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Meta-Analysis of Transcriptomic Studies of Blood and Six Brain Regions Identifies a Consensus of 15 Cross-Tissue Mechanisms in Alzheimer's Disease and Suggests an Origin of Cross-Study Heterogeneity.血液和六个脑区转录组学研究的荟萃分析确定了阿尔茨海默病中15种跨组织机制的共识,并揭示了跨研究异质性的根源。
Am J Med Genet B Neuropsychiatr Genet. 2025 Jun;198(4):e33019. doi: 10.1002/ajmg.b.33019. Epub 2024 Dec 16.
2
Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer's disease dementia.基于大脑和血液组织参考转录组数据的贝叶斯全基因组 TWAS 鉴定出 141 个阿尔茨海默病痴呆风险基因。
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本文引用的文献

1
The complex genetic architecture of Alzheimer's disease: novel insights and future directions.阿尔茨海默病的复杂遗传结构:新的见解和未来方向。
EBioMedicine. 2023 Apr;90:104511. doi: 10.1016/j.ebiom.2023.104511. Epub 2023 Mar 10.
2
Linking the Amyloid, Tau, and Mitochondrial Hypotheses of Alzheimer's Disease and Identifying Promising Drug Targets.将阿尔茨海默病的淀粉样蛋白、tau 和线粒体假说联系起来,并确定有前途的药物靶点。
Biomolecules. 2022 Nov 11;12(11):1676. doi: 10.3390/biom12111676.
3
Emerging roles of innate and adaptive immunity in Alzheimer's disease.先天免疫和适应性免疫在阿尔茨海默病中的新作用。
Immunity. 2022 Dec 13;55(12):2236-2254. doi: 10.1016/j.immuni.2022.10.016. Epub 2022 Nov 8.
4
Neuronal cell death mechanisms in Alzheimer's disease: An insight.阿尔茨海默病中的神经元细胞死亡机制:深入剖析
Front Mol Neurosci. 2022 Aug 25;15:937133. doi: 10.3389/fnmol.2022.937133. eCollection 2022.
5
An accurate fully automated panel of plasma biomarkers for Alzheimer's disease.用于阿尔茨海默病的准确、全自动的血浆生物标志物面板。
Alzheimers Dement. 2023 Apr;19(4):1204-1215. doi: 10.1002/alz.12751. Epub 2022 Aug 11.
6
Polygenic resilience scores capture protective genetic effects for Alzheimer's disease.多基因弹性评分可捕捉阿尔茨海默病的保护遗传效应。
Transl Psychiatry. 2022 Jul 25;12(1):296. doi: 10.1038/s41398-022-02055-0.
7
New insights into the genetic etiology of Alzheimer's disease and related dementias.阿尔茨海默病及相关痴呆症的遗传学病因新见解。
Nat Genet. 2022 Apr;54(4):412-436. doi: 10.1038/s41588-022-01024-z. Epub 2022 Apr 4.
8
Reducing voltage-dependent potassium channel Kv3.4 levels ameliorates synapse loss in a mouse model of Alzheimer's disease.降低电压依赖性钾通道Kv3.4水平可改善阿尔茨海默病小鼠模型中的突触损失。
Brain Neurosci Adv. 2022 Mar 24;6:23982128221086464. doi: 10.1177/23982128221086464. eCollection 2022 Jan-Dec.
9
Roles and Mechanisms of the Protein Quality Control System in Alzheimer's Disease.蛋白质质量控制系统在阿尔茨海默病中的作用和机制。
Int J Mol Sci. 2021 Dec 29;23(1):345. doi: 10.3390/ijms23010345.
10
Somatostatin-evoked Aβ catabolism in the brain: Mechanistic involvement of α-endosulfine-K channel pathway.脑内生长抑素诱导的 Aβ 代谢:α-内磺肽-K 通道途径的机制参与。
Mol Psychiatry. 2022 Mar;27(3):1816-1828. doi: 10.1038/s41380-021-01368-8. Epub 2021 Nov 4.

