Search

Background We developed a novel approach to minimize batch effects when assigning samples to batches. Our algorithm selects a batch allocation, among all possible ways of assigning samples to batches, that minimizes differences in average propensity score between batches. This strategy was compared to randomization and stratified randomization in a case–control study (30 per group) with a covariate (case vs control, represented as β1, set to be null) and two biologically relevant confounding variables (age, represented as β2, and hemoglobin A1c (HbA1c), represented as β3). Gene expression values were obtained from a publicly available dataset of expression data obtained from pancreas islet cells. Batch effects were simulated as twice the median biological variation across the gene expression dataset and were added to the publicly available dataset to simulate a batch effect condition. Bias was calculated as the absolute difference between observed betas under the batch allocation strategies and the true beta (no batch effects). Bias was also evaluated after adjustment for batch effects using ComBat as well as a linear regression model. In order to understand performance of our optimal allocation strategy under the alternative hypothesis, we also evaluated bias at a single gene associated with both age and HbA1c levels in the ‘true’ dataset (CAPN13 gene). Results Pre-batch correction, under the null hypothesis (β1), maximum absolute bias and root mean square (RMS) of maximum absolute bias, were minimized using the optimal allocation strategy. Under the alternative hypothesis (β2 and β3 for the CAPN13 gene), maximum absolute bias and RMS of maximum absolute bias were also consistently lower using the optimal allocation strategy. ComBat and the regression batch adjustment methods performed well as the bias estimates moved towards the true values in all conditions under both the null and alternative hypotheses. Although the differences between methods were less pronounced following batch correction, estimates of bias (average and RMS) were consistently lower using the optimal allocation strategy under both the null and alternative hypotheses. Conclusions Our algorithm provides an extremely flexible and effective method for assigning samples to batches by exploiting knowledge of covariates prior to sample allocation.

Background Oxylipins are formed from oxidation of omega-6 (n6) and omega-3 (n3) fatty acids (FAs). Evidence for inflammatory effects comes mostly from adults. Methods Oxylipins from n6 FA (27 n6-oxylipins) and n3 FA (12 n3-oxylipins) were measured through ultra-high-performance liquid chromatography-mass spectrometry (LC-MS/MS) in plasma from 111 children at risk of type 1 diabetes (age 1-17 years) studied longitudinally. Oxylipin precursor FAs (arachidonic acid, linoleic acid, alpha-linolenic acid, docosahexaenoic acid, eicosapentaenoic acid) were measured in red blood cell (RBC) membrane and plasma. Precursor FAs dietary intake was measured through food frequency questionnaire and environmental tobacco smoke (ETS) through questionnaires. Linear mixed models were used to test oxylipins with predictors. Results Age associated with 15 n6- and 6 n3-oxylipins; race/ethnicity associated with 3 n6- and 1 n3-oxylipins; sex associated with 2 n6-oxylipins. ETS associated with lipoxin-A4. Oxylipins associated with precursor FAs in plasma more often than RBC. RBC levels and dietary intake of precursor FAs more consistently associated with n3-oxylipins than with n6-oxylipins. Conclusions In healthy children, oxylipin levels change with age. Oxylipins associated with precursor FAs more often in plasma than RBC or diet, suggesting that inflammatory regulation leading to FA release into plasma may also be a determinant of oxylipin generation. Impact This is the first study to examine predictors of oxylipins in healthy children at risk of type 1 diabetes.In healthy children at risk of type 1 diabetes, many oxylipins change with age, and most oxylipins do not differ by sex or race/ethnicity.Environmental tobacco smoke exposure was associated with the presence of lipoxin A4.Omega-6- and omega-3-related oxylipin levels were consistently associated with their respective precursor fatty acid levels measured in the plasma.Proportionally more omega-3 compared to omega-6 oxylipins were associated with dietary intake and red blood cell membrane levels of the respective precursor fatty acid.

