Ann Cashion

Peripheral Total Tau in Military Personnel Who Sustain Traumatic Brain Injuries During Deployment.

IMPORTANCEnApproximately one-third of military personnel who deploy for combat operations sustain 1 or more traumatic brain injuries (TBIs), which increases the risk for chronic symptoms of postconcussive disorder, posttraumatic stress disorder, and depression and for the development of chronic traumatic encephalopathy. Elevated concentrations of tau are observed in blood shortly following a TBI, but, to our knowledge, the role of tau elevations in blood in the onset and maintenance of chronic symptoms after TBI has not been investigated.nnnOBJECTIVESnTo assess peripheral tau levels in military personnel exposed to TBI and to examine the relationship between chronic neurological symptoms and tau elevations.nnnDESIGN, SETTING, AND PARTICIPANTSnObservational assessment from September 2012 to August 2014 of US military personnel at the Madigan Army Medical Center who had been deployed within the previous 18 months. Plasma total tau concentrations were measured using a novel ultrasensitive single-molecule enzyme-linked immunosorbent assay. Classification of participants with and without self-reported TBI was made using the Warrior Administered Retrospective Casualty Assessment Tool. Self-reported symptoms of postconcussive disorder, posttraumatic stress disorder, and depression were determined by the Neurobehavioral Symptom Inventory, the Posttraumatic Stress Disorder Checklist Military Version, and the Quick Inventory of Depressive Symptomatology, respectively. Group differences in tau concentrations were determined through analysis of variance models, and area under the receiver operating characteristic curve determined the sensitivity and specificity of tau concentrations in predicting TBI and chronic symptoms. Seventy participants with self-reported TBI on the Warrior Administered Retrospective Casualty Assessment Tool and 28 control participants with no TBI exposure were included.nnnMAIN OUTCOMES AND MEASURESnConcentration of total tau in peripheral blood.nnnRESULTSnConcentrations of plasma tau were significantly elevated in the 70 participants with self-reported TBI compared with the 28 controls (mean [SD], 1.13 [0.78] vs 0.63 [0.48] pg/mL, respectively; F1,97u2009=u20094.97; Pu2009=u2009.03). Within the self-reported TBI cases, plasma total tau concentrations were significantly associated with having a medical record of TBI compared with self-reported TBI only (mean [SD], 1.57 [0.92] vs 0.85 [0.52] pg/mL, respectively; F1,69u2009=u20096.15; Pu2009=u2009.02) as well as reporting the occurrence of 3 of more TBIs during deployment compared with fewer than 3 TBIs (mean [SD], 1.52 [0.82] vs 0.82 [0.60] pg/mL, respectively; F1,69u2009=u20098.57; Pu2009=u2009.008). The severity of total postconcussive symptoms correlated with total tau concentrations in the self-reported TBI group (ru2009=u20090.37; Pu2009=u2009.003).nnnCONCLUSIONS AND RELEVANCEnMilitary personnel who report multiple TBIs have long-term elevations in total tau concentration. The total tau concentration relates to symptoms of postconcussive disorder.

Development of an Analysis Pipeline Characterizing Multiple Hypervariable Regions of 16S rRNA Using Mock Samples

Objectives There is much speculation on which hypervariable region provides the highest bacterial specificity in 16S rRNA sequencing. The optimum solution to prevent bias and to obtain a comprehensive view of complex bacterial communities would be to sequence the entire 16S rRNA gene; however, this is not possible with second generation standard library design and short-read next-generation sequencing technology. Methods This paper examines a new process using seven hypervariable or V regions of the 16S rRNA (six amplicons: V2, V3, V4, V6-7, V8, and V9) processed simultaneously on the Ion Torrent Personal Genome Machine (Life Technologies, Grand Island, NY). Four mock samples were amplified using the 16S Ion Metagenomics Kit™ (Life Technologies) and their sequencing data is subjected to a novel analytical pipeline. Results Results are presented at family and genus level. The Kullback-Leibler divergence (DKL), a measure of the departure of the computed from the nominal bacterial distribution in the mock samples, was used to infer which region performed best at the family and genus levels. Three different hypervariable regions, V2, V4, and V6-7, produced the lowest divergence compared to the known mock sample. The V9 region gave the highest (worst) average DKL while the V4 gave the lowest (best) average DKL. In addition to having a high DKL, the V9 region in both the forward and reverse directions performed the worst finding only 17% and 53% of the known family level and 12% and 47% of the genus level bacteria, while results from the forward and reverse V4 region identified all 17 family level bacteria. Conclusions The results of our analysis have shown that our sequencing methods using 6 hypervariable regions of the 16S rRNA and subsequent analysis is valid. This method also allowed for the assessment of how well each of the variable regions might perform simultaneously. Our findings will provide the basis for future work intended to assess microbial abundance at different time points throughout a clinical protocol.

The National Institutes of Health/National Institutes of Nursing Research intramural research program and the development of the National Institutes of Health Symptom Science Model.

The National Institute of Nursing Research (NINR) intramural research program conducts basic and biobehavioral symptom science research and provides training opportunities to the next generation of scientists. Recently, the NINR developed the Symptom Science Model to guide research. The model begins by identifying a complex symptom, which is then characterized into a phenotype with biological and clinical data, followed by the application of genomic and other discovery methodologies to illuminate targets for therapeutic and clinical interventions. Using the Symptom Science Model, the intramural program organizes and implements biobehavioral, symptom management, and tissue injury research. The model is also used as a framework for training and career development opportunities including on-campus trainings and research fellowship. The scientific goal of the intramural program is to enhance patient outcomes including health-related quality of life. Achieving this goal requires a long-term vision, continued resource investments, and a commitment to mentoring our next generation of scientists.