血液和六个脑区转录组学研究的荟萃分析确定了阿尔茨海默病中15种跨组织机制的共识,并揭示了跨研究异质性的根源。

Meta-Analysis of Transcriptomic Studies of Blood and Six Brain Regions Identifies a Consensus of 15 Cross-Tissue Mechanisms in Alzheimer's Disease and Suggests an Origin of Cross-Study Heterogeneity.

作者信息

Hou Jiahui, Hess Jonathan L, Zhang Chunling, van Rooij Jeroen G J, Hearn Gentry C, Fan Chun Chieh, Faraone Stephen V, Fennema-Notestine Christine, Lin Shu-Ju, Escott-Price Valentina, Seshadri Sudha, Holmans Peter, Tsuang Ming T, Kremen William S, Gaiteri Chris, Glatt Stephen J

机构信息

Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab), Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, Syracuse, New York, USA.

Department of Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, New York, USA.

出版信息

Am J Med Genet B Neuropsychiatr Genet. 2025 Jun;198(4):e33019. doi: 10.1002/ajmg.b.33019. Epub 2024 Dec 16.

DOI:10.1002/ajmg.b.33019
PMID:39679839
原文链接:
https://pmc.ncbi.nlm.nih.gov/articles/PMC12048288/
Abstract

The comprehensive genome-wide nature of transcriptome studies in Alzheimer's disease (AD) should provide a reliable description of disease molecular states. However, the genes and molecular systems nominated by transcriptomic studies do not always overlap. Even when results do align, it is not clear if those observations represent true consensus across many studies. A couple of sources of variation have been proposed to explain this variability, including tissue-of-origin and cohort type, but its basis remains uncertain. To address this variability and extract reliable results, we utilized all publicly available blood or brain transcriptomic datasets of AD, comprised of 24 brain studies with 4007 samples from six different brain regions, and eight blood studies with 1566 samples. We identified a consensus of AD-associated genes across brain regions and AD-associated gene-sets across blood and brain, generalizable machine learning and linear scoring classifiers, and significant contributors to biological diversity in AD datasets. While AD-associated genes did not significantly overlap between blood and brain, our findings highlighted 15 dysregulated processes shared across blood and brain in AD. The top five most significantly dysregulated processes were DNA replication, metabolism of proteins, protein localization, cell cycle, and programmed cell death. Conversely, addressing the discord across studies, we found that large-scale gene co-regulation patterns can account for a significant fraction of variability in AD datasets. Overall, this study ranked and characterized a compilation of genes and molecular systems consistently identified across a large assembly of AD transcriptome studies in blood and brain, providing potential candidate biomarkers and therapeutic targets.

摘要

阿尔茨海默病(AD)转录组研究的全基因组综合特性应能提供对疾病分子状态的可靠描述。然而,转录组研究提名的基因和分子系统并不总是重叠的。即使结果一致,也不清楚这些观察结果是否代表众多研究中的真正共识。已提出一些变异来源来解释这种变异性,包括起源组织和队列类型,但其基础仍不确定。为了解决这种变异性并提取可靠结果,我们利用了所有公开可用的AD血液或脑转录组数据集,其中包括24项脑研究,来自六个不同脑区的4007个样本,以及八项血液研究,1566个样本。我们确定了跨脑区的AD相关基因共识以及跨血液和脑的AD相关基因集、可推广的机器学习和线性评分分类器,以及AD数据集中生物多样性的重要贡献因素。虽然血液和脑中的AD相关基因没有明显重叠,但我们的研究结果突出了AD中血液和脑共有的15个失调过程。最显著失调的前五个过程是DNA复制、蛋白质代谢、蛋白质定位、细胞周期和程序性细胞死亡。相反,为了解决研究之间的不一致性,我们发现大规模基因共调控模式可解释AD数据集中很大一部分变异性。总体而言,本研究对在大量血液和脑AD转录组研究中一致鉴定出的基因和分子系统进行了排名和表征,提供了潜在的候选生物标志物和治疗靶点。