Cellular RNA levels define heterotrophic substrate-uptake rate sub-guilds in activated sludge microbial communities
Bibliographic record
Abstract
A heterotrophic-specialist model was proposed previously to divide wastewater treatment plant (WWTP) heterotrophs into sub-guilds of consumers of readily or slowly degradable substrates (RDS or SDS, respectively). The substrate degradation rate model coupled to metabolic considerations predicted that RNA and polyhydroxyalkanoate (PHA) levels would be positively correlated in the activated sludge communities with high RNA and PHA occurring in RDS-consumers, and low RNA with no PHA accumulation occurring in SDS-consumers because their external substrates are always present. This prediction was verified in previous studies and in the current one. Thus, RNA and PHA levels were used as biomarkers of the RDS- and SDS-consumer sub-guilds for cell sorting using flow cytometry of samples from three WWTPs. Subsequently, 16S rRNA gene amplicon sequencing revealed that the sorted groups were highly similar over time and among WWTPs, and demonstrated a clear segregation by RNA levels. Predicted ecophysiological traits based on 16S rRNA phylogeny suggested that the high-RNA population showed RDS-consumer traits such as higher rrn copy numbers per genome. Using a mass-flow immigration model, it appeared that the high-RNA populations exhibited high immigration rates more frequently than low-RNA populations, but the differences in frequencies were less with increasing solids residence times.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".