Sex-specific and cell-specific regulation of ER stress and neuroinflammation after traumatic brain injury in juvenile mice.

Endoplasmic reticulum (ER) stress and neuroinflammation play an important role in secondary brain damage after traumatic brain injury (TBI). Due to the complex brain cytoarchitecture, multiple cell types are affected by TBI. However, cell type-specific and sex-specific responses to ER stress and neuroinflammation remain unclear. Here we investigated differential regulation of ER stress and neuroinflammatory pathways in neurons and microglia during the acute phase post-injury in a mouse model of impact acceleration TBI in both males and females. We found that TBI resulted in significant weight loss only in males, and sensorimotor impairment and depressive-like behaviors in both males and females at the acute phase post-injury. By concurrently isolating neurons and microglia from the same brain sample of the same animal, we were able to evaluate the simultaneous responses in neurons and microglia towards ER stress and neuroinflammation in both males and females. We discovered that the ER stress and anti-inflammatory responses were significantly stronger in microglia, especially in female microglia, compared with the male and female neurons. Whereas the degree of phosphorylated-tau (pTau) accumulation was significantly higher in neurons, compared with the microglia. In conclusion, TBI resulted in behavioral deficits and cell type-specific and sex-specific responses to ER stress and neuroinflammation, and abnormal protein accumulation at the acute phase after TBI in immature mice.

New Empirical Bayes Models to Jointly Analyze Multiple RNA-Sequencing Data in a Hypophosphatasia Disease Study.

Hypophosphatasia is a rare inherited metabolic disorder caused by the deficiency of tissue-nonspecific alkaline phosphatase. More severe and early onset cases present symptoms of muscle weakness, diminished motor coordination, and epileptic seizures. These neurological manifestations are poorly characterized. Thus, it is urgent to discover novel differentially expressed genes for investigating the genetic mechanisms underlying the neurological manifestations of hypophosphatasia. RNA-sequencing data offer a high-resolution and highly accurate transcript profile. In this study, we apply an empirical Bayes model to RNA-sequencing data acquired from the spinal cord and neocortex tissues of a mouse model, individually, to more accurately estimate the genetic effects without bias. More importantly, we further develop two integration methods, weighted gene approach and weighted Z method, to incorporate two RNA-sequencing data into a model for enhancing the effects of genetic markers in the diagnostics of hypophosphatasia disease. The simulation and real data analysis have demonstrated the effectiveness of our proposed integration methods, which can maximize genetic signals identified from the spinal cord and neocortex tissues, minimize the prediction error, and largely improve the prediction accuracy in risk prediction.

A weighted empirical Bayes risk prediction model using multiple traits.

With rapid advances in high-throughput sequencing technology, millions of single-nucleotide variants (SNVs) can be simultaneously genotyped in a sequencing study. These SNVs residing in functional genomic regions such as exons may play a crucial role in biological process of the body. In particular, non-synonymous SNVs are closely related to the protein sequence and its function, which are important in understanding the biological mechanism of sequence evolution. Although statistically challenging, models incorporating such SNV annotation information can improve the estimation of genetic effects, and multiple responses may further strengthen the signals of these variants on the assessment of disease risk. In this work, we develop a new weighted empirical Bayes method to integrate SNV annotation information in a multi-trait design. The performance of this proposed model is evaluated in simulation as well as a real sequencing data; thus, the proposed method shows improved prediction accuracy compared to other approaches.

Penalized Variable Selection for Lipid-Environment Interactions in a Longitudinal Lipidomics Study.

Lipid species are critical components of eukaryotic membranes. They play key roles in many biological processes such as signal transduction, cell homeostasis, and energy storage. Investigations of lipid-environment interactions, in addition to the lipid and environment main effects, have important implications in understanding the lipid metabolism and related changes in phenotype. In this study, we developed a novel penalized variable selection method to identify important lipid-environment interactions in a longitudinal lipidomics study. An efficient Newton-Raphson based algorithm was proposed within the generalized estimating equation (GEE) framework. We conducted extensive simulation studies to demonstrate the superior performance of our method over alternatives, in terms of both identification accuracy and prediction performance. As weight control via dietary calorie restriction and exercise has been demonstrated to prevent cancer in a variety of studies, analysis of the high-dimensional lipid datasets collected using 60 mice from the skin cancer prevention study identified meaningful markers that provide fresh insight into the underlying mechanism of cancer preventive effects.

