Whole genome sequencing-based classification of human-related Haemophilus species and detection of antimicrobial resistance genes.
Antibiotic resistance
H. haemolyticus
H. influenzae
Haemophilus
Identification
Molecular differentiation
Pangenome-wide association study
Precision medicine
Whole genome sequencing
Journal
Genome medicine
ISSN: 1756-994X
Titre abrégé: Genome Med
Pays: England
ID NLM: 101475844
Informations de publication
Date de publication:
09 02 2022
09 02 2022
Historique:
received:
31
05
2021
accepted:
24
01
2022
entrez:
10
2
2022
pubmed:
11
2
2022
medline:
17
3
2022
Statut:
epublish
Résumé
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. 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. 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 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.
Sections du résumé
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
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.
Identifiants
pubmed: 35139905
doi: 10.1186/s13073-022-01017-x
pii: 10.1186/s13073-022-01017-x
pmc: PMC8830169
doi:
Substances chimiques
Anti-Bacterial Agents
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
13Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s).
Références
Winslow C, Broadhurst J, Buchanan R, Krumwiede C, Rogers L, Smith G. The Families and Genera of the Bacteria: Preliminary Report of the Committee of the Society of American Bacteriologists on Characterization and Classification of Bacterial Types. J Bacteriol. 1917;2(5):505–66.
pubmed: 16558764
pmcid: 378727
doi: 10.1128/jb.2.5.505-566.1917
Thjötta T, Avery OT. Studies on bacterial nutrition : II. Growth accessory substances in the cultivation of Hemophilic bacilli. J Exp Med. 1921;34(1):97.
pubmed: 19868543
pmcid: 2128063
doi: 10.1084/jem.34.1.97
Nørskov-Lauritsen N. Classification, identification, and clinical significance of Haemophilus and Aggregatibacter species with host specificity for humans. Clin Microbiol Rev. 2014;27(2):214–40.
pubmed: 24696434
pmcid: 3993099
doi: 10.1128/CMR.00103-13
Mukundan D, Ecevit Z, Patel M, Marrs CF, Gilsdorf JR. Pharyngeal colonization dynamics of Haemophilus influenzae and Haemophilus haemolyticus in healthy adult carriers. J Clin Microbiol. 2007;45(10):3207–17.
pubmed: 17687018
pmcid: 2045313
doi: 10.1128/JCM.00492-07
Van Eldere J, Slack MPE, Ladhani S, Cripps AW. Non-typeable Haemophilus influenzae, an under-recognised pathogen. Lancet Infect Dis. 2014;14:1281–92.
pubmed: 25012226
doi: 10.1016/S1473-3099(14)70734-0
Peltola H. Worldwide Haemophilus influenzae type b disease at the beginning of the 21st century: global analysis of the disease burden 25 years after the use of the polysaccharide vaccine and a decade after the advent of conjugates. Clin Microbiol Rev. American Society for Microbiology (ASM). 2000;13:302–17.
pubmed: 10756001
pmcid: 100154
doi: 10.1128/CMR.13.2.302
McCormick DW, Molyneux EM. Bacterial meningitis and haemophilus influenzae type b conjugate Vaccine, Malawi. Emerg Infect Dis. 2011 Apr;17(4):688–90.
pubmed: 21470461
pmcid: 3377403
doi: 10.3201/eid1704.101045
Musher DM. Haemophilus species. In Medical Microbiology: 4th edition (Samuel Baron). The University of Texas Medical Branch at Galveston. Medical Microbiology. University of Texas Medical Branch at Galveston; 1996.
Bakaletz LO, Novotny LA. Nontypeable Haemophilus influenzae (NTHi). Trends Microbiol. Elsevier Ltd. 2018;26:727–8.
pubmed: 29793827
doi: 10.1016/j.tim.2018.05.001
WHO priority list [Internet]. Available from: https://www.who.int/medicines/areas/rational_use/prioritization-of-pathogens/en/ .
