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Note to the Reader The full text of a 1995 NSF grant submission that outlines our research objectives and methods. The focus is on mapping quantitative trait loci that affect CNS development.
Print Friendly Background. There has not yet been a concerted effort to
isolate genes that control neuron number (neurogenic or proneural genes)
in vertebrates. The main problems have been an insufficient density of
genetic markers and the amount of work required to get reliable data on
cell numbers from hundreds of animals. However, a number of important
technical advances now make it practical to map neurogenic genes. These
advances include (i) a quadrupling of the density of the genetic map of
the mouse, (ii) the ease of genotyping large numbers of marker loci
distributed across the entire mouse genome by the polymerase chain
reaction, (iii) a far more powerful conceptual framework for mapping
quantitative trait loci (QTLs) that underlie complex genetic traits, and
(iv) significantly more reliable and economical ways to estimate numbers
of neurons. Methods. This research focuses on four discrete neuron types
in the mouse CNS that are amenable to accurate and economical
quantitative analysis: retinal ganglion cells (large projection
neurons), photoreceptors, horizontal cells (smaller interneurons), and
neurons in the dorsal lateral geniculate nucleus. Accurate
stereological, electron microscopic, and immunocytochemical methods are
used to count these cell populations. The total population of single
cell types differ as much as twofold between inbred strains of mice. The
chromosomal regions that harbor genes that control this variation are
being identified using recombinant inbred strains and
intercross-backcross progeny. To map neurogenic candidate genes to
within ±5 cM, the DNA from panels of 150-300 progeny is typed by the
polymerase chain reaction and analyzed using the maximum likelihood
interval method. Developmental studies of inbred and recombinant inbred
strains are being carried out in parallel to determine whether these
genes control rates of neuron production or rates of survival. Significance. Genes that modulate absolute numbers of neurons
are key determinants of development, individual behavior, species
differences in brain structure, and rates of brain evolution. Isolating
these genes is a first and crucial step in discovering proteins that
normally regulate the proliferation and death of neurons early in
development. Understanding how allelic variants of these genes modulate
absolute numbers of neurons will ultimately provide much insight into
the molecular biology of neuron generation and death. The long-term aim of this proposal is to characterize genetic
mechanisms that underlie the remarkable variation in cell number in the
mammalian brain. In this proposal we request support for an analysis of
neuron number in the mouse visual system. Our immediate goals are to:
1. Measure the strength of genetic and environmental control on
variation in neuron number. We will study four well-defined and
interconnected cell populations in the mouse primary visual system
(photoreceptors, horizontal cells, retinal ganglion cells, and neurons
in the dorsal lateral geniculate nucleus). As part of this analysis we
will test the hypothesis that a relatively small number of genes have
large and specific effects on the size of discrete neuron populations.
2. Map these complex CNS traits. Quantitative trait loci (QTLs) will
be mapped using both recombinant inbred strains and the maximum
likelihood interval method of Lander and Botstein (1989) on intercross
progeny. Mapping genes is a first and crucial step in gaining access to
molecular mechanisms that control neuron number and brain size. 3. Determine whether the variation in neuron number is due to
differences in cell production or cell death. Large numbers of neurons
often die during normal development. It is important to determine what
processes neurogenic or proneural genes affect. This will be done by
studying the development of the same four cell populations in inbred and
recombinant inbred strains of mice. Variation in brain weight and neuron number. The weight of the
vertebrate brain varies enormously, from as little as 7 mg in
free-living deep-sea teleosts (Fine et al., 1987) to 7 kg in elephants.
This million-fold variation is associated with great differences in
neuron numbers. In mammals, the size of homologous populations vary at
least 5,000-fold and this variation contributes to the behavioral
diversity of this order (Williams
& Herrup 1988). There is also wide-ranging variation within single
species. Numbers of cells in the trigeminal mesenchephalic nucleus of
Xenopus ranges from 185 to 284 (Kollros & Thiesse 1985). Numbers of
neurons in the dorsal lateral geniculate nucleus of rhesus monkeys range
from 1.0 to 1.6 million (Williams
& Rakic 1988a). Variation is less marked but equally pervasive in
the nervous systems of invertebrates (Goodman 1979, Macagno 1980,
Stewart et al. 1986). The variation in neuron populations among members
of a species provides one of the most important foundations for
evolutionary change in brain structure (Armstrong & Bergeron 1985,
Finlay 1992). Genetic factors control neuron number. A significant fraction
of the variation among members of a single species is caused by
environmental and developmental effects. A case in point is the
variation in numbers of neurons among isogenic grasshoppers (Goodman
1976). Variation of this type is not heritable and does not contribute
to evolutionary change. In contrast, a large, still uncharted component
of variation has a genetic basis. Given the complexity of neuron
production and death (McConnell 1991, Oppenheim 1991), products of
numerous genes—perhaps hundreds—must have specific effects on specific
neuronal populations. Products of other genes, for example, hormones and
trophic factors, may have global or pleiotropic effects (Berg 1982,
Garcia et al. 1992). Mapping genes that control natural variation. One approach to
the problem of the genetic control of neuron number exploits variation
among inbred mice. During the process of inbreeding heterozygous loci
are forced into homozygosity. Some strains become homozygous for one of
the original alleles, other strains for other alleles. The often large
phenotypic variation in quantitative traits among inbred strains is
principally due to additive genetic factors (Falconer 1981). These
inbred strains are therefore superb tools with which to isolate genes
that underlie complex phenotypic traits. The work of Wimer and
colleagues (1969, 1976) is a good example of how to apply this approach
to the genetics of the mammalian CNS. They were able to demonstrate
large strain differences in forebrain structure and were also able to
estimate the genetic determination of these differences. However, their
work was constrained by the problem of estimating cell number in the
hippocampus and cortex of large numbers of mice. Perhaps for this reason
they did not estimate numbers of effective gene loci controlling
variation in CNS structure. Furthermore, until the development of large
sets of recombinant inbred strains by Taylor in the 1970s and 1980s
(Taylor 1976, 1989) there was also no effective way to map complex
quantitative traits (Klein 1978). The situation is now much improved.
