Genetic Analysis of Variation in Neuron Number

 

 

 

 

A Dissertation

Presented for

The Graduate Studies Council

The University of Tennessee, Memphis

 

In Partial Fulfillment

Of the requirements for the Degree

Doctor of Philosophy

From the University of Tennessee

 

 

By

Richelle Cutler Strom

December 1999

 

 

 

 

 

 

 

 

 

 

 

 

Copyright ©1999 Richelle Cutler Strom

All rights reserved

 

 

 

 

 

 

ACKNOWLEDGMENTS

I would like to thank my mentor, Dr. Robert Williams, for his guidance and patience. I would like to thank Dr. Dan Goldowitz for his teaching early in my graduate training (in the mouse room and laboratory), and also later for his assistance as a committee member. I also thank my committee members, Drs. Andrea Elberger, Karen Hasty, and Michael Dockter for their assistance. I would like to acknowledge the people who have assisted me in the technical aspects of my dissertation research. I especially thank Mrs. Xiyun Peng for her expert technical assistance with genotyping. I thank Kathy Troughton for her assistance in sectioning the optic nerves and for teaching me electron microscopy. I also like to thank Richard Cushing for his help with the histology. I would like to express my gratitude to my UT friends whom have made graduate school a recreative experience. These friends are Drs. Dennis Rice, Kristin Hamre, Mike Fowler, Guomin Zhou and Toya Kimble, Qing Tang, and Trisha Jensen.

I would like to thank the members of my family, especially my mother and father, who encouraged me to pursue this path from the start. Finally, I would like to express my sincere appreciation to my husband Jimmy for his support and understanding. He gave me the strength to "just do it".

 

 

 

