Anand Amit, Punnen Julius, Nagamalesh U M, Selvam Sabariya, Bysani Madhusudhan, Venkatesh Ramya, Nawin Kriti, Garg Shilpa, Raghuraman Bagirath, Shetty Varun, Kumaran Senthil, Dokania Manoj, Narayan Pradeep, Udwadia Ankita, Gunawardhana Kushan, Gordon David, Ramarao Manjunath, Zhao Lei, Gulia Jyoti
Bristol Myers Squibb, Bengaluru, India.
Narayana Institute of Cardiac Sciences, Narayana Health, Bengaluru, India.
J Mol Cell Cardiol Plus. 2025 Jan 3;11:100282. doi: 10.1016/j.jmccpl.2025.100282. eCollection 2025 Mar.
Heart failure (HF) is a complex syndrome. Despite availability of multiple treatment options, the mortality remains high and the quality of life poor. Better understanding of the underlying pathophysiological processes can lead to development of novel therapies. Multiple comparative transcriptomics studies, which revealed gene level changes in the key pathophysiological pathways in failing hearts, point towards heterogeneity from interplay of disease stage, etiologies and ethnicity. Transcriptomic characterization of HF in patients from different ethnicities can potentially help in understanding the heterogeneity imparted by various factors and the core elements in heart failure.
METHODS & RESULTS: An integrated analysis of bulk transcriptome and microRNA sequencing from the cardiac tissues of 30 South Asian (SA) patients having HF with reduced ejection fraction (HFrEF) and 19 control subjects was conducted. Plasma miRNAs from a subset of HFrEF and control patients were also sequenced to understand their biomarker potential. The altered transcriptome from the myocardium of SA HFrEF patients reflected cardiac muscle contraction, cellular energetics, altered immune signaling and extracellular matrix remodelling as predominant pathophysiological mechanisms. The SA HFrEF patients also showed dysregulation of multiple microRNAs in cardiac tissue like miR-216, miR-217, miR-184 and miR-9983. Many of these miRNAs, such as miR184 and few others, showed altered levels in both the plasma and cardiac tissue of HFrEF patients suggesting their biomarker potential. The diversity in the HFrEF transcriptomes from published studies led us to examine the core HF genes in our cohort. A gene signature generated using machine learning (ML) from the top dysregulated genes in SA HFrEF cohort stratified HF from controls in other cohorts. The sensitivity of the HF gene signature was further improved when union of two cohorts was used as a training set. Our ML analyses developed a core HF gene signature consisting of 21 genes that can stratify HF patients from controls with 98 % sensitivity in all the tested cohorts.
This study reveals molecular changes underlying the pathophysiology as reflected by coding and regulatory non-coding components of transcriptome from South Asian patients and uncovers a conserved gene signature for HF.
心力衰竭（HF）是一种复杂的综合征。尽管有多种治疗选择，但死亡率仍然很高，生活质量较差。更好地理解潜在的病理生理过程有助于开发新的治疗方法。多项比较转录组学研究揭示了衰竭心脏关键病理生理途径中的基因水平变化，表明疾病阶段、病因和种族相互作用导致了异质性。对不同种族患者的HF进行转录组特征分析可能有助于理解各种因素赋予的异质性以及心力衰竭的核心要素。
对30例射血分数降低的心力衰竭（HFrEF）南亚（SA）患者和19例对照者的心脏组织进行了批量转录组和微小RNA测序的综合分析。还对一部分HFrEF患者和对照患者的血浆微小RNA进行了测序，以了解其作为生物标志物的潜力。SA HFrEF患者心肌中转录组的改变反映出心肌收缩、细胞能量代谢、免疫信号改变和细胞外基质重塑是主要的病理生理机制。SA HFrEF患者的心脏组织中还存在多种微小RNA失调，如miR-216、miR-217、miR-184和miR-9983。其中许多微小RNA，如miR184等，在HFrEF患者的血浆和心脏组织中水平均发生改变，表明其具有作为生物标志物的潜力。已发表研究中HFrEF转录组的多样性促使我们在我们的队列中研究核心HF基因。使用机器学习（ML）从SA HFrEF队列中上调最明显的基因生成的基因特征能够将HF与其他队列中的对照区分开来。当将两个队列的联合作为训练集时，HF基因特征的敏感性进一步提高。我们的ML分析开发了一个由21个基因组成的核心HF基因特征，该特征在所有测试队列中能够以98%的敏感性将HF患者与对照区分开来。
本研究揭示了南亚患者转录组的编码和调控非编码成分所反映的病理生理基础的分子变化，并发现了一个保守的HF基因特征。