Quantitative genetics has moved way beyond estimates of heritability,
dominance, and epistasis. Complex quantitative traits can now be mapped
using recombinant inbred strains (Plomin et al. 1991, Belknap et al.
1992ab) and the maximum likelihood interval method (Lander & Botstein
1989). The genetic dissection of complex traits—of which neuron number
is an excellent example—has only begun in the last few years using these
new and powerful methods (Lander & Schork 1994). Number of effective gene loci. If large numbers of genes
collectively control variation in many different parts of the nervous
system, then allelic differences at any one locus will be associated
with minimal differences in phenotype. This is referred to as the
infinitesimal model of polygenic action (Lai et al. 1994). In contrast,
if a small number of genes control the variation, then individual
quantitative trait loci (QTLs) will often have large effects. Such
neurogenic and proneural QTLs can in principle be mapped. Recent work on
the control of bristle number in Drosophila provides reason for
optimism. Mackay and Langley (1990) and Lai and colleagues (1994) have
shown that normal allelic variants at the achaete-scute complex and at
the scabrous locus each account for 5 to 10% of the natural variation in
numbers of these sensory organs. We have begun a comparable analysis of
variation in neuron numbers among inbred strains of mice. Our results
demonstrate that small numbers of genes control a large fraction of the
strain variation in ganglion cell number. Additive genetic factors
account for about 40% of the variation, and as much as half of this
variation appears to be controlled by one, or possibly two QTLs (see
Preliminary Results). An analysis of four related neuron populations. We propose to
analyze four well-defined populations of neurons at different levels of
the primary visual system: photoreceptors, horizontal cells, retinal
ganglion cells, and neurons in the dorsal lateral geniculate nucleus. We
recently finished a system-level comparison of cell populations in the
retina and dorsal lateral geniculate nucleus of two species of cat that
provides an example of the power of a system-level comparative analysis
(Williams et al. 1993). By carrying equally detailed studies of inbred
mice and test cross progeny derived from strains with high and low
neuron number, we will be able to (i) measure the contributions that
genetic factors make to variation in neuron number, (ii) map QTLs that
underlie these complex polygenic traits, (iii) measure the phenotypic
and genetic correlation between neuron populations, and (iv) determine
whether neurogenic QTLs affect the proliferation or death of neurons. In
this application we use the term neurogenic quite broadly to describe
loci that have an effect on final neuron populations. Such genes can
control either the production or survival of neurons. These genes could
be expressed in neurons, glia, or even in cells in non-neural organs
such as liver or placenta. Trophic interactions, cell death, and control of neuron number.
One important idea in developmental neuroscience is that sizes of
neuronal populations are carefully matched by trophic interactions (Berg
1982, Williams & Herrup 1988, Purves 1988). A major role assigned to
cell death is to bring interconnected populations into better
quantitative alignment. While there is much evidence that favors this
hypothesis, there are large gaps in what we know about this process. For
example, accurate data on the normal range of variation in ratios of
cells are to our knowledge not yet available for a single pair of
interconnected neuron populations. This is one of the reasons that we
have chosen to study interconnected populations belonging to a single
system. Photoreceptors are connected intimately with horizontal cells,
and retinal ganglion cells with neurons in the dorsal lateral geniculate
nucleus. With this design we can measure correlation between numbers of
neurons. We will also be able to test whether common genetic mechanisms
control the production of these interconnected neurons, or alternatively
whether correlations in numbers emerge only after a period of
quantitative adjustment achieved via cell death. Recent work in both the
cat (Williams
et al. 1993) and several rodent species (Finlay & Pallas 1989) has
shown that there can be large variations in ratios and absolute numbers
of neurons even within the visual system of closely related species or
strains. For example, we have found that strains C57BL/6J and A/J have
equal numbers of ganglion cells, but C57BL/6J has twice the number of
horizontal cells. Consequently, we can test the hypothesis that the four
populations are controlled, at least in part, by unique QTLs. Project history and long-term strategy. For the past ten years
both of us (RW and DG) have been interested in the control of neuron
number (Chalupa et al. 1984, Williams et al. 1986, Williams & Rakic
1988a, Hecktroth et al. 1988, Goldowitz 1989, Smeyne & Goldowitz 1990,
Williams & Goldowitz 1992ab, Williams et al. 1993, Rice et al. 1995,
Scheetz et al. 1995). Much of this work examined effects of experimental
manipulations on cell number—increased availability of target tissue,
decreased neuronal activity, heterochronic shifts in neuron production,
and effects of specific mutations. Several years ago we began to collect
data on retinal ganglion cell number from inbred and outcrossed strains
of mice as a prelude to making chimeras between strains with large
phenotypic differences (Williams & Goldowitz 1992a). Because of the
rapid refinement of the genetic map of the mouse, combined with the ease
with which polymorphic microsatellites can now be typed, such data can
now be put to another and potentially more interesting use—namely,
identifying, mapping, and ultimately characterizing neurogenic QTLs. Mapping neurogenic genes is an important first step in characterizing
the genetic basis of neuron proliferation, survival, and death. A
daunting second step is to then identify and characterize candidate
genes. In this application we do not propose any positional cloning. Our
research strengths are in neuroscience, and it therefore makes sense for
us to (i) measure strain differences, (ii) estimate numbers of effective
gene loci, (iii) map of QTLs to ±5 cM, and (iv) study the development of
strain variation. Our rationale is that if we succeed in generating 4-8
QTLs, this will provide a reasonable size pool for a candidate gene
approach in subsequent years. It may then also be worthwhile to generate
recombinant congenic or consomic strains to isolate effects of single
QTLs on nearly isogenic backgrounds (Moen et al 1991, Groot et al 1992).
