Goddard's Astrophysics Science Division Annual Report 2013

The Astrophysics Science Division (ASD) at Goddard Space Flight Center (GSFC) is one of the largest and most diverse astrophysical organizations in the world, with activities spanning a broad range of topics in theory, observation, and mission and technology development. Scientific research is carried out over the entire electromagnetic spectrum from gamma rays to radio wavelengths as well as particle physics and gravitational radiation. Members of ASD also provide the scientific operations for two orbiting astrophysics missions Fermi Gamma-ray Space Telescope and Swift as well as the Science Support Center for Fermi. A number of key technologies for future missions are also under development in the Division, including X-ray mirrors, space-based interferometry, high contrast imaging techniques to search for exoplanets, and new detectors operating at gamma-ray, X-ray, ultraviolet, infrared, and radio wavelengths. The overriding goals of ASD are to carry out cutting-edge scientific research, provide Project Scientist support for spaceflight missions, implement the goals of the NASA Strategic Plan, serve and support the astronomical community, and enable future missions by conceiving new concepts and inventing new technologies

TRPV6 as a putative genomic susceptibility locus influencing racial disparities in cancer

It is well established that African Americans exhibit higher incidence, higher mortality, and more aggressive forms of some cancers, including those of breast, prostate, colon, stomach, and cervix. Here we examine the ancestral haplotype of the TRPV6 calcium channel as a putative genomic factor in this racial divide. The minor (ancestral) allele frequency is 60% in people of African ancestry, but between 1 and 11% in all other populations. Research on TRPV6 structure/function, its association with specific cancers, and the evolutionary-ecological conditions which impacted selection of its haplotypes are synthesized to provide evidence for TRPV6 as a germline susceptibility locus in cancer. Recently elucidated mechanisms of TRPV6 channel deactivation are discussed in relation to the location of the allele favored in selection, suggesting a reduced capacity to inactivate the channel in those who have the ancestral haplotype. This could result in an excessively high cellular Ca2+, which has been implicated in cancer, for those in settings where calcium intake is far higher than in their ancestral environment. A recent report associating increasing calcium intake with a pattern of increase in aggressive prostate cancer in African-American but not European-American men may be related. If TRPV6 is found to be associated with cancer, further research would be warranted to improve risk assessment and examine interventions with the aim of improving cancer outcomes for people of African ancestr

Cracking the “Sepsis” Code: Assessing Time Series Nature of EHR data, and Using Deep Learning for Early Sepsis Prediction

On a yearly basis, sepsis costs US hospitals more than any other health condition. A majority of patients who suffer from sepsis are not diagnosed at the time of admission. Early detection and antibiotic treatment of sepsis are vital to improve outcomes for these patients, as each hour of delayed treatment is associated with increased mortality. In this study our goal is to predict sepsis 12 hours before its diagnosis using vitals and blood tests routinely taken in the ICU. We have investigated the performance of several machine learning algorithms including XGBoost, CNN, CNN-LSTM and CNN-XGBoost. Contrary to our expectations, XGBoost outperforms all of the sequential models and yields the best hour-by-hour prediction, perhaps due to the way we imputed missing values, losing signal that relates to the time-series nature of the EHR data. We added feature engineering to detect change points in tests and vitals, resulting in 5% improvement in XGBoost. Our team, USF-Sepsis-Phys, achieved a utility score of 0.22 (untuned threshold) and an average of the three reported AUCs (test sets A, B, C) of 0.82. As expected with this AUC, the same model with tuned threshold (not run in the PhysioNet challenge) performed significantly better, as evaluated with 3-fold cross-validation of the entire PhyisoNet training set

