Damage Repair Versus Aging in Biofilms
Bibliographic record
Abstract
ABSTRACT The extent of senescence due to damage accumulation (or aging) is evidently evolvable as it varies hugely between species and is not universal, suggesting that its fitness advantages depend on life history and environment. In contrast, repair of damage is present in all organisms studied. Repair and segregation of damage have not always been considered as alternatives, despite the fundamental trade-off between investing resources into repair or growth. For unicellular organisms, unrepaired damage could be divided asymmetrically between daughter cells, leading to aging of one and rejuvenation of the other. Repair of unicells has been shown to be advantageous in well-mixed environments such as chemostats. However, most microorganisms live in spatially structured systems such as biofilms with gradients of environmental conditions and cellular physiology as well as clonal population structure. We asked whether this clonal structure might favor aging by damage segregation as this can be seen as a division of labor strategy, akin to the germline soma division in multicellular organisms. We used an individual-based model with a newly developed adaptive repair strategy where cells respond to their current intracellular damage levels by investing into repair machinery accordingly. We found that the new adaptive repair strategy was advantageous whenever efficient and optimal, both in biofilms and chemostats. Thus, biofilms do not favor a germline soma-like division of labor between daughter cells in terms of damage segregation. We suggest that damage segregation is only beneficial when active and effective, extrinsic mortality is high and a degree of multicellularity is present. IMPORTANCE Damage is an inevitable consequence of life, leading to a trade-off between allocating resources into damage repair or into growth whilst allowing aging, i.e. , segregation of damage upon cell division. Few studies considered repair as an alternative to aging. Moreover, all previous studies merely considered well-mixed environments, although the vast majority of unicellular organisms live in spatially structured environments, exemplified by biofilms, and fitness advantages in well-mixed systems often turn into disadvantages in spatially structured systems. We compared the fitness consequences of aging versus damage repair in biofilms with an individual-based model implementing an adaptive repair mechanism based on sensing damage. We found that aging is not beneficial. Instead, it is useful as a stress response to deal with damage that failed to be repaired when (i) clearly asymmetric cell division is feasible; (ii) extrinsic mortality is high; and (iii) a degree of multicellularity is present.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".