CARFMAP: A Curated Pathway Map of Cardiac Fibroblasts

Vitamin C Regulates the Profibrotic Activity of Fibroblasts in In Vitro Replica Settings of Myocardial Infarction

Extracellular collagen remodeling is one of the central mechanisms responsible for the structural and compositional coherence of myocardium in patients undergoing myocardial infarction (MI). Activated primary cardiac fibroblasts following myocardial infarction are extensively investigated to establish anti-fibrotic therapies to improve left ventricular remodeling. To systematically assess vitamin C functions as a potential modulator involved in collagen fibrillogenesis in an in vitro model mimicking heart tissue healing after MI. Mouse primary cardiac fibroblasts were isolated from wild-type C57BL/6 mice and cultured under normal and profibrotic (hypoxic + transforming growth factor beta 1) conditions on freshly prepared coatings mimicking extracellular matrix (ECM) remodeling during healing after an MI. At 10 μg/mL, vitamin C reprogramed the respiratory mitochondrial metabolism, which is effectively associated with a more increased accumulation of intracellular reactive oxygen species (iROS) than the number of those generated by mitochondrial reactive oxygen species (mROS). The mRNA/protein expression of subtypes I, III collagen, and fibroblasts differentiations markers were upregulated over time, particularly in the presence of vitamin C. The collagen substrate potentiated the modulator role of vitamin C in reinforcing the structure of types I and III collagen synthesis by reducing collagen V expression in a timely manner, which is important in the initiation of fibrillogenesis. Altogether, our study evidenced the synergistic function of vitamin C at an optimum dose on maintaining the equilibrium functionality of radical scavenger and gene transcription, which are important in the initial phases after healing after an MI, while modulating the synthesis of de novo collagen fibrils, which is important in the final stage of tissue healing.

miR-29b-3p inhibits post-infarct cardiac fibrosis by targeting FOS

Background: Cardiac fibrosis after myocardial infarction (MI) is a major cause of heart deterioration. Recently, the roles of microRNAs (miRNAs) in various cardiovascular diseases associated with cardiac fibrosis have been extensively investigated. The present study aimed to investigate the role and mechanism of miR-29b-3p in cardiac fibrosis after MI.

Methods: miR-29b-3p expression in TGF-β1-activated cardiac fibroblasts (CFs) was detected by qRT-PCR. Cell Counting Kit-8 (CCK-8) and Trans-well assays were performed to evaluate CFs proliferation and migration ability, respectively. Protein expressions of α-SMA, collagen I, collagen III, MMP2, and MMP9 were examined by Western blot assay. Bioinformatics, luciferase, and RNA immunoprecipitation (RIP) assays were carried out to determine whether FOS was targeted by miR-29b-3p.

Results: TGF-β1 treatment dose-dependently curbed miR-29b-3p expression in CFs. miR-29b-3p restrained the promotive impacts of TGF-β1 on CFs proliferation, migration, and differentiation. FOS was affirmed to be a target of miR-29b-3p, elevated expression of FOS reversed the inhibitory effects of miR-29b-3p on cell proliferation, migration, and differentiation in TGF-β1-activated CFs.

Conclusion: miR-29b-3p degraded the pro-fibrosis effect induced by TGF-β1 via targeting FOS, providing a prospective therapeutic avenue for cardiac fibrosis after MI.

Informing epidemic (research) responses in a timely fashion by knowledge management - a Zika virus use case

The response of pathophysiological research to emerging epidemics often occurs after the epidemic and, as a consequence, has little to no impact on improving patient outcomes or on developing high-quality evidence to inform clinical management strategies during the epidemic. Rapid and informed guidance of epidemic (research) responses to severe infectious disease outbreaks requires quick compilation and integration of existing pathophysiological knowledge. As a case study we chose the Zika virus (ZIKV) outbreak that started in 2015 to develop a proof-of-concept knowledge repository. To extract data from available sources and build a computationally tractable and comprehensive molecular interaction map we applied generic knowledge management software for literature mining, expert knowledge curation, data integration, reporting and visualization. A multi-disciplinary team of experts, including clinicians, virologists, bioinformaticians and knowledge management specialists, followed a pre-defined workflow for rapid integration and evaluation of available evidence. While conventional approaches usually require months to comb through the existing literature, the initial ZIKV KnowledgeBase (ZIKA KB) was completed within a few weeks. Recently we updated the ZIKA KB with additional curated data from the large amount of literature published since 2016 and made it publicly available through a web interface together with a step-by-step guide to ensure reproducibility of the described use case. In addition, a detailed online user manual is provided to enable the ZIKV research community to generate hypotheses, share knowledge, identify knowledge gaps, and interactively explore and interpret data. A workflow for rapid response during outbreaks was generated, validated and refined and is also made available. The process described here can be used for timely structuring of pathophysiological knowledge for future threats. The resulting structured biological knowledge is a helpful tool for computational data analysis and generation of predictive models and opens new avenues for infectious disease research. ZIKV Knowledgebase is available at www.zikaknowledgebase.eu.

