Ethnicity, consanguinity, and genetic architecture of hypertrophic cardiomyopathy.

AIMS: Hypertrophic cardiomyopathy (HCM) is characterized by phenotypic heterogeneity that is partly explained by the diversity of genetic variants contributing to disease. Accurate interpretation of these variants constitutes a major challenge for diagnosis and implementing precision medicine, especially in understudied populations. The aim is to define the genetic architecture of HCM in North African cohorts with high consanguinity using ancestry-matched cases and controls. METHODS AND RESULTS: Prospective Egyptian patients (n = 514) and controls (n = 400) underwent clinical phenotyping and genetic testing. Rare variants in 13 validated HCM genes were classified according to standard clinical guidelines and compared with a prospective HCM cohort of majority European ancestry (n = 684). A higher prevalence of homozygous variants was observed in Egyptian patients (4.1% vs. 0.1%, P = 2 × 10-7), with variants in the minor HCM genes MYL2, MYL3, and CSRP3 more likely to present in homozygosity than the major genes, suggesting these variants are less penetrant in heterozygosity. Biallelic variants in the recessive HCM gene TRIM63 were detected in 2.1% of patients (five-fold greater than European patients), highlighting the importance of recessive inheritance in consanguineous populations. Finally, rare variants in Egyptian HCM patients were less likely to be classified as (likely) pathogenic compared with Europeans (40.8% vs. 61.6%, P = 1.6 × 10-5) due to the underrepresentation of Middle Eastern populations in current reference resources. This proportion increased to 53.3% after incorporating methods that leverage new ancestry-matched controls presented here. CONCLUSION: Studying consanguineous populations reveals novel insights with relevance to genetic testing and our understanding of the genetic architecture of HCM.

Health inequity in genomic personalized medicine in underrepresented populations: a look at the current evidence.

Improvements in sequencing technology coupled with dramatic declines in the cost of genome sequencing have led to a proportional growth in the size and number of genetic datasets since the release of the human genetic sequence by The Human Genome Project (HGP) international consortium. The HGP was undeniably a significant scientific success, a turning point in human genetics and the beginning of human genomics. This burst of genetic information has led to a greater understanding of disease pathology and the potential of employing this data to deliver more precise patient care. Hence, the recognition of high-penetrance disease-causing mutations which encode drivers of disease has made the management of most diseases more specific. Nonetheless, while genetic scores are becoming more extensively used, their application in the real world is expected to be limited due to the lack of diversity in the data used to construct them. Underrepresented populations, such as racial and ethnic minorities, low-income individuals, and those living in rural areas, often experience greater health disparities and worse health outcomes compared to the general population. These disparities are often the result of systemic barriers, such as poverty, discrimination, and limited access to healthcare. Addressing health inequity in underrepresented populations requires addressing the underlying social determinants of health and implementing policies and programs which promoted health equity and reduce disparities. This can include expanding access to affordable healthcare, addressing poverty and unemployment, and promoting policies that combat discrimination and racism.

Applications of machine learning in metabolomics: Disease modeling and classification.

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios.

Fenofibrate ameliorates olanzapine's side effects without altering its central effect: emphasis on FGF-21-adiponectin axis.

The present work was designed to investigate whether fenofibrate could ameliorate olanzapine deleterious effect on insulin resistance via its effect on fibroblast growth factor-21 (FGF-21)-adiponectin axis without affecting olanzapine antipsychotic effect in postweaning socially isolated reared female rats. Treatment with olanzapine (6 mg/kg, intraperitoneally) or fenofibrate (100 mg/kg, orally) have been started 5 weeks after isolation, then behavioral tests, hippocampal content of neurotransmitters, and brain-derived neurotrophic factor (BDNF) were assessed. Moreover, insulin resistance, lipid profile, FGF-21, adiponectin, inflammatory, and oxidative stress markers of adipose tissue were assessed. Treatment of isolated-reared animals with olanzapine, or fenofibrate significantly ameliorated the behavioral and biochemical changes induced by postweaning social isolation. Co-treatment showed additive effects in improving hippocampal BDNF level. Besides, fenofibrate reduced the elevation in weight gain, adiposity index, insulin resistance, lipid profile, and FGF-21 level induced by olanzapine treatment. Also, fenofibrate increased adiponectin level which was reduced upon olanzapine treatment. Moreover, fenofibrate improved both adipose tissue oxidative stress and inflammatory markers elevation as a result of olanzapine treatment. Fenofibrate could ameliorate olanzapine-induced insulin resistance without affecting its central effect in isolated reared rats via its action on FGF-21-adiponectin axis.

The Egyptian Collaborative Cardiac Genomics (ECCO-GEN) Project: defining a healthy volunteer cohort.

The integration of comprehensive genomic and phenotypic data from diverse ethnic populations offers unprecedented opportunities toward advancements in precision medicine and novel diagnostic technologies. Current reference genomic databases are not representative of the global human population, making variant interpretation challenging, especially in underrepresented populations, such as the North African population. To address this, the Egyptian Collaborative Cardiac Genomics (ECCO-GEN) Project launched a study comprising 1000 individuals free of cardiovascular disease (CVD). Here, we present the first 391 Egyptian healthy volunteers recruited to establish a pilot phenotyped control cohort. All individuals underwent detailed clinical investigation, including cardiac magnetic resonance imaging (MRI), and were sequenced using a targeted panel of 174 genes with reported roles in inherited cardiac conditions. We identified 1262 variants in 27 cardiomyopathy genes of which 15.1% were not captured in current global and regional genetic reference databases (here: gnomAD and Great Middle Eastern Variome). The ECCO-GEN project aims at defining the genetic landscape of an understudied population and providing individual-level genetic and phenotypic data to support future studies in CVD and population genetics.

