Identifying Conserved Generic Aspergillus spp. Co-Expressed Gene Modules Associated with Germination Using Cross-Platform and Cross-Species Transcriptomics.
Tim J H BaltussenJordy P M CoolenPaul E VerweijJan DijksterhuisWillem J G MelchersPublished in: Journal of fungi (Basel, Switzerland) (2021)
Aspergillus spp. is an opportunistic human pathogen that may cause a spectrum of pulmonary diseases. In order to establish infection, inhaled conidia must germinate, whereby they break dormancy, start to swell, and initiate a highly polarized growth process. To identify critical biological processes during germination, we performed a cross-platform, cross-species comparative analysis of germinating A. fumigatus and A. niger conidia using transcriptional data from published RNA-Seq and Affymetrix studies. A consensus co-expression network analysis identified four gene modules associated with stages of germination. These modules showed numerous shared biological processes between A. niger and A. fumigatus during conidial germination. Specifically, the turquoise module was enriched with secondary metabolism, the black module was highly enriched with protein synthesis, the darkgreen module was enriched with protein fate, and the blue module was highly enriched with polarized growth. More specifically, enriched functional categories identified in the blue module were vesicle formation, vesicular transport, tubulin dependent transport, actin-dependent transport, exocytosis, and endocytosis. Genes important for these biological processes showed similar expression patterns in A. fumigatus and A. niger, therefore, they could be potential antifungal targets. Through cross-platform, cross-species comparative analysis, we were able to identify biologically meaningful modules shared by A. fumigatus and A. niger, which underscores the potential of this approach.
Keyphrases
- network analysis
- rna seq
- single cell
- poor prognosis
- high throughput
- endothelial cells
- gene expression
- transcription factor
- genome wide identification
- electronic health record
- randomized controlled trial
- protein protein
- genetic diversity
- oxidative stress
- artificial intelligence
- data analysis
- human health
- case control
- pluripotent stem cells