Molecular Mechanisms of DBNL in Heart Failure: From Macrophage Immunometabolism to Therapeutic Implications

Summary

We present a protocol to integrate multiomics Mendelian randomization, single-cell transcriptomics, and structure-based in silico screening to systematically delineate the Drebrin Like (DBNL)-macrophage axis in heart failure. We suggest DBNL as a druggable immunometabolic target.

Abstract

Heart failure (HF) remains a major global health challenge, with limited effective treatments targeting its core pathophysiological mechanisms. In this study, an integrated multiomics approach combining Mendelian randomization (MR) and single-cell RNA sequencing (scRNA-seq) was used to identify potential biomarkers and therapeutic targets for HF. We utilized data from genome-wide association studies (GWASs), expression quantitative trait loci (eQTLs), methylation quantitative trait loci (mQTLs), and protein quantitative trait loci (pQTLs) to investigate the genetic mechanisms of HF. Single-cell RNA sequencing was performed to analyse gene expression in cardiac macrophages, and pseudotime analysis was used to study the dynamic regulation of DBNL during macrophage differentiation. Molecular docking and dynamics simulations identified pirinixic acid (WY-14643; PubChem CID: 4594; synonyms: 50892-23-4; WY-14643) as a potential regulator of DBNL. Multiomics analysis revealed that DBNL (Drebrin-like) is a key gene associated with HF risk. Single-cell RNA sequencing revealed that DBNL is expressed mainly in cardiac macrophages and is upregulated under pathological conditions. The expression of DBNL by macrophages is associated with immune metabolism and profibrotic pathways, particularly the IL-6/JAK/STAT3, PI3K/AKT/mTOR, and TGF-β signalling pathways. Pseudotime analysis indicated that DBNL has a dynamic regulatory effect on macrophage differentiation, especially in chronic inflammation. Molecular docking and dynamic simulations have shown that pyridine acid has a potential role in regulating DBNL. This study elucidates the role of DBNL in the progression of heart failure and suggests its potential as a therapeutic target. Importantly, the therapeutic relevance is inferred from computational predictions. These findings offer novel insights into immune metabolism and macrophage-mediated intercellular communication, establishing a theoretical basis for future applications in the optimization of DBNL-targeted drugs and functional research. Nonetheless, additional experimental validation and preclinical studies are needed to substantiate the clinical efficacy of DBNL as a therapeutic target.

Introduction

Heart failure (HF) represents a significant and growing global health challenge that affects millions of individuals worldwide and places considerable economic strain on healthcare systems¹. Consequently, there is a pressing need for efficacious interventions in heart failure (HF) management.

The complex pathophysiology of heart failure originates from impaired myocardial contractility of the left ventricle, leading to a cascade of systemic complications, such as pulmonary and peripheral congestion². However, directly addressing the fundamental pathophysiological mechanisms that underlie the p....

Protocol

Data Download
Gene Expression Data
The single-cell RNA sequencing (scRNA-seq) data utilized in the present study were sourced from the Gene Expression Omnibus (GEO) repository maintained by the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/geo/), specifically from the dataset with the accession number⁹ GSE161470 (human cardiac tissue comprising four control specimens and one pathological specimen). This dataset was originally published by Zhang et al. in 2022¹⁰. The primary objective of the original investigation was to examine the cellula....

Results

Identification of DBNL as a gene associated with heart failure risk through Mendelian randomization and colocalization analysis
The outcome identifier for heart failure-related samples, GCST90162626, was acquired from the summary statistics dataset. Causal relationships among 1,247 pairs of expression quantitative trait loci (eQTLs) and outcomes were determined utilizing the extract_instruments and extract_outcome_data functions (Supplementary Table 2). Subsequent Mendelian randomiza.......

Discussion

Although inflammation constitutes a fundamental aspect of the pathophysiology of heart failure (HF), previous investigations have predominantly concentrated on generalized inflammatory mediators and cell surface markers^17,18,24. This focus has constrained the elucidation of the specific cellular mechanisms involved. To overcome these limitations, we utilized an integrative approach combining multiomics Mendelian randomization (M.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
CellMarker Database	Harbin Medical University (CellMarker 2.0)	http://bio-bigdata.hrbmu.edu.cn/CellMarker/	CellMarker Database; For querying tissue/cell type marker genes during single-cell annotation
Comparative Toxicogenomics Database (CTD)	CTD (Duke University et al.)	http://ctdbase.org/	CTD Database; For retrieving interactions between chemicals, genes, phenotypes, and diseases, used for drug prediction
deCODE Plasma pQTL Dataset (2021, 4,907 aptamers, 35,559 European samples)	deCODE genetics	https://www.decode.com/summarydata/	2021 pQTL data release; Used as pQTL exposure data for MR analysis
eQTLGen Whole Blood eQTL Data	eQTLGen Consortium	https://www.eqtlgen.org/	eQTLGen phase I/II (specific version as per usage); Used as eQTL exposure data for MR and colocalization analysis
GEO Single-cell RNA-seq Dataset GSE161470 (Human Cardiac Tissue: 4 control + 1 pathological sample)	NCBI Gene Expression Omnibus (GEO)	https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE161470	GSE161470; To obtain single-cell RNA sequencing data of human heart tissue for downstream analysis
GWAS Catalog	NHGRI–EBI	https://www.ebi.ac.uk/gwas/	GWAS Catalog; To retrieve HF-related GWAS information, annotations, and auxiliary analysis
Heart Failure GWAS Summary Statistics (GCST90162626)	NHGRI–EBI GWAS Catalog	https://www.ebi.ac.uk/gwas/studies/GCST90162626	Study accession: GCST90162626; Used as outcome data for MR and colocalization analysis
Molecular Signatures Database (MSigDB) v7.0	Broad Institute	https://www.gsea-msigdb.org/gsea/msigdb	MSigDB v7.0; For GSEA / GSVA background gene sets and pathway annotations
Pirinixic acid (WY-14643) Structure Data (PubChem)	NCBI PubChem	https://pubchem.ncbi.nlm.nih.gov/compound/5694	PubChem CID: 5694; Used to obtain ligand 3D structure for molecular docking and dynamics simulation
UniProt Protein Database (DBNL, UniProt ID: Q9UJU6)	UniProt Consortium	https://www.uniprot.org/uniprot/Q9UJU6	UniProt: Q9UJU6; Used to obtain the amino acid sequence of DBNL for structure prediction and docking
Whole Blood mQTL Meta-Analysis (Ref 11, 3,701 European samples, 426,636 mQTL traits)	Authors / Consortium (according to your reference 11)	Pending author to add original database URL / DOI	Reference DOI or Dataset ID; Used as mQTL exposure data for MR analysis