Metagenomics analysis reveals features unique to Indian distal gut microbiota
Bibliographic record
Abstract
Various factors including diet, age, geography, culture and socio-economic status have a role in determining the composition of the human gut microbiota. The human gut microbial composition is known to be altered in disease conditions. Considering the important role of the gut microbiome in maintaining homeostasis and overall health, it is important to understand the microbial diversity and the functional metagenome of the healthy gut. Here, we characterized the microbiota of 31 fecal samples from healthy individuals of Indian ethnic tribes from Ladakh, Jaisalmer and Khargone by shotgun metagenomic sequencing. Sequence analysis revealed that Bifidobacterium and Prevotella were the key microbes contributing to the differences among Jaisalmer, Khargone and Ladakh samples at the genus level. Our correlation network study identified carbohydrate-active enzymes and carbohydrate binding proteins that are associated with specific genera in the different Indian geographical regions studied. Network analysis of carbohydrate-active enzymes and genus abundance revealed that the presence of different carbohydrate-active enzymes is driven by differential abundance of genera. The correlation networks were different in the different geographical regions, and these interactions suggest the role of less abundant genera in shaping the gut environment. We compared our data with samples from different countries and found significant differences in taxonomic composition and abundance of carbohydrate-active enzymes in the gut microbiota as compared to the other countries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".