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Cellular O‐Glycome Reporter/Amplification (CORA) to Explore O‐Glycans of Living Cells

2016· article· en· W4389024857 on OpenAlexfundno aff
Matthew R. Kudelka, Aristotelis Antonopoulos, Yingchun Wang, Duc M. Duong, Xuezheng Song, Nicholas T. Seyfried, Anne Dell, Stuart M. Haslam, Richard D. Cummings, Tongzhong Ju

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research CouncilNational Institutes of HealthResearch and Development Corporation of Newfoundland and Labrador
KeywordsGlycomeGlycanGlycosylationGlycomicsChemistryBiochemistryFucosylationComputational biologyBiologyGlycoprotein

Abstract

fetched live from OpenAlex

O‐glycosylation is present on over 80% of proteins that traverse the secretory apparatus and plays key roles in many biological processes. However, the repertoire of O‐glycans synthesized by cells and thus their function are difficult to determine. Current strategies to evaluate O‐glycans from cells utilize chemical release, such as alkaline β‐elimination, prior to analysis by mass spectrometry or other technologies. However, β‐elimination is inefficient, potentially biased, and results in O‐glycan degradation. To address this challenge, we developed a technology to amplify and profile mucin‐type O‐glycans synthesized by living cells, termed Cellular O‐Glycome Reporter/Amplification (CORA). We developed a chemical O‐glycan precursor that when incubated with live cells crosses the plasma membrane, is taken up by the Golgi Apparatus, and is modified by glycosyltransferases in situ , before being secreted from cells as a variety of modified O‐glycan derivatives, allowing easy purification for analysis by HPLC and mass spectrometry (MS). CORA detected O‐glycans observed by β‐elimination as well as many additional complex structures with ~100–1000‐fold increase in sensitivity over conventional O‐glycan analyses. Furthermore, CORA coupled with computational modeling allowed us to predict the diversity of the human O‐glycome. To our knowledge, CORA is the first technology for glycome amplification and thus could offer new opportunities for understanding the role of glycans in health and disease. Support or Funding Information This work was supported by National Institutes of Health Grant U01CA168930 to TJ and RDC, P41GM103694 to RDC, Georgia Cancer Coalition (now Georgia Research Alliance, GRA) Award to TJ, and by Biotechnology and Biological Sciences Research Council grant BB/K016164/1 (AD and SMH for Core Support for Collaborative Research). AD is supported by a Wellcome Trust Senior Investigator Award. We also acknowledge support from the Emory Integrated Proteomics Core.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.289
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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