Header

UZH-Logo

Maintenance Infos

Novel Organism Verification and Analysis (NOVA) study: identification of 35 clinical isolates representing potentially novel bacterial taxa using a pipeline based on whole genome sequencing


Muigg, Veronika; Seth-Smith, Helena M B; Adam, Kai-Manuel; Weisser, Maja; Hinic, Vladimira; Blaich, Annette; Roloff, Tim; Heininger, Ulrich; Schmid, Hanna; Kohler, Maurus; Graf, Lukas; Winterflood, Dylan M; Schlaepfer, Pascal; Goldenberger, Daniel (2024). Novel Organism Verification and Analysis (NOVA) study: identification of 35 clinical isolates representing potentially novel bacterial taxa using a pipeline based on whole genome sequencing. BMC Microbiology, 24(1):14.

Abstract

BACKGROUND
Reliable species identification of cultured isolates is essential in clinical bacteriology. We established a new study algorithm named NOVA - Novel Organism Verification and Analysis to systematically analyze bacterial isolates that cannot be characterized by conventional identification procedures MALDI-TOF MS and partial 16 S rRNA gene sequencing using Whole Genome Sequencing (WGS).

RESULTS
We identified a total of 35 bacterial strains that represent potentially novel species. Corynebacterium sp. (n = 6) and Schaalia sp. (n = 5) were the predominant genera. Two strains each were identified within the genera Anaerococcus, Clostridium, Desulfovibrio, and Peptoniphilus, and one new species was detected within Citrobacter, Dermabacter, Helcococcus, Lancefieldella, Neisseria, Ochrobactrum (Brucella), Paenibacillus, Pantoea, Porphyromonas, Pseudoclavibacter, Pseudomonas, Psychrobacter, Pusillimonas, Rothia, Sneathia, and Tessaracoccus. Twenty-seven of 35 strains were isolated from deep tissue specimens or blood cultures. Seven out of 35 isolated strains identified were clinically relevant. In addition, 26 bacterial strains that could only be identified at the species level using WGS analysis, were mainly organisms that have been identified/classified very recently.

CONCLUSION
Our new algorithm proved to be a powerful tool for detection and identification of novel bacterial organisms. Publicly available clinical and genomic data may help to better understand their clinical and ecological role. Our identification of 35 novel strains, 7 of which appear to be clinically relevant, shows the wide range of undescribed pathogens yet to define.

Abstract

BACKGROUND
Reliable species identification of cultured isolates is essential in clinical bacteriology. We established a new study algorithm named NOVA - Novel Organism Verification and Analysis to systematically analyze bacterial isolates that cannot be characterized by conventional identification procedures MALDI-TOF MS and partial 16 S rRNA gene sequencing using Whole Genome Sequencing (WGS).

RESULTS
We identified a total of 35 bacterial strains that represent potentially novel species. Corynebacterium sp. (n = 6) and Schaalia sp. (n = 5) were the predominant genera. Two strains each were identified within the genera Anaerococcus, Clostridium, Desulfovibrio, and Peptoniphilus, and one new species was detected within Citrobacter, Dermabacter, Helcococcus, Lancefieldella, Neisseria, Ochrobactrum (Brucella), Paenibacillus, Pantoea, Porphyromonas, Pseudoclavibacter, Pseudomonas, Psychrobacter, Pusillimonas, Rothia, Sneathia, and Tessaracoccus. Twenty-seven of 35 strains were isolated from deep tissue specimens or blood cultures. Seven out of 35 isolated strains identified were clinically relevant. In addition, 26 bacterial strains that could only be identified at the species level using WGS analysis, were mainly organisms that have been identified/classified very recently.

CONCLUSION
Our new algorithm proved to be a powerful tool for detection and identification of novel bacterial organisms. Publicly available clinical and genomic data may help to better understand their clinical and ecological role. Our identification of 35 novel strains, 7 of which appear to be clinically relevant, shows the wide range of undescribed pathogens yet to define.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Downloads

3 downloads since deposited on 31 Jan 2024
3 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Medical Microbiology
Dewey Decimal Classification:610 Medicine & health
570 Life sciences; biology
Scopus Subject Areas:Life Sciences > Microbiology
Health Sciences > Microbiology (medical)
Language:English
Date:5 January 2024
Deposited On:31 Jan 2024 15:20
Last Modified:30 Jun 2024 01:38
Publisher:BioMed Central
ISSN:1471-2180
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1186/s12866-023-03163-7
PubMed ID:38178003
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)