Simberkoff MS. Haemophilus and Moraxella infections. In: Goldman L, Schafer AI, editors. Goldman’s Cecil Medicine: 24th edition. Philadelphia: Elsevier Inc; 2012. p. 1861–4.
doi: 10.1016/B978-1-4377-1604-7.00308-0
Anderson R, Wang X, Briere EC, Katz LS, Cohn AC, Clark TA, et al. Haemophilus haemolyticus isolates causing clinical disease. J Clin Microbiol. 2012;50(7):2462–5.
pubmed: 22573587
pmcid: 3405640
doi: 10.1128/JCM.06575-11
Nørskov-Lauritsen N, Bruun B, Andersen C, Kilian M. Identification of haemolytic Haemophilus species isolated from human clinical specimens and description of Haemophilus sputorum sp. nov. Int J Med Microbiol. 2012;302(2):78–83.
pubmed: 22336150
doi: 10.1016/j.ijmm.2012.01.001
Nørskov-Lauritsen N, Bruun B, Kilian M. Multilocus sequence phylogenetic study of the genus Haemophilus with description of Haemophilus pittmaniae sp. nov. Int J Syst Evol Microbiol. 2005;55(1):449–56.
pubmed: 15653917
doi: 10.1099/ijs.0.63325-0
Kus JV, Shuel M, Soares D, Hoang W, Law D, Tsang RSW. Identification and characterization of “Haemophilus quentini” strains causing invasive disease in Ontario, Canada (2016 to 2018). J Clin Microbiol. 2019;57(12):e01254-19.
Murphy TF, Kirkham C, Sikkema DJ. Neonatal, urogenital isolates of biotype 4 nontypeable Haemophilus influenzae express a variant P6 outer membrane protein molecule. Infect Immun. 1992;60(5):2016–22.
pubmed: 1373403
pmcid: 257109
doi: 10.1128/iai.60.5.2016-2022.1992
Brenner DJ, Mayer LW, Carlone GM, Harrison LH, Bibb WF, Brandileone MC, et al. Biochemical, genetic, and epidemiologic characterization of Haemophilus influenzae biogroup aegyptius (Haemophilus aegyptius) strains associated with Brazilian purpuric fever. J Clin Microbiol. 1988;26(8):1524–34.
pubmed: 3262623
pmcid: 266654
doi: 10.1128/jcm.26.8.1524-1534.1988
Lewis DA, Mitjà O. Haemophilus ducreyi: from sexually transmitted infection to skin ulcer pathogen. Curr Opin Infect Dis. Lippincott Williams and Wilkins. 2016;29:52–7.
pubmed: 26658654
doi: 10.1097/QCO.0000000000000226
Cooke FJ, Slack MPE. Infectious diseases. In: Fourth. 2017.
Hedegaard J, Okkels H, Bruun B, Kilian M, Mortensen KK, Nørskov-Lauritsen N. Phylogeny of the genus Haemophilus as determined by comparison of partial infB sequences. The GenBank accession numbers for the sequences reported in this paper are AJ289629 through AJ289694, AJ290742 through AJ290767, and AJ295746. Microbiology. 2001;147(9):2599–609.
pubmed: 11535800
doi: 10.1099/00221287-147-9-2599
Pickering J, Richmond PC, Kirkham L-AS. Molecular tools for differentiation of non-typeable Haemophilus influenzae from Haemophilus haemolyticus. Front Microbiol. 2014;5(DEC):664.
pubmed: 25520712
pmcid: 4251515
Osman KL, Jefferies JMC, Woelk CH, Devos N, Pascal TG, Mortier MC, et al. Patients with chronic obstructive pulmonary disease harbour a variation of Haemophilus species. Sci Rep. 2018;8(1):1–11.
Price EP, Harris TM, Spargo J, Nosworthy E, Beissbarth J, Chang AB, et al. Simultaneous identification of Haemophilus influenzae and Haemophilus haemolyticus using real-time PCR. Future Microbiol. 2017;12(7):585–93.
pubmed: 28604066
doi: 10.2217/fmb-2016-0215
Nürnberg S, Claus H, Krone M, Vogel U, Lâm TT. Discriminative potential of the vitek MS in vitro diagnostic device regarding Haemophilus influenzae and Haemophilus haemolyticus. J Clin Microbiol Am Soc Microbiol. 2020;58:e00278-20.
