Condition-dependent amorphous protein agglomerates control cytoplasmic rheology
Bibliographic record
Abstract
Summary Molecular crowding in the bacterial cytoplasm restricts the diffusion of large molecules, impacting cellular processes. However, how nutrient availability influences cytoplasmic rheology is not well understood. With single-particle tracking in Escherichia coli , we observed a threefold variation in the diffusion of a 40-nm particle across exponential growth conditions. Previously suggested determinants of rheology did not account for this variation; instead, we found a strong anticorrelation between the diffusion coefficient and the abundance of amino acid metabolism proteins, persisting upon genetic perturbations and showing that lower diffusion is associated with increased viscoelasticity. Photoactivated light microscopy revealed that some amino acid metabolism proteins form clusters. Electron microscopy showed that these proteins could form amorphous agglomerates at physiological concentrations in vitro, likely driven by their low intrinsic disorder, high compactness and hydropathy score. These findings show that protein agglomerates regulate cytoplasmic rheology in a condition-dependent manner, suggesting an underappreciated level of cytoplasmic organization. Highlights Diffusion of 40-nm particles varies threefold across growth conditions in E. coli Cytoplasmic diffusion inversely correlates with COG-E protein abundance COG-E proteins form agglomerates that increase cytoplasmic viscoelasticity Protein compactness and hydrophobicity predict condition-dependent crowding effects
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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 | 0.001 | 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".