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Keywords:

  • Chromosome substitution strain;
  • endophenotype;
  • home cage environment;
  • psychiatric disorder;
  • quantitative trait locus;
  • shelter preference

Abstract

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgment

Interspecies genetic analysis of neurobehavioral traits is critical for identifying neurobiological mechanisms underlying psychiatric disorders, and for developing models for translational research. Recently, after screening a chromosome substitution strain panel in an automated home cage environment, chromosomes 15 and 19 were identified in female mice for carrying genetic loci that contribute to increased avoidance behavior (sheltering preference). Furthermore, we showed that the quantitative trait locus (QTL) for baseline avoidance behavior on chromosome 15 is homologous with a human linkage region for bipolar disorder (8q24). Similarly, we now performed comparative analysis on the QTL for avoidance behavior found on chromosome 19 and correspondingly revealed an overlap of the mouse interval and human homologous region 10q23-24, which has been previously linked to bipolar disorders. By means of a comparative genetic strategy within the human homologous region, we describe an association for TLL2 with bipolar disorder using the genome-wide association study (GWAS) data set generated by the Wellcome Trust Case Control Consortium (WTCCC). On the basis of genetic homology and mood stabilizer sensitivity, our data indicate the intriguing possibility that mouse home cage avoidance behavior may translate to a common biochemical mechanisms underlying bipolar disorder susceptibility. These findings pave new roads for the identification of the molecular mechanisms and novel treatment possibilities for this psychiatric disorder, as well as for the validity of translational research of associated psychiatric endophenotypes.

Despite the fact that mood disorders rank among the top 10 causes of worldwide disability (Murray & Lopez 1996), no selective and effective etiology-directed treatment options have been developed. One concern is that the underlying pathophysiology of these disorders is largely unknown (de Mooij-van Malsen et al. 2008). By using interspecies trait genetics in combination with endophenotype approaches (Gould & Gottesman 2006; Kas et al. 2007; Kas & Van Ree 2004) susceptibility genes can be identified involved in these complex disorders that pin-point to underlying neurobiological mechanisms and to new treatment targeting strategies.

Recently, we tested a mouse chromosome substitution strain (CSS) panel, based on C57BL/6J as host and A/J as donor strain, in a designed home cage environment for increased avoidance behavior (de Mooij-van Malsen et al. 2009a). By using these strains, mouse chromosomes 15 and 19 (Fig. 1) were identified in female mice for carrying genetic loci that contribute to increased avoidance behavior, measured by the preference for sheltered feeding in an automated home cage environment (Kas et al. 2008). In a previous study, we showed that the quantitative trait locus (QTL) interval for baseline avoidance behavior on chromosome 15 is homologous with a human linkage region for bipolar disorder (8q24). Integrating the homologous mouse QTL-interval with genotypes of a large data set of bipolar patients and control subject revealed two associated genes of which one, adenylyl cyclase 8 (Adcy8) was differentially expressed in specific brain regions of mouse strains that differ in avoidance behavior levels. Interestingly, we showed significant differences in expression of Adcy8 in mouse brain regions involved in the regulation of emotion (de Mooij-van Malsen et al. 2009a). Furthermore, we showed that this mouse avoidance behavior was sensitive to mood stabilizer treatment, providing genetic homology and predictive validity for this mouse behavioral phenotype and bipolar disorders.

image

Figure 1. Distance moved and feeding location preference for CSS19 females. (a) Distance moved (horizontal activity level) during the 3 days of home cage environment testing for C57BL/6J and CSS19 females. (b) Feeding duration on the two different platforms during the 3 days of home cage environment testing. CSS19 females showed a significant preference for the sheltered platform on the second and third day of testing.

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On the basis of the current literature, mouse chromosome 19 has not often been implicated in anxiety-related behaviors. Possibly, as we have showed for avoidance behavior in the automated home cage environment, anxiety-related behaviors in mice may be gender specific which need to be taken into account during the study design and analysis. Furthermore, most behavioral tests used in these studies are short-lasting tests (5–60 min) and reflect novelty-induced behaviors, whereas the home cage environment is able to measure avoidance levels under baseline conditions and over various circadian cycles. Interestingly, Laarakker et al. (2008) found that CSS19 mice showed increased avoidance behavior on the modified holeboard. To determine the location of the QTL for avoidance behavior on chromosome 19, a CSS19-F2 population was generated and tested in the home cage environment and QTL analysis was performed. Here, we applied QTL mapping and the genetic information from the mouse QTL-interval was integrated with that from the homologous human linkage region for bipolar disorder. By means of a comparative genetic strategy within the homologous 10q23-24 region, we describe an association for TLL2 with bipolar disorder using the genome-wide association study (GWAS) data set generated by the Wellcome Trust Case Control Consortium (WTCCC; http://www.wtccc.org.uk/).

