School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China.
School of Public Health and the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
EBioMedicine. 2024 Sep;107:105305. doi: 10.1016/j.ebiom.2024.105305. Epub 2024 Aug 23.
Tissue-specific analysis of the transcriptome is critical to elucidating the molecular basis of complex traits, but central tissues are often not accessible. We propose a methodology, Multi-mOdal-based framework to bridge the Transcriptome between PEripheral and Central tissues (MOTPEC).
Multi-modal regulatory elements in peripheral blood are incorporated as features for gene expression prediction in 48 central tissues. To demonstrate the utility, we apply it to the identification of BMI-associated genes and compare the tissue-specific results with those derived directly from surrogate blood.
MOTPEC models demonstrate superior performance compared with both baseline models in blood and existing models across the 48 central tissues. We identify a set of BMI-associated genes using the central tissue MOTPEC-predicted transcriptome data. The MOTPEC-based differential gene expression (DGE) analysis of BMI in the central tissues (including brain caudate basal ganglia and visceral omentum adipose tissue) identifies 378 genes overlapping the results from a TWAS of BMI, while only 162 overlapping genes are identified using gene expression in blood. Cellular perturbation analysis further supports the utility of MOTPEC for identifying trait-associated gene sets and narrowing the effect size divergence between peripheral blood and central tissues.
The MOTPEC framework improves the gene expression prediction accuracy for central tissues and enhances the identification of tissue-specific trait-associated genes.
This research is supported by the National Natural Science Foundation of China 82204118 (D.Z.), the seed funding of the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province (2020E10004), the National Institutes of Health (NIH) Genomic Innovator Award R35HG010718 (E.R.G.), NIH/NHGRI R01HG011138 (E.R.G.), NIH/NIA R56AG068026 (E.R.G.), NIH Office of the Director U24OD035523 (E.R.G.), and NIH/NIGMS R01GM140287 (E.R.G.).
研究转录组的组织特异性对于阐明复杂性状的分子基础至关重要，但中心组织通常无法获得。我们提出了一种方法，即基于多模态的外周与中枢组织转录组桥接框架（MOTPEC）。
将外周血中的多模态调控元件纳入基因表达预测的特征，以识别 48 种中枢组织中的基因。为了验证其效用，我们将其应用于 BMI 相关基因的鉴定，并将组织特异性结果与直接来自替代血液的结果进行比较。
MOTPEC 模型在血液中的基线模型和现有的 48 种中枢组织模型中表现出优越的性能。我们使用中枢组织 MOTPEC 预测的转录组数据确定了一组 BMI 相关基因。MOTPEC 基于 BMI 的中枢组织差异基因表达（DGE）分析（包括脑尾状核基底节和内脏网膜脂肪组织）鉴定出 378 个与 TWAS 中 BMI 结果重叠的基因，而仅使用血液中的基因表达鉴定出 162 个重叠基因。细胞扰动分析进一步支持 MOTPEC 用于鉴定与性状相关的基因集并缩小外周血和中枢组织之间的效应大小差异的效用。
MOTPEC 框架提高了中枢组织的基因表达预测准确性，并增强了组织特异性性状相关基因的鉴定。
本研究得到国家自然科学基金 82204118（D.Z.）、浙江省智能预防医学重点实验室种子基金 2020E10004、美国国立卫生研究院（NIH）基因组创新奖 R35HG010718（E.R.G.）、NIH/NHGRI R01HG011138（E.R.G.）、NIH/NIA R56AG068026（E.R.G.）、NIH 主任办公室 U24OD035523（E.R.G.）和 NIH/NIGMS R01GM140287（E.R.G.）的资助。
