Lipocalin 2 is essential for chronic kidney disease progression in mice and humans
Bibliographic record
Abstract
Mechanisms of progression of chronic kidney disease (CKD), a major health care burden, are poorly understood. EGFR stimulates CKD progression, but the molecular networks that mediate its biological effects remain unknown. We recently showed that the severity of renal lesions after nephron reduction varied substantially among mouse strains and required activation of EGFR. Here, we utilized two mouse strains that react differently to nephron reduction--FVB/N mice, which develop severe renal lesions, and B6D2F1 mice, which are resistant to early deterioration--coupled with genome-wide expression to elucidate the molecular nature of CKD progression. Our results showed that lipocalin 2 (Lcn2, also known as neutrophil gelatinase-associated lipocalin [NGAL]), the most highly upregulated gene in the FVB/N strain, was not simply a marker of renal lesions, but an active player in disease progression. In fact, the severity of renal lesions was dramatically reduced in Lcn2-/- mice. We discovered that Lcn2 expression increased upon EGFR activation and that Lcn2 mediated its mitogenic effect during renal deterioration. EGFR inhibition prevented Lcn2 upregulation and lesion development in mice expressing a dominant negative EGFR isoform, and hypoxia-inducible factor 1α (Hif-1α) was crucially required for EGFR-induced Lcn2 overexpression. Consistent with this, cell proliferation was dramatically reduced in Lcn2-/- mice. These data are relevant to human CKD, as we found that LCN2 was increased particularly in patients who rapidly progressed to end-stage renal failure. Together our results uncover what we believe to be a novel function for Lcn2 and a critical pathway leading to progressive renal failure and cystogenesis.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".