Information encoded in a network of inflammation proteins predicts clinical outcome after myocardial infarction

A developmental lineage-based gene co-expression network for mouse pancreatic β-cells reveals a role for Zfp800 in pancreas development

To gain a deeper understanding of pancreatic β-cell development, we used iterative weighted gene correlation network analysis to calculate a gene co-expression network (GCN) from 11 temporally and genetically defined murine cell populations. The GCN, which contained 91 distinct modules, was then used to gain three new biological insights. First, we found that the clustered protocadherin genes are differentially expressed during pancreas development. Pcdhγ genes are preferentially expressed in pancreatic endoderm, Pcdhβ genes in nascent islets, and Pcdhα genes in mature β-cells. Second, after extracting sub-networks of transcriptional regulators for each developmental stage, we identified 81 zinc finger protein (ZFP) genes that are preferentially expressed during endocrine specification and β-cell maturation. Third, we used the GCN to select three ZFPs for further analysis by CRISPR mutagenesis of mice. Zfp800 null mice exhibited early postnatal lethality, and at E18.5 their pancreata exhibited a reduced number of pancreatic endocrine cells, alterations in exocrine cell morphology, and marked changes in expression of genes involved in protein translation, hormone secretion and developmental pathways in the pancreas. Together, our results suggest that developmentally oriented GCNs have utility for gaining new insights into gene regulation during organogenesis.

Network medicine in Cardiovascular Research

The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.

Peripheral Blood RNAs and Left Ventricular Dysfunction after Myocardial Infarction: Towards Translation into Clinical Practice

The treatment and early outcome of patients with acute myocardial infarction (MI) have dramatically improved the past decades, but the incidence of left ventricular (LV) dysfunction post-MI remains high. Peripheral blood RNAs reflect pathophysiological changes during acute MI and the inflammatory process. Therefore, these RNAs are promising new markers to molecularly phenotype patients and improve the early identification of patients at risk of subsequent LV dysfunction. We here discuss the coding and long non-coding RNAs that can be measured in peripheral blood of patients with acute MI and list the advantages and limitations for implementation in clinical practice. Although some studies provide preliminary evidence of their diagnostic and prognostic potential, the use of these makers has not yet been implemented in clinical practice. The added value of RNAs to improve treatment and outcome remains to be determined in larger clinical studies. International consortia are now catalyzing renewed efforts to investigate novel RNAs that may improve post-MI outcome in a precision-medicine approach. Graphical Abstract Peripheral blood RNAs reflect the inflammatory changes in acute MI. A number of studies provide preliminary evidence of their prognostic potential, although the use of these makers has not yet been assessed in clinical practice.

Fluid-based assays and precision medicine of cardiovascular diseases: the ‘hope’ for Pandora’s box?

Progresses in liquid-based assays may provide novel useful non-invasive indicators of cardiovascular (CV) diseases. By analysing circulating cells or their products in blood, saliva and urine samples, we can investigate molecular changes present at specific time points in each patient allowing sequential monitoring of disease evolution. For example, an increased number of circulating endothelial cells may be a diagnostic biomarker for diabetic nephropathy and heart failure with preserved ejection fraction. The assessment of circulating cell-free DNA (cfDNA) levels may be useful to predict severity of acute myocardial infarction, as well as diagnose heart graft rejection. Remarkably, circulating epigenetic biomarkers, including DNA methylation, histone modifications and non-coding RNAs are key pathogenic determinants of CV diseases representing putative useful biomarkers and drug targets. For example, the unmethylated FAM101A gene may specifically trace cfDNA derived from cardiomyocyte death providing a powerful diagnostic biomarker of apoptosis during ischaemia. Moreover, changes in plasma levels of circulating miR-92 may predict acute coronary syndrome onset in patients with diabetes. Now, network medicine provides a framework to analyse a huge amount of big data by describing a CV disease as a result of a chain of molecular perturbations rather than a single defect (reductionism). We outline advantages and challenges of liquid biopsy with respect to traditional tissue biopsy and summarise the main completed and ongoing clinical trials in CV diseases. Furthermore, we discuss the importance of combining fluid-based assays, big data and network medicine to improve precision medicine and personalised therapy in this field.

Network Medicine in Pathobiology

The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline of network medicine has rapidly evolved in parallel, providing an unbiased, comprehensive biological framework through which to interrogate and integrate systematically these large-scale, multi-omic data to enhance our understanding of disease mechanisms and to design drugs that reflect a deep knowledge of molecular pathobiology. In this review, we discuss the key principles of network medicine and the human disease network and explore the latest applications of network medicine in this multi-omic era. We also highlight the current conceptual and technological challenges, which serve as exciting opportunities by which to improve and expand the network-based applications beyond the artificial boundaries of the current state of human pathobiology.

