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Genetic Analysis of Variation in Neuron Number
Richelle Cutler Strom

Chapter  7: Discussion

I have mapped eight major effect QTLs that modulate neuron number in mice. These QTLs are the first loci known to control normal variation in cell number in the vertebrate CNS. Identifying the specific genes that these QTLs reside in awaits candidate gene analysis. However, even if the QTLs are not cloned in the near future, once the mouse and human genome is completely sequenced, the identification of QTLs mapped in mouse will help to assign the function of genes. Today, with the advent of expressed site sequence tag mapping, genes are being mapped at a breakneck speed. Thus, the bottleneck now is identifying gene function and QTL mapping will certainly aid in this task.

In chapter 5, I mapped a QTL, Nnc1, that is responsible for more than half of the genetic variance in ganglion cell number in mice and generates the pronounced bimodality found among strain averages (see Chapter 5, (Williams et al., 1998). Thyroid hormone receptor alpha is a superb candidate gene for Nnc1 and the reduced ganglion cell number in the Thra null mice provides further support for Thra’s influence on ganglion cell number. Further support for Thra can be acquired by first identifying the genetic variant at the Thra locus and determining whether the variant is associated with variation in ganglion cell number in other strains.

The genetic variants can be found by sequencing the gene from the high and low strains. Sequencing has become so efficient that entire intervals have been sequenced to identify a genetic mutation (Yu et al., 1996). Another way to locate the genetic variants is to run short segments of the gene on a neutral 5% polyacrylamide gel and look for shifts in the band migration between the DNA from high and low strains. This technique is called single strand conformation polymorphism (SSCP) and can detect even a single point mutation (Orita et al., 1988).

A candidate gene must be proved definitively. Proof for a candidate gene can be obtained if the quantitative phenotype changes in a transgenic animal expressing the allelic variant. In the same concept, gene candidates can also be tested by transferring the QTL interval onto another background strain with the generation of a congenic mouse strain (Smithies and Maeda, 1995). For polygenic traits, such as epilepsy, congenics have been used to refine the location of putative QTLs and measure QTL effects (Frankel, 1995).


Nature of the QTL

Substantial trait variation within a population in the wild result from the absence of selective pressures and indicates that the trait is not important for survival. However, the magnitude of the variation in brain weight and ganglion cell among inbred strains is probably not representative of the wild populations from which they were derived. During the inbreeding process inbred strains may have encountered new selection pressures that could have resulted in a non-random selection of alleles. Thus, the allele frequency among inbred strains may not represent the wild populations from which they were derived. Surprisingly, the genetic divergence found among the inbred strains averages 40%, which is larger than that expected from mixing the progenitor subspecies (Crusio, 1992). The wide divergence between strains could result from a selection of heterozygosity during the early generations of inbreeding and then the subsequent fixation of one allele (Fitch and Atchley, 1985). Alternatively, the large genetic variation among mice could result from the absence of selective pressures in the sustained life of the laboratory mouse, allowing over generations the accumulation of mutations that would be deleterious in the wild. However, it is worth emphasizing that an extensive variety of mice were sampled from a wide range of ages, both sexes, and taken from different litters and different mothers within strains. The environmental range that we have sampled is appreciable and is typical of most research colonies, perhaps even stable wild populations. Nevertheless, the specificity of the genetic variation should still be representative and the increased genetic diversity between inbred strains of mice is advantageous for the detection of quantitative trait loci with small effects.

The human brain size is roughly twice as big as that of a chimpanzee’s yet their genetic code differs by only 1.6%. How is it that this small genetic difference can account for the substantial morphological differences found between chimpanzees and humans? It is possible if the genetic variation effects the expression parameters of key genes during development, such as growth hormones, which have widespread growth effects (Slack and Ruvkun, 1997). Genetic variation within key genes could produce effects by changing their level or timing of gene expression. For example, a higher level of IGF-I expressed under the control of the metallothionine promoter in transgenic mice results in brains weighing 50% more and containing 21% more DNA compared to normal mice (D'ercole, 1993). Differences in the timing of gene expression can affect the timing of developmental events resulting in heterochronic differences between species. An example of heterochrony is the difference in duration of brain growth between chimpanzees and humans. Brain growth ceases at birth in chimpanzees, but continues to grow for another two years after birth in humans (Raff, 1996). Evidence that heterochrony exists in developing brain processes between species of Mus was found from like-genotype cells clustering in the brains of interspecies chimeric mice when they are typically intermixed within intraspecies chimeras (Goldowitz, 1989). There is no doubt that heterochrony plays a significant role in the generation of evolutionary differences.

