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