The immune system plays a major role in human health and disease, and understanding genetic causes of interindividual variability of immune responses is vital. Here, we isolate monocytes from 134 genotyped individuals, stimulate these cells with three defined microbe-associated molecular patterns (LPS, MDP, and 5′-ppp-dsRNA), and profile the transcriptomes at three time points. Mapping expression quantitative trait loci (eQTL), we identify 417 response eQTLs (reQTLs) with varying effects between conditions. We characterize the dynamics of genetic regulation on early and late immune response and observe an enrichment of reQTLs in distal cis-regulatory elements. In addition, reQTLs are enriched for recent positive selection with an evolutionary trend towards enhanced immune response. Finally, we uncover reQTL effects in multiple GWAS loci and show a stronger enrichment for response than constant eQTLs in GWAS signals of several autoimmune diseases. This demonstrates the importance of infectious stimuli in modifying genetic predisposition to disease.

Electronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge. We present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes. PatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu. Supplementary data are available at Bioinformatics online.

Adverse drug effects (ADEs) in children are common and may result in disability and death, necessitating post-marketing monitoring of their use. Evaluating drug safety is especially challenging in children due to the processes of growth and maturation, which can alter how children respond to treatment. Current drug safety-signal-detection methods do not account for these dynamics. We recently developed a method called disproportionality generalized additive models (dGAMs) to better identify safety signals for drugs across child-development stages. We used dGAMs on a database of 264,453 pediatric adverse-event reports and found 19,438 ADEs signals associated with development and validated these signals against a small reference set of pediatric ADEs. Using our approach, we can hypothesize on the ontogenic dynamics of ADE signals, such as that montelukast-induced psychiatric disorders appear most significant in the second year of life. Additionally, we integrated pediatric enzyme expression data and found that pharmacogenes with dynamic childhood expression, such as CYP2C18 and CYP27B1, are associated with pediatric ADEs. We curated KidSIDES, a database of pediatric drug safety signals, for the research community and developed the Pediatric Drug Safety portal (PDSportal) to facilitate evaluation of drug safety signals across childhood growth and development. This study was supported by grants from the National Institutes of Health (NIH).

Clinical and experimental studies have suggested that the duration of left ventricular assist device (LVAD) support may affect remodeling of the failing heart. We aimed to 1) characterize the changes in Ca²⁺/calmodulin-dependent protein kinase type-IIδ (CaMKIIδ), growth signaling, structural proteins, fibrosis, apoptosis, and gene expression before and after LVAD support and 2) assess whether the duration of support correlated with improvement or worsening of reverse remodeling. Left ventricular apex tissue and serum pairs were collected in patients with dilated cardiomyopathy ( n = 25, 23 men and 2 women) at LVAD implantation and after LVAD support at cardiac transplantation/LVAD explantation. Normal cardiac tissue was obtained from healthy hearts ( n = 4) and normal serum from age-matched control hearts ( n = 4). The duration of LVAD support ranged from 48 to 1,170 days (median duration: 270 days). LVAD support was associated with CaMKIIδ activation, increased nuclear myocyte enhancer factor 2, sustained histone deacetylase-4 phosphorylation, increased circulating and cardiac myostatin (MSTN) and MSTN signaling mediated by SMAD2, ongoing structural protein dysregulation and sustained fibrosis and apoptosis (all P < 0.05). Increased CaMKIIδ phosphorylation, nuclear myocyte enhancer factor 2, and cardiac MSTN significantly correlated with the duration of support. Phosphorylation of SMAD2 and apoptosis decreased with a shorter duration of LVAD support but increased with a longer duration of LVAD support. Further study is needed to define the optimal duration of LVAD support in patients with dilated cardiomyopathy. NEW & NOTEWORTHY A long duration of left ventricular assist device support may be detrimental for myocardial recovery, based on myocardial tissue experiments in patients with prolonged support showing significantly worsened activation of Ca²⁺/calmodulin-dependent protein kinase-IIδ, increased nuclear myocyte enhancer factor 2, increased myostatin and its signaling by SMAD2, and apoptosis as well as sustained histone deacetylase-4 phosphorylation, structural protein dysregulation, and fibrosis.

Adverse drugs effects (ADEs) in children are common and may result in disability and death. The current paediatric drug safety landscape, including clinical trials, is limited as it rarely includes children and relies on extrapolation from adults. Children are not small adults but go through an evolutionarily conserved and physiologically dynamic process of growth and maturation. Novel quantitative approaches, integrating observations from clinical trials and drug safety databases with dynamic mechanisms, can be used to systematically identify ADEs unique to childhood. In this perspective, we discuss three critical research directions using systems biology methodologies and novel informatics to improve paediatric drug safety, namely child versus adult drug safety profiles, age‐dependent drug toxicities and genetic susceptibility of ADEs across childhood. We argue that a data‐driven framework that leverages observational data, biomedical knowledge and systems biology modelling will reveal previously unknown mechanisms of pediatric adverse drug events and lead to improved paediatric drug safety.

Background: Identifying adverse drugs effects (ADEs) in children, overall and within pediatric age groups, is essential for preventing disability and death from marketed drugs. At the same time, however, detection is challenging due to dynamic biological processes during growth and maturation, called ontogeny, that alter pharmacokinetics and pharmacodynamics. As a result, methodologies in pediatric drug safety have been limited to event surveillance and have not focused on investigating adverse event mechanisms. There is an opportunity to identify drug event patterns within observational databases for evaluating ontogenic-mediated adverse event mechanisms. The first step of which is to establish statistical models that can identify temporal trends of adverse effects across childhood.Results: Using simulation, we evaluated a population stratification method (the proportional reporting ratio or PRR) and a population modeling method (the generalized additive model or GAM) to identify and quantify ADE risk at varying reporting rates and dynamics. We found that GAMs showed improved performance over the PRR in detecting dynamic drug event reporting across child development stages. Moreover, GAMs exhibited normally distributed and robust ADE risk estimation at all development stages by sharing information across child development stages.Conclusions: Our study underscores the opportunity for using population modeling techniques, which leverage drug event reporting across development stages, as biologically-inspired detection methods for evaluating ontogenic mechanisms.