Treatment- and population-dependent activity patterns of behavioral and expression QTLs

Systems genetics analysis of the LXS recombinant inbred mouse strains:Genetic and molecular insights into acute ethanol tolerance

We have been using the Inbred Long- and Short-Sleep mouse strains (ILS, ISS) and a recombinant inbred panel derived from them, the LXS, to investigate the genetic underpinnings of acute ethanol tolerance which is considered to be a risk factor for alcohol use disorders (AUDs). Here, we have used RNA-seq to examine the transcriptome of whole brain in 40 of the LXS strains 8 hours after a saline or ethanol "pretreatment" as in previous behavioral studies. Approximately 1/3 of the 14,184 expressed genes were significantly heritable and many were unique to the pretreatment. Several thousand cis- and trans-eQTLs were mapped; a portion of these also were unique to pretreatment. Ethanol pretreatment caused differential expression (DE) of 1,230 genes. Gene Ontology (GO) enrichment analysis suggested involvement in numerous biological processes including astrocyte differentiation, histone acetylation, mRNA splicing, and neuron projection development. Genetic correlation analysis identified hundreds of genes that were correlated to the behaviors. GO analysis indicated that these genes are involved in gene expression, chromosome organization, and protein transport, among others. The expression profiles of the DE genes and genes correlated to AFT in the ethanol pretreatment group (AFT-Et) were found to be similar to profiles of HDAC inhibitors. Hdac1, a cis-regulated gene that is located at the peak of a previously mapped QTL for AFT-Et, was correlated to 437 genes, most of which were also correlated to AFT-Et. GO analysis of these genes identified several enriched biological process terms including neuron-neuron synaptic transmission and potassium transport. In summary, the results suggest widespread genetic effects on gene expression, including effects that are pretreatment-specific. A number of candidate genes and biological functions were identified that could be mediating the behavioral responses. The most prominent of these was Hdac1 which may be regulating genes associated with glutamatergic signaling and potassium conductance.

Linkage mapping of yeast cross protection connects gene expression variation to a higher-order organismal trait

Gene expression variation is extensive in nature, and is hypothesized to play a major role in shaping phenotypic diversity. However, connecting differences in gene expression across individuals to higher-order organismal traits is not trivial. In many cases, gene expression variation may be evolutionarily neutral, and in other cases expression variation may only affect phenotype under specific conditions. To understand connections between gene expression variation and stress defense phenotypes, we have been leveraging extensive natural variation in the gene expression response to acute ethanol in laboratory and wild Saccharomyces cerevisiae strains. Previous work found that the genetic architecture underlying these expression differences included dozens of “hotspot” loci that affected many transcripts in trans. In the present study, we provide new evidence that one of these expression QTL hotspot loci affects natural variation in one particular stress defense phenotype—ethanol-induced cross protection against severe doses of H₂O₂. A major causative polymorphism is in the heme-activated transcription factor Hap1p, which we show directly impacts cross protection, but not the basal H₂O₂ resistance of unstressed cells. This provides further support that distinct cellular mechanisms underlie basal and acquired stress resistance. We also show that Hap1p-dependent cross protection relies on novel regulation of cytosolic catalase T (Ctt1p) during ethanol stress in a wild oak strain. Because ethanol accumulation precedes aerobic respiration and accompanying reactive oxygen species formation, wild strains with the ability to anticipate impending oxidative stress would likely be at an advantage. This study highlights how strategically chosen traits that better correlate with gene expression changes can improve our power to identify novel connections between gene expression variation and higher-order organismal phenotypes.

A major goal in genetics is to understand how individuals with different genetic makeups respond to their environment. Understanding these “gene-environment interactions” is important for the development of personalized medicine. For example, gene-environment interactions can explain why some people are more sensitive to certain drugs or are more likely to get certain cancers. While the underlying causes of gene-environment interactions are unclear, one possibility is that differences in gene expression across individuals are responsible. In this study, we examined that possibility using baker’s yeast as a model. We were interested in a phenomenon called acquired stress resistance, where cells exposed to a mild dose of one stress can become resistant to an otherwise lethal dose of severe stress. This response is observed in diverse organisms ranging from bacteria to humans, though the specific mechanisms governing acquisition of higher stress resistance are poorly understood. To understand the differences between yeast strains with and without the ability to acquire further stress resistance, we employed genetic mapping. We found that part of the variation in acquired stress resistance was due to sequence differences in a key regulatory protein, thus providing new insight into how different individuals respond to acute environmental change.

