Individualized interactomes for network-based precision medicine in hypertrophic cardiomyopathy with implications for other clinical pathophenotypes

Cardiovascular Risk Assessment and Prevention in Cardio-Oncology: Beyond Traditional Risk Factors

This review goes beyond traditional approaches in cardio-oncology, highlighting often-neglected factors impacting patient care. Social determinants, environment, health care access, and gut microbiome significantly influence patient outcomes. Powerful tools like multi-omics and wearable technologies offer deeper insights into real-world experiences. The future lies in integrating these advancements with established practices to achieve precision cardio-oncology care. By crafting tailored therapies and continuously updating comprehensive management plans based on real-time data, we can unlock the full potential of personalized care for all patients.

A highly sensitive electrochemical aptasensor for detecting broad-spectrum estrogen molecules in clinical samples

The lack of sensitive and accurate monitoring methods for in vivo estrogen levels presents challenges for better prevention of estrogen-induced diseases. We have developed a label-free electrochemical aptasensor that demonstrates high sensitivity and selectivity for the broad-spectrum detection of estrogen molecules. This biosensor uses gold nanoparticles for electrochemical signal amplification and aptamers for broad-spectrum target recognition, enabling precise detection of trace amounts of estrogen in serum samples. The aptasensor demonstrates high sensitivity in estrogen detection, with a linear detection range of 0.01-1 nM and a minimum detection limit of 3 pM. It also exhibits excellent selectivity and interference resistance, with a detection error of less than 19 %, even in the presence of high concentrations of other biological substances. Additionally, molecular dynamics simulations were performed to provide insights into the molecular mechanism of aptamer broad-spectrum recognition and construction principles underlying the sensor. We anticipate that this aptasensor will serve as a robust, convenient, and cost-effective detection method, offering a valuable solution for the prevention of estrogen-induced diseases.

Identification and validation of differentially expressed disulfidptosis-related genes in hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is one of the most common cardiovascular diseases with no effective treatment due to its complex pathogenesis. A novel cell death, disulfidptosis, has been extensively studied in the cancer field but rarely in cardiovascular diseases. This study revealed the potential relationship between disulfidptosis and hypertrophic cardiomyopathy and put forward a predictive model containing disulfidptosis-associated genes (DRGs) of GYS1, MYH10, PDMIL1, SLC3A2, CAPZB, showing excellent performance by SVM machine learning model. The results were further validated by western blot, RNA sequencing and immunohistochemistry in a TAC mice model. In addition, resveratrol was selected as a therapeutic drug targeting core genes using the CTD database. In summary, this study provides new perspectives for exploring disulfidptosis-related biomarkers and potential therapeutic targets for hypertrophic cardiomyopathy.

DysRegNet: Patient-specific and confounder-aware dysregulated network inference towards precision therapeutics

BACKGROUND AND PURPOSE

Gene regulation is frequently altered in diseases in unique and patient-specific ways. Hence, personalised strategies have been proposed to infer patient-specific gene-regulatory networks. However, existing methods do not scale well because they often require recomputing the entire network per sample. Moreover, they do not account for clinically important confounding factors such as age, sex or treatment history. Finally, a user-friendly implementation for the analysis and interpretation of such networks is missing.

EXPERIMENTAL APPROACH

We present DysRegNet, a method for inferring patient-specific regulatory alterations (dysregulations) from bulk gene expression profiles. We compared DysRegNet to the well-known SSN method, considering patient clustering, promoter methylation, mutations and cancer-stage data.

KEY RESULTS

We demonstrate that both SSN and DysRegNet produce interpretable and biologically meaningful networks across various cancer types. In contrast to SSN, DysRegNet can scale to arbitrary sample numbers and highlights the importance of confounders in network inference, revealing an age-specific bias in gene regulation in breast cancer. DysRegNet is available as a Python package (https://github.com/biomedbigdata/DysRegNet_package), and analysis results for 11 TCGA cancer types are available through an interactive web interface (https://exbio.wzw.tum.de/dysregnet).

CONCLUSION AND IMPLICATIONS

DysRegNet introduces a novel bioinformatics tool enabling confounder-aware and patient-specific network analysis to unravel regulatory alteration in complex diseases.

NetMedPy: A Python package for Large-Scale Network Medicine Screening

Network medicine leverages the quantification of information flow within sub-cellular networks to elucidate disease etiology and comorbidity, as well as to predict drug efficacy and identify potential therapeutic targets. However, current Network Medicine toolsets often lack computationally efficient data processing pipelines that support diverse scoring functions, network distance metrics, and null models. These limitations hamper their application in large-scale molecular screening, hypothesis testing, and ensemble modeling. To address these challenges, we introduce NetMedPy, a highly efficient and versatile computational package designed for comprehensive Network Medicine analyses.

DREAMER: Exploring Common Mechanisms of Adverse Drug Reactions and Disease Phenotypes through Network-Based Analysis

Adverse drug reactions (ADRs) are a major concern in clinical healthcare, significantly affecting patient safety and drug development. This study introduces DREAMER, a novel network-based method for exploring the mechanisms underlying ADRs and disease phenotypes at a molecular level by leveraging a comprehensive knowledge graph obtained from various datasets. By considering drugs and diseases that cause similar phenotypes, and investigating their commonalities regarding their impact on specific modules of the protein-protein interaction network, DREAMER can robustly identify protein sets associated with the biological mechanisms underlying ADRs and unravel the causal relationships that contribute to the observed clinical outcomes. Applying DREAMER to 649 ADRs, we identified proteins associated with the mechanism of action for 67 ADRs across multiple organ systems. In particular, DREAMER highlights the importance of GABAergic signaling and proteins of the coagulation pathways for personality disorders and intracranial hemorrhage, respectively. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes.

Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers

Hypertrophic cardiomyopathy (HCM) is a hereditary cardiac disorder marked by anomalous thickening of the myocardium, representing a significant contributor to mortality. While the involvement of immune inflammation in the development of cardiac ailments is well-documented, its specific impact on HCM pathogenesis remains uncertain. Five distinct machine learning algorithms, namely LASSO, SVM, RF, Boruta and XGBoost, were utilized to discover new biomarkers associated with HCM. A unique nomogram was developed using two newly identified biomarkers and subsequently validated. Furthermore, samples of HCM and normal heart tissues were gathered from our institution to confirm the variance in expression levels and prognostic significance of GATM and MGST1. Five novel biomarkers (DARS2, GATM, MGST1, SDSL and ARG2) associated with HCM were identified. Subsequent validation revealed that GATM and MGST1 exhibited significant diagnostic utility for HCM in both the training and test cohorts, with all AUC values exceeding 0.8. Furthermore, a novel risk assessment model for HCM patients based on the expression levels of GATM and MGST1 demonstrated favourable performance in both the training (AUC = 0.88) and test cohorts (AUC = 0.9). Furthermore, our study revealed that GATM and MGST1 exhibited elevated expression levels in HCM tissues, demonstrating strong discriminatory ability between HCM and normal cardiac tissues (AUC of GATM = 0.79; MGST1 = 0.86). Our findings suggest that two specific cell types, monocytes and multipotent progenitors (MPP), may play crucial roles in the pathogenesis of HCM. Notably, GATM and MGST1 were found to be highly expressed in various tumours and showed significant prognostic implications. Functionally, GATM and MGST1 are likely involved in xenobiotic metabolism and epithelial mesenchymal transition in a wide range of cancer types. GATM and MGST1 have been identified as novel biomarkers implicated in the progression of both HCM and cancer. Additionally, monocytes and MPP may also play a role in facilitating the progression of HCM.

Decoding the Foodome: Molecular Networks Connecting Diet and Health

Diet, a modifiable risk factor, plays a pivotal role in most diseases, from cardiovascular disease to type 2 diabetes mellitus, cancer, and obesity. However, our understanding of the mechanistic role of the chemical compounds found in food remains incomplete. In this review, we explore the "dark matter" of nutrition, going beyond the macro- and micronutrients documented by national databases to unveil the exceptional chemical diversity of food composition. We also discuss the need to explore the impact of each compound in the presence of associated chemicals and relevant food sources and describe the tools that will allow us to do so. Finally, we discuss the role of network medicine in understanding the mechanism of action of each food molecule. Overall, we illustrate the important role of network science and artificial intelligence in our ability to reveal nutrition's multifaceted role in health and disease.

Necroptosis and immune infiltration in hypertrophic cardiomyopathy: novel insights from bioinformatics analyses

Background

Hypertrophic Cardiomyopathy (HCM), a widespread genetic heart disorder, is largely associated with sudden cardiac fatality. Necroptosis, an emerging type of programmed cell death, plays a fundamental role in several cardiovascular diseases.

Aim

This research utilized bioinformatics analysis to investigate necroptosis's implication in HCM.

Methods

The study retrieved RNA sequencing datasets GSE130036 and GSE141910 from the Gene Expression Omnibus (GEO) database. It detected necroptosis-linked differentially expressed genes (NRDEGs) by reviewing both the gene set for necroptosis and the differently expressed genes (DEGs). The enriched signaling pathway of HCM was assessed using GSEA, while common DEGs were studied through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Concurrently, the Protein-Protein Interaction network (PPI) proved useful for identifying central genes. CIBERSORT facilitated evaluating the correlation between distinct immune cell-type prevalence and NRDEGs by analyzing immune infiltration patterns. Lastly, GSE141910 dataset validated the expression ranks of NRDEGs and immune-cell penetration.

Results

The investigation disclosed significant enrichment and activation of the necroptosis pathway in HCM specimens. Seventeen diverse genes, including CYBB, BCL2, and JAK2 among others, were identified in the process. PPI network scrutiny classified nine of these genes as central genes. Results from GO and KEGG enrichment analyses showed substantial connections of these genes to pathways pertaining to the HIF-1 signaling track, necroptosis, and NOD-like receptor signaling process. Moreover, an imbalance in M2 macrophage cells in HCM samples was observed. Finally, CYBB, BCL2, and JAK2 emerged as vital genes and were validated using the GSE141910 dataset.

Conclusion

These results indicate necroptosis as a probable underlying factor in HCM, with immune cell infiltration playing a part. Additionally, CYBB, BCL2, JAK2 could act as potential biomarkers for recognizing HCM. This information forms crucial insights into the basic mechanisms of HCM and could enhance its diagnosis and management.

Transcriptomic Analyses and Experimental Validation Identified Immune-Related lncRNA-mRNA Pair MIR210HG-BPIFC Regulating the Progression of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a disease in which the myocardium of the heart becomes asymmetrically thickened, malformed, disordered, and loses its normal structure and function. Recent studies have demonstrated the significant involvement of inflammatory responses in HCM. However, the precise role of immune-related long non-coding RNAs (lncRNAs) in the pathogenesis of HCM remains unclear. In this study, we performed a comprehensive analysis of immune-related lncRNAs in HCM. First, transcriptomic RNA-Seq data from both HCM patients and healthy individuals (GSE180313) were reanalyzed thoroughly. Key HCM-related modules were identified using weighted gene co-expression network analysis (WGCNA). A screening for immune-related lncRNAs was conducted within the key modules using immune-related mRNA co-expression analysis. Based on lncRNA-mRNA pairs that exhibit shared regulatory microRNAs (miRNAs), we constructed a competing endogenous RNA (ceRNA) network, comprising 9 lncRNAs and 17 mRNAs that were significantly correlated. Among the 26 lncRNA-mRNA pairs, only the MIR210HG-BPIFC pair was verified by another HCM dataset (GSE130036) and the isoprenaline (ISO)-induced HCM cell model. Furthermore, knockdown of MIR210HG increased the regulatory miRNAs and decreased the mRNA expression of BPIFC correspondingly in AC16 cells. Additionally, the analysis of immune cell infiltration indicated that the MIR210HG-BPIFC pair was potentially involved in the infiltration of naïve CD4+ T cells and CD8+ T cells. Together, our findings indicate that the decreased expression of the lncRNA-mRNA pair MIR210HG-BPIFC was significantly correlated with the pathogenesis of the disease and may be involved in the immune cell infiltration in the mechanism of HCM.

In-depth proteomic analysis identifies key gene signatures predicting therapeutic efficacy of anti-PD-1/PD-L1 monotherapy in non-small cell lung cancer

Background

Immunotherapy has opened up a new era of individualized treatment for non-small cell lung cancer (NSCLC) with negative driver gene mutations. Anti-programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) antibodies have been the main options for immunotherapy over the past decade. Screening for predictive markers of anti-PD-1/PD-L1-responsive patients remains a focus in the field of immunotherapy, especially on the protein level in which relevant proteomic biomarkers are still lacking.

