Dissipation of lysosome pH impairs formation and collapses existing LPS-induced lysosome tubules in macrophages
Bibliographic record
Abstract
Lysosomes degrade extracellular and intracellular materials targeted for disposal. Lysosomes display an acidic milieu established by the V-ATPase that promotes this hydrolytic activity and powers membrane transport. Interestingly, while lysosomes are typically globular in morphology, lipopolysaccharide-activated macrophages reorganize lysosomes into an expanded tubular network. This tubulation process requires microtubules, dynein and kinesin motors, and the Rab7 and Arl8b GTPases. Here, we sought to determine if the V-ATPase and the lysosomal pH are important for LPS-mediated lysosome tubulation in macrophages. We found that inhibition of the V-ATPase prevented lysosome tubulation and collapsed preformed tubules. However, the V-ATPase also controls mTORC1 activity, which itself is needed for tubulation. To distinguish between lysosomal pH and mTORC1, we turned to NH4Cl. NH4Cl alkalinized lysosomal pH but did not interfere with mTORC1 activity; yet NH4Cl blocked lysosome tubulation showing that an acidic lysosomal pH is needed for lysosome remodelling. Moreover, clamping the pH to either acidic or alkaline pH values caused tubules to collapse, indicating that the pH gradient promotes tubulation, rather than a specific pH value. To better understand how the lysosomal pH helps with lysosome tubulation, we examined microtubule organization and lysosome movement; dissipation of the acidic lysosomal pH did not alter these properties, suggesting that motors remained associated with lysosomes. On the other hand, while LPS did not alter the average pH of spherical or tubular lysosomes, growing tubules displayed a more acidic peripheral end relative to the pericentral end. Based on this observation, we propose that a localized pH gradient along the tubule may enable tubulation by modulating factors that catalyse tubule growth.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".