Multi-context genetic modeling of transcriptional regulation resolves novel disease loci.

A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We introduce CONTENT-a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We apply CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. We find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell-type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increases the number of locus-phenotype associations discovered by over 51% relative to previous methods across 22 complex traits.

Methylation risk scores are associated with a collection of phenotypes within electronic health record systems.

Inference of clinical phenotypes is a fundamental task in precision medicine, and has therefore been heavily investigated in recent years in the context of electronic health records (EHR) using a large arsenal of machine learning techniques, as well as in the context of genetics using polygenic risk scores (PRS). In this work, we considered the epigenetic analog of PRS, methylation risk scores (MRS), a linear combination of methylation states. We measured methylation across a large cohort (n = 831) of diverse samples in the UCLA Health biobank, for which both genetic and complete EHR data are available. We constructed MRS for 607 phenotypes spanning diagnoses, clinical lab tests, and medication prescriptions. When added to a baseline set of predictive features, MRS significantly improved the imputation of 139 outcomes, whereas the PRS improved only 22 (median improvement for methylation 10.74%, 141.52%, and 15.46% in medications, labs, and diagnosis codes, respectively, whereas genotypes only improved the labs at a median increase of 18.42%). We added significant MRS to state-of-the-art EHR imputation methods that leverage the entire set of medical records, and found that including MRS as a medical feature in the algorithm significantly improves EHR imputation in 37% of lab tests examined (median R2 increase 47.6%). Finally, we replicated several MRS in multiple external studies of methylation (minimum p-value of 2.72 × 10-7) and replicated 22 of 30 tested MRS internally in two separate cohorts of different ethnicity. Our publicly available results and weights show promise for methylation risk scores as clinical and scientific tools.

Single-cell RNA-seq reveals cell type-specific molecular and genetic associations to lupus.

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Knowledge of circulating immune cell types and states associated with SLE remains incomplete. We profiled more than 1.2 million peripheral blood mononuclear cells (162 cases, 99 controls) with multiplexed single-cell RNA sequencing (mux-seq). Cases exhibited elevated expression of type 1 interferon-stimulated genes (ISGs) in monocytes, reduction of naïve CD4+ T cells that correlated with monocyte ISG expression, and expansion of repertoire-restricted cytotoxic GZMH+ CD8+ T cells. Cell type-specific expression features predicted case-control status and stratified patients into two molecular subtypes. We integrated dense genotyping data to map cell type-specific cis-expression quantitative trait loci and to link SLE-associated variants to cell type-specific expression. These results demonstrate mux-seq as a systematic approach to characterize cellular composition, identify transcriptional signatures, and annotate genetic variants associated with SLE.

Functional characterisation of the amyotrophic lateral sclerosis risk locus GPX3/TNIP1.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a complex, late-onset, neurodegenerative disease with a genetic contribution to disease liability. Genome-wide association studies (GWAS) have identified ten risk loci to date, including the TNIP1/GPX3 locus on chromosome five. Given association analysis data alone cannot determine the most plausible risk gene for this locus, we undertook a comprehensive suite of in silico, in vivo and in vitro studies to address this. METHODS: The Functional Mapping and Annotation (FUMA) pipeline and five tools (conditional and joint analysis (GCTA-COJO), Stratified Linkage Disequilibrium Score Regression (S-LDSC), Polygenic Priority Scoring (PoPS), Summary-based Mendelian Randomisation (SMR-HEIDI) and transcriptome-wide association study (TWAS) analyses) were used to perform bioinformatic integration of GWAS data (Ncases = 20,806, Ncontrols = 59,804) with 'omics reference datasets including the blood (eQTLgen consortium N = 31,684) and brain (N = 2581). This was followed up by specific expression studies in ALS case-control cohorts (microarray Ntotal = 942, protein Ntotal = 300) and gene knockdown (KD) studies of human neuronal iPSC cells and zebrafish-morpholinos (MO). RESULTS: SMR analyses implicated both TNIP1 and GPX3 (p < 1.15 × 10-6), but there was no simple SNP/expression relationship. Integrating multiple datasets using PoPS supported GPX3 but not TNIP1. In vivo expression analyses from blood in ALS cases identified that lower GPX3 expression correlated with a more progressed disease (ALS functional rating score, p = 5.5 × 10-3, adjusted R2 = 0.042, Beffect = 27.4 ± 13.3 ng/ml/ALSFRS unit) with microarray and protein data suggesting lower expression with risk allele (recessive model p = 0.06, p = 0.02 respectively). Validation in vivo indicated gpx3 KD caused significant motor deficits in zebrafish-MO (mean difference vs. control ± 95% CI, vs. control, swim distance = 112 ± 28 mm, time = 1.29 ± 0.59 s, speed = 32.0 ± 2.53 mm/s, respectively, p for all < 0.0001), which were rescued with gpx3 expression, with no phenotype identified with tnip1 KD or gpx3 overexpression. CONCLUSIONS: These results support GPX3 as a lead ALS risk gene in this locus, with more data needed to confirm/reject a role for TNIP1. This has implications for understanding disease mechanisms (GPX3 acts in the same pathway as SOD1, a well-established ALS-associated gene) and identifying new therapeutic approaches. Few previous examples of in-depth investigations of risk loci in ALS exist and a similar approach could be applied to investigate future expected GWAS findings.

