Preterm infants harbour diverse <i>Klebsiella</i> populations, including atypical species that encode and produce an array of antimicrobial resistance- and virulence-associated factors
Bibliographic record
Abstract
ABSTRACT Klebsiella spp. are frequently enriched in the gut microbiota of preterm neonates, and overgrowth is associated with necrotizing enterocolitis, nosocomial infections and late-onset sepsis. Little is known about the genomic and phenotypic characteristics of preterm-associated Klebsiella as previous studies have focussed on recovery of antimicrobial-resistant isolates or culture-independent molecular analyses. Faecal samples from a UK cohort of healthy and sick preterm neonates ( n =109) were screened on MacConkey agar to isolate lactose-positive Enterobacteriaceae . Whole-genome sequences were generated for isolates. Approximately one-tenth of faecal samples harboured Klebsiella spp. ( Klebsiella pneumoniae , 7.3 %; Klebsiella quasipneumoniae , 0.9 %; Klebsiella grimontii , 2.8 %; Klebsiella michiganensis , 1.8 %). Isolates recovered from NEC- and sepsis-affected infants and those showing no signs of clinical infection (i.e. ‘healthy’) encoded multiple β-lactamases, which may prove problematic when defining treatment regimens for NEC or sepsis, and suggest ‘healthy’ preterm infants contribute to the resistome. No difference was observed between isolates recovered from ‘healthy’ and sick infants with respect to in vitro siderophore production (all encoded enterobactin in their genomes). All K. pneumoniae , K. quasipneumoniae , K. grimontii and K. michiganensis faecal isolates tested were able to reside and persist in macrophages, indicating their immune evasion abilities. Using a curated dataset of Klebsiella oxytoca , K. grimontii and K. michiganensis whole-genome sequences, metapangenome analyses of published metagenomic data confirmed our findings regarding the presence of K. michiganensis in the preterm gut, and highlight the importance of refined analyses with curated sequence databases when studying closely related species present in metagenomic data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".