Differences in Perceptions of Incivility Among Disciplines in Higher Education.

BACKGROUND: Incivility occurs in various forms in higher education and negatively affects teaching and learning outcomes. It has not been determined if incivility is more prevalent in one discipline than another. PURPOSE: The purpose of this study was to compare faculty and student perceptions of incivility across disciplines at a large public university. METHODS: In this descriptive comparative study, a convenience sample of 156 faculty and 421 students completed the Incivility in Higher Education-Revised survey electronically. RESULTS: The total sample was 577. Nursing reported the highest level of perceived incivility, with all other disciplines also reporting some level of incivility. Faculty perceived more incivility than students. CONCLUSIONS: With a national awareness of incivility in nursing education, this study shows that incivility also exists in other disciplines and is a starting point for addressing its impact on higher education.

Enhanced Platelet-Activating Factor Synthesis Facilitates Acute and Delayed Effects of Ethanol-Intoxicated Thermal Burn Injury.

Thermal burn injuries in patients who are alcohol-intoxicated result in greater morbidity and mortality. Murine models combining ethanol and localized thermal burn injury reproduce the systemic toxicity seen in human subjects, which consists of both acute systemic cytokine production with multiple organ dysfunction, as well as a delayed systemic immunosuppression. However, the exact mechanisms for these acute and delayed effects are unclear. These studies sought to define the role of the lipid mediator platelet-activating factor in the acute and delayed effects of intoxicated burn injury. Combining ethanol and thermal burn injury resulted in increased enzymatic platelet-activating factor generation in a keratinocyte cell line in vitro, human skin explants ex vivo, as well as in murine skin in vivo. Further, the acute increase in inflammatory cytokines, such as IL-6, and the systemic immunosuppressive effects of intoxicated thermal burn injury were suppressed in mice lacking platelet-activating factor receptors. Together, these studies provide a potential mechanism and treatment strategies for the augmented toxicity and immunosuppressive effects of thermal burn injury in the setting of acute ethanol exposure, which involves the pleotropic lipid mediator platelet-activating factor.

Increased urinary angiotensin converting enzyme 2 and neprilysin in patients with type 2 diabetes.

Angiotensin converting enzyme 2 (ACE2) and neprilysin (NEP) are metalloproteases that are highly expressed in the renal proximal tubules. ACE2 and NEP generate renoprotective angiotensin (1-7) from angiotensin II and angiotensin I, respectively, and therefore could have a major role in chronic kidney disease (CKD). Recent data demonstrated increased urinary ACE2 in patients with diabetes with CKD and kidney transplants. We tested the hypothesis that urinary ACE2, NEP, and a disintegrin and metalloproteinase 17 (ADAM17) are increased and could be risk predictors of CKD in patients with diabetes. ACE2, NEP, and ADAM17 were investigated in 20 nondiabetics (ND) and 40 patients with diabetes with normoalbuminuria (Dnormo), microalbuminuria (Dmicro), and macroalbuminuria (Dmacro) using ELISA, Western blot, and fluorogenic and mass spectrometric-based enzyme assays. Logistic regression model was applied to predict the risk prediction. Receiver operating characteristic curves were drawn, and prediction accuracies were calculated to explore the effectiveness of ACE2 and NEP in predicting diabetes and CKD. Results demonstrated that there is no evidence of urinary ACE2 and ADAM17 in ND subjects, but both enzymes were increased in patients with diabetes, including Dnormo. Although there was no detectable plasma ACE2 activity, there was evidence of urinary and plasma NEP in all the subjects, and urinary NEP was significantly increased in Dmicro patients. NEP and ACE2 showed significant correlations with metabolic and renal characteristics. In summary, urinary ACE2, NEP, and ADAM17 are increased in patients with diabetes and could be used as early biomarkers to predict the incidence or progression of CKD at early stages among individuals with type 2 diabetes.

