Salmonella enteritidis izolātu pilna genoma sekvenēšanas datu analīze
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Date
2020
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Rīgas Stradiņa universitāte
Rīga Stradiņš University
Rīga Stradiņš University
Abstract
Ievads. Salmonella enterica ir nozīmīgs patogēns, kas var izraisīt dažāda smaguma infekcijas cilvēkiem un dzīvniekiem. Netifoīdā Salmonella tiek pārnesta ar dzīvnieku izcelsmes produktiem (galvenokārt caur olām, gaļu un mājputnu produktiem), un produktiem, kas ir kontaminēti ar dzīvnieku fēcēm un/vai notekūdeņiem, kā arī ir bijuši saskarē ar dzīvniekiem un dzīvnieku vidi. Pilna genoma sekvenēšana ir atzīta tehnoloģija, kurai ir nozīmīga loma sabiedrības veselībā, īpaši pārtikas drošībā, izmantojot dažādus rīkus patogēnu molekulārajai tipēšanai, fenotipu prognozēšanai un filoģenēzes konstruēšanai.
Darba mērķis. Šī pētījuma mērķis bija salīdzināt dažādus datu analīzes rīkus un to nodrošināto izšķirtspēju epidemioloģiskā izmeklēšanā.
Materiāli un metodes. Mēs veicam retrospektīvu datu analīzi Salmonella enterica izolātiem no cilvēkiem (n=11) un mājputnu gaļas (n=6), kas bija iegūti 2019. gada septembrī un oktobrī. Salmonella enterica pilna genoma sekvenēšanas dati tika izmantoti in silico serotipēšanā ((SeqSero, SISTR (Salmonella In Silico Typing Resource)), Multi-Locus Sequence Typing tipēšanā (MLST 2.0, SISTR), core genoma MLST (cgMLSTFinder 1.1), un uz SNP balstītu datu analīzes metodei (CSI Phylogeny). Datu analīzes rīki, kurus mēs izmantojām, bija brīvi pieejami no Center for Genomic Epidemiology.
Rezultāti. Visi analizētie izolāti bija Salmonella enterica serovars enteritīdis, ST 11. Septiņpadsmit izolātiem bija 7 dažādi cgMLST tipi. Izmantojot uz SNP balstītu pilna genoma sekvenēšanas datu analīzi mēs identificējām 532 SNPs, kas klaisificē analizētos paraugus vismaz 3 klasteros.
Secinājumi. Mēs varam secināt, ka dažādi serotipēšanas un MLST datu analīzes rīki parādīja vienādus rezultātus visiem analizētajiem S. eneterica izolātiem. cgMLST un SNP analīzes rezultāti demonstrēja visaugstāko izšķirtspēju un lai arī šīs metodes ir balstītas uz dažādām datu analīzes pieejām, izolātu klasteri abos gadījumos bija līdzīgi.
Objectives. Salmonella enterica (SE) is a significant foodborne pathogen that can cause infections of varying severity to human and animal. The nontyphoidal Salmonella (NTS) is transmitted through animal products (mainly through eggs, meats, and poultry products) and produce contaminated with animal feces and/or human sewage, and contact with animals and animal environment. Whole genome sequencing (WGS) is recognized technology that plays significant role in public health, especially food safety with various tools for pathogen molecular typing, phenotypic prediction and phylogeny construction. The aim of this study was to compare different data analysis tools and their provided discriminatory power in epidemiological investigations. Materials and Methods. We did a retrospective data analysis of Salmonella enterica isolates from human (n=11) and poultry (n=6) collected in September and October 2019. WGS data of S. enterica were used for in silico serotyping (SeqSero, SISTR (Salmonella In Silico Typing Resource)), Multi-Locus Sequence Typing (MLST 2.0, SISTR), and core genome MLST (cgMLSTFinder 1.1). and SNP based data analysis method (CSI Phylogeny). Data analysis tools we used were free available from Center for Genomic Epidemiology. Results. All analyzed isolates belonged to Salmonella enterica serovar Enteritidis, ST 11. Seventeen isolates represented 7 various cgMLST types. Using SNP based WGS analysis method we identified 532 SNPs that classify analyzed samples in at least 3 clusters. Conclusions. We can conclude that various data analysis tools for serotype and MLST represented the same results for all analyzed SE strains. Results of cgMLST and SNP analysis demonstrated the highest discriminatory power and although these methods are based on different data analysis approaches clusters of isolates in both cases were similar.
Objectives. Salmonella enterica (SE) is a significant foodborne pathogen that can cause infections of varying severity to human and animal. The nontyphoidal Salmonella (NTS) is transmitted through animal products (mainly through eggs, meats, and poultry products) and produce contaminated with animal feces and/or human sewage, and contact with animals and animal environment. Whole genome sequencing (WGS) is recognized technology that plays significant role in public health, especially food safety with various tools for pathogen molecular typing, phenotypic prediction and phylogeny construction. The aim of this study was to compare different data analysis tools and their provided discriminatory power in epidemiological investigations. Materials and Methods. We did a retrospective data analysis of Salmonella enterica isolates from human (n=11) and poultry (n=6) collected in September and October 2019. WGS data of S. enterica were used for in silico serotyping (SeqSero, SISTR (Salmonella In Silico Typing Resource)), Multi-Locus Sequence Typing (MLST 2.0, SISTR), and core genome MLST (cgMLSTFinder 1.1). and SNP based data analysis method (CSI Phylogeny). Data analysis tools we used were free available from Center for Genomic Epidemiology. Results. All analyzed isolates belonged to Salmonella enterica serovar Enteritidis, ST 11. Seventeen isolates represented 7 various cgMLST types. Using SNP based WGS analysis method we identified 532 SNPs that classify analyzed samples in at least 3 clusters. Conclusions. We can conclude that various data analysis tools for serotype and MLST represented the same results for all analyzed SE strains. Results of cgMLST and SNP analysis demonstrated the highest discriminatory power and although these methods are based on different data analysis approaches clusters of isolates in both cases were similar.
Description
Medicīna
Medicine
Veselības aprūpe
Health Care
Medicine
Veselības aprūpe
Health Care
Keywords
Salmonella enterica, Pilna genoma sekvenēšana, kodola genoms, datu analīze, Salmonella enterica, Whole genome sequencing, core genome, data analysis