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.

Sepsis and Mortality Prediction in Very Low Birth Weight Infants: Analysis of HeRO and nSOFA

OBJECTIVE

Scores to predict sepsis or define sepsis severity could improve care for very low birth weight (VLBW) infants. The heart rate characteristics (HRC) index (HeRO score) was developed as an early warning system for late-onset sepsis (LOS), and also rises before necrotizing enterocolitis (NEC). The neonatal sequential organ failure assessment (nSOFA) was developed to predict sepsis-associated mortality using respiratory, hemodynamic, and hematologic data. The aim of this study was to analyze the HRC index and nSOFA near blood cultures in VLBW infants relative to diagnosis and sepsis-associated mortality.

STUDY DESIGN

Retrospective, single-center study of VLBW infants from 2011 to 2019. We analyzed HRC index and nSOFA around blood cultures diagnosed as LOS/NEC. In a subgroup of the cohort, we analyzed HRC and nSOFA near the first sepsis-like illness (SLI) or sepsis ruled-out (SRO) compared with LOS/NEC. We compared scores by diagnosis and mortality during treatment.

RESULTS

We analyzed 179 LOS/NEC, 93 SLI, and 96 SRO blood culture events. In LOS/NEC, the HRC index increased before the blood culture, while nSOFA increased at the time of culture. Both scores were higher in nonsurvivors compared with survivors and in LOS/NEC compared with SRO. The nSOFA 12 hours after the time of blood culture predicted mortality during treatment better than any other time point analyzed (area under the curve 0.91).

CONCLUSION

The HRC index provides earlier warning of imminent sepsis, whereas nSOFA after blood culture provides better prediction of mortality.

KEY POINTS

· The HRC index and nSOFA provide complementary information on sepsis risk and sepsis-related mortality risk.. · This study adds to existing literature evaluating these risk scores independently by analyzing them together and in cases of not only proven but also suspected infections.. · The impact of combining risk models could be improved outcomes for premature infants..

Network model-based screen for FDA-approved drugs affecting cardiac fibrosis

Cardiac fibrosis is a significant component of pathological heart remodeling, yet it is not directly targeted by existing drugs. Systems pharmacology approaches have the potential to provide mechanistic frameworks with which to predict and understand how drugs modulate biological systems. Here, we combine network modeling of the fibroblast signaling network with 36 unique drug-target interactions from DrugBank to predict drugs that modulate fibroblast phenotype and fibrosis. Galunisertib was predicted to decrease collagen and α-SMA expression, which we validated in human cardiac fibroblasts. In vivo fibrosis data from the literature validated predictions for 10 drugs. Further, the model was used to identify network mechanisms by which these drugs work. Arsenic trioxide was predicted to induce fibrosis by AP1-driven TGFβ expression and MMP2-driven TGFβ activation. Entresto (valsartan/sacubitril) was predicted to suppress fibrosis by valsartan suppression of ERK signaling and sacubitril enhancement of PKG activity, both of which decreased Smad3 activity. Overall, this study provides a framework for integrating drug-target mechanisms with logic-based network models, which can drive further studies both in cardiac fibrosis and other conditions.

Network Analysis Reveals a Distinct Axis of Macrophage Activation in Response to Conflicting Inflammatory Cues

Macrophages are subject to a wide range of cytokine and pathogen signals in vivo, which contribute to differential activation and modulation of inflammation. Understanding the response to multiple, often-conflicting cues that macrophages experience requires a network perspective. In this study, we integrate data from literature curation and mRNA expression profiles obtained from wild type C57/BL6J mice macrophages to develop a large-scale computational model of the macrophage signaling network. In response to stimulation across all pairs of nine cytokine inputs, the model predicted activation along the classic M1-M2 polarization axis but also a second axis of macrophage activation that distinguishes unstimulated macrophages from a mixed phenotype induced by conflicting cues. Along this second axis, combinations of conflicting stimuli, IL-4 with LPS, IFN-γ, IFN-β, or TNF-α, produced mutual inhibition of several signaling pathways, e.g., NF-κB and STAT6, but also mutual activation of the PI3K signaling module. In response to combined IFN-γ and IL-4, the model predicted genes whose expression was mutually inhibited, e.g., iNOS or Nos2 and Arg1, or mutually enhanced, e.g., Il4rα and Socs1, validated by independent experimental data. Knockdown simulations further predicted network mechanisms underlying functional cross-talk, such as mutual STAT3/STAT6-mediated enhancement of Il4rα expression. In summary, the computational model predicts that network cross-talk mediates a broadened spectrum of macrophage activation in response to mixed pro- and anti-inflammatory cytokine cues, making it useful for modeling in vivo scenarios.

Computational model predicts paracrine and intracellular drivers of fibroblast phenotype after myocardial infarction

The fibroblast is a key mediator of wound healing in the heart and other organs, yet how it integrates multiple time-dependent paracrine signals to control extracellular matrix synthesis has been difficult to study in vivo. Here, we extended a computational model to simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction (MI) in response to time-dependent data for nine paracrine stimuli. This computational model was validated against dynamic collagen expression and collagen area fraction data from post-infarction rat hearts. The model predicted that while many features of the fibroblast phenotype at inflammatory or maturation phases of healing could be recapitulated by single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking the reparative phase required paired stimuli (e.g. TGFβ and endothelin-1). Virtual overexpression screens simulated with either static cytokine pairs or post-MI paracrine dynamic predicted phase-specific regulators of collagen expression. Several regulators increased (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the reparative phase. NADPH oxidase (NOX) overexpression sustained collagen expression from reparative to maturation phases, driven by TGFβ and endothelin positive feedback loops. Interleukin-1 overexpression had mixed effects, both enhancing collagen via the TGFβ positive feedback loop and suppressing collagen via NFκB and BAMBI (BMP and activin membrane-bound inhibitor) incoherent feed-forward loops. These model-based predictions reveal network mechanisms by which the dynamics of paracrine stimuli and interacting signaling pathways drive the progression of fibroblast phenotypes and fibrosis after myocardial infarction.

Computational modeling of cardiac fibroblasts and fibrosis

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.

A computational model of cardiac fibroblast signaling predicts context-dependent drivers of myofibroblast differentiation

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.

The myoendothelial junction: connections that deliver the message

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.