Lipocalin 2 attenuates oligodendrocyte loss and immune cell infiltration in mouse models for multiple sclerosis
Bibliographic record
Abstract
Abstract Multiple sclerosis (MS) is a central nervous system disease characterized by both degenerative and inflammatory processes. Various mediators are involved in the interplay of degeneration and innate immunity on one hand and peripheral adaptive immunity on the other hand. The secreted protein lipocalin 2 (LCN2) is an inflammatory modulator in a variety of pathologies. Although elevated intrathecal levels of LCN2 have been reported in MS patients, it's functional role is widely unknown. Here, we identified a subpopulation of astrocytes as a source of LCN2 in MS lesions and respective animal models. We investigated the functional role of LCN2 for both autoimmune and degenerative aspects in three MS mouse models including both wild type (WT) and Lcn2 −/− mouse strains. While the experimental autoimmune encephalomyelitis (EAE) model reflects primary autoimmunity, the cuprizone model reflects selective oligodendrocyte loss and demyelination. In addition, we included a combinatory Cup/EAE model in which primary cytodegeneration is followed by inflammatory lesions within the forebrain. While in the EAE model, the disease outcome was comparable in between the two mouse strains, cuprizone intoxicated Lcn2 −/− animals showed an increased loss of oligodendrocytes. In the Cup/EAE model, Lcn2 −/− animals showed increased inflammation when compared to WT mice. Together, our results highlight LCN2 as a potentially protective molecule in MS lesion formation, which might be able to limit loss of oligodendrocytes immune‐cell invasion. Despite these findings, it is not yet clear which glial cell phenotype (and to which extent) contributes to the observed neuroprotective effects, that is, microglia and/or astroglia or even endothelial cells in the brain.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".