Identification of Proteins Associated with Multilamellar Bodies Produced by Dictyostelium discoideum
Bibliographic record
Abstract
Dictyostelium discoideum amoebae produce and secrete multilamellar bodies (MLBs) when fed digestible bacteria. The aim of the present study was to elucidate the proteic content of MLBs. The lipid composition of MLBs is mainly amoebal in origin, suggesting that MLB formation is a protozoa-driven process that could play a significant role in amoebal physiology. We identified four major proteins on purified MLBs using mass spectrometry in order to better understand the molecular mechanisms governing MLB formation and, eventually, to elucidate the true function of MLBs. These proteins were SctA, PhoPQ, PonC and a protein containing a cytidine/deoxycytidylate deaminase (CDD) zinc-binding region. SctA is a component of pycnosomes, which are membranous materials that are continuously secreted by amoebae. The presence of SctA on MLBs was confirmed by immunofluorescence and Western blotting using a specific anti-SctA antibody. The CDD protein may be one of the proteins recognized by the H36 antibody, which was used as a MLB marker in a previous study. The function of the CDD protein is unknown. Immunofluorescence and flow cytometric analyses confirmed that the H36 antibody is a better marker of MLBs than the anti-SctA antibody. This study is an additional step to elucidate the potential role of MLBs and revealed that only a small set of proteins appeared to be present on MLBs.
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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 | 0.001 | 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".