Methods

We collected samples from 23 patients with NSCLC who received anti-PD-1/PD-L1 monotherapy and were followed up for three years. The proteomic profile of the tumor was obtained by mass spectrometry (MS). Meanwhile, we combined the RNA sequencing (RNA-seq) data of 27 patients treated with anti-PD-1/PD-L1 therapy in a previous study to establish an integrated gene network. Weighted correlation network analysis (WGCNA) and elastic network were implemented to screen the top gene modules for predicting treatment-responsive patients. Gene expression related mutational patterns were also retrieved for validation in the Memorial Sloan-Kettering Cancer Center (MSKCC) cohort.

Results

Our results showed the gene expression profile of MOXD1, PHAF1, KRT7, ANKRD30A, TMEM184A, KIR3DL1, and KCNK4 could better predict the durable response to anti-PD-1/PD-L1 therapy, with the specificity and sensitivity of 0.76 and 0.6, respectively. Besides, the mutational gene profile associated with these genes also suggested an association with favorable response in the MSKCC cohort. Patient-specific protein-protein interaction (PPI) network also indicated strong correlation among KRT7, TMEM184A and ANKRD30A.

Conclusions

Our study indicated that key gene signatures identified by machine learning model could be utilized for clinical screening of patients who might benefit from anti-PD-1 therapy. Further mechanistic investigations around these genes are warranted.

Transcriptome analysis of human hypertrophic cardiomyopathy reveals inhibited cardiac development pathways in children

The epidemiological, etiological, and clinical characteristics vary greatly between pediatric (P-HCM) and adult (A-HCM) hypertrophic cardiomyopathy (HCM) patients, and the understanding of the heterogeneous pathogenesis mechanisms is insufficient to date. In this study, we aimed to comprehensively assess the respective transcriptome signatures and uncover the essential differences in gene expression patterns among A-HCM and P-HCM. The transcriptome data of adults were collected from public data (GSE89714), and novel pediatric data were first obtained by RNA sequencing from 14 P-HCM and 9 infantile donor heart samples. Our study demonstrates the common signatures of myofilament or protein synthesis and calcium ion regulation pathways in HCM. Mitochondrial function is specifically dysregulated in A-HCM, whereas the inhibition of cardiac developing networks typifies P-HCM. These findings not only distinguish the transcriptome characteristics in children and adults with HCM but also reveal the potential mechanism of the higher incidence of septal defects in P-HCM patients.

Myocardial Fibrosis in Hypertrophic Cardiomyopathy: A Perspective from Fibroblasts

Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease and the leading cause of sudden cardiac death in young people. Mutations in genes that encode structural proteins of the cardiac sarcomere are the more frequent genetic cause of HCM. The disease is characterized by cardiomyocyte hypertrophy and myocardial fibrosis, which is defined as the excessive deposition of extracellular matrix proteins, mainly collagen I and III, in the myocardium. The development of fibrotic tissue in the heart adversely affects cardiac function. In this review, we discuss the latest evidence on how cardiac fibrosis is promoted, the role of cardiac fibroblasts, their interaction with cardiomyocytes, and their activation via the TGF-β pathway, the primary intracellular signalling pathway regulating extracellular matrix turnover. Finally, we summarize new findings on profibrotic genes as well as genetic and non-genetic factors involved in the pathophysiology of HCM.

Analysis of risk stratification and prevention of sudden death in pediatric patients with hypertrophic cardiomyopathy: Dilemmas and clarity

Hypertrophic cardiomyopathy (HCM) has been considered the most common cause of sudden death (SD) in the young. However, introduction of implantable cardioverter-defibrillators (ICDs) in HCM has proved highly effective and the mainstay of preventing SD in children, adolescents, and adults by terminating malignant ventricular tachyarrhythmias. Nevertheless, ICD decision making is generally regarded as more difficult in pediatrics, and the strategy for selecting ICD patients from this population remains without consensus. Prospective studies in HCM children and adolescents have shown the American Heart Association/American College of Cardiology traditional major risk marker strategy to be reliable with >90% sensitivity in selecting patients for SD prevention. International data in >2000 young HCM patients assembled over 20 years who were stratified by major risk markers showed ICDs effectively prevented SD in 20%. Alternatively, novel quantitative risk scoring initiatives provide 5-year risk estimates that are potentially useful as adjunctive tools to facilitate discussion of prophylactic ICD risks vs benefit but are as yet unsupported by prospective outcome studies. Risk scoring strategies are characterized by reasonable discriminatory statistical power (C-statistic 0.69-0.76) for identifying patients with SD events but with relatively low sensitivity, albeit with specificity comparable with the risk marker strategy. While some reticence for obligating healthy-appearing young patients to lifelong device implants is understandable, underutilization of the ICD in high-risk children and adolescents can represent a lost opportunity for fulfilling the long-standing aspiration of SD prevention. This review provides a critical assessment of the current strengths and weaknesses of SD risk stratification strategies in young HCM patients in an effort to clarify clinical decision making in this challenging subpopulation.

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Previous studies have revealed that patients with hypertrophic cardiomyopathy (HCM) exhibit differences in symptom severity and prognosis, indicating potential HCM subtypes among these patients. Here, 793 patients with HCM were recruited at an average follow-up of 32.78 ± 27.58 months to identify potential HCM subtypes by performing consensus clustering on the basis of their echocardiography features. Furthermore, we proposed a systematic method for illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learning modeling and interactome network detection techniques based on whole-exome sequencing data. Another independent cohort that consisted of 414 patients with HCM was recruited to replicate the findings. Consequently, two subtypes characterized by different clinical outcomes were identified in HCM. Patients with subtype 2 presented asymmetric septal hypertrophy associated with a stable course, while those with subtype 1 displayed left ventricular systolic dysfunction and aggressive progression. Machine learning modeling based on personal whole-exome data identified 46 genes with mutation burden that could accurately predict subtype propensities. Furthermore, the patients in another cohort predicted as subtype 1 by the 46-gene model presented increased left ventricular end-diastolic diameter and reduced left ventricular ejection fraction. By employing echocardiography and genetic screening for the 46 genes, HCM can be classified into two subtypes with distinct clinical outcomes.

Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping

The complex landscape of cardiovascular diseases encompasses a wide range of related pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous phenotypes. This variety of manifestations poses significant challenges in the development of treatment strategies. The increasing availability of precise phenotypic and multiomics data of cardiovascular disease patient populations has spurred the development of a variety of computational disease subtyping techniques to identify distinct subgroups with unique underlying pathogeneses. In this review, we outline the essential components of computational approaches to select, integrate, and cluster omics and clinical data in the context of cardiovascular disease research. We delve into the challenges faced during different stages of the analysis, including feature selection and extraction, data integration, and clustering algorithms. Next, we highlight representative applications of subtyping pipelines in heart failure and coronary artery disease. Finally, we discuss the current challenges and future directions in the development of robust subtyping approaches that can be implemented in clinical workflows, ultimately contributing to the ongoing evolution of precision medicine in health care.

Edge and modular significance assessment in individual-specific networks

Individual-specific networks, defined as networks of nodes and connecting edges that are specific to an individual, are promising tools for precision medicine. When such networks are biological, interpretation of functional modules at an individual level becomes possible. An under-investigated problem is relevance or "significance" assessment of each individual-specific network. This paper proposes novel edge and module significance assessment procedures for weighted and unweighted individual-specific networks. Specifically, we propose a modular Cook's distance using a method that involves iterative modeling of one edge versus all the others within a module. Two procedures assessing changes between using all individuals and using all individuals but leaving one individual out (LOO) are proposed as well (LOO-ISN, MultiLOO-ISN), relying on empirically derived edges. We compare our proposals to competitors, including adaptions of OPTICS, kNN, and Spoutlier methods, by an extensive simulation study, templated on real-life scenarios for gene co-expression and microbial interaction networks. Results show the advantages of performing modular versus edge-wise significance assessments for individual-specific networks. Furthermore, modular Cook's distance is among the top performers across all considered simulation settings. Finally, the identification of outlying individuals regarding their individual-specific networks, is meaningful for precision medicine purposes, as confirmed by network analysis of microbiome abundance profiles.

InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop

Deciphering cell-type-specific 3D structures of chromatin is challenging. Here, we present InferLoop, a novel method for inferring the strength of chromatin interaction using single-cell chromatin accessibility data. The workflow of InferLoop is, first, to conduct signal enhancement by grouping nearby cells into bins, and then, for each bin, leverage accessibility signals for loop signals using a newly constructed metric that is similar to the perturbation of the Pearson correlation coefficient. In this study, we have described three application scenarios of InferLoop, including the inference of cell-type-specific loop signals, the prediction of gene expression levels and the interpretation of intergenic loci. The effectiveness and superiority of InferLoop over other methods in those three scenarios are rigorously validated by using the single-cell 3D genome structure data of human brain cortex and human blood, the single-cell multi-omics data of human blood and mouse brain cortex, and the intergenic loci in the GWAS Catalog database as well as the GTEx database, respectively. In addition, InferLoop can be applied to predict loop signals of individual spots using the spatial chromatin accessibility data of mouse embryo. InferLoop is available at https://github.com/jumphone/inferloop.

Network medicine: an approach to complex kidney disease phenotypes

Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.

Pursuing functional biomarkers in complex disease: Focus on pulmonary arterial hypertension

A major gap in diagnosis, classification, risk stratification, and prediction of therapeutic response exists in pulmonary arterial hypertension (PAH), driven in part by a lack of functional biomarkers that are also disease-specific. In this regard, leveraging big data-omics analyses using innovative approaches that integrate network medicine and machine learning correlated with clinically useful indices or risk stratification scores is an approach well-positioned to advance PAH precision medicine. For example, machine learning applied to a panel of 48 cytokines, chemokines, and growth factors could prognosticate PAH patients with immune-dominant subphenotypes at elevated or low-risk for mortality. Here, we discuss strengths and weaknesses of the most current studies evaluating omics-derived biomarkers in PAH. Progress in this field is offset by studies with small sample size, pervasive limitations in bioinformatics, and lack of standardized methods for data processing and interpretation. Future success in this field, in turn, is likely to hinge on mechanistic validation of data outputs in order to couple functional biomarker data with target-specific therapeutics in clinical practice.

Multiomics Network Medicine Approaches to Precision Medicine and Therapeutics in Cardiovascular Diseases

Cardiovascular diseases (CVD) are the leading cause of death worldwide and display complex phenotypic heterogeneity caused by many convergent processes, including interactions between genetic variation and environmental factors. Despite the identification of a large number of associated genes and genetic loci, the precise mechanisms by which these genes systematically influence the phenotypic heterogeneity of CVD are not well understood. In addition to DNA sequence, understanding the molecular mechanisms of CVD requires data from other omics levels, including the epigenome, the transcriptome, the proteome, as well as the metabolome. Recent advances in multiomics technologies have opened new precision medicine opportunities beyond genomics that can guide precise diagnosis and personalized treatment. At the same time, network medicine has emerged as an interdisciplinary field that integrates systems biology and network science to focus on the interactions among biological components in health and disease, providing an unbiased framework through which to integrate systematically these multiomics data. In this review, we briefly present such multiomics technologies, including bulk omics and single-cell omics technologies, and discuss how they can contribute to precision medicine. We then highlight network medicine-based integration of multiomics data for precision medicine and therapeutics in CVD. We also include a discussion of current challenges, potential limitations, and future directions in the study of CVD using multiomics network medicine approaches.

Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond

A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.

Evidence of Advanced Pulmonary Vascular Remodeling in Obstructive Hypertrophic Cardiomyopathy With Pulmonary Hypertension

BACKGROUND

Elevated mean pulmonary artery pressure (mPAP) is common in patients with hypertrophic cardiomyopathy (HCM) and heart failure symptoms. However, dynamic left ventricular (LV) outflow tract obstruction may confound interpretation of pulmonary hypertension (PH) pathophysiologic features in HCM when relying on resting invasive hemodynamic data alone.

RESEARCH QUESTION

Do structural changes to the lung vasculature clarify PH pathophysiologic features in patients with HCM with progressive heart failure?