Termination of pregnancy services in Irish general practice from January 2019 to June 2019.

OBJECTIVE: To describe how a general-practitioner-delivered community medical abortion service is provided in Ireland, including a description of the unique model of care delivered within the framework of new legislation. To investigate the characteristics and contraceptive choices of women attending in the first six months of the service. STUDY DESIGN: Twenty-seven general practitioners conducted a retrospective chart review. We described the service and analyzed demographic characteristics, treatment outcomes, adverse events, and contraception use. We defined treatment success as complete abortion without surgical intervention. RESULTS: Twenty-seven general practitioners from the Southern Task Group on Abortion and Reproductive Topics (START) group collected data from 475 women who had attended requesting medical abortion from January 1st 2019 to June 30th 2019. Out of these, 315 (66%) were more than 25 years old, and 261 (55%) had at least one child. The mean gestational age at initial presentation was 49 days. Five (1%) had a gestational age which exceeded 84 days. Four hundred and twenty (89%) proceeded with community medical abortion following an initial consultation. The process was completed without the need for surgical intervention in 412 (98%) cases. Six (1.4%) women had a mild post-treatment infection, and received community treatment with oral antibiotics. Thirty-three (7.9%) patients were referred to hospital for additional evaluation following treatment. Two hundred ninety (69%) adopted contraception post abortion; only 160 (34%) were using contraception prior to pregnancy. CONCLUSION: The general-practitioner-delivered community medical abortion service described in the study is safe, effective, and accessible for the majority of, but not all women seeking abortion. The model of care used in Ireland provides an ideal opportunity to discuss contraceptive choice. IMPLICATIONS STATEMENT: This review provides demographic, efficacy, and safety data for the general-practitioner-provided community medical abortion service in Ireland. An effective and largely accessible model of care is demonstrated. These findings can help inform legislative review, clinical guidelines, and generate hypotheses for future research.

Digitization of Measurement-Based Care Pathways in Mental Health Through REDCap and Electronic Health Record Integration: Development and Usability Study.

BACKGROUND: The delivery of standardized self-report assessments is essential for measurement-based care in mental health. Paper-based methods of measurement-based care data collection may result in transcription errors, missing data, and other data quality issues when entered into patient electronic health records (EHRs). OBJECTIVE: This study aims to help address these issues by using a dedicated instance of REDCap (Research Electronic Data Capture; Vanderbilt University)-a free, widely used electronic data capture platform-that was established to enable the deployment of digitized self-assessments in clinical care pathways to inform clinical decision making. METHODS: REDCap was integrated with the primary clinical information system to facilitate the real-time transfer of discrete data and PDF reports from REDCap into the EHR. Both technical and administrative components were required for complete implementation. A technology acceptance survey was also administered to capture physicians' and clinicians' attitudes toward the new system. RESULTS: The integration of REDCap with the EHR transitioned clinical workflows from paper-based methods of data collection to electronic data collection. This resulted in significant time savings, improved data quality, and valuable real-time information delivery. The digitization of self-report assessments at each appointment contributed to the clinic-wide implementation of the major depressive disorder integrated care pathway. This digital transformation facilitated a 4-fold increase in the physician adoption of this integrated care pathway workflow and a 3-fold increase in patient enrollment, resulting in an overall significant increase in major depressive disorder integrated care pathway capacity. Physicians' and clinicians' attitudes were overall positive, with almost all respondents agreeing that the system was useful to their work. CONCLUSIONS: REDCap provided an intuitive patient interface for collecting self-report measures and accessing results in real time to inform clinical decisions and an extensible backend for system integration. The approach scaled effectively and expanded to high-impact clinics throughout the hospital, allowing for the broad deployment of complex workflows and standardized assessments, which led to the accumulation of harmonized data across clinics and care pathways. REDCap is a flexible tool that can be effectively leveraged to facilitate the automatic transfer of self-report data to the EHR; however, thoughtful governance is required to complement the technical implementation to ensure that data standardization, data quality, patient safety, and privacy are maintained.

