Dissection of a QTL Hotspot on Mouse Distal Chromosome 1 that Modulates Neurobehavioral Phenotypes and Gene Expression

A remarkably diverse set of traits maps to a region on mouse distal chromosome 1 (Chr 1) that corresponds to human Chr 1q21–q23. This region is highly enriched in quantitative trait loci (QTLs) that control neural and behavioral phenotypes, including motor behavior, escape latency, emotionality, seizure susceptibility (Szs1), and responses to ethanol, caffeine, pentobarbital, and haloperidol. This region also controls the expression of a remarkably large number of genes, including genes that are associated with some of the classical traits that map to distal Chr 1 (e.g., seizure susceptibility). Here, we ask whether this QTL-rich region on Chr 1 (Qrr1) consists of a single master locus or a mixture of linked, but functionally unrelated, QTLs. To answer this question and to evaluate candidate genes, we generated and analyzed several gene expression, haplotype, and sequence datasets. We exploited six complementary mouse crosses, and combed through 18 expression datasets to determine class membership of genes modulated by Qrr1. Qrr1 can be broadly divided into a proximal part (Qrr1p) and a distal part (Qrr1d), each associated with the expression of distinct subsets of genes. Qrr1d controls RNA metabolism and protein synthesis, including the expression of ∼20 aminoacyl-tRNA synthetases. Qrr1d contains a tRNA cluster, and this is a functionally pertinent candidate for the tRNA synthetases. Rgs7 and Fmn2 are other strong candidates in Qrr1d. FMN2 protein has pronounced expression in neurons, including in the dendrites, and deletion of Fmn2 had a strong effect on the expression of few genes modulated by Qrr1d. Our analysis revealed a highly complex gene expression regulatory interval in Qrr1, composed of multiple loci modulating the expression of functionally cognate sets of genes

Functional characterization of allelic variants of pulmonary adenoma susceptibility 1 (Pas1) genes for their cancer modifier activity

The pulmonary adenoma susceptibility 1 (Pas1) locus affects inherited predisposition and resistance to chemically induced lung tumorigenesis in mice. A/J and C57BL6/J mouse strains carry the susceptibility and resistance allele, respectively. In the present study we identified and genotyped 64 polymorphisms in the Pas1 locus in 29 mouse inbred strains in order to reduce the region containing the Pas1 locus, and we succeeded in delimiting the Pas1 locus to a minimal region of 468kb containing six genes (Bcat1, Lrmp, Cascl, Ghiso, Kras2 and Lmn-rs1). That region defined a core Pas1 haplotype (A/J- and C57BL/6J-type) with 42 tightly linked markers, including intragenic polymorphisms in five genes (Bcat1, Lrmpf Casc1, Ghiso and Kras2) and amino-acid changes in three genes (Lrmp, Casc1 and Lmn-rs1). mRNA expression study of these candidate genes revealed that, in (A/JxC57BL/6J)F1 mouse lung tumors, the Lmna-rs1 gene was completely downregulated, whereas allele-specific downregulation of the C57BL/6J-derived allele was observed at the Casc1 gene, suggesting the potential role of these genes in tumor suppression. These first results indicate a complex multigenic nature of the Pas1 locus, and point to a functional role for both intronic and exonic polymorphisms of the six genes of the Pas1 haplotype in lung tumor susceptibility. Then we continued by testing functional activity of candidate genes in vitro. In vitro colony formation assay of human lung cancer cell lines A549 and NCI-H520 transfected with the allelic variants of the four genes revealed allele-specific modulations of colony numbers by Lmna-rs1 and Casc1, but not by Lrmp and Ghiso. We found that inhibition of clonogenicity by allelic forms of Pas1 candidate genes was not mediated by induction of apoptosis. These findings provide evidence that allelic variants of mouse Pas1 candidate genes differentially modulate growth of human cancer cells

Prioritizing genes responsible for host resistance to influenza using network approaches

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Genetic control of alphavirus pathogenesis

Alphaviruses, members of the positive-sense, single-stranded RNA virus family Togaviridae, represent a re-emerging public health concern worldwide as mosquito vectors expand into new geographic ranges. Members of the alphavirus genus tend to induce clinical disease characterized by rash, arthralgia, and arthritis (chikungunya virus, Ross River virus, and Semliki Forest virus) or encephalomyelitis (eastern equine encephalitis virus, western equine encephalitis virus, and Venezuelan equine encephalitis virus), though some patients who recover from the initial acute illness may develop long-term sequelae, regardless of the specific infecting virus. Studies examining the natural disease course in humans and experimental infection in cell culture and animal models reveal that host genetics play a major role in influencing susceptibility to infection and severity of clinical disease. Genome-wide genetic screens, including loss of function screens, microarrays, RNA-sequencing, and candidate gene studies, have further elucidated the role host genetics play in the response to virus infection, with the immune response being found in particular to majorly influence the outcome. This review describes the current knowledge of the mechanisms by which host genetic factors influence alphavirus pathogenesis and discusses emerging technologies that are poised to increase our understanding of the complex interplay between viral and host genetics on disease susceptibility and clinical outcome

