Shotgun metagenomics reveals the microbiome and resistome of water harvesting ponds used by Kenyan rural smallholders
Bibliographic record
Abstract
Summary Water harvesting ponds are essential to smallholder farming across sub-Saharan Africa, yet their role in antimicrobial resistance (AMR) transmission remains unclear. Using shotgun metagenomics, we characterised the microbiome and resistome of 16 rural Kenyan ponds, detecting 582 antibiotic resistance gene (ARG) subtypes across 27 classes. Five ARG types (bacitracin, multidrug, polymyxin, beta-lactam and rifamycin) accounted for most ARGs (90.6%). Genome-resolved analyses recovered 1,542 metagenome-assembled genomes, including non-tuberculous Mycolicibacterium carrying rifamycin resistance ( rbpA ) and virulence factors including type VII secretion systems, dormancy regulators, and antigen 85 complex. ARG–mobile genetic element co-localisation was rare, and resistome risk scores were moderate (∼22.4), indicating limited horizontal transfer potential compared to agricultural or hospital effluents. These ponds act as moderate, persistent environmental AMR reservoirs linked to farming practices. Strengthened antimicrobial stewardship, improved manure management and vegetative buffer zones could help mitigate AMR dissemination and support safer rural water systems under a One Health framework.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".