We will also assess the quality of the physical map in the mouse in
regions in which QTLs have been mapped and consider whether any of the
QTLs are amenable to higher resolution mapping and positional cloning.
In the long-term we may also continue to define and map additional
neurogenic QTLs that affect other cell populations in other parts of the
nervous system. It may be possible to define classes or systems of
neurons influenced by common QTLs. Other long-term directions include
cloning and characterizing specific neurogenic genes. This type of
analysis might lead to the production of mice in which candidate
neurogenic genes have been manipulated by transgenic experiments or
eliminated by homologous recombination. At the current pace of progress,
it is hard to predict what the best research directions will be in four
years. Large segments of the mouse genome will have been physically
mapped within this period, and within ten years a great deal of the
mouse and human genomes will have been sequenced. We can expect to have
a high resolution genetic map of essentially all murine genes within a
decade. Some of the studies we are now beginning were not practical even
two years ago because of the paucity of PCR-typable marker loci in
mouse. The approach that we are using has not yet been exploited to map
neurogenic genes in any vertebrate. The findings that are summarize in
this section are intended to serve as a proof of concept. We have now
studied the ganglion cell population in 40 different strains, including
(i) standard inbred mice, (ii) wild inbred mice, (iii) outcrossed and
randomly bred mice, (iv) F1, F2, and N2 hybrid mice, and (v) recombinant
inbred mice. We have quantified more than 285 cases by quantitative
electron microscopy; 155 in the last four months alone. This is the
largest systematic study of a neuron population in mouse, and possibly,
in any vertebrate. This information is immediately useful in estimating
the magnitude of change that might be achieved by means of selection and
in estimating the similarity between relatives. In the context of this
application, this work also provides a firm basis for the search for,
and mapping of, neurogenic QTLs. In the next several pages we present a
synopsis of our work on the ganglion cell population as an demonstration
of what we expect to accomplish for each of the other three cell
populations. Work in progress under other support. At the earliest stages
(1991-1993) a part of this work was supported by a grant from the NIH to
study the clonal structure of the mouse retina (RO1-EY08868, now
unfunded). At that time we were trying to identify strains of mice with
large differences in cell number to exploit in generating aggregation
chimeras (Williams & Goldowitz 1992a, Rice et al 1995). This work is now
supported by a small intramural grant from the University of Tennessee
($9,600 for one year). Three papers on the genetic analysis of the ganglion cell population
are in preparation or submission. The first deals with the magnitude of
natural variation in neuron number and provides estimates of broad- and
narrow-sense heritability. The second paper describes work in which we
have estimated gene dominance, maternal effect, and the number of
effective gene loci controlling ganglion cell number. The final paper
will describe mapping data from the BXD recombinant inbred strains. We are adding new information and cases daily. The most current
dataset and manuscripts are accessible for the reviewers of this
application on Internet using Mosaic, Netscape, or other World-Wide Web
readers at the URL address http://www.nervenet.org/main/maincontrol.html.
The main conclusions of this work can be summarized as follows: Each of the neuron populations will be studied in stages, moving from
an analysis of strain variation, through mapping, and concluding with
developmental studies. Stage 1. The first stage involves measuring the variation
within and between different strains of inbred and outcrossed mice. The
immediate purpose is to determine the degree to which additive genetic
factors modulate the size of the target cell population (ganglion cells,
horizontal cells, photoreceptors, and neurons in the lateral geniculate
nucleus). We have completed this analysis for one population of
neurons—retinal ganglion cells. More than 280 individuals belonging to
40 strains of mice have been studied, including standard inbred,
recombinant inbred, wild inbred, F1, F2, and N2 hybrids, and several
different types of genetically heterogeneous strains. This is the
largest quantitative dataset yet produced for any neuron population in
any vertebrate. Comments. Variation among members of the genus Mus. We have
discussed variation as a tool that gives us access to neurogenic loci.
But variation in neuron number, whether environmental or genetic in
cause, is an interesting and important topic in its own right,
particularly in exploring the relationship between neuronal populations
and behavioral capacity. An obvious question is whether mice with
different numbers of ganglion cells have differences in visual acuity.
Does the twofold difference in horizontal cell number between A/J and
C57BL/6J affect adaptation or the center-surround structure of receptive
fields? We hope that our careful quantification of different neuronal
populations will catalyze studies of strain differences in visual
performance. We will measure variation within and among 15 or more genetically
well characterized and common inbred strains of mice (129/J, A/J, AKR/J,
BALB/c, C3H/HeJ, C57BL/6J, C57BL/KsJ, CBA/CaJ, CE/J, DBA/2J, LP/J, NZB/BinJ,
NZW/LacJ, PL/J, and SJL/J). All strains have been inbred by successive
brother-to-sister matings for more than 80 generations and are
consequently homozygous at essentially all loci. These strains were
initially selected by us without regard to any known CNS or ocular
characteristics, even the presence or absence of known retinal
mutations, such as rd (LaVail & Mullen 1976). Provided that the sample
of strains is sufficiently large, then differences among inbred strains
provides an unbiased estimate of additive genetic control (Hegmann and
Possidente, 1981).Of the 15 strains, 7 strains (A/J, AKR/J, BALB/c, C3H/HeJ,
C57BL/6J, DBA/2J, LP/J) have been genotyped by Dietrich and colleagues
(1994) at more than 5250 polymorphic microsatellite loci. Heritability estimates. Complementary methods will be used to
calculate heritability; that is, the strength of genetic determination
of the phenotypic variation. This analysis will allow us to decide how
successful a genetic dissection of phenotypic variation is likely to be.