Methods

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgment

Mice and behavioral testing

For genetic mapping of the genetic locus on mouse chromosome 19, the C57BL/6J and C57BL/6J-Chr 19A/J/NaJ strains (Jackson Laboratory, Bar Harbor, ME, USA; abbreviated to CSS19), were used as progenitor strains. The F1 generation was derived by reciprocal mating of C57BL/6J and CSS19 animals. The F1-hybrids were inter-crossed producing an F2 progeny (n = 85 females, n = 96 males). Mice were group housed while not in the experiment. For 3 days automated monitoring of home cage avoidance behavior only for female F2 animals were studied, since for this phenotype male CSS19 mice did not deviate significantly from male C57BL/6J animals (de Mooij-van Malsen et al. 2009a). We automatically registered multiday behavioral observations in a designed home cage environment, assessing the animals' reduced preference for exposed areas (avoidance behavior) independent of motor activity levels under both novelty and baseline conditions (PhenoTyper® PT10S/P/N Version 1.01 combined with Ethovision®, Noldus Information Technology, Wageningen, the Netherlands). In the automated home cage environment the animals can choose to eat at two different feeding platforms, where they had ad libitum access to regular chow. At one feeding platform the animals could eat while exposed to the environment and at the other while sheltered. The preference for visiting the sheltered feeding platform (and therefore, avoiding the exposed feeding platform) is considered a measure for avoidance behavior. Behavior was continuously measured for 3 days. All experimental procedures were approved by the ethical committee for animal experimentation of the University Medical Center Utrecht, the Netherlands.

Genetic map construction

For the generation of a genetic map of mouse chromosome 19, 15 microsatellites were selected based on a haplotype difference between C57BL/6J and A/J of at least 8 bp (please note that in CSS19, except for chromosome 19, all other chromosomes will be homozygous for C57BL/6J genetic background). These markers were dispersed throughout mouse chromosome 19 (D19Mit59, D19Mit109, D19Mit61, D19Mit16, D19Mit106, D19Mit86, D19Mit46, D19Mit65, D19Mit119, D19Mit10, D19Mit123, D19Mit36, D19Mit1, D19Mit34 and D19Mit137). Genotyping, QTL and statistical analysis methods have been described previously (de Mooij-van Malsen et al. 2009b; Kas et al. 2009). (Cox et al. (2009) have constructed a revised genetic map of the mouse genome and demonstrated that utilization of the revised map improves QTL mapping. Therefore, marker positions were taken from this map by using the ‘mouse map converter’ (http://cgd.jax.org/mousemapconverter/).

Genome-wide association study

Genome-wide association study data was downloaded from the WTCCC with formal data access permission via the European Genotype Archive (EGA). Single nucleotide polymorphisms (SNPs) with missingness ≥1%, minor allele frequency (MAF) ≤1%, Hardy-Weinberg equilibrium (HWE) P-value ≤ 1 × 10−3 and P-value ≤ 1 × 10−5 in controls and cases (1868 cases vs. 2938 basic controls) were filtered, as previously described (WTCCC, 2007, #42733). For the selected mouse genes, the human location was determined with the University of California Santa Cruz genome browser liftover tool (http://genome.ucsc.edu/cgi-bin/), and P values of the GWAS for all SNPs located within the region were examined. Statistical test were performed using logistic regression analysis with case-control status and the dependent variable.

Results

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgment

For genetic mapping of the locus on mouse chromosome 19, the C57BL/6J and C57BL/6J-Chr 19A/J/NaJ strains (CSS19) were used as progenitor strains. CSS19 females showed a significant preference for the sheltered platform, without any differences in total activity [Fig. 1; for statistical analysis please refer to (de Mooij-van Malsen et al. 2009a)]. Even though consistent during the circadian cycle, these differences are predominantly found during the dark phase, as the animals hardly eat during the light phase. Female F2 mice were longitudinally screened for avoidance behavior in an automated home cage environment for 3 days (de Mooij-van Malsen et al. 2009a; Kas et al. 2008). Two QTLs were identified on mouse chromosome 19, located at 39.178.007–45.559.561 bp and 56.496.429–61.923.313 bp [−1.0 LOD support interval (Fig. 2a)], accounting for respectively 18.3% and 12.7% of the variance in the baseline avoidance behavior in the F2-population.

image

Figure 2. Quantitative trait locus analysis for the visit preference during baseline. (a) Genetic map [left; mouse chromosome 19; the revised Shifman map (Cox et al. 2009)] and LOD score plot (right) for the difference between the visit duration on the sheltered and exposed platform (‘delta’) on the third day of home cage environment testing in the (C57BL/6J × CSS19) F2 population (n = 85). The dashed vertical line represents the threshold value (1.72) of the LOD score considered significant for chromosome-wide linkage. (b) Association of TLL2 SNPs in the human GWA data set for bipolar disorder. Three SNPs, located within a single gene, were found to be nominally associated with bipolar disorder (P < 0.001).