Abstract

There are large differences in neuron number both within and between species. This variation in neuron number results from both non-genetic and genetic factors. Non-genetic factors, such as litter size and parity, and genetic cofactors, such as sex, age, and body weight, can influence neuron number. However, the cumulative variance in neuron number that can be explained by the summation of these factors is unknown. Genetic variation can explain a substantial portion of the variation in neuron number. However, the identity of the genetic factors and the manner in which they influence neuron number are not known. In this dissertation, I have analyzed the components of environmental and genetic variation contributing to variation in neuron number among inbred strains of mice. I have focused on variation in neuron number on a large scale, by using the surrogate measure of whole brain weight, and variation within a distinct neuron population, retinal ganglion cells.
     Brain weight ranges from 403 mg to 495 mg among standard inbred strains of mice. I have assessed the relative importance of environmental and genetic factors in the variation of brain weight in 27 standard inbred strains, two sets of recombinant inbred strains, and four F2 intercrosses. I first estimated the portion of variance in brain weight due to sex, age, body weight, litter size, and parity by regression analysis. Sex, age, parity, and body weight account for approximately half of the variance in brain weight, with body weight being the most important variable. However, the remaining variance in brain weight is substantial. Significant genetic variation was evident from the significantly lower variance in brain weight within strains compared to across strains, or within heterogeneous mice. Heritability of brain weight in the inbred strains is 0.50 . The broad platykurtic and nearly bimodal probability density distributions of two intercrosses indicate that genes with major effects on brain weight are segregating within these crosses. In addition, my estimates of the minimum number of genes modulating brain weight within the crosses ranged from one to six. The high heritability and low effective gene number indicate that it should be possible to map some of the genes modulating brain weight between strains. Genes that produce variation in a quantitative trait, such as brain weight, are called quantitative trait loci (QTLs).
     I mapped QTLs responsible for variation in brain weight using two recombinant inbred sets (BXD and AXB/BXA) and one F2 intercross (ABXDF2, n = 517. Using linkage analysis, with composite interval mapping, I detected four significant QTLs affecting brain weight on Chrs 7, 11, and 14. The brain weight QTLs have been named Brain size control 1, 2, 3, and 4 (Bsc1, 2, 3, & 4). Bsc1 maps to proximal Chr 11 at 12 cM and has a LOD score of 8.4. Bsc2 maps to distal Chr 7 at 65.2 cM and has a LOD score of 6.7. Bsc3and Bsc4 map to Chr 14 at 25 cM and 59.1 cM and have LOD scores of 6.8 and 5.9, respectively. Secondary brain weight QTLs were mapped to Chrs 1, 5, 8, 11, 14, 18, and X. In the ABXD5F2 cross, three QTLs, Bsc3 and Bsc4, and a secondary QTL on Chr 18, were each estimated to explain between 4% to 6% of the variance in brain weight. These three QTLs account for 60% of the total genetic variance and 15% of the total phenotypic variance in brain weight in the ABXDF2 cross.
     Retinal ganglion cell numbers range from 50,600 to 69,000 among standard inbred strains of mice. I examined the contribution of environmental and genetic factors to the variation in ganglion cell number among 17 standard inbred strains, 26 BXD recombinant inbred strains, and two F2 intercrosses. Variation in ganglion cell number was not correlated with age, sex, or body weight, within any of the inbred strains. However, ganglion cell number was significantly correlated with brain weight across strains and within heterogeneous mice, accounting for 20% to 30% of the variance in ganglion cell number. Genetic variation was evident from the significantly larger variance in ganglion cell number among strains compared to within strains. A bimodal probability distribution of ganglion cell number from 57 inbred strains, in addition to the estimate of one to three genes modulating ganglion cell number, indicates that there are genes with large effects on ganglion cell number. The heritability of retinal ganglion cell number in the standard inbred mouse strains is 0.48. These results indicate that it should be feasible to map some of the QTLs modulating ganglion cell number among inbred strains.
     I used the BXD recombinant inbred set and two F2 intercrosses (CCASF2, n = 112, 32CASF2, n = 140) in linkage analysis, with composite interval mapping, to map genes responsible for variation ganglion cell number. I mapped four significant QTLs affecting ganglion cell number to Chrs 1, 7, 11, and 16. The ganglion cell QTLs have been named Neuron number control 1, 2, 3, and 4 (Nnc1, 2, 3, & 4). Nnc1 maps to Chr 11 at 57 cM and has a LOD score of 6.7. Nnc2 maps to Chr 7 at 65 cM and has a LOD score of 5.9. Nnc3 maps to 82 cM on Chr 1 and has a LOD score of 9.3, and Nnc4 maps to Chr 16 at 41.5 cM and has a LOD score of 6.0. Thyroid hormone receptor alpha (Thra) was identified as a superb candidate gene for Nnc1. I tested Thra as a candidate by comparing the ganglion cell number in transgenic mice carrying a null transgene at the Thra locus with ganglion cell number from mice carrying a wild-type Thra. The mice with the null Thra transgene had significantly lower ganglion cell numbers compared to the wild-type mice. The result supports Thra as a candidate gene for Nnc1.
     Finally, I examined the developmental mechanisms responsible for the differences in ganglion cell among strains of mice. I estimated ganglion cell production in strains with high ganglion cell number and strains with low ganglion cell number by counting ganglion cells at postnatal day zero. Approximately 77% of the variation among adult strains result from differences in the production of ganglion cells. Thus, the variation in adult ganglion cell number among inbred mouse strains results predominantly from differences in cell production. Collectively, the results indicate that some of the Nnc1, 2, 3, & 4, QTLs are likely to modulate ganglion cell number by influencing cell production.
     In summary, I have demonstrated, 1) the proportion of variance in brain weight and ganglion cell number explained by genetic and non-genetic factors, 2) the location of QTLs producing variation in brain weight and ganglion cell number, and 3) the predominant mechanism generating variation in ganglion cell number is cell production. Finally, of significance, the mapping studies prove that it is possible to map the genes that are responsible for both global and discrete quantitative variation within the mouse brain.

Table of Contents

Chapter 1: Introduction


Forward genetic approach*
Exploiting natural genetic variation*
Dissertation research*
Importance of neuron number*
Developmental control of neuron number*

Chapter 2: Genetic and Environmental Control of Brain Weight Variation


Introduction*
Materials and methods*
Results*
Discussion*

Chapter 3: Mapping Genes Controlling Variation in Brain Weight Using Recombinant Inbred strains and F2 Intercross Progeny


Introduction*
Materials and methods*
Results*
Discussion*

Chapter 4: Genetic and Environmental Control of Retinal Ganglion Cell Variation


Introduction*
Materials and methods*
Results*
Discussion*

Chapter 5: Mapping Genes Controlling Variation in Retinal Ganglion Cell Number Using Recombinant Inbred Strains and F2 Intercross Progeny


Introduction*
Materials and methods*
Results*
Discussion*

Chapter 6: Developmental Mechanisms Controlling Retinal Ganglion Cell Variation Among Inbred Mouse Strains


