Bacteria Getting into Shape: Genetic Determinants of <i>E. coli</i> Morphology
Bibliographic record
Abstract
ABSTRACT Perturbation of cellular processes is a prevailing approach to understanding biology. To better understand the complicated biology that defines bacterial shape, a sensitive, high-content platform was developed to detect multiple morphological defect phenotypes using microscopy. We examined morphological phenotypes across the Escherichia coli K-12 deletion (Keio) collection at the mid-exponential growth phase, revealing 111 deletions perturbing shape. Interestingly, 64% of these were uncharacterized mutants, illustrating the complex nature of shape maintenance and regulation in bacteria. To understand the roles these genes play in defining morphology, 53 mutants with knockouts resulting in abnormal cell shape were crossed with the Keio collection in high throughput, generating 1,373 synthetic lethal interactions across 1.7 million double deletion mutants. This analysis yielded a highly populated interaction network spanning and linking multiple phenotypes, with a preponderance of interactions involved in transport, oxidation-reduction, and metabolic processes. IMPORTANCE Genetic perturbations of cellular functions are a prevailing approach to understanding cell systems, which are increasingly being practiced in very high throughput. Here, we report a high-content microscopy platform tailored to bacteria, which probes the impact of genetic mutation on cell morphology. This has particular utility in revealing elusive and subtle morphological phenotypes associated with blocks in nonessential cellular functions. We report 111 nonessential mutations impacting E. coli morphology, with nearly half of those genes being poorly annotated or uncharacterized. Further, these genes appear to be tightly linked to transport or redox processes within the cell. The screening platform is simple and low cost and is broadly applicable to any bacterial genomic library or chemical collection. Indeed, this is a powerful tool in understanding the biology behind bacterial shape.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".