Metagenomics Versus Metatranscriptomics of the Murine Gut Microbiome for Assessing Microbial Metabolism During Inflammation
Bibliographic record
Abstract
inference of the metagenome. While they link the microbiome with metabolic abnormalities associated with disease phenotypes, they do not capture microbial gene expression patterns that occur in response to the multitude of stimuli that constantly ambush the gut environment. Metatranscriptomics closes that gap, but its implementation is more expensive and tedious. We assessed the metabolic perturbations associated with gut inflammation using shotgun metagenomics and metatranscriptomics. Shotgun metagenomics detected changes in abundance of bacterial taxa known to be SCFA producers, which favors gut homeostasis. Bacteria in the phylum Firmicutes were found at decreased abundance, while those in phyla Bacteroidetes and Proteobacteria were found at increased abundance. Surprisingly, inferring the coding capacity of the microbiome from shotgun metagenomics data did not result in any statistically significant difference, suggesting functional redundancy in the microbiome or poor resolution of shotgun metagenomics data to profile bacterial pathways, especially when sequencing is not very deep. Obviously, the ability of metatranscriptomics libraries to detect transcripts expressed at basal (or simply low) levels is also dependent on sequencing depth. Nevertheless, metatranscriptomics informed about contrasting roles of bacteria during inflammation. Functions involved in nutrient transport, immune suppression and regulation of tissue damage were dramatically upregulated, perhaps contributed by homeostasis-promoting bacteria. Functions ostensibly increasing bacteria pathogenesis were also found upregulated, perhaps as a consequence of increased abundance of Proteobacteria. Bacterial protein synthesis appeared downregulated. In summary, shotgun metagenomics was useful to profile bacterial population composition and taxa relative abundance, but did not inform about differential gene content associated with inflammation. Metatranscriptomics was more robust for capturing bacterial metabolism in real time. Although both approaches are complementary, it is often not possible to apply them in parallel. We hope our data will help researchers to decide which approach is more appropriate for the study of different aspects of the microbiome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".