Genome-Wide Detection of Gene Coexpression Domains Showing Linkage to Regions Enriched with Polymorphic Retrotransposons in Recombinant Inbred Mouse Strains

Regulatory effects of the Uty/Ddx3y locus on neighboring chromosome Y genes and autosomal mRNA transcripts in adult mouse non-reproductive cells

In addition to sperm-related genes, the male-specific chromosome Y (chrY) contains a class of ubiquitously expressed and evolutionary conserved dosage-sensitive regulator genes that include the neighboring Uty, Ddx3y and (in mice) Eif2s3y genes. However, no study to date has investigated the functional impact of targeted mutations of any of these genes within adult non-reproductive somatic cells. We thus compared adult male mice carrying a gene trap within their Uty gene (Uty^GT) to their wild-type (WT) isogenic controls, and performed deep sequencing of RNA and genome-wide profiling of chromatin features in extracts from either cardiac tissue, cardiomyocyte-specific nuclei or purified cardiomyocytes. The apparent impact of Uty^GT on gene transcription concentrated mostly on chrY genes surrounding the locus of insertion, i.e. Uty, Ddx3y, long non-coding RNAs (lncRNAs) contained within their introns and Eif2s3y, in addition to possible effects on the autosomal Malat1 lncRNA. Notwithstanding, Uty^GT also caused coordinate changes in the abundance of hundreds of mRNA transcripts related to coherent cell functions, including RNA processing and translation. The results altogether indicated that tightly co-regulated chrY genes had nonetheless more widespread effects on the autosomal transcriptome in adult somatic cells, most likely due to mechanisms other than just transcriptional regulation of corresponding protein-coding genes.

Short-Term Genetic Selection for Adolescent Locomotor Sensitivity to Delta9-Tetrahydrocannabinol (THC)

Cannabis use is linked to positive and negative outcomes. Identifying genetic targets of susceptibility to the negative effects of cannabinoid use is of growing importance. The current study sought to complete short-term selective breeding for adolescent sensitivity and resistance to the locomotor effects of a single 10 mg/kg THC dose in the open field. Selection for THC-locomotor sensitivity was moderately heritable, with the greatest estimates of heritability seen in females from the F2 to S3 generations. Selection for locomotor sensitivity also resulted in increased anxiety-like activity in the open field. These results are the first to indicate that adolescent THC-locomotor sensitivity can be influenced via selective breeding. Development of lines with a genetic predisposition for THC-sensitivity or resistance to locomotor effects allow for investigation of risk factors, differences in consequences of THC use, identification of correlated behavioral responses, and detection of genetic targets that may contribute to heightened cannabinoid sensitivity.

Acute and long-term effects of Δ9-tetrahydrocannabinol on object recognition and anxiety-like activity are age- and strain-dependent in mice

Use of exogenous cannabinoids disrupts the fine-tuned endocannabinoid receptor system, possibly leading to alterations in cognition, memory, and emotional processes that endure long after cannabinoid use has stopped. Long-term adolescent use may uniquely disrupt these behaviors when compared to adult use. The current study explored the acute and long-term behavioral effects of six 10mg/kg Δ9-tetrahydrocannabinol (THC) injections across the adolescent or early adult period in male inbred C57Bl/6J and DBA/2J mice. The acute and prolonged effects of THC on object memory using the novel object recognition task, unconditioned anxiety in the elevated plus maze and open field, and sedative effects in the open field were examined. Acute THC treatment resulted in anxiogenic activity in both strains, but only caused sedation in B6 mice. Repeated THC treatment resulted in a protracted effect on object recognition, but not unconditioned anxiety, assessed 4weeks later. In both strains, an adolescent history of THC treatment disrupted later object recognition. Interestingly, in B6 mice an adult history of THC exposure appeared to rescue a deficit in object recognition observed in vehicle-treated adults. Repeated THC administration also produced a protracted effected on CB1R protein expression. Animals treated with THC in adolescence maintained increased levels of CB1R protein expression compared to their adult THC-treated counterparts at five weeks following the last injection. These results indicate that THC use may have long-lasting effects with adolescence being a unique period of susceptibility.

