Deep culturing the fecal microbiota of healthy laying hens
Bibliographic record
Abstract
BACKGROUND: The microbiota is implicated in several aspects of livestock health and disease. Understanding the structure and function of the poultry microbiota would be a valuable tool for improving their health and productivity since the microbiota can likely be optimized for metrics that are important to the industry such as improved feed conversion ratio, lower greenhouse gas emissions, and higher levels of competitive exclusion against pathogens. Most research into understanding the poultry microbiota has relied on culture-independent methods; however, the pure culture of bacteria is essential to elucidating the roles of individual bacteria in the microbiota and developing novel probiotic products for poultry production. RESULTS: In this study, we have used a deep culturing approach consisting of 76 culture conditions to generate a culture collection of 1,240 bacterial isolates from healthy chickens. We then compared the taxonomy of cultured isolates to the taxonomic results of metagenomic sequencing to estimate what proportion of the microbiota was cultured. Metagenomic sequencing detected DNA from 545 bacterial species while deep culturing was able to produce isolates for 128 bacterial species. Some bacterial families, such as Comamonadaceae and Neisseriaceae were only detected via culturing - indicating that metagenomic analysis may not provide a complete taxonomic census of the microbiota. To further examine sub-species diversity in the poultry bacteriome, we whole genome sequenced 114 Escherichia coli isolates from 6 fecal samples and observed a great deal of diversity. CONCLUSIONS: Deep culturing and metagenomic sequencing approaches to examine the diversity of the microbiota within an individual will yield different results. In this project we generated a culture collection of enteric bacteria from healthy laying hens that can be used to further understand the role of specific commensals within the broader microbiota context and have made this collection available to the community. Isolates from this collection can be requested by contacting the corresponding author and will be provided at cost.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".