Regulation of TLR10 Expression and Its Role in Chemotaxis of Human Neutrophils
Bibliographic record
Abstract
Toll-like receptors are innate immune receptors that play a critical role in pathogen-associated molecular pattern recognition. TLR10 was recently identified and very limited data are available on its expression, mechanisms that regulate its expression, and its role in primary immune cells. To study the expression pattern of TLR10 in primary immune cells, we examined TLR10 protein expression in naive and Escherichia coli lipopolysaccharide (LPS)-activated human neutrophils. Human neutrophils challenged with LPS showed a decrease in total and surface TLR10 expression at 90 min. TLR10 in LPS-activated neutrophils colocalized with flotallin-1, a lipid raft marker, and EEA-1, an early endosomal marker, to suggest its endocytosis. There was increased colocalization of TLR10 with TLR4 at LPS 60 min followed by decrease at later LPS treatment times. Treatment with TLR4 neutralizing antibody decreased cytoplasmic localization of TLR10 in LPS-treated neutrophils. Reactive oxygen species (ROS) depletion and neutralization of p65 subunit of NF-κB in LPS-treated neutrophils decreased TLR10 expression. Live cell imaging of LPS-activated neutrophils showed TLR10 translocation in the leading edge and TLR10 knockdown in neutrophils reduced their fMLP-induced chemotaxis and the number of neutrophils with pseudopodia but without affecting the expression of key proteins of actin nucleation process, ARP-3 and Diap1. Taken together, our findings show that neutrophil activation alters TLR10 expression through ROS production and NF-κB regulation, and TLR10 knockdown reduced neutrophil chemotaxis.
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) | 0.002 | 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".