Cardiac troponin T (TNNT2) mutations in chinese dilated cardiomyopathy patients

Mass Spectrometric ITEM-FOUR Analysis Reveals Coding Single-Nucleotide Polymorphisms in Human Cardiac Troponin T That Evade Detection by Sandwich ELISAs Which Use Monoclonal Antibodies M7 and M11.7 from the Elecsys Troponin T® Assay

Immunoassays for cardiac troponin, such as the Elecsys® hs-TnT, have become the gold standard for myocardial infarction diagnostics. While various protein/chemical factors affecting the troponin complex and thus its diagnostic accuracy have been investigated, the role of coding single-nucleotide polymorphisms remains underexplored. To evaluate potential cSNP-induced interference with antibody binding in the Elecsys® hs-TnT immunoassay, we applied ITEM-FOUR, a mass spectrometry-based method that quantifies changes in antibody binding upon amino acid substitutions in epitope peptides. Candidate cSNPs were selected from the dbSNP database and were mapped to human cardiac troponin T by molecular modeling. Consuming micromolar antibody concentrations and microliter sample volumes, two wild-type and 17 cSNP-derived variant epitope peptides-six for monoclonal antibody M7 and eleven for monoclonal antibody M11.7-were investigated to reveal the binding motifs "V131-K134-E138-A142" for M7 and "E146-I150-R154-E157" for M11.7. Loss of binding to M11.7 was observed for substitutions Q148R (rs730880232), R154W (rs483352832), and R154Q (rs745632066), whereas the E138K (rs730881100) exchange disrupted binding of M7. Except for cSNP Q148R, they are associated with cardiomyopathies, placing affected individuals at risk of both underlying heart disease and false-negative hs-TnT assay results in cases of myocardial infarction. Our results highlight the need to account for cSNP-related interferences in antibody-based diagnostics. ITEM-FOUR offers a powerful approach for tackling this challenge, fostering next-generation assay development.

DS-MVP: identifying disease-specific pathogenicity of missense variants by pre-training representation

Accurately predicting the pathogenicity of missense variants is crucial for improving disease diagnosis and advancing clinical research. However, existing computational methods primarily focus on general pathogenicity predictions, overlooking assessments of disease-specific conditions. In this study, we propose DS-MVP, a method capable of predicting disease-specific pathogenicity of missense variants in human genomes. DS-MVP first leverages a deep learning model pre-trained on a large general pathogenicity dataset to learn rich representation of missense variants. It then fine-tunes these representations with an XGBoost model on smaller datasets for specific diseases. We evaluated the learned representation by testing it on multiple binary pathogenicity datasets and gene-level statistics, demonstrating that DS-MVP outperforms existing state-of-the-art methods, such as MetaRNN and AlphaMissense. Additionally, DS-MVP excels in multi-label and multi-class classification, effectively classifying disease-specific pathogenic missense variants based on disease conditions. It further enhances predictions by fine-tuning the pre-trained model on disease-specific datasets. Finally, we analyzed the contributions of the pre-trained model and various feature types, with gene description corpus features from large language model and genetic feature fusion contributing the most. These results underscore that DS-MVP represents a broader perspective on pathogenicity prediction and holds potential as an effective tool for disease diagnosis.

Dilated cardiomyopathy: a new insight into the rare but common cause of heart failure

Heart failure is a global health burden responsible for high morbidity and mortality with a prevalence of greater than 60 million individuals worldwide. One of the major causes of heart failure is dilated cardiomyopathy (DCM), characterized by associated systolic dysfunction. During the last few decades, there have been remarkable advances in our understanding about the genetics of dilated cardiomyopathy. The genetic causes were initially thought to be associated with mutations in genes encoding proteins that are localized to cytoskeleton and sarcomere only; however, with the advancement in mechanistic understanding, the roles of ion channels, Z-disc, mitochondria, nuclear proteins, cardiac transcription factors (e.g., NKX-2.5, TBX20, GATA4), and the factors involved in calcium homeostasis have also been identified and found to be implicated in both familial and sporadic DCM cases. During past few years, next-generation sequencing (NGS) has been established as a diagnostic tool for genetic analysis and it has added significantly to the existing candidate gene list for DCM. The animal models have also provided novel insights to develop a better treatment strategy based on phenotype-genotype correlation, epigenetic and phenomic profiling. Most of the DCM biomarkers that are used in routine genetic and clinical testing are structural proteins, but during the last few years, the role of mi-RNA has also emerged as a biomarker due to their accessibility through noninvasive methods. Our increasing genetic knowledge can improve the clinical management of DCM by bringing clinicians and geneticists on one platform, thereby influencing the individualized clinical decision making and leading to precision medicine.

Targeted Next Generation Sequencing for Genetic Mutations of Dilated Cardiomyopathy

BACKGROUND

Approximately one-third of cases of dilated cardiomyopathy (DCM) are caused by genetic mutations. With new sequencing technologies, numerous variants have been associated with this inherited cardiomyopathy, however the prevalence and genotype-phenotype correlations in different ethnic cohorts remain unclear. This study aimed to investigate the variants in Chinese DCM patients and correlate them with clinical presentations and prognosis.

METHODS AND RESULTS

From September 2013 to December 2016, 70 index patients underwent DNA sequencing for 12 common disease-causing genes with next generation sequencing. Using a bioinformatics filtering process, 12 rare truncating variants (7 nonsense variants, 4 frameshift variants, and 1 splice site variant) and 29 rare missense variants were identified. Of these, 3 patients were double heterozygotes and 10 patients were compound heterozygotes. Overall, 47.1% (33/70) of the index patients had the seputatively pathogenic variants. The majority (33/41, 80.4%) of these variants were located in titin (TTN). More than 80% of the TTN variants (27/33, 81.8%) were distributed in the A band region of the sarcomere. Patients carrying these variants did not have a different phenotype in disease severity, clinical outcome and reversibility of ventricular function compared with non-carriers.

CONCLUSIONS

Several new rare variants were identified in a Chinese population in this study, indicating that there are ethnic differences in genetic mutations in DCM patients. TTN remains the major disease-causing gene. Our results could be a reference for future genetic tests in Chinese populations. No specific genotype-phenotype correlations were found, however a prospective large cohort study may be needed to confirm our findings.