Buono, Sean A. and Kelly, Reagan J. and Topaz, Nadav and Retchless, Adam C. and Silva, Hideky and Chen, Alexander and Ramos, Edward and Doho, Gregory and Khan, Agha Nabeel and Okomo-Adhiambo, Margaret A. and Hu, Fang and Marasini, Daya and Wang, Xin (2020) Web-Based Genome Analysis of Bacterial Meningitis Pathogens for Public Health Applications Using the Bacterial Meningitis Genomic Analysis Platform (BMGAP). Frontiers in Genetics, 11. ISSN 1664-8021
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Abstract
Web-Based Genome Analysis of Bacterial Meningitis Pathogens for Public Health Applications Using the Bacterial Meningitis Genomic Analysis Platform (BMGAP) Sean A. Buono Reagan J. Kelly Nadav Topaz Adam C. Retchless Hideky Silva Alexander Chen Edward Ramos Gregory Doho Agha Nabeel Khan Margaret A. Okomo-Adhiambo Fang Hu Daya Marasini Xin Wang
Effective laboratory-based surveillance and public health response to bacterial meningitis depends on timely characterization of bacterial meningitis pathogens. Traditionally, characterizing bacterial meningitis pathogens such as Neisseria meningitidis (Nm) and Haemophilus influenzae (Hi) required several biochemical and molecular tests. Whole genome sequencing (WGS) has enabled the development of pipelines capable of characterizing the given pathogen with equivalent results to many of the traditional tests. Here, we present the Bacterial Meningitis Genomic Analysis Platform (BMGAP): a secure, web-accessible informatics platform that facilitates automated analysis of WGS data in public health laboratories. BMGAP is a pipeline comprised of several components, including both widely used, open-source third-party software and customized analysis modules for the specific target pathogens. BMGAP performs de novo draft genome assembly and identifies the bacterial species by whole-genome comparisons against a curated reference collection of 17 focal species including Nm, Hi, and other closely related species. Genomes identified as Nm or Hi undergo multi-locus sequence typing (MLST) and capsule characterization. Further typing information is captured from Nm genomes, such as peptides for the vaccine antigens FHbp, NadA, and NhbA. Assembled genomes are retained in the BMGAP database, serving as a repository for genomic comparisons. BMGAP’s species identification and capsule characterization modules were validated using PCR and slide agglutination from 446 bacterial invasive isolates (273 Nm from nine different serogroups, 150 Hi from seven different serotypes, and 23 from nine other species) collected from 2017 to 2019 through surveillance programs. Among the validation isolates, BMGAP correctly identified the species for all 440 isolates (100% sensitivity and specificity) and accurately characterized all Nm serogroups (99% sensitivity and 98% specificity) and Hi serotypes (100% sensitivity and specificity). BMGAP provides an automated, multi-species analysis pipeline that can be extended to include additional analysis modules as needed. This provides easy-to-interpret and validated Nm and Hi genome analysis capacity to public health laboratories and collaborators. As the BMGAP database accumulates more genomic data, it grows as a valuable resource for rapid comparative genomic analyses during outbreak investigations.
