Gene expression analysis of mouse chromosome substitution strains

The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA.
Mammalian Genome (Impact Factor: 3.07). 07/2006; 17(6):598-614. DOI: 10.1007/s00335-005-0176-y
Source: PubMed


An analysis of transcriptional variation in the liver using a panel of B.A chromosome substitution strains identified 4209 transcripts that are differentially expressed relative to the C57BL/6J background and 1010 transcripts that are differentially expressed between C57BL/6J and A/J strains. A subset of these strains (substituting Chromosomes 1, 6, and 15) was used to identify 386 additional differentially expressed transcripts in the kidney. Approximately 15% of differentially expressed transcripts are located on the substituted chromosome. These cis-QTL are codirectionally expressed with the donor strain A/J. By comparison, trans-regulated loci comprise 85% of differentially expressed transcripts, often show opposite direction of change compared with A/J, and can be regulated by multiple chromosome substitutions. Gene expression differences in this study provide evidence for transgressive segregation: Only 438 of 4209 QTL in liver were inside the parental range. By combining QTL data with known biological functions, we were able to identify physiologic pathways altered in multiple strains. In many cases the same pathways were altered by multiple distinct chromosome substitutions. Taken together, these results suggest that widespread epistatic background effects may result in complex and overlapping transcriptional relationships among different chromosome substitution strains. Transcriptional profiling of chromosome substitution strains reveals a complex genetic architecture of transcriptional regulation.

