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Biochemical, machine learning and molecular approaches for the differential diagnosis of Mucopolysaccharidoses

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Abstract

This study was aimed to construct classification and regression tree (CART) model of glycosaminoglycans (GAGs) for the differential diagnosis of Mucopolysaccharidoses (MPS). Two-dimensional electrophoresis and liquid chromatography–tandem mass spectrometry (LC–MS/MS) were used for the qualitative and quantitative analysis of GAGs. Specific enzyme assays and targeted gene sequencing were performed to confirm the diagnosis. Machine learning tools were used to develop CART model based on GAG profile. Qualitative and quantitative CART models showed 96.3% and 98.3% accuracy, respectively, in the differential diagnosis of MPS. The thresholds of different GAGs diagnostic of specific MPS types were established. In 60 MPS positive cases, 46 different mutations were identified in six specific genes. Among 31 different mutations identified in IDUA, nine were nonsense mutations and two were gross deletions while the remaining were missense mutations. In IDS gene, four missense, two frameshift, and one deletion were identified. In NAGLU gene, c.1693C > T and c.1914_1914insT were the most common mutations. Two ARSB, one case each of SGSH and GALNS mutations were observed. LC–MS/MS-based GAG pattern showed higher accuracy in the differential diagnosis of MPS. The mutation spectrum of MPS, specifically in IDUA and IDS genes, is highly heterogeneous among the cases studied.

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Acknowledgements

We thank Mr. Rajeev Sindhi, MD, Sandor Lifesciences Pvt. Ltd. for providing necessary infrastructure for the study. We thank all the families who had participated in the study.

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Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, Institute of Genetics & Hospital for Genetics Diseases, Osmania University, Begumpet, Hyderabad, Telangana, India

    Srilatha Kadali & Vijaya Lakshmi Bodiga

  2. Department of Biochemical Genetics, Sandor Lifesciences Pvt. Ltd, Banjara Hills, Hyderabad, Telangana, India

    Srilatha Kadali & Shaik Mohammad Naushad

  3. Rainbow Children’s Hospital, Banjara Hills, Hyderabad, Telangana, India

    Akella Radha Rama Devi

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  1. Srilatha Kadali
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  2. Shaik Mohammad Naushad
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  3. Akella Radha Rama Devi
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  4. Vijaya Lakshmi Bodiga
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Contributions

SK participated in the study design, analysis, interpretation of data, and drafting the manuscript. SMN performed statistical analysis, interpretation of the data, and drafting of the manuscript. ARRD participated in recruitment of the patients, interpretation of clinical reports, and counseled the affected families. VLB carried out the conception and design of the study, interpretation of data, and final approval of the manuscript.

Corresponding author

Correspondence to Vijaya Lakshmi Bodiga.

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Conflict of interest

The authors declare that they have no competing interests (financial or non-financial) in the present study.

Ethical approval

The study was approved by the Institutional ethical committee for biomedical research, Institute of Genetics and Faculty of Science, Osmania University. This study complied with the ethical principles outlined in the Declaration of Helsinki.

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Informed written consents were obtained from patients/guardians along with detailed clinical history during their enrollment for the study.

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Kadali, S., Naushad, S.M., Radha Rama Devi, A. et al. Biochemical, machine learning and molecular approaches for the differential diagnosis of Mucopolysaccharidoses. Mol Cell Biochem 458, 27–37 (2019). https://doi.org/10.1007/s11010-019-03527-6

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  • DOI: https://doi.org/10.1007/s11010-019-03527-6

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