SCHIZOPHRENIA DIAGNOSIS: LEUKOCYTE MULTIGENE SIGNATURES

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 该项目将基于转录的白细胞(白细胞)RNA的高密度微阵列测量，测试对精神分裂症患者和匹配的对照组受试者进行生物分类的可行性。先前的研究表明，通过测量单个基因的mRNA转录，白细胞(白细胞)基因表达水平与精神分裂症之间存在关联。研究人员假设，精神分裂症患者表现出不同于正常健康对照组的特征白细胞多基因表达模式或特征。这项研究旨在生成服用抗精神病药物的精神分裂症患者和未服用抗精神病药物的精神分裂症患者的多基因表达数据，以避免抗精神病药物衍生的基因表达变化的潜在混杂因素。这项研究中产生的数据将被用来使用聚类和监督学习算法来创建分类多基因表达谱。这个项目的总体目标是成功地创建一个分类的、多基因的精神分裂症标记物图谱，能够正确地区分服药和非服药的精神分裂症患者与对照组。 微阵列分析将被用来检测20名未服用抗精神病药物的精神分裂症患者、12名接受抗精神病药物治疗的精神分裂症患者和14名年龄匹配的健康对照组的白细胞样本中的基因表达。为了招募服用抗精神病药物的幼稚患者，调查人员将对在贝尔维尤医院中心综合精神病学急救计划(CPEP)和罗克兰县当地社区设施中出现的精神分裂症患者进行筛查。为了招募药物治疗的精神分裂症受试者，他们将对出现在纽约奥兰治堡罗克兰精神病中心(RPC)的精神分裂症患者进行筛查。对照受试者将通过内森·克莱恩研究所健康志愿者池(NKI HVP)招募。 具体目标如下：1)在本项目的两年时间里，采集20名抗精神病药物的精神分裂症患者、12名药物治疗的精神分裂症患者和14名年龄匹配的健康对照组的血白细胞。1b)利用Affymetrix基因芯片微阵列技术检测白细胞样本中的全球基因表达。2)结合我们初步研究的白细胞基因表达数据集(n=12)和本项目的白细胞基因表达数据集(n=46)，使用聚类和分类算法识别和验证区分精神分裂症患者和正常对照组的多基因指纹图谱。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This project will test the feasibility of a biological classification of schizophrenic patients and matched control subjects, based on high-density microarray measurement of transcribed white blood cell (leukocyte) RNA. Previous studies have shown associations between white blood cell (leukocyte) gene expression levels and schizophrenia, by measurement of mRNA transcripts for individual genes. The investigators hypothesize that patients with schizophrenia exhibit a characteristic leukocyte multigene expression pattern or signature that differs from normal healthy control subjects. This study is designed to generate multigene expression data from both neuroleptic-medicated and neuroleptic-naive schizophrenic patients, in order to avoid the potential confounder of neuroleptic drug-derived gene expression changes. The data generated in this study will be used to create a classifying multigene expression profile using clustering and supervised learning algorithms. The overall objective of this project is to successfully create a classifying, multigene marker profile for schizophrenia that can correctly distinguish both medicated and non-medicated schizophrenic patients from control subjects. Micro-array analysis will be performed to measure the gene expression in leukocyte samples from 20 neuroleptic-naive schizophrenic patients, 12 neuroleptic-treated schizophrenic patients, and 14 age- matched healthy control subjects. To recruit neuroleptic-naive patient subjects, the investigators will screen schizophrenic patients who present at the Bellevue Hospital Center Comprehensive Psychiatric Emergency Program (CPEP), and Rockland County local community facilities. To recruit medicated schizophrenic subjects they will screen Schizophrenic patients who present at Rockland Psychiatric Center (RPC), Orangeburg, New York. Control subjects will be recruited through the Nathan Kline Institute Healthy Volunteer Pool (NKI HVP). The specific aims are as follows: 1a) To collect blood leukocytes from twenty neuroleptic-na¿ve schizophrenics, twelve medicated schizophrenics, and fourteen age-matched healthy control subjects over the two-year period of this project. 1b) Employ Affymetrix GeneChip microarray technology to measure global gene expression in the leukocyte samples. 2) To combine the leukocyte gene expression datasets from our preliminary study (n=12) with that of this project (n=46), and use clustering and classification algorithms to identify and validate multi-gene fingerprints that differentiate schizophrenic subjects from normal healthy controls.