Expression Levels of Obesity-Related Genes Are Associated with Weight Change in Kidney Transplant Recipients

Background The aim of this study was to investigate the association of gene expression profiles in subcutaneous adipose tissue with weight change in kidney transplant recipients and to gain insights into the underlying mechanisms of weight gain. Methodology/Principal Findings A secondary data analysis was done on a subgroup (nu200a=u200a26) of existing clinical and gene expression data from a larger prospective longitudinal study examining factors contributing to weight gain in transplant recipients. Measurements taken included adipose tissue gene expression profiles at time of transplant, baseline and six-month weight, and demographic data. Using multivariate linear regression analysis controlled for race and gender, expression levels of 1553 genes were significantly (p<0.05) associated with weight change. Functional analysis using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes classifications identified metabolic pathways that were enriched in this dataset. Furthermore, GeneIndexer literature mining analysis identified a subset of genes that are highly associated with obesity in the literature and Ingenuity pathway analysis revealed several significant gene networks associated with metabolism and endocrine function. Polymorphisms in several of these genes have previously been linked to obesity. Conclusions/Significance We have successfully identified a set of molecular pathways that taken together may provide insights into the mechanisms of weight gain in kidney transplant recipients. Future work will be done to determine how these pathways may contribute to weight gain.

Pre‐transplant predictors of one yr weight gain after kidney transplantation

Clinically useful predictors of weight gain could be used to reduce the epidemic of post‐kidney transplant obesity and resulting co‐morbidities. The purpose of this study was to identify predictors of weight gain at 12 months following kidney transplant in a cohort of 96 recipients. Demographic, clinical, and environmental data were obtained at transplant and 12 months. Descriptive, correlational, and Bayesian network analysis were used to identify predictors. For the 52 (55.9%) recipients who gained weight, the average amount gained was 9.18 ± 6.59 kg. From the 15 baseline factors that met inclusion criteria, Bayesian network modeling identified four baseline predictors for weight gain: younger age, higher carbohydrate consumption, higher trunk fat percentage, and higher perception of mental health quality of life. Three are modifiable through either pre‐ or immediate post‐transplant clinical intervention programs.

Food Availability as a Determinant of Weight Gain Among Renal Transplant Recipients

Excessive weight gain is common after renal transplantation, but it is unknown whether environmental factors, such as food availability, contribute to this important clinical problem. We evaluated the effects of food availability (fast food restaurants, convenience stores, and grocery stores within 1, 2, and 3 mile buffers of transplant recipients residences) on body mass index (BMI) change during the first year post-transplant. Participants (nu2009=u2009299) resided in Memphis, Tennessee. BMI increased by 1.42 units (pu2009<u2009.001) corresponding to an average weight gain of 9.25u2009lbs (5.43%) during the first year post-transplant. The number of grocery stores within 1 mile of recipients residence was associated with an increase in BMI (pu2009<u2009.05), but fast food restaurants and convenience stores were not significantly associated with BMI change.

Challenges in evaluating next-generation sequence data for clinical decisions

The views expressed in this article are tho organizations, institutions, government agen * Corresponding author: Janet K. Williams, 50 E-mail address: janet-williams@uiowa.ed 0029-6554/

National Institutes of Health Symptom Science Model sheds light on patient symptoms

see front matter 2015 Elsevi http://dx.doi.org/10.1016/j.outlook.2014.08.00 Although genome sequencing using next-generation sequencing (NGS) has the potential to provide answers to clinical diagnosis and treatment questions (Atwal et al., 2014), there is limited evidence for many of these tests to guide clinical use. Genetic testing using NGS can yield a large amount of information, only some of which may be informative in answering the question for which the test was ordered and in some cases creates uncertainty and increased complexity of clinical care (Feero, 2014). These tests can be expensive, and the lack of evidence may influence whether thirdparty payers will cover the test cost. Regardless, genome sequencing using NGS (either whole genome sequencing [WGS] or whole exome sequencing [WES]) is beginning to be used clinically to identify causative variants in patients in whom several gene variants may result in similar clinical diseases. It is also used to

Genomics, clinical research, and learning health care systems: Strategies to improve patient care

Since the establishment of the nursing profession, identifying and alleviating the subjective symptoms experienced by patients has been at the core of nursing practice. In supporting the scientific foundation for clinical practice, nursing science has maintained a consistent commitment to prevent, manage, and eliminate symptoms. Scientists from the intramural research program at the National Institute of Nursing Research (NINR), a component of the National Institutes of Health, developed a National Institutes of Health Symptom Science Model (NIH-SSM) to guide symptom science research programs engaged in the use of emerging omic methods such as the genotyping of symptom phenotypes. The NIH-SSM was developed based on the NINR intramural research programs success in designing and implementing methods for examining identified symptoms or symptom clusters. The NIH-SSM identifies the research process of characterizing symptom phenotypes, identifying and testing biomarkers, and ultimately developing clinical interventions in cancer-related fatigue, gastrointestinal disorders, and traumatic brain injuries. The purpose of this article was to demonstrate how scientists can apply the NIH-SSM, leading the broader scientific community in advancing personalized and precise clinical interventions.

Older Age Results in Differential Gene Expression after Mild Traumatic Brain Injury and Is Linked to Imaging Differences at Acute Follow-up

Genomics, clinical research, and learning health care systems: Strategies to improve patient care Janet K. Williams, PhD, RN, FAAN*, Ann K. Cashion, PhD, RN, FAAN, Sam Shekar, MD, MPH, Geoffrey S. Ginsburg, MD, PhD American Academy of Nursing, The University of Iowa, Iowa City, IA National Institute of Nursing Research, National Institutes of Health, Bethesda, MD Northrop Grumman Information Systems Health Division, McLean, VA Duke University, Durham, NC