Network modeling predicts personalized gene expression and drug responses in valve myofibroblasts cultured with patient sera

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

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Multiscale computational model predicts how environmental changes and drug treatments affect microvascular remodeling in fibrotic disease

Investigating the molecular, cellular, and tissue-level changes caused by disease, and the effects of pharmacological treatments across these biological scales, necessitates the use of multiscale computational modeling in combination with experimentation. Many diseases dynamically alter the tissue microenvironment in ways that trigger microvascular network remodeling, which leads to the expansion or regression of microvessel networks. When microvessels undergo remodeling in idiopathic pulmonary fibrosis (IPF), functional gas exchange is impaired due to loss of alveolar structures and lung function declines. Here, we integrated a multiscale computational model with independent experiments to investigate how combinations of biomechanical and biochemical cues in IPF alter cell fate decisions leading to microvascular remodeling. Our computational model predicted that extracellular matrix (ECM) stiffening reduced microvessel area, which was accompanied by physical uncoupling of endothelial cell (ECs) and pericytes, the cells that comprise microvessels. Nintedanib, an FDA-approved drug for treating IPF, was predicted to further potentiate microvessel regression by decreasing the percentage of quiescent pericytes while increasing the percentage of pericytes undergoing pericyte-myofibroblast transition (PMT) in high ECM stiffnesses. Importantly, the model suggested that YAP/TAZ inhibition may overcome the deleterious effects of nintedanib by promoting EC-pericyte coupling and maintaining microvessel homeostasis. Overall, our combination of computational and experimental modeling can explain how cell decisions affect tissue changes during disease and in response to treatments.

The molecular mechanisms of extracellular matrix-derived hydrogel therapy in idiopathic pulmonary fibrosis models

Idiopathic Pulmonary Fibrosis (IPF) is a progressively debilitating lung condition characterized by oxidative stress, cell phenotype shifts, and excessive extracellular matrix (ECM) deposition. Recent studies have shown promising results using decellularized ECM-derived hydrogels produced through pepsin digestion in various lung injury models and even a human clinical trial for myocardial infarction. This study aimed to characterize the composition of ECM-derived hydrogels, assess their potential to prevent fibrosis in bleomycin-induced IPF models, and unravel their underlying molecular mechanisms of action. Porcine lungs were decellularized and pepsin-digested for 48 h. The hydrogel production process, including visualization of protein molecular weight distribution and hydrogel gelation, was characterized. Peptidomics analysis of ECM-derived hydrogel contained peptides from 224 proteins. Probable bioactive and cell-penetrating peptides, including collagen IV, laminin beta 2, and actin alpha 1, were identified. ECM-derived hydrogel treatment was administered as an early intervention to prevent fibrosis advancement in rat models of bleomycin-induced pulmonary fibrosis. ECM-derived hydrogel concentrations of 1 mg/mL and 2 mg/mL showed subtle but noticeable effects on reducing lung inflammation, oxidative damage, and protein markers related to fibrosis (e.g., alpha-smooth muscle actin, collagen I). Moreover, distinct changes were observed in macroscopic appearance, alveolar structure, collagen deposition, and protein expression between lungs that received ECM-derived hydrogel and control fibrotic lungs. Proteomic analyses revealed significant protein and gene expression changes related to cellular processes, pathways, and components involved in tissue remodeling, inflammation, and cytoskeleton regulation. RNA sequencing highlighted differentially expressed genes associated with various cellular processes, such as tissue remodeling, hormone secretion, cell chemotaxis, and cytoskeleton engagement. This study suggests that ECM-derived hydrogel treatment influence pathways associated with tissue repair, inflammation regulation, cytoskeleton reorganization, and cellular response to injury, potentially offering therapeutic benefits in preventing or mitigating lung fibrosis.

