John K. Belknap, Ph.D. - US grants

Quantitative trait loci (QTLs) are chromosomal regions containing genes (polygenes) that influence a quantitative (or complex) trait such as alcohol (ethanol) withdrawal severity. During the last four years of present R01 funding, we have mapped several QTLs that jointly have a major influence on the severity of alcohol withdrawal in populations derived from the C57BL/6 (B6) and DBA/2 (D2) inbred mouse strains. The three largest QTLs (LOD>4, p<10/-5) are on distal chromosome 1, mid chromosome 4 and proximal chromosome 11. Based on mouse-human linkage homology, the human counterparts of the three mouse QTLs map to human chromosomes 1q21- 32, 9p23, and 5q31-35, respectively. Using congenic strains developed to isolate each of the three QTLs against a uniform (inbred) genetic background, we propose to continue these studies toward the eventual identification of the genes that underly each QTL. To accomplish this, we propose the following. (1) Narrow the QTL interval down from our present approximately 20 cM to approximately 1 cM using interval specific congenic strains. (2) Mouse and human gene databases will be searched to find candidate genes that map within our 1 cM QTL interval. In the case of human databases, this will involve regions of known mouse-human linkage homology. (3) Promising candidate genes will be scanned for base pair differences between B6 and D2 genotypes by SSCP and tested for alcohol withdrawal regulated expression differences using Northern blot analysis. (4) When most brain mRNA transcripts for evidence of alcohol withdrawal gene regulation specific to individual QTL regions isolated in a congenic strain. Where such regulation is found, a cDNA reverse transcribed from the differentially regulated mRNA species can be sequenced and serve as a 3' tag for a specific gene or genes. This tag can be used to identify the gene by matching its sequence to those in mouse or human gene/EST databases developed as part of the Human Genome Project, or as a probe for full length cDNA cloning and sequencing. Finally, DD-PCR will also be used to detect differential regulation due to alcohol withdrawal without regard to genotype (QTL), for this will pick up additional genes not polymorphic in our mouse populations.

The first objective of the proposed work is to continue bidirectional selective breeding to produce three lines of mice which differ maximally from one another in their genetically-based sensitivity to analgesic (antinociceptive) effects of opiate drugs (narcotic analgesics). To this end, three lines will be maintained: one will be selectively bred in the direction of a high antinociceptive response, another in the direction of a low antinociceptive response, and the third will be a (nonselected) control line. Selection will be based on latencies on the hot plate assay in response to levorphanol, morphine-like opioid agonist. The second objective is to conduct studies to determine the mechanisms which account for the selection line differences, and a search made for other effects of opioids which may share common mechanisms with the antinociceptive response. The third objective is to screen a considerable number of novel analgesics using these selection lines to determine the extent to which their mechanisms of action overlap with levorphanol or morphine. These animals should be a valuable resource for research concerning the actions of analgesic drugs and mechanisms of pain in general. Every reasonable effort will be made to make these selection lines available to other investigators who might wish to use them.

It is proposed to conduct investigations to utilize and further characterize newly developed genetic lines of mice which have been selectively-bred for either increased or decreased handling- induced convulsions (HIC) following withdrawal from ethanol (WSP and WSR lines). It is planned to determine if these genetically- based differences in susceptibility to HIC associated with physical dependence on ethanol are genetically related to susceptibility (cross-susceptibility) to HIC and other withdrawal signs associated with physical dependence on benzodiazepines, barbiturates, acetaldehyde, anesthetic gases, t-butanol, and several other sedative-hypnotic drugs. If so, then this would suggest that the mechanisms responsible for the intensity of HIC following ethanol withdrawal are also operative with these other drugs and/or withdrawal signs. Conversely, if ethanol HIC susceptibility does not generalize to these other drugs and/or withdrawal signs monitored, then independent controlling mechanisms are implicated. The development of these selected lines allows a novel approach to be utilized to determine the degree of commonality that exists among a number of drug dependencies with respect to alcohol. Additional studies are proposed to assess the role of the GABA and benzodiazepine receptors in the genesis of the alcohol withdrawal syndrome, utilizing in vitro receptor binding and receptor autoradiography in the WSP, WSR mice. The latter technique in particular will allow the detection of very small changes in receptor density caused by chronic intoxication and subsequent withdrawal from ethanol.

