Molecular and Cellular Biochemistry

, Volume 458, Issue 1–2, pp 27–37 | Cite as

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

  • Srilatha Kadali
  • Shaik Mohammad Naushad
  • Akella Radha Rama Devi
  • Vijaya Lakshmi BodigaEmail author


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.


Mucopolysaccharidoses Glycosaminoglycans LC–MS/MS CART model Machine learning tools Mutation analysis 



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.

Author 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.

Compliance with ethical standards

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.

Informed consent

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


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Srilatha Kadali
    • 1
    • 2
  • Shaik Mohammad Naushad
    • 2
  • Akella Radha Rama Devi
    • 3
  • Vijaya Lakshmi Bodiga
    • 1
    Email author
  1. 1.Department of Biochemistry and Molecular Biology, Institute of Genetics & Hospital for Genetics DiseasesOsmania UniversityHyderabadIndia
  2. 2.Department of Biochemical GeneticsSandor Lifesciences Pvt. LtdHyderabadIndia
  3. 3.Rainbow Children’s HospitalHyderabadIndia

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