Insights into therapeutic targets and biomarkers using integrated multi-'omics' approaches for dilated and ischemic cardiomyopathies

Integrated multi-omics analysis describes immune profiles in ischemic heart failure and identifies PTN as a novel biomarker

Introduction

Heart failure is a leading global cause of mortality, with ischemic heart failure (IHF) being a major contributor. IHF is primarily driven by coronary artery disease, and its underlying mechanisms are not fully understood, particularly the role of immune responses and inflammation in cardiac muscle remodeling. This study aims to elucidate the immune landscape of heart failure using multi-omics data to identify biomarkers for preventing cardiac fibrosis and disease progression.

Methods

We utilized multi-omics data to elucidate the intricate immune landscape of heart failure at various regulatory levels. Given the substantial size of our transcriptomic dataset, we used diverse machine learning techniques to identify key mRNAs. For smaller datasets such as our proteomic dataset, we applied multilevel data cleansing and enhancement using principles from network biology. This comprehensive analysis led to the development of a scalable, integrated -omics analysis pipeline.

Results

Pleiotrophin (PTN) had shown significant upregulation in multiple datasets and the activation of various molecules associated with dysplastic cardiac remodeling. By synthesizing these data with experimental validations, PTN was identified as a potential biomarker.

Discussion

The present study not only provides a comprehensive perspective on immune dynamics in IHF but also offers valuable insights for the identification of biomarkers, discovery of therapeutic targets, and development of drugs.

Using Omics to Identify Novel Therapeutic Targets in Heart Failure

Omics refers to the measurement and analysis of the totality of molecules or biological processes involved within an organism. Examples of omics data include genomics, transcriptomics, epigenomics, proteomics, metabolomics, and more. In this review, we present the available literature reporting omics data on heart failure that can inform the development of novel treatments or innovative treatment strategies for this disease. This includes polygenic risk scores to improve prediction of genomic data and the potential of multiomics to more efficiently identify potential treatment targets for further study. We also discuss the limitations of omic analyses and the barriers that must be overcome to maximize the utility of these types of studies. Finally, we address the current state of the field and future opportunities for using multiomics to better personalize heart failure treatment strategies.

Multi-omic analyses and network biology in cardiovascular disease

Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.

Multi-omics integration to identify the genetic expression and protein signature of dilated and ischemic cardiomyopathy

Introduction

Heart failure (HF) is a complex clinical syndrome leading to high morbidity. In this study, we aimed to identify the gene expression and protein signature of HF main causes, namely dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM).

Methods

Omics data were accessed through GEO repository for transcriptomic and PRIDE repository for proteomic datasets. Sets of differentially expressed genes and proteins comprising DCM (DiSig) and ICM (IsSig) signatures were analyzed by a multilayered bioinformatics approach. Enrichment analysis via the Gene Ontology was performed through the Metascape platform to explore biological pathways. Protein-protein interaction networks were analyzed via STRING db and Network Analyst.

Results

Intersection of transcriptomic and proteomic analysis showed 10 differentially expressed genes/proteins in DiSig (AEBP1, CA3, HBA2, HBB, HSPA2, MYH6, SERPINA3, SOD3, THBS4, UCHL1) and 15 differentially expressed genes/proteins in IsSig (AEBP1, APOA1, BGN, CA3, CFH, COL14A1, HBA2, HBB, HSPA2, LTBP2, LUM, MFAP4, SOD3, THBS4, UCHL1). Common and distinct biological pathways between DiSig and IsSig were retrieved, allowing for their molecular characterization. Extracellular matrix organization, cellular response to stress and transforming growth factor-beta were common between two subphenotypes. Muscle tissue development was dysregulated solely in DiSig, while immune cells activation and migration in IsSig.

Discussion

Our bioinformatics approach sheds light on the molecular background of HF etiopathology showing molecular similarities as well as distinct expression differences between DCM and ICM. DiSig and IsSig encompass an array of "cross-validated" genes at both transcriptomic and proteomic level, which can serve as novel pharmacological targets and possible diagnostic biomarkers.

Mapping of structural arrangement of cells and collective calcium transients: an integrated framework combining live cell imaging using confocal microscopy and UMAP-assisted HDBSCAN-based approach

Live cell calcium (Ca2+) imaging is one of the important tools to record cellular activity during in vitro and in vivo preclinical studies. Specially, high-resolution microscopy can provide valuable dynamic information at the single cell level. One of the major challenges in the implementation of such imaging schemes is to extract quantitative information in the presence of significant heterogeneity in Ca2+ responses attained due to variation in structural arrangement and drug distribution. To fill this gap, we propose time-lapse imaging using spinning disk confocal microscopy and machine learning-enabled framework for automated grouping of Ca2+ spiking patterns. Time series analysis is performed to correlate the drug induced cellular responses to self-assembly pattern present in multicellular systems. The framework is designed to reduce the large-scale dynamic responses using uniform manifold approximation and projection (UMAP). In particular, we propose the suitability of hierarchical DBSCAN (HDBSCAN) in view of reduced number of hyperparameters. We find UMAP-assisted HDBSCAN outperforms existing approaches in terms of clustering accuracy in segregation of Ca2+ spiking patterns. One of the novelties includes the application of non-linear dimension reduction in segregation of the Ca2+ transients with statistical similarity. The proposed pipeline for automation was also proved to be a reproducible and fast method with minimal user input. The algorithm was used to quantify the effect of cellular arrangement and stimulus level on collective Ca2+ responses induced by GPCR targeting drug. The analysis revealed a significant increase in subpopulation containing sustained oscillation corresponding to higher packing density. In contrast to traditional measurement of rise time and decay ratio from Ca2+ transients, the proposed pipeline was used to classify the complex patterns with longer duration and cluster-wise model fitting. The two-step process has a potential implication in deciphering biophysical mechanisms underlying the Ca2+ oscillations in context of structural arrangement between cells.

Computational pharmacology: New avenues for COVID-19 therapeutics search and better preparedness for future pandemic crises

The COVID-19 pandemic created an unprecedented global healthcare emergency prompting the exploration of new therapeutic avenues, including drug repurposing. A large number of ongoing studies revealed pervasive issues in clinical research, such as the lack of accessible and organised data. Moreover, current shortcomings in clinical studies highlighted the need for a multi-faceted approach to tackle this health crisis. Thus, we set out to explore and develop new strategies for drug repositioning by employing computational pharmacology, data mining, systems biology, and computational chemistry to advance shared efforts in identifying key targets, affected networks, and potential pharmaceutical intervention options. Our study revealed that formulating pharmacological strategies should rely on both therapeutic targets and their networks. We showed how data mining can reveal regulatory patterns, capture novel targets, alert about side-effects, and help identify new therapeutic avenues. We also highlighted the importance of the miRNA regulatory layer and how this information could be used to monitor disease progression or devise treatment strategies. Importantly, our work bridged the interactome with the chemical compound space to better understand the complex landscape of COVID-19 drugs. Machine and deep learning allowed us to showcase limitations in current chemical libraries for COVID-19 suggesting that both in silico and experimental analyses should be combined to retrieve therapeutically valuable compounds. Based on the gathered data, we strongly advocate for taking this opportunity to establish robust practices for treating today's and future infectious diseases by preparing solid analytical frameworks.