Use of Telehealth by Surgical Specialties During the COVID-19 Pandemic.

Importance: While telehealth use in surgery has shown to be feasible, telehealth became a major modality of health care delivery during the COVID-19 pandemic. Objective: To assess patterns of telehealth use across surgical specialties before and during the COVID-19 pandemic. Design, Setting, and Participants: Insurance claims from a Michigan statewide commercial payer for new patient visits with a surgeon from 1 of 9 surgical specialties during one of the following periods: prior to the COVID-19 pandemic (period 1: January 5 to March 7, 2020), early pandemic (period 2: March 8 to June 6, 2020), and late pandemic (period 3: June 7 to September 5, 2020). Exposures: Telehealth implementation owing to the COVID-19 pandemic in March 2020. Main Outcomes and Measures: (1) Conversion rate defined as the rate of weekly new patient telehealth visits divided by mean weekly number of total new patient visits in 2019. This outcome adjusts for a substantial decrease in outpatient care during the pandemic. (2) Weekly number of new patient telehealth visits divided by weekly number of total new patient visits. Results: Among 4405 surgeons in the cohort, 2588 (58.8%) performed telehealth in any patient care context. Specifically for new patient visits, 1182 surgeons (26.8%) used telehealth. A total of 109 610 surgical new outpatient visits were identified during the pandemic. The median (interquartile range) age of telehealth patients was 46.8 (34.1-58.4) years compared with 52.6 (38.3-62.3) years for patients who received care in-person. Prior to March 2020, less than 1% (8 of 173 939) of new patient visits were conducted through telehealth. Telehealth use peaked in April 2020 (week 14) and facilitated 34.6% (479 of 1383) of all new patient visits during that week. The telehealth conversion rate peaked in April 2020 (week 15) and was equal to 8.2% of the 2019 mean weekly new patient visit volume. During period 2, a mean (SD) of 16.6% (12.0%) of all new patient surgical visits were conducted via telehealth (conversion rate of 5.1% of 2019 mean weekly new patient visit volumes). During period 3, 3.0% (2168 of 71 819) of all new patient surgical visits were conducted via telehealth (conversion rate of 2.5% of 2019 new patient visit volumes). Mean (SD) telehealth conversion rates varied by specialty with urology being the highest (14.3% [7.7%]). Conclusions and Relevance: Results from this study showed that telehealth use grew across all surgical specialties in Michigan in response to the COVID-19 pandemic. While rates of telehealth use have declined as in-person care has resumed, telehealth use remains substantially higher across all surgical specialties than it was prior to the pandemic.

Instantaneous velocity estimation for the four swimming strokes using a 3-axis accelerometer: Validation on paralympic athletes.

Inertial measurement unit systems (IMU) are increasingly used in sports. However, no algorithm evaluating the instantaneous swimming velocity has been validated on the four swimming strokes, or on dive start. Our objective is to develop and validate a new method to measure instantaneous swimming velocity on athletes presenting various musculoskeletal and neurological impairments and swimming one of the four swimming strokes. Seven Paralympic athletes were involved and performed a total of 18 trials of 50 m in real conditions. All trials were recorded with a 3-axis accelerometer, a tethered device and a video camera. The instantaneous velocity was computed by drift-free integration of the forward acceleration. For all trials, Bland-Altman analyses showed a bias of 0.03-0.06 m.s-1 with the tethered device, with 95% LOA lower than 0.31-0.80 m.s-1, RMSE was 0.14-0.39 m.s-1, and ICC amounted 0.494-0.941. No significant difference was found for the mean 50 m velocities between the accelerometer, the tethered device and the video camera. The results obtained for freestyle were comparable to those obtained with GPS in outdoor pools. Those obtained for backstroke, breaststroke and butterfly were very encouraging. Our method is simple, reliable, advantageous compared to tethered devices, and could be easily used in the field by coaches.

