Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis.
Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis.
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通过考虑聚糖生物合成来纠正糖基质中的稀疏性和相互依赖性。
DOI：
10.1038/s41467-021-25183-5
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发表时间：
2021-08-17
影响因子：
16.6
通讯作者：
Lewis NE
中科院分区：
文献类型：
--
作者：
Bao B;Kellman BP;Chiang AWT;Zhang Y;Sorrentino JT;York AK;Mohammad MA;Haymond MW;Bode L;Lewis NE
聚糖是基本的细胞构建模块，参与许多有机体功能。糖组学的进展正在阐明聚糖的重要作用。尽管如此，正确分析大型糖组学数据集仍然具有挑战性，因为每种聚糖的丰度取决于许多其他共享许多中间生物合成步骤的聚糖。此外，样品中测量的聚糖的重叠可能很低。我们通过 GlyCompare 解决这些挑战，这是一种糖组学数据分析方法，可解释所有测量的聚糖的共享生物合成步骤，以纠正糖组学中的稀疏性和非独立性，从而能够直接比较不同的糖谱并提高统计能力。使用 GlyCompare，我们研究了糖工程促红细胞生成素的不同 N-聚糖谱。我们获得了具有生物学意义的突变细胞糖谱聚类，并鉴定了岩藻糖基转移酶突变体对四触角结构的敲除特异性效应。我们进一步分析母乳寡糖谱，发现母亲的岩藻糖基转移酶依赖性分泌状态间接影响唾液酸化。最后，我们将我们的方法应用于粘蛋白型 O-聚糖、神经节苷脂和位点特异性组成糖基化数据，以揭示组织和疾病特异性聚糖的呈现。我们以子结构为导向的方法将使研究人员能够充分利用糖组学数据不断增长的能力和规模。糖组学可以揭示重要的分子变化，但测量的聚糖高度互连且与常见的统计方法不兼容，从而在分析过程中引入陷阱。在这里，作者开发了一种方法来识别样品之间的聚糖依赖性，以促进比较糖组学。
Glycans are fundamental cellular building blocks, involved in many organismal functions. Advances in glycomics are elucidating the essential roles of glycans. Still, it remains challenging to properly analyze large glycomics datasets, since the abundance of each glycan is dependent on many other glycans that share many intermediate biosynthetic steps. Furthermore, the overlap of measured glycans can be low across samples. We address these challenges with GlyCompare, a glycomic data analysis approach that accounts for shared biosynthetic steps for all measured glycans to correct for sparsity and non-independence in glycomics, which enables direct comparison of different glycoprofiles and increases statistical power. Using GlyCompare, we study diverse N-glycan profiles from glycoengineered erythropoietin. We obtain biologically meaningful clustering of mutant cell glycoprofiles and identify knockout-specific effects of fucosyltransferase mutants on tetra-antennary structures. We further analyze human milk oligosaccharide profiles and find mother’s fucosyltransferase-dependent secretor-status indirectly impact the sialylation. Finally, we apply our method on mucin-type O-glycans, gangliosides, and site-specific compositional glycosylation data to reveal tissues and disease-specific glycan presentations. Our substructure-oriented approach will enable researchers to take full advantage of the growing power and size of glycomics data. Glycomics can uncover important molecular changes but measured glycans are highly interconnected and incompatible with common statistical methods, introducing pitfalls during analysis. Here, the authors develop an approach to identify glycan dependencies across samples to facilitate comparative glycomics.
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影响因子：
16.6
作者：
Benedetti E;Pučić-Baković M;Keser T;Wahl A;Hassinen A;Yang JY;Liu L;Trbojević-Akmačić I;Razdorov G;Štambuk J;Klarić L;Ugrina I;Selman MHJ;Wuhrer M;Rudan I;Polasek O;Hayward C;Grallert H;Strauch K;Peters A;Meitinger T;Gieger C;Vilaj M;Boons GJ;Moremen KW;Ovchinnikova T;Bovin N;Kellokumpu S;Theis FJ;Lauc G;Krumsiek J
通讯作者：
Krumsiek J
影响因子：
30.3
作者：
Bojar, Daniel;Powers, Rani K.;Collins, James J.
通讯作者：
Collins, James J.
影响因子：
4.6
作者：
Doherty M;Theodoratou E;Walsh I;Adamczyk B;Stöckmann H;Agakov F;Timofeeva M;Trbojević-Akmačić I;Vučković F;Duffy F;McManus CA;Farrington SM;Dunlop MG;Perola M;Lauc G;Campbell H;Rudd PM
通讯作者：
Rudd PM
影响因子：
5.8
作者：
Klein, Joshua;Carvalho, Luis;Zaia, Joseph
通讯作者：
Zaia, Joseph
影响因子：
16.6
作者：
Gutierrez, Jahir M.;Feizi, Amir;Lewis, Nathan E.
通讯作者：
Lewis, Nathan E.