RapidAIM 2.0: a high-throughput assay to study functional response of human gut microbiome to xenobiotics
Bibliographic record
Abstract
Abstract Our gut microbiome functions like an organ, having its own set of functions and roles which can be modulated by various types of xenobiotic and biotic components. High-throughput screening approaches that are established based on in vitro or ex vivo cell, tissue or organ models greatly accelerate drug discovery and our understanding of biological and pathological processes within these systems. There was a lack of a high-throughput compatible functional screening approach of the gut microbiome until we recently developed the RapidAIM (Rapid Assay of Individual Microbiome). RapidAIM combines an optimized culturing model, which maintains the taxonomic and functional profiles of the human gut microbiome in vitro , and a high-throughput metaproteomics workflow to gain deep functional insights into microbiome responses. This protocol describes the most recently optimized 2.0 version of RapidAIM, consisting of extensive details on stool sample collection, biobanking, in vitro culturing and stimulation, microbiome sample processing, and metaproteomics measurement and data analysis. To demonstrate the typical outcome of the protocol, we show an example of using RapidAIM 2.0 to evaluate the effect of prebiotic kestose on ex vivo individual human gut microbiomes biobanked with five different workflows; we also show that kestose had consistent functional effects across individuals and can be used as positive control in the assay.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".