Open Access Methodology article

Optimizing multiplex SNP-based data analysis for genotyping of Mycobacterium tuberculosis isolates

Sarah Sengstake1*, Nino Bablishvili2, Anja Schuitema1, Nino Bzekalava2, Edgar Abadia34, Jessica de Beer5, Nona Tadumadze2, Maka Akhalaia6, Kiki Tuin7, Nestani Tukvadze2, Rusudan Aspindzelashvili2, Elizabeta Bachiyska8, Stefan Panaiotov8, Christophe Sola4, Dick van Soolingen109, Paul Klatser1, Richard Anthony1 and Indra Bergval1

Author Affiliations

1 KIT Biomedical Research, Royal Tropical Institute, Meibergdreef 39, 1105 AZ Amsterdam, The Netherlands

2 National TB Reference Laboratory, National Center for Tuberculosis and Lung Diseases, 50 Maruashvili Street, 0101 Tbilisi, Georgia

3 Instituto Venezolano de Investigaciones Cientificas (IVIC), Laboratorio de Genética Molecular, CMBC, Caracas, Venezuela

4 Institute of Genetics and Microbiology, UMR 8621 CNRS/UPS11, Buildings 400 et 409, Faculty of Sciences - University Paris-Sud 11, 15, rue Georges Clémenceau, 91405 Orsay, France

5 Tuberculosis Reference Laboratory, Centre for Infectious Disease Control, Centre for Infectious Disease Research, Diagnostics and Perinatal Screening, National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands

6 National Reference Laboratory, National Center for Tuberculosis Problems, Ministry Health of the Republic Kazakhstan, 5, Bekhozhin str, Almaty 050010, Republic of Kazakhstan

7 MRC-Holland, Willem Schoutenstraat 6, 1057 DN Amsterdam, The Netherlands

8 National Center of Infectious and Parasitic Diseases, 26 Yanko Sakazov blvd, Sofia 1504, Bulgaria

9 Tuberculosis Reference Laboratory, Centre for Infectious Disease Control, National Institute for Public Health and the Environment, P.O. Box 1, 3720 BA Bilthoven, The Netherlands

10 Department of Pulmonary Disease, University Centre for Chronic Diseases, and Department of Medical Microbiology, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, Netherlands

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BMC Genomics 2014, 15:572  doi:10.1186/1471-2164-15-572

Published: 7 July 2014

Abstract

Background

Multiplex ligation-dependent probe amplification (MLPA) is a powerful tool to identify genomic polymorphisms. We have previously developed a single nucleotide polymorphism (SNP) and large sequence polymorphisms (LSP)-based MLPA assay using a read out on a liquid bead array to screen for 47 genetic markers in the Mycobacterium tuberculosis genome. In our assay we obtain information regarding the Mycobacterium tuberculosis lineage and drug resistance simultaneously. Previously we called the presence or absence of a genotypic marker based on a threshold signal level. Here we present a more elaborate data analysis method to standardize and streamline the interpretation of data generated by MLPA. The new data analysis method also identifies intermediate signals in addition to classification of signals as positive and negative. Intermediate calls can be informative with respect to identifying the simultaneous presence of sensitive and resistant alleles or infection with multiple different Mycobacterium tuberculosis strains.

Results

To validate our analysis method 100 DNA isolates of Mycobacterium tuberculosis extracted from cultured patient material collected at the National TB Reference Laboratory of the National Center for Tuberculosis and Lung Diseases in Tbilisi, Republic of Georgia were tested by MLPA. The data generated were interpreted blindly and then compared to results obtained by reference methods. MLPA profiles containing intermediate calls are flagged for expert review whereas the majority of profiles, not containing intermediate calls, were called automatically. No intermediate signals were identified in 74/100 isolates and in the remaining 26 isolates at least one genetic marker produced an intermediate signal.

Conclusion

Based on excellent agreement with the reference methods we conclude that the new data analysis method performed well. The streamlined data processing and standardized data interpretation allows the comparison of the Mycobacterium tuberculosis MLPA results between different experiments. All together this will facilitate the implementation of the MLPA assay in different settings.

Keywords:
Mycobacterium tuberculosis; MLPA; Data analysis; SNP typing; MAGPIX; Drug resistance; MTBC lineage; Republic of Georgia