Glycan-mediated interactions between bacteria, rotavirus and the host cells provide an additional mechanism of antiviral defence
Bibliographic record
Abstract
Limited efficacy of rotavirus (RV) vaccines in children in developing countries and in animals remains a significant problem necessitating further search for additional approaches to control RV-associated gastroenteritis. During cell attachment and entry events, RV interacts with cell surface O-glycans including histo-blood group antigens (HBGAs). Besides modulation of the protective immunity against RV, several commensal and probiotic bacteria were shown to express HBGA-like substances suggesting that they may affect RV attachment and entry into the host cells. Moreover, some beneficial bacteria have been shown to possess the ability to bind host HBGAs via sugar specific proteins called lectins. However, limited research has been done to evaluate the effects of HBGA-expressing and/or HBGA-binding bacteria on RV infection. The aim of this study was to investigate the ability of selected commensal and probiotic bacteria to bind different RV strains via HBGAs and to block RV infection of IPEC-J2 cells. Our data indicated that Gram-negative probiotic Escherichia coli Nissle 1917 (E. coli Nissle 1917) and commensal Gram-positive (Streptococcus bovis and Bifidobacterium adolescentis) and Gram-negative (Bacteroides thetaiotaomicron, Clostridium clostridioforme and Escherichia coli G58 (E. coli G58) bacteria of swine origin expressed HBGAs which correlated with their ability to bind group A and C RVs. Additionally, Gram-positive E. coli 1917 and E. coli G58 demonstrated the ability to block RV attachment onto IPEC-J2 cells. Taken together, our results support the hypothesis that physical interactions between RVs and HBGA-expressing beneficial bacteria may limit RV replication.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".