Analyses of differentially expressed genes after exposure to acute stress, acute ethanol, or a combination of both in mice

Alcohol abuse is a complex disorder, which is confounded by other factors, including stress. In the present study, we examined gene expression in the hippocampus of BXD recombinant inbred mice after exposure to ethanol (NOE), stress (RSS), and the combination of both (RSE). Mice were given an intraperitoneal (i.p.) injection of 1.8 g/kg ethanol or saline, and subsets of both groups were exposed to acute restraint stress for 15 min or controls. Gene expression in the hippocampus was examined using microarray analysis. Genes that were significantly (p < 0.05, q < 0.1) differentially expressed were further evaluated. Bioinformatic analyses were predominantly performed using tools available at GeneNetwork.org, and included gene ontology, presence of cis-regulation or polymorphisms, phenotype correlations, and principal component analyses. Comparisons of differential gene expression between groups showed little overlap. Gene Ontology demonstrated distinct biological processes in each group with the combined exposure (RSE) being unique from either the ethanol (NOE) or stress (RSS) group, suggesting that the interaction between these variables is mediated through diverse molecular pathways. This supports the hypothesis that exposure to stress alters ethanol-induced gene expression changes and that exposure to alcohol alters stress-induced gene expression changes. Behavior was profiled in all groups following treatment, and many of the differentially expressed genes are correlated with behavioral variation within experimental groups. Interestingly, in each group several genes were correlated with the same phenotype, suggesting that these genes are the potential origins of significant genetic networks. The distinct sets of differentially expressed genes within each group provide the basis for identifying molecular networks that may aid in understanding the complex interactions between stress and ethanol, and potentially provide relevant therapeutic targets. Using Ptp4a1, a candidate gene underlying the quantitative trait locus for several of these phenotypes, and network analyses, we show that a large group of differentially expressed genes in the NOE group are highly interrelated, some of which have previously been linked to alcohol addiction or alcohol-related phenotypes.

Natural gene expression variation studies in yeast

The rise of sequence information across different yeast species and strains is driving an increasing number of studies in the emerging field of genomics to associate polymorphic variants, mRNA abundance and phenotypic differences between individuals. Here, we gathered evidence from recent studies covering several layers that define the genotype-phenotype gap, such as mRNA abundance, allele-specific expression and translation efficiency to demonstrate how genetic variants co-evolve and define an individual's genome. Moreover, we exposed several antecedents where inter- and intra-specific studies led to opposite conclusions, probably owing to genetic divergence. Future studies in this area will benefit from the access to a massive array of well-annotated genomes and new sequencing technologies, which will allow the fine breakdown of the complex layers that delineate the genotype-phenotype map. Copyright © 2016 John Wiley & Sons, Ltd.

Joint mouse-human phenome-wide association to test gene function and disease risk

Phenome-wide association is a novel reverse genetic strategy to analyze genome-to-phenome relations in human clinical cohorts. Here we test this approach using a large murine population segregating for ∼5 million sequence variants, and we compare our results to those extracted from a matched analysis of gene variants in a large human cohort. For the mouse cohort, we amassed a deep and broad open-access phenome consisting of ∼4,500 metabolic, physiological, pharmacological and behavioural traits, and more than 90 independent expression quantitative trait locus (QTL), transcriptome, proteome, metagenome and metabolome data sets—by far the largest coherent phenome for any experimental cohort (www.genenetwork.org). We tested downstream effects of subsets of variants and discovered several novel associations, including a missense mutation in fumarate hydratase that controls variation in the mitochondrial unfolded protein response in both mouse and Caenorhabditis elegans, and missense mutations in Col6a5 that underlies variation in bone mineral density in both mouse and human.

Phenome-wide association is a novel method that links sequence variants to a spectrum of phenotypes and diseases. Here the authors generate detailed mouse genetic and phenome data which links their phenome-wide association study (PheWAS) of mouse to corresponding PheWAS in human.

