Construction of stable mouse artificial chromosome from native mouse chromosome 10 for generation of transchromosomic mice

Mammalian artificial chromosomes derived from native chromosomes have been applied to biomedical research and development by generating cell sources and transchromosomic (Tc) animals. Human artificial chromosome (HAC) is a precedent chromosomal vector which achieved generation of valuable humanized animal models for fully human antibody production and human pharmacokinetics. While humanized Tc animals created by HAC vector have attained significant contributions, there was a potential issue to be addressed regarding stability in mouse tissues, especially highly proliferating hematopoietic cells. Mouse artificial chromosome (MAC) vectors derived from native mouse chromosome 11 demonstrated improved stability, and they were utilized for humanized Tc mouse production as a standard vector. In mouse, however, stability of MAC vector derived from native mouse chromosome other than mouse chromosome 11 remains to be evaluated. To clarify the potential of mouse centromeres in the additional chromosomes, we constructed a new MAC vector from native mouse chromosome 10 to evaluate the stability in Tc mice. The new MAC vector was transmitted through germline and stably maintained in the mouse tissues without any apparent abnormalities. Through this study, the potential of additional mouse centromere was demonstrated for Tc mouse production, and new MAC is expected to be used for various applications.

Construction of Stable Mouse Artificial Chromosome from Native Mouse Chromosome 10 for Generation of Transchromosomic Mice

Mammalian artificial chromosomes derived from native chromosomes have been applied to biomedical research and development by generating cell sources and transchromosomic (Tc) animals. Human artificial chromosome (HAC) is a precedent chromosomal vector which achieved generation of valuable humanized animal models for fully human antibody production and human pharmacokinetics. While humanized Tc animals created by HAC vector have attained significant contributions, there was a potential issue to be addressed regarding stability in mouse tissues, especially highly proliferating hematopoietic cells. Mouse artificial chromosome (MAC) vectors derived from native mouse chromosome 11 demonstrated improved stability, and they were utilized for humanized Tc mouse production as a standard vector. In mouse, however, stability of MAC vector derived from native mouse chromosome other than mouse chromosome 11 remains to be evaluated. To clarify the potential of mouse centromeres in the additional chromosomes, we constructed a new MAC vector from native mouse chromosome 10 to evaluate the stability in Tc mice. The new MAC vector was transmitted through germline and stably maintained in the mouse tissues without any apparent abnormalities. Through this study, the potential of additional mouse centromere was demonstrated for Tc mouse production, and new MAC is expected to be used for various applications.

Identification of a New QTL Region on Mouse Chromosome 1 Responsible for Male Hypofertility: Phenotype Characterization and Candidate Genes

Male fertility disorders often have their origin in disturbed spermatogenesis, which can be induced by genetic factors. In this study, we used interspecific recombinant congenic mouse strains (IRCS) to identify genes responsible for male infertility. Using ultrasonography, in vivo and in vitro fertilization (IVF) and electron microscopy, the phenotyping of several IRCS carrying mouse chromosome 1 segments of Mus spretus origin revealed a decrease in the ability of sperm to fertilize. This teratozoospermia included the abnormal anchoring of the acrosome to the nucleus and a persistence of residual bodies at the level of epididymal sperm midpiece. We identified a quantitative trait locus (QTL) responsible for these phenotypes and we have proposed a short list of candidate genes specifically expressed in spermatids. The future functional validation of candidate genes should allow the identification of new genes and mechanisms involved in male infertility.

22q11.2 deletion syndrome and schizophrenia

22q11.2 deletion is a common microdeletion that causes an array of developmental defects including 22q11.2 deletion syndrome (22q11DS) or DiGeorge syndrome and velocardiofacial syndrome. About 30% of patients with 22q11.2 deletion develop schizophrenia. Mice with deletion of the ortholog region in mouse chromosome 16qA13 exhibit schizophrenia-like abnormal behaviors. It is suggested that the genes deleted in 22q11DS are involved in the pathogenesis of schizophrenia. Among these genes, COMT, ZDHHC8, DGCR8, and PRODH have been identified as schizophrenia susceptibility genes. And DGCR2 is also found to be associated with schizophrenia. In this review, we focused on these five genes and reviewed their functions in the brain and the potential pathophysiological mechanisms in schizophrenia, which will give us a deeper understanding of the pathology of schizophrenia.

