Quantum processor-inspired machine learning in the biomedical sciences.

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired by recent advances in physical quantum processors, we evaluated several unconventional machine-learning (ML) strategies on actual human tumor data, namely "Ising-type" methods, whose objective function is formulated identical to simulated annealing and quantum annealing.

Enhancing reproducibility of gene expression analysis with known protein functional relationships: The concept of well-associated protein.

Identification of differentially expressed genes (DEGs) is well recognized to be variable across independent replications of genome-wide transcriptional studies. These are often employed to characterize disease state early in the process of discovery and prioritize novel targets aimed at addressing unmet medical need. Increasing reproducibility of biological findings from these studies could potentially positively impact the success rate of new clinical interventions.

Chronic mTOR activation induces a degradative smooth muscle cell phenotype.

Smooth muscle cell (SMC) proliferation has been thought to limit the progression of thoracic aortic aneurysm and dissection (TAAD) because loss of medial cells associates with advanced disease. We investigated effects of SMC proliferation in the aortic media by conditional disruption of Tsc1, which hyperactivates mTOR complex 1. Consequent SMC hyperplasia led to progressive medial degeneration and TAAD.

Molecular profiling of rheumatoid arthritis patients reveals an association between innate and adaptive cell populations and response to anti-tumor necrosis factor.

Background: The goal of this study is to use comprehensive molecular profiling to characterize clinical response to anti-TNF therapy in a real-world setting and identify reproducible markers differentiating good responders and non-responders in rheumatoid arthritis (RA).

Methods: Whole-blood mRNA, plasma proteins, and glycopeptides were measured in two cohorts of biologic-naïve RA patients (n = 40 and n = 36) from the Corrona CERTAIN (Comparative Effectiveness Registry to study Therapies for Arthritis and Inflammatory coNditions) registry at baseline and after 3 months of anti-TNF treatment. Response to treatment was categorized by EULAR criteria.

Endothelial TGF-β signalling drives vascular inflammation and atherosclerosis.

Atherosclerosis is a progressive vascular disease triggered by interplay between abnormal shear stress and endothelial lipid retention. A combination of these and, potentially, other factors leads to a chronic inflammatory response in the vessel wall, which is thought to be responsible for disease progression characterized by a buildup of atherosclerotic plaques. Yet molecular events responsible for maintenance of plaque inflammation and plaque growth have not been fully defined.

Endothelial ERK1/2 signaling maintains integrity of the quiescent endothelium.

To define the role of ERK1/2 signaling in the quiescent endothelium, we induced endothelial knockout in adult mice. This resulted in a rapid onset of hypertension, a decrease in eNOS expression, and an increase in endothelin-1 plasma levels, with all mice dying within 5 wk. Immunostaining and endothelial fate mapping showed a robust increase in TGFβ signaling leading to widespread endothelial-to-mesenchymal transition (EndMT).

Enhancing Retrosynthetic Reaction Prediction with Deep Learning Using Multiscale Reaction Classification.

Chemical synthesis planning is a key aspect in many fields of chemistry, especially drug discovery. Recent implementations of machine learning and artificial intelligence techniques for retrosynthetic analysis have shown great potential to improve computational methods for synthesis planning. Herein, we present a multiscale, data-driven approach for retrosynthetic analysis with deep highway networks (DHN).

M281, an Anti-FcRn Antibody: Pharmacodynamics, Pharmacokinetics, and Safety Across the Full Range of IgG Reduction in a First-in-Human Study.

M281 is a fully human, anti-neonatal Fc receptor (FcRn) antibody that inhibits FcRn-mediated immunoglobulin G (IgG) recycling to decrease pathogenic IgG while preserving IgG production. A randomized, double-blind, placebo-controlled, first-in-human study with 50 normal healthy volunteers was designed to probe safety and the physiological maximum for reduction of IgG. Intravenous infusion of single ascending doses up to 60 mg/kg induced dose-dependent serum IgG reductions, which were similar across all IgG subclasses.

Dynamic balance of pro- and anti-inflammatory signals controls disease and limits pathology.

Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long-term control of infection while also preventing an over-zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro- and anti-inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback.

Th17 cytokines differentiate obesity from obesity-associated type 2 diabetes and promote TNFα production.

Objective: T cell inflammation plays pivotal roles in obesity-associated type 2 diabetes (T2DM). The identification of dominant sources of T cell inflammation in humans remains a significant gap in understanding disease pathogenesis. It was hypothesized that cytokine profiles from circulating T cells identify T cell subsets and T cell cytokines that define T2DM-associated inflammation.

Strategies for efficient numerical implementation of hybrid multi-scale agent-based models to describe biological systems.

Biologically related processes operate across multiple spatiotemporal scales. For computational modeling methodologies to mimic this biological complexity, individual scale models must be linked in ways that allow for dynamic exchange of information across scales. A powerful methodology is to combine a discrete modeling approach, agent-based models (ABMs), with continuum models to form hybrid models.

A multi-scale approach to designing therapeutics for tuberculosis.

Approximately one third of the world's population is infected with Mycobacterium tuberculosis. Limited information about how the immune system fights M. tuberculosis and what constitutes protection from the bacteria impact our ability to develop effective therapies for tuberculosis.

Computational modeling predicts IL-10 control of lesion sterilization by balancing early host immunity-mediated antimicrobial responses with caseation during mycobacterium tuberculosis infection.

Although almost a third of the world's population is infected with the bacterial pathogen Mycobacterium tuberculosis, our understanding of the functions of many immune factors involved in fighting infection is limited. Determining the role of the immunosuppressive cytokine IL-10 at the level of the granuloma has proven difficult because of lesional heterogeneity and the limitations of animal models. In this study, we take an in silico approach and, through a series of virtual experiments, we predict several novel roles for IL-10 in tuberculosis granulomas: 1) decreased levels of IL-10 lead to increased numbers of sterile lesions, but at the cost of early increased caseation; 2) small increases in early antimicrobial activity cause this increased lesion sterility; 3) IL-10 produced by activated macrophages is a major mediator of early antimicrobial activity and early host-induced caseation; and 4) increasing levels of infected macrophage derived IL-10 promotes bacterial persistence by limiting the early antimicrobial response and preventing lesion sterilization.

A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment.

While active tuberculosis (TB) is a treatable disease, many complex factors prevent its global elimination. Part of the difficulty in developing optimal therapies is the large design space of antibiotic doses, regimens and combinations. Computational models that capture the spatial and temporal dynamics of antibiotics at the site of infection can aid in reducing the design space of costly and time-consuming animal pre-clinical and human clinical trials.

Macrophage polarization drives granuloma outcome during Mycobacterium tuberculosis infection.

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), induces formation of granulomas, structures in which immune cells and bacteria colocalize. Macrophages are among the most abundant cell types in granulomas and have been shown to serve as both critical bactericidal cells and targets for M. tuberculosis infection and proliferation throughout the course of infection.

Multi-scale modeling predicts a balance of tumor necrosis factor-α and interleukin-10 controls the granuloma environment during Mycobacterium tuberculosis infection.

Interleukin-10 (IL-10) and tumor necrosis factor-α (TNF-α) are key anti- and pro-inflammatory mediators elicited during the host immune response to Mycobacterium tuberculosis (Mtb). Understanding the opposing effects of these mediators is difficult due to the complexity of processes acting across different spatial (molecular, cellular, and tissue) and temporal (seconds to years) scales. We take an in silico approach and use multi-scale agent based modeling of the immune response to Mtb, including molecular scale details for both TNF-α and IL-10.