Molecular analysis of sarcomeric and non-sarcomeric genes in patients with hypertrophic cardiomyopathy.

Background: Hypertrophic cardiomyopathy (HCM) is a common genetic heart disorder characterized by unexplained left ventricle hypertrophy associated with non-dilated ventricular chambers. Several genes encoding heart sarcomeric proteins have been associated to HCM, but a small proportion of HCM patients harbor alterations in other non-sarcomeric loci. The variable expression of HCM seems influenced by genetic modifier factors and new sequencing technologies are redefining the understanding of genotype–phenotype relationships, even if the interpretations of the numerous identified variants pose several challenges. Methods and results: We investigated 62 sarcomeric and non-sarcomeric genes in 41 HCM cases and in 3 HCM-related disorders patients. We employed an integrated approach that combines multiple tools for the prediction, annotation and visualization of functional variants. Genotype–phenotype correlations were carried out for inspecting the involvement of each gene in age onset and clinical variability of HCM. The 80% of the non-syndromic patients showed at least one rare non-synonymous variant (nsSNV) and among them, 58% carried alterations in sarcomeric loci, 14% in desmosomal and 7% in other non-sarcomeric ones without any sarcomere change. Statistical analyses revealed an inverse correlation between the number of nsSNVs and age at onset, and a relationship between the clinical variability and number and type of variants. Conclusions: Our results extend the mutational spectrum of HCM and contribute in defining the molecular pathogenesis and inheritance pattern(s) of this condition. Besides, we delineate a specific procedure for the identification of the most likely pathogenetic variants for a next generation sequencing approach embodied in a clinical context

Prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric DNA variants found in patients with hypertrophic cardiomyopathy

AbstractGenomic technologies are redefining the understanding of genotype–phenotype relationships and over the past decade, many bioinformatics algorithms have been developed to predict functional consequences of single nucleotide variants. This article presents the data from a comprehensive computational workflow adopted to assess the biomedical impact of the DNA variants resulting from the experimental study “Molecular analysis of sarcomeric and non-sarcomeric genes in patients with hypertrophic cardiomyopathy” (Bottillo et al., 2016) [1]. Several different independently methods were employed to predict the functional consequences of alleles that result in amino acid substitutions, to study the effect of some DNA variants over the splicing process and to investigate the impact of a sequence variant with respect to the evolutionary conservation

Identification of a variant hotspot in "MYBPC3" and of a novel "CSRP3" autosomal recessive alteration in a cohort of Polish patients with hypertrophic cardiomyopathy

INTRODUCTION Hypertrophic cardiomyopathy (HCM) is a heart disorder caused by autosomal dominant alterations affecting both sarcomeric genes and other nonsarcomeric loci in a minority of cases. However, in some patients, the occurrence of the causal pathogenic variant or variants in homozygosity, compound heterozygosity, or double heterozygosity has also been described. Most of the HCM pathogenic variants are missense and unique, but truncating mutations of the MYBPC3 gene have been reported as founder pathogenic variants in populations from Finland, France, Japan, Iceland, Italy, and the Netherlands. OBJECTIVES This study aimed to assess the genetic background of HCM in a cohort of Polish patients. PATIENTS AND METHODS Twenty‑nine Polish patients were analyzed by a next‑generation sequencing panel including 404 cardiovascular genes. RESULTS Pathogenic variants were found in 41% of the patients, with ultra‑rare MYBPC3 c.2541C>G (p.Tyr847Ter) mutation standing for a variant hotspot and correlating with a lower age at HCM diagnosis. Among the nonsarcomeric genes, the CSRP3 mutation was found in a single case carrying the novel c.364C>T (p.Arg122Ter) variant in homozygosity. With this finding, the total number of known HCM cases with human CSRP3 knockout cases has reached 3. CONCLUSIONS This report expands the mutational spectrum and the inheritance pattern of HCM

Functional characterization of a novel truncating mutation in Lamin A/C gene in a family with a severe cardiomyopathy with conduction defects

