Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization.

Translation of genomic discovery, such as single-cell sequencing data, to clinical decisions remains a longstanding bottleneck in the field. Meanwhile, computational systems biological models, such as cellular metabolism models and cell signaling pathways, have emerged as powerful approaches to provide efficient predictions in metabolites and gene expression levels, respectively. However, there has been limited research on the integration between these two models. This work develops a methodology for integrating computational models of probabilistic gene regulatory networks with a constraint-based metabolism model. By using probabilistic reasoning with Bayesian Networks, we aim to predict cell-specific changes under different interventions, which are embedded into the constraint-based models of metabolism. Applications to single-cell sequencing data of glioblastoma brain tumors generate predictions about the effects of pharmaceutical interventions on the regulatory network and downstream metabolisms in different cell types from the tumor microenvironment. The model presents possible insights into treatments that could potentially suppress anaerobic metabolism in malignant cells with minimal impact on other cell types' metabolism. The proposed integrated model can guide therapeutic target prioritization, the formulation of combination therapies, and future drug discovery. This model integration framework is also generalizable to other applications, such as different cell types, organisms, and diseases.

Distinct association between chronic Epstein-Barr virus infection and T cell compartments from pediatric heart, kidney, and liver transplant recipients.

Chronic Epstein-Barr virus (EBV) infection after pediatric organ transplantation (Tx) accounts for significant morbidity and mortality. The risk of complications, such as posttransplant lymphoproliferative disorders, in high viral load (HVL) carriers is the highest in heart Tx recipients. However, the immunologic signatures of such a risk have been insufficiently defined. Here, we assessed the phenotypic, functional, and transcriptomic profiles of peripheral blood CD8+/CD4+ T cells, including EBV-specific T cells, in 77 pediatric heart, kidney, and liver Tx recipients and established the relationship between memory differentiation and progression toward exhaustion. Unlike kidney and liver HVL carriers, heart HVL carriers displayed distinct CD8+ T cells with (1) up-regulation of interleukin-21R, (2) decreased naive phenotype and altered memory differentiation, (3) accumulation of terminally exhausted (TEX PD-1+T-bet-Eomes+) and decrease of functional precursors of exhausted (TPEX PD-1intT-bet+) effector subsets, and (4) transcriptomic signatures supporting the phenotypic changes. In addition, CD4+ T cells from heart HVL carriers displayed similar changes in naive and memory subsets, elevated Th1 follicular helper cells, and plasma interleukin-21, suggesting an alternative inflammatory mechanism that governs T cell responses in heart Tx recipients. These results may explain the different incidences of EBV complications and may help improve the risk stratification and clinical management of different types of Tx recipients.

Metabolomic profiles in relapsing-remitting and progressive multiple sclerosis compared to healthy controls: a five-year follow-up study.

INTRODUCTION AND OBJECTIVES: Multiple sclerosis (MS) is a disease of the central nervous system associated with immune dysfunction, demyelination, and neurodegeneration. The disease has heterogeneous clinical phenotypes such as relapsing-remitting MS (RRMS) and progressive multiple sclerosis (PMS), each with unique pathogenesis. Metabolomics research has shown promise in understanding the etiologies of MS disease. However, there is a paucity of clinical studies with follow-up metabolomics analyses. This 5-year follow-up (5YFU) cohort study aimed to investigate the metabolomics alterations over time between different courses of MS patients and healthy controls and provide insights into metabolic and physiological mechanisms of MS disease progression. METHODS: A cohort containing 108 MS patients (37 PMS and 71 RRMS) and 42 controls were followed up for a median of 5 years. Liquid chromatography-mass spectrometry (LC-MS) was applied for untargeted metabolomics profiling of serum samples of the cohort at both baseline and 5YFU. Univariate analyses with mixed-effect ANCOVA models, clustering, and pathway enrichment analyses were performed to identify patterns of metabolites and pathway changes across the time effects and patient groups. RESULTS AND CONCLUSIONS: Out of 592 identified metabolites, the PMS group exhibited the most changes, with 219 (37%) metabolites changed over time and 132 (22%) changed within the RRMS group (Bonferroni adjusted P < 0.05). Compared to the baseline, there were more significant metabolite differences detected between PMS and RRMS classes at 5YFU. Pathway enrichment analysis detected seven pathways perturbed significantly during 5YFU in MS groups compared to controls. PMS showed more pathway changes compared to the RRMS group.

