61 metabolic measures; n = 995) were performed. In addition, confirmatory genome-wide analyses of urine metabolites were conducted (n = 578). The fully automated quantitative regression-based spectral analysis is demonstrated for creatinine and glucose (n = 4548). Results:Intra-assay metabolite variations were mostly &lt;5%, indicating high robustness and accuracy of urine NMR spectroscopy methodology per se. Intra-individual metabolite variations were large, ranging from 6% to 194%. However, population-based inter-individual metabolite variations were even larger (from 14% to 1655%), providing a sound base for epidemiological applications. Metabolic associations between urine and serum were found to be clearly weaker than those within serum and within urine, indicating that urinary metabolomics data provide independent metabolic information. Two previous genome-wide hits for formate and 2-hydroxyisobutyrate were replicated at genome-wide significance. Conclusion:Quantitative urine metabolomics data suggest broad novelty for systems epidemiology. A roadmap for an open access methodology is provided