Cooperation between a hierarchical set of recruitment sites targets the X chromosome for dosage compensation

In many organisms, it remains unclear how X chromosomes are specified for dosage compensation, since DNA sequence motifs shown to be important for dosage compensation complex (DCC) recruitment are themselves not X-specific. Here, we addressed this problem in C. elegans. We found that the DCC recruiter, SDC-2, is required to maintain open chromatin at a small number of primary DCC recruitment sites, whose sequence and genomic context are X-specific. Along the X, primary recruitment sites are interspersed with secondary sites, whose function is X-dependent. A secondary site can ectopically recruit the DCC when additional recruitment sites are inserted either in tandem or at a distance (>30 kb). Deletion of a recruitment site on the X results in reduced DCC binding across several megabases surrounded by topologically associating domain (TAD) boundaries. Our work elucidates that hierarchy and long-distance cooperativity between gene-regulatory elements target a single chromosome for regulation.

DOI:http://dx.doi.org/10.7554/eLife.23645.001

The DNA inside living cells is organized in structures called chromosomes. In many animals, females have two X chromosomes, whereas males have only one. To ensure that females do not end up with a double dose of the proteins encoded by the genes on the X chromosome, animals use a process called dosage compensation to correct this imbalance. The mechanisms underlying this process vary between species, but they typically involve a regulatory complex that binds to the X chromosomes of one sex to modify gene expression.

Caenorhabditis elegans, for example, is a species of nematode worm in which individuals with two X chromosomes are hermaphrodites and those with one X chromosome are males. In C. elegans, a regulatory complex, called the dosage compensation complex, attaches to both X chromosomes of a hermaphrodite, and reduces the expression of the genes on each by half to match the level seen in the males. Previous research has shown that short DNA sequences, known as motifs, recruit the dosage compensation complex to the X chromosomes. However, these sequences are also found on the other chromosomes and, until now, it was not known why the complex was only recruited to the X chromosomes.

Albritton et al. now show the X chromosomes have a ‘hierarchical’ recruitment system. A few sites on the X chromosomes contain clusters of a specific DNA motif, which initiate the process and attract the dosage compensation complex more strongly than other sites. These ‘strong’ recruitment sites are placed across the length of the X chromosomes and cooperate with several ‘weaker’ ones located in between. This way, multiple recruitment sites can cooperate over a long distance, while non-sex chromosomes, which have only one or two stronger recruitment sites, do not have thisadvantage.

Hierarchy and cooperativity may be general features of gene expression, in which proteins are targeted to chromosomes without the need for having specific motifs at every recruitment site. The way DNA sequences are distributed across the genome may give us clues about their role. Thus, knowing how genomes are structured will help us identify disrupted areas in diseases such as cancer.

DOI:http://dx.doi.org/10.7554/eLife.23645.002

Association of a Network of Interferon-Stimulated Genes with a Locus Encoding a Negative Regulator of Non-conventional IKK Kinases and IFNB1

Functional genomic analysis of gene expression in mice allowed us to identify a quantitative trait locus (QTL) linked in trans to the expression of 190 gene transcripts and in cis to the expression of only two genes, one of which was Ypel5. Most of the trans-expression QTL genes were interferon-stimulated genes (ISGs), and their expression in mouse macrophage cell lines was stimulated in an IFNB1-dependent manner by Ypel5 silencing. In human HEK293T cells, YPEL5 silencing enhanced the induction of IFNB1 by pattern recognition receptors and phosphorylation of TBK1/IKBKE kinases, whereas co-immunoprecipitation experiments revealed that YPEL5 interacted physically with IKBKE. We thus found that the Ypel5 gene (contained in a locus linked to a network of ISGs in mice) is a negative regulator of IFNB1 production and innate immune responses that interacts functionally and physically with TBK1/IKBKE kinases.

