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Purpose of Review: Heart failure (HF) is often accompanied by a constellation of comorbidities, leading to diverse patient presentations and clinical trajectories. While traditional methods have provided valuable insights into our understanding of HF, network medicine approaches seek to leverage these complex relationships by analyzing disease at a systems level. This review introduces the concepts of network medicine and explores the use of comorbidity networks to study HF and heart disease., Recent Findings: Comorbidity networks are used to understand disease trajectories, predict outcomes, and uncover potential molecular mechanisms through identification of genes and pathways relevant to comorbidity. These networks have shown the importance of non-cardiovascular comorbidities to the clinical journey of patients with HF. However, the community should be aware of important limitations in developing and implementing these methods. Network approaches hold promise for unraveling the impact of comorbidities in the complex presentation and genetics of HF. Methods that consider comorbidity presence and timing have the potential to help optimize management strategies and identify pathophysiological mechanisms., Competing Interests: Declarations. Human and Animal Rights and Informed Consent: This article does not contain any studies with human or animal subjects performed by any of the authors. Competing Interests: The authors declare no competing interests., (© 2024. The Author(s).)

Inflammation, fibrosis and metabolic stress critically promote heart failure with preserved ejection fraction (HFpEF). Exposure to high-fat diet and nitric oxide synthase inhibitor N[w]-nitro-l-arginine methyl ester (L-NAME) recapitulate features of HFpEF in mice. To identify disease-specific traits during adverse remodeling, we profiled interstitial cells in early murine HFpEF using single-cell RNAseq (scRNAseq). Diastolic dysfunction and perivascular fibrosis were accompanied by an activation of cardiac fibroblast and macrophage subsets. Integration of fibroblasts from HFpEF with two murine models for heart failure with reduced ejection fraction (HFrEF) identified a catalog of conserved fibroblast phenotypes across mouse models. Moreover, HFpEF-specific characteristics included induced metabolic, hypoxic and inflammatory transcription factors and pathways, including enhanced expression of Angiopoietin-like 4 (Angptl4) next to basement membrane compounds, such as collagen IV (Col4a1). Fibroblast activation was further dissected into transcriptional and compositional shifts and thereby highly responsive cell states for each HF model were identified. In contrast to HFrEF, where myofibroblast and matrifibrocyte activation were crucial features, we found that these cell states played a subsidiary role in early HFpEF. These disease-specific fibroblast signatures were corroborated in human myocardial bulk transcriptomes. Furthermore, we identified a potential cross-talk between macrophages and fibroblasts via SPP1 and TNFɑ with estimated fibroblast target genes including Col4a1 and Angptl4. Treatment with recombinant ANGPTL4 ameliorated the murine HFpEF phenotype and diastolic dysfunction by reducing collagen IV deposition from fibroblasts in vivo and in vitro. In line, ANGPTL4, was elevated in plasma samples of HFpEF patients and particularly high levels associated with a preserved global-longitudinal strain. Taken together, our study provides a comprehensive characterization of molecular fibroblast activation patterns in murine HFpEF, as well as the identification of Angiopoietin-like 4 as central mechanistic regulator with protective effects., Competing Interests: Declarations. Conflict of interest: JSR reports funding from GSK and Sanofi and fees from Travere Therapeutics, and Astex., (© 2024. The Author(s).)

