Regulatory cascade involving transcriptional and N-end rule pathways in rice under submergence.

The rice SUB1A-1 gene, which encodes a group VII ethylene response factor (ERFVII), plays a pivotal role in rice survival under flooding stress, as well as other abiotic stresses. In Arabidopsis, five ERFVII factors play roles in regulating hypoxic responses. A characteristic feature of Arabidopsis ERFVIIs is a destabilizing N terminus, which functions as an N-degron that targets them for degradation via the oxygen-dependent N-end rule pathway of proteolysis, but permits their stabilization during hypoxia for hypoxia-responsive signaling. Despite having the canonical N-degron sequence, SUB1A-1 is not under N-end rule regulation, suggesting a distinct hypoxia signaling pathway in rice during submergence. Herein we show that two other rice ERFVIIs gene, ERF66 and ERF67, are directly transcriptionally up-regulated by SUB1A-1 under submergence. In contrast to SUB1A-1, ERF66 and ERF67 are substrates of the N-end rule pathway that are stabilized under hypoxia and may be responsible for triggering a stronger transcriptional response to promote submergence survival. In support of this, overexpression of ERF66 or ERF67 leads to activation of anaerobic survival genes and enhanced submergence tolerance. Furthermore, by using structural and protein-interaction analyses, we show that the C terminus of SUB1A-1 prevents its degradation via the N-end rule and directly interacts with the SUB1A-1 N terminus, which may explain the enhanced stability of SUB1A-1 despite bearing an N-degron sequence. In summary, our results suggest that SUB1A-1, ERF66, and ERF67 form a regulatory cascade involving transcriptional and N-end rule control, which allows rice to distinguish flooding from other SUB1A-1-regulated stresses.

Text mining for identifying topics in the literatures about adolescent substance use and depression.

BACKGROUND: Both adolescent substance use and adolescent depression are major public health problems, and have the tendency to co-occur. Thousands of articles on adolescent substance use or depression have been published. It is labor intensive and time consuming to extract huge amounts of information from the cumulated collections. Topic modeling offers a computational tool to find relevant topics by capturing meaningful structure among collections of documents. METHODS: In this study, a total of 17,723 abstracts from PubMed published from 2000 to 2014 on adolescent substance use and depression were downloaded as objects, and Latent Dirichlet allocation (LDA) was applied to perform text mining on the dataset. Word clouds were used to visually display the content of topics and demonstrate the distribution of vocabularies over each topic. RESULTS: The LDA topics recaptured the search keywords in PubMed, and further discovered relevant issues, such as intervention program, association links between adolescent substance use and adolescent depression, such as sexual experience and violence, and risk factors of adolescent substance use, such as family factors and peer networks. Using trend analysis to explore the dynamics of proportion of topics, we found that brain research was assessed as a hot issue by the coefficient of the trend test. CONCLUSIONS: Topic modeling has the ability to segregate a large collection of articles into distinct themes, and it could be used as a tool to understand the literature, not only by recapturing known facts but also by discovering other relevant topics.

A stochastic inference of de novo CNV detection and association test in multiplex schizophrenia families.

The copy number variation (CNV) is a type of genetic variation in the genome. It is measured based on signal intensity measures and can be assessed repeatedly to reduce the uncertainty in PCR-based typing. Studies have shown that CNVs may lead to phenotypic variation and modification of disease expression. Various challenges exist, however, in the exploration of CNV-disease association. Here we construct latent variables to infer the discrete CNV values and to estimate the probability of mutations. In addition, we propose to pool rare variants to increase the statistical power and we conduct family studies to mitigate the computational burden in determining the composition of CNVs on each chromosome. To explore in a stochastic sense the association between the collapsing CNV variants and disease status, we utilize a Bayesian hierarchical model incorporating the mutation parameters. This model assigns integers in a probabilistic sense to the quantitatively measured copy numbers, and is able to test simultaneously the association for all variants of interest in a regression framework. This integrative model can account for the uncertainty in copy number assignment and differentiate if the variation was de novo or inherited on the basis of posterior probabilities. For family studies, this model can accommodate the dependence within family members and among repeated CNV data. Moreover, the Mendelian rule can be assumed under this model and yet the genetic variation, including de novo and inherited variation, can still be included and quantified directly for each individual. Finally, simulation studies show that this model has high true positive and low false positive rates in the detection of de novo mutation.

MicroRNA expression aberration as potential peripheral blood biomarkers for schizophrenia.

Since brain tissue is not readily accessible, a new focus in search of biomarkers for schizophrenia is blood-based expression profiling of non-protein coding genes such as microRNAs (miRNAs), which regulate gene expression by inhibiting the translation of messenger RNAs. This study aimed to identify potential miRNA signature for schizophrenia by comparing genome-wide miRNA expression profiles in patients with schizophrenia vs. healthy controls. A genome-wide miRNA expression profiling was performed using a Taqman array of 365 human miRNAs in the mononuclear leukocytes of a learning set of 30 cases and 30 controls. The discriminating performance of potential biomarkers was validated in an independent testing set of 60 cases and 30 controls. The expression levels of the miRNA signature were then evaluated for their correlation with the patients' clinical symptoms, neurocognitive performances, and neurophysiological functions. A seven-miRNA signature (hsa-miR-34a, miR-449a, miR-564, miR-432, miR-548d, miR-572 and miR-652) was derived from a supervised classification with internal cross-validation, with an area under the curve (AUC) of receiver operating characteristics of 93%. The putative signature was then validated in the testing set, with an AUC of 85%. Among these miRNAs, miR-34a was differentially expressed between cases and controls in both the learning (P = 0.005) and the testing set (P = 0.002). These miRNAs were differentially correlated with patients' negative symptoms, neurocognitive performance scores, and event-related potentials. The results indicated that the mononuclear leukocyte-based miRNA profiling is a feasible way to identify biomarkers for schizophrenia, and the seven-miRNA signature warrants further investigation.