Dual Linkage of a Locus to Left Ventricular Mass and a Cardiac Gene Co-Expression Network Driven by a Chromosome Domain

We have previously reported Lvm1 as a quantitative trait locus (QTL) on chromosome 13 that links to cardiac left ventricular mass (LVM) in a panel of AxB/BxA mouse recombinant inbred strains (RIS). When performing a gene expression QTL (eQTL) analysis, we detected 33 cis-eQTLs that correlated with LVM. Among the latter, a group of eight cis-eQTLs clustered in a genomic region smaller than 6 Mb and surrounding the Lvm1 peak on chr13. Co-variant analysis indicated that all eight genes correlated with the phenotype in a causal rather than a reactive fashion, a finding that (despite its functional interest) did not provide grounds to prioritize any of these candidate genes. As a complementary approach, we performed weighted gene co-expression network analysis, which allowed us to detect 49 modules of highly connected genes. The module that correlated best with LVM: (1) showed linkage to a module QTL whose boundaries matched closely those of the phenotypic Lvm1 QTL on chr13; (2) harbored a disproportionately high proportion of genes originating from a small genomic region on chromosome 13 (including the 8 previously detected cis-eQTL genes); (3) contained genes that, beyond their individual level of expression, correlated with LVM as a function of their inter-connectivity; and (4) showed increased abundance of polymorphic insertion–deletion elements in the same region. Taken together, these data suggest that a domain on chromosome 13 constitutes the biologic principle responsible for the organization and linkage of the gene co-expression module, and indicate a mechanism whereby genetic variants within chromosome domains may associate to phenotypic changes via coordinate changes in the expression of several genes. One other possible implication of these findings is that candidate genes to consider as contributors to a particular phenotype should extend further than those that are closest to the QTL peak.

Network statistics of genetically-driven gene co-expression modules in mouse crosses

In biology, networks are used in different contexts as ways to represent relationships between entities, such as for instance interactions between genes, proteins or metabolites. Despite progress in the analysis of such networks and their potential to better understand the collective impact of genes on complex traits, one remaining challenge is to establish the biologic validity of gene co-expression networks and to determine what governs their organization. We used WGCNA to construct and analyze seven gene expression datasets from several tissues of mouse recombinant inbred strains (RIS). For six out of the 7 networks, we found that linkage to “module QTLs” (mQTLs) could be established for 29.3% of gene co-expression modules detected in the several mouse RIS. For about 74.6% of such genetically-linked modules, the mQTL was on the same chromosome as the one contributing most genes to the module, with genes originating from that chromosome showing higher connectivity than other genes in the modules. Such modules (that we considered as “genetically-driven”) had network statistic properties (density and centralization) that set them apart from other modules in the network. Altogether, a sizeable portion of gene co-expression modules detected in mouse RIS panels had genetic determinants as their main organizing principle. In addition to providing a biologic interpretation validation for these modules, these genetic determinants imparted on them particular properties that set them apart from other modules in the network, to the point that they can be predicted to a large extent on the basis of their network statistics.

iBMQ: a R/Bioconductor package for integrated Bayesian modeling of eQTL data

MOTIVATION

Recently, mapping studies of expression quantitative loci (eQTL) (where gene expression levels are viewed as quantitative traits) have provided insight into the biology of gene regulation. Bayesian methods provide natural modeling frameworks for analyzing eQTL studies, where information shared across markers and/or genes can increase the power to detect eQTLs. Bayesian approaches tend to be computationally demanding and require specialized software. As a result, most eQTL studies use univariate methods treating each gene independently, leading to suboptimal results.

RESULTS

We present a powerful, computationally optimized and free open-source R package, iBMQ. Our package implements a joint hierarchical Bayesian model where all genes and SNPs are modeled concurrently. Model parameters are estimated using a Markov chain Monte Carlo algorithm. The free and widely used openMP parallel library speeds up computation. Using a mouse cardiac dataset, we show that iBMQ improves the detection of large trans-eQTL hotspots compared with other state-of-the-art packages for eQTL analysis.

AVAILABILITY

The R-package iBMQ is available from the Bioconductor Web site at http://bioconductor.org and runs on Linux, Windows and MAC OS X. It is distributed under the Artistic Licence-2.0 terms.

CONTACT

christian.deschepper@ircm.qc.ca or rgottard@fhcrc.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.