Article

Characterization of the quantitative trait locus for haloperidol-induced catalepsy on distal mouse chromosome 1.

Department of Veterans Affairs, Richard L. Roudebush Medical Center, Indianapolis, IN 46202, USA.
Genes Brain and Behavior (Impact Factor: 3.6). 04/2008; 7(2):214-23. DOI: 10.1111/j.1601-183X.2007.00340.x
Source: PubMed

ABSTRACT We report here the confirmation of the quantitative trait locus for haloperidol-induced catalepsy on distal chromosome (Chr) 1. We determined that this quantitative trait locus was captured in the B6.D2-Mtv7a/Ty congenic mouse strain, whose introgressed genomic interval extends from approximately 169.1 to 191.3 Mb. We then constructed a group of overlapping interval-specific congenic strains to further break up the interval and remapped the locus between 177.5 and 183.4 Mb. We next queried single nucleotide polymorphism (SNP) data sets and identified three genes with nonsynonymous coding SNPs in the quantitative trait locus. We also queried two brain gene expression data sets and found five known genes in this 5.9-Mb interval that are differentially expressed in both whole brain and striatum. Three of the candidate quantitative trait genes were differentially expressed using quantitative real-time polymerase chain reaction analyses. Overall, the current study illustrates how multiple approaches, including congenic fine mapping, SNP analysis and microarray gene expression screens, can be integrated both to reduce the quantitative trait locus interval significantly and to detect promising candidate quantitative trait genes.

0 Bookmarks
 · 
76 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We performed short-term bi-directional selective breeding for haloperidol-induced catalepsy, starting from three mouse populations of increasingly complex genetic structure: an F2 intercross, a heterogeneous stock (HS) formed by crossing four inbred strains (HS4) and a heterogeneous stock (HS-CC) formed from the inbred strain founders of the Collaborative Cross (CC). All three selections were successful, with large differences in haloperidol response emerging within three generations. Using a custom differential network analysis procedure, we found that gene coexpression patterns changed significantly; importantly, a number of these changes were concordant across genetic backgrounds. In contrast, absolute gene-expression changes were modest and not concordant across genetic backgrounds, in spite of the large and similar phenotypic differences. By inferring strain contributions from the parental lines, we are able to identify significant differences in allelic content between the selected lines concurrent with large changes in transcript connectivity. Importantly, this observation implies that genetic polymorphisms can affect transcript and module connectivity without large changes in absolute expression levels. We conclude that, in this case, selective breeding acts at the subnetwork level, with the same modules but not the same transcripts affected across the three selections.
    PLoS ONE 03/2013; 8(3):e58951. DOI:10.1371/journal.pone.0058951 · 3.53 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Prior studies with crosses of the FVB/NJ (FVB; seizure-induced cell death-susceptible) mouse and the C57BL/6J (B6; seizure-induced cell death-resistant) mouse revealed the presence of a quantitative trait locus (QTL) on chromosome 15 that influenced susceptibility to kainic acid-induced cell death (Sicd2). In an earlier study, we confirmed that the Sicd2 interval harbors gene(s) conferring strong protection against seizure-induced cell death through the creation of the FVB.B6-Sicd2 congenic strain, and created three interval-specific congenic lines (ISCLs) that encompass Sicd2 on chromosome 15 to fine-map this locus. To further localise this Sicd2 QTL, an additional congenic line carrying overlapping intervals of the B6 segment was created (ISCL-4), and compared with the previously created ISCL-1-ISCL-3 and assessed for seizure-induced cell death phenotype. Whereas all of the ISCLs showed reduced cell death associated with the B6 phenotype, ISCL-4, showed the most extensive reduction in seizure-induced cell death throughout all hippocampal subfields. In order to characterise the susceptibility loci on Sicd2 by use of this ISCL and identify compelling candidate genes, we undertook an integrative genomic strategy of comparing exon transcript abundance in the hippocampus of this newly developed chromosome 15 subcongenic line (ISCL-4) and FVB-like littermates. We identified 10 putative candidate genes that are alternatively spliced between the strains and may govern strain-dependent differences in susceptibility to seizure-induced excitotoxic cell death. These results illustrate the importance of identifying transcriptomics variants in expression studies, and implicate novel candidate genes conferring susceptibility to seizure-induced cell death.
    European Journal of Neuroscience 09/2013; DOI:10.1111/ejn.12351 · 3.67 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: High-throughput next-generation sequencing is now entering its second decade. However, it was not until 2008 that the first report of sequencing the brain transcriptome appeared (Mortazavi, Williams, Mccue, Schaeffer, & Wold, 2008). These authors compared short-read RNA-Seq data for mouse whole brain with microarray results for the same sample and noted both the advantages and disadvantages of the RNA-Seq approach. While RNA-Seq provided exon level resolution, the majority of the reads were provided by a small proportion of highly expressed genes and the data analysis was exceedingly complex. Over the past 6 years, there have been substantial improvements in both RNA-Seq technology and data analysis. This volume contains 11 chapters that detail various aspects of sequencing the brain transcriptome. Some of the chapters are very methods driven, while others focus on the use of RNA-Seq to study such diverse areas as development, schizophrenia, and drug abuse. This chapter briefly reviews the transition from microarrays to RNA-Seq as the preferred method for analyzing the brain transcriptome. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and noncoding RNAs, is superior for gene network construction, detects alternative spliced transcripts, and can be used to extract genotype information, e.g., nonsynonymous coding single nucleotide polymorphisms. RNA-Seq embraces the complexity of the brain transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior-disease relationships is substantial.
    International Review of Neurobiology 01/2014; 116C:1-19. DOI:10.1016/B978-0-12-801105-8.00001-1 · 2.46 Impact Factor

Full-text (2 Sources)

Download
0 Downloads
Available from
Apr 1, 2015