Deep Metaproteomics Approach for the Study of Human Microbiomes
Bibliographic record
Abstract
Host-microbiome interactions have been shown to play important roles in human health and diseases. Most of the current studies of the microbiome have been performed by genomic approaches through next-generation sequencing. Technologies, such as metaproteomics, for functional analysis of the microbiome are needed to better understand the intricate host-microbiome interactions. However, significant efforts to improve the depth and resolution of gut metaproteomics are still required. In this study, we combined an efficient sample preparation technique, high resolution mass spectrometry, and metaproteomic bioinformatics tools to perform ultradeep metaproteomic analysis of human gut microbiome from stool. We reported the deepest analysis of the microbiome to date with an average of 20 558 protein groups identified per sample analysis. Moreover, strain resolution taxonomic and pathway analysis using deep metaproteomics revealed strain level variations, in particular for Faecalibacterium prausnitzii, in the microbiome from the different individuals. We also reported that the human proteins identified in stool samples are functionally enriched in extracellular region pathways and in particular those proteins involved in defense response against microbial organisms. Deep metaproteomics is a promising approach to perform in-depth microbiome analysis and simultaneously reveals both human and microbial changes that are not readily apparent using the standard genomic approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".