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Record W3011206529 · doi:10.1101/2020.03.16.994491

Clusters of bacterial RNA polymerase are biomolecular condensates that assemble through liquid-liquid phase separation

2020· preprint· en· W3011206529 on OpenAlexafffund
A.-M. Ladouceur, Bhaval Parmar, Stefan Biedzinski, J.S. Wall, S. Graydon Tope, David V. Cohn, Anthony Kim, Nicolas Soubry, Rodrigo Reyes‐Lamothe, Stephanie C. Weber

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill University
FundersInstitute of GeneticsNatural Sciences and Engineering Research Council of CanadaKeio UniversityNational Science Foundation
KeywordsNucleoidRNA polymeraseRNABiologyBiophysicsPolymeraseCell biologyIntracellularDNAChemistryBiochemistryGeneEscherichia coli

Abstract

fetched live from OpenAlex

Abstract Once described as mere “bags of enzymes”, bacterial cells are in fact highly organized, with many macromolecules exhibiting non-uniform localization patterns. Yet the physical and biochemical mechanisms that govern this spatial heterogeneity remain largely unknown. Here, we identify liquid-liquid phase separation (LLPS) as a mechanism for organizing clusters of RNA polymerase (RNAP) in E. coli . Using fluorescence imaging, we show that RNAP quickly transitions from a dispersed to clustered localization pattern as cells enter log phase in nutrient-rich media. RNAP clusters are sensitive to hexanediol, a chemical that dissolves liquid-like compartments in eukaryotic cells. In addition, we find that the transcription antitermination factor NusA forms droplets in vitro and in vivo , suggesting that it may nucleate RNAP clusters. Finally, we use single-molecule tracking to characterize the dynamics of cluster components. Our results indicate that RNAP and NusA molecules move inside clusters, with mobilities faster than a DNA locus but slower than bulk diffusion through the nucleoid. We conclude that RNAP clusters are biomolecular condensates that assemble through LLPS. This work provides direct evidence for LLPS in bacteria and suggests that this process serves as a universal mechanism for intracellular organization across the tree of life. Significance Bacterial cells are small and were long thought to have little to no internal structure. However, advances in microscopy have revealed that bacteria do indeed contain subcellular compartments. But how these compartments form has remained a mystery. Recent progress in larger, more complex eukaryotic cells has identified a novel mechanism for intracellular organization known as liquid-liquid phase separation. This process causes certain types of molecules to concentrate within distinct compartments inside the cell. Here, we demonstrate that the same process also occurs in bacteria. This work, together with a growing body of literature, suggests that liquid-liquid phase separation is a universal mechanism for intracellular organization that extends across the tree of life.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.294
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2020
Admission routes2
Has abstractyes

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