It is proposed to conduct investigations to utilize and further characterize newly developed genetic lines of mice which have been selectively-bred for either increased or decreased handling- induced convulsions (HIC) following withdrawal from ethanol (WSP and WSR lines). It is planned to determine if these genetically- based differences in susceptibility to HIC associated with physical dependence on ethanol are genetically related to susceptibility (cross-susceptibility) to HIC and other withdrawal signs associated with physical dependence on benzodiazepines, barbiturates, acetaldehyde, anesthetic gases, t-butanol, and several other sedative-hypnotic drugs. If so, then this would suggest that the mechanisms responsible for the intensity of HIC following ethanol withdrawal are also operative with these other drugs and/or withdrawal signs. Conversely, if ethanol HIC susceptibility does not generalize to these other drugs and/or withdrawal signs monitored, then independent controlling mechanisms are implicated. The development of these selected lines allows a novel approach to be utilized to determine the degree of commonality that exists among a number of drug dependencies with respect to alcohol. Additional studies are proposed to assess the role of the GABA and benzodiazepine receptors in the genesis of the alcohol withdrawal syndrome, utilizing in vitro receptor binding and receptor autoradiography in the WSP, WSR mice. The latter technique in particular will allow the detection of very small changes in receptor density caused by chronic intoxication and subsequent withdrawal from ethanol.

The PCR Genotyping Core involves all genotyping procedures needed for QTL mapping once the behavioral, pharmacological and neurochemical data have been collected and genomic DNA samples extracted as part of other components. This Core is central to the major theme of QTL mapping, and is required to meet almost all of the Specific Aims in Research Components 3,4 and 7. Genomic DNA will be extracted from spleen except when genotyped animals are needed as breeders, then tail tip samples will be used. Each genotyping involve the determination of which alleles an individual mouse possesses at one microsatellite marker locus, and is accomplished by a single PCR reaction. Genotyping many individuals for many markers is required for QTL confirmation and mapping in segregating (noninbred) populations where each mouse is a unique genotype. Over the 5-year period of requested support, we propose to genotype 5 B6D2F2 populations (92 mice each), 6 selection line F2 populations (80 mice each), 8 selection lines (30 mice each), 8 inbred strains ancestral to the selection liens (5 mice each), 3 phenotypic selected lines (160 mice each), eight sets of genotypic selection lines (144 mice each) and develop 16 congenic strain sets by seven generations of backcrossing (25 mice per set per generation). The grand total for the requested 5-year period of support is 52,000 PCR reactions. This Core will be functional in all years of requested Center funding.

DESCRIPTION: (Applicant's Abstract) Extensive preliminary data demonstrate genetic influences on behavioral responses to cocaine (COC) and methamphetamine (MA) in C57Bl/6J (B6), DBA/2J (D2) and 25 of their recombinant inbred (BXD Rl) strains of mice. The responses studied include several related to high-dose toxicity (e.g. MA stereotyped chewing, climbing, exophthalmos, and temperature changes; COC seizures) and low-dose locomotor activition/sensitization responses to COC and MA. Using quantitative trait locus (QTL) mapping methods, we have provisionally identified the location of multiple QTLs for each drug response in the mouse genome. A quantitative trait locus (QTL) is a chromosome site containing gene or genes which appear to influence these responses to psychostimulants. The statistical approach we employ requires that the provisional QTLs we have identified must be further tested in additional populations before linkage in the proposed chromosomal regions can be accepted. The proposed studies will rigorously assess the strength of the provisional genetic mapping assignments by examining F2 individuals from the cross of B6 and D2 progenitors, and lines selectively bred from the F2 population to differ in drug response. In each of these populations, allele frequencies for the associated markers should cosegregate in individual mice with differences in magnitude of the drug response mapped. For those QTLs rigorously verified, congenic strains will be developed by backcrossing the relevant chromosomal region onto a progenitor B6 or D2 strain. These congenic strains will provide a powerful genetic animal model for future mechanism-oriented tests of candidate gene function. Where a candidate gene is not obvious, the congenics will also facilitate location of the relevant gene through positional cloning. Because of the high degree of similarity (synteny) between the mouse and human genomes, identification of the location of specific genes in mice will allow rapid identification of their human counterparts. The proposed studies build on 5 years of previous work and represent progress toward the identification of specific genes underlying susceptibility to aspects of psychostimulant toxicity and activation/sensitization.

