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Sialotranscriptomics of the argasid tick Ornithodoros moubata along the trophogonic cycle.

Ana OleagaBeatriz SorianoCarlos LlorensRicardo Pérez-Sánchez
Published in: PLoS neglected tropical diseases (2021)
The argasid tick Ornithodoros moubata is the main vector of human relapsing fever (HRF) and African swine fever (ASF) in Africa. Salivary proteins are part of the host-tick interface and play vital roles in the tick feeding process and the host infection by tick-borne pathogens; they represent interesting targets for immune interventions aimed at tick control. The present work describes the transcriptome profile of salivary glands of O. moubata and assesses the gene expression dynamics along the trophogonic cycle using Illumina sequencing. De novo transcriptome assembling resulted in 71,194 transcript clusters and 41,011 annotated transcripts, which represent 57.6% of the annotation success. Most salivary gene expression takes place during the first 7 days after feeding (6,287 upregulated transcripts), while a minority of genes (203 upregulated transcripts) are differentially expressed between 7 and 14 days after feeding. The functional protein groups more abundantly overrepresented after blood feeding were lipocalins, proteases (especially metalloproteases), protease inhibitors including the Kunitz/BPTI-family, proteins with phospholipase A2 activity, acid tail proteins, basic tail proteins, vitellogenins, the 7DB family and proteins involved in tick immunity and defence. The complexity and functional redundancy observed in the sialotranscriptome of O. moubata are comparable to those of the sialomes of other argasid and ixodid ticks. This transcriptome provides a valuable reference database for ongoing proteomics studies of the salivary glands and saliva of O. moubata aimed at confirming and expanding previous data on the O. moubata sialoproteome.
Keyphrases
  • gene expression
  • rna seq
  • single cell
  • genome wide
  • dna methylation
  • endothelial cells
  • multiple sclerosis
  • mass spectrometry
  • multidrug resistant
  • artificial intelligence
  • emergency department
  • deep learning
  • case control