Estimates of genomic heritability and genome-wide association study for fatty acids profile in Santa Inês sheep

Background: Despite the health concerns and nutritional importance of fatty acids, there is a relative paucity of studies in the literature that report genetic or genomic parameters, especially in the case of sheep populations. To investigate the genetic architecture of fatty acid composition of sheep, we conducted genome-wide association studies (GWAS) and estimated genomic heritabilities for fatty acid profile in Longissimus dorsi muscle of 216 male sheep. Results: Genomic heritability estimates for fatty acid content ranged from 0.25 to 0.46, indicating that substantial genetic variation exists for the evaluated traits. Therefore, it is possible to alter fatty acid profiles through selection. Twenty-seven genomic regions of 10 adjacent SNPs associated with fatty acids composition were identified on chromosomes 1, 2, 3, 5, 8, 12, 14, 15, 16, 17, and 18, each explaining ≥0.30% of the additive genetic variance. Twenty-three genes supporting the understanding of genetic mechanisms of fat composition in sheep were identified in these regions, such as DGAT2, TRHDE, TPH2, ME1, C6, C7, UBE3D, PARP14, and MRPS30. Conclusions: Estimates of genomic heritabilities and elucidating important genomic regions can contribute to a better understanding of the genetic control of fatty acid deposition and improve the selection strategies to enhance meat quality and health attributes

Systems Biology Approach to Identifying Host Interactive Pathways Modulating the Severity of Streptococcal Sepsis

Clinical outcomes of infectious diseases are controlled by complex interactions between the host and the pathogen. Epidemiological, genetic and molecular studies in my mentor’s laboratory provided evidence that in invasive Group A streptococcal (GAS) infections, genetic variations in both bacteria and patients influenced the severity of GAS sepsis. Allelic variations in class II human leukocyte antigens (HLA) contributed significantly to differences in the severity of group A streptococcal sepsis caused by the same virulent strain of the bacteria. HLA class II molecules present streptococcal superantigens (SAgs) to T cells, and variations in HLA class II molecules can strongly influence SAg responses. However, the bacteria produce a very large number of additional virulence factors that participate in the pathogenesis of this complex disease, and it is likely that host genes besides HLA class II molecules are also participating in modulating the severity of GAS sepsis. The main focus of this Ph.D. project was to identify additional host genes and pathways that may be modulating the severity of GAS sepsis. To achieve this goal I applied a systems genetics approach, involving genome wide association studies (GWAS) of GAS sepsis in the Advanced Recombinant Inbred (ARI) panel of BXD mouse strains. We used this panel of ARI-BXD strains as a genetically diverse reference population to study differential severity of GAS sepsis as ARI-BXD strains diversity mimics the genetic diversity of human population. We assessed several traits associated with differential host responses to GAS sepsis, and analyzed variations in these traits in the context of mice genotypic variability, using genome-wide scans and the sophisticated analysis tools of WebQTL. This allowed us to map quantitative trait loci (QTL) associated with modulating susceptibility to severe GAS sepsis on chromosome (Chr) 2 and Chr X. The mapped QTLs strongly predicted disease severity (accounting for 25–30% of variance), and harbored highly polymorphic genes known to play important roles in innate immune responses. Based on linkage analyses, gene ontology, co-citation networks, and variations in gene expression, we identified interleukin 1 (IL1) and prostaglandin E (PGE) pathways as prime candidates associated with modulating the severity of GAS sepsis. To further investigate mechanisms underlying differential host susceptibility, we analyzed genome-wide differential gene expression in blood and spleens of uninfected vs. infected mice belonging to highly resistant or susceptible BXD strains, at selected times post infection. Our transcriptional analyses revealed common pathways between susceptible and resistant strains associated with innate immune response, e.g. Interferon signaling pathway. Since our data has pointed to a strong association of differential response to GAS with innate immune responses, we explored if differences in the numbers of relevant immune cells among the BXD strains played a role in their differential susceptibility to GAS. We found no significant differences in numbers or percentages of immune cell populations between susceptible and resistant strains under normal, uninfected conditions. However, depletion of neutrophils and/or macrophages significantly increased the severity of GAS sepsis in both resistant and susceptible strains. Taken together, our data suggested that differences in mobilization and /or function of these cells between susceptible and resistant strains might play a role in modulating differential severity of GAS sepsis. In conclusion, we found that variations in the severity of GAS sepsis have a strong genetic component that is complex and multigenic. Different combinations of genetic variants influenced theonset, progression, and severity of GAS sepsis and disease and ultimate outcome. Our overall approach of systems genetics, where we systematically dissected genetic, molecular, cellular and functional differences that may be associated with differential host susceptibility to GAS provided us with tremendous insight into disease mechanism. The knowledge gained can help the development of better diagnostics and means to predict disease severity based on a set of genetic and prognostic biomarkers to help customize patient care, to apply effective and more targeted therapeutic interventions and improve disease outcomes in septic patients