Computational screen for sex-specific drug effects in a cardiac fibroblast signaling network model

Heart disease is the leading cause of death in both men and women. Cardiac fibrosis is the uncontrolled accumulation of extracellular matrix proteins, which can exacerbate the progression of heart failure, and there are currently no drugs approved specifically to target matrix accumulation in the heart. Computational signaling network models (SNMs) can be used to facilitate discovery of novel drug targets. However, the vast majority of SNMs are not sex-specific and/or are developed and validated using data skewed towards male in vitro and in vivo samples. Biological sex is an important consideration in cardiovascular health and drug development. In this study, we integrate a cardiac fibroblast SNM with estrogen signaling pathways to create sex-specific SNMs. The sex-specific SNMs demonstrated high validation accuracy compared to in vitro experimental studies in the literature while also elucidating how estrogen signaling can modulate the effect of fibrotic cytokines via multi-pathway interactions. Further, perturbation analysis and drug screening uncovered several drug compounds predicted to generate divergent fibrotic responses in male vs. female conditions, which warrant further study in the pursuit of sex-specific treatment recommendations for cardiac fibrosis. Future model development and validation will require more generation of sex-specific data to further enhance modeling capabilities for clinically relevant sex-specific predictions of cardiac fibrosis and treatment.

scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets

Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among gene modules. As state-of-the-art applications, we first applied scTIGER to scRNA-seq datasets of prostate cancer cells, and successfully identified the dynamic regulatory networks of AR, ERG, PTEN and ATF3 for same-cell type between prostatic cancerous and normal conditions, and two-cell types within the prostatic cancerous environment. We then applied scTIGER to scRNA-seq data from neurons with and without fear memory and detected specific regulatory networks for BDNF, CREB1 and MAPK4. Additionally, scTIGER demonstrates robustness against high levels of dropout noise in scRNA-seq data.

Vascular smooth muscle cell mechanotransduction through serum and glucocorticoid inducible kinase-1 promotes interleukin-6 production and macrophage accumulation in murine hypertension

Objective

The objective of this investigation was to demonstrate that in vivo induction of hypertension (HTN) and in vitro cyclic stretch of aortic vascular smooth muscle cells (VSMCs) can cause serum and glucocorticoid-inducible kinase (SGK-1)-dependent production of cytokines to promote macrophage accumulation that may promote vascular pathology.

Methods

HTN was induced in C57Bl/6 mice with angiotensin II infusion (1.46 mg/kg/day × 21 days) with or without systemic infusion of EMD638683 (2.5 mg/kg/day × 21 days), a selective SGK-1 inhibitor. Systolic blood pressure was recorded. Abdominal aortas were harvested to quantify SGK-1 activity (pSGK-1/SGK-1) by immunoblot. Flow cytometry quantified the abundance of CD11b+/F480+ cells (macrophages). Plasma interleukin (IL)-6 and monocyte chemoattractant protein-1 (MCP-1) was assessed by enzyme-linked immunosorbent assay. Aortic VSMCs from wild-type mice were subjected to 12% biaxial cyclic stretch (Stretch) for 3 or 12 hours with or without EMD638683 (10 μM) and with or without SGK-1 small interfering RNA with subsequent quantitative polymerase chain reaction for IL-6 and MCP-1 expression. IL-6 and MCP-1 in culture media were analyzed by enzyme-linked immunosorbent assay. Aortic VSMCs from SGK-1flox+/+ mice were transfected with Cre-Adenovirus to knockdown SGK-1 (SGK-1KD VSMCs) and underwent parallel tension experimentation. Computational modeling was used to simulate VSMC signaling. Statistical analysis included analysis of variance with significance at a P value of <.05.

Results

SGK-1 activity, abundance of CD11b+/F4-80+ cells, and plasma IL-6 were increased in the abdominal aorta of mice with HTN and significantly reduced by treatment with EMD638683. This outcome mirrored the increased abundance of IL-6 in media from Stretch C57Bl/6 VSMCs and attenuation of the effect with EMD638683 or SGK-1 small interfering RNA. C57Bl/6 VSMCs also responded to Stretch with increased MCP-1 expression and secretion into the culture media. Further supporting the integral role of mechanical signaling through SGK-1, target gene expression and cytokine secretion was unchanged in SGK-1KD VSMCs with Stretch, and computer modeling confirmed SGK-1 as an intersecting node of signaling owing to mechanical strain and angiotensin II.

Conclusions

Mechanical activation of SGK-1 in aortic VSMCs can promote inflammatory signaling and increased macrophage abundance, therefore this kinase warrants further exploration as a pharmacotherapeutic target to abrogate hypertensive vascular pathology.