Effective laboratory-based surveillance and public health response to bacterial meningitis depends on timely characterization of bacterial meningitis pathogens. Traditionally, characterizing bacterial meningitis pathogens such as Neisseria meningitidis (Nm) and Haemophilus influenzae (Hi) required several biochemical and molecular tests. Whole genome sequencing (WGS) has enabled the development of pipelines capable of characterizing the given pathogen with equivalent results to many of the traditional tests. Here, we present the Bacterial Meningitis Genomic Analysis Platform (BMGAP): a secure, web-accessible informatics platform that facilitates automated analysis of WGS data in public health laboratories. BMGAP is a pipeline comprised of several components, including both widely used, open-source third-party software and customized analysis modules for the specific target pathogens. BMGAP performs de novo draft genome assembly and identifies the bacterial species by whole-genome comparisons against a curated reference collection of 17 focal species including Nm, Hi, and other closely related species. Genomes identified as Nm or Hi undergo multi-locus sequence typing (MLST) and capsule characterization. Further typing information is captured from Nm genomes, such as peptides for the vaccine antigens FHbp, NadA, and NhbA. Assembled genomes are retained in the BMGAP database, serving as a repository for genomic comparisons. BMGAP’s species identification and capsule characterization modules were validated using PCR and slide agglutination from 446 bacterial invasive isolates (273 Nm from nine different serogroups, 150 Hi from seven different serotypes, and 23 from nine other species) collected from 2017 to 2019 through surveillance programs. Among the validation isolates, BMGAP correctly identified the species for all 440 isolates (100% sensitivity and specificity) and accurately characterized all Nm serogroups (99% sensitivity and 98% specificity) and Hi serotypes (100% sensitivity and specificity). BMGAP provides an automated, multi-species analysis pipeline that can be extended to include additional analysis modules as needed. This provides easy-to-interpret and validated Nm and Hi genome analysis capacity to public health laboratories and collaborators. As the BMGAP database accumulates more genomic data, it grows as a valuable resource for rapid comparative genomic analyses during outbreak investigations.
11 26 2020 601870 10.3389/fgene.2020.601870 1 10.3389/crossmark-policy frontiersin.org true https://creativecommons.org/licenses/by/4.0/ 10.3389/fgene.2020.601870 https://www.frontiersin.org/articles/10.3389/fgene.2020.601870/full https://www.frontiersin.org/articles/10.3389/fgene.2020.601870/full Clin. Vaccine Immunol. Bambini 21 966 2014 Neisseria adhesin A variation and revised nomenclature scheme. 10.1128/cvi.00825-13 J. Comput. Biol. Bankevich 19 455 2012 SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. 10.1089/cmb.2012.0021 J. Clin. Microbiol. Birtles 43 6007 2005 Multilocus sequence typing of Neisseria meningitidis directly from clinical samples and application of the method to the investigation of meningococcal disease case clusters. 10.1128/jcm.43.12.6007-6014.2005 Lancet Infect. Dis. Bozio 18 1360 2018 10.1016/S1473-3099(18)30476-6 Outbreak of Neisseria meningitidis serogroup C outside the meningitis belt-Liberia, 2017: an epidemiological and laboratory investigation. BMC Bioinformatics Camacho 10 2009 BLAST+: architecture and applications. 10.1186/1471-2105-10-421 Lancet Infect. Dis. Caugant 18 1295 2018 Metagenomics for investigation of an unusual meningococcal outbreak. 10.1016/s1473-3099(18)30499-7 Office of Advanced Molecular Diagnostics (OAMD) Portal. User Guide for CDC’s SAMS Partner Portal. PLoS One Dolan Thomas 6 2011 sodC-based real-time PCR for detection of Neisseria meningitidis. 10.1371/journal.pone.0019361 J. Clin. Microbiol. Feavers 37 3883 1999 Multilocus sequence typing and antigen gene sequencing in the investigation of a meningococcal disease outbreak. 10.1128/jcm.37.12.3883-3887.1999 Wellcome Open Res. Jolley 3 2018 Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications. 