Exploiting Comparative Omics to Understand the Pathogenic and Virulence-Associated Protease: Anti-Protease Relationships in the Zoonotic Parasites Fasciola hepatica and Fasciola gigantica .
Krystyna CwiklinskiJohn Pius DaltonPublished in: Genes (2022)
The helminth parasites, Fasciola hepatica and Fasciola gigantica , are the causative agents of fasciolosis, a global and economically important disease of people and their livestock. Proteases are pivotal to an array of biological processes related to parasitism (development, feeding, immune evasion, virulence) and therefore their action requires strict regulation by parasite anti-proteases (protease inhibitors). By interrogating the current publicly available Fasciola spp. large sequencing datasets, including several genome assemblies and life cycle stage-specific transcriptome and proteome datasets, we reveal the complex profile and structure of proteases and anti-proteases families operating at various stages of the parasite's life cycle. Moreover, we have discovered distinct profiles of peptidases and their cognate inhibitors expressed by the parasite stages in the intermediate snail host, reflecting the different environmental niches in which they move, develop and extract nutrients. Comparative genomics revealed a similar cohort of peptidase inhibitors in F. hepatica and F. gigantica but a surprisingly reduced number of cathepsin peptidases genes in the F. gigantica genome assemblies. Chromosomal location of the F. gigantica genes provides new insights into the evolution of these gene families, and critical data for the future analysis and interrogation of Fasciola spp. hybrids spreading throughout the Asian and African continents.
Keyphrases
- life cycle
- genome wide
- single cell
- rna seq
- plasmodium falciparum
- escherichia coli
- dna methylation
- pseudomonas aeruginosa
- copy number
- genome wide identification
- staphylococcus aureus
- high throughput
- biofilm formation
- epithelial mesenchymal transition
- gene expression
- antimicrobial resistance
- oxidative stress
- electronic health record
- heavy metals
- machine learning
- current status
- transcription factor
- heat shock
- genome wide analysis
- artificial intelligence
- genetic diversity