Using an uncertainty-coding matrix in Bayesian regression models for haplotype-specific risk detection in family association studies.

Haplotype association studies based on family genotype data can provide more biological information than single marker association studies. Difficulties arise, however, in the inference of haplotype phase determination and in haplotype transmission/non-transmission status. Incorporation of the uncertainty associated with haplotype inference into regression models requires special care. This task can get even more complicated when the genetic region contains a large number of haplotypes. To avoid the curse of dimensionality, we employ a clustering algorithm based on the evolutionary relationship among haplotypes and retain for regression analysis only the ancestral core haplotypes identified by it. To integrate the three sources of variation, phase ambiguity, transmission status and ancestral uncertainty, we propose an uncertainty-coding matrix which combines these three types of variability simultaneously. Next we evaluate haplotype risk with the use of such a matrix in a Bayesian conditional logistic regression model. Simulation studies and one application, a schizophrenia multiplex family study, are presented and the results are compared with those from other family based analysis tools such as FBAT. Our proposed method (Bayesian regression using uncertainty-coding matrix, BRUCM) is shown to perform better and the implementation in R is freely available.

Absence of evidence for an association between resistin gene variants and insulin resistance in an Asian population with low and high blood pressure.

AIMS: Although the function of resistin in human biology is unclear, some evidence suggests resistin gene variants influence insulin resistance, and insulin resistance-related hypertension. We searched for associations between common resistin gene variants and factors related to insulin resistance in Asian individuals with high or low blood pressure (BP). METHODS: Non-diabetic Chinese or Japanese sibling pairs were included if one had extreme hypertension and the other was either hypertensive or hypotensive. Four common, non-coding single nucleotide polymorphisms (SNPs) were identified by sequencing the resistin gene in 24 hypertensive probands. Generalized estimating equations (GEEs)-based regressions were then performed to test for SNP associations using the entire study population (n=1556). RESULTS: Of 72 tests, only one was significant at the 0.05 level; 3.5 significant tests were expected by chance alone. High variability in insulin and triglyceride levels created wide confidence intervals, thus the negative results are not conclusive for these phenotypes. However, the large sample size resulted in narrow confidence intervals for BMI, fasting and 120min post-load glucose, and high and low density lipoprotein cholesterol (LDL-C). CONCLUSION: Several factors associated with insulin resistance are not likely influenced by the resistin gene in non-diabetic Asian individuals with high and low blood pressure.

Directly measured insulin resistance and the assessment of clustered cardiovascular risks in hypertension.

BACKGROUND: The purpose of the study was to use factor analysis to investigate the contribution of a directly measured insulin sensitivity index, steady-state plasma glucose (SSPG) from insulin suppression test (IST), to a clustering of cardiovascular risk factors in hypertensive subjects. METHODS: A total of 204 nondiabetic hypertensive patients who received IST for SSPG were included for current analysis. Factor analysis was performed to explore the contribution of SSPG as additional information to a clustering of risk factors in these subjects. RESULTS: In factor analysis, SSPG aggregated with metabolic variables in an obesity-hyperinsulinemia domain that included two factors: one with positive loadings for SSPG, 2-h glucose, and Log 2-h insulin; and the other with positive loadings for body mass index, waist circumference, and fasting glucose. Fasting insulin linked the two factors together and explained 38.3% of the total variance. Systolic and diastolic blood pressures were loaded on a blood pressure domain separately. The third domain consisted of two factors: one with positive loadings for Log triglycerides and negative loading for high-density lipoprotein cholesterol; and the other with positive loadings for Log triglycerides and non-high-density lipoprotein cholesterol. The model loaded without SSPG explained a proportion of the total variance (78.5%) similar to that achieved with the model loaded with SSPG (77.1%). CONCLUSIONS: Directly measured insulin sensitivity index SSPG clustered with 2-h glucose and Log 2-h insulin in factor analysis in a cohort consisting entirely of hypertensive subjects. However, the contribution of SSPG as additional information to explain the total variance seems to be insignificant.

A Bayes regression approach to array-CGH data.

This paper develops a Bayes regression model having change points for the analysis of array-CGH data by utilizing not only the underlying spatial structure of the genomic alterations but also the observation that the noise associated with the ratio of the fluorescence intensities is bigger when the intensities get smaller. We show that this Bayes regression approach is particularly suitable for the analysis of cDNA microarray-CGH data, which are generally noisier than those using genomic clones. A simulation study and a real data analysis are included to illustrate this approach.