Allometric scaling of interspecific <scp>RNA</scp> transcript abundance to extend the use of metatranscriptomics in characterizing complex communities
Bibliographic record
Abstract
Metatranscriptomics allows profiling of community mRNA and rRNA transcript abundance under certain environmental conditions. However, variations in the proportion of RNA transcripts across different community size structures remain less explained, thus limiting the possible applications of metatranscriptomics in community studies. Here, we extended the assumptions of the growth-rate hypothesis (GRH) and the metabolic theory of ecology (MTE) to validate the allometric scaling of interspecific RNA transcript (mRNA and rRNA) abundance through metatranscriptomic analysis of mock communities consisting of model organisms. The results suggest that body size imposes significant constraints on RNA transcript abundance. Interestingly, the relationship between the total mitochondrial transcript abundance (mRNA and rRNA slopes were -0.30 and -0.28, respectively) and body size aligned with the MTE assumptions with slopes close to -¼, while the nuclear transcripts displayed much steeper slopes (mRNA and rRNA slopes were -0.33 and -0.40, respectively). The assumed temperature dependence was not observed in this study. At the gene level, the allometric slopes range from 0 to -1. Overall, the above results showed that larger individuals have lesser RNA transcript abundance per tissue mass than smaller ones regardless of temperature. Analyses of field-collected microcrustacean zooplankton samples demonstrated that the correction of size effect, using the allometric exponents derived from the model organism mock community, explains better the patterns of interspecific RNA transcripts abundance within the metatranscriptome. Integrating allometry with metatranscriptomics can extend the use of RNA transcript reads in estimating ecological processes within complex communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".