A new model calling procedure for Illumina BeadArray data.

BACKGROUND: Accurate genotype calling for high throughput Illumina data is an important step to extract more genetic information for a large scale genome wide association studies. Many popular calling algorithms use mixture models to infer genotypes of a large number of single nucleotide polymorphisms in a fast and efficient way. In practice, mixture models are mostly restricted to infer genotypes for common SNPs where their minor allele frequencies are quite large. However, it is still challenging to accurately genotype rare variants, especially for some rare variants where the boundaries of their genotypes are not clearly defined. RESULTS: To further improve the call accuracy and the quality of genotypes on rare variants, a new model calling procedure, named M-D, is proposed to infer genotypes for the Illumina BeadArray data. In this calling procedure, a Gaussian Mixture Model and a Dirichlet Process Gaussian Mixture Model are integrated to infer genotypes. CONCLUSIONS: Applications to Illumina data illustrate that this new approach can improve calling performance compared to other popular genotyping algorithms.

Radiation therapy generates platelet-activating factor agonists.

Pro-oxidative stressors can suppress host immunity due to their ability to generate oxidized lipid agonists of the platelet-activating factor-receptor (PAF-R). As radiation therapy also induces reactive oxygen species, the present studies were designed to define whether ionizing radiation could generate PAF-R agonists and if these lipids could subvert host immunity. We demonstrate that radiation exposure of multiple tumor cell lines in-vitro, tumors in-vivo, and human subjects undergoing radiation therapy for skin tumors all generate PAF-R agonists. Structural characterization of radiation-induced PAF-R agonistic activity revealed PAF and multiple oxidized glycerophosphocholines that are produced non-enzymatically. In a murine melanoma tumor model, irradiation of one tumor augmented the growth of the other (non-treated) tumor in a PAF-R-dependent process blocked by a cyclooxygenase-2 inhibitor. These results indicate a novel pathway by which PAF-R agonists produced as a byproduct of radiation therapy could result in tumor treatment failure, and offer important insights into potential therapeutic strategies that could improve the overall antitumor effectiveness of radiation therapy regimens.

Evacuation performance evaluation tool.

OBJECTIVE: Hospitals conduct evacuation exercises to improve performance during emergency events. An essential aspect in this process is the creation of reliable and valid evaluation tools. The objective of this article is to describe the development and implications of a disaster evacuation performance tool that measures one portion of the very complex process of evacuation. DESIGN: Through the application of the Delphi technique and DeVellis's framework, disaster and neonatal experts provided input in developing this performance evaluation tool. Following development, content validity and reliability of this tool were assessed. SETTING: Large pediatric hospital and medical center in the Midwest. PARTICIPANTS: The tool was pilot tested with an administrative, medical, and nursing leadership group and then implemented with a group of 68 healthcare workers during a disaster exercise of a neonatal intensive care unit (NICU). RESULTS: The tool has demonstrated high content validity with a scale validity index of 0.979 and inter-rater reliability G coefficient (0.984, 95% CI: 0.948-0.9952). CONCLUSIONS: The Delphi process based on the conceptual framework of DeVellis yielded a psychometrically sound evacuation performance evaluation tool for a NICU.

M(3)-S: a genotype calling method incorporating information from samples with known genotypes.

BACKGROUND: A key challenge in analyzing high throughput Single Nucleotide Polymorphism (SNP) arrays is the accurate inference of genotypes for SNPs with low minor allele frequencies. A number of calling algorithms have been developed to infer genotypes for common SNPs, but they are limited in their performance in calling rare SNPs. The existing algorithms can be broadly classified into three categories, including: population-based methods, SNP-based methods, and a hybrid of the two approaches. Despite the relatively better performance of the hybrid approach, it is still challenging to analyze rare SNPs. RESULTS: We propose to utilize information from samples with known genotypes to develop a two stage genotyping procedure, namely M(3)-S, for rare SNP calling. This new approach can improve genotyping accuracy through clearly defining the boundaries of genotype clusters from samples with known genotypes, and enlarge the call rate by combining the simulated data based on the inferred genotype clusters information with the study population. CONCLUSIONS: Applications to real data demonstrates that this new approach M(3)-S outperforms existing methods in calling rare SNPs.