A 3-gene panel improves the prediction of left ventricular dysfunction after acute myocardial infarction

BACKGROUND

Identification of patients at risk of poor outcome after acute myocardial infarction (MI) would allow tailoring healthcare to each individual. However, lack of prognostication tools renders this task challenging. Previous investigations suggested that blood transcriptome analysis may inform about prognosis after MI. We aim to independently confirm the value of gene expression profiles in the blood to predict left ventricular (LV) dysfunction after MI.

METHODS AND RESULTS

Five genes (LMNB1, MMP9, TGFBR1, LTBP4 and TNXB) selected from previous studies were measured in peripheral blood samples obtained at reperfusion in 449 MI patients. 79 patients had LV dysfunction as attested by an ejection fraction (EF) ≤40% at 4-month follow-up and 370 patients had a preserved LV function (EF>40%). LMNB1, MMP9 and TGFBR1 were up-regulated in patients with LV dysfunction and LTBP4 was down-regulated, as compared with patients with preserved LV function. The 5 genes were significant univariate predictors of LV dysfunction. In multivariable analyses adjusted with traditional risk factors and corrected for model overfitting, a panel of 3 genes - TNXB, TGFBR1 and LTBP4 - improved the prediction of a clinical model (p=0.00008) and provided a net reclassification index of 0.45 [0.23-0.69], p=0.0002 and an integrated discrimination improvement of 0.05 [0.02-0.09], p=0.001. Bootstrap internal validation confirmed the incremental predictive value of the 3-gene panel.

CONCLUSION

A 3-gene panel can aid to predict LV dysfunction after MI. Further independent validation is required before considering these findings for molecular diagnostic assay development.

Systems biology: An emerging strategy for discovering novel pathogenetic mechanisms that promote cardiovascular disease

Reductionist theory proposes that analyzing complex systems according to their most fundamental components is required for problem resolution, and has served as the cornerstone of scientific methodology for more than four centuries. However, technological gains in the current scientific era now allow for the generation of large datasets that profile the proteomic, genomic, and metabolomic signatures of biological systems across a range of conditions. The accessibility of data on such a vast scale has, in turn, highlighted the limitations of reductionism, which is not conducive to analyses that consider multiple and contemporaneous interactions between intermediates within a pathway or across constructs. Systems biology has emerged as an alternative approach to analyze complex biological systems. This methodology is based on the generation of scale-free networks and, thus, provides a quantitative assessment of relationships between multiple intermediates, such as protein-protein interactions, within and between pathways of interest. In this way, systems biology is well positioned to identify novel targets implicated in the pathogenesis or treatment of diseases. In this review, the historical root and fundamental basis of systems biology, as well as the potential applications of this methodology are discussed with particular emphasis on integration of these concepts to further understanding of cardiovascular disorders such as coronary artery disease and pulmonary hypertension.

Studying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA-Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from Peripheral Blood Mononuclear Cells

Myocardial infarction (MI) is a serious heart disease and a leading cause of mortality and morbidity worldwide. Although some molecules (genes, miRNAs and transcription factors (TFs)) associated with MI have been studied in a specific pathological context, their dynamic characteristics in gene expressions, biological functions and regulatory interactions in MI progression have not been fully elucidated to date. In the current study, we analyzed time-series RNA expression data from peripheral blood mononuclear cells. We observed that significantly differentially expressed genes were sharply up- or down-regulated in the acute phase of MI, and then changed slowly until the chronic phase. Biological functions involved at each stage of MI were identified. Additionally, dynamic miRNA–TF co-regulatory networks were constructed based on the significantly differentially expressed genes and miRNA–TF co-regulatory motifs, and the dynamic interplay of miRNAs, TFs and target genes were investigated. Finally, a new panel of candidate diagnostic biomarkers (STAT3 and ICAM1) was identified to have discriminatory capability for patients with or without MI, especially the patients with or without recurrent events. The results of the present study not only shed new light on the understanding underlying regulatory mechanisms involved in MI progression, but also contribute to the discovery of true diagnostic biomarkers for MI.

Pathway mapping and development of disease-specific biomarkers: protein-based network biomarkers

It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network-based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein-protein or gene-gene interactions that can be monitored and evaluated at different stages and time-points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue-generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases-specific, stage-specific, severity-specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed.

