<i>Lactobacillus salivarius</i> HHuMin-U Activates Innate Immune Defense against Norovirus Infection through TBK1-IRF3 and NF-κB Signaling Pathways
Bibliographic record
Abstract
The composition of commensal bacteria plays a critical role in controlling immune responses in the intestine. Studies have shown that specific bacterial strains may have the capacity to enhance host immune defense against gastrointestinal viral infections. While norovirus is known to be the most common cause of gastroenteritis, leading to an estimated 200,000 deaths every year, identification of bacterial strains with protective effects against norovirus infection remains elusive. Here, we discovered Lactobacillus salivarius HHuMin-U (HHuMin-U) as a potent antiviral strain against norovirus infection. HHuMin-U significantly suppressed murine norovirus replication and lowered viral RNA titers in macrophages. The transcriptome sequencing (RNA sequencing) analysis revealed that HHuMin-U markedly enhanced the expression level of antiviral interferon-stimulated genes compared to mock treatment. HHuMin-U treatment dose-dependently induced type I interferons (IFN-α and IFN-β) and tumor necrosis factor-α production in mouse and human macrophages, promoting antiviral innate responses against norovirus infection. Investigation on the molecular mechanism demonstrated that HHuMin-U can activate nuclear factor κB and TANK-binding kinase 1 (TBK1)–interferon regulatory factor 3 signaling pathways, leading to the phosphorylation of signal transducer and activator of transcription 1 and signal transducer and activator of transcription 2, the key mediators of interferon-stimulated genes. Finally, oral administration of HHuMin-U increased IFN-β levels in the ileum of mice and altered the gut microbiome profile. These results suggest the species/strain-specific importance of gut microbial composition for antiviral immune responses and the potential use of HHuMin-U as a probiotic agent.
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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) | 0.001 | 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".