In one paragraphArticle in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
9 authors.
Xingyu LiuState Key Laboratory of Genetic Engineering, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.ORCID 0000-0003-0008-7815 Li YiState Key Laboratory of Genetic Engineering, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.ORCID 0009-0002-2872-2569 Zongtao LinDepartment of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri, United States.ORCID 0000-0002-6017-338X Siyu ChenDepartment of Chemistry, University of California, Davis, Davis, California, United States.ORCID 0009-0003-9584-6905 Shunyang WangDepartment of Chemistry, University of California, Davis, Davis, California, United States.ORCID 0000-0001-9851-2968 Ying ShengDepartment of Chemistry, University of California, Davis, Davis, California, United States.ORCID 0000-0002-6002-9783 Carlito B LebrillaDepartment of Chemistry, University of California, Davis, Davis, California, United States.ORCID 0000-0001-7190-5323 Benjamin A GarciaDepartment of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri, United States.ORCID 0000-0003-3596-4750 Yixuan XieState Key Laboratory of Genetic Engineering, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.ORCID 0000-0002-8512-6053 Funding
Research Education ComponentP30AG066444 · NIA · WASHINGTON UNIVERSITY · PI Susan Lynn Stark · 2020 to 2026
$28.7MShared Resources Core 2: Quantitative Proteomics CoreP01CA196539 · NCI · ROCKEFELLER UNIVERSITY · PI YOUNG, MICHAEL WARREN · 2015 to 2024
$17.6MThe incorporation of human milk oligosaccharides in brain glycoconjugatesR01GM049077 · NIGMS · UNIVERSITY OF CALIFORNIA DAVIS · PI LEBRILLA, CARLITO B · 1999 to 2024
$6.3MViral modulation of epitranscriptomic mechanismsR01AI118891 · NIAID · WASHINGTON UNIVERSITY · PI GARCIA, BENJAMIN A, WEITZMAN, MATTHEW D. · 2015 to 2025
$5.4MComprehensive Characterization of Glycosylation Alterations in Alzheimer’s DiseaseR01AG062240 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI JIN, LEE-WAY, LEBRILLA, CARLITO B · 2018 to 2022
$3.3MQuantitative mass spectrometry for comprehending epigenetic mechanisms in a new underlying neurological developmental disorderR01HD106051 · NICHD · WASHINGTON UNIVERSITY · PI Benjamin A Garcia · 2022 to 2026
$2.6MNCI NIH HHS P01 CA196539NIAID NIH HHS R01 AI118891NIA NIH HHS P30 AG066444NIA NIH HHS R01 AG062240NICHD NIH HHS R01 HD106051NIGMS NIH HHS R01 GM049077
6 · The paper itselfAbstract
Protein-protein interactions (PPIs) provide essential insights into the complex molecular mechanisms and signaling pathways within cells that regulate development and disease-related phenotypes. However, for membrane proteins, the impact of various forms of glycosylation has often been overlooked in PPI studies. In this study, we introduce a novel approach, glycan-dependent affinity purification followed by mass spectrometry (GAP-MS), to assess variations in PPIs for any glycoprotein of interest under different glycosylation conditions. As a proof of principle, we selected four glycoproteins-BSG, CD44, EGFR, and SLC3A2-as baits to compare their co-purified partners across five metabolically controlled glycan conditions. The findings demonstrate the capability of GAP-MS to identify PPIs influenced by altered glycosylation states, establishing a foundation for systematically exploring the Glycan-Dependent Protein Interactome (GDPI) for other glycoproteins of interest.
Identifiers
PMID39554187
PMCPMC11565926
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