Ovine Fetal Immune Response to Cache Valley Virus Infection

Cache Valley virus (CVV)-induced malformations have been previously reproduced in ovine fetuses. To evaluate the development of the antiviral response by the early, infected fetus, before the development of immunocompetency, ovine fetuses at 35 days of gestation were inoculated in utero with CVV and euthanized at 7, 10, 14, 21, and 28 days postinfection. The antiviral immune response in immature fetuses infected with CVV was evaluated. Gene expression associated with an innate, immune response was quantified by real-time quantitative PCR. The upregulated genes in infected fetuses included ISG15, Mx1, Mx2, IL-1, IL-6, TNF-α, TLR-7, and TLR-8. The amount of Mx1 protein, an interferon-stimulated GTPase capable of restricting growth of bunyaviruses, was elevated in the allantoic and amniotic fluid in infected fetuses. ISG15 protein expression was significantly increased in target tissues of infected animals. B lymphocytes and immunoglobulin-positive cells were detected in lymphoid tissues and in the meninges of infected animals. These results demonstrated that the infected ovine fetus is able to initiate an innate and adaptive immune response much earlier than previously known, which presumably contributes to viral clearance in infected animals

Natural climate solutions

Our thanks for inputs by L. Almond, A. Baccini, A. Bowman, S. CookPatton, J. Evans, K. Holl, R. Lalasz, A. Nassikas, M. Spalding, M. Wolosin, and expert elicitation respondents. Our thanks for datasets developed by the Hansen lab and the NESCent grasslands working group (C. Lehmann, D. Griffith, T. M. Anderson, D. J. Beerling, W. Bond, E. Denton, E. Edwards, E. Forrestel, D. Fox, W. Hoffmann, R. Hyde, T. Kluyver, L. Mucina, B. Passey, S. Pau, J. Ratnam, N. Salamin, B. Santini, K. Simpson, M. Smith, B. Spriggs, C. Still, C. Strömberg, and C. P. Osborne). This study was made possible by funding from the Doris Duke Charitable Foundation. Woodbury was supported in part by USDA-NIFA Project 2011-67003-30205 Data deposition: A global spatial dataset of reforestation opportunities has been deposited on Zenodo (https://zenodo.org/record/883444). This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1710465114/-/DCSupplemental.Peer reviewedPublisher PD

Leveraging Genomic Associations in Precision Digital Care for Weight Loss: Cohort Study

Background: The COVID-19 pandemic has highlighted the urgency of addressing an epidemic of obesity and associated inflammatory illnesses. Previous studies have demonstrated that interactions between single-nucleotide polymorphisms (SNPs) and lifestyle interventions such as food and exercise may vary metabolic outcomes, contributing to obesity. However, there is a paucity of research relating outcomes from digital therapeutics to the inclusion of genetic data in care interventions. Objective: This study aims to describe and model the weight loss of participants enrolled in a precision digital weight loss program informed by the machine learning analysis of their data, including genomic data. It was hypothesized that weight loss models would exhibit a better fit when incorporating genomic data versus demographic and engagement variables alone. Methods: A cohort of 393 participants enrolled in Digbi Health’s personalized digital care program for 120 days was analyzed retrospectively. The care protocol used participant data to inform precision coaching by mobile app and personal coach. Linear regression models were fit of weight loss (pounds lost and percentage lost) as a function of demographic and behavioral engagement variables. Genomic-enhanced models were built by adding 197 SNPs from participant genomic data as predictors and refitted using Lasso regression on SNPs for variable selection. Success or failure logistic regression models were also fit with and without genomic data. Results: Overall, 72.0% (n=283) of the 393 participants in this cohort lost weight, whereas 17.3% (n=68) maintained stable weight. A total of 142 participants lost 5% bodyweight within 120 days. Models described the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. Incorporating genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13, respectively. The logistic model improved the pseudo R 2 value from 0.193 to 0.285. Gender, engagement, and specific SNPs were significantly associated with weight loss. SNPs within genes involved in metabolic pathways processing food and regulating fat storage were associated with weight loss in this cohort: rs17300539_G (insulin resistance and monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, and cholesterol metabolism), and rs4074995_A (calcium-potassium transport and serum calcium levels). The models described greater average weight loss for participants with more risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks. Conclusions: Including genomic information when modeling outcomes of a digital precision weight loss program greatly enhanced the model accuracy. Interpretable weight loss models indicated the efficacy of coaching informed by participants’ genomic risk, accompanied by active engagement of participants in their own success. Although large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss using genetic risk, with digitally delivered recommendations alongside health coaching to improve intervention efficac