Background/Aims: Truncating LMNA gene mutations occur in many inherited cardiomyopathy cases, but the molecular mechanisms involved in the disease they cause have not yet been systematically investigated. Here, we studied a novel frameshift LMNA variant (p.D243Gfs*4) identified in three members of an Italian family co-segregating with a severe form of cardiomyopathy with conduction defects. Methods: HEK293 cells and HL-1 cardiomyocytes were transiently transfected with either Lamin A or D243Gfs*4 tagged with GFP (or mCherry). D243Gfs*4 expression, cellular localization and its effects on diverse cellular mechanisms were evaluated with western blotting, laser-scanning confocal microscopy and video-imaging analysis in single cells. Results: When expressed in HEK293 cells, GFP- (or mCherry)-tagged LMNA D243Gfs*4 colocalized with calnexin within the ER. ER mislocalization of LMNA D243Gfs*4 did not significantly induce ER stress response, abnormal Ca2+ handling and apoptosis when compared with HEK293 cells expressing another truncated mutant of LMNA (R321X) which similarly accumulates within the ER. Of note, HEK293-LMNA D243Gfs*4 cells showed a significant reduction of connexin 43 (CX43) expression level, which was completely rescued by activation of the WNT/β-catenin signaling pathway. When expressed in HL-1 cardiomyocytes, D243Gfs*4 significantly impaired the spontaneous Ca2+ oscillations recorded in these cells as result of propagation of the depolarizing waves through the gap junctions between non-transfected cells surrounding a cell harboring the mutation. Furthermore, mCh-D243Gfs*4 HL-1 cardiomyocytes showed reduced CX43-dependent Lucifer Yellow (LY) loading and propagation. Of note, activation of β-catenin rescued both LY loading and LMNA D243Gfs*4 -HL-1 cells spontaneous activity propagation. Conclusion: Overall, the present results clearly indicate the involvement of the aberrant CX43 expression/activity as a pathogenic mechanism for the conduction defects associated to this LMNA truncating alteration

mRNA therapy corrects defective glutathione metabolism and restores ureagenesis in preclinical argininosuccinic aciduria

The urea cycle enzyme argininosuccinate lyase (ASL) enables the clearance of neurotoxic ammonia and the biosynthesis of arginine. Patients with ASL deficiency present with argininosuccinic aciduria, an inherited metabolic disease with hyperammonemia and a systemic phenotype coinciding with neurocognitive impairment and chronic liver disease. Here, we describe the dysregulation of glutathione biosynthesis and upstream cysteine utilization in ASL-deficient patients and mice using targeted metabolomics and in vivo positron emission tomography (PET) imaging using ( S)-4-(3-18F-fluoropropyl)-l-glutamate ([18F]FSPG). Up-regulation of cysteine metabolism contrasted with glutathione depletion and down-regulated antioxidant pathways. To assess hepatic glutathione dysregulation and liver disease, we present [18F]FSPG PET as a noninvasive diagnostic tool to monitor therapeutic response in argininosuccinic aciduria. Human hASL mRNA encapsulated in lipid nanoparticles improved glutathione metabolism and chronic liver disease. In addition, hASL mRNA therapy corrected and rescued the neonatal and adult Asl-deficient mouse phenotypes, respectively, enhancing ureagenesis. These findings provide mechanistic insights in liver glutathione metabolism and support clinical translation of mRNA therapy for argininosuccinic aciduria. </p

A machine-learning based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease

Background: Mechanisms of myocardial ischemia in obstructive and non-obstructive coronary artery disease (CAD), and the interplay between clinical, functional, biological and psycho-social features, are still far to be fully elucidated. Objectives: To develop a machine-learning (ML) model for the supervised prediction of obstructive versus non-obstructive CAD. Methods: From the EVA study, we analysed adults hospitalized for IHD undergoing conventional coronary angiography (CCA). Non-obstructive CAD was defined by a stenosis &lt; 50% in one or more vessels. Baseline clinical and psycho-socio-cultural characteristics were used for computing a Rockwood and Mitnitski frailty index, and a gender score according to GENESIS-PRAXY methodology. Serum concentration of inflammatory cytokines was measured with a multiplex flow cytometry assay. Through an XGBoost classifier combined with an explainable artificial intelligence tool (SHAP), we identified the most influential features in discriminating obstructive versus non-obstructive CAD. Results: Among the overall EVA cohort (n = 509), 311 individuals (mean age 67 ± 11&nbsp;years, 38% females; 67% obstructive CAD) with complete data were analysed. The ML-based model (83% accuracy and 87% precision) showed that while obstructive CAD was associated with higher frailty index, older age and a cytokine signature characterized by IL-1β, IL-12p70 and IL-33, non-obstructive CAD was associated with a higher gender score (i.e., social characteristics traditionally ascribed to women) and with a cytokine signature characterized by IL-18, IL-8, IL-23. Conclusions: Integrating clinical, biological, and psycho-social features, we have optimized a sex- and gender-unbiased model that discriminates obstructive and non-obstructive CAD. Further mechanistic studies will shed light on the biological plausibility of these associations. Clinical trial registration: NCT02737982

