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通过基于网络的多组学整合阐明人乳寡糖生物合成基因。

Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration.

作者信息

Kellman Benjamin P, Richelle Anne, Yang Jeong-Yeh, Chapla Digantkumar, Chiang Austin W T, Najera Julia A, Liang Chenguang, Fürst Annalee, Bao Bokan, Koga Natalia, Mohammad Mahmoud A, Bruntse Anders Bech, Haymond Morey W, Moremen Kelley W, Bode Lars, Lewis Nathan E

机构信息

Department of Pediatrics, University of California, San Diego, La Jolla, CA, 92093, USA.

Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA.

出版信息

Nat Commun. 2022 May 4;13(1):2455. doi: 10.1038/s41467-022-29867-4.

DOI:10.1038/s41467-022-29867-4
PMID:35508452
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9068700/
Abstract

Human Milk Oligosaccharides (HMOs) are abundant carbohydrates fundamental to infant health and development. Although these oligosaccharides were discovered more than half a century ago, their biosynthesis in the mammary gland remains largely uncharacterized. Here, we use a systems biology framework that integrates glycan and RNA expression data to construct an HMO biosynthetic network and predict glycosyltransferases involved. To accomplish this, we construct models describing the most likely pathways for the synthesis of the oligosaccharides accounting for >95% of the HMO content in human milk. Through our models, we propose candidate genes for elongation, branching, fucosylation, and sialylation of HMOs. Our model aggregation approach recovers 2 of 2 previously known gene-enzyme relations and 2 of 3 empirically confirmed gene-enzyme relations. The top genes we propose for the remaining 5 linkage reactions are consistent with previously published literature. These results provide the molecular basis of HMO biosynthesis necessary to guide progress in HMO research and application with the goal of understanding and improving infant health and development.

摘要

人乳寡糖(HMOs)是对婴儿健康和发育至关重要的丰富碳水化合物。尽管这些寡糖在半个多世纪前就已被发现,但其在乳腺中的生物合成在很大程度上仍未得到充分表征。在此,我们使用一个整合聚糖和RNA表达数据的系统生物学框架来构建HMO生物合成网络,并预测其中涉及的糖基转移酶。为实现这一目标,我们构建了描述寡糖合成最可能途径的模型,这些寡糖占人乳中HMO含量的95%以上。通过我们的模型,我们提出了HMO延伸、分支、岩藻糖基化和唾液酸化的候选基因。我们的模型汇总方法恢复了2个先前已知的基因-酶关系中的2个以及3个经实验证实的基因-酶关系中的2个。我们为其余5个连接反应提出的顶级基因与先前发表的文献一致。这些结果为HMO生物合成提供了分子基础,这对于指导HMO研究和应用的进展、以理解和改善婴儿健康与发育为目标而言是必要的。

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Nat Commun. 2021 Aug 17;12(1):4988. doi: 10.1038/s41467-021-25183-5.
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Review of Machine Learning Methods for the Prediction and Reconstruction of Metabolic Pathways.用于代谢途径预测与重建的机器学习方法综述
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Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication.
Glycosyltransferases: glycoengineers in human milk oligosaccharide synthesis and manufacturing.
糖基转移酶:人乳寡糖合成与制造中的糖基工程酶
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Robust and High-Resolution All-Ion Fragmentation LC-ESI-IM-MS Analysis for In-Depth Characterization or Profiling of Up to 200 Human Milk Oligosaccharides.用于深入表征或分析多达200种人乳寡糖的稳健且高分辨率的全离子碎裂液相色谱-电喷雾离子化-离子淌度质谱分析
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Recent advances in the science of human milk oligosaccharides.人乳寡糖科学的最新进展
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