Shared Molecular Mechanisms of Hypertrophic Cardiomyopathy and Its Clinical Presentations: Automated Molecular Mechanisms Extraction Approach

Hypertrophic Cardiomyopathy: Genetic Foundations, Outcomes, Interconnections, and Their Modifiers

Hypertrophic cardiomyopathy (HCM) is the most prevalent heritable cardiomyopathy. HCM is considered to be caused by mutations in cardiac sarcomeric protein genes. Recent research suggests that the genetic foundation of HCM is much more complex than originally postulated. The clinical presentations of HCM are very variable. Some mutation carriers remain asymptomatic, while others develop severe HCM, terminal heart failure, or sudden cardiac death. Heterogeneity regarding both genetic mutations and the clinical course of HCM hinders the establishment of universal genotype-phenotype correlations. However, some trends have been identified. The presence of a mutation in some genes encoding sarcomeric proteins is associated with earlier HCM onset, more severe left ventricular hypertrophy, and worse clinical outcomes. There is a diversity in the mechanisms implicated in the pathogenesis of HCM. They may be classified into groups, but they are interrelated. The lack of known supplementary elements that control the progression of HCM indicates that molecular mechanisms that exist between genotype and clinical presentations may be crucial. Secondary molecular changes in pathways implicated in HCM pathogenesis, post-translational protein modifications, and epigenetic factors affect HCM phenotypes. Cardiac loading conditions, exercise, hypertension, diet, alcohol consumption, microbial infection, obstructive sleep apnea, obesity, and environmental factors are non-molecular aspects that change the HCM phenotype. Many mechanisms are implicated in the course of HCM. They are mostly interconnected and contribute to some extent to final outcomes.

Subtypes and Mechanisms of Hypertrophic Cardiomyopathy Proposed by Machine Learning Algorithms

Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM.

Recent Trends in Computational Biomedical Research

Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields [...].

Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms' representation

Background

Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task.

Results

We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance.

Conclusions

Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00279-2.