AIM To screen for a mutation of the cardiac troponin T gene in two families where there had been sudden deaths without an increase in left ventricular mass but with myocardial disarray suggesting hypertrophic cardiomyopathy. METHODS DNA from affected individuals from both families was used to screen the cardiac troponin T gene on an exon by exon basis. Mutation screening was achieved by polymerase chain reaction and direct sequencing. Where appropriate, a mutation was confirmed by restriction digest. RESULTS A novel missense mutation of exon 9 was found in the affected individuals of one of the families. This mutation at amino acid 94 resulted in the substitution of arginine for leucine and was not found in 100 normal control samples. A mutation of the cardiac troponin T gene was excluded in the second family. CONCLUSIONS A mutation of the gene for the sarcomeric protein cardiac troponin T can cause familial hypertrophic cardiomyopathy with marked myocyte disarray and frequent premature sudden death in the absence of myocardial hypertrophy at clinical or macroscopic level.

Familial hypertrophic cardiomyopathy is a heterogeneous disease caused by mutations in eight different genes that encode cardiac sarcomeric proteins. Relatives of affected persons are at low risk for the disease in later life if the electrocardiographic and two-dimensional echocardiographic findings are normal in early adulthood. The only reported exceptions are patients with mutations in the gene for cardiac myosin-binding protein C, in whom the disease typically develops in middle age.^1,2 We describe a 57-year-old man with hypertrophic cardiomyopathy caused by a mutation in the gene for troponin T.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that causes loss of joint function and significantly reduces quality of life. Plasma metabolite concentrations of disease-modifying anti-rheumatic drugs (DMARDs) can influence treatment efficacy and toxicity. This study explored the relationship between DMARD-metabolising gene variants and plasma metabolite levels in RA patients. DMARD metabolite concentrations were determined by tandem mass-spectrometry in plasma samples from 100 RA patients with actively flaring disease collected at two intervals. Taqman probes were used to discriminate single-nucleotide polymorphism (SNP) genotypes in cohort genomic DNA: rs246240 (ABCC1), rs1476413 (MTHFR), rs2231142 (ABCG2), rs3740065 (ABCC2), rs4149081 (SLCO1B1), rs4846051 (MTHFR), rs10280623 (ABCB1), rs16853826 (ATIC), rs17421511 (MTHFR) and rs717620 (ABCC2). Mean plasma concentrations of methotrexate (MTX) and MTX-7-OH metabolites were higher (p < 0.05) at baseline in rs4149081 GA genotype patients. Patients with rs1476413 SNP TT or CT alleles have significantly higher (p < 0.001) plasma poly-glutamate metabolites at both study time points and correspondingly elevated disease activity scores. Patients with the rs17421511 SNP AA allele reported significantly lower pain scores (p < 0.05) at both study intervals. Genotyping strategies could help prioritise treatments to RA patients most likely to gain clinical benefit whilst minimizing toxicity.

The current global pandemic due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has taken a substantial number of lives across the world. Although few vaccines have been rolled-out, a number of vaccine candidates are still under clinical trials at various pharmaceutical companies and laboratories around the world. Considering the intrinsic nature of viruses in mutating and evolving over time, persistent efforts are needed to develop better vaccine candidates. In this study, various immuno-informatics tools and bioinformatics databases were deployed to derive consensus B-cell and T-cell epitope sequences of SARS-CoV-2 spike glycoprotein. This approach has identified four potential epitopes which have the capability to initiate both antibody and cell-mediated immune responses, are non-allergenic and do not trigger autoimmunity. These peptide sequences were also evaluated to show 99.82% of global population coverage based on the genotypic frequencies of HLA binding alleles for both MHC class-I and class-II and are unique for SARS-CoV-2 isolated from human as a host species. Epitope number 2 alone had a global population coverage of 98.2%. Therefore, we further validated binding and interaction of its constituent T-cell epitopes with their corresponding HLA proteins using molecular docking and molecular dynamics simulation experiments, followed by binding free energy calculations with molecular mechanics Poisson–Boltzmann surface area, essential dynamics analysis and free energy landscape analysis. The immuno-informatics pipeline described and the candidate epitopes discovered herein could have significant impact upon efforts to develop globally effective SARS-CoV-2 vaccines.

Throughout the COVID-19 pandemic, valuable datasets have been collected on the effects of the virus SARS-CoV-2. In this study, we combined whole genome sequencing data with clinical data (including clinical outcomes, demographics, comorbidity, treatment information) for 929 patient cases seen at a large UK hospital Trust between March 2020 and May 2021. We identified associations between acute physiological status and three measures of disease severity; admission to the intensive care unit (ICU), requirement for intubation, and mortality. Whilst the maximum National Early Warning Score (NEWS2) was moderately associated with severe COVID-19 (A = 0.48), the admission NEWS2 was only weakly associated (A = 0.17), suggesting it is ineffective as an early predictor of severity. Patient outcome was weakly associated with myriad factors linked to acute physiological status and human genetics, including age, sex and pre-existing conditions. Overall, we found no significant links between viral genomics and severe outcomes, but saw evidence that variant subtype may impact relative risk for certain sub-populations. Specific mutations of SARS-CoV-2 appear to have little impact on overall severity risk in these data, suggesting that emerging SARS-CoV-2 variants do not result in more severe patient outcomes. However, our results show that determining a causal relationship between mutations and severe COVID-19 in the viral genome is challenging. Whilst improved understanding of the evolution of SARS-CoV-2 has been achieved through genomics, few studies on how these evolutionary changes impact on clinical outcomes have been seen due to complexities associated with data linkage. By combining viral genomics with patient records in a large acute UK hospital, this study represents a significant resource for understanding risk factors associated with COVID-19 severity. However, further understanding will likely arise from studies of the role of host genetics on disease progression.