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A molecular and morphological study of Ascaris suum in a human-pig contact scenario in northeastern Brazil.

Polyanna Araújo Alves BacelarJéssica Pereira Dos SantosDeiviane Aparecida CalegarDenilson de Araújo E SilvaDaniella Nobre LealBrenda Bulsara Costa EvangelistaElis Regina Chaves Dos ReisJacenir Reis Dos Santos MalletFilipe Anibal Carvalho-CostaLauren Hubert JaegerKerla Joeline Lima Monteiro
Published in: Revista brasileira de parasitologia veterinaria = Brazilian journal of veterinary parasitology : Orgao Oficial do Colegio Brasileiro de Parasitologia Veterinaria (2023)
The aim of the present study was to assess morphologic and genetic data on ascariasis in swine (Sus scrofa domesticus) and humans in low-resource rural and periurban communities in the state of Piauí, Brazil. Our cross-sectional survey included 100 fecal samples obtained from swine and 682 samples from humans. Fifteen pigs were necropsied. Human and porcine fecal samples were examined to identify Ascaris eggs. Parasites obtained in the swine necropsies were studied using scanning electron microscopy (SEM), and the mitochondrial gene encoding the cytochrome oxidase 1 (cox1) enzyme was partially amplified and sequenced for molecular taxonomy and phylogenetic analyses. The overall prevalence of Ascaris eggs in the swine fecal samples was 16/100 (16%). No Ascaris eggs were identified in the human fecal samples. SEM of six worms recovered from pigs demonstrated morphological characteristics of A. suum. Cox1 sequences were compatible with A. suum reference sequences. Original and reference (GenBank) nucleotide sequences were organized into clusters that did not segregate the parasites by host species or and region. The largest haplogroups were dominated by haplotypes H01, H02 and H31. In the communities studied, there was no epidemiological evidence of the zoonotic transmission of ascariasis at the human-swine interface.
Keyphrases
  • endothelial cells
  • induced pluripotent stem cells
  • pluripotent stem cells
  • oxidative stress
  • genome wide
  • gene expression
  • machine learning
  • high resolution