Cardiac fibroblasts support heart function, and aberrant fibroblast signaling can lead to fibrosis and cardiac dysfunction. Yet how signaling molecules drive myofibroblast differentiation and fibrosis in the complex signaling environment of cardiac injury remains unclear. We developed a large-scale computational model of cardiac fibroblast signaling in order to identify regulators of fibrosis under diverse signaling contexts. The model network integrates 10 signaling pathways, including 91 nodes and 134 reactions, and it correctly predicted 80% of independent previous experiments. The model predicted key fibrotic signaling regulators (e.g. reactive oxygen species, tissue growth factor β (TGFβ) receptor), whose function varied depending on the extracellular environment. We characterized how network structure relates to function, identified functional modules, and predicted cross-talk between TGFβ and mechanical signaling, which was validated experimentally in adult cardiac fibroblasts. This study provides a systems framework for predicting key regulators of fibroblast signaling across diverse signaling contexts.

IMPORTANCE Infection in neonates remains a substantial problem. Advances for this population are hindered by the absence of a consensus definition for sepsis. In adults, the Sequential Organ Failure Assessment (SOFA) operationalizes mortality risk with infection and defines sepsis. The generalizability of the neonatal SOFA (nSOFA) for neonatal late-onset infection-related mortality remains unknown. OBJECTIVE To determine the generalizability of the nSOFA for neonatal late-onset infection-related mortality across multiple sites.

A vast amount of investigation has centered on how the endothelium and smooth muscle communicate. From this evidence, myoendothelial junctions have emerged as critical anatomical structures to regulate heterocellular cross talk. Indeed, there is now evidence that the myoendothelial junction serves as a signaling microdomain to organize proteins used to facilitate vascular heterocellular communication. This review highlights the evolving role of myoendothelial junctions in the context of vascular cell-cell communication.

Altered fibroblast behavior can lead to pathologic changes in the heart such as arrhythmia, diastolic dysfunction, and systolic dysfunction. Computational models are increasingly used as a tool to identify potential mechanisms driving a phenotype or potential therapeutic targets against an unwanted phenotype. Here we review how computational models incorporating cardiac fibroblasts have clarified the role for these cells in electrical conduction and tissue remodeling in the heart. Models of fibroblast signaling networks have primarily focused on fibroblast cell lines or fibroblasts from other tissues rather than cardiac fibroblasts, specifically, but they are useful for understanding how fundamental signaling pathways control fibroblast phenotype. In the future, modeling cardiac fibroblast signaling, incorporating -omics and drug-interaction data into signaling network models, and utilizing multi-scale models will improve the ability of in silico studies to predict potential therapeutic targets against adverse cardiac fibroblast activity.

2The fibroblast is a key mediator of wound healing in the heart and other organs, yet how 2 3 it integrates multiple time-dependent paracrine signals to control extracellular matrix 2 4 synthesis has been difficult to study in vivo. Here, we extended a computational model to 2 5 simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction in 2 6 response to time-dependent data for nine paracrine stimuli. This computational model 2 7was validated against dynamic collagen expression and collagen area fraction data from 2 8 post-infarction rat hearts. The model predicted that while many features of the fibroblast 2 9 phenotype at inflammatory or maturation phases of healing could be recapitulated by 3 0 single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking 3 1 of the proliferative phase required paired stimuli (e.g. TGFβ and angiotensin-II). Virtual 3 2 overexpression screens with static cytokine pairs and after myocardial infarction 3 3 predicted phase-specific regulators of collagen expression. Several regulators increased 3 4 (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the 3 5 proliferative phase. NADPH oxidase overexpression sustained collagen expression from 3 6 proliferative to maturation phases, driven by TGFβ and endothelin positive feedback 3 7 loops. Interleukin-1 overexpression suppressed collagen via NFκB and BAMBI (BMP 3 8 and activin membrane-bound inhibitor) incoherent feedforward loops, but it then later 3 9 sustained collagen expression due to the TGFβ positive feedback loop. These model-4 0 based predictions reveal network mechanisms by which the dynamics of paracrine stimuli 4 1 and interacting signaling pathways drive the progression of fibroblast phenotypes and 4 2 fibrosis after myocardial infarction. 4 3 4 4 4 5Wound healing is a complex process that involves a dynamic interplay between 4 6 inflammatory and proliferative signaling. This process is especially important following 4 7 injury to the heart, where cardiomyocytes are unable to regenerate. Scar formation and 4 8 the preservation of viable heart muscle are important for continued cardiac function[1]. 4 9 8 1 understanding how fibroblasts respond during the different phases of wound healing 8 2 could identify mechanisms by which fibrosis develops in other organs. 8 3 Myocardial infarct healing is notoriously difficult to investigate because it 8 4 involves many dynamic and interacting signaling processes. Fibroblasts are particularly 8 5 difficult to study in situ during wound healing because they can differentiate from many 8 6 different cell types and there is no clear consensus on fibroblast markers[20].8 7Computational modeling has been a useful method for investigating complex dynamic 8 8 processes in many areas of biology. Although models have been constructed to study the 8 9 wound healing process post-MI[21], no such model has yet been applied to study 9 0 5 fibroblast intracellular signaling and phenotypic changes during m...