Mechanisms underlying the cardiovascular-kidney-metabolic (CKM) syndrome are unknown, although key small molecule metabolites may be involved. Bulk and spatial metabolomics identified adenine to be upregulated and specifically enriched in coronary blood vessels in hearts from patients with diabetes and left ventricular hypertrophy. Single nucleus gene expression studies revealed that endothelial methylthioadenosine phosphorylase (MTAP) was increased in human hearts with hypertrophic cardiomyopathy. The urine adenine/creatinine ratio in patients was predictive of incident heart failure with preserved ejection fraction. Heart adenine and MTAP gene expression was increased in a 2-hit mouse model of hypertrophic heart disease and in a model of diastolic dysfunction with diabetes. Inhibition of MTAP blocked adenine accumulation in the heart, restored heart dysfunction in mice with type 2 diabetes and prevented ischemic heart damage in a rat model of myocardial infarction. Mechanistically, adenine-induced impaired mitophagy was reversed by reduction of mTOR. These studies indicate that endogenous adenine is in a causal pathway for heart failure and ischemic heart disease in the context of CKM syndrome., Competing Interests: Competing interests: Dr. Margulies holds research grants from Amgen and serves as a scientific consultant/advisory board member for Bristol Myers Squibb and Amgen. Dr. Sharma serves on the data safety board for Cara Therapeutics and holds equity in SygnaMap. All other authors declare that they have no competing interests. Dr. Tuttle has received investigator-initiated grant support (to Providence Inland Northwest Health) from Travere and Bayer outside of the submitted work; consultancy fees from AstraZeneca, Boehringer Ingelheim, Eli Lilly and Company, Novo Nordisk and Travere; speaker fees from AstraZeneca, Eli Lilly, and Novo Nordisk. Dr. Julia Saez-Rodriguez reports funding from GSK, Pfizer and Sanofi & fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Owkin, Pfizer and Grunenthal.

Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type-centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here, we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities., Competing Interests: RR, JL, DD, BV No competing interests declared, JS reports funding from GSK, Pfizer and Sanofi and fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Pfizer and Grunenthal, (© 2023, Ramirez Flores et al.)

Background: Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles., Methods: We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature., Results: We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance., Conclusions: We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients., (© 2023. The Author(s).)

Background Transcriptomic studies have contributed to fundamental knowledge of myocardial remodeling in human heart failure (HF). However, the key HF genes reported are often inconsistent between studies, and systematic efforts to integrate evidence from multiple patient cohorts are lacking. Here, we aimed to provide a framework for comprehensive comparison and analysis of publicly available data sets resulting in an unbiased consensus transcriptional signature of human end-stage HF. Methods and Results We curated and uniformly processed 16 public transcriptomic studies of left ventricular samples from 263 healthy and 653 failing human hearts. First, we evaluated the degree of consistency between studies by using linear classifiers and overrepresentation analysis. Then, we meta-analyzed the deregulation of 14 041 genes to extract a consensus signature of HF. Finally, to functionally characterize this signature, we estimated the activities of 343 transcription factors, 14 signaling pathways, and 182 micro RNAs, as well as the enrichment of 5998 biological processes. Machine learning approaches revealed conserved disease patterns across all studies independent of technical differences. These consistent molecular changes were prioritized with a meta-analysis, functionally characterized and validated on external data. We provide all results in a free public resource (https://saezlab.shinyapps.io/reheat/) and exemplified usage by deciphering fetal gene reprogramming and tracing the potential myocardial origin of the plasma proteome markers in patients with HF. Conclusions Even though technical and sampling variability confound the identification of differentially expressed genes in individual studies, we demonstrated that coordinated molecular responses during end-stage HF are conserved. The presented resource is crucial to complement findings in independent studies and decipher fundamental changes in failing myocardium.

Purpose of Review: The goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on "omics" and clinical data. We address some limitations of this data, as well as their future potential., Recent Findings: Omics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling. Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.

Veins grafted into an arterial environment undergo a complex vascular remodeling process. Pathologic vascular remodeling often results in stenosed or occluded conduit grafts. Understanding this complex process is important for improving the outcome of patients with coronary and peripheral artery disease undergoing surgical revascularization. Using in vivo murine cell lineage-tracing models, we show that endothelial-derived cells contribute to neointimal formation through endothelial-to-mesenchymal transition (EndMT), which is dependent on early activation of the Smad2/3-Slug signaling pathway. Antagonism of transforming growth factor-β (TGF-β) signaling by TGF-β neutralizing antibody, short hairpin RNA-mediated Smad3 or Smad2 knockdown, Smad3 haploinsufficiency, or endothelial cell-specific Smad2 deletion resulted in decreased EndMT and less neointimal formation compared to controls. Histological examination of postmortem human vein graft tissue corroborated the changes observed in our mouse vein graft model, suggesting that EndMT is operative during human vein graft remodeling. These data establish that EndMT is an important mechanism underlying neointimal formation in interpositional vein grafts, and identifies the TGF-β-Smad2/3-Slug signaling pathway as a potential therapeutic target to prevent clinical vein graft stenosis.