Alcohol selected mice: Comparative withdrawal syndromes

酒精选择小鼠：比较戒断综合症

基本信息

批准号：
7268985
负责人：
JOHN K BELKNAP
金额：
$ 26.88万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
1988
资助国家：
美国
起止时间：
1988-09-01 至 2009-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7268985
关键词：
Accounting Acute Affect Air Alcohol Withdrawal Seizures Alcohol withdrawal syndrome Alcoholic Intoxication Alcohols Aldehydes Alleles Analysis of Variance Appendix Brain Breathing Breeding Butanols Candidate Disease Gene Cataloging Catalogs Chromosomes Chromosomes, Human, Pair 1 Chronic Clinical Communities Condition Congenic Mice Congenic Strain Consumption Control Groups Convulsions Core Facility Coupling Data Databases Detection Development Diazepam Disease model Dissection Distal Drug abuse Ethanol Excision Faculty Fostering Frequencies Funding Future Gene Expression Gene Expression Microarray Analysis Gene Expression Regulation Gene Frequency Generations Genes Genetic Genetic Polymorphism Genetic Predisposition to Disease Genetic Variation Genome Genotype Goals Grant Haplotypes Hour Hypnotics and Sedatives In Situ Hybridization Inbred Strain Individual Institutes Intoxication Joints Laboratories Lead Link Location Maps Measures Membrane Structure and Function Messenger RNA Methods Microarray Analysis Microsatellite Repeats Modeling Monitor Mus National Institute on Alcohol Abuse and Alcoholism Nature Neuro-Oncological Ventral Antigen 2 Nitrous Oxide Numbers Pathway interactions Pentobarbital Personal Satisfaction Pharmaceutical Preparations Phenotype Physical Dependence Population Positioning Attribute Predisposition Problem Solving Process Process Measure Progress Reports Proteins Purpose QTL Genes Quantitative Trait Loci Regulation Relapse Relative (related person)Reporting Research Resistance Resolution Reverse Transcriptase Polymerase Chain Reaction Rodent Model Role Running Sampling Score Seizures Sequence Analysis Severities Site Sorting - Cell Movement Source Standards of Weights and Measures Statistically Significant Study Subject Substance Withdrawal Syndrome Synaptic Membranes Tertiary Protein Structure Testing Thinking Time Tissue-Specific Gene Expression Tissues Transcript Transgenic Mice Translating United States National Institutes of Health Variant Western Blotting Withdrawal Work alcohol and other drug alcohol exposure base brain tissue chronic alcohol ingestion clinically relevant comparative comparison group congenic cost day design drinking drug withdrawal follow-up gene discovery gene function genetic analysis indexing interest killings mouse genome neurochemistry neurophysiology new technology preference problem drinker promoter protein function research study success trait trend unpublished works vapor

项目摘要

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.

描述（由申请人提供）：定量性状基因座（QTL）是影响定量或多焦点性状的染色体染色体（等位基因）上的位点。在我们目前的RO1赠款中，使用QTL分析来检测和绘制十个QTL，影响急性或慢性酒精（ETOH）戒断严重程度，使用C57BL/6（B6）X DBA/2（d2）之间的杂交区域对广泛的染色体区域，戒断（d2）和戒断seeizure seeizure seeizure（IP2）X撤回Prone（IP2）x撤回Prone（IP2）x撤回（IP2）x撤回（IP2）X ecizure CairaL seizure Comperant（IR1 IR1）。已经确定了该基因是其中之一，并且为此而取得了良好的进步。在此提案中，我们计划迈向B6XD2和IP2XLR1跨越慢性酒精戒断模型的七个QTL的基因鉴定，而不是过去使用的急性模型。该方法是将基因表达微阵列和QTL数据结合在同一小鼠中，以充分利用这两种全基因组发现基因组方法的互补优势和劣势。最近的工作表明，大多数基因的基因表达的个体变异，如转录本（mRNA）丰度所索引，通常由多个基因座（QTL）控制。通过在同一小鼠上结合微阵列和QTL（定量性状基因座）分析，我们建议通过确定哪些转录物分离出戒断变化以及哪些QTL的转录差异，以及哪些QTL与哪些QTLS共同影响特定的基因的变化，并且对于特定的GENE（S）和戒断性的差异，并且哪些QTL会影响哪些转录物，并通过确定哪些转录分离来检测这些转录。我们将使用用于高和低慢性酒精戒断严重程度的短期选定系列，以验证鉴定基因表达的差异基因，以验证戒断严重程度。对先天性菌株，单倍型分析和基因功能分析的专业高分辨率映射群体的分析将提供候选基因之间的分化越来越多，这将有助于鉴定负责七个已知的QTLS影响慢性酒精戒断严重性的基因。这些研究的另一个主要重点是确定整个基因组中基因调节的慢性中毒的影响，因为这将提供有关机制的重要线索。我们将使用代表小鼠基因组中所有基因中绝大多数的新近可用的Affymetrix小鼠430 2.0 Genechip（R）。