Role of Hypoxia Inducible Factor-1α (HIF-1α) in Innate Defense against Uropathogenic Escherichia coli Infection
Bibliographic record
Abstract
Uropathogenic E. coli (UPEC) is the primary cause of urinary tract infections (UTI) affecting approximately 150 million people worldwide. Here, we revealed the importance of transcriptional regulator hypoxia-inducible factor-1 α subunit (HIF-1α) in innate defense against UPEC-mediated UTI. The effects of AKB-4924, a HIF-1α stabilizing agent, were studied using human uroepithelial cells (5637) and a murine UTI model. UPEC adherence and invasion were significantly reduced in 5637 cells when HIF-1α protein was allowed to accumulate. Uroepithelial cells treated with AKB-4924 also experienced reduced cell death and exfoliation upon UPEC challenge. In vivo, fewer UPEC were recovered from the urine, bladders and kidneys of mice treated transurethrally with AKB-4924, whereas increased bacteria were recovered from bladders of mice with a HIF-1α deletion. Bladders and kidneys of AKB-4924 treated mice developed less inflammation as evidenced by decreased pro-inflammatory cytokine release and neutrophil activity. AKB-4924 impairs infection in uroepithelial cells and bladders, and could be correlated with enhanced production of nitric oxide and antimicrobial peptides cathelicidin and β-defensin-2. We conclude that HIF-1α transcriptional regulation plays a key role in defense of the urinary tract against UPEC infection, and that pharmacological HIF-1α boosting could be explored further as an adjunctive therapy strategy for serious or recurrent UTI.
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) | 0.000 | 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".