Sierra Y, González-Díaz A, Carrera-Salinas A, Berbel D, Vázquez-Sánchez D, Tubau F, et al. Genome-wide analysis of urogenital and respiratory multidrug-resistant Haemophilus parainfluenzae. J Antimicrob Chemother. 2021;76(7):1741–51.
pubmed: 33792695
doi: 10.1093/jac/dkab109
Kitts PA, Church DM, Thibaud-Nissen F, Choi J, Hem V, Sapojnikov V, et al. Assembly: a resource for assembled genomes at NCBI. Nucleic Acids Res. 2016;44(D1):D73–80.
pubmed: 26578580
doi: 10.1093/nar/gkv1226
Escalona M, Rocha S, Posada D. A comparison of tools for the simulation of genomic next-generation sequencing data. Nat Rev Genet. Nature Publishing Group. 2016;17:459–69.
pubmed: 27320129
pmcid: 5224698
doi: 10.1038/nrg.2016.57
Leinonen R, Sugawara H, Shumway M. The sequence read archive. Nucleic Acids Res. 2011;39(SUPPL. 1):D19.
pubmed: 21062823
doi: 10.1093/nar/gkq1019
NCBI [Internet]. Available from: https://www.ncbi.nlm.nih.gov/ .
Andrews S. FastQC [Internet]. Available from: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ .
Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20(1):257.
pubmed: 31779668
pmcid: 6883579
doi: 10.1186/s13059-019-1891-0
European Society of Clinical Microbiology and Infectious Diseases. EUCAST clinical breakpoints [Internet]. Available from: https://eucast.org/clinical_breakpoints/ .
Seemann T. Shovill [Internet]. Available from: https://github.com/tseemann/shovill .
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455.
pubmed: 22506599
pmcid: 3342519
doi: 10.1089/cmb.2012.0021
Souvorov A, Agarwala R, Lipman DJ. SKESA: strategic k-mer extension for scrupulous assemblies. Genome Biol. 2018;19(1):1–13.
doi: 10.1186/s13059-018-1540-z
Petit RA. Shovill-se [Internet]. Available from: https://github.com/rpetit3/shovill/blob/single-end-reads/bin/shovill-se .
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30(14):2068–9.
pubmed: 24642063
doi: 10.1093/bioinformatics/btu153
Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S, Holden MTG, et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31(22):3691–3.
pubmed: 26198102
pmcid: 4817141
doi: 10.1093/bioinformatics/btv421
Bayliss SC, Thorpe HA, Coyle NM, Sheppard SK, Feil EJ. PIRATE: A fast and scalable pangenomics toolbox for clustering diverged orthologues in bacteria. Gigascience. 2019;8(10):1–9.
doi: 10.1093/gigascience/giz119
Inouye M, Dashnow H, Raven LA, Schultz MB, Pope BJ, Tomita T, et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med. 2014;6(11):90.
pubmed: 25422674
pmcid: 4237778
doi: 10.1186/s13073-014-0090-6
Price MN, Dehal PS, Arkin AP. FastTree 2 - approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5(3):e9490.
pubmed: 20224823
pmcid: 2835736
doi: 10.1371/journal.pone.0009490
Rambaut A. Figtree [Internet]. Available from: https://github.com/rambaut/figtree .
Letunic I, Bork P. Interactive Tree of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 2019;47(W1):W256–9.
pubmed: 30931475
pmcid: 6602468
doi: 10.1093/nar/gkz239
Inkscape [Internet]. Available from: https://inkscape.org/ .
Diricks M. HaemoSeq. 2021. Github. https://github.com/ngs-fzb/HaemoSeq .
Watts SC, Holta KE. HICAP: In silico serotyping of the haemophilus influenzae capsule locus. J Clin Microbiol. 2019;57(6):e00190-19.
