Mural Wnt/ß-catenin signaling regulates Lama2 expression to promote neurovascular unit maturation.

Neurovascular unit and barrier maturation rely on vascular basement membrane (vBM) composition. Laminins, a major vBM component, are crucial for these processes, yet the signaling pathway(s) that regulate their expression remain unknown. Here, we show that mural cells have active Wnt/ß-catenin signaling during central nervous system development in mice. Bulk RNA sequencing and validation using postnatal day 10 and 14 wild-type versus adenomatosis polyposis coli downregulated 1 (Apcdd1-/-) mouse retinas revealed that Lama2 mRNA and protein levels are increased in mutant vasculature with higher Wnt/ß-catenin signaling. Mural cells are the main source of Lama2, and Wnt/ß-catenin activation induces Lama2 expression in mural cells in vitro. Markers of mature astrocytes, including aquaporin 4 (a water channel in astrocyte endfeet) and integrin-α6 (a laminin receptor), are upregulated in Apcdd1-/- retinas with higher Lama2 vBM deposition. Thus, the Wnt/ß-catenin pathway regulates Lama2 expression in mural cells to promote neurovascular unit and barrier maturation.

Alterations in the kallikrein-kinin system predict death after heart transplant.

Heart transplantation remains the definitive treatment for end stage heart failure. Because availability is limited, risk stratification of candidates is crucial for optimizing both organ allocations and transplant outcomes. Here we utilize proteomics prior to transplant to identify new biomarkers that predict post-transplant survival in a multi-institutional cohort. Microvesicles were isolated from serum samples and underwent proteomic analysis using mass spectrometry. Monte Carlo cross-validation (MCCV) was used to predict survival after transplant incorporating select recipient pre-transplant clinical characteristics and serum microvesicle proteomic data. We identified six protein markers with prediction performance above AUROC of 0.6, including Prothrombin (F2), anti-plasmin (SERPINF2), Factor IX, carboxypeptidase 2 (CPB2), HGF activator (HGFAC) and low molecular weight kininogen (LK). No clinical characteristics demonstrated an AUROC > 0.6. Putative biological functions and pathways were assessed using gene set enrichment analysis (GSEA). Differential expression analysis identified enriched pathways prior to transplant that were associated with post-transplant survival including activation of platelets and the coagulation pathway prior to transplant. Specifically, upregulation of coagulation cascade components of the kallikrein-kinin system (KKS) and downregulation of kininogen prior to transplant were associated with survival after transplant. Further prospective studies are warranted to determine if alterations in the KKS contributes to overall post-transplant survival.

A database of pediatric drug effects to evaluate ontogenic mechanisms from child growth and development.

BACKGROUND: 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. METHODS: We recently developed a method called disproportionality generalized additive models (dGAMs) to better identify safety signals for drugs across child-development stages. FINDINGS: 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. CONCLUSIONS: 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. FUNDING: This study was supported by grants from the National Institutes of Health (NIH).

No population left behind: Improving paediatric drug safety using informatics and systems biology.

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.

Evaluating risk detection methods to uncover ontogenic-mediated adverse drug effect mechanisms in children.

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.

Plasma kallikrein predicts primary graft dysfunction after heart transplant.

BACKGROUND: Primary graft dysfunction (PGD) is the leading cause of early mortality after heart transplant. Pre-transplant predictors of PGD remain elusive and its etiology remains unclear. METHODS: Microvesicles were isolated from 88 pre-transplant serum samples and underwent proteomic evaluation using TMT mass spectrometry. Monte Carlo cross validation (MCCV) was used to predict the occurrence of severe PGD after transplant using recipient pre-transplant clinical characteristics and serum microvesicle proteomic data. Putative biological functions and pathways were assessed using gene set enrichment analysis (GSEA) within the MCCV prediction methodology. RESULTS: Using our MCCV prediction methodology, decreased levels of plasma kallikrein (KLKB1), a critical regulator of the kinin-kallikrein system, was the most predictive factor identified for PGD (AUROC 0.6444 [0.6293, 0.6655]; odds 0.1959 [0.0592, 0.3663]. Furthermore, a predictive panel combining KLKB1 with inotrope therapy achieved peak performance (AUROC 0.7181 [0.7020, 0.7372]) across and within (AUROCs of 0.66-0.78) each cohort. A classifier utilizing KLKB1 and inotrope therapy outperforms existing composite scores by more than 50 percent. The diagnostic utility of the classifier was validated on 65 consecutive transplant patients, resulting in an AUROC of 0.71 and a negative predictive value of 0.92-0.96. Differential expression analysis revealed a enrichment in inflammatory and immune pathways prior to PGD. CONCLUSIONS: Pre-transplant level of KLKB1 is a robust predictor of post-transplant PGD. The combination with pre-transplant inotrope therapy enhances the prediction of PGD compared to pre-transplant KLKB1 levels alone and the resulting classifier equation validates within a prospective validation cohort. Inflammation and immune pathway enrichment characterize the pre-transplant proteomic signature predictive of PGD.

Pediatric data from the All of Us research program: demonstration of pediatric obesity over time.

OBJECTIVE: To describe and demonstrate use of pediatric data collected by the All of Us Research Program. MATERIALS AND METHODS: All of Us participant physical measurements and electronic health record (EHR) data were analyzed including investigation of trends in childhood obesity and correlation with adult body mass index (BMI). RESULTS: We identified 19 729 participants with legacy pediatric EHR data including diagnoses, prescriptions, visits, procedures, and measurements gathered since 1980. We found an increase in pediatric obesity diagnosis over time that correlates with BMI measurements recorded in participants' adult EHRs and those physical measurements taken at enrollment in the research program. DISCUSSION: We highlight the availability of retrospective pediatric EHR data for nearly 20 000 All of Us participants. These data are relevant to current issues such as the rise in pediatric obesity. CONCLUSION: All of Us contains a rich resource of retrospective pediatric EHR data to accelerate pediatric research studies.

ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data.

OBJECTIVES: Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement. MATERIALS AND METHODS: We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format. RESULTS: ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept. CONCLUSION: ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu).

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.

MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: 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 INFORMATION: Supplementary data are available at Bioinformatics online.

Structural and functional cardiac profile after prolonged duration of mechanical unloading: potential implications for myocardial recovery.

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.

Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations.

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.Insight into the genetic influence on the immune response is important for the understanding of interindividual variability in human pathologies. Here, the authors generate transcriptome data from human blood monocytes stimulated with various immune stimuli and provide a time-resolved response eQTL map.