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Inferring the molecular affinity of Indian pangolin with extant Manidae species based on mitochondrial genes: a wildlife forensic perspective.

Ved Prakash KumarAnkita RajpootMalay Ashvinkumar ShuklaParag NigamSurendra Prakash Goyal
Published in: Mitochondrial DNA. Part B, Resources (2018)
Pangolins are the world`s most trafficked mammalian species classified under family Manidae and face severe threat of extinction, largely due to the illicit trade of its parts and products, especially scales, in international markets. Pangolin scales are believed to be used in Traditional Chinese Medicines (TCM) and meat is used as delicacies in restaurants. Of the eight extant species of pangolin, morphological discrimination is easy but the situation becomes precarious once the scales and meat samples are seized and it is difficult to identify species based on morphology in such cases. However, wildlife DNA forensics has played an instrumental role in the identification of species from such type of materials. The present study investigated that three mitochondrial genes (Cyt b, 16S rRNA, and 12S rRNA) clearly showed the variation among seven extant pangolin species (Manis culionensis; possibly extinct), whereas, maximum variation was obtained in cytochrome b when compared to another two mitochondrial genes. The present study revealed that obtained SNPs based on short sequence length (Intervals) within the three mitochondrial genes will be helpful to design the short molecular marker and species-specific probe that is used in wildlife forensic for identifying pangolin species from the degraded sample. We also advocate using more than one molecular marker for species discrimination so as to minimize any false identification of the mammal's species reported in the trade. Furthermore, data generated from the study would help in strengthening the DNA database of Indian pangolin species.
Keyphrases
  • genome wide
  • bioinformatics analysis
  • emergency department
  • mass spectrometry
  • early onset
  • artificial intelligence
  • electronic health record
  • single cell
  • deep learning