Examining the effects of an anti-Salmonella bacteriophage preparation, BAFASAL, on <i>ex vivo</i> human gut microbiome composition and function using a multi-omics approach
Bibliographic record
Abstract
Abstract Salmonella infections (salmonellosis) pose serious health risks to humans, usually via contamination in our food chain. This foodborne pathogen causes major food losses and human illnesses that result in significant economic impacts. Pathogens such as Salmonella have traditionally been kept at bay through the use of antibiotics, but antibiotic overuse within the food industry has led to the development of numerous multidrug-resistant bacterial strains. Thus, governments are now restricting antibiotic use, forcing the industry to search for alternatives to secure safe food chains. Bacteriophages, viruses that infect and kill bacteria, are currently being investigated and used as replacement treatments and prophylactics due to their specificity and efficacy. They are generally regarded as safe alternatives to antibiotics as they are natural components of the ecosystem. One example is BAFASEL, a commercial bacteriophage mixture that specifically targets Salmonella and is currently approved for use in poultry farming. However, when specifically used in the industry they can also make their way into humans through our food chain or exposure as is the case for antibiotics. In particular, agricultural workers could be repeatedly exposed to bacteriophages supplemented in animal feeds. To the best of our knowledge, no studies have investigated the effects of such exposure to bacteriophages on the human gut microbiome. In this study, we used a novel in vitro assay called RapidAIM to investigate BAFASAL’s potential impact on five individual human gut microbiomes. Multi-omics analyses, including 16S rRNA gene sequencing and metaproteomic, revealed that ex vivo human gut microbiota composition and function were unaffected by BAFASAL treatment providing an additional measure for its safety. Due to the critical role of the gut microbiome in human health and the known role of bacteriophages in regulation of microbiome composition and function, we suggest assaying the impact of bacteriophage-cocktails on the human gut microbiome as a part of their safety assessment. Graphical Abstract
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".