Development and Validation of a High-Throughput Short Sequence Typing Scheme for <i>Serratia marcescens</i> Pure Culture and Environmental DNA
Bibliographic record
Abstract
Abstract Molecular typing methods are used to characterize the relatedness between bacterial isolates involved in infections. These approaches rely mostly on discrete loci or whole genome sequences (WGS) analyses of pure cultures. On the other hand, their application to environmental DNA profiling to evaluate epidemiological relatedness amongst patients and environments has received less attention. We developed a specific, high-throughput short sequence typing (HiSST) method for the opportunistic human pathogen Serratia marcescens . Genes displaying the highest polymorphism were retrieved from the core genome of 60 S. marcescens strains. Bioinformatics analyses showed that use of only three loci (within bssA, gabR and dhaM ) distinguished strains with the same level of efficiency than average nucleotide identity scores of whole genomes. This HiSST scheme was applied to an epidemiological survey of S. marcescens in a neonatal intensive care unit (NICU). In a first case study, a strain responsible for an outbreak in the NICU was found in a sink drain of this unit, by using HiSST scheme and confirmed by WGS. The HiSST scheme was also applied to environmental DNA extracted from sink-environment samples. Diversity of S. marcescens was modest, with 11, 6 and 4 different sequence types (ST) of gabR, bssA and dhaM loci amongst 19 sink drains, respectively. Epidemiological relationships amongst sinks were inferred on the basis of pairwise comparisons of ST profiles. Further research aimed at relating ST distribution patterns to environmental features encompassing sink location, utilization and microbial diversity is needed to improve the surveillance and management of opportunistic pathogens.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".