Coexpression network analysis of human candida infection reveals key modules and hub genes responsible for host-pathogen interactions

Abstract

Invasive fungal infections are a significant reason for morbidity and mortality among organ transplant recipients. Therefore, it is critical to investigate the host and candida niches to understand the epidemiology of fungal infections in transplantation. Candida albicans is an opportunistic fungal pathogen that causes fatal invasive mucosal infections, particularly in solid organ transplant patients. Therefore, identifying and characterizing these genes would play a vital role in understanding the complex regulation of host-pathogen interactions. Using 32 RNA-sequencing samples of human cells infected with C. albicans, we developed WGCNA coexpression networks and performed DESeq2 differential gene expression analysis to identify the genes that positively correlate with human candida infection. Using hierarchical clustering, we identified 5 distinct modules. We studied the inter- and intramodular gene network properties in the context of sample status traits and identified the highly enriched genes in the correlated modules. We identified 52 genes that were common in the most significant WGCNA turquoise module and differentially expressed genes in human endothelial cells (HUVEC) infection vs. control samples. As a validation step, we identified the differentially expressed genes from the independent Candida-infected human oral keratinocytes (OKF6) samples and validated 30 of the 52 common genes. We then performed the functional enrichment analysis using KEGG and GO. Finally, we performed protein-protein interaction (PPI) analysis using STRING and CytoHubba from 30 validated genes. We identified 8 hub genes (JUN, ATF3, VEGFA, SLC2A1, HK2, PTGS2, PFKFB3, and KLF6) that were enriched in response to hypoxia, angiogenesis, vasculogenesis, hypoxia-induced signaling, cancer, diabetes, and transplant-related disease pathways. The discovery of genes and functional pathways related to the immune system and gene coexpression and differential gene expression analyses may serve as novel diagnostic markers and potential therapeutic targets.

Two transcription factors, AcREM14 and AcC3H1, enhance the resistance of kiwifruit Actinidia chinensis var. chinensis to Pseudomonas syringae pv. actinidiae

“…WGCNA has been frequently used to study plant interactions with pathogenic bacteria [ 50–52 ]. Wu et al .…”

“…[ 53 ] constructed a co-expression network by WGCNA, which enabled the identification of a total of 5010 DEGs in 15 co-expression gene modules, and 4 modules showed significant association with smut resistance. The researchers identified 38 hub genes by calculating their connectivity within the corresponding network [ 50 ]. Here, we used WGCNA to correlate the expression profiles of RH12 and SH14 with the virulence phenotype and identified four highly correlated modules.…”

“…The filtered reads were aligned to the kiwifruit reference genome ( http://kiwifruitgenome.org/ ) using HISAT2 v2.1.0 [ 63 ]. Transcript assembly was conducted using StringTie v1.3.3b [ 50 ] and read counts were calculated using the built-in prepDE.py script. The DEseq2 package v1.16.1 [ 64 ], was used to screen for transcripts with absolute expression fold-changes >2 and P < 0.05.…”

The Dual Roles of Activating Transcription Factor 3 (ATF3) in Inflammation, Apoptosis, Ferroptosis, and Pathogen Infection Responses

“…This once again highlights the complexity and significance of ATF3's regulatory mechanisms. Notably, Candida albicans, a fungal pathogen that infects humans, significantly upregulates ATF3 after infecting cells [189,190]. This may suggest that ATF3 plays a role in Candida albicans infections.…”

Modelling Pathogen Response of the Human Immune System in a Reduced State Space

“…The study of the immune system dynamics is carried out at two primary levels: (i) intracellular gene networks and switching in cell states (such as [3]- [5]), and (ii) cell-cell interactions leading to adaptations analyzed at cell population levels (such as [6]- [9]). The timescale is another differentiating aspect among the studies, ranging from minutes (e.g.…”