STUDY DESIGN AND METHODS

Clinical data and ultrarare lung autopsy specimens were acquired retrospectively from the National Institutes of Health (1975-1992). Patients were included based on the availability of lung tissue and recorded mPAP. Discarded tissue from rejected lung donors served as control specimens. Histomorphology was performed on pulmonary arterioles and veins. Comparisons were calculated using the Student t test and Mann-Whitney U test; Pearson correlation was used to assess association between morphometric measurements and HCM cardiac and hemodynamic measurements.

RESULTS

The HCM cohort (n = 7; mean ± SD age, 43 ± 18 years; 71% men) showed maximum mean ± SD LV wall thickness of 25 ± 2.8 mm, mean ± SD outflow tract gradient of 90 ± 30 mm Hg, median mPAP of 25 mm Hg (interquartile range [IQR], 6 mm Hg), median pulmonary artery wedge pressure (PAWP) of 16 mm Hg (IQR, 4 mm Hg), and median pulmonary vascular resistance of 1.8 Wood units (WU; IQR, 2.4 WU). Compared with control samples (n = 5), patients with HCM showed greater indexed pulmonary arterial hypertrophy (20.7 ± 7.2% vs 49.7 ± 12%; P < .001) and arterial wall fibrosis (11.5 ± 3.4 mm vs 21.0 ± 4.7 mm; P < .0001), which correlated with mPAP (r = 0.84; P = .018), PAWP (r = 0.74; P = .05), and LV outflow tract gradient (r = 0.78; P = .035). Compared with control samples, pulmonary vein thickness was increased by 2.9-fold (P = .008) in the HCM group, which correlated with mPAP (r = 0.81; P = .03) and LV outflow tract gradient (r = 0.83; P = .02).

INTERPRETATION

To the best of our knowledge, these data demonstrate for the first time that in patients with obstructive HCM, heart failure is associated with pathogenic pulmonary vascular remodeling even when mPAP is elevated only mildly. These observations clarify PH pathophysiologic features in HCM, with future implications for clinical strategies that mitigate outflow tract obstruction.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Proline and glucose metabolic reprogramming supports vascular endothelial and medial biomass in pulmonary arterial hypertension

In pulmonary arterial hypertension (PAH), inflammation promotes a fibroproliferative pulmonary vasculopathy. Reductionist studies emphasizing single biochemical reactions suggest a shift toward glycolytic metabolism in PAH; however, key questions remain regarding the metabolic profile of specific cell types within PAH vascular lesions in vivo. We used RNA-Seq to profile the transcriptome of pulmonary artery endothelial cells (PAECs) freshly isolated from an inflammatory vascular injury model of PAH ex vivo, and these data were integrated with information from human gene ontology pathways. Network medicine was then used to map all aa and glucose pathways to the consolidated human interactome, which includes data on 233,957 physical protein-protein interactions. Glucose and proline pathways were significantly close to the human PAH disease module, suggesting that these pathways are functionally relevant to PAH pathobiology. To test this observation in vivo, we used multi-isotope imaging mass spectrometry to map and quantify utilization of glucose and proline in the PAH pulmonary vasculature at subcellular resolution. Our findings suggest that elevated glucose and proline avidity underlie increased biomass in PAECs and the media of fibrosed PAH pulmonary arterioles. Overall, these data show that anabolic utilization of glucose and proline are fundamental to the vascular pathology of PAH.

Monitoring of Serological, Cellular and Genomic Biomarkers in Transplantation, Computational Prediction Models and Role of Cell-Free DNA in Transplant Outcome

The process and evolution of an organ transplant procedure has evolved in terms of the prevention of immunological rejection with the improvement in the determination of immune response genes. These techniques include considering more important genes, more polymorphism detection, more refinement of the response motifs, as well as the analysis of epitopes and eplets, its capacity to fix complement, the PIRCHE algorithm and post-transplant monitoring with promising new biomarkers that surpass the classic serum markers such as creatine and other similar parameters of renal function. Among these new biomarkers, we analyze new serological, urine, cellular, genomic and transcriptomic biomarkers and computational prediction, with particular attention to the analysis of donor free circulating DNA as an optimal marker of kidney damage.

"Transplantomics" for predicting allograft rejection: real-life applications and new strategies from Network Medicine

Although decades of the reductionist approach achieved great milestones in optimizing the immunosuppression therapy, traditional clinical parameters still fail in predicting both acute and chronic (mainly) rejection events leading to higher rates across all solid organ transplants. To clarify the underlying immune-related cellular and molecular mechanisms, current biomedical research is increasingly focusing on "transplantomics" which relies on a huge quantity of big data deriving from genomics, transcriptomics, epigenomics, proteomics, and metabolomics platforms. The AlloMap (gene expression) and the AlloSure (donor-derived cell-free DNA) tests represent two successful examples of how omics and liquid biopsy can really improve the precision medicine of heart and kidney transplantation. One of the major challenges in translating big data in clinically useful biomarkers is the integration and interpretation of the different layers of omics datasets. Network Medicine offers advanced bioinformatic-molecular strategies which were widely used to integrate large omics datasets and clinical information in end-stage patients to prioritize potential biomarkers and drug targets. The application of network-oriented approaches to clarify the complex nature of graft rejection is still in its infancy. Here, we briefly discuss the real-life clinical applications derived from omics datasets as well as novel opportunities for establishing predictive tests in solid organ transplantation. Also, we provide an original "graft rejection interactome" and propose network-oriented strategies which can be useful to improve precision medicine of solid organ transplantation.

Extracellular Vesicles and Ischemic Cardiovascular Diseases

Characterized by coronary artery obstruction or stenosis, ischemic cardiovascular diseases as advanced stages of coronary heart diseases commonly lead to left ventricular aneurysm, ventricular septal defect, and mitral insufficiency. Extracellular vesicles (EVs) secreted by diverse cells in the body exert roles in cell-cell interactions and intrinsic cellular regulations. With a lipid double-layer membrane and biological components such as DNA, protein, mRNA, microRNAs (miRNA), and siRNA inside, the EVs function as paracrine signaling for the pathophysiology of ischemic cardiovascular diseases and maintenance of the cardiac homeostasis. Unlike stem cell transplantation with the potential tumorigenicity and immunogenicity, the EV-based therapeutic strategy is proposed to satisfy the demand for cardiac repair and regeneration while the circulating EVs detected by a noninvasive approach can act as precious biomarkers. In this chapter, we extensively summarize the cardioprotective functions of native EVs and bioengineered EVs released from stem cells, cardiomyocytes, cardiac progenitor cells (CPCs), endothelial cells, fibroblast, smooth muscle cells, and immune cells. In addition, the potential of EVs as robust molecule biomarkers is discussed for clinical diagnosis of ischemic cardiovascular disease, attributed to the same pathology of EVs as that of their origin. Finally, we highlight EV-based therapy as a biocompatible alternative to direct cell-based therapy for ischemic cardiovascular diseases.

