Multiomics analysis of umbilical cord hematopoietic stem cells from a multiethnic cohort of Hawaii reveals the intergenerational effect of maternal prepregnancy obesity and risks for cancers.

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
Yuheng Du, Paula A Benny, Yuchen Shao, Ryan J Schlueter, Alexandra Gurary, Annette Lum-Jones, Cameron B Lassiter, Fadhl M AlAkwaa, Maarit Tiirikainen, Dena Towner, W Steven Ward, Lana X Garmire
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引用次数: 0

Abstract

Background: Maternal obesity is a health concern that may predispose newborns to a high risk of medical problems later in life. To understand the intergenerational effect of maternal obesity, we hypothesized that the maternal obesity effect is mediated by epigenetic changes in the CD34+/CD38-/Lin- hematopoietic stem cells (uHSCs) in the offspring. To investigate this, we conducted a DNA methylation centric multiomics study. We measured DNA methylation and gene expression of the CD34+/CD38-/Lin- uHSCs and metabolomics of the cord blood, all from a multiethnic cohort from Kapiolani Medical Center for Women and Children in Honolulu, Hawaii (n=72, collected between 2016 and 2018).

Results: Differential methylation analysis unveiled a global hypermethylation pattern in the maternal prepregnancy obese group (BH adjusted P < 0.05), after adjusting for major clinical confounders. KEGG pathway enrichment, WGCNA, and PPI analyses revealed that hypermethylated CpG sites were involved in critical biological processes, including cell cycle, protein synthesis, immune signaling, and lipid metabolism. Utilizing Shannon entropy on uHSCs methylation, we discerned notably higher quiescence of uHSCs impacted by maternal obesity. Additionally, the integration of multiomics data-including methylation, gene expression, and metabolomics-provided further evidence of dysfunctions in adipogenesis, erythropoietin production, cell differentiation, and DNA repair, aligning with the findings at the epigenetic level. Furthermore, we trained a random forest classifier using the CpG sites in the genes of the top pathways associated with maternal obesity, and applied it to predict cancer versus adjacent normal sample labels in 14 Cancer Genome Atlas (TCGA) cancer types. Five of 14 cancers showed balanced accuracy of 0.6 or higher: LUSC (0.87), PAAD (0.83), KIRC (0.71), KIRP (0.63) and BRCA (0.60).

Conclusions: This study revealed the significant correlation between prepregnancy maternal obesity and multiomics-level molecular changes in the uHSCs of offspring, particularly at the DNA methylation level. These maternal-obesity-associated epigenetic markers in uHSCs may contribute to increased risks in certain cancers of the offspring. Larger and multicenter cohort validation studies are warranted to follow up the current single-site study.

来自夏威夷多种族队列的脐带造血干细胞的多组学分析揭示了母亲孕前肥胖和癌症风险的代际影响。
背景:产妇肥胖是一个健康问题,可能使新生儿在以后的生活中有较高的医疗问题风险。为了了解母体肥胖的代际效应,我们假设母体肥胖效应是由后代CD34+/CD38-/Lin-造血干细胞(uhsc)的表观遗传变化介导的。为了研究这一点,我们进行了一项以DNA甲基化为中心的多组学研究。我们测量了脐带血的DNA甲基化和CD34+/CD38-/Lin- uhsc的基因表达以及代谢组学,这些都来自夏威夷檀香山Kapiolani妇女和儿童医疗中心的多种族队列(n=72,收集于2016年至2018年)。结果:在调整主要临床混杂因素后,差异甲基化分析揭示了孕妇孕前肥胖组的整体高甲基化模式(BH调整P < 0.05)。KEGG通路富集、WGCNA和PPI分析显示,高甲基化的CpG位点参与关键的生物过程,包括细胞周期、蛋白质合成、免疫信号传导和脂质代谢。利用香农熵分析uhsc甲基化,我们发现母亲肥胖对uhsc的影响明显更高。此外,多组学数据(包括甲基化、基因表达和代谢组学)的整合提供了脂肪生成、促红细胞生成素产生、细胞分化和DNA修复功能障碍的进一步证据,与表观遗传水平的研究结果一致。此外,我们利用与母亲肥胖相关的顶端通路基因中的CpG位点训练了一个随机森林分类器,并将其应用于预测14种癌症基因组图谱(TCGA)癌症类型的癌症与邻近正常样本标签。14种癌症中有5种的平衡准确度为0.6或更高:LUSC (0.87), PAAD (0.83), KIRC (0.71), KIRP(0.63)和BRCA(0.60)。结论:本研究揭示了孕前母亲肥胖与后代uhsc多组学水平的分子变化之间的显著相关性,特别是在DNA甲基化水平上。uhsc中这些与母亲肥胖相关的表观遗传标记可能会增加后代患某些癌症的风险。在目前的单点研究之后,有必要进行更大规模的多中心队列验证研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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