Sulfur Isotope Fractionation during the Evolutionary Adaptation of a Sulfate-Reducing Bacterium
Bibliographic record
Abstract
Dissimilatory sulfate reduction is a microbial catabolic pathway that preferentially processes less massive sulfur isotopes relative to their heavier counterparts. This sulfur isotope fractionation is recorded in ancient sedimentary rocks and generally is considered to reflect a phenotypic response to environmental variations rather than to evolutionary adaptation. Modern sulfate-reducing microorganisms isolated from similar environments can exhibit a wide range of sulfur isotope fractionations, suggesting that adaptive processes influence the sulfur isotope phenotype. To date, the relationship between evolutionary adaptation and isotopic phenotypes has not been explored. We addressed this by studying the covariation of fitness, sulfur isotope fractionation, and growth characteristics in Desulfovibrio vulgaris Hildenborough in a microbial evolution experiment. After 560 generations, the mean fitness of the evolved lineages relative to the starting isogenic population had increased by ∼ 17%. After 927 generations, the mean fitness relative to the initial ancestral population had increased by ∼ 20%. Growth rate in exponential phase increased during the course of the experiment, suggesting that this was a primary influence behind the fitness increases. Consistent changes were observed within different selection intervals between fractionation and fitness. Fitness changes were associated with changes in exponential growth rate but changes in fractionation were not. Instead, they appeared to be a response to changes in the parameters that govern growth rate: yield and cell-specific sulfate respiration rate. We hypothesize that cell-specific sulfate respiration rate, in particular, provides a bridge that allows physiological controls on fractionation to cross over to the adaptive realm.
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 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) | 0.000 | 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 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".