The Transcriptional Regulation of Genes Involved in the Immune Innate Response of Keratinocytes Co-Cultured with Trichophyton rubrum Reveals Important Roles of Cytokine GM-CSF.
Monise Fazolin PetrucelliBruna Aline M CantelliMozart MarinsAna Lucia FachinPublished in: Journal of fungi (Basel, Switzerland) (2022)
Trichophyton rubrum is the most causative agent of dermatophytosis worldwide. The keratinocytes are the first line of defense during infection, triggering immunomodulatory responses. Previous dual RNA-seq data showed the upregulation of several human genes involved in immune response and epithelial barrier integrity during the co-culture of HaCat cells with T. rubrum . This work evaluates the transcriptional response of this set of genes during the co-culture of HaCat with different stages of T. rubrum conidia development and viability. Our results show that the developmental stage of fungal conidia and their viability interfere with the transcriptional regulation of innate immunity genes. The CSF2 gene encoding the cytokine GM-CSF is the most overexpressed, and we report for the first time that CSF2 expression is contact and conidial-viability-dependent during infection. In contrast, CSF2 transcripts and GM-CSF secretion levels were observed when HaCat cells were challenged with bacterial LPS. Furthermore, the secretion of proinflammatory cytokines was dependent on the conidia developmental stage. Thus, we suggest that the viability and developmental stage of fungal conidia interfere with the transcriptional patterns of genes encoding immunomodulatory proteins in human keratinocytes with regard to important roles of GM-CSF during infection.
Keyphrases
- immune response
- rna seq
- endothelial cells
- genome wide
- induced apoptosis
- poor prognosis
- cerebrospinal fluid
- genome wide identification
- transcription factor
- single cell
- cell cycle arrest
- magnetic resonance
- dna methylation
- signaling pathway
- magnetic resonance imaging
- oxidative stress
- genome wide analysis
- copy number
- long non coding rna
- machine learning
- wound healing
- big data
- pluripotent stem cells
- artificial intelligence