Crowdsourcing a crisis response for COVID-19 in oncology.

Crowdsourcing efforts are currently underway to collect and analyze data from patients with cancer who are affected by the COVID-19 pandemic. These community-led initiatives will fill key knowledge gaps to tackle crucial clinical questions on the complexities of infection with the causative coronavirus SARS-Cov-2 in the large, heterogeneous group of vulnerable patients with cancer.

CONFINED: distinguishing biological from technical sources of variation by leveraging multiple methylation datasets.

Methylation datasets are affected by innumerable sources of variability, both biological (cell-type composition, genetics) and technical (batch effects). Here, we propose a reference-free method based on sparse canonical correlation analysis to separate the biological from technical sources of variability. We show through simulations and real data that our method, CONFINED, is not only more accurate than the state-of-the-art reference-free methods for capturing known, replicable biological variability, but it is also considerably more robust to dataset-specific technical variability than previous approaches. CONFINED is available as an R package as detailed at https://github.com/cozygene/CONFINED .

Modeling the temporal dynamics of the gut microbial community in adults and infants.

Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and functions of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of taxa depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of microbial community temporal dynamics. MTV-LMM can identify time-dependent microbes (i.e., microbes whose abundance can be predicted based on the previous microbial composition) in longitudinal studies, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on real and synthetic time series datasets, and found that MTV-LMM outperforms commonly used methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance of taxa at later time points is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM, we demonstrate that a considerable portion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome.

FEAST: fast expectation-maximization for microbial source tracking.

A major challenge of analyzing the compositional structure of microbiome data is identifying its potential origins. Here, we introduce fast expectation-maximization microbial source tracking (FEAST), a ready-to-use scalable framework that can simultaneously estimate the contribution of thousands of potential source environments in a timely manner, thereby helping unravel the origins of complex microbial communities ( https://github.com/cozygene/FEAST ). The information gained from FEAST may provide insight into quantifying contamination, tracking the formation of developing microbial communities, as well as distinguishing and characterizing bacteria-related health conditions.

Thermal physiology: A new dimension of the pace-of-life syndrome.

Current syndrome research focuses primarily on behaviour with few incorporating components of physiology. One such syndrome is the pace-of-life syndrome (POLS) which describes covariation between behaviour, metabolism, immunity, hormonal response, and life-history traits. Despite the strong effect temperature has on behaviour, thermal physiology has yet to be considered within this syndrome framework. We proposed the POLS to be extended to include a new dimension, the cold-hot axis. Under this premise, it is predicted that thermal physiology and behaviour would covary, whereby individual positioning along the thermal continuum would coincide with that of the behavioural continuum. This hypothesis was tested by measuring thermal traits of delicate skinks (Lampropholis delicata) and linking it to their behaviour. Principal components analysis and structural equation modelling were used to determine if traits were structured within the POLS and to characterize the direction of their interactions. Model results supported the inclusion of the cold-hot axis into the POLS and indicated that thermal physiology was the driver of this relationship, in that thermal traits either constrained or promoted activity, exploration, boldness and social behaviour. This study highlights the need to integrate thermal physiology within a syndrome framework.

Targeting bacterial biofilms via surface engineering of gold nanoparticles.