Morquio A Syndrome-Associated Mutations: A Review of Alterations in the GALNS gene and a New Locus-Specific Database

Morquio A syndrome (mucopolysaccharidosis IVA) is an autosomal recessive disorder that results from deficient activity of the enzyme Nacetylgalactosamine-6-sulfatase (GALNS) due to alterations in the GALNS gene, which causes major skeletal and connective tissue abnormalities and effects on multiple organ systems. The GALNS alterations associated with Morquio A are numerous and heterogeneous, and new alterations are continuously identified. To aid detection and interpretation of GALNS alterations, from previously published research, we provide a comprehensive and upto-date listing of 277 unique GALNS alterations associated with Morquio A identified from 1,091 published GALNS alleles. In agreement with previous findings, most reported GALNS alterations are missense changes and even the most frequent alterations are relatively uncommon. We found that 48% of patients are assessed as homozygous for a GALNS alteration, 39% are assessed as heterozygous for two identified GALNS alterations, and in 13% of patients only one GALNS alteration is detected. We report here the creation of a locus-specific database for the GALNS gene (http://galns.mutdb.org/) that catalogs all reported alterations in GALNS to date. We highlight the challenges both in alteration detection and genotype– phenotype interpretation caused in part by the heterogeneity of GALNS alterations and provide recommendations for molecular testing of GALNS

State of the Art Review on Genetics and Precision Medicine in Arrhythmogenic Cardiomyopathy.

Arrhythmogenic cardiomyopathy (ACM) is an inherited cardiomyopathy characterised by ventricular arrhythmia and an increased risk of sudden cardiac death (SCD). Numerous genetic determinants and phenotypic manifestations have been discovered in ACM, posing a significant clinical challenge. Further to this, wider evaluation of family members has revealed incomplete penetrance and variable expressivity in ACM, suggesting a complex genotype-phenotype relationship. This review details the genetic basis of ACM with specific genotype-phenotype associations, providing the reader with a nuanced perspective of this condition; whilst also proposing a future roadmap to delivering precision medicine-based management in ACM

Digital Therapeutics Care Utilizing Genetic and Gut Microbiome Signals for the Management of Functional Gastrointestinal Disorders: Results From a Preliminary Retrospective Study

Diet and lifestyle-related illnesses including functional gastrointestinal disorders (FGIDs) and obesity are rapidly emerging health issues worldwide. Research has focused on addressing FGIDs via in-person cognitive-behavioral therapies, diet modulation and pharmaceutical intervention. Yet, there is paucity of research reporting on digital therapeutics care delivering weight loss and reduction of FGID symptom severity, and on modeling FGID status and symptom severity reduction including personalized genomic SNPs and gut microbiome signals. Our aim for this study was to assess how effective a digital therapeutics intervention personalized on genomic SNPs and gut microbiome signals was at reducing symptomatology of FGIDs on individuals that successfully lost body weight. We also aimed at modeling FGID status and FGID symptom severity reduction using demographics, genomic SNPs, and gut microbiome variables. This study sought to train a logistic regression model to differentiate the FGID status of subjects enrolled in a digital therapeutics care program using demographic, genetic, and baseline microbiome data. We also trained linear regression models to ascertain changes in FGID symptom severity of subjects at the time of achieving 5% or more of body weight loss compared to baseline. For this we utilized a cohort of 177 adults who reached 5% or more weight loss on the Digbi Health personalized digital care program, who were retrospectively surveyed about changes in symptom severity of their FGIDs and other comorbidities before and after the program. Gut microbiome taxa and demographics were the strongest predictors of FGID status. The digital therapeutics program implemented, reduced the summative severity of symptoms for 89.42% (93/104) of users who reported FGIDs. Reduction in summative FGID symptom severity and IBS symptom severity were best modeled by a mixture of genomic and microbiome predictors, whereas reduction in diarrhea and constipation symptom severity were best modeled by microbiome predictors only. This preliminary retrospective study generated diagnostic models for FGID status as well as therapeutic models for reduction of FGID symptom severity. Moreover, these therapeutic models generate testable hypotheses for associations of a number of biomarkers in the prognosis of FGIDs symptomatology