An Empirical Bayes risk prediction model using multiple traits for sequencing data.

The rapidly developing sequencing technologies have led to improved disease risk prediction through identifying many novel genes. Many prediction methods have been proposed to use rich genomic information to predict binary disease outcomes. It is intuitive that these methods can be further improved by making efficient use of the rich information in measured quantitative traits that are correlated with binary outcomes. In this article, we propose a novel Empirical Bayes prediction model that uses information from both quantitative traits and binary disease status to improve risk prediction. Our method is built on a new statistic that better infers the gene effect on multiple traits, and it also enjoys the good theoretical properties. We then consider using sequencing data by combining information from multiple rare variants in individual genes to strengthen the signals of causal genetic effects. In simulation study, we find that our proposed Empirical Bayes approach is superior to other existing methods in terms of feature selection and risk prediction. We further evaluate the effectiveness of our proposed method through its application to the sequencing data provided by the Genetic Analysis Workshop 18.

A bivariate variance components model for mapping iQTLs underlying endosperm traits.

Genomic imprinting plays a pivotal role in early stage development in plants. Linkage analysis has been proven to be useful in mapping imprinted quantitative trait loci (iQTLs) underlying imprinting phenotypic traits in natural populations or experimental crosses. For correlated traits, studies have shown that multivariate genetic linkage analysis can improve QTL mapping power and precision, especially when a QTL has a pleiotropic effect on several traits. In addition, the joint analysis of multiple traits can test a number of biologically interesting hypotheses, such as pleiotropic effects vs close linkage. Motivated by a triploid maize endosperm dataset, we extended the variance components linkage analysis model incorporating imprinting effect proposed by Li and Cui (2010) to a bivariate trait modeling framework, aimed to improve the mapping precision and to identify pleiotropic imprinting effects. We proposed to partition the genetic variance of a QTL into sex-specific allelic variance components, to model and test the imprinting effect of an iQTL on two traits. Both simulation studies and real data analysis show the power and utility of the method.

M(3): an improved SNP calling algorithm for Illumina BeadArray data.

SUMMARY: Genotype calling from high-throughput platforms such as Illumina and Affymetrix is a critical step in data processing, so that accurate information on genetic variants can be obtained for phenotype-genotype association studies. A number of algorithms have been developed to infer genotypes from data generated through the Illumina BeadStation platform, including GenCall, GenoSNP, Illuminus and CRLMM. Most of these algorithms are built on population-based statistical models to genotype every SNP in turn, such as GenCall with the GenTrain clustering algorithm, and require a large reference population to perform well. These approaches may not work well for rare variants where only a small proportion of the individuals carry the variant. A fundamentally different approach, implemented in GenoSNP, adopts a single nucleotide polymorphism (SNP)-based model to infer genotypes of all the SNPs in one individual, making it an appealing alternative to call rare variants. However, compared to the population-based strategies, more SNPs in GenoSNP may fail the Hardy-Weinberg Equilibrium test. To take advantage of both strategies, we propose a two-stage SNP calling procedure, named the modified mixture model (M(3)), to improve call accuracy for both common and rare variants. The effectiveness of our approach is demonstrated through applications to genotype calling on a set of HapMap samples used for quality control purpose in a large case-control study of cocaine dependence. The increase in power with M(3) is greater for rare variants than for common variants depending on the model. AVAILABILITY: M(3) algorithm: http://bioinformatics.med.yale.edu/group. CONTACT: name@bio.com; hongyu.zhao@yale.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Functional mapping of dynamic traits with robust t-distribution.

