Parasitology Research

, Volume 112, Issue 8, pp 2925–2932 | Cite as

Isolation and molecular characterization of potentially pathogenic Acanthamoeba genotypes from diverse water resources including household drinking water from Khyber Pakhtunkhwa, Pakistan

  • Tania Tanveer
  • Abdul Hameed
  • Ambreen Gul Muazzam
  • Suk-Yul Jung
  • Asma Gul
  • Abdul MatinEmail author
Original Paper


Acanthamoeba, an opportunistic protozoan pathogen, is ubiquitous in nature, and therefore plays a predatory role and helps control microbial communities in the ecosystem. These Acanthamoeba species are recognized as opportunistic human pathogens that may cause blinding keratitis and rare but fatal granulomatous encephalitis. To date, there is not a single report demonstrating Acanthamoeba isolation and identification from environmental sources in Pakistan, and that is the aim of this study. Acanthamoeba were identified by morphological characteristics of their cysts on non-nutrient agar plates seeded with Escherichia coli. Additionally, the polymerase chain reaction (PCR) was performed with genus-specific primers followed by direct sequencing of the PCR product for molecular identification. Furthermore, our PCR and sequencing results confirmed seven different pathogenic and nonpathogenic genotypes, including T2–T10, T4, T5, T7, T15, T16, and T17. To the best of our knowledge, we have identified and isolated Acanthamoeba sp., for the first time, from water resources of Khyber Pakhtunkhwa, Pakistan. There is an urgent need to address (1) the pathogenic potential of the identified genotypes and (2) explore other environmental sources from the country to examine the water quality and the current status of Acanthamoeba species in Pakistan, which may be a potential threat for public health across the country.


Keratitis Acanthamoeba Keratitis Acanthamoeba Isolation Acanthamoeba Species Single Polymerase Chain Reaction Product 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors are thankful to Brig. Muhammad Farooq Murawat, Asim Wazir Khan Naizi, Sohail Khan Naizi, Junaid Khan, Abu Turab Khan, and Ghazala Shabnum for their tremendous support in sample collection and transportation. This work was partially supported by grants from Institute of Biomedical and Genetic Engineering, Islamabad, Pakistan.

Conflicts of interest

Authors have no conflict of interest to declare.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tania Tanveer
    • 1
    • 2
  • Abdul Hameed
    • 1
  • Ambreen Gul Muazzam
    • 1
  • Suk-Yul Jung
    • 3
  • Asma Gul
    • 2
  • Abdul Matin
    • 1
    • 4
    Email author
  1. 1.Institute of Biomedical and Genetic EngineeringIslamabadPakistan
  2. 2.Department of Environmental SciencesInternational Islamic UniversityIslamabadPakistan
  3. 3.Department of Biomedical Laboratory Science, Molecular Diagnosis Research InstituteNamseoul UniversityCheonanSouth Korea
  4. 4.Department of Medical Lab TechnologyUniversity of HaripurHaripurPakistan

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