Whole brain and brain regional coexpression network interactions associated with predisposition to alcohol consumption.

To identify brain transcriptional networks that may predispose an animal to consume alcohol, we used weighted gene coexpression network analysis (WGCNA). Candidate coexpression modules are those with an eigengene expression level that correlates significantly with the level of alcohol consumption across a panel of BXD recombinant inbred mouse strains, and that share a genomic region that regulates the module transcript expression levels (mQTL) with a genomic region that regulates alcohol consumption (bQTL). To address a controversy regarding utility of gene expression profiles from whole brain, vs specific brain regions, as indicators of the relationship of gene expression to phenotype, we compared candidate coexpression modules from whole brain gene expression data (gathered with Affymetrix 430 v2 arrays in the Colorado laboratories) and from gene expression data from 6 brain regions (nucleus accumbens (NA); prefrontal cortex (PFC); ventral tegmental area (VTA); striatum (ST); hippocampus (HP); cerebellum (CB)) available from GeneNetwork. The candidate modules were used to construct candidate eigengene networks across brain regions, resulting in three "meta-modules", composed of candidate modules from two or more brain regions (NA, PFC, ST, VTA) and whole brain. To mitigate the potential influence of chromosomal location of transcripts and cis-eQTLs in linkage disequilibrium, we calculated a semi-partial correlation of the transcripts in the meta-modules with alcohol consumption conditional on the transcripts' cis-eQTLs. The function of transcripts that retained the correlation with the phenotype after correction for the strong genetic influence, implicates processes of protein metabolism in the ER and Golgi as influencing susceptibility to variation in alcohol consumption. Integration of these data with human GWAS provides further information on the function of polymorphisms associated with alcohol-related traits

Rat Mammary Extracellular Matrix Composition and Response to Ibuprofen Treatment During Postpartum Involution by Differential GeLC–MS/MS Analysis

Breast cancer patients diagnosed within five years following pregnancy have increased metastasis and decreased survival. A hallmark of postpartum biology that may contribute to this poor prognosis is mammary gland involution, involving massive epithelial cell death and dramatic stromal remodeling. Previous studies show pro-tumorigenic properties of extracellular matrix (ECM) isolated from rodent mammary glands undergoing postpartum involution. More recent work demonstrates systemic ibuprofen treatment during involution decreases its tumor-promotional nature. Utilizing a proteomics approach, we identified relative differences in the composition of mammary ECM isolated from nulliparous rats and those undergoing postpartum involution, with and without ibuprofen treatment. GeLC–MS/MS experiments resulted in 20327 peptide identifications that mapped to 884 proteins with a <0.02% false discovery rate. Label-free quantification yielded several ECM differences between nulliparous and involuting glands related to collagen-fiber organization, cell motility and attachment, and cytokine regulation. Increases in known pro-tumorigenic ECM proteins osteopontin, tenascin-C, and laminin-α1 and pro-inflammatory proteins STAT3 and CD68 further identify candidate mediators of breast cancer progression specific to the involution window. With postpartum ibuprofen treatment, decreases in tenascin-C and three laminin chains were revealed. Our data suggest novel ECM mediators of breast cancer progression and demonstrate a protective influence of ibuprofen on mammary ECM composition

Severity of idiopathic scoliosis is associated with differential methylation : An epigenome‐wide association study of monozygotic twins with idiopathic scoliosis

Epigenetic mechanisms may contribute to idiopathic scoliosis (IS). We identified 8 monozygotic twin pairs with IS, 6 discordant (Cobb angle difference >10°) and 2 concordant (Cobb angle difference ≤2°). Genome‐wide methylation in blood was measured with the Infinium HumanMethylation EPIC Beadchip. We tested for differences in methylation and methylation variability between discordant twins and tested the association between methylation and curve severity in all twins. Differentially methylated region (DMR) analyses identified gene promoter regions. Methylation at cg12959265 (chr. 7 DPY19L1) was less variable in cases (false discovery rate (FDR) = 0.0791). We identified four probes (false discovery rate, FDR < 0.10); cg02477677 (chr. 17, RARA gene), cg12922161 (chr. 2 LOC150622 gene), cg08826461 (chr. 2), and cg16382077 (chr. 7) associated with curve severity. We identified 57 DMRs where hyper‐ or hypo‐methylation was consistent across the region and 28 DMRs with a consistent association with curve severity. Among DMRs, 21 were correlated with bone methylation. Prioritization of regions based on methylation concordance in bone identified promoter regions for WNT10A (WNT signaling), NPY (regulator of bone and energy homeostasis), and others predicted to be relevant for bone formation/remodeling. These regions may aid in understanding the complex interplay between genetics, environment, and IS

Characteristics of candidate modules associated with alcohol consumption.

<p>Candidate modules from all whole brain and each brain regional network are shown. The first column depicts the network from which the candidate module was derived and the second column is the module name. The direction of the correlation is not reported as these are unsigned networks. N/A indicates there were no common eQTLs among the probesets. The mQTL location reports the chromosome and Mb location for the highest peak.</p

WGCNA Network Summary

1<p>Significant association with alcohol consumption is defined as FDR <0.05 or Fisher's unadjusted p-value <0.01 for the association between module eigengene and alcohol consumption (Rodriguez et al., 1994; Phillips et al., 1994).</p

Reproducibility of Candidate Modules and Conservation of Candidate Modules across Brain Regions and Whole Brain.

<p>Conservation of candidate coexpression modules across individual brain regions and whole brain is represented by a Z summary score (color scale: 0 (black) to 10 (bright red)) (Langfelder et al., 2011; see text). In this graphic, Z summary scores above 10 are truncated to 10. The coexpression modules on the vertical axis are followed by an abbreviation indicating the network from which the module is derived: wb, whole brain; cer, cerebellum; hip, hippocampus; na, nucleus accumbens; pfc, prefrontal cortex; str, striatum; vta, ventral tegmental area. For each module, the Z summary score for conservation within each of the other datasets is shown. In addition, the average bootstrapped Z summary score is illustrated for the dataset from which the module was originally derived (represents reproducibility of candidate module in its original dataset). *Average Z summary score for reproducibility is within one SD of 2.</p

Meta-Module Characteristics.

<p>The size of each meta-module in the eigengene network is reported as number of eigengenes and number of probesets/genes. The hub gene is the highest connected gene of all probesets in the meta-module. ¹Unadjusted Fisher's p-value is for association of meta-eigengene and alcohol consumption. ²The mQTL for the meta-module is reported as the location of the highest peak (Chr:Mb).</p

Flow Chart of Analysis Procedure for Whole Brain (A) and Brain Regional (B) Microarray Data.

<p>Whole brain microarray data were filtered for SNPs between C57BL/6 and DBA/2 mice, and for expression above background levels. The remaining probesets were subjected to WGCNA, and the resulting coexpression modules were filtered by correlation of eigengene with alcohol consumption data, followed by determination of overlap of mQTLs and alcohol bQTLs, to identify “candidate modules”. B. Microarray data for the indicated brain regions were obtained from GeneNetwork (<a href="http://www.genenetwork.org" target="_blank">www.genenetwork.org</a>), and subjected to WGCNA (using the same probesets as were used for the whole brain data). Candidate modules were identified and characterized within each network, and were used to create an eigengene network that demonstrates gene coexpression within and between brain regions.</p