An integrative functional genomics approach towards quantitative trait gene nomination in existing and emerging mouse genetic reference populations

An approach that has been widely applied for the genetic dissection of complex traits is Quantitative Trait Locus (QTL) mapping. QTL mapping identifies genomic regions that harbor polymorphisms, responsible for the observed variation in a complex trait. If these polymorphisms are located within a gene, then these genes are called Quantitative Trait Genes (QTG). Prior to advancements in QTL mapping populations, QTL mapping resolution was often poor, resulting in large QTL intervals. Therefore, after mapping a QTL, fine mapping was initiated to further reduce the QTL interval and to identify the QTG. While successful, fine mapping using genetic approaches have been extremely time and resource intensive, making it the rate-limiting step in QTG discovery. Thus far, only a few QTGs have been successfully identified and validated. The disproportionate ratio of QTLs mapped to QTGs identified has been a cause of concern. Successful QTG discovery relies on the power and resolution with which QTLs are mapped and the genetic architecture of the underlying QTL mapping population. Here, QTL mapping performance in two recently developed QTL mapping populations, namely the expanded BXD Recombinant Inbred (RI) strain panel and the collaborative cross (CC) are assessed. Results indicate that while both the expanded BXD RI strain panel and the CC improve QTL mapping resolution, the CC is able to achieve greater precision and resolution in QTL v mapping. However, neither the BXD RI nor the CC facilitates gene level resolution in QTL mapping. Recent studies have used the integration and convergence of evidence among functional genomics studies as a successful strategy towards the efficient and rapid nomination of QTG. Here, the complementary in silico approach of integrative functional genomics, using GeneWeaver (www.geneweaver.org), is applied towards the reduction of two cocaine-induced locomotor activation QTLs, mapped in the expanded BXD RI strain panel. Integrative functional genomic analyses of these QTLs led to the nomination of Rab3b as a putative QTG. Functional assessment of Rab3b using Rab3bcd knockout mice reveals its role in acute habituation mediated cocaine response, serving as evidence of the efficiency and utility of integrative functional genomics for the identification of highly relevant QTG

Positional cloning of genes contributing to variability in nociceptive and analgesic phenotypes

Variability between individuals in pain and analgesia phenotypes is often observed in both clinical and experimental settings. The source of this variability has long been attributed to the interplay of environmental and genetic factors, but we have only recently begun identifying these determinants. Experiments comparing isogenic strains of mice have suggested that different pain tests may share a genetic basis; likewise, analgesic magnitude induced by disparate drug classes may be influenced by a common set of genes.We have presently used a quantitative trait locus mapping strategy to search for genes responsible for variability in analgesic response to five analgesic drugs (the opioids morphine and U50,488, and the non-opioid drugs clonidine, epibatidine, and WIN55,212-2). We first used a B6129PF2 intercross population to map sensitivity to clonidine, morphine, and WIN55,212-2 to a 30 cM region on distal Chromosome 1. In silico and congenic strain mapping techniques allowed us to refine this linkage, as well as generalize it to four of the five drugs and seven mouse strains. Kcnj9 (GIRK3) was identified as a likely candidate gene underlying response to multiple analgesic modalities. We showed that this gene is differentially expressed between C57BL/6 and 129P3 strains in the periaqueductal gray. We also determined that Kcnj9 null mutant mice exhibit attenuated analgesic responses.We previously mapped nociceptive response to the formalin test to two loci on Chr. 9 and 10 using an AB6F2 cross. Using in silico mapping, we identified several haplotypes near the Atp1b3 gene on Chr. 9 (a subunit of the sodium-potassium pump) that correlated highly with early phase formalin response. We then showed that pharmacological antagonism of the sodium-potassium channel eliminates the strain difference observed between A and C57BL/6 mice, supporting a role for this gene in determining response to formalin. Positional cloning of the Chr. 10 locus, employing recombinant and congenic strains, allowed us to refine the location of the locus to a <3 cM interval. A small number of genes in this region were identified as differentially expressed by microarray analysis, providing a short list of candidate genes for follow-up investigation