Multiscale Coupling of an Agent-Based Model of Tissue Fibrosis and a Logic-Based Model of Intracellular Signaling

Wound healing and fibrosis following myocardial infarction (MI) is a dynamic process involving many cell types, extracellular matrix (ECM), and inflammatory cues. As both incidence and survival rates for MI increase, management of post-MI recovery and associated complications are an increasingly important focus. Complexity of the wound healing process and the need for improved therapeutics necessitate a better understanding of the biochemical cues that drive fibrosis. To study the progression of cardiac fibrosis across spatial and temporal scales, we developed a novel hybrid multiscale model that couples a logic-based differential equation (LDE) model of the fibroblast intracellular signaling network with an agent-based model (ABM) of multi-cellular tissue remodeling. The ABM computes information about cytokine and growth factor levels in the environment including TGFβ, TNFα, IL-1β, and IL-6, which are passed as inputs to the LDE model. The LDE model then computes the network signaling state of individual cardiac fibroblasts within the ABM. Based on the current network state, fibroblasts make decisions regarding cytokine secretion and deposition and degradation of collagen. Simulated fibroblasts respond dynamically to rapidly changing extracellular environments and contribute to spatial heterogeneity in model predicted fibrosis, which is governed by many parameters including cell density, cell migration speeds, and cytokine levels. Verification tests confirmed that predictions of the coupled model and network model alone were consistent in response to constant cytokine inputs and furthermore, a subset of coupled model predictions were validated with in vitro experiments with human cardiac fibroblasts. This multiscale framework for cardiac fibrosis will allow for systematic screening of the effects of molecular perturbations in fibroblast signaling on tissue-scale extracellular matrix composition and organization.

Inhibition of CXCR4 inhibits the proliferation and osteogenic potential of fibroblasts from ankylosing spondylitis via the Wnt/β‑catenin pathway

Ankylosing spondylitis (AS) is an autoimmune condition characterized by chronic inflammation and abnormal ossification as the primary features of the disease. The aim of the present study was to investigate the role of C‑X‑C chemokine receptor type 4 (CXCR4) in ossification from patients with AS. CXCR4 expression was assessed by western blot analysis and immunohistochemistry analysis of tissues obtained from patients with AS and controls. Fibroblasts were isolated, cultured and incubated with AMD 3100 and stromal cell‑derived factor‑1 to inhibit and promote CXCR4 levels, respectively. CXCR4 was upregulated in hip synovial tissues from patients with AS compared with that observed in controls. AS fibroblasts exhibited increased proliferation and growth rates. Inhibition of CXCR4 increased the phosphorylation of β‑catenin and downregulated the expression of β‑catenin, v‑myc avian myelocytomatosis viral oncogene homolog, cyclin D1 and osteocalcin. Alizarin red staining demonstrated a decrease in biomineralization activity following the inhibition of CXCR4. These data support the hypothesis that inhibiting CXCR4 in patients with AS may suppress the ossification of fibroblasts.

Cardiac Fibroblast Activation Post-Myocardial Infarction: Current Knowledge Gaps

In response to myocardial infarction (MI), the wound healing response of the left ventricle (LV) comprises overlapping inflammatory, proliferative, and maturation phases, and the cardiac fibroblast is a key cell type involved in each phase. It has recently been appreciated that, early post-MI, fibroblasts transform to a proinflammatory phenotype and secrete cytokines and chemokines as well as matrix metalloproteinases (MMPs). Later post-MI, fibroblasts are activated to anti-inflammatory and proreparative phenotypes and generate anti-inflammatory and proangiogenic factors and extracellular matrix (ECM) components that form the infarct scar. Additional studies are needed to systematically examine how fibroblast activation shifts over the timeframe of the MI response and how modulation at different activation stages could alter wound healing and LV remodeling in distinct ways. This review summarizes current fibroblast knowledge as the foundation for a discussion of existing knowledge gaps.

Combinatorial Ranking of Gene Sets to Predict Disease Relapse: The Retinoic Acid Pathway in Early Prostate Cancer

Background

Quantitative high-throughput data deposited in consortia such as International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) present opportunities and challenges for computational analyses.

Methods

We present a computational strategy to systematically rank and investigate a large number (2¹⁰–2²⁰) of clinically testable gene sets, using combinatorial gene subset generation and disease-free survival (DFS) analyses. This approach integrates protein–protein interaction networks, gene expression, DNA methylation, and copy number data, in association with DFS profiles from patient clinical records.

Results

As a case study, we applied this pipeline to systematically analyze the role of ALDH1A2 in prostate cancer (PCa). We have previously found this gene to have multiple roles in disease and homeostasis, and here we investigate the role of the associated ALDH1A2 gene/protein networks in PCa, using our methodology in combination with PCa patient clinical profiles from ICGC and TCGA databases. Relationships between gene signatures and relapse were analyzed using Kaplan–Meier (KM) log-rank analysis and multivariable Cox regression. Relative expression versus pooled mean from diploid population was used for z-statistics calculation. Gene/protein interaction network analyses generated 11 core genes associated with ALDH1A2; combinatorial ranking of the power set of these core genes identified two gene sets (out of 2¹¹ − 1 = 2,047 combinations) with significant correlation with disease relapse (KM log rank p < 0.05). For the more significant of these two sets, referred to as the optimal gene set (OGS), patients have median survival 62.7 months with OGS alterations compared to >150 months without OGS alterations (p = 0.0248, hazard ratio = 2.213, 95% confidence interval = 1.1–4.098). Two genes comprising OGS (CYP26A1 and RDH10) are strongly associated with ALDH1A2 in the retinoic acid (RA) pathways, suggesting a major role of RA signaling in early PCa progression. Our pipeline complements human expertise in the search for prognostic biomarkers in large-scale datasets.