LsrL modulates Lsr2-induced chromatin structure to tune biosynthetic gene cluster regulation in <i>Streptomyces venezuelae</i>
Bibliographic record
Abstract
Abstract Specialized biosynthetic gene clusters in Streptomyces are subject to complex regulation involving both transcriptional control and chromosome organization. The nucleoid-associated protein Lsr2 silences many of these clusters, yet how it shapes the global chromatin structure and how its conserved paralog LsrL contributes to this process remain poorly understood. In this study, we applied a multi-omics approach, combining transcriptional activity, genome-wide protein-DNA binding profiles, and three-dimensional chromosome conformation to characterize the coordination of Lsr2 and LsrL in exerting transcriptional control and genome architecture in Streptomyces venezuelae . In line with established Lsr2 functions, we find that Lsr2 sets broad transcriptional boundaries, while LsrL acts in a more context-specific manner that depends on the presence of Lsr2 and may function to reinforce or modulate Lsr2-mediated silencing. Loss of Lsr2 reshaped the chromatin landscape genome-wide, relieving its restriction on short-range contacts, triggering strong transcriptional changes and new domain boundaries near de-repressed biosynthetic gene clusters. These findings establish Lsr2 as a dominant but contextually modulated regulator whose interplay with LsrL coordinates specialized metabolism with higher-order chromosome organization.
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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) | 0.001 | 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".