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Available from: Keith Shockley, Jul 09, 2014
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    • "A genetic analysis of morphological traits of yeast has revealed a complex QTL system with transgressive variation which has been correlated with gene expression analysis to produced results similar to the regulation of Ucp1 and brown fat induction (Nogami et al., 2007). Others have conducted a methodological global gene expression analysis of liver and kidney with chromosome substitution strains between A/J and B6 mice to assess allelic effects on gene expression and found that allelic variation at several chromosomes affect expression of 4209 transcripts (Shockley and Churchill, 2006). Many of the transcripts had levels of expression that exceeded those found in the parental strains, suggestive of transgressive variation (Shockley and Churchill, 2006). "
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    ABSTRACT: Evidence that adult humans have functional brown adipose tissue has stirred interest in the possibility that the impressive effectiveness of induction of brown adipocytes to reduce obesity in mice may be translated to the human condition. A major focus recently on the identification of signaling and transcription factor that stimulate the induction of brown adipocytes has come from transgenic and gene KO models. However, these models have created a very complex picture of the regulatory mechanisms for brown fat induction. In this review insights into the critical regulatory pathways involved in brown adipocyte induction in the retroperitoneal fat depot of mice are described from quantitative trait locus (QTL) analysis of allelic variability determining Ucp1 levels and brown adipocyte induction in A/J vs. B6 mice. The key observation is that recombinant genotypes, found in recombinant inbred stains and backcross and intercross progeny, show transgressive variation for Ucp1 mRNA levels. These genetic crosses also show that the levels of Ucp1 mRNA are determined by interactions that control the levels of PPARα, PGC-1α, and type 2 deiodinase (DIO2) and that each factor is controlled by a subset of QTLs that also control Ucp1 expression. These results indicate that induction of Ucp1 in the retroperitoneal fat depot involves synergy between signaling and transcription factors that vary depending upon the environmental conditions. Inherent in this model is the idea that there is a high level of redundancy that can involve any factor with the potential to influence expression of the core factors, PPARα, PGC-1a, and DIO2.
    Frontiers in Endocrinology 10/2011; 2:64. DOI:10.3389/fendo.2011.00064
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    • "The third filter, requiring a gene expression difference in the parental lines of the BXD, may be conservative, because an absence of a difference in the parental lines doesn't necessarily preclude heritability in the BXD. Shockley and Churchill [38] found more gene expression differences between A/J:C57BL/6J consomic lines than between the A/J and C57BL/6J parental inbred strains. One interpretation is that A/J and C57BL/6J carry compensating (epistatic) increaser and decreaser alleles that are segregated in the consomic lines. "
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    ABSTRACT: Successful strategies for QTL gene identification benefit from combined experimental and bioinformatic approaches. Unique design aspects of the BXD recombinant inbred line mapping panel allow use of archived gene microarray expression data to filter likely from unlikely candidates. This prompted us to propose a simple five-filter protocol for candidate nomination. To filter more likely from less likely candidates, we required candidate genes near to the QTL to have mRNA abundance that correlated with the phenotype among the BXD lines as well as differed between the parental lines C57BL/6J and DBA/2J. We also required verification of mRNA abundance by an independent method, and finally we required either differences in protein levels or confirmed DNA sequence differences. QTL mapping of mouse forebrain weight in 34 BXD RI lines found significant association on chromosomes 1 and 11, with each C57BL/6J allele increasing weight by more than half a standard deviation. The intersection of gene lists that were within +/- 10 Mb of the strongest associated location, that had forebrain mRNA abundance correlated with forebrain weight among the BXD, and that had forebrain mRNA abundance differing between C57BL/6J and DBA/2J, produced two candidates, Tnni1 (troponin 1) and Asb3 (ankyrin repeat and SOCS box-containing protein 3). Quantitative RT-PCR confirmed the direction of an increased expression in C57BL/6J genotype over the DBA/2J genotype for both genes, a difference that translated to a 2-fold difference in Asb3 protein. Although Tnni1 protein differences could not be confirmed, a 273 bp indel polymorphism was discovered 1 Kb upstream of the transcription start site. Delivery of well supported candidate genes following a single quantitative trait locus mapping experiment is difficult. However, by combining available gene expression data with QTL mapping, we illustrated a five-filter protocol that nominated Asb3 and Tnni1 as candidates affecting increased mouse forebrain weight. We recommend our approach when (1) investigators are working with phenotypic differences between C57BL/6J and DBA/2J, and (2) gene expression data are available on that relate to the phenotype of interest. Under these circumstances, measurement of the phenotype in the BXD lines will likely also deliver excellent candidate genes.
    BMC Genomics 10/2008; 9(1):444. DOI:10.1186/1471-2164-9-444 · 3.99 Impact Factor
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    • "Genome-wide measurements have revealed high rates of genetic variation in gene expression (typically 410% of genes) in humans (Enard et al., 2002; Rockman and Wray, 2002; Bray et al., 2003; Lo et al., 2003; Whitney et al., 2003; Morley et al., 2004; Pastinen et al., 2004; Radich et al., 2004), mice (Cowles et al., 2002; Schadt et al., 2003; Shockley and Churchill, 2006), fish (Oleksiak et al., 2002, 2005), flies (Jin et al., 2001; Wayne and McIntyre, 2002; Meiklejohn et al., 2003; Rifkin et al., 2003; Nuzhdin et al., 2004; Ranz et al., 2004), yeast (Cavalieri et al., 2000; Brem et al., 2002; Townsend et al., 2003; Yvert et al., 2003; Fay et al., 2004), plants (Kirst et al., 2005; Vuylsteke et al., 2005; Lai et al., 2006) and bacteria (Le et al., 2005). Patterns of expression divergence have also been compared between sexes (Jin et al., 2001; Ranz et al., 2003; Gibson et al., 2004), across developmental stages (Rifkin et al., 2003), among tissue types (Enard et al., 2002; Whitehead and Crawford, 2005; Khaitovich et al., 2005a, b) and over different environments (Fay et al., 2004; Landry et al., 2006). "
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    ABSTRACT: Surveys of gene expression reveal extensive variability both within and between a wide range of species. Compelling cases have been made for adaptive changes in gene regulation, but the proportion of expression divergence attributable to natural selection remains unclear. Distinguishing adaptive changes driven by positive selection from neutral divergence resulting from mutation and genetic drift is critical for understanding the evolution of gene expression. Here, we review the various methods that have been used to test for signs of selection in genomic expression data. We also discuss properties of regulatory systems relevant to neutral models of gene expression. Despite some potential caveats, published studies provide considerable evidence for adaptive changes in gene expression. Future challenges for studies of regulatory evolution will be to quantify the frequency of adaptive changes, identify the genetic basis of expression divergence and associate changes in gene expression with specific organismal phenotypes.
    Heredity 03/2008; 100(2):191-9. DOI:10.1038/sj.hdy.6801000 · 3.81 Impact Factor
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