Introduction*
Materials and methods*
Results*
Discussion*

Chapter 7: Discussion


Genetic Variation*
Summary*

References

Vita

List of Tables

 Table 2.1 Average brain weights for 27 standard inbred strains corrected for sex, age, and body weight.
Table 2.2 Brain weight for Mus species and subspecies corrected for sex, age and body weight.
Table 2.3 Average brain weight for F1 hybrids and their parental strains.
Table 2.4 Inbred strain multiple regression analysis for brain weight.
Table 2.5 CCASF2 multiple regression analysis for brain weight.
Table 2.6 32CASF2 multiple regression analysis for brain weight.
Table 2.7 ABXD5F2 multiple regression analysis for brain weight.
Table 2.8 AC3HF2 multiple regression analysis for brain weight.
Table 2.9 C3HAF2 multiple regression analysis for brain weight.
Table 2.10 BXD multiple regression analysis for brain weight.
Table 2.11 AXB multiple regression analysis for brain weight.
Table 2.12 BXA multiple regression analysis for brain weight.
Table 2.13 Heritability, brain and body weight correlation, and gene number for BXD, AXB/BXA, and F2 intercross mice.
Table 3.1 Corrected brain weight and body weight data for BXD strains and parentals, C57BL/6J and DBA/2J.
Table 3.2 Corrected brain weight and body weight data for AXB/BXA strains and parentals, A/J and C57BL/6J.
Table 3.3 BXD strain distribution pattern for brain weight and genotypes at five loci on Chr 11.
Table 4.1 Average ganglion cell number for 18 standard inbred strains.
Table 4.2 Average ganglion cell number in inbred representatives of wild strains.
Table 4.3 Average ganglion cell population in heterogenous mice.
Table 4.4 Average ganglion cell number in F1 hybrids and parental strains.
Table 5.1 Corrected retinal ganglion cell number for BXD strains and parents, C57BL/6J and DBA/2J.
Table 5.2 BXD strain distribution pattern for retinal ganglion cell number and genotypes at five loci on Chr 11.
Table 6.1 Ganglion cell number and percentage cell loss.

 

List of Figures

 Figure 2.1. Lineage chart of the genus Mus with fixed brain weights for strains, species, and subspecies of mice.
Figure 2.2. Probability density distribution of corrected brain weights for 27 inbred strains.
Figure 2.3. Probability density distributions of corrected brain weight for recombinant progeny and their parental strains.
Figure 2.4 Regression of ABXD5F2 brain weight and body weight.
Figure 2.5 Polynomial regression of ABXD5F2 brain weight and age.
Figure 2.6 Regression of ABXD5F2 brain weight and parity.
Figure 3.1 Microsatellite marker positions for ABXD5F2 intercross screening.
Figure 3.2 PCR amplified microsatellite polymorphic between A/J and BXD5 and separated by electrophoresis on a 3% agarose gel.
Figure 3.3 Permutation of BXD brain weight data for conditional genome-wide significance testing.
Figure 3.4 Linkage map demonstrates the QTL Bsc1 on Chr 11 in the BXD data set.
Figure 3.5 Linkage map demonstrates the QTL Bsc2 on Chr 7 in the AXB/BXA data set.
Figure 3.6 Linkage map demonstrates the QTLs Bsc3 and Bsc4 on Chr 14 in the ABXD5F2 data set.
Figure 4.1 Method for estimating ganglion cell numbers. Ganglion cell number for strains, species, and subspecies of mice.
Figure 4.2 Probability density of retinal ganglion cell number for 57 inbred strains.
Figure 4.3 Scatterplot of retinal ganglion cell number for CCASF2, CCASF1, and parental strains.
Figure 5.1Linkage map demonstrates the QTL Nnc1 on Chr 11 in the BXD data set.
Figure 5.2Linkage map demonstrates the QTL Nnc2 on Chr 14 in the BXD data set.
Figure 5.3Ganglion cell numbers for transgenic mice carrying homozygous null Nnc1 candidate genes (-1), heterozygous null (0) and wildtype genes (1).
Figure 5.4 Linkage map demonstrates the QTL Nnc3 on distal Chr 1 in the CCASF2 data set.
Figure 5.5 Linkage map demonstrates Nnc4 on Chr 16 in the 32CASTF2 data set.
Figure 6.1 Bimodal distribution of adult ganglion cell averages for 60 inbred strains.
Figure 6.2 Cross-section of a neonatal optic nerve.
Figure 6.3 Regression of P0 and adult ganglion cell number averages for ten strains.
Figure 6.4. Regression of numbers of cells that are lost (number at P0 minus the number at maturity) and adult ganglion cell number from our data.

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