Identifying Immune Cell Infiltration and Hub Genes During the Myocardial Remodeling Process After Myocardial Infarction

Purpose

Myocardial remodeling after myocardial infarction (MI) is a complex repair process following myocardial injury, characterized by the infiltration of multiple types of immune cells. However, the underlying molecular mechanism of myocardial remodeling after MI remains obscure. This study aimed to identify the hub differential expression genes (DEGs) of myocardial remodeling after MI and determine the distribution of immune cells infiltrating the pathology.

Methods

We downloaded GSE132143, GSE151834, and GSE176092 data from the GEO database. The GSE132143 dataset was used to identify DEGs, perform functional annotation, and screen hub genes based on protein-protein interaction (PPI) analysis. The GSE151834 dataset was used to validate the expression of hub genes. CIBERSORTx analysis was performed to explore the immune microenvironment in myocardial remodeling after MI. After conducting a literature review, we selected P3H3 to confirm the expression by utilizing immunohistochemistry and qRT-PCR. Finally, the snRNA-seq data in dataset GSE176092 was used for clarifying the expression of these hub genes in various cell clusters.

Results

We found 975 DEGs in myocardial remodeling after MI. Four hub genes (P3H3, COL15A1, COL16A1, COL27A1) were identified and were verified in the GSE151834 dataset. According to immune infiltration analysis, CD4+ naive T cells, regulatory T cells, monocytes, M2 macrophages, and neutrophils were involved in the pathological process of myocardial remodeling after MI. Additionally, in vitro experiments verified that P3h3 expression was significantly elevated in myocardial remodeling after MI. The snRNA-seq data analyzed that P3h3, Col15a1, Col16a1, and Col27a1 were highly expressed in fibroblasts of post-MI.

Conclusion

This study identified four hub genes P3H3, COL15A1, COL16A1, and COL27A1, particularly P3H3, as potential targets for targeted therapy in MI patients.

The Microenvironment of the Pathogenesis of Cardiac Hypertrophy

Pathological cardiac hypertrophy is a key risk factor for the development of heart failure and predisposes individuals to cardiac arrhythmia and sudden death. While physiological cardiac hypertrophy is adaptive, hypertrophy resulting from conditions comprising hypertension, aortic stenosis, or genetic mutations, such as hypertrophic cardiomyopathy, is maladaptive. Here, we highlight the essential role and reciprocal interactions involving both cardiomyocytes and non-myocardial cells in response to pathological conditions. Prolonged cardiovascular stress causes cardiomyocytes and non-myocardial cells to enter an activated state releasing numerous pro-hypertrophic, pro-fibrotic, and pro-inflammatory mediators such as vasoactive hormones, growth factors, and cytokines, i.e., commencing signaling events that collectively cause cardiac hypertrophy. Fibrotic remodeling is mediated by cardiac fibroblasts as the central players, but also endothelial cells and resident and infiltrating immune cells enhance these processes. Many of these hypertrophic mediators are now being integrated into computational models that provide system-level insights and will help to translate our knowledge into new pharmacological targets. This perspective article summarizes the last decades' advances in cardiac hypertrophy research and discusses the herein-involved complex myocardial microenvironment and signaling components.

Benchmarking of protein interaction databases for integration with manually reconstructed signalling network models

Protein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, Omnipath and Signor for their ability to recover manually curated edges from three logic-based network models of cardiac hypertrophy, mechano-signalling and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano-signalling (85 of 125 interactions, 68%) and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well-conserved pathways, they performed worse at recovering tissue-specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of protein kinase C autophosphorylation and Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signalling. KEY POINTS: Protein interaction databases are used to recover signalling interactions from previously developed network models. The five protein interaction databases benchmarked recovered well-conserved pathways, but did poorly at recovering tissue-specific pathways and transcriptional regulation, indicating the importance of manual curation. We identify new signalling interactions not previously used in the network models, including a role for Ca2+ /calmodulin-dependent protein kinase II phosphorylation of CREB in cardiomyocyte hypertrophy.

Computational Screen for Sex-Specific Drug Effects in a Cardiac Fibroblast Network Model

Heart disease is the leading cause of death in both men and women. Cardiac fibrosis is the uncontrolled accumulation of extracellular matrix proteins which can exacerbate the progression of heart failure, and there are currently no drugs approved specifically to target matrix accumulation in the heart. Computational signaling network models (SNMs) can be used to facilitate discovery of novel drug targets. However, the vast majority of SNMs are not sex-specific and/or are developed and validated using data skewed towards male in vitro and in vivo samples. Biological sex is an important consideration in cardiovascular health and drug development. In this study, we integrate a previously constructed cardiac fibroblast SNM with estrogen signaling pathways to create sex-specific SNMs. The sex-specific SNMs maintained previously high validation when compared to in vitro experimental studies in the literature. A sex-specific perturbation analysis and drug screen uncovered several potential pathways that warrant further study in the pursuit of sex-specific treatment recommendations for cardiac fibrosis.