Ethyl alcohol is a toxin that, when consumed at high levels, produces organ damage and death. One way to prevent or ameliorate this damage in humans is to reduce the exposure of organs to alcohol by reducing alcohol ingestion. Both the propensity to consume large volumes of alcohol and the susceptibility of human organs to alcohol-induced damage exhibit a strong genetic influence. We have developed an integrative genetic/genomic approach to identify transcriptional networks that predispose complex traits, including propensity for alcohol consumption and propensity for alcohol-induced organ damage. In our approach, the phenotype is assessed in a panel of recombinant inbred (RI) rat strains, and quantitative trait locus (QTL) analysis is performed. Transcriptome data from tissues/organs of naïve RI rat strains are used to identify transcriptional networks using Weighted Gene Coexpression Network Analysis (WGCNA). Correlation of the first principal component of transcriptional coexpression modules with the phenotype across the rat strains, and overlap of QTLs for the phenotype and the QTLs for the coexpression modules (module eigengene QTL) provide the criteria for identification of the functionally related groups of genes that contribute to the phenotype (candidate modules). While we previously identified a brain transcriptional module whose QTL overlapped with a QTL for levels of alcohol consumption in HXB/BXH RI rat strains and 12 selected rat lines, this module did not account for all of the genetic variation in alcohol consumption. Our search for QTL overlap and correlation of coexpression modules with phenotype can, however, be applied to any organ in which the transcriptome has been measured, and this represents a holistic approach in the search for genetic contributors to complex traits. Previous work has implicated liver/brain interactions, particularly involving inflammatory/immune processes, as influencing alcohol consumption levels. We have now analyzed the liver transcriptome of the HXB/BXH RI rat panel in relation to the behavioral trait of alcohol consumption. We used RNA-Seq and microarray data to construct liver transcriptional networks, and identified a liver candidate transcriptional coexpression module that explained 24% of the genetic variance in voluntary alcohol consumption. The transcripts in this module focus attention on liver secretory products that influence inflammatory and immune signaling pathways. We propose that these liver secretory products can interact with brain mechanisms that affect alcohol consumption, and targeting these pathways provides a potential approach to reducing high levels of alcohol intake and also protecting the integrity of the liver and other organs.

The ILSXISS (LXS) recombinant inbred (RI) panel of mice is a valuable resource for genetic mapping studies of complex traits, due to its genetic diversity and large number of strains. Male and female mice from this panel were used to investigate genetic influences on alcohol consumption in the “drinking in the dark” (DID) model. Male mice (38 strains) and female mice (36 strains) were given access to 20 % ethanol during the early phase of their circadian dark cycle for four consecutive days. The first principal component of alcohol consumption measures on days 2, 3, and 4 was used as a phenotype (DID phenotype) to calculate QTLs, using a SNP marker set for the LXS RI panel. Five QTLs were identified, three of which included a significant genotype by sex interaction, i.e., a significant genotype effect in males and not females. To investigate candidate genes associated with the DID phenotype, data from brain microarray analysis (Affymetrix Mouse Exon 1.0 ST Arrays) of male LXS RI strains were combined with RNA-Seq data (mouse brain transcriptome reconstruction) from the parental ILS and ISS strains in order to identify expressed mouse brain transcripts. Candidate genes were determined based on common eQTL and DID phenotype QTL regions and correlation of transcript expression levels with the DID phenotype. The resulting candidate genes (in particular, Arntl/Bmal1) focused attention on the influence of circadian regulation on the variation in the DID phenotype in this population of mice.

Aminoquinoline derivatives were evaluated against a panel of receptors/channels/transporters in radioligand binding experiments. One of these derivatives (DCUK-OEt) displayed micromolar affinity for brain γ-aminobutyric acid type A (GABAA) receptors. DCUK-OEt was shown to be a positive allosteric modulator (PAM) of GABA currents with α1β2γ2, α1β3γ2, α5β3γ2 and α1β3δ GABAA receptors, while having no significant PAM effect on αβ receptors or α1β1γ2, α1β2γ1, α4β3γ2 or α4β3δ receptors. DCUK-OEt modulation of α1β2γ2 GABAA receptors was not blocked by flumazenil. The subunit requirements for DCUK-OEt actions distinguished DCUK-OEt from other currently known modulators of GABA function (e.g., anesthetics, neurosteroids or ethanol). Simulated docking of DCUK-OEt at the GABAA receptor suggested that its binding site may be at the α + β- subunit interface. In slices of the central amygdala, DCUK-OEt acted primarily on extrasynaptic GABAA receptors containing the α1 subunit and generated increases in extrasynaptic “tonic” current with no significant effect on phasic responses to GABA. DCUK-OEt is a novel chemical structure acting as a PAM at particular GABAA receptors. Given that neurons in the central amygdala responding to DCUK-OEt were recently identified as relevant for alcohol dependence, DCUK-OEt should be further evaluated for the treatment of alcoholism.