Efficient Electrochemical Sensor Based on Gold Nanoclusters/Carbon Ionic Liquid Crystal for Sensitive Determination of Neurotransmitters and Anti-Parkinson Drugs.

Purpose: Herein we introduce a simple and sensitive sensor for the electrochemical determination of neurotransmitters compounds and anti-Parkinson drugs. Methods: The electrochemical sensor (Au/CILCE) based on gold nanoclusters modified carbon ionic liquid crystal (ILC) electrode was characterized using scanning electron microscopy and voltammetry measurements. Results: The effect of ionic liquid type in the carbon paste composite for the electro-catalytic oxidation of L-dopa was evaluated. Highest current response was obtained in case of ILC compared to other studied kinds of ionic liquids. The effective combination of gold nanoclusters and ILC resulted in extra advantages including large surface area and high ionic conductivity of the nanocomposite. L-dopa is considered one of the most important prescribed medicines for treating Parkinson's disease. Moreover, a binary therapy using L-dopa and carbidopa proved effective and promising as it avoids the short comings of L-dopa mono-therapy for Parkinson's patients. The Au/CILCE can detect L-dopa in human serum in the linear concentration range of 0.1 µM to 90 µM with detection and quantification limits of 4.5 nM and 15.0 nM, respectively. Also, the Au/CILCE sensor can simultaneously and sensitively detect L-dopa in the presence of carbidopa with low detection limits. Conclusion: The sensor is advantageous to be applicable for electrochemical sensing of other biologically electroactive species.

Pre-mRNA splicing repression triggers abiotic stress signaling in plants.

Alternative splicing (AS) of precursor RNAs enhances transcriptome plasticity and proteome diversity in response to diverse growth and stress cues. Recent work has shown that AS is pervasive across plant species, with more than 60% of intron-containing genes producing different isoforms. Mammalian cell-based assays have discovered various inhibitors of AS. Here, we show that the macrolide pladienolide B (PB) inhibits constitutive splicing and AS in plants. Also, our RNA sequencing (RNA-seq) data revealed that PB mimics abiotic stress signals including salt, drought and abscisic acid (ABA). PB activates the abiotic stress- and ABA-responsive reporters RD29A::LUC and MAPKKK18::uidA in Arabidopsis thaliana and mimics the effects of ABA on stomatal aperture. Genome-wide analysis of AS by RNA-seq revealed that PB perturbs the splicing machinery and leads to a striking increase in intron retention and a reduction in other forms of AS. Interestingly, PB treatment activates the ABA signaling pathway by inhibiting the splicing of clade A PP2C phosphatases while still maintaining to some extent the splicing of ABA-activated SnRK2 kinases. Taken together, our data establish PB as an inhibitor and modulator of splicing and a mimic of abiotic stress signals in plants. Thus, PB reveals the molecular underpinnings of the interplay between stress responses, ABA signaling and post-transcriptional regulation in plants.

Selective A3 adenosine receptor agonist protects against doxorubicin-induced cardiotoxicity.

PURPOSE: Doxorubicin (DOX) is an effective anticancer drug; however, its clinical use is limited by its cardiotoxic effect. Adenosine was proved to mediate anti-inflammatory effects and protected from myocardial ischemia/reperfusion injury. So the present work was designed to examine the effectiveness of a selective A3 adenosine receptor agonist (Cl-IB-MECA) in DOX-induced cardiotoxicity and to elucidate the underlying mechanisms via studying its effect on different oxidative stress, inflammatory and apoptotic markers. METHODS: Firstly the potential cardioprotective dose of Cl-IB-MECA was screened in male Wistar rats at different doses (20, 40 and 80 µg/kg; i.v) against a single dose of DOX (15 mg/kg; i.p). Secondly, the dose of 40 µg/kg Cl-IB-MECA was selected for further assessment of the cardioprotective mechanisms. RESULTS: Cl-IB-MECA at a dose 40 µg/kg (i.v) protects against DOX-induced bradycardia, elevated creatine kinase isoenzyme-MB and histopathological changes. Also, it significantly ameliorates oxidative stress injury evoked by DOX as evidenced by inhibition of reduced glutathione depletion and lipid peroxidation as well as elevation of antioxidant enzyme activities. Additionally, DOX provoked inflammatory responses by increasing the expressions of nuclear factor kappa B and the levels of tumor necrosis factor alpha. Cl-IB-MECA pretreatment significantly inhibited these inflammatory responses. Furthermore, DOX induced apoptotic tissue damage by increasing cytochrome c expressions which was suppressed by Cl-IB-MECA pretreatment. CONCLUSION: Cl-IB-MECA protects against DOX-induced cardiotoxicity through restoration of the oxidant/antioxidant status and consequential suppression of DOX-induced inflammatory responses and abrogation of the resultant apoptotic signals.