Computational analysis of cortical neuronal excitotoxicity in a large animal model of neonatal brain injury.

BACKGROUND: Neonatal hypoxic brain injury is a major cause of intellectual and developmental disability. Hypoxia causes neuronal dysfunction and death in the developing cerebral cortex due to excitotoxic Ca2+-influx. In the translational piglet model of hypoxic encephalopathy, we have previously shown that hypoxia overactivates Ca2+/Calmodulin (CaM) signaling via Sarcoma (Src) kinase in cortical neurons, resulting in overexpression of proapoptotic genes. However, identifying the exact relationship between alterations in neuronal Ca2+-influx, molecular determinants of cell death, and the degree of hypoxia in a dynamic system represents a significant challenge. METHODS: We used experimental and computational methods to identify molecular events critical to the onset of excitotoxicity-induced apoptosis in the cerebral cortex of newborn piglets. We used 2-3-day-old piglets (normoxic [Nx], hypoxic [Hx], and hypoxic + Src-inhibitor-treatment [Hx+PP2] groups) for biochemical analysis of ATP production, Ca2+-influx, and Ca2+/CaM-dependent protein kinase kinase 2 (CaMKK2) expression. We then used SimBiology to build a computational model of the Ca2+/CaM-Src-kinase signaling cascade, simulating Nx, Hx, and Hx+PP2 conditions. To evaluate our model, we used Sobol variance decomposition, multiparametric global sensitivity analysis, and parameter scanning. RESULTS: Our model captures important molecular trends caused by hypoxia in the piglet brain. Incorporating the action of Src kinase inhibitor PP2 further validated our model and enabled predictive analysis of the effect of hypoxia on CaMKK2. We determined the impact of a feedback loop related to Src phosphorylation of NMDA receptors and activation kinetics of CaMKII. We also identified distinct modes of signaling wherein Ca2+ level alterations following Src kinase inhibition may not be a linear predictor of changes in Bax expression. Importantly, our model indicates that while pharmacological pre-treatment significantly reduces the onset of abnormal Ca2+-influx, there exists a window of intervention after hypoxia during which targeted modulation of Src-NMDAR interaction kinetics in combination with PP2 administration can reduce Ca2+-influx and Bax expression to similar levels as pre-treatment. CONCLUSIONS: Our model identifies new dynamics of critical components in the Ca2+/CaM-Src signaling pathway leading to neuronal injury and provides a feasible framework for drug efficacy studies in translational models of neonatal brain injury for the prevention of intellectual and developmental disabilities.

Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer.

Individual computational models of single myeloid, lymphoid, epithelial, and cancer cells were created and combined into multi-cell computational models and used to predict the collective chemokine, cytokine, and cellular biomarker profiles often seen in inflamed or cancerous tissues. Predicted chemokine and cytokine output profiles from multi-cell computational models of gingival epithelial keratinocytes (GE KER), dendritic cells (DC), and helper T lymphocytes (HTL) exposed to lipopolysaccharide (LPS) or synthetic triacylated lipopeptide (Pam3CSK4) as well as multi-cell computational models of multiple myeloma (MM) and DC were validated using the observed chemokine and cytokine responses from the same cell type combinations grown in laboratory multi-cell cultures with accuracy. Predicted and observed chemokine and cytokine responses of GE KER + DC + HTL exposed to LPS and Pam3CSK4 matched 75% (15/20, p = 0.02069) and 80% (16/20, P = 0.005909), respectively. Multi-cell computational models became 'personalized' when cell line-specific genomic data were included into simulations, again validated with the same cell lines grown in laboratory multi-cell cultures. Here, predicted and observed chemokine and cytokine responses of MM cells lines MM.1S and U266B1 matched 75% (3/4) and MM.1S and U266B1 inhibition of DC marker expression in co-culture matched 100% (6/6). Multi-cell computational models have the potential to identify approaches altering the predicted disease-associated output profiles, particularly as high throughput screening tools for anti-inflammatory or immuno-oncology treatments of inflamed multi-cellular tissues and the tumor microenvironment.