We will use Hegmann and Possidente's method (1981) to estimate the
additive genetic component from standard inbred mice alone. This method
assumes that the variance present among a set of fully inbred mice is
approximately twice that in the population that gave rise to the set of
inbred strains. We will also use methods described in Falconer (1981)
and Crusio (1992) and compare genetically uniform and genetically
heterogeneous populations of mice. The genetically uniform mice used for
this calculation are either F1 hybrids or standard inbred strains. In
estimating heritability of ganglion cell number we correct for scaling
problems caused by the use of standard deviations. Heritability is
computed using the coefficient of variation. Evolution, fitness, variation, and why studying the mouse visual
system is a good idea. It is often stated that phenotypic traits
that are highly variable within a single population contribute less to
fitness than those traits that are tightly regulated (Mayr 1970). The
rationale is that selective pressures will tend to trim away the
extremes thereby minimizing genetic load. Conversely, the retention of
high phenotypic variability implies reduced selection of a
characteristic. There are obvious exceptions, including sexual
dimorphism and within-sex bimodalities associated with niche selection
(GC Williams 1992). But in general, an analysis of variation in a
natural population can provide insight into the degree of selection that
acts on a quantitative trait. Traits that are important in fitness also
tend to have high levels of directional dominance and low heritabilities
in response to selection (Hahn & Haber 1978). In contrast, traits that
are relatively free of selection display more additive genetic variation
and comparatively high heritability. Another way of thinking about this
is that genetic load and multiple alleles are not as well tolerated or
maintained in those system most exposed to selection. The reason this
may be worth mentioning in the context of this application is as a
counterpoint to the criticism that the mouse is not noted for its high
visual acuity and that this mammal is therefore an unwise choice. Mice
are nocturnal and rely more heavily on other sensory modalities to get
around. However, because high levels of variation and the retention of
high levels of polymorphisms are useful, if not essential, for mapping
QTLs, the low level of selection on visual system performance in the
mouse actually is of considerable advantage in mapping the set of genes
that influence neuron populations in this system. Genes that control
numbers of neurons in the mouse visual system may display higher than
average levels of polymorphism than a more visual competent mammalian
species. Stage 2. Our purpose is to estimate the number of effective
QTLs that control variation between strains. This involves counting
neuron populations in F1 and F2 progeny generated between high and low
cell number strains. Comments. The ability to map a QTL is critically dependent
upon the amount of phenotypic variance that it controls. There is no a
piori reason why quantitative traits must be polygenic, and even if
traits are controlled by many genes, if one or two of the QTLs account
for a disproportionately large share of the phenotypic variance, then
these QTLs should be mappable. Several methods are available to determine the probability that a
given complex quantitative trait can be broken into sufficiently large
mappable pieces. We have used three methods in our study of the ganglion
cell population. First, we have studied the phenotypes of a large and
randomly selected group of inbred strains of mice. Discontinuities in
the normal probability plots of strain averages indicate single QTL
effects (Fentress 1992, Belknap et al. 1992b). A second method involves
crosses between high and low strains. A comparison between variance in
F1 and F2 progeny can be used to estimate minimum numbers of effective
gene loci (Wright 1978, Falconer, 1981). If the variance in the F2 is
great relative to that in the F1, then this suggests that the variation
is controlled by a few genes with large effects. Wright's equation for
the number of effective gene loci is generally biased in a direction
that gives higher, rather than lower estimates of the number of
effective loci (Wright 1978). The third method involves phenotyping
recombinant inbred strains. If the variance of the quantitative
parameter is low or quantitative single gene effects are large, then it
is in principle possible to detect phenotypes corresponding to a limited
number of independent major QTLs. Stage 3. The third stage is mapping neurogenic QTLs. Two
complementary methods will be used. The first method depends on the use
of recombinant inbred (RI) strains of mice (Bailey 1981). The second
method is molecular linkage analysis. This method involves typing
intercross progeny between high and low strains. In many cases we will
exploit the large genotypic and phenotypic differences between CAST/Ei
(a wild inbred strain) and standard inbred strains, such as BALB/c, DBA/2J,
and C57BL/6J. We have begun this analysis for the ganglion cell
population. Comments. Recombinant inbred strains. A major use of
recombinant inbred strains is in mapping quantitative trait loci (Klein
1978, Plomin et al. 1991, Nesbitt 1992, Neumann 1992, Belknap et al.