Potts CC, Topaz N, Rodriguez-Rivera LD, Hu F, Chang HY, Whaley MJ, et al. Genomic characterization of Haemophilus influenzae: a focus on the capsule locus. BMC Genomics. 2019;20(1):733.
pubmed: 31606037
pmcid: 6790013
doi: 10.1186/s12864-019-6145-8
Pinto M, González-Díaz A, Machado MP, Duarte S, Vieira L, Carriço JA, et al. Insights into the population structure and pan-genome of Haemophilus influenzae. Infect Genet Evol. 2019;(67):126–35.
SRST2 [Internet]. Available from: https://github.com/katholt/srst2 .
Letunic I, Bork P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics. 2007;23(1):127–8.
pubmed: 17050570
doi: 10.1093/bioinformatics/btl529
Connor TR, Corander J, Hanage WP. Population subdivision and the detection of recombination in non-typable Haemophilus influenzae. Microbiology (United Kingdom). 2012;158(12):2958–64.
pubmed: 23038806
pmcid: 4083659
Witherden EA, Bajanca-Lavado MP, Tristram SG, Nunes A. Role of inter-species recombination of the ftsI gene in the dissemination of altered penicillin-binding-protein-3-mediated resistance in Haemophilus influenzae and Haemophilus haemolyticus. J Antimicrob Chemother. 2014;69(6):1501–9.
pubmed: 24562614
doi: 10.1093/jac/dku022
Harris TM, Price EP, Sarovich DS, Nørskov-Lauritsen N, Beissbarth J, Chang AB, et al. Comparative genomic analysis identifies x-factor (Haemin)-independent haemophilus haemolyticus: a formal re-classification of “haemophilus intermedius”. Microb Genomics. 2020;6(1):e000303.
González-Díaz A, Tubau F, Pinto M, Sierra Y, Cubero M, Càmara J, et al. Identification of polysaccharide capsules among extensively drug-resistant genitourinary Haemophilus parainfluenzae isolates. Sci Rep. 2019;9(1):4481.
Bush K, Jacoby GA. Updated functional classification of β-lactamases. Antimicrob Agents Chemother. 2010;54(3):969.
pubmed: 19995920
doi: 10.1128/AAC.01009-09
Satola SW, Collins JT, Napier R, Farley MM. Capsule gene analysis of invasive haemophilus influenzae: accuracy of serotyping and prevalence of IS1016 among nontypeable isolates. J Clin Microbiol. 2007;45(10):3230.
pubmed: 17699642
pmcid: 2045354
doi: 10.1128/JCM.00794-07
Cope EK, Goldstein-Daruech N, Kofonow JM, Christensen L, McDermott B, Monroy F, et al. Regulation of virulence gene expression resulting from streptococcus pneumoniae and nontypeable haemophilus influenzae interactions in chronic disease. Miyaji EN, editor. PLoS One. 2011;6(12):e28523.
pubmed: 22162775
pmcid: 3230614
doi: 10.1371/journal.pone.0028523
Bervoets I, Charlier D. Diversity, versatility and complexity of bacterial gene regulation mechanisms: opportunities and drawbacks for applications in synthetic biology. FEMS Microbiol Rev. Oxford University Press. 2019;43:304–39.
pubmed: 30721976
pmcid: 6524683
doi: 10.1093/femsre/fuz001
De Gier C, Kirkham LAS, NØrskov-Lauritsen N. Complete deletion of the fucose operon in haemophilus influenzae is associated with a cluster in multilocus sequence analysis-based phylogenetic group II related to haemophilus haemolyticus: implications for identification and typing. J Clin Microbiol. 2015;53(12):3773–8.
pubmed: 26378279
pmcid: 4652104
doi: 10.1128/JCM.01969-15
Price EP, Sarovich DS, Nosworthy E, Beissbarth J, Marsh RL, Pickering J, et al. Haemophilus influenzae: using comparative genomics to accurately identify a highly recombinogenic human pathogen. BMC Genomics. 2015;16(1):641.
pubmed: 26311542
pmcid: 4551764
doi: 10.1186/s12864-015-1857-x
Frickmann H, Christner M, Donat M, Berger A, Essig A, Podbielski A, et al. Rapid Discrimination of Haemophilus influenzae, H. parainfluenzae, and H. haemolyticus by Fluorescence In Situ Hybridization (FISH) and Two Matrix-Assisted Laser-Desorption-Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF-MS) Platforms. PLoS One. 2013;8(4):e63222.
