Genome-Wide Classification of Myb Domain-Containing Protein Families in Entamoeba invadens .
Patricia CuellarElizabeth J Castañeda-OrtizCésar Rosales-ZarzaCarlos E Martínez-RodríguezIsrael Canela-PérezMario Alberto RodríguezJesús ValdésElisa Irene Azuara-LiceagaPublished in: Genes (2024)
Entamoeba histolytica , the causative agent of amebiasis, is the third leading cause of death among parasitic diseases globally. Its life cycle includes encystation, which has been mostly studied in Entamoeba invadens , responsible for reptilian amebiasis. However, the molecular mechanisms underlying this process are not fully understood. Therefore, we focused on the identification and characterization of Myb proteins, which regulate the expression of encystation-related genes in various protozoan parasites. Through bioinformatic analysis, we identified 48 genes in E. invadens encoding MYB-domain-containing proteins. These were classified into single-repeat 1R (20), 2R-MYB proteins (27), and one 4R-MYB protein. The in-silico analysis suggests that these proteins are multifunctional, participating in transcriptional regulation, chromatin remodeling, telomere maintenance, and splicing . Transcriptomic data analysis revealed expression signatures of eimyb genes, suggesting a potential orchestration in the regulation of early and late encystation-excystation genes. Furthermore, we identified probable target genes associated with reproduction, the meiotic cell cycle, ubiquitin-dependent protein catabolism, and endosomal transport. In conclusion, our findings suggest that E. invadens Myb proteins regulate stage-specific proteins and a wide array of cellular processes. This study provides a foundation for further exploration of the molecular mechanisms governing encystation and unveils potential targets for therapeutic intervention in amebiasis.
Keyphrases
- transcription factor
- genome wide
- cell cycle
- data analysis
- genome wide identification
- dna methylation
- poor prognosis
- binding protein
- randomized controlled trial
- gene expression
- cell proliferation
- protein protein
- machine learning
- single cell
- copy number
- small molecule
- oxidative stress
- deep learning
- drug delivery
- risk assessment
- life cycle
- climate change
- cancer therapy
- long non coding rna
- molecular dynamics simulations
- human health