Assessing species biomass contributions in microbial communities via metaproteomics
Bibliographic record
Abstract
Abstract Assessment of microbial community composition is the cornerstone of microbial ecology. Microbial community composition can be analyzed by quantifying cell numbers or by quantifying biomass for individual populations. However, as cell volumes can differ by orders of magnitude, these two approaches yield vastly different results. Methods for quantifying cell numbers are already available (e.g. fluorescence in situ hybridization, 16S rRNA gene amplicon sequencing), yet methods for assessing community composition in terms of biomass are lacking. We developed metaproteomics based methods for assessing microbial community composition using protein abundance as a measure for biomass contributions of individual populations. We optimized the accuracy and sensitivity of the method using artificially assembled microbial communities and found that it is less prone to some of the biases found in sequencing-based methods. We applied the method using communities from two different environments, microbial mats from two alkaline soda lakes and saliva from multiple individuals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 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".