bioRxivBioinformaticsPreprint
Computational identification of candidate human long noncoding RNAs associated with PBMC responses to Candida auris and its cell wall components
Abstract
Candida auris is an emerging multidrug-resistant fungal pathogen. However, little is known about the role of long noncoding RNAs (lncRNAs) in the human immune response to this fungus. This study aimed to identify and prioritize novel lncRNAs associated with the response of human peripheral blood mononuclear cells (PBMCs) to C. auris and its cell wall components. We reanalyzed RNA-sequencing data from peripheral blood mononuclear cells obtained from three healthy donors. The cells were exposed to live C. auris, purified mannan, purified {beta}-glucan, or RPMI control for 4 h or 24 h. We identified 607 putative novel polyadenylated lncRNA loci that were absent from GENCODE Release 48. Of the 449 novel loci tested for differential expression, 102 responded to at least one stimulus. Novel-lncRNA responses were limited at 4 h but increased markedly at 24 h. At 24 h, 78 novel lncRNAs responded to live C. auris, 34 to mannan, and 11 to {beta}-glucan. Many of these responses are specific to a particular stimulus. Among the 102 responsive lncRNAs, 94 were significantly coexpressed with protein-coding genes. The coding genes of the most highly connected lncRNAs were associated with innate immunity, defense responses, lysosomal functions, and signaling pathways. Temporal analysis revealed 19 novel lncRNAs whose responses changed between 4 and 24 h. Fourteen novel lncRNAs were also differentially expressed and were selected for further analysis. Combining temporal response, coexpression, and genomic proximity identified 4 candidate cis-associated pairs: MSTRG.32011-TLR2, MSTRG.5763-DUSP5, MSTRG.25627-HCK, and MSTRG.18159-ITGB3. These findings reveal a predominantly late and stimulus-specific novel-lncRNA response to C. auris and provide prioritized candidates for experimental validation.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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