A Novel Mapping Strategy Utilizing Mouse Chromosome Substitution Strains Identifies Multiple Epistatic Interactions That Regulate Complex Traits

The genetic contribution of additive vs. non-additive (epistatic) effects in the regulation of complex traits is unclear. While genome-wide association studies typically ignore gene-gene interactions, in part because of the lack of statistical power for detecting them, mouse chromosome substitution strains (CSSs) represent an alternate approach for detecting epistasis given their limited allelic variation. Therefore, we utilized CSSs to identify and map both additive and epistatic loci that regulate a range of hematologic- and metabolism-related traits, as well as hepatic gene expression. Quantitative trait loci (QTL) were identified using a CSS-based backcross strategy involving the segregation of variants on the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. In the liver transcriptomes of offspring from this cross, we identified and mapped additive QTL regulating the hepatic expression of 768 genes, and epistatic QTL pairs for 519 genes. Similarly, we identified additive QTL for fat pad weight, platelets, and the percentage of granulocytes in blood, as well as epistatic QTL pairs controlling the percentage of lymphocytes in blood and red cell distribution width. The variance attributed to the epistatic QTL pairs was approximately equal to that of the additive QTL; however, the SNPs in the epistatic QTL pairs that accounted for the largest variances were undetected in our single locus association analyses. These findings highlight the need to account for epistasis in association studies, and more broadly demonstrate the importance of identifying genetic interactions to understand the complete genetic architecture of complex traits.

Genetics of mouse behavioral and peripheral neural responses to sucrose

Mice of the C57BL/6ByJ (B6) strain have higher consumption of, and stronger peripheral neural responses to, sucrose solution than do mice of the 129P3/J (129) strain. To identify quantitative trait loci (QTLs) responsible for this strain difference and evaluate the contribution of peripheral taste responsiveness to individual differences in sucrose intake, we produced an intercross (F2) of 627 mice, measured their sucrose consumption in two-bottle choice tests, recorded the electrophysiological activity of the chorda tympani nerve elicited by sucrose in a subset of F2 mice, and genotyped the mice with DNA markers distributed in every mouse chromosome. We confirmed a sucrose consumption QTL (Scon2, or Sac) on mouse chromosome (Chr) 4, harboring the Tas1r3 gene, which encodes the sweet taste receptor subunit T1R3 and affects both behavioral and neural responses to sucrose. For sucrose consumption, we also detected five new main-effect QTLs Scon6 (Chr2), Scon7 (Chr5), Scon8 (Chr8), Scon3 (Chr9) and a sex-specific QTL Scon9 (Chr15), and an interacting QTL pair Scon4 (Chr1) and Scon3 (Chr9). No additional QTLs for the taste nerve responses to sucrose were detected besides the previously known one on Chr4 (Scon2). Identification of the causal genes and variants for these sucrose consumption QTLs may point to novel mechanisms beyond peripheral taste sensitivity that could be harnessed to control obesity and diabetes.

A novel mapping strategy utilizing mouse chromosome substitution strains identifies multiple epistatic interactions that regulate complex traits

The genetic contribution of additive versus non-additive (epistatic) effects in the regulation of complex traits is unclear. While genome-wide association studies typically ignore gene-gene interactions, in part because of the lack of statistical power for detecting them, mouse chromosome substitution strains (CSSs) represent an alternate and powerful model for detecting epistasis given their limited allelic variation. Therefore, we utilized CSSs to identify and map both additive and epistatic loci that regulate a range of hematologic- and metabolism-related traits, as well as hepatic gene expression. Quantitative trait loci (QTLs) were identified using a CSS-based backcross strategy involving the segregation of variants on the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. In the liver transcriptomes of offspring from this cross, we identified and mapped additive QTLs regulating the hepatic expression of 768 genes, and epistatic QTL pairs for 519 genes. Similarly, we identified additive QTLs for fat pad weight, platelets, and the percentage of granulocytes in blood, as well as epistatic QTL pairs controlling the percentage of lymphocytes in blood and red cell distribution width. The variance attributed to the epistatic QTL pairs was approximately equal to that of the additive QTLs; however, the SNPs in the epistatic QTL pairs that accounted for the largest variances were undetected in our single locus association analyses. These findings highlight the need to account for epistasis in association studies, and more broadly demonstrate the importance of identifying genetic interactions to understand the complete genetic architecture of complex traits.