Executable Models of Signaling Pathways and Application to HIV

Executable models of signaling pathways enable transforming static pictures of signaling pathways into models that can be used for in-silico experimentation, thereby facilitating development of mechanistic models. Development of executable models for large-scale dataset is rare and require defining signal flow and signal integration. We develop Boolean Omics Network Invariant-Time Analysis (BONITA), for signal propagation, signal integration, and pathway analysis. Our signal propagation approach models heterogeneity in transcriptomic data as arising from intercellular heterogeneity rather than intracellular stochasticity, and propagates binary signals repeatedly across networks. Logic rules defining signal integration are inferred by genetic algorithm and are refined by local search. The rules determine the impact of each node in a pathway, which is used to score the probability of the pathway’s modulation by chance. We have comprehensively tested BONITA for application to transcriptomics data from translational studies and identified higher sensitivity at lower levels of pathway modulation compared to state-of-the-art pathway analysis methods. We have applied BONITA and other network methods to investigate two key problems in HIV biology: B cell responses to vaccination and higher incidence of atherosclerosis in people living with HIV, using a systems biology approach. The first study looks at completed phase I and IIa trials of HIV vaccines to estimate durability of antibody response to protein or MVA-boosted vaccination and uncover underlying molecular differences in B cells. Our mixed effects models of antibody dynamics after HIV vaccination revealed half-lives of gp120-specific antibodies were longer but peak magnitudes were lower for Modified Vaccinia Ankara (MVA)-boosted regimens than protein-boosted regimens, leading to higher total area under the curve for protein regimens. To investigate molecular signatures of MVA and protein boosted trials we performed RNA sequencing of gp120-specific B cells from durable and transient vaccine responders in HVTN 094, 205 (MVA-boosted) and HVTN 105 (protein-boosted vaccines). BONITA reveals integration of key FCRL genes with BCR signaling pathway to influence difference between protein and MVA boosted regimens. Secondly, to elucidate factors that lead to development of atherosclerosis at higher levels in people living with HIV, we collected mRNA and miRNA expression in addition to cytokine levels in people living with HIV with and without atherosclerosis. These revealed a number of miRNAs that were clearly different between these groups. Analysis of an integrated network of all data types revealed increased importance of miRNAs in network regulation of HIV+ group in contrast with increased importance of cytokines in the HIV+AS+ group

Obstetric Penicillin Allergy Evaluations

Rationale: Group B streptococcal (GBS) colonization is associated with adverse pregnancy outcomes. Penicillins are first-line therapy for peripartum prophylaxis in colonized women, making delabeling of PCN allergy in this population particularly important

Middle-way flexible docking: Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α.

There is a vast gulf between the two primary strategies for simulating protein-ligand interactions. Docking methods significantly limit or eliminate protein flexibility to gain great speed at the price of uncontrolled inaccuracy, whereas fully flexible atomistic molecular dynamics simulations are expensive and often suffer from limited sampling. We have developed a flexible docking approach geared especially for highly flexible or poorly resolved targets based on mixed-resolution Monte Carlo (MRMC), which is intended to offer a balance among speed, protein flexibility, and sampling power. The binding region of the protein is treated with a standard atomistic force field, while the remainder of the protein is modeled at the residue level with a Gō model that permits protein flexibility while saving computational cost. Implicit solvation is used. Here we assess three facets of the MRMC approach with implications for other docking studies: (i) the role of receptor flexibility in cross-docking pose prediction; (ii) the use of non-equilibrium candidate Monte Carlo (NCMC) and (iii) the use of pose-clustering in scoring. We examine 61 co-crystallized ligands of estrogen receptor α, an important cancer target known for its flexibility. We also compare the performance of the MRMC approach with Autodock smina. Adding protein flexibility, not surprisingly, leads to significantly lower total energies and stronger interactions between protein and ligand, but notably we document the important role of backbone flexibility in the improvement. The improved backbone flexibility also leads to improved performance relative to smina. Somewhat unexpectedly, our implementation of NCMC leads to only modestly improved sampling of ligand poses. Overall, the addition of protein flexibility improves the performance of docking, as measured by energy-ranked poses, but we do not find significant improvements based on cluster information or the use of NCMC. We discuss possible improvements for the model including alternative coarse-grained force fields, improvements to the treatment of solvation, and adding additional types of NCMC moves

Executable models of immune signaling pathways in HIV-associated atherosclerosis

Atherosclerosis (AS)-associated cardiovascular disease is an important cause of mortality in an aging population of people living with HIV (PLWH). This elevated risk has been attributed to viral infection, anti-retroviral therapy, chronic inflammation, and lifestyle factors. However, the rates at which PLWH develop AS vary even after controlling for length of infection, treatment duration, and for lifestyle factors. To investigate the molecular signaling underlying this variation, we sequenced 9368 peripheral blood mononuclear cells (PBMCs) from eight PLWH, four of whom have atherosclerosis (AS+). Additionally, a publicly available dataset of PBMCs from persons before and after HIV infection was used to investigate the effect of acute HIV infection. To characterize dysregulation of pathways rather than just measuring enrichment, we developed the single-cell Boolean Omics Network Invariant Time Analysis (scBONITA) algorithm. scBONITA infers executable dynamic pathway models and performs a perturbation analysis to identify high impact genes. These dynamic models are used for pathway analysis and to map sequenced cells to characteristic signaling states (attractor analysis). scBONITA revealed that lipid signaling regulates cell migration into the vascular endothelium in AS+ PLWH. Pathways implicated included AGE-RAGE and PI3K-AKT signaling in CD8+ T cells, and glucagon and cAMP signaling pathways in monocytes. Attractor analysis with scBONITA facilitated the pathway-based characterization of cellular states in CD8+ T cells and monocytes. In this manner, we identify critical cell-type specific molecular mechanisms underlying HIV-associated atherosclerosis using a novel computational method