A4 INVESTIGATING MICROBIAL ENGRAFTMENT VIA A COMPREHENSIVE CULTURE-ENRICHED AND CULTURE-INDEPENDENT METAGENOMICS IN PATIENTS WITH ULCERATIVE COLITIS
Bibliographic record
Abstract
Fecal microbiota transplantation (FMT) has demonstrated considerable success in the treatment of Clostridium difficile infection by reducing pathogen burden and correcting microbial dysbiosis in the host. Our lab is part of a team investigating the efficacy of FMT for patients suffering from ulcerative colitis, and we have previously shown preliminary success in using FMT for treatment of patients with ulcerative colitis. Microbial engraftment in fecal transplant studies has been very difficult to demonstrate, especially given the low DNA sequence resolution provided in 16SrRNA amplicon sequencing and the challenges in refinement of the high-quality genomes from metagenomic samples. Building a longitudinal comprehensive database of a healthy donor involved in two randomized control trial studies of fecal microbiome transplant for UC patients. Identifying the bacterial genomes, genes, functions, and metabolites that were transferred from a donor to patients with data from before and after FMT. We used a culture-enriched metagenomics (CEMG) approach together with other shotgun metagenomic techniques such as assembly and binning to construct high-quality metagenome assembled genomes (MAGs) from a single donor. We compared the presence of MAGs in a single donor at different time points and created a comprehensive DNA sequence library contains functional annotation and taxonomic assignment. The donor database was used to track the engraftment of genes and genome in 8 FMT recipients from our previous randomized control trial using metagenomic mapping for each patient with data from before and after FMT. CEMG approach combined with in silico assembly-based methods allowed us not to only recover the highest number of MAGs refined from a single donor (203 MAGs) but also, we were able to predict novel metabolites, higher number of functional and carbohydrate activities that were not investigated earlier. We were able to predict metagenome assembled genomes, genes, and functions that were not present before FMT but they were detected in high abundance after fecal microbiota transplantation. The combination of CEMG and direct shotgun sequencing tackles some of the challenges present in the metagenomics analysis, particularly by increasing the number as well as the accuracy of the MAGs. Our novel culture-dependent approach provides higher genomic resolution that can predict bacterial engraftment in fecal microbiota transplantation for patients with ulcerative colitis. CCC, CIHR
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".