Quantitative proteomics identifies ferritin in the innate immune response of<i>C. elegans</i>
Bibliographic record
Abstract
When encountering a pathogen, all organisms evoke a protective response by inducing defense mechanisms to help fight off the invader. The invertebrate model organism Caenorhabditis elegans has proven to be valuable for studies of the host response and the small nematode mounts a substantial transcriptional response to numerous pathogens. Here, we use global quantitative proteomics to profile the response to infection with E. coli strain LF82 isolated from patients suffering from Crohn's disease, an inflammatory bowel disease. We show that LF82 infection induces more than one hundred proteins. The response share many functional categories with other innate immunity studies in C. elegans, but also identifies novel host immune effector proteins. We demonstrate functional relevance for four LF82 induced proteins, including a lysozyme and a C-type lectin. The ferritin homolog FTN-2 was shown to be necessary for the full protective response against the Gram-negative LF82 and the Gram-positive pathogen Staphylococcus aureus. This study is the first to demonstrate a role for ferritin in the innate immune response of C. elegans, and our results suggests that quantitative proteomics is an attractive approach for identifying additional components in the complex immune response of the nematode.
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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".