Functional mapping has been a powerful tool in mapping quantitative trait loci (QTL) underlying dynamic traits of agricultural or biomedical interest. In functional mapping, multivariate normality is often assumed for the underlying data distribution, partially due to the ease of parameter estimation. The normality assumption however could be easily violated in real applications due to various reasons such as heavy tails or extreme observations. Departure from normality has negative effect on testing power and inference for QTL identification. In this work, we relax the normality assumption and propose a robust multivariate t-distribution mapping framework for QTL identification in functional mapping. Simulation studies show increased mapping power and precision with the t distribution than that of a normal distribution. The utility of the method is demonstrated through a real data analysis.

Large-scale risk prediction applied to Genetic Analysis Workshop 17 mini-exome sequence data.

We consider the application of Efron's empirical Bayes classification method to risk prediction in a genome-wide association study using the Genetic Analysis Workshop 17 (GAW17) data. A major advantage of using this method is that the effect size distribution for the set of possible features is empirically estimated and that all subsequent parameter estimation and risk prediction is guided by this distribution. Here, we generalize Efron's method to allow for some of the peculiarities of the GAW17 data. In particular, we introduce two ways to extend Efron's model: a weighted empirical Bayes model and a joint covariance model that allows the model to properly incorporate the annotation information of single-nucleotide polymorphisms (SNPs). In the course of our analysis, we examine several aspects of the possible simulation model, including the identity of the most important genes, the differing effects of synonymous and nonsynonymous SNPs, and the relative roles of covariates and genes in conferring disease risk. Finally, we compare the three methods to each other and to other classifiers (random forest and neural network).

Designs for linkage analysis and association studies of complex diseases.

Genetic linkage analysis has been a traditional means for identifying regions of the genome with large genetic effects that contribute to a disease. Following linkage analysis, association studies are widely pursued to fine-tune regions with significant linkage signals. For complex diseases which often involve function of multi-genetic variants each with small or moderate effect, linkage analysis has little power compared to association studies. In this chapter, we give a brief review of design issues related to linkage analysis and association studies with human genetic data. We introduce methods commonly used for linkage and association studies and compared the relative merits of the family-based and population-based association studies. Compared to candidate gene studies, a genomewide blind searching of disease variant is proving to be a more powerful approach. We briefly review the commonly used two-stage designs in genome-wide association studies. As more and more biological evidences indicate the role of genomic imprinting in disease, identifying imprinted genes becomes critically important. Design and analysis in genetic mapping imprinted genes are introduced in this chapter. Recent efforts in integrating gene expression analysis and genetic mapping, termed expression quantitative trait loci (eQTLs) mapping or genetical genomics analysis, offer new prospect in elucidating the genetic architecture of gene expression. Designs in genetical genomics analysis are also covered in this chapter.

Functional mapping imprinted quantitative trait loci underlying developmental characteristics.

BACKGROUND: Genomic imprinting, a phenomenon referring to nonequivalent expression of alleles depending on their parental origins, has been widely observed in nature. It has been shown recently that the epigenetic modification of an imprinted gene can be detected through a genetic mapping approach. Such an approach is developed based on traditional quantitative trait loci (QTL) mapping focusing on single trait analysis. Recent studies have shown that most imprinted genes in mammals play an important role in controlling embryonic growth and post-natal development. For a developmental character such as growth, current approach is less efficient in dissecting the dynamic genetic effect of imprinted genes during individual ontology. RESULTS: Functional mapping has been emerging as a powerful framework for mapping quantitative trait loci underlying complex traits showing developmental characteristics. To understand the genetic architecture of dynamic imprinted traits, we propose a mapping strategy by integrating the functional mapping approach with genomic imprinting. We demonstrate the approach through mapping imprinted QTL controlling growth trajectories in an inbred F2 population. The statistical behavior of the approach is shown through simulation studies, in which the parameters can be estimated with reasonable precision under different simulation scenarios. The utility of the approach is illustrated through real data analysis in an F2 family derived from LG/J and SM/J mouse stains. Three maternally imprinted QTLs are identified as regulating the growth trajectory of mouse body weight. CONCLUSION: The functional iQTL mapping approach developed here provides a quantitative and testable framework for assessing the interplay between imprinted genes and a developmental process, and will have important implications for elucidating the genetic architecture of imprinted traits.