Molecular interaction networks and drug development: Novel approach to drug target identification and drug repositioning

Conventional drug discovery requires identifying a protein target believed to be important for disease mechanism and screening compounds for those that beneficially alter the target's function. While this approach has been an effective one for decades, recent data suggest that its continued success is limited largely owing to the highly prevalent irreducibility of biologically complex systems that govern disease phenotype to a single primary disease driver. Network medicine, a new discipline that applies network science and systems biology to the analysis of complex biological systems and disease, offers a novel approach to overcoming these limitations of conventional drug discovery. Using the comprehensive protein-protein interaction network (interactome) as the template through which subnetworks that govern specific diseases are identified, potential disease drivers are unveiled and the effect of novel or repurposed drugs, used alone or in combination, is studied. This approach to drug discovery offers new and exciting unbiased possibilities for advancing our knowledge of disease mechanisms and precision therapeutics.

Community Detection in Protein-Protein Interaction Networks and Applications

The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

Novel Genes Involved in Hypertrophic Cardiomyopathy: Data of Transcriptome and Methylome Profiling

Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disease; its pathogenesis is still being intensively studied to explain the reasons for the significant genetic and phenotypic heterogeneity of the disease. To search for new genes involved in HCM development, we analyzed gene expression profiles coupled with DNA methylation profiles in the hypertrophied myocardia of HCM patients. The transcriptome analysis identified significant differences in the levels of 193 genes, most of which were underexpressed in HCM. The methylome analysis revealed 1755 nominally significant differentially methylated positions (DMPs), mostly hypomethylated in HCM. Based on gene ontology enrichment analysis, the majority of biological processes, overrepresented by both differentially expressed genes (DEGs) and DMP-containing genes, are involved in the regulation of locomotion and muscle structure development. The intersection of 193 DEGs and 978 DMP-containing genes pinpointed eight common genes, the expressions of which correlated with the methylation levels of the neighboring DMPs. Half of these genes (AUTS2, BRSK2, PRRT1, and SLC17A7), regulated by the mechanism of DNA methylation, were underexpressed in HCM and were involved in neurogenesis and synapse functioning. Our data, suggesting the involvement of innervation-associated genes in HCM, provide additional insights into disease pathogenesis and expand the field of further research.

From novel discovery tools and biomarkers to precision medicine-basic cardiovascular science highlights of 2021/22

Here, we review the highlights of cardiovascular basic science published in 2021 and early 2022 on behalf of the European Society of Cardiology Council for Basic Cardiovascular Science. We begin with non-coding RNAs which have emerged as central regulators cardiovascular biology, and then discuss how technological developments in single-cell 'omics are providing new insights into cardiovascular development, inflammation, and disease. We also review recent discoveries on the biology of extracellular vesicles in driving either protective or pathogenic responses. The Nobel Prize in Physiology or Medicine 2021 recognized the importance of the molecular basis of mechanosensing and here we review breakthroughs in cardiovascular sensing of mechanical force. We also summarize discoveries in the field of atherosclerosis including the role of clonal haematopoiesis of indeterminate potential, and new mechanisms of crosstalk between hyperglycaemia, lipid mediators, and inflammation. The past 12 months also witnessed major advances in the field of cardiac arrhythmia including new mechanisms of fibrillation. We also focus on inducible pluripotent stem cell technology which has demonstrated disease causality for several genetic polymorphisms in long-QT syndrome and aortic valve disease, paving the way for personalized medicine approaches. Finally, the cardiovascular community has continued to better understand COVID-19 with significant advancement in our knowledge of cardiovascular tropism, molecular markers, the mechanism of vaccine-induced thrombotic complications and new anti-viral therapies that protect the cardiovascular system.

What Causes Hypertrophic Cardiomyopathy?

Hypertrophic cardiomyopathy (HCM) is a global and relatively common cause of patient morbidity and mortality and is among the first reported monogenic cardiac diseases. For 30 years, the basic etiology of HCM has been attributed largely to variants in individual genes encoding cardiac sarcomere proteins, with the implication that HCM is fundamentally a genetic disease. However, data from clinical and network medicine analyses, as well as contemporary genetic studies show that single gene variants do not fully explain the broad and diverse HCM clinical spectrum. These transformative advances place a new focus on possible novel interactions between acquired disease determinants and genetic context to produce complex HCM phenotypes, also offering a measure of caution against overemphasizing monogenics as the principal cause of this disease. These new perspectives in which HCM is not a uniformly genetic disease but likely explained by multifactorial etiology will also unavoidably impact how HCM is viewed by patients and families in the clinical practicing community going forward, including relevance to genetic counseling and access to healthcare insurance and psychosocial wellness.

Identification of circular RNAs in cardiac hypertrophy and cardiac fibrosis

Cardiac hypertrophy initially serves as an adaptive response to physiological and pathological stimuli. Sustained hypertrophy progress to pathological cardiac hypertrophy, cardiac fibrosis and ultimately lead to heart failure, one of the leading medical causes of mortality worldwide. Intervention of pathological cardiac hypertrophy can effectively reduce the occurrence of heart failure. Abundant factors, such as adrenergic, angiotensin, and endothelin (ET-1) receptors, have been shown to participate in the regulation of pathological cardiac hypertrophy. Recently, an increasing number of studies have indicated that circRNA and circRNA-miRNA–mRNA network regulation is indispensable for the posttranscriptional regulation of mRNA in cardiac hypertrophy. In our study, the morphological, cardiac function and pathological changes during cardiac hypertrophy were investigated. RNA sequencing identified 93 circRNAs that were differentially expressed in the TAC_2w group, and 55 circRNAs in the TAC_4w group compared with the sham group. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses identified several significant pathways, including hypertrophic cardiomyopathy, extracellular matrix (ECM)-receptor interaction and focal adhesion. Coexpression analyses were performed for differentially expressed circRNAs and differentially expressed mRNAs. Based on gene set enrichment analysis (GSEA), 8 circRNAs (mmu-Nfkb1_0001, mmu-Smad4_0007, mmu-Hecw2_0009, mmu-Itgbl1_0002, mmu-Lrrc2_0005, mmu-Cpeb3_0007, mmu-Ryr2_0040, and mmu-Rtn4_0001) involved in cardiac hypertrophy and cardiac fibrosis were identified. We validated some key circRNAs by qPCR. The crucial coexpression of circRNA–mRNA and its interaction with miRNA showed the possible mechanism of circRNAs in the process of cardiac dysfunction. Our results may provide promising targets for the treatment of pathological cardiac hypertrophy and fibrosis.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension

Pulmonary hypertension is a complex disease with multiple causes, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of pulmonary hypertension pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular -omics profiling, and other modalities. In this review, we summarize key data sets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and subphenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.

