Identification of the hydrophobic glycoproteins of Caenorhabditis elegans
Bibliographic record
Abstract
Hydrophobic proteins such as integral membrane proteins are difficult to separate, and therefore to study, at a proteomics level. However, the Asn-linked (N-linked) carbohydrates (N-glycans) contained in membrane glycoproteins are important in differentiation, embryogenesis, inflammation, cancer and metastasis, and other vital cellular processes. Thus, the identification of these proteins and their sites of glycosylation in a well-characterized model organism is the first step toward understanding the mechanisms by which N-glycans and their associated proteins function in vivo. In this report, a proteomics method recently developed by our group was applied to identify 117 hydrophobic N-glycosylated proteins of Caenorhabditis elegans extracts by analysis of 195 glycopeptides containing 199 Asn-linked oligosaccharides. Most of the proteins identified are involved in cell adhesion, metabolism, or the transport of small molecules. In addition, there are 18 proteins for which no function is known or predictable by sequence homologies and two proteins which were previously predicted to exist only on the basis of genomic sequences in the C. elegans database. Because N-glycosylation is initiated in the lumen of the endoplasmic reticulum (ER), our data can be used to reassess the previously predicted subcellular localizations of these proteins. As well, the identification of N-glycosylation sites helps establish the membrane topology of the associated glycoproteins. Caenorhabditis elegans strains are presently available with mutations in 17 of the genes we have identified. The powerful genetic tools available for C. elegans can be used to make other strains with mutations in genes encoding N-glycosylated proteins and thereby determine N-glycan function.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".