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Record W4220715531 · doi:10.3390/pathogens11040426

Sugar Coating: Utilisation of Host Serum Sialoglycoproteins by Schistosoma mansoni as a Potential Immune Evasion Mechanism

2022· article· en· W4220715531 on OpenAlexafffund
Maude Dagenais, Jared Q. Gerlach, Timothy G. Geary, Thavy Long

Bibliographic record

VenuePathogens · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSchistosoma mansoniImmune systemSialic acidBiologyGlycanGlycoproteinCell biologyEffectorAntigenHost (biology)ImmunologyMicrobiologyHelminthsBiochemistrySchistosomiasis

Abstract

fetched live from OpenAlex

Parasitic helminths resort to various mechanisms to evade and modulate their host’s immune response, several of which have been described for Schistosoma mansoni. We recently reported the presence of sialic acid residues on the surface of adult S. mansoni extracellular vesicles (EVs). We now report that these sialylated molecules are mammalian serum proteins. In addition, our data suggest that most sialylated EV-associated proteins do not elicit a humoral response upon injection into mice, or in sera obtained from infected animals. Sialic acids frequently terminate glycans on the surface of vertebrate cells, where they serve important functions in physiological processes such as cell adhesion and signalling. Interestingly, several pathogens have evolved ways to mimic or utilise host sialic acid beneficially by coating their own proteins, thereby facilitating cell invasion and providing protection from host immune effectors. Together, our results indicate that S. mansoni EVs are coated with host glycoproteins, which may contribute to immune evasion by masking antigenic sites, protecting EVs from removal from serum and aiding in cell adhesion and entry to exert their functions.

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 categoriesInsufficient payload (model declined to judge)
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 score0.053
Threshold uncertainty score0.997

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.237
Teacher spread0.228 · 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.

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

Citations7
Published2022
Admission routes2
Has abstractyes

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