Multi-modal Proteomic Characterization of Lysosomal Function and Proteostasis in Progranulin-Deficient Neurons
Bibliographic record
Abstract
Abstract Progranulin (PGRN) is a lysosomal protein implicated in various neurodegenerative diseases. Over 70 mutations discovered in the GRN gene all result in reduced expression of PGRN protein. However, the detailed molecular function of PGRN within lysosomes and the impact of PGRN deficiency on lysosomal biology remain unclear. Here we leveraged multifaceted proteomic techniques to comprehensively characterize how PGRN deficiency changes the molecular and functional landscape of neuronal lysosomes. Using lysosome proximity labeling and immuno-purification of intact lysosomes, we characterized lysosome compositions and interactomes in both human induced pluripotent stem cell (iPSC)-derived glutamatergic neurons (i 3 Neurons) and mouse brains. Using dynamic stable isotope labeling by amino acids in cell culture (dSILAC) proteomics, we measured global protein half-lives in i 3 Neurons for the first time and characterized the impact of progranulin deficiency on neuronal proteostasis. Together, this study indicated that PGRN loss impairs the lysosome’s degradative capacity with increased levels of v-ATPase subunits on the lysosome membrane, increased catabolic enzymes within the lysosome, elevated lysosomal pH, and pronounced alterations in neuron protein turnover. Collectively, these results suggested PGRN as a critical regulator of lysosomal pH and degradative capacity, which in turn influences global proteostasis in neurons. The multi-modal techniques developed here also provided useful data resources and tools to study the highly dynamic lysosome biology in neurons.
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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".