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The QTL-interval at 39.2–45.6 Mb is homologous with a human genomic region (10q23-24) repeatedly identified in human linkage studies of bipolar disorder (Liu et al. 2003; Savitz et al. 2007). Using comparative genomics, the genes in the mouse QTL were tested in a human GWA data set for bipolar disorder. Three SNPs, located within a single gene, were found to be nominally associated with bipolar disorder (P < 0.001; Fig. 2b). By using the combined control data set, a significant association was found for TLL2 (rs2861579, P = 0.0016); and rs10786291, P = 0.002). To assess empirical levels of significance adjusting for multiple testing in the region, the PLINK MPERM procedure was performed on the 768 SNP region and the TLL2 SNP rs2861579 did not reached formal significance but remained suggestive (Pcorrected = 0.47).

Discussion

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgment

As Lewis and Tomlinson (2012) indicated, mouse models can be of great help in the localization and functional identification of genetic linkage regions found in human GWA studies. Here we show that the reversed methodology of starting with rodent behavioral genetic research can be just as valuable in defining regions of interest to be highlighted for more specific analysis in human psychiatric patients. By means of a comparative genetic strategy that was based on genetic mapping of mouse avoidance behavior, we identified a genetic locus on mouse chromosome 19 that is homologous to a linkage region for bipolar disorders in human. On the basis of a genetic association study using an extensive genotype data set of bipolar disorder cases and controls, an association between TLL2 within this homologous region was identified. This is, in addition to the previously reported association of ADCY8, we have now identified a second linkage region on the basis of genetic homology with mouse avoidance behavior and bipolar disorder in human subjects. In addition to the genetic homology, this mouse behavior is also altered by treatment with a mood stabilizer (de Mooij-van Malsen et al. 2009a). This raises the intriguing possibility that TLL2 and ADCY8 encode a behavioral endophenotype of bipolar disorder. Remarkably, both mouse regions were only found for female mice. Even though often debated, gender differences can be found in the prevalence of subtypes of bipolar spectrum disorder. The world health organizations' ‘World Mental Health survey initiative on bipolar spectrum disorders recently showed that: lifetime rates of bipolar-I and sub-threshold bipolar were greater in males than in females, whereas females had higher rates of bipolar-II than their male counterparts’. The WTCCC data set did not show any gender effects for our loci, but also combined cases with bipolar-I, bipolar-II and schizoaffective bipolar disorder. Whether or not the genes identified in our research are functionally involved in the susceptibility or development of one or more subtypes and/or endophenotypes remains to be established in future research.

The 10q23-24 region has previously been implicated in bipolar disorder by linkage analysis (Liu et al. 2003; Savitz et al. 2007), but TLL2 itself has not yet been specifically associated with the disorder. However, meta analysis of GWAS data recently performed by the Psychiatric Genetics Consortium (PGC; Major Depressive Disorder Working Group of the Psychiatric, GC 2013; http://www.broadinstitute.org/mpg/ricopili/), suggests association of TLL2 with major depressive disorder. This is very much in line with our mouse avoidance phenotype, as this mouse behavior may translate to the depressive features shown in both major depressive and bipolar depressive disorders. Further, a SNP close to the 3′ end of TLL2 has also been found to be associated with attention deficit hyperactivity disorder (ADHD) (Sharp et al. 2009). A high comorbidity exists between bipolar disorders, major depressive disorder (MDD) and ADHD in both adults and children. For example, rates of ADHD between 57% and 98% have been shown in childhood bipolar disorder patients (Wozniak et al. 1995a,1995b), not caused by shared symptoms (Milberger et al. 1995). As our behavioral measurements dissociate motor activity levels from avoidance behavior, TLL2 may be interesting for future research on the molecular background of the possible comorbid endophenotypes (e.g. attention vs. activity) involved in these psychiatric disorders.

On the basis of Gene Ontology (www.geneontology.org), TLL2 and ADCY8 are implicated in several cellular processes, including calcium ion binding and calcium-dependent adenylyl cyclase signaling, respectively. The association of calcium signaling molecules with mood disorders has also recently been reported at the genetic and cellular level (Ripke et al. 2011; Sklar et al. 2011), including association with the major L-type calcium channel α subunits found in the brain (Ferreira et al. 2008). Calcium signaling is also the target of several mood stabilizing drugs. As little is known about the underlying molecular mechanisms for these mood disorders, our findings could provide a better understanding of disease biology to facilitate early detection and etiology-directed treatment development.

References

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgment
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Acknowledgment

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgment

The authors declare that they have no competing financial interests.