1992ab, 1993, Plomin & McClearn 1993). There are two significant
advantages of RI strains for mapping: (i) one can obtain an estimate of
average phenotype associated with a fixed recombinant genotype, and (ii)
one does not have to genotype animals, because the RI strains have
already been typed for many hundreds of loci. To perform this type of analysis, mice from each of the RI strains is
phenotyped for neuron number. We intend to use adults of both sexes. One
of the largest set of RI lines was derived from a single cross between a
C57BL/6 female and a DBA male, and is referred to as the BXD RI strain
set. For our purposes this RI line is ideal because of the large
genotypic and phenotypic differences between parental strains (Taylor
1972). Twenty six isogenic recombinant strains from this mating can now
be purchased from Jackson Laboratory. A group of animals from each line
of BXD mice is typed (i.e., ganglion cell axons are counted), and the
average phenotype is compared to that of the parental strains (high,
low, or intermediate). Over 1300 gene loci have now been typed and
mapped in the BXD lines (Williams 1994). Consequently, the combination
of phenotypes with the different RI lines can be read like a code. By
matching the neuron number data with pre-existing data for mapped loci,
it is usually possible to localize a gene to within 5-10 cM. While the RI strains are commonly used to map Mendelian traits, they
can also be used to map QTLs (Plomin 1994, Manly 1994). The correlation
between a set of previously typed genes and the quantitative trait is
computed. Each of the parental alleles of previously typed genes is
assigned a value of 0 or 1. This is similar to the analysis performed on
a standard test cross progeny in mapping a QTL, except that no
additional genotyping is required. The calculations are performed by the
program Map Manager QTL (Manly 1994), but can also be done directly in a
spreadsheet program such as Excel. The result is a map of the genome in
which chromosomal segments are assigned correlations, and lod scores are
assigned by the probability of there being a linkage between the QTL and
allele distribution patterns (Figures 2, 3). The power of RI analysis depends upon the number of RI strains that
are typed. For example we have typed 11 of 26 BXD RI lines and the lod
scores for putative ganglion cell QTLs do not yet exceed 2.8. Given the
large numbers of comparisons that underlie this analysis, a lod score of
more than 3, and preferably of up to 4, is needed to confirm a linkage
(Lander & Schork 1994). By completing the analysis of the entire BXD
series and by adding additional cases to the BXD strains that we have
already begun to type, we expect to be able to define at least one QTL
within a short period of time. Linkage using polymorphic microsatellite loci. A linkage
analysis involves finding the set of alleles at polymorphic loci that
tend to segregate with a particular phenotype (high or low cell number)
in intercross or backcross progeny. To map the ganglion cell number QTLs,
inbred BALB/c mice (a high strain) are mated to inbred CAST/Ei mice (a
low strain) to produce F1 hybrids. These hybrids are intercrossed to
produce F2 in which the CAST/Ei and BALB/c alleles assort and recombine.
Cells are counted in the individual F2 progeny when these mice have
reached maturity (30-40 days of age). We then look for an association or
correlation between CAST/Ei alleles at microsatellite loci and a low
cell number phenotype. Conversely, we look for an association between
BALB/c alleles and a high cell number phenotype. The stregth of these
correlations is assessed by computing lod scores. Backcross versus intercross progeny. To map recessive
Mendelian mutations in the mouse the standard operating procedure is to
type backcross progeny. However, in mapping a QTL it is advantageous to
generate F2 progeny, because both parents are hybrids and there will be
approximately twice as many recombinants between parental alleles. This
is an especially important factor when it is expensive or difficult to
phenotype animals. A drawback of genotyping F2 progeny is greater
complexity of PCR products generated at microsatellite loci. There are
three combinations of alleles that need to be distinguished on single
lanes of the gels. For example, in a CAST/Ei by BALB/c intercross, these
combinations are C/C, B/C, and B/B. In practice, it is sometimes
difficult to genotype particular combinations of products—what are
called dominant molecular markers—and it is therefore necessary to
introduce genotype categories such as "not C/C" and "not B/B". There are
currently two QTL programs available—MAPMAKER (Lander et al 1987) and
Map Manager QTL (Manly 1994)—both of which handle dominant marker
genotype data. Another difference between backcross and intercross
progeny concerns the complexity of potential epistatic interactions. In
a backcross there are only three combinations of alleles at two unlinked
loci (C/C-C/C, C/C-B/C, and C/C-B/B). However, in an F2 intercross there
are nine allele combinations, and if one intends to test a particular
two locus model then there are advantages in testing the less
heterogeneous N2 progeny. Given the complex epistatic interactions
between neurogenic loci in Drosophila (Campos-Ortega & Knust 1990) this
is a factor that we have to consider seriously. But for the time-being
we have decided that the increase in meioses and the associated increase
in map resolution in an F2 panel outweigh other factors. The QTL interval mapping method takes advantage of the large number
of microsatellite DNA markers (6,000) that are being generated by Bill
Dietrich, Eric Lander, and collegues (see Dietrich et al 1992).
Detection of polymorphisms is comparatively easy, involving PCR, gel
electrophoresis, and ethidium bromide staining. The Portable Dictionary
of the Mouse Genome (Williams 1994, see the Dictionary server on the
World-Wide Web at the URL address http://www.nervenet.org/) will be used
to select appropriate MIT microsatellite loci to type. cDNA strategies in relationship to mapping neurogenic QTLs.