A novel heat shock protein alpha 8 (Hspa8) molecular network mediating responses to stress- and ethanol-related behaviors

Genetic differences mediate individual differences in susceptibility and responses to stress and ethanol, although, the specific molecular pathways that control these responses are not fully understood. Heat shock protein alpha 8 (Hspa8) is a molecular chaperone and member of the heat shock protein family that plays an integral role in the stress response and that has been implicated as an ethanol-responsive gene. Therefore, we assessed its role in mediating responses to stress and ethanol across varying genetic backgrounds. The hippocampus is an important mediator of these responses, and thus, was examined in the BXD family of mice in this study. We conducted bioinformatic analyses to dissect genetic factors modulating Hspa8 expression, identify downstream targets of Hspa8, and examined its role. Hspa8 is trans-regulated by a gene or genes on chromosome 14 and is part of a molecular network that regulates stress- and ethanol-related behaviors. To determine additional components of this network, we identified direct or indirect targets of Hspa8 and show that these genes, as predicted, participate in processes such as protein folding and organic substance metabolic processes. Two phenotypes that map to the Hspa8 locus are anxiety-related and numerous other anxiety- and/or ethanol-related behaviors significantly correlate with Hspa8 expression. To more directly assay this relationship, we examined differences in gene expression following exposure to stress or alcohol and showed treatment-related differential expression of Hspa8 and a subset of the members of its network. Our findings suggest that Hspa8 plays a vital role in genetic differences in responses to stress and ethanol and their interactions.

Identification of candidate genes that underlie the QTL on chromosome 1 that mediates genetic differences in stress-ethanol interactions

Alcoholism, stress, and anxiety are strongly interacting heritable, polygenetic traits. In a previous study, we identified a quantitative trait locus (QTL) on murine chromosome (Chr) 1 between 23.0 and 31.5 Mb that modulates genetic differences in the effects of ethanol on anxiety-related phenotypes. The goal of the present study was to extend the analysis of this locus with a focus on identifying candidate genes using newly available data and tools. Anxiety-like behavior was evaluated with an elevated zero maze following saline or ethanol injections (1.8 g/kg) in C57BL/6J, DBA2J, and 72 BXD strains. We detected significant effects of strain and treatment and their interaction on anxiety-related behaviors, although surprisingly, sex was not a significant factor. The Chr1 QTL is specific to the ethanol-treated cohort. Candidate genes in this locus were evaluated using now standard bioinformatic criteria. Collagen 19a1 (Col19a1) and family sequence 135a (Fam135a) met most criteria but have lower expression levels and lacked biological verification and, therefore, were considered less likely candidates. In contrast, two other genes, the prenylated protein tyrosine phosphate family member Ptp4a1 (protein tyrosine phosphate 4a1) and the zinc finger protein Phf3 (plant homeoDomain finger protein 3) met each of our bioinformatic criteria and are thus strong candidates. These findings are also of translational relevance because both Ptp4a1 and Phf3 have been nominated as candidates genes for alcohol dependence in a human genome-wide association study. Our findings support the hypothesis that variants in one or both of these genes modulate heritable differences in the effects of ethanol on anxiety-related behaviors.

MicroRNA and cancer--a brief overview

MicroRNAs (miRNAs) are short non-coding RNAs with a length of ∼22 nucleotides, involved in posttranscriptional regulation of gene expression. Until now, over 2588 miRNAs have been identified in humans and the list is growing. MicroRNAs have an important role in all biological processes and aberrant miRNA expression is associated with many diseases including cancer. In the year 2002 the first connection between cancer and miRNA deregulation was discovered. Since then, a lot of information about the key role which miRNAs play in cancer development and drug resistance has been gained. However, there is still a long way to go to fully understand the miRNA world. In this review, we briefly describe miRNA biogenesis and discuss the role of miRNAs in cancer development and drug resistance. Finally we explain how miRNAs can be used as biomarkers and as a novel therapeutic approach in cancer.

Extensive cis-regulatory variation robust to environmental perturbation in Arabidopsis

cis- and trans-acting factors affect gene expression and responses to environmental conditions. However, for most plant systems, we lack a comprehensive map of these factors and their interaction with environmental variation. Here, we examined allele-specific expression (ASE) in an F1 hybrid to study how alleles from two Arabidopsis thaliana accessions affect gene expression. To investigate the effect of the environment, we used drought stress and developed a variance component model to estimate the combined genetic contributions of cis- and trans-regulatory polymorphisms, environmental factors, and their interactions. We quantified ASE for 11,003 genes, identifying 3318 genes with consistent ASE in control and stress conditions, demonstrating that cis-acting genetic effects are essentially robust to changes in the environment. Moreover, we found 1618 genes with genotype x environment (GxE) interactions, mostly cis x E interactions with magnitude changes in ASE. We found fewer trans x E interactions, but these effects were relatively less robust across conditions, showing more changes in the direction of the effect between environments; this confirms that trans-regulation plays an important role in the response to environmental conditions. Our data provide a detailed map of cis- and trans-regulation and GxE interactions in A. thaliana, laying the ground for mechanistic investigations and studies in other plants and environments.