Individualized Medicine in Africa: Bringing the Practice Into the Realms of Population Heterogeneity

The declared aim of "personalized", "stratified" or "precision" approaches is to place individual variation, as ascertained through genomic and various other biomarkers, at the heart of Scientific Medicine using it to predict risk of disease or response to therapy and to tailor interventions and target therapies so as to maximize benefit and minimize risk for individual patients and efficiency for the health care system overall. It is often contrasted to current practices for which the scientific base is rooted in concepts of a "universal biology" and a "typical" or "average patient" and in which variation is ignored. Yet both approaches equally overlook the hierarchical nature of human variation and the critical importance of differences between populations. Impact of genetic heterogeneity has to be seen within that context to be meaningful and subsequently useful. In Africa such complexity is compounded by the high effective size of its populations, their diverse histories and the diversity of the environmental terrains they occupy, rendering analysis of gene environment interactions including the establishment of phenotype genotype correlations even more cumbersome. Henceforth "Individualized" methods and approaches can only magnify the shortcomings of universal approaches if adopted without due regard to these complexities. In the current perspective we review examples of potential hurdles that may confront biomedical scientists and analysts in genomic medicine in clinical and public health genomics in Africa citing specific examples from the current SARS-COV2 pandemic and the challenges of establishing reference biobanks and pharmacogenomics reference values.

Fibroblasts orchestrate cellular crosstalk in the heart through the ECM

Cell communication is needed for organ function and stress responses, especially in the heart. Cardiac fibroblasts, cardiomyocytes, immune cells, and endothelial cells comprise the major cell types in ventricular myocardium that together coordinate all functional processes. Critical to this cellular network is the non-cellular extracellular matrix (ECM) that provides structure and harbors growth factors and other signaling proteins that affect cell behavior. The ECM is not only produced and modified by cells within the myocardium, largely cardiac fibroblasts, it also acts as an avenue for communication among all myocardial cells. In this Review, we discuss how the development of therapeutics to combat cardiac diseases, specifically fibrosis, relies on a deeper understanding of how the cardiac ECM is intertwined with signaling processes that underlie cellular activation and behavior.

Altered intercellular communication and extracellular matrix signaling as a potential disease mechanism in human hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is considered a primary disorder of the sarcomere resulting in unexplained left ventricular hypertrophy but the paradoxical association of nonmyocyte phenotypes such as fibrosis, mitral valve anomalies and microvascular occlusion is unexplained. To understand the interplay between cardiomyocyte and nonmyocyte cell types in human HCM, single nuclei RNA-sequencing was performed on myectomy specimens from HCM patients with left ventricular outflow tract obstruction and control samples from donor hearts free of cardiovascular disease. Clustering analysis based on gene expression patterns identified a total of 34 distinct cell populations, which were classified into 10 different cell types based on marker gene expression. Differential gene expression analysis comparing HCM to Normal datasets revealed differences in sarcomere and extracellular matrix gene expression. Analysis of expressed ligand-receptor pairs across multiple cell types indicated profound alteration in HCM intercellular communication, particularly between cardiomyocytes and fibroblasts, fibroblasts and lymphocytes and involving integrin β1 and its multiple extracellular matrix (ECM) cognate ligands. These findings provide a paradigm for how sarcomere dysfunction is associated with reduced cardiomyocyte secretion of ECM ligands, altered fibroblast ligand-receptor interactions with other cell types and increased fibroblast to lymphocyte signaling, which can further alter the ECM composition and promote nonmyocyte phenotypes.

DYRK1B-STAT3 Drives Cardiac Hypertrophy and Heart Failure by Impairing Mitochondrial Bioenergetics

BACKGROUND

Heart failure is a global public health issue that is associated with increasing morbidity and mortality. Previous studies have suggested that mitochondrial dysfunction plays critical roles in the progression of heart failure; however, the underlying mechanisms remain unclear. Because kinases have been reported to modulate mitochondrial function, we investigated the effects of DYRK1B (dual-specificity tyrosine-regulated kinase 1B) on mitochondrial bioenergetics, cardiac hypertrophy, and heart failure.

METHODS

We engineered DYRK1B transgenic and knockout mice and used transverse aortic constriction to produce an in vivo model of cardiac hypertrophy. The effects of DYRK1B and its downstream mediators were subsequently elucidated using RNA-sequencing analysis and mitochondrial functional analysis.

RESULTS

We found that DYRK1B expression was clearly upregulated in failing human myocardium and in hypertrophic murine hearts, as well. Cardiac-specific DYRK1B overexpression resulted in cardiac dysfunction accompanied by a decline in the left ventricular ejection fraction, fraction shortening, and increased cardiac fibrosis. In striking contrast to DYRK1B overexpression, the deletion of DYRK1B mitigated transverse aortic constriction-induced cardiac hypertrophy and heart failure. Mechanistically, DYRK1B was positively associated with impaired mitochondrial bioenergetics by directly binding with STAT3 to increase its phosphorylation and nuclear accumulation, ultimately contributing toward the downregulation of PGC-1α (peroxisome proliferator-activated receptor gamma coactivator-1α). Furthermore, the inhibition of DYRK1B or STAT3 activity using specific inhibitors was able to restore cardiac performance by rejuvenating mitochondrial bioenergetics.

CONCLUSIONS

Taken together, the findings of this study provide new insights into the previously unrecognized role of DYRK1B in mitochondrial bioenergetics and the progression of cardiac hypertrophy and heart failure. Consequently, these findings may provide new therapeutic options for patients with heart failure.