10.12688/wellcomeopenres.14826.1 Nucl. Acids Res. Kichenaradja 38 D62 2010 10.1093/nar/gkp947 ISbrowser: an extension of ISfinder for visualizing insertion sequences in prokaryotic genomes. Emerg. Infect. Dis. Kretz 22 1762 2016 10.3201/eid2210.160468 Whole-Genome Characterization of Epidemic Neisseria meningitidis Serogroup C and Resurgence of Serogroup W. Niger, 2015. Nat. Methods Langmead 9 357 2012 Fast gapped-read alignment with Bowtie 2. 10.1038/nmeth.1923 Toolkit for Processing Sequences in FASTA/Q Formats. Li 2020 Proc. Natl. Acad. Sci. U S A. Maiden 95 3140 1998 Multilocus sequence typing: a portable approach to the identification of clones within populations of pathogenic microorganisms. 10.1073/pnas.95.6.3140 J. Clin. Microbiol. Marjuki 57 2019 10.1128/JCM.01609-18 Whole-Genome Sequencing for Characterization of Capsule Locus and Prediction of Serogroup of Invasive Meningococcal Isolates. EMBNET J. Martin 17 10 2011 Cutadapt removes adapter sequences from high-throughput sequencing reads. 10.14806/ej.17.1.200 Genomics Miller 95 315 2010 Assembly algorithms for next-generation sequencing data. 10.1016/j.ygeno.2010.03.001 Euro. Surveill. Nadon 22 2017 10.2807/1560-7917.ES.2017.22.23.30544 PulseNet International: Vision for the implementation of whole genome sequencing (WGS) for global food-borne disease surveillance. Genome Biol. Ondov 17 2016 10.1186/s13059-016-0997-x Mash: fast genome and metagenome distance estimation using MinHash. BMC Genomics Potts 20 2019 Genomic characterization of Haemophilus influenzae: a focus on the capsule locus. 10.1186/s12864-019-6145-8 Nucl. Acids Res. Pruitt 35 D61 2007 10.1093/nar/gkl842 NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. mSphere Retchless 1 2016 10.1128/mSphere.00201-16 The establishment and diversification of epidemic-associated serogroup W meningococcus in the African meningitis belt, 1994 to 2012. Lancet Infect. Dis. Sidikou 16 1288 2016 Emergence of epidemic Neisseria meningitidis serogroup C in Niger, 2015: an analysis of national surveillance data. 10.1016/s1473-3099(16)30253-5 Nucl. Acids Res. Siguier 34 D32 2006 10.1093/nar/gkj014 ISfinder: the reference centre for bacterial insertion sequences. Methods Mol. Biol. Siguier 859 91 2012 Exploring bacterial insertion sequences with ISfinder: objectives, uses, and future developments. 10.1007/978-1-61779-603-6_5 BMC Bioinformatics Talevich 13 2012 Bio.Phylo: a unified toolkit for processing, analyzing and visualizing phylogenetic trees in Biopython. 10.1186/1471-2105-13-209 BMC Infect. Dis. Topaz 18 2018 BMScan: using whole genome similarity to rapidly and accurately identify bacterial meningitis causing species. 10.1186/s12879-018-3324-1 PLoS One Vuong 11 2016 Development of Real-Time PCR Methods for the Detection of Bacterial Meningitis Pathogens without DNA Extraction. 10.1371/journal.pone.0147765 Vaccine Wang 29 4739 Prevalence and genetic diversity of candidate vaccine antigens among invasive Neisseria meningitidis isolates in the United States. 10.1016/j.vaccine.2011.04.092 Int. J. Med. Microbiol. Wang 301 303 Detection of bacterial pathogens in Mongolia meningitis surveillance with a new real-time PCR assay to detect Haemophilus influenzae. 10.1016/j.ijmm.2010.11.004 J. Clin. Microbiol. Wang 50 702 2012 Clinical validation of multiplex real-time PCR assays for detection of bacterial meningitis pathogens. 10.1128/jcm.06087-11 Sci. Rep. Whaley 8 2018 10.1038/s41598-018-33622-5 Whole genome sequencing for investigations of meningococcal outbreaks in the United States: a retrospective analysis. Next generation sequencing: translation to clinical diagnostics. Wong 2013 10.1007/978-1-4614-7001-4 Data_Sheet_1.PDF 10.3389/fgene.2020.601870.s001 https://www.frontiersin.org/articles/10.3389/fgene.2020.601870/supplementary-material/10.3389/fgene.2020.601870.s001
Item Type: | Article |
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Subjects: | Grantha Library > Medical Science |
Depositing User: | Unnamed user with email support@granthalibrary.com |
Date Deposited: | 31 Jan 2023 11:40 |
Last Modified: | 19 Jul 2024 07:46 |
URI: | http://asian.universityeprint.com/id/eprint/107 |