A multi-modal framework improves prediction of tissue-specific gene expression from a surrogate tissue.

IF 9.7 1区医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

EBioMedicine Pub Date : 2024-09-01 Epub Date: 2024-08-23 DOI:10.1016/j.ebiom.2024.105305

Yue Xu, Chunfeng He, Jiayao Fan, Yuan Zhou, Chunxiao Cheng, Ran Meng, Ya Cui, Wei Li, Eric R Gamazon, Dan Zhou

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引用次数: 0

Abstract

Background: 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).

Methods: 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.

Findings: 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.

Interpretation: The MOTPEC framework improves the gene expression prediction accuracy for central tissues and enhances the identification of tissue-specific trait-associated genes.

Funding: 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.).

查看原文本刊更多论文

多模式框架提高了从替代组织预测组织特异性基因表达的能力。

背景：组织特异性转录组分析对于阐明复杂性状的分子基础至关重要，但中心组织往往无法获得。我们提出了一种方法--基于多模式调控要素的框架（MOTPEC）--来连接外周血和中心组织的转录组：方法：将外周血中的多模式调控因子作为特征纳入 48 个中心组织的基因表达预测。为了证明其实用性，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从代用血液中得出的结果进行比较：研究结果：与血液中的基线模型和 48 种中心组织中的现有模型相比，MOTPEC 模型表现出更优越的性能。我们利用中心组织 MOTPEC 预测的转录组数据确定了一组与 BMI 相关的基因。基于 MOTPEC 的中心组织（包括大脑尾状基底节和内脏网膜脂肪组织）BMI 差异基因表达（DGE）分析确定了 378 个与 BMI TWAS 结果重叠的基因，而使用血液中的基因表达确定的重叠基因只有 162 个。细胞扰动分析进一步支持了 MOTPEC 在确定性状相关基因集和缩小外周血与中心组织之间效应大小差异方面的实用性：MOTPEC框架提高了中心组织基因表达预测的准确性，并加强了组织特异性性状相关基因的鉴定：本研究得到了国家自然科学基金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.）。

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来源期刊

EBioMedicine Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology

CiteScore

17.70

自引率

0.90%

发文量

579

审稿时长

5 weeks

期刊介绍： eBioMedicine is a comprehensive biomedical research journal that covers a wide range of studies that are relevant to human health. Our focus is on original research that explores the fundamental factors influencing human health and disease, including the discovery of new therapeutic targets and treatments, the identification of biomarkers and diagnostic tools, and the investigation and modification of disease pathways and mechanisms. We welcome studies from any biomedical discipline that contribute to our understanding of disease and aim to improve human health.