Zhu B, Xiao D, Zhang H, Zhang Y, Gao Y, Xu L, et al. MALDI-TOF MS distinctly differentiates nontypable Haemophilus influenzae from Haemophilus haemolyticus. PLoS One. 2013;8(2):e56139.
Frickmann H, Podbielski A, Essig A, Schwarz NG, Zautner AE. Difficulties in species identification within the genus Haemophilus—a pilot study addressing a significant problem for routine diagnostics. Eur J Microbiol Immunol. 2014;4(2):99–105.
doi: 10.1556/EuJMI.4.2014.2.2
Slouka D, Hanakova J, Kostlivy T, Skopek P, Kubec V, Babuska V, et al. Epidemiological and microbiological aspects of the peritonsillar abscess. Int J Environ Res Public Health. 2020;17(11):1–10.
doi: 10.3390/ijerph17114020
Loens K, Van Heirstraeten L, Malhotra-Kumar S, Goossens H, Ieven M. Optimal sampling sites and methods for detection of pathogen possibly causing community-acquired lower respiratory tract infections. J Clin Microbiol. American Society for Microbiology Journals. 2009;47:21–31.
pubmed: 19020070
doi: 10.1128/JCM.02037-08
Murphy TF, Sethi S, Klingman KL, Brueggemann AB, Doern GV. Simultaneous respiratory tract colonization by multiple strains of nontypeable Haemophilus influenzae in chronic obstructive pulmonary disease: implications for antibiotic therapy. J Infect Dis. 1999;180(2):404–9.
pubmed: 10395856
doi: 10.1086/314870
Smith-Vaughan HC, Leach AJ, Shelby-James TM, Kemp K, Kemp DJ, Mathews JD. Carriage of multiple ribotypes of non-encapsulated Haemophilus influenzae in Aboriginal infants with otitis media. Epidemiol Infect. 1996;116(2):177–83.
pubmed: 8620909
pmcid: 2271621
doi: 10.1017/S0950268800052419
Gröschel MI, Walker TM, van der Werf TS, Lange C, Niemann S, Merker M. Pathogen-based precision medicine for drug-resistant tuberculosis. Leong JM, editor. PLOS Pathog. 2018;14(10):e1007297.
pubmed: 30335850
pmcid: 6193714
doi: 10.1371/journal.ppat.1007297
Hendriksen RS, Bortolaia V, Tate H, Tyson GH, Aarestrup FM, McDermott PF. Using genomics to track global antimicrobial resistance. Front Public Health. Frontiers Media S.A. 2019;7:242.
pubmed: 31552211
pmcid: 6737581
doi: 10.3389/fpubh.2019.00242
Gupta SK, Padmanabhan BR, Diene SM, Lopez-Rojas R, Kempf M, Landraud L, et al. ARG-annot, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob Agents Chemother. 2014;58(1):212–20.
pubmed: 24145532
pmcid: 3910750
doi: 10.1128/AAC.01310-13
Bioproject database [Internet]. Available from: https://www.ncbi.nlm.nih.gov/bioproject/ .
Diricks M, Kohl TA, Käding N, Leshchinskiy V, Hauswaldt S, Jiménez Vázquez O, et al. Whole genome sequencing based classification of human-related Haemophilus species and detection of antimicrobial resistance genes. BioProject PRJEB43356, NCBI Sequence Read Archive 2021. https://www.ncbi.nlm.nih.gov/sra/?term=PRJEB43356 (2021).