TMEM16F Expressed in Kupffer Cells Regulates Liver Inflammation and Metabolism to Protect Against <i>Listeria Monocytogenes</i>
Bibliographic record
Abstract
Infection by bacteria leads to tissue damage and inflammation, which need to be tightly controlled by host mechanisms to avoid deleterious consequences. It is previously reported that TMEM16F, a calcium-activated lipid scramblase expressed in various immune cell types including T cells and neutrophils, is critical for the control of infection by bacterium Listeria monocytogenes (Lm) in vivo. This function correlated with the capacity of TMEM16F to repair the plasma membrane (PM) damage induced in T cells in vitro, by the Lm toxin listeriolysin O (LLO). However, whether the protective effect of TMEM16F on Lm infection in vivo is mediated by an impact in T cells, or in other cell types, is not determined. Herein, the immune cell types and mechanisms implicated in the protective effect of TMEM16F against Lm in vivo are elucidated. Cellular protective effects of TMEM16F correlated with its capacity of lipid scrambling and augment PM fluidity. Using cell type-specific TMEM16F-deficient mice, the indication is obtained that TMEM16F expressed in liver Kupffer cells (KCs), but not in T cells or B cells, is key for protection against Listeria in vivo. In the absence of TMEM16F, Listeria induced PM rupture and fragmentation of KCs in vivo. KC death associated with greater liver damage, inflammatory changes, and dysregulated liver metabolism. Overall, the results uncovered that TMEM16F expressed in Kupffer cells is crucial to protect the host against Listeria infection. This influence is associated with the capacity of Kupffer cell-expressed TMEM16F to prevent excessive inflammation and abnormal liver metabolism.
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) | 0.001 | 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".