BackgroundNew approaches are needed for large-scale predictive modeling of cellular signaling networks. While mass action and enzyme kinetic approaches require extensive biochemical data, current logic-based approaches are used primarily for qualitative predictions and have lacked direct quantitative comparison with biochemical models.ResultsWe developed a logic-based differential equation modeling approach for cell signaling networks based on normalized Hill activation/inhibition functions controlled by logical AND and OR operators to characterize signaling crosstalk. Using this approach, we modeled the cardiac β1-adrenergic signaling network, including 36 reactions and 25 species. Direct comparison of this model to an extensively characterized and validated biochemical model of the same network revealed that the new model gave reasonably accurate predictions of key network properties, even with default parameters. Normalized Hill functions improved quantitative predictions of global functional relationships compared with prior logic-based approaches. Comprehensive sensitivity analysis revealed the significant role of PKA negative feedback on upstream signaling and the importance of phosphodiesterases as key negative regulators of the network. The model was then extended to incorporate recently identified protein interaction data involving integrin-mediated mechanotransduction.ConclusionsThe normalized-Hill differential equation modeling approach allows quantitative prediction of network functional relationships and dynamics, even in systems with limited biochemical data.

High-throughput, ‘omic’ methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of ‘omic’ data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator.

Cardiac excitation-contraction coupling is a highly coordinated process that is controlled by protein kinase signaling pathways, including Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) and protein kinase A (PKA). Increased CaMKII expression and activity (as occurs during heart failure) destabilizes EC coupling and may lead to sudden cardiac death. To better understand mechanisms of cardiac CaMKII function, we integrated dynamic CaMKII-dependent regulation of key Ca(2+) handling targets with previously validated models of cardiac EC coupling, Ca(2+)/calmodulin-dependent activation of CaMKII, and β-adrenergic activation of PKA. Model predictions are validated against CaMKII-overexpression data from rabbit ventricular myocytes. The model demonstrates how overall changes to Ca(2+) handling during CaMKII overexpression are explained by interactions between individual CaMKII substrates. CaMKII and PKA activities during β-adrenergic stimulation may synergistically facilitate inotropic responses and contribute to a CaMKII-Ca(2+)-CaMKII feedback loop. CaMKII regulated early frequency-dependent acceleration of relaxation and EC coupling gain (which was highly sarcoplasmic reticulum Ca(2+) load-dependent). Additionally, the model identifies CaMKII-dependent ryanodine receptor hyperphosphorylation as a proarrhythmogenic trigger. In summary, we developed a detailed computational model of CaMKII and PKA signaling in cardiac myocytes that provides unique insights into their regulation of normal and pathological Ca(2+) handling.

Acidosis is a fundamental feature of the tumor microenvironment, which directly regulates tumor cell invasion by affecting immune cell function, clonal cell evolution, and drug resistance. Despite the important association of tumor microenvironment acidosis with tumor cell invasion, relatively little is known regarding which areas within a tumor are acidic and how acidosis influences gene expression to promote invasion. Here, we injected a labeled pH-responsive peptide to mark acidic regions within tumors. Surprisingly, acidic regions were not restricted to hypoxic areas and overlapped with highly proliferative, invasive regions at the tumor-stroma interface, which were marked by increased expression of matrix metalloproteinases and degradation of the basement membrane. RNA-seq analysis of cells exposed to low pH conditions revealed a general rewiring of the transcriptome that involved RNA splicing and enriched for targets of RNA binding proteins with specificity for AU-rich motifs. Alternative splicing of Mena and CD44, which play important isoform-specific roles in metastasis and drug resistance, respectively, was sensitive to histone acetylation status. Strikingly, this program of alternative splicing was reversed in vitro and in vivo through neutralization experiments that mitigated acidic conditions. These findings highlight a previously underappreciated role for localized acidification of tumor microenvironment in the expression of an alternative splicingdependent tumor invasion program.Significance: This study expands our understanding of acidosis within the tumor microenvironment and indicates that acidosis induces potentially therapeutically actionable changes to alternative splicing.

Highlights d High rate of NF1 loss in the R0 compared to neoadjuvant chemotherapy (NACT) group d Lower chromothripsis-like pattern and higher neoantigens in the R0 versus NACT group d Increased number of infiltrated T cells and decreased macrophages in the R0 group d Significant transcriptomic and proteomic variations between HGSC subgroups