Integrated Graph Propagation and Optimization with Biological Applications.

Mathematical models that utilize network representations have proven to be valuable tools for investigating biological systems. Often dynamic models are not feasible due to their complex functional forms that rely on unknown rate parameters. Network propagation has been shown to accurately capture the sensitivity of nodes to changes in other nodes; without the need for dynamic systems and parameter estimation. Node sensitivity measures rely solely on network structure and encode a sensitivity matrix that serves as a good approximation to the Jacobian matrix. The use of a propagation-based sensitivity matrix as a Jacobian has important implications for network optimization. This work develops Integrated Graph Propagation and OptimizatioN (IGPON), which aims to identify optimal perturbation patterns that can drive networks to desired target states. IGPON embeds propagation into an objective function that aims to minimize the distance between a current observed state and a target state. Optimization is performed using Broyden's method with the propagationbased sensitivity matrix as the Jacobian. IGPON is applied to simulated random networks, DREAM4 in silico networks, and over-represented pathways from STAT6 knockout data and YBX1 knockdown data. Results demonstrate that IGPON is an effective way to optimize directed and undirected networks that are robust to uncertainty in the network structure.

Expanded Imaging Classification of Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: The Mayo Clinic imaging classification of autosomal dominant polycystic kidney disease (ADPKD) uses height-adjusted total kidney volume (htTKV) and age to identify patients at highest risk for disease progression. However, this classification applies only to patients with typical diffuse cystic disease (class 1). Because htTKV poorly predicts eGFR decline for the 5%-10% of patients with atypical morphology (class 2), imaging-based risk modeling remains unresolved. METHODS: Of 558 adults with ADPKD in the HALT-A study, we identified 25 patients of class 2A with prominent exophytic cysts (class 2Ae) and 43 patients of class 1 with prominent exophytic cysts; we recalculated their htTKVs to exclude exophytic cysts. Using original and recalculated htTKVs in association with imaging classification in logistic and mixed linear models, we compared predictions for developing CKD stage 3 and for eGFR trajectory. RESULTS: Using recalculated htTKVs increased specificity for developing CKD stage 3 in all participants from 82.6% to 84.2% after adjustment for baseline age, eGFR, BMI, sex, and race. The predicted proportion of class 2Ae patients developing CKD stage 3 using a cutoff of 0.5 for predicting case status was better calibrated to the observed value of 13.0% with recalculated htTKVs (45.5%) versus original htTKVs (63.6%). Using recalculated htTKVs reduced the mean paired difference between predicted and observed eGFR from 17.6 (using original htTKVs) to 4.0 ml/min per 1.73 m2 for class 2Ae, and from -1.7 (using original htTKVs) to 0.1 ml/min per 1.73 m2 for class 1. CONCLUSIONS: Use of a recalculated htTKV measure that excludes prominent exophytic cysts facilitates inclusion of class 2 patients and reclassification of class 1 patients in the Mayo classification model.

Long-term trajectory of kidney function in autosomal-dominant polycystic kidney disease.

Autosomal dominant polycystic kidney disease (ADPKD) is characterized by cyst and kidney growth, which is hypothesized to cause loss of functioning renal mass and eventually end-stage kidney disease. However, the time course of decline in glomerular filtration rate (GFR) is poorly defined. The Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease study is a 14-year observational cohort study of 241 adults with ADPKD. As an estimate of the rate of kidney growth, participants were stratified into 5 subclasses based on baseline age and magnetic resonance imaging measurements of total kidney volume (TKV) according to the method of Irazabal. GFR trajectories spanning over four decades of life were reconstructed and fitted using mixed polynomial models, which were validated using data from the HALT-PKD study. GFR trajectories were nonlinear, with a period of relative stability in most participants, followed by accelerating decline. The shape and slope of these trajectories were strongly associated with baseline Irazabal class. Patients with PKD1 mutations had a steeper GFR decline than patients with PKD2 mutations or with no detected mutation, largely mediated by the effect of genotype on Irazabal class. Thus, GFR decline in ADPKD is nonlinear, and its trajectory throughout adulthood can be predicted from a single measurement of kidney volume. These models can be used for clinical prognostication, clinical trial design, and patient selection for clinical interventions. Our findings support a causal link between growth in kidney volume and GFR decline, adding support for the use of TKV as a surrogate endpoint in clinical trials.