An X-ray burst from a magnetar enlightening the mechanism of fast radio bursts

Fast radio bursts (FRBs) are millisecond radio pulses originating from powerful enigmatic sources at extragalactic distances. Neutron stars with large magnetic fields (magnetars) have been considered as the sources powering the FRBs, but the connection requires further substantiation. Here we report the detection by the AGILE satellite on 28 April 2020 of an X-ray burst in temporal coincidence with a bright FRB-like radio burst from the Galactic magnetar SGR 1935+2154. The burst observed in the hard X-ray band (18-60 keV) lasted about 0.5 s, it is spectrally cut off above 80 keV and implies an isotropically emitted energy of about 1040 erg. This event demonstrates that a magnetar can produce X-ray bursts in coincidence with FRB-like radio bursts. It also suggests that FRBs associated with magnetars can emit X-ray bursts. We discuss SGR 1935+2154 in the context of FRBs with low-intermediate radio energies in the range 1038-1040 erg. Magnetars with magnetic fields B ≈ 1015 G may power these FRBs, and new data on the search for X-ray emission from FRBs are presented. We constrain the bursting X-ray energy of the nearby FRB 180916 to be less than 1046 erg, smaller than that observed in giant flares from Galactic magnetars

The Sex-Specific Detrimental Effect of Diabetes and Gender-Related Factors on Pre-admission Medication Adherence Among Patients Hospitalized for Ischemic Heart Disease: Insights From EVA Study

Background: Sex and gender-related factors have been under-investigated as relevant determinants of health outcomes across non-communicable chronic diseases. Poor medication adherence results in adverse clinical outcomes and sex differences have been reported among patients at high cardiovascular risk, such as diabetics. The effect of diabetes and gender-related factors on medication adherence among women and men at high risk for ischemic heart disease (IHD) has not yet been fully investigated.Aim: To explore the role of sex, gender-related factors, and diabetes in pre-admission medication adherence among patients hospitalized for IHD.Materials and Methods: Data were obtained from the Endocrine Vascular disease Approach (EVA) (ClinicalTrials.gov Identifier: NCT02737982), a prospective cohort of patients admitted for IHD. We selected patients with baseline information regarding the presence of diabetes, cardiovascular risk factors, and gender-related variables (i.e., gender identity, gender role, gender relations, institutionalized gender). Our primary outcome was the proportion of pre-admission medication adherence defined through a self-reported questionnaire. We performed a sex-stratified analysis of clinical and gender-related factors associated with pre-admission medication adherence.Results: Two-hundred eighty patients admitted for IHD (35% women, mean age 70), were included. Around one-fourth of the patients were low-adherent to therapy before hospitalization, regardless of sex. Low-adherent patients were more likely diabetic (40%) and employed (40%). Sex-stratified analysis showed that low-adherent men were more likely to be employed (58 vs. 33%) and not primary earners (73 vs. 54%), with more masculine traits of personality, as compared with medium-high adherent men. Interestingly, women reporting medication low-adherence were similar for clinical and gender-related factors to those with medium-high adherence, except for diabetes (42 vs. 20%, p = 0.004). In a multivariate adjusted model only employed status was associated with poor medication adherence (OR 0.55, 95%CI 0.31–0.97). However, in the sex-stratified analysis, diabetes was independently associated with medication adherence only in women (OR 0.36; 95%CI 0.13–0.96), whereas a higher masculine BSRI was the only factor associated with medication adherence in men (OR 0.59, 95%CI 0.35–0.99).Conclusion: Pre-admission medication adherence is common in patients hospitalized for IHD, regardless of sex. However, patient-related factors such as diabetes, employment, and personality traits are associated with adherence in a sex-specific manner

Prediction and visualization data for the interpretation of sarcomeric and non-sarcomeric DNA variants found in patients with hypertrophic cardiomyopathy

Genomic technologies are redefining the understanding of geno-type–phenotype relationships and over the past decade, manybioinformatics algorithms have been developed to predict func-tional consequences of single nucleotide variants. This articlepresents the data from a comprehensive computational workflowadopted to assess the biomedical impact of the DNA variantsresulting from the experimental study“Molecular analysis of sar-comeric and non-sarcomeric genes in patients with hypertrophiccardiomyopathy”(Bottillo et al., 2016)[1]. Several different inde-pendently methods were employed to predict the functionalconsequences of alleles that result in amino acid substitutions, tostudy the effect of some DNA variants over the splicing process and o investigate the impact of a sequence variant with respect to theevolutionary conservation