Substractive hybridization cDNA methods have recently been exploited to
define sets of genes expressed in the nervous system preferentially
during early stages of brain development. These genes include the 10
Nedd genes, Ndpp1, and Ndn (Kumar et al. 1992, Aizawa et al. 1992,
Sazuka et al. 1992). The spatio-temporal expression of several of these
genes has been characterized, but their functional signficance is
unknown. A cDNA-based approach defines members of the large set of genes
expressed during brain develoment. Assessing which of the hundreds or
thousands of developmentally expressed neural genes is worth pursuing
will be difficult. However, given sufficient map data on neurogenic QTLs
known to modulate the size of neuronal populations, it will be possible
to rationally select small subsets of candidate cDNAs for more detailed
study. Candidate genes for QTLs. Neurogenic genes in Drosophila, such
as Notch, Delta, almondex, vnd, and daughterless, control the relative
proportion of neuroectodermal precursors that migrate into the embyro to
become neuroblasts (Campos-Ortega & Knust 1990). Members of the
achaete-scute complex (T1a, T2-T5) control the relative abundance of
different classes of neurons. Homologs of several of these Drosophila
neurogenic genes have been cloned in mouse including two unmapped
achaete-scute homologs, Mash1 and Mash2 (Franco del Amo et al 1993), and
two Notch homologs,Notch1 and Notch2 (Guillemot & Joyner 1993). Both
Notch1 and Mash1 are expressed in the ventricular zone early in murine
develoment. Mash1 expression begins as early as E10.5 in the brain and
E12.5 in the retina. Its expression is restricted to neural tissues,
both in the peripheral and central nervous systems. In contrast, Notch1
expression is much more widespread and transcripts can be detected in
all three germ layers (Lardelli & Lendahl 1993). The role of these
murine homologs is still unknown. As we map QTLs we will review the set
of nearby genes, most especially homologs of known neurogenic and cell
death genes (Bcl2 and homologs of nematode ced genes; Ellis & Horvitz
1986), helix-loop-helix transcription factors, kinases such as Trkb,
etc., that are known to map within ±10 cM. Stage 4. The final stage involves an analysis of neuron number
during development in selected RI and inbred strains. The underlying
question is how do QTLs affect neuron number? Do they modulate
proliferation or cell death? Comment. In the case of the ganglion cell population we will
count the number of axons in the optic nerves of high and low strains
during development. Most counts will initially be done on neonates. This
stage of development precedes any appreciable ganglion cell loss (MA
Williams et al 1990). If differences between high and low strains are
already detected at P0, then this provides evidence that the gene acts
on the kinetics of ganglion cell production. However, if no differences
are noted at birth, then this supports the hypothesis that cell death is
responsible for the strain differences. Retinal ganglion cells and geniculate neurons are both known to
undergo massive cell loss in the mouse and other mammals (MA Williams et
al. 1990, Williams & Rakic 1988a). Both populations will be studied at
birth using previously described methods (Williams et al. 1986, Williams
& Rakic 1988ab). Photoreceptors are not subject to significant cell
death (Young 1984, Goldowitz & Williams, in progress) and therefore a
developmental analysis is not planned. It is not yet known whether
horizontal cells are subject to neuron loss during development (cf.
Robinson 1988). A major advance that makes this research possible is the development
of rapid, accurate, and economical methods to count populations of
neurons. While estimating numbers of neurons in a progeny panel of
200-500 animals is still a daunting task, we now know that this is
practical over a 3-4 year period. In the first part of this Methods
section we review four key counting methods: quantitative surveys of the
optic nerve, wholemounts analysis of immunohistochemically defined
cells, wholemount analysis of photoreceptors, and direct 3-dimensional
counting of neurons in the LGN. We have used all of these methods
extensively in the past several years. Synopsis of EM method. To estimate ganglion cell number, 20 to
25 evenly spaced electron micrographs of the optic nerve are taken at
X10,000 to X20,000. A low power micrograph (X200) of the entire
ultrathin section is taken to determine the total nerve cross-sectional
area. Axons are counted directly on each negative. The density of axons
in this sample is multiplied by the cross-sectional area of the nerve.
Three lines of evidence indicate that there is a 1-to-1 numerical
relation between retinal ganglion cells and optic axons: (i) numbers of
retrogradely labeled ganglion cells closely match those of optic axons
(Rice et al 1995), (ii) reconstructions of single axons indicated that
they rarely if ever branch in the optic nerve, even early in development
(Williams & Rakic, 1985), and (iii) counts of axons at the proximal and
distal ends of the optic nerve do not differ significantly (Lia et al.
1984, Robinson et al. 1987). Fixation and processing of EM tissue. Mice are anesthetized
with an intraperitoneal injection of Avertin and are perfused
transcardially with 0.9% phosphate buffered saline and fixative using a
small peristaltic pump. The electron microscopy fixative is injected for
2 minutes (5 ml of 1.25% glutaraldehyde and 1.0% paraformaldehyde in
0.125 M phosphate buffer). An additional 5 ml of second double-strength
fixative is injected for another 2 minutes. The head is removed and
placed in the stronger fixative overnight at 4°C. Optic nerves are
subsequently osmicated, stained with uranyl acetate, dehydrated, and
embedded in Spurr's resin. Thin sections of the nerve are placed on slot
grids and are photographed at a primary magnification of x10,000 to
x20,000 on a JEOL 2000 microscope. Brains from these cases will be
sectioned on a sledge microtome to analyze cells in the LGN (see below).
Accuracy of EM method. Three main factors influence the
accuracy of electron microscopic estimates. The first factor is the
quality of the images used for counting. If the tissue is poorly
preserved then counts will underestimate the population because small
unmyelinated fibers are not resolved. We have studied the correlation
between the quality of tissue and the z score of the count (z scores
were calculated within strains) for a dataset consisting of 250 cases.
Average, good, and excellent cases have z scores of -0.14, -0.03, and
+0.05, respectively. The difference in the average z scores is trivial
(less than a 750 neurons per estimate). Poorly fixed tissue typically
yield low counts (average z = -0.5) because small unmyelinated fibers
cannot be counted reliably. Such tissue is discarded. The second factor is the accuracy with which the image magnification
is calibrated. At each sampling session a waffle-type calibration grid
is photographed at the same magnification used to sample the nerve.
These calibration images are used to compute sample area to within 2%
accuracy. The accuracy of the calibration grid has been verified by
comparing different calibration grids and by determining axon number
using a ratiometric method that requires no information on absolute
sample areas. The third factor is the adequacy of sampling. The greater the number
of sites, the lower the sampling error and the more accurate the count.
The accuracy of any single estimate has to be weighed against its cost.
