English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background Bacteria belonging to the genus  Haemophilus  cause a wide range of diseases in humans. Recently,  H. influenzae  was classified by the WHO as priority pathogen due to the wide spread of ampicillin resistant strains. However, other  Haemophilus  spp. are often misclassified as  H. influenzae.  Therefore, we established an accurate and rapid whole genome sequencing (WGS) based classification and serotyping algorithm and combined it with the detection of resistance genes. Methods A gene presence/absence-based classification algorithm was developed, which employs the open-source gene-detection tool SRST2 and a new classification database comprising 36 genes, including capsule loci for serotyping. These genes were identified using a comparative genome analysis of 215 strains belonging to ten human-related  Haemophilus  (sub)species (training dataset). The algorithm was evaluated on 1329 public short read datasets (evaluation dataset) and used to reclassify 262 clinical  Haemophilus  spp. isolates from 250 patients (German cohort). In addition, the presence of antibiotic resistance genes within the German dataset was evaluated with SRST2 and correlated with results of traditional phenotyping assays. Results The newly developed algorithm can differentiate between clinically relevant  Haemophilus  species including, but not limited to,  H. influenzae ,  H. haemolyticus , and  H. parainfluenzae . It can also identify putative haemin-independent  H. haemolyticus  strains and determine the serotype of typeable  Haemophilus  strains. The algorithm performed excellently in the evaluation dataset (99.6% concordance with reported species classification and 99.5% with reported serotype) and revealed several misclassifications. Additionally, 83 out of 262 (31.7%) suspected  H. influenzae  strains from the German cohort were in fact  H. haemolyticus  strains, some of which associated with mouth abscesses and lower respiratory tract infections .  Resistance genes were detected in 16 out of 262 datasets from the German cohort. Prediction of ampicillin resistance, associated with  bla  TEM-1D , and tetracycline resistance, associated with  tetB , correlated well with available phenotypic data. Conclusions Our new classification database and algorithm have the potential to improve diagnosis and surveillance of  Haemophilus  spp. and can easily be coupled with other public genotyping and antimicrobial resistance databases. Our data also point towards a possible pathogenic role of  H. haemolyticus  strains, which needs to be further investigated.

Whole genome sequencing-based classification of human-related Haemophilus species and detection of antimicrobial resistance genes