Bacterial biofilms are associated with persistent infections that are resistant to conventional antibiotics and substantially complicate patient care. Surface engineered nanoparticles represent a novel, unconventional approach for disruption of biofilms and targeting of bacterial pathogens. Herein, we describe the role of surface charge of gold nanoparticles (AuNPs) on biofilm disruption and bactericidal activity towards Staphylococcus aureus and Pseudomonas aeruginosa which are important ventilator associated pneumonia (VAP) pathogens. In addition, we study the toxicity of charged AuNPs on human bronchial epithelial cells. While 100% positively charged AuNP surface was uniformly toxic to both bacteria and epithelial cells, reducing the extent of positive charge on the AuNP surface at moderate concentrations prevented epithelial cell toxicity. Reducing surface charge was however also less effective in killing bacteria. Conversely, increasing AuNP concentration while maintaining a low level of positivity continued to be bactericidal and disrupt the bacterial biofilm and was less cytotoxic to epithelial cells. These initial in vitro studies suggest that modulation of AuNP surface charge could be used to balance effects on bacteria vs. airway cells in the context of VAP, but the therapeutic window in terms of concentration vs. surface positive charge may be limited. Additional factors such as hydrophobicity may need to be considered in order to design AuNPs with specific, beneficial effects on bacterial pathogens and their biofilms.

The accuracy of emergency medical dispatcher-assisted layperson-caller pulse check using the medical priority dispatch system protocol.

INTRODUCTION: Knowing the pulse rate of a patient in a medical emergency can help to determine patient acuity and the level of medical care required. Little evidence exists regarding the ability of a 911 layperson-caller to accurately determine a conscious patient's pulse rate. Hypothesis The hypothesis of this study was that, when instructed by a trained emergency medical dispatcher (EMD) using the scripted Medical Priority Dispatch System (MPDS) protocol Pulse Check Diagnostic Tool (PCDxT), a layperson-caller can detect a carotid pulse and accurately determine the pulse rate in a conscious person. METHODS: This non-randomized and non-controlled prospective study was conducted at three different public locations in the state of Utah (USA). A healthy, mock patient's pulse rate was obtained using an electrocardiogram (ECG) monitor. Layperson-callers, in turn, initiated a simulated 911 phone call to an EMD call-taker who provided instructions for determining the pulse rate of the patient. Layperson accuracy was assessed using correlations between the layperson-caller's finding and the ECG reading. RESULTS: Two hundred sixty-eight layperson-callers participated; 248 (92.5%) found the pulse of the mock patient. There was a high correlation between pulse rates obtained using the ECG monitor and those found by the layperson-callers, overall (94.6%, P < .001), and by site, gender, and age. CONCLUSIONS: Layperson-callers, when provided with expert, scripted instructions by a trained 911 dispatcher over the phone, can accurately determine the pulse rate of a conscious and healthy person. Improvements to the 911 instructions may further increase layperson accuracy.

Prevalence and determinants of hyperglycemia in very low birth weight infants: cohort analyses of the NIRTURE study.

OBJECTIVES: To investigate the prevalence and determinants of hyperglycemia in the preterm population, as part of the Neonatal Insulin Therapy in Europe (NIRTURE) Trial. STUDY DESIGN: We conducted prospective cohort analyses of continuous glucose monitoring data from control infants participating in an international randomized controlled trial. Data were collected from 188 very low birth weight infants (<1500 g). RESULTS: In the first week of life, 80% of infants had evidence of glucose levels >8 mmol/L, and 32% had glucose levels >10 mmol/L >10% of the time. Independent risk factors for hyperglycemia included increasing prematurity, small size at birth, use of inotropes, lipid infusions, and sepsis. There was a lack of association between rate of dextrose infused and risk of hyperglycemia. CONCLUSION: The prevalence of hyperglycemia in the very low birth weight infant is high, with marked variability in prevalence between infants, not simply related to rates of glucose infused, but to other potentially modifiable risk factors.

Understanding biophysicochemical interactions at the nano-bio interface.

Rapid growth in nanotechnology is increasing the likelihood of engineered nanomaterials coming into contact with humans and the environment. Nanoparticles interacting with proteins, membranes, cells, DNA and organelles establish a series of nanoparticle/biological interfaces that depend on colloidal forces as well as dynamic biophysicochemical interactions. These interactions lead to the formation of protein coronas, particle wrapping, intracellular uptake and biocatalytic processes that could have biocompatible or bioadverse outcomes. For their part, the biomolecules may induce phase transformations, free energy releases, restructuring and dissolution at the nanomaterial surface. Probing these various interfaces allows the development of predictive relationships between structure and activity that are determined by nanomaterial properties such as size, shape, surface chemistry, roughness and surface coatings. This knowledge is important from the perspective of safe use of nanomaterials.