MétaCan
Menu
Back to cohort
Record W1978750313 · doi:10.1093/glycob/cwt034

A proposal for the naming of N-glycosylation pathway components in Archaea

2013· article· en· W1978750313 on OpenAlexaff
Jerry Eichler, Ken F. Jarrell, Sonja‐Verena Albers

Bibliographic record

VenueGlycobiology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsArchaeaGlycosylationComputational biologyBiologyComputer scienceGeneticsBacteria

Abstract

fetched live from OpenAlex

To the Editor, In 1976, the long-held opinion that N-glycosylation was a trait restricted to eukaryotes was overturned when the surface-layer glycoprotein from the archaeon Halobacterium salinarum was shown to undergo such post-translational modification (Mescher and Strominger, 1976). Largely based on genomic studies, it is now believed that N-glycosylation is a common process in Archaea (Kaminski et al., 2013). Analysis of the limited number of characterized archaeal N-linked glycan reveals diversity in composition unparalleled in either eukaryal or bacterial N-glycosylation (Schwarz and Aebi, 2011; Eichler, 2013). Such variety is indicative of N-glycosylation in Archaea being largely mediated by different, species-specific pathways. Current biochemical descriptions of archaeal N-glycosylation systems support this claim (Jarrell et al., 2010; Eichler, 2013; Meyer and Albers, 2013). As such, it is essential that workers in the field adhere to a common nomenclature for the genes and proteins involved in the archaeal version of this post-translational modification. Indeed, by adopting an agreed upon naming system, some of the ambiguity plaguing the field of bacterial protein glycosylation might be avoided. For instance, although PglB, PglC, PglD, PglE and PglF all contribute to N-glycosylation in Campylobacter jejuni and O-glycosylation in Neisseria gonorrhoeae, proteins bearing the same name serve very different roles in each system (Linton et al., 2005; Aas et al., 2007). In 2006, Chaban et al. proposed the use of the abbreviation agl (for archaeal glycosylation) to identify genes involved in the archaeal version of N-glycosylation, along the guidelines for naming bacterial genes outlined in Demerec et al. (1966). Studies in Haloferax volcanii, Methanococcus voltae and Methanococcus maripaludis thus annotated proteins serving various N-glycosylation-related roles as AglA to AglZ (for review, see Eichler, 2013). More recently, however, additional genes encoding proteins implicated in Sulfolobus acidocaldarius N-glycosylation were annotated and published as agl1-agl4 and agl16 (Meyer et al., 2011, 2013). In this case, the nomenclature adopted is similar to that used for naming yeast genes, where a three letter abbreviation is followed by a number (Cherry et al., 2012). In the absence of an accepted set of rules for naming archaeal genes, we believe that the community would be best served by maintaining use of the agl/Agl abbreviation followed by a number for naming new archaeal N-glycosylation genes/proteins, given that all of the letters of the alphabet have already been used in this context. Indeed, given that archaeal N-glycosylation presents aspects of the both its bacterial and eukaryal counterparts (see Calo et al., 2010; Jarrell et al., 2010; Meyer and Albers, 2011), adopting a combination of bacterial and eukaryal nomenclature rules for naming genes involved in the archaeal version of this post-translational modification is fitting. By adopting an open-ended number-based system for naming novel archaeal N-glycosylation to be identified in the future, the enormous variety of genes/proteins responsible for the diversity seen in archaeal N-glycosylation could be nonetheless linked via the common agl abbreviation. At the same time, a shared N-glycosylation-related function would be assigned the same name in all species, as in the case of aglB, encoding the archaeal oligosaccharyltransferase. By the same logic, as the precise roles of Agl proteins become clear, components could be renamed with the first letter/number assigned that specific role. While such a naming strategy could result in a given archaeal species containing agl genes bearing widely spaced letters and/or numbers, it would allow for easy identification of similarities in N-glycosylation pathways that will likely prove to be otherwise largely species-specific. As members of the archaeal N-glycosylation research community, we propose that the agl-based nomenclature outlined above be adopted for annotating any relevant new genes and proteins identified. To facilitate such efforts, researchers are invited to visit the aglgenes website (www.bgu.ac.il/aglgenes), where an updated listing of agl sequences is provided.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.269
Teacher spread0.247 · 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 teacher head, not a consensus.

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

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

Citations13
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueGlycobiologySame topicGlycosylation and Glycoproteins ResearchFrench-language works237,207