In practice we have found that an analysis of 20 to 25 negatives gives
us a SEM of ±1.5 axons per micrograph. The result is that our estimate
from single cases typically have a sampling errors of ±4,000 on a base
of 60,000. Replicate counts (n=20 pairs) of the same nerve have a
correlation of 0.95. This level of accuracy is sufficient for the
analysis of inbred strains, RI strains, and test cross progeny. Approximately 10-25% of all axons in midorbital parts of the adult
mouse optic nerve are unmyelinated. To ensure that they are accurately
tallied, the count is performed while wearing 2.5X surgical binocular
magnifying glasses. All counts on the negatives are double-checked on
the light box also using 2.5X surgical binocular magnifying glasses.
Doing the analysis directly on negatives reduces the expense and time
required to analyze an optic nerve by more than a factor of two. Immunohistochemical analysis of wholemounts. Counting
calbindin-positive horizontal cells has also proven to give remarkably
reliable data in wholemount preparations. For example, analysis of
approximately 12 sites in each of four A/J mice gave averages that
ranged from 21.44 to 23.67 cells/sample. This very low level of within
strain variation, coupled to the very large between strain variation
(see Preliminary Results), encourages a QTL analysis using quantitative
immunocytochemical procedures. We have not yet demonstrated that we have
labeled all horizontal cells in mouse retina [there are 2-3 separate
categories in some diurnal species (Kolb et al 1992)] and for this
reason we need to be careful to state that we will be counting only
calbindin-positive horizontal cells. We suspect that this includes the
vast majority of horizontal cells in this species (cf. Hamano et al.
1990). Other antibodies which we will test within the next year, are
known to selectively label horizontal cells in mouse, including R4 and
R5 and vimentin (Dräger et al. 1984). Animals are perfused with 4%
paraformaldehyde in phosphate buffer. Retinas are removed and immersed
overnight in 30% sucrose prior to quick freezing at -80°C. Calbindin
polyclonal antibodies (Sigma Co. C8666, calbindin-D-28 kDa) are used to
label horizontal cells. To improve antibody penetration, the duration
has been increased (3-4 days in primary antibody, 2 days in secondary
antibody, anti-mouse IgG). In nocturnal species with rod dominated
retinas all, or nearly all, horizontal cell bodies and some of their
proximal dendrites express high levels of calbinidin (Hamano et al
1990). The calbindin-positive horizontal cells are located in a single
plane positioned approximately 40 microns from the retinal surfaces. We
have now analyzed 42 retinas from 25 cases belonging to five inbred
strains, one F1, and one outcrossed wild species of mouse (M. caroli).
Large and very consistent strain differences have been characterized.
The phenotype of the five F1 hybrids generated from C57BL/6J (high) and
A/J (low) parents was almost precisely at the parental midpoint. Wholemount analysis of photoreceptors. We have carried out
several quantitative studies of photoreceptors in wholemounts of mice
and other species (Wikler et al. 1990, Williams 1991, Williams et al.
1993). A major advantage of the wholemount is that minimal tissue
processing is involved. Shrinkage and cutting artifacts are usually not
a problem. Counting photoreceptors is a straightforward process and it
takes approximately 2-3 hours to sample a single mouse retina (10 to 20
evenly distributed sample sites). The mean density of photoreceptor
inner segments is multiplied by the area of the retina to obtain a final
estimate of the photoreceptor population. We have done this in several
strains of mice and have already found in excess of 30% variation in
this small sample. Tissue for the wholemount analysis will be perfused
as for immunohistochemistry (4% paraformaldehyde). The retina will be
prepared as a wholemount in Gelvatol between two coverslips. A
microscope equipped with Nomarski optics and video image enhancement
equipment is used for this analysis (Williams 1991). The small size of
photoreceptor inner segments in mouse requires the use of monochromatic
green light to improve resolution. Sampling Protocol. We will sample sites using a 0.5 to 1.0 mm
grid over the entire retina. This is an unbiased method. Typically about
15 to 25 sites will be sampled. The mouse does not have a pronounced
central-to-peripheral gradient in cell density, and this number of
sample sites is already known to give estimates that have a standard
error of the mean of approximately ±5%. Retinas will be processed and
counted in batches to ensure uniform application of counting criteria.
Direct 3-dimensional counting. Eight years ago I developed a
method of counting neurons called direct 3-D counting that is
insensitive that avoids the inaccuracies that surround the Abercrombie
correction for split cells (Williams & Rakic 1988b). Both microscopes in
my lab are equipped for this method. Provided neuronal populations have
a relatively uniform packing density, it can take less than two hours of
analysis to obtain estimates that have a sampling error of under 5%.
Estimating the volume of a nuclear region, for example the dorsal
lateral geniculate nucleus of the mouse takes well under 1 hour.
