Schizophrenia Biomarkers: Blood vs Brain Gene Expression
Schizophrenia Biomarkers: Blood vs Brain Gene Expression
批准号:
6957341
负责人:
MING T. TSUANG
金额:
$21.57万
依托单位国家:
美国
项目类别:
财政年份:
2005
资助国家:
美国
项目状态:
已结题
起止时间:
2005-09-27 至 2007-08-31
关键词:
antipsychotic agentsbehavioral geneticsbioinformaticsbiomarkerbipolar depressionblood testsbrainclinical researchdiagnosis design /evaluationdiagnostic testsearly diagnosisgene expressiongene expression profilinghuman subjecthuman tissuelymphocytemental disorder diagnosismessenger RNAmicroarray technologynucleic acid quantitation /detectionpatient oriented researchpharmacogeneticspostmortempsychopharmacologyschizophreniatissue /cell culture
中文摘要
描述(申请人提供):精神分裂症的诊断目前基本上依赖于使用了近25年的相同的临床访谈、症状识别和诊断标准的应用方法。考虑到精神分裂症的病因中有相当大的遗传贡献,一套基于生物学的诊断标记可能会更有效地验证诊断及其亚型,并提高诊断的敏感性和特异性。此外,如果能够在外周血而不是死后脑组织中识别诊断标记,那么早期识别和改进治疗的希望可能就会实现。作为建立这种基于生物学的标记的第一步,该项目的主要目标是识别和验证外周血细胞中区分精神分裂症患者和对照对象的高度可靠和特定的基因表达模式。我们已经生成了初步数据,确定了精神分裂症患者、双相情感障碍患者和对照组之间的基因表达谱差异;在拟议的项目中,将使用寡核苷酸微阵列和先进的统计技术的新应用来扩展和验证这些初步发现。为此,我们提出了以下具体目标:1)量化精神分裂症患者和对照受试者外周血淋巴细胞的基因表达水平;2)量化从对照受试者培养的淋巴细胞应用典型和非典型抗精神病药物所诱导的基因表达水平,以排除对药物有反应但对疾病过程没有特异性的候选生物标记物基因;3)优先考虑候选基因,用于后续验证和确认为疾病生物标记物,使用控制推断错误率的新的数据分析和统计方法;4)通过更精确的mRNA定量技术验证顶级候选基因在外周血淋巴细胞中的差异表达;5)验证精神分裂症患者和对照组死后脑组织中TOP候选基因的差异表达。长期目标(除了这项建议之外)将是复制和扩展我们的发现,测试基于外周血液的基因表达谱的其他潜在混杂因素,并测试相对于精神病学对照组的特异性。最终,基于生物学的诊断标记物可以大大提高早期识别、干预和预防的可能性,并有助于识别疾病的病因。
英文摘要
DESCRIPTION (provided by applicant): The diagnosis of schizophrenia presently relies on essentially the same methods of clinical interviewing, symptom identification, and application of diagnostic criteria that have been in use for nearly 25 years. Given the considerable genetic contribution toward the etiology of schizophrenia, a biologically based set of diagnostic markers may be more efficient for validating the diagnosis and its subtypes, and for improving diagnostic sensitivity and specificity. Moreover, if diagnostic markers can be identified in peripheral blood rather than in postmortem brain tissue, the promise of early identification and improved treatment might be realized. As a first step toward establishing such biologically based markers, the main objective of this project is to identify and validate highly reliable and specific patterns of gene expression in peripheral blood cells that differentiate patients with schizophrenia from control subjects. We have generated pilot data identifying differences in gene expression profiles between patients with schizophrenia, patients with bipolar disorder, and control subjects; in the proposed project, novel applications of oligonucleotide microarrays and advanced statistical techniques will be used to extend and validate these preliminary findings. To accomplish this, we propose the following specific aims: 1) Quantify gene expression levels in peripheral lymphocytes from patients with schizophrenia and control subjects; 2) Quantify levels of gene expression induced by the application of typical and atypical antipsychotic medications to cultured lymphocytes obtained from control subjects, in order to rule out candidate biomarker genes that respond to medication but are not specific to the disease process; 3) Prioritize candidate genes for subsequent verification and validation as disease biomarkers using novel data-analytic and statistical methods that control the rate of inferential errors; 4) Verify the differential expression of top candidate genes in peripheral lymphocytes by more precise mRNA quantification techniques; and 5) Validate the differential expression of top candidate genes in postmortem brain tissue obtained from patients with schizophrenia and control subjects. Long-term goals (beyond this proposal) will be to replicate and extend our findings, test for other potential confounders of peripheral bloodbased gene expression profiling, and test for specificity vis-a-vis psychiatric comparison groups. Ultimately, biologically based diagnostic markers may substantially enhance the possibilities for early identification, intervention, and prevention, and also facilitate the identification of etiologic factors in the illness.
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