Genetic architecture of ethanol-responsive transcriptome variation in Saccharomyces cerevisiae strains

Natural variation in gene expression is pervasive within and between species, and it likely explains a significant fraction of phenotypic variation between individuals. Phenotypic variation in acute systemic responses can also be leveraged to reveal physiological differences in how individuals perceive and respond to environmental perturbations. We previously found extensive variation in the transcriptomic response to acute ethanol exposure in two wild isolates and a common laboratory strain of Saccharomyces cerevisiae. Many expression differences persisted across several modules of coregulated genes, implicating trans-acting systemic differences in ethanol sensing and/or response. Here, we conducted expression QTL mapping of the ethanol response in two strain crosses to identify the genetic basis for these differences. To understand systemic differences, we focused on "hotspot" loci that affect many transcripts in trans. Candidate causal regulators contained within hotspots implicate upstream regulators as well as downstream effectors of the ethanol response. Overlap in hotspot targets revealed additive genetic effects of trans-acting loci as well as "epi-hotspots," in which epistatic interactions between two loci affected the same suites of downstream targets. One epi-hotspot implicated interactions between Mkt1p and proteins linked to translational regulation, prompting us to show that Mkt1p localizes to P bodies upon ethanol stress in a strain-specific manner. Our results provide a glimpse into the genetic architecture underlying natural variation in a stress response and present new details on how yeast respond to ethanol stress.

Systems genetics in "-omics" era: current and future development

The systems genetics is an emerging discipline that integrates high-throughput expression profiling technology and systems biology approaches for revealing the molecular mechanism of complex traits, and will improve our understanding of gene functions in the biochemical pathway and genetic interactions between biological molecules. With the rapid advances of microarray analysis technologies, bioinformatics is extensively used in the studies of gene functions, SNP-SNP genetic interactions, LD block-block interactions, miRNA-mRNA interactions, DNA-protein interactions, protein-protein interactions, and functional mapping for LD blocks. Based on bioinformatics panel, which can integrate "-omics" datasets to extract systems knowledge and useful information for explaining the molecular mechanism of complex traits, systems genetics is all about to enhance our understanding of biological processes. Systems biology has provided systems level recognition of various biological phenomena, and constructed the scientific background for the development of systems genetics. In addition, the next-generation sequencing technology and post-genome wide association studies empower the discovery of new gene and rare variants. The integration of different strategies will help to propose novel hypothesis and perfect the theoretical framework of systems genetics, which will make contribution to the future development of systems genetics, and open up a whole new area of genetics.

Distinct gene loci control the host response to influenza H1N1 virus infection in a time-dependent manner

Background

There is strong but mostly circumstantial evidence that genetic factors modulate the severity of influenza infection in humans. Using genetically diverse but fully inbred strains of mice it has been shown that host sequence variants have a strong influence on the severity of influenza A disease progression. In particular, C57BL/6J, the most widely used mouse strain in biomedical research, is comparatively resistant. In contrast, DBA/2J is highly susceptible.

Results

To map regions of the genome responsible for differences in influenza susceptibility, we infected a family of 53 BXD-type lines derived from a cross between C57BL/6J and DBA/2J strains with influenza A virus (PR8, H1N1). We monitored body weight, survival, and mean time to death for 13 days after infection. Qivr5 (quantitative trait for influenza virus resistance on chromosome 5) was the largest and most significant QTL for weight loss. The effect of Qivr5 was detectable on day 2 post infection, but was most pronounced on days 5 and 6. Survival rate mapped to Qivr5, but additionally revealed a second significant locus on chromosome 19 (Qivr19). Analysis of mean time to death affirmed both Qivr5 and Qivr19. In addition, we observed several regions of the genome with suggestive linkage. There are potentially complex combinatorial interactions of the parental alleles among loci. Analysis of multiple gene expression data sets and sequence variants in these strains highlights about 30 strong candidate genes across all loci that may control influenza A susceptibility and resistance.

Conclusions

We have mapped influenza susceptibility loci to chromosomes 2, 5, 16, 17, and 19. Body weight and survival loci have a time-dependent profile that presumably reflects the temporal dynamic of the response to infection. We highlight candidate genes in the respective intervals and review their possible biological function during infection.