Reliable quantitative estimates of neuron number in the mouse LGN can be
obtained in about three hours. Brains of adult mice, previously fixed
for EM analysis (see above), will be protected in 30% sucrose and cut
frozen at 50 microns on a sledge microtome. The microtome is equipped
with a specimen stage encoder to allow accurate determination of mean
section thickness. Complete or alternating series of sections through
the thalamus will be stained with cresyl violet. Some animals will
receive bilateral intraocular injections of 1.0 \0xB5l of 50% HRP. The
purpose of this protocol is to allow us to more accurately delimit the
boundaries of the LGN. In animals that have had intraocular peroxidase
injections 24 h prior to sacrifice, alternate series of sections through
the thalamus will also be processed using the tetramethylbenzidine
reaction and counterstained with neutral red. Volume of the LGN will be
calculated using methods described in Williams and Rakic (1988ab). Intercross progeny for QTL mapping. We plan an interspecies
intercross between CAST/Ei and BALB/c. As part of an experiment now in
progress (Williams, Cutler, and Goldowitz, in progress), we have already
begun using the MIT primers to detect polymorphism among strains of
mice. PCR conditions are uniform for all primer pairs (24 cycles at
annealing temperature of 55 °C) and are described in the paper by
Dietrich and colleagues (1992). The PCR product is typically run out on
a small sequencing gel. Sequencing polyacylamide gels are usually used
because the microsatellite products of the PCR are typically short (100
to 300 bp), and because the differences in repeat number between strains
are often only a few base pairs. However, in our case we will usually
generate interspecies intercrosses (e.g., CAST/Ei and BALB/c) in which
the differences in the size of PCR products are greater than 10 bp. The
PCR products of these intercross progeny can be run out on a 3% GTG (FMC
Inc.) agarose minigel. We have successfully detected the published
polymorphisms for each of three MIT microsatellite loci that we have
tested on agarose gels using only ethidium bromide for labeling of
bands. Scanning the genome for QTLs with microsatellites. The
selection of PCR primers for whole genome QTL scanning involves
selecting a set of approximately 50-100 evenly spaced marker loci in
which the product differences between CAST/Ei and BALB/c are greater
than 10-20 base pairs. Both of these strains have already been typed at
all the 5,000 microsatellite loci (Dietrich et al. 1992), so finding a
subset of 100 markers that fulfill this condition is simple. We have
done this analysis and have generated a list of several hundred
candidate markers. The mouse map is about 1,500 cM in length.
Consequently, the average distance between any locus and its neighboring
microsatellite loci will be about 6-7 cM. If one intended to type each
of 50 F2 progeny with each of 100 of these markers, then one would need
to carry out 5,000 PCR reactions. There is a much more efficient
alternative involving a pooled DNA approach (Taylor & Phillips 1994).
DNA from the highest and lowest 20 animals is pooled for PCR reactions
with each of about 50 PCR primers (30 cM average spacing, adequate for
detecting linkage).This involves setting up only 100 reactions. Every
lane will be heterozygous (two bands per lane) since 20 cases are
pooled. However, at those microsatellite loci that are linked to QTLs
associated with the high and low phenotypes there will be marked
quantitative asymmetry in the density of the two bands in each lane.
These asymmetric patterns should be reciprocal on adjacent lanes from
high and low pools. One can subsequently test whether nearby MIT loci
also exhibit the same asymmetries. After this linkage screening process,
all progeny are typed for microsatellites within the candidate QTL
regions. This allows the informative recombinants to be scored, thus
refining the map position of a QTL. Documentation. UV-excited gels that have been treated with
ethidium bromide will be photographed using Polaroid instant film. We
have just completed an extensive analysis of genomic DNA from a family
of dogs (the achiasmatic mutation), and have experience in documenting
and analyzing PCR products (Ezer et al. in submission). Calculating linkage between loci. The analysis consists of
comparing the distribution pattern of the phenotype (high or low neuron
or receptor number) with the distribution pattern of parental alleles at
the polymorphic microsatellite loci. The first level of analysis is
simply to detect a linkage, the second level is to estimate QTL position
more precisely by looking at phenotypes of recombinants. Actual
calculations will be performed using the program Map Manager QTL from
Ken Manly, Roswell Park. This program runs on Macintosh computers.
MAPMAKER QTL is also available from the Whitehead Institute. This
program runs on UNIX. We have a Sun workstation and we will compare
results of the two programs. Statistical Criteria. Lander and Shork (1994) provide a table
of the critical lod scores to use with several different experimental
designs. For analysis of RI lines they suggest a value of 3.9 and for
backcross or intercrosses, a lod score of 3.3. These values correspond
to a genome-wide significance level of .05 and apply to two-sided tests
in which alleles from either parental strain may increase neuron number.
A cross between CAST/Ei and BALB/c only tests for genes that are
polymorphic between these two strains. Obviously, these two strains may
share the same alleles at several loci affecting cell number. However,
given the large differences (these strains were derived from different
species of mice, Mus castaneus and M. domesticus), this cross will be
highly informative. It also has the advantage that the MIT
microsatelitte markers will differ substantially in length and we will
consequently be able to type most PCR products on agarose gels. Developmental analysis of cell number. As in the adult, the size of
the ganglion cell population will be estimated by counting axons in the
optic nerve. The developmetnal analysis of the optic nerve of the mouse
is not difficult and will involve the same methods described in detail
in Williams et al (1986) and Williams et al (1992). The key factor is
excellent fixation of tissue on the day of birth (P0). We will analyze
optic nerves from the following strains as part of this study: parental
stocks used in all test cross panels and recombinant inbred lines (CAST/Ei,
BALB/c, DBA/2J, and C57BL/6). In addition, we will analyze several other
high and low strains. One strain we will study is C57BL/Ks. This Kaliss
strain is a high cell number strain closely related to C57BL/6. We have
already begun this analysis. A difference in ganglion cell numbers between strains before ganglion
cell death in the mouse (Linden & Pinto 1985, Young 1985) indicates that
proliferation or commitment differ between the strains. In this case, we
will study progressively younger ages to determine just how early
differences can be detected. The same type of analysis will be performed for LGN neurons. Accurate
estimates of the number of neurons in the LGN of neonatal mice will
require use of anterograde HRP labeling of retinal afferents to define
the medial border of the nucleus. HRP (50%) and DMSO (5%) in water will
be injected using a micropipette loaded with 200 \0xB5l of solution.
Animals will survive for a 24-hour-period and will then be perfused with
mixed aldehydes. The HRP reaction product will be demonstrated using
tetramethylbenzidine as a chromogen. I have done this type of experiment
in prenatal cats (Williams & Chlaupa 1982). The actual quantitative
analysis of neurons in the LGN and criteria to be used in distinguishing
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