Predicting response to BET inhibitors using computational modeling: A BEAT AML project study.

Despite advances in understanding the molecular pathogenesis of acute myeloid leukaemia (AML), overall survival rates remain low. The ability to predict treatment response based on individual cancer genomics using computational modeling will aid in the development of novel therapeutics and personalize care. Here, we used a combination of genomics, computational biology modeling (CBM), ex vivo chemosensitivity assay, and clinical data from 100 randomly selected patients in the Beat AML project to characterize AML sensitivity to a bromodomain (BRD) and extra-terminal (BET) inhibitor. Computational biology modeling was used to generate patient-specific protein network maps of activated and inactivated protein pathways translated from each genomic profile. Digital drug simulations of a BET inhibitor (JQ1) were conducted by quantitatively measuring drug effect using a composite AML disease inhibition score. 93% of predicted disease inhibition scores matched the associated ex vivo IC50 value. Sensitivity and specificity of CBM predictions were 97.67%, and 64.29%, respectively. Genomic predictors of response were identified. Patient samples harbouring chromosomal aberrations del(7q) or -7, +8, or del(5q) and somatic mutations causing ERK pathway dysregulation, responded to JQ1 in both in silico and ex vivo assays. This study shows how a combination of genomics, computational modeling and chemosensitivity testing can identify network signatures associating with treatment response and can inform priority populations for future clinical trials of BET inhibitors.

Histatin 5 binds to Porphyromonas gingivalis hemagglutinin B (HagB) and alters HagB-induced chemokine responses.

Histatins are human salivary gland peptides with anti-microbial and anti-inflammatory activities. In this study, we hypothesized that histatin 5 binds to Porphyromonas gingivalis hemagglutinin B (HagB) and attenuates HagB-induced chemokine responses in human myeloid dendritic cells. Histatin 5 bound to immobilized HagB in a surface plasmon resonance (SPR) spectroscopy-based biosensor system. SPR spectroscopy kinetic and equilibrium analyses, protein microarray studies, and I-TASSER structural modeling studies all demonstrated two histatin 5 binding sites on HagB. One site had a stronger affinity with a KD1 of 1.9 µM and one site had a weaker affinity with a KD2 of 60.0 µM. Binding has biological implications and predictive modeling studies and exposure of dendritic cells both demonstrated that 20.0 µM histatin 5 attenuated (p < 0.05) 0.02 µM HagB-induced CCL3/MIP-1α, CCL4/MIP-1ß, and TNFα responses. Thus histatin 5 is capable of attenuating chemokine responses, which may help control oral inflammation.

Defensin DEFB103 bidirectionally regulates chemokine and cytokine responses to a pro-inflammatory stimulus.

Human ß defensin DEFB103 acts as both a stimulant and an attenuator of chemokine and cytokine responses: a dichotomy that is not entirely understood. Our predicted results using an in silico simulation model of dendritic cells and our observed results in human myeloid dendritic cells, show that DEFB103 significantly (p < 0.05) enhanced 6 responses, attenuated 7 responses, and both enhanced/attenuated the CXCL1 and TNF responses to Porphyromonas gingivalis hemagglutinin B (HagB). In murine JAWSII dendritic cells, DEFB103 significantly attenuated, yet rarely enhanced, the Cxcl2, Il6, and Csf3 responses to HagB; and in C57/BL6 mice, DEFB103 significantly enhanced, yet rarely attenuated, the Cxcl1, Csf1, and Csf3 responses. Thus, DEFB103 influences pro-inflammatory activities with the concentration of DEFB103 and order of timing of DEFB103 exposure to dendritic cells, with respect to microbial antigen exposure to cells, being paramount in orchestrating the onset, magnitude, and composition of the chemokine and cytokine response.