[unreadable] DESCRIPTION (provided by applicant): A quantitative trait locus (QTL) is a site on a chromosome containing genes (alleles) influencing a quantitative or multilocus trait. In our present RO1 grant, QTL analysis was used to detect and map ten QTLs influencing either acute or chronic alcohol (EtOH) withdrawal severity to broad chromosomal regions using crosses between the C57BL/6 (B6) x DBA/2 (D2) and the Withdrawal Seizure Prone (IP2) x Withdrawal Seizure Resistant (IR1) inbred strains. The gene has been identified which accounts for one of these, and good progress toward this end has been made for another. In this proposal we plan to advance toward gene identification for the seven QTLs focused on the chronic alcohol withdrawal model in B6xD2 and IP2xlR1 crosses rather than the acute model used in the past. The approach is to combine gene expression microarray and QTL data taken in the same mice to take full advantage of the complementary strengths and weaknesses of these two genome-wide methods of gene discovery. Recent work has shown that individual variation in gene expression in brain for most genes, as indexed by transcript (mRNA) abundance, is often controlled by multiple loci (QTLs). By combining microarray and QTL (quantitative trait locus) analysis on the same mice, we propose to detect those transcripts most likely to have a direct influence oh withdrawal severity by determining which transcripts segregate with withdrawal variation and which do not, and also which QTLs jointly influence variation in transcript abundance for particular gene(s) and withdrawal severity. We will use short-term selected lines bred for high and low chronic alcohol withdrawal severity to verify differential gene expression for candidate genes cosegregating with withdrawal severity. Analyses of specialized high resolution mapping populations of congenic strains, haplotype analyses, and analyses of gene function will provide increasing differentiation among candidate genes which will contribute importantly toward the identification of those genes responsible for the seven known QTLs influencing chronic alcohol withdrawal severity. Another major focus of these studies is to determine the effects of chronic intoxication, either with or without withdrawal, on gene regulation throughout the genome, as this will provide important clues concerning mechanism. We will use the newly available Affymetrix mouse 430 2.0 Genechip(r) representing the vast majority of all genes in the mouse genome. [unreadable] [unreadable]

During the past 5 years of support, we have identified chromosomal regions (called quantitative trait loci, QTLs) that contain genes involved in ethanol responses including preference/consumption and withdrawal. We have progressed to fine map several of these QTLs to ~1 megabase (Mb) intervals. For a QTL on chromosome 4, we have progressed to identify the causative gene or quantitative trait gene (QTG) as Mpdz, and have identified high-quality QTG candidates for other ethanol response QTLs as well as candidates and gene networks implicated by knockout and microarray analyses. These gene candidates and gene networks now require rigorous testing to be accepted as having causal roles in ethanol response. An important goal of behavioral genomics is to identify individual genes and gene networks underlying phenotypic variation, and to elucidate the mechanism by which the gene or gene network affects behavior. In the next 5 years of support, the role of the Core will continue to provide expertise for both candidate gene hypothesis-driven (e.g., RNA interference [RNAi]) and hypothesis-generating (e.g., weighted gene co-expression networks) analyses in mice and nonhuman primates. Complementary strategies will emphasize identification and definitive proof of genes and gene networks involved in ethanol preference/consumption, withdrawal, and genetically correlated traits (including impulsivity). Genes will be tested for differential expression and/or sequence (coding and regulatory) using appropriate animal models. Priority for expression and sequence comparisons will be determined based on several criteria, including putative biological role and likely relevance to ethanol action. Database sequence information will also be used to design oligonucleotide primers that flank genes of interest for real-time quantitative PCR (QPCR) to test for genotype-differences in expression. In some cases, PCR amplification of the coding and regulatory regions from appropriate strains will be needed for DNA sequencing of PCR products to identify sequence differences. Both units of Core Component #3 (the Molecular Genetics Unit [MGU] and the Bioinformatics &Biostatistics Unit [BBU]) will be active in all years of requested Center support. The BBU will focus on statistical, computational and bioinformatics support, especially microarray data analysis, QTL analysis, gene network analyses and further central database development. Both units will support all five Center research components, as well as pilot projects in Component #10 and several other NIH (ROI, R37, UOI, KOI, F31 and F32) and VA grants.

The Biostatistics and Genetics Core (BGC) Component 2 will provide data processing, database coordination and maintenance, data analysis and statistical support for each of the other cores and projects within the Methamphetamine Abuse Research Center (MARC). Quantitative genetic analysis support will also be given to those Cores using mouse genetic models. This single unit serves as an important interface for the transmission of diverse types of data and processed information among core and project investigators. It assures that data and information from different cores and projects will be processed uniformly and in such a way as to allow for their smooth interface where appropriate. The environment provided by a single data management and biostatistics unit also facilitates the effective and timely return of processed data and information to the core and project investigators in support of their objectives. In addition, this Core will facilitate data sharing for investigators within and outside the center. This Core will continue to assist in the use of public databases and computer software, and provide the computer infrastructure for the microarray and QTL studies. Training in statistical and genetic methods and experimental design will also be provided on a one-on-one and as-needed basis. This Core will also develop new analytical programs tailored to the needs of MARC investigators, particularly newer clustering algorithms. As the Center continues to evolve, we will continue development of a central data warehouse for the MARC on our secure server.