Inflammatory serum factors from aortic valve stenosis patients modulate sex differences in valvular myofibroblast activation and osteoblast-like differentiation

Aortic valve stenosis (AVS) is a sexually dimorphic cardiovascular disease that is driven by fibrosis and calcification of the aortic valve leaflets. Circulating inflammatory factors present in serum from AVS patients contribute to sex differences in valve fibro-calcification by driving the activation of valvular interstitial cells (VICs) to myofibroblasts and/or osteoblast-like cells. However, the molecular mechanisms by which inflammatory factors contribute to sex-specific valve fibro-calcification remain largely unknown. In this study, we identified inflammatory factors present in serum samples from AVS patients that regulate sex-specific myofibroblast activation and osteoblast-like differentiation. After correlating serum proteomic datasets with clinical and in vitro myofibroblast datasets, we identified annexin A2 and cystatin C as candidate inflammatory factors that correlate with both AVS patient severity and myofibroblast activation measurements in vitro. Validation experiments utilizing hydrogel biomaterials as cell culture platforms that mimic the valve extracellular matrix confirmed that annexin A2 and cystatin C promote sex-specific VIC activation to myofibroblasts via p38 MAPK signaling. Additionally, annexin A2 and cystatin C increase osteoblast-like differentiation primarily in male VICs. Our results implicate serum inflammatory factors as potential AVS biomarkers that also contribute to sexually dimorphic AVS progression by driving VIC myofibroblast activation and/or osteoblast-like differentiation. Collectively, the results herein further our overall understanding as to how biological sex may impact inflammation-driven AVS and may lead to the development of sex-specific drug treatment strategies.

Potential ferroptosis key genes in calcific aortic valve disease

Calcific aortic valve disease (CAVD) is a highly prevalent condition that comprises a disease continuum, ranging from microscopic changes to profound fibro-calcific leaflet remodeling, culminating in aortic stenosis, heart failure, and ultimately premature death. Ferroptosis has been hypothesized to contribute to the pathogenesis of CAVD. We aimed to study the association between ferroptosis genes and CAVD and reveal the potential roles of ferroptosis in CAVD. CAVD-related differentially expressed genes (DEGs) were identified via bioinformatic analysis of Datasets GSE51472 and GSE12644 obtained from Gene Expression Omnibus. A ferroptosis dataset containing 259 genes was obtained from the Ferroptosis Database. We then intersected with CAVD-related DEGs to identify the ferroptosis DEGs. Subsequently, protein–protein interaction networks and functional enrichment analyses were performed for ferroptosis DEGs. Then, we used miRWalk3.0 to predict the target pivotal microRNAs. An in vitro model of CAVD was constructed using human aortic valve interstitial cells. The qRT-PCR and western blotting methods were used to validate the ferroptosis DEGs identified by the microarray data. A total of 21 ferroptosis DEGs in CAVD containing 12 upregulated and nine downregulated genes were identified. The results of the Gene Set Enrichment Analysis (GSEA) and analysis of the KEGG pathway by WebGestalt indicated that the ferroptosis DEGs were enriched in six signaling pathways among which NAFLD (including IL-6, BID, and PRKAA2 genes) and HIF-1 (including IL-6, HIF-1, and HMOX1 genes) signaling pathways were also verified by DAVID and/or Metascape. Finally, the in vitro results showed that the mRNA and protein expression levels of IL-6, HIF-1α, HMOX1, and BID were higher, while the levels of PRKAA2 were lower in the Pi-treated group than those in the control group. However, the addition of ferrostatin-1 (a selective ferroptosis inhibitor) significantly reversed the above changes. Therefore, IL-6, HIF-1α, HMOX1, BID, and PRKAA2 are potential key genes closely associated with ferroptosis in CAVD. Further work is required to explore the underlying ferroptosis-related molecular mechanisms and provide possible therapeutic targets for CAVD.