Using systems biology approaches to understand cardiac inflammation and extracellular matrix remodeling in the setting of myocardial infarction

Inflammation and extracellular matrix (ECM) remodeling are important components regulating the response of the left ventricle to myocardial infarction (MI). Significant cellular- and molecular-level contributors can be identified by analyzing data acquired through high-throughput genomic and proteomic technologies that provide expression levels for thousands of genes and proteins. Large-scale data provide both temporal and spatial information that need to be analyzed and interpreted using systems biology approaches in order to integrate this information into dynamic models that predict and explain mechanisms of cardiac healing post-MI. In this review, we summarize the systems biology approaches needed to computationally simulate post-MI remodeling, including data acquisition, data analysis for biomarker classification and identification, data integration to build dynamic models, and data interpretation for biological functions. An example for applying a systems biology approach to ECM remodeling is presented as a reference illustration.

miRNome in myocardial infarction: Future directions and perspective

MicroRNAs (miRNAs), which are small and non-coding RNAs, are genome encoded from viruses to humans. They contribute to various developmental, physiological and pathological processes in living organisms. A huge amount of research results revealed that miRNAs regulate these processes also in the heart. miRNAs may have cell-type-specific or tissue-specific expression patterns or may be expressed ubiquitously. Primary studies of miRNA involvement in hypertrophy, heart failure and myocardial infarction analyzed miRNAs that are enriched in or specific for cardiomyocytes; however, growing evidence suggest that other miRNAs, not cardiac or muscle-specific, play a significant role in cardiovascular disease. Abnormal miRNA regulation has been shown to be involved in cardiac diseases, suggesting that miRNAs might affect cardiac structure and function. In this review, we focus on miRNAs that have been found to contribute to the pathogenesis of myocardial infarction (MI) and the response post-MI and characterized as diagnostic, prognostic and therapeutic targets. The majority of these studies were performed using mouse and rat models of MI, with a focus on the identification of basic cellular and molecular pathways involved in MI and in the response post-MI. Much research has also been performed on animal and human plasma samples from MI individuals to identify miRNAs that are possible prognostic and/or diagnostic targets of MI and other MI-related diseases. A large proportion of research is focused on miRNAs as promising therapeutic targets and biomarkers of drug responses and/or stem cell treatment approaches. However, only a few studies have described miRNA expression in human heart tissue following MI.

Selecting biologically informative genes in co-expression networks with a centrality score

BACKGROUND

Measures of node centrality in biological networks are useful to detect genes with critical functional roles. In gene co-expression networks, highly connected genes (i.e., candidate hubs) have been associated with key disease-related pathways. Although different approaches to estimating gene centrality are available, their potential biological relevance in gene co-expression networks deserves further investigation. Moreover, standard measures of gene centrality focus on binary interaction networks, which may not always be suitable in the context of co-expression networks. Here, I also investigate a method that identifies potential biologically meaningful genes based on a weighted connectivity score and indicators of statistical relevance.

RESULTS

The method enables a characterization of the strength and diversity of co-expression associations in the network. It outperformed standard centrality measures by highlighting more biologically informative genes in different gene co-expression networks and biological research domains. As part of the illustration of the gene selection potential of this approach, I present an application case in zebrafish heart regeneration. The proposed technique predicted genes that are significantly implicated in cellular processes required for tissue regeneration after injury.

CONCLUSIONS

A method for selecting biologically informative genes from gene co-expression networks is provided, together with free open software.

Deciphering the molecular basis of human cardiovascular disease through network biology

PURPOSE OF REVIEW

This review introduces the fundamental concepts of network medicine and explores the feasibility and potential impact of network-based methods on predicting and ameliorating individual manifestations of human cardiovascular disease.

RECENT FINDINGS

Complex cardiovascular diseases rarely result from an abnormality in a single molecular effector, but, rather, nearly always are the net result of multiple pathobiological pathways that interact through an interconnected network. In the postgenomic era, a framework has emerged of the potential complexity of the interacting pathways that govern molecular actions in the human cell. As a result, network approaches have been developed to understand more comprehensively those interconnections that influence human disease. 'Network medicine' has already led to tangible discoveries of novel disease genes and pathways as well as improved mechanisms for rational drug development.

SUMMARY

As methodologies evolve, network medicine may better capture the complexity of human pathogenesis and, thus, re-define personalized disease classification and therapies.

Systems pathology: a critical review

The technological advances of the last twenty years together with the dramatic increase in computational power have injected new life into systems-level thinking in Medicine. This review emphasizes the close relationship of Systems Pathology to Systems Biology and delineates the differences between Systems Pathology and Clinical Systems Pathology. It also suggests an algorithm to support the application of systems-level thinking to clinical research, proposes applying systems-level thinking to the health care systems and forecasts an acceleration of preventive medicine as a result of the coupling of personal genomics with systems pathology.

Role of clinical bioinformatics in the development of network-based Biomarkers

Network biomarker as a new type of biomarkers with protein-protein interactions was initiated and investigated with the integration of knowledge on protein annotations, interaction, and signaling pathway. A number of methodologies and computational programs have been developed to integrate selected proteins into the knowledge-based networks via the combination of genomics, proteomics and bioinformatics. Alterations of network biomarkers can be monitored and evaluated at different stages and time points during the development of diseases, named dynamic network biomarkers. Dynamic network biomarkers should be furthermore correlated with clinical informatics, including patient complaints, history, therapies, clinical symptoms and signs, physician's examinations, biochemical analyses, imaging profiles, pathologies and other measurements.