Genetic variation has been found in two key regulatory genes that function in the somatropic growth axis. Size variants of growth hormone and insulin growth factor 2, are found to segregate in large and small body sized mice (Elliott et al., 1990; Winkelman and Hodgetts, 1992). How the genetic variants produce differences in body size are not known. Alternatively, some genes may function only to create quantitative variation by tweaking the efficacy of key genes. Many genes are redundant, since knocking out some genes result in no obvious phenotype. This genetic redundancy could serve to maintain quantitative variation in the species. A species with an abundance of quantitative variation would be better equipped to adapt through natural selection in the presence of environmental pressure. However, it remains to be shown whether these "disposable" genes actually contribute to genetic variation.

Recently, much progress has been made characterizing the molecular basis of natural variation in bristle number in Drosophila (Long, 1995). Five QTLs that associate with variation in bristle number have been mapped to Chr 3. Genes that map nearby and are known to be involved in bristle formation were selected as candidate genes. One of the candidate genes is Delta. Delta is a transmembrane protein involved in the lateral inhibition process of bristle formation. Delta serves as the ligand for Notch and can activate the Notch pathway and suppress the neurogenic fate. A lower level of Delta expression in a cell would down regulate Notch leading to a neurogenic cell fate such as the bristle, an external sensory organ of the peripheral nervous system. Two sites that associate with variation in abdominal and sternopleural bristle number have been identified in the introns of the candidate gene Delta. How the sequence variants in Delta’s introns change Delta’s function and modify bristle formation is not yet known. However, if the two sites in Delta are located within enhancer sequences, which are known to reside within introns, the expression level of Delta could be altered. The two genetic variants in Delta are individually responsible for 12% and 6% of the total genetic variation in bristle number on Chr 3. This large effect size demonstrates that natural quantitative variation in vertebrates can result from a small number of loci with large effects.

Macromutations in genes that affect neuron number can provide insight into the genetic control of neuron number. For example, in Drosophila a mutation called gene minibrain (mnb) results in a marked reduction in the optic lobes and central brain hemispheres. The mnb gene encodes a cell type-specific serine-threonine protein kinase involved in the regulation of cell division (Tejedor et al., 1995). The mouse and human homologs of mnb have been mapped to Chr 16 and 21, respectively (Shindoh et al., 1996). Transgenic mice carrying extra dosages of the gene mnb have learning deficits, implicating mnb as the critical gene that leads to the learning problems and smaller brain size characteristic of trisomy 21, or Down syndrome (Smith et al., 1997). Thus, the critical role mnb plays in cortical neurogenesis is conserved from Drosophila to humans. A mouse mutation called megencephly causes hypertrophy of the brain resulting in a 25% larger brain size (Donahue et al., 1996). This mutation was mapped to mid-distal Chr 6 in the mouse. It is not known how the megencephly mutation increases cell number or whether a human homolog is responsible for megencephaly-related syndromes in humans, e.g., Sotos syndrome, Robinow syndrome, Canavan's disease, and Alexander disease. Natural allelic variants in genes such as mnb and megencephaly could produce natural variation in brain weight.

The work presented here on the natural variation of neuron number in mice has provided a glimpse into the genetic bases of variation in neuron number. Our understanding of the genetic bases of natural variation in CNS structure both within and between species is still in its infancy.In light of the recent advances in mapping tools and genomic databases, the future holds much promise for rapid advances in our knowledge of the genetic bases of natural variation in the brain.


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