Please note that we have stopped the regular imports of Gene Expression Omnibus (GEO) data into ArrayExpress. This may not be the latest version of this experiment.
E-GEOD-64705 - microRNA profiling in the zoonotic parasite Echinococcus canadensis using a high-throughput approach
Released on 9 February 2015, last updated on 14 February 2015
Echinococcus canadensis, Echinococcus granulosus
microRNAs (miRNAs), a class of small non-coding RNAs, are key regulators of gene expression at post-transcriptional level and play essential roles in fundamental biological processes such as development and metabolism. Here, we perform a comprehensive analysis of miRNAs in the zoonotic parasite E. canadensis G7, one of the causative agents of the neglected disease cystic echinococcosis. Small RNA libraries from protoscoleces and cyst walls of E. canadensis G7 and protoscoleces of E. granulosus sensu stricto G1 were sequenced using Illumina technology. As a result, we found transcriptional evidence of 37 miRNAs thus expanding the miRNA repertoire of E. canadensis G7. Differential expression analysis showed significant regulated miRNAs between life cycle stages of E. canadensis G7. We confirmed the remarkable loss of conserved miRNA families in E. canadensis, reflecting their low morphological complexity and high adaptation to parasitism. This study will provide valuable information for better understanding the complex biology of this parasite and could help to find new potential targets for therapy and/or diagnosis. Small RNA libraries from protoscoleces and cyst walls of E. canadensis G7 and protoscoleces of E. granulosus sensu stricto G1 were sequenced using Illumina technology. For each sample type, two libraries were constructed from two independent samples in order to have biological replicates.
RNA-seq of non coding RNA
Natalia Macchiaroli <email@example.com>, Laura Kamenetzky, Lucas Maldonado, Magdalena Zarowiecki, Mara C Rosenzvit, Marcela Cucher
microRNA profiling in the zoonotic parasite Echinococcus canadensis using a high-throughput approach. Macchiaroli N, Cucher M, Zarowiecki M, Maldonado L, Kamenetzky L, Rosenzvit M. , PMID:25656283