Delivery of parasite Cdg7_Flc_0990 RNA transcript into intestinal epithelial cells during Cryptosporidium parvum infection suppresses host cell gene transcription through epigenetic mechanisms.
Yang WangAi-Yu GongShibin MaXian-Ming ChenJuliane K Strauss-SoukupXian-Ming ChenPublished in: Cellular microbiology (2017)
Cryptosporidial infection causes dysregulated transcription of host genes key to intestinal epithelial homeostasis, but the underlying mechanisms remain obscure. Previous studies demonstrate that several Cryptosporidium parvum (C. parvum) RNA transcripts are selectively delivered into epithelial cells during host cell invasion and may modulate gene transcription in infected cells. We report here that C. parvum infection suppresses the transcription of LRP5, SLC7A8, and IL33 genes in infected intestinal epithelium. Trans-suppression of these genes in infected host cells is associated with promoter enrichment of suppressive epigenetic markers (i.e., H3K9me3). Cdg7_FLc_0990, a C. parvum RNA that has previously demonstrated to be delivered into the nuclei of infected epithelial cells, is recruited to the promoter regions of LRP5, SLC7A8, and IL33 genes. Cdg7_FLc_0990 appears to be recruited to their promoter regions together with G9a, a histone methyltransferase for H3K9 methylation. The PR domain zinc finger protein 1, a G9a-interacting protein, is required for the assembly of Cdg7_FLc_0990 to the G9a complex and gene-specific enrichment of H3K9 methylation. Our data demonstrate that cryptosporidial infection induces epigenetic histone methylations in infected cells through nuclear transfer of parasite Cdg7_Flc_0990 RNA transcript, resulting in transcriptional suppression of the LRP5, SLC7A8, and IL33 genes.
Keyphrases
- dna methylation
- genome wide
- genome wide identification
- transcription factor
- gene expression
- induced apoptosis
- copy number
- cell cycle arrest
- genome wide analysis
- signaling pathway
- endoplasmic reticulum stress
- mesenchymal stem cells
- rna seq
- amino acid
- oxidative stress
- cell proliferation
- cell therapy
- deep learning
- bone marrow
- machine learning
- protein protein
- protein kinase
- binding protein
- life cycle