FGF21 (Fibroblast Growth Factor 21) Defines a Potential Cardiohepatic Signaling Circuit in End-Stage Heart Failure

BACKGROUND

Extrinsic control of cardiomyocyte metabolism is poorly understood in heart failure (HF). FGF21 (Fibroblast growth factor 21), a hormonal regulator of metabolism produced mainly in the liver and adipose tissue, is a prime candidate for such signaling.

METHODS

To investigate this further, we examined blood and tissue obtained from human subjects with end-stage HF with reduced ejection fraction at the time of left ventricular assist device implantation and correlated serum FGF21 levels with cardiac gene expression, immunohistochemistry, and clinical parameters.

RESULTS

Circulating FGF21 levels were substantially elevated in HF with reduced ejection fraction, compared with healthy subjects (HF with reduced ejection fraction: 834.4 [95% CI, 628.4-1040.3] pg/mL, n=40; controls: 146.0 [86.3-205.7] pg/mL, n=20, P=1.9×10-5). There was clear FGF21 staining in diseased cardiomyocytes, and circulating FGF21 levels negatively correlated with the expression of cardiac genes involved in ketone metabolism, consistent with cardiac FGF21 signaling. FGF21 gene expression was very low in failing and nonfailing hearts, suggesting extracardiac production of the circulating hormone. Circulating FGF21 levels were correlated with BNP (B-type natriuretic peptide) and total bilirubin, markers of chronic cardiac and hepatic congestion.

CONCLUSIONS

Circulating FGF21 levels are elevated in HF with reduced ejection fraction and appear to bind to the heart. The liver is likely the main extracardiac source. This supports a model of hepatic FGF21 communication to diseased cardiomyocytes, defining a potential cardiohepatic signaling circuit in human HF.

Hypertrophic Cardiomyopathy: New Concepts and Therapies

Hypertrophic cardiomyopathy (HCM), a relatively common, globally distributed, and often inherited myocardial disorder, transformed over the last several years into a treatable condition with the emergence of effective management options that alter natural history at all ages. Now available are a matured risk stratification algorithm selecting patients for prophylactic implantable defibrillators that prevent arrhythmic sudden death; low-risk, high-benefit surgical myectomy to reverse progressive heart failure symptoms due to left ventricular outflow obstruction; anticoagulation prophylaxis to prevent atrial fibrillation-mediated embolic stroke; and heart transplant for refractory end-stage disease in the absence of obstruction. Those strategies have resulted in reduction of HCM-related morbidity and reduction of mortality to 0.5% per year.

Construction and contextualization approaches for protein-protein interaction networks

Protein-protein interaction network (PPIN) analysis is a widely used method to study the contextual role of proteins of interest, to predict novel disease genes, disease or functional modules, and to identify novel drug targets. PPIN-based analysis uses both generic and context-specific networks. Multiple contextualization methodologies have been described, such as shortest-path algorithms, neighborhood-based methods, and diffusion/propagation algorithms. This review discusses these methods, provides intuitive representations of PPIN contextualization, and also examines how the quality of such context-specific networks could be improved by considering additional sources of evidence. As a heuristic, we observe that tasks such as identifying disease genes, drug targets, and protein complexes should consider local neighborhoods, while uncovering disease mechanisms and discovering disease-pathways would gain from diffusion-based construction.

Precision Medicine in Patients with Differential Diabetic Phenotypes: Novel Opportunities from Network Medicine

INTRODUCTION

Diabetes mellitus (DM) comprises differential clinical phenotypes ranging from rare monogenic to common polygenic forms, such as type 1 (T1DM), type 2 (T2DM), and gestational diabetes, which are associated with cardiovascular complications. Also, the high- -risk prediabetic state is rising worldwide, suggesting the urgent need for early personalized strategies to prevent and treat a hyperglycemic state.

OBJECTIVE

We aim to discuss the advantages and challenges of Network Medicine approaches in clarifying disease-specific molecular pathways, which may open novel ways for repurposing approved drugs to reach diabetes precision medicine and personalized therapy.

CONCLUSION

The interactome or protein-protein interactions (PPIs) is a useful tool to identify subtle molecular differences between precise diabetic phenotypes and predict putative novel drugs. Despite being previously unappreciated as T2DM determinants, the growth factor receptor-bound protein 14 (GRB14), calmodulin 2 (CALM2), and protein kinase C-alpha (PRKCA) might have a relevant role in disease pathogenesis. Besides, in silico platforms have suggested that diflunisal, nabumetone, niflumic acid, and valdecoxib may be suitable for the treatment of T1DM; phenoxybenzamine and idazoxan for the treatment of T2DM by improving insulin secretion; and hydroxychloroquine reduce the risk of coronary heart disease (CHD) by counteracting inflammation. Network medicine has the potential to improve precision medicine in diabetes care and enhance personalized therapy. However, only randomized clinical trials will confirm the clinical utility of network- oriented biomarkers and drugs in the management of DM.

Mass-Spectrometry-Based Functional Proteomic and Phosphoproteomic Technologies and Their Application for Analyzing Ex Vivo and In Vitro Models of Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant disease thought to be principally caused by mutations in sarcomeric proteins. Despite extensive genetic analysis, there are no comprehensive molecular frameworks for how single mutations in contractile proteins result in the diverse assortment of cellular, phenotypic, and pathobiological cascades seen in HCM. Molecular profiling and system biology approaches are powerful tools for elucidating, quantifying, and interpreting dynamic signaling pathways and differential macromolecule expression profiles for a wide range of sample types, including cardiomyopathy. Cutting-edge approaches combine high-performance analytical instrumentation (e.g., mass spectrometry) with computational methods (e.g., bioinformatics) to study the comparative activity of biochemical pathways based on relative abundances of functionally linked proteins of interest. Cardiac research is poised to benefit enormously from the application of this toolkit to cardiac tissue models, which recapitulate key aspects of pathogenesis. In this review, we evaluate state-of-the-art mass-spectrometry-based proteomic and phosphoproteomic technologies and their application to in vitro and ex vivo models of HCM for global mapping of macromolecular alterations driving disease progression, emphasizing their potential for defining the components of basic biological systems, the fundamental mechanistic basis of HCM pathogenesis, and treating the ensuing varied clinical outcomes seen among affected patient cohorts.