DeORFanizing Candida albicans Genes using Co-Expression

Abstract

AbstractFunctional characterization of open reading frames in non-model organisms, such as the common opportunistic fungal pathogen Candida albicans, can be labor intensive. To meet this challenge, we built a comprehensive and unbiased co-expression network for C. albicans, which we call CalCEN, from data collected from 853 RNA sequencing runs from 18 large scale studies deposited in the NCBI Sequence Read Archive. Retrospectively, CalCEN is highly predictive of known gene function annotations and can be synergistically combined with sequence similarity and interaction networks in Saccharomyces cerevisiae through orthology for additional accuracy in gene function prediction. To prospectively demonstrate the utility of the co-expression network in C. albicans, we predicted the function of under-annotated open reading frames (ORF)s and identified CCJ1 as a novel cell cycle regulator in C. albicans. This study provides a tool for future systems biology analyses of gene function in C. albicans. We provide a computational pipeline for building and analyzing the co-expression network and CalCEN itself at (http://github.com/momeara/CalCEN).ImportanceCandida albicans is a common and deadly fungal pathogen of humans, yet the genome of this organism contains many genes of unknown function. By determining gene function, we can help identify essential genes, new virulence factors, or new regulators of drug resistance, and thereby give new targets for antifungal development. Here, we use information from large scale RNAseq studies and generate a C. albicans co-expression network (CalCEN) that is robust and able to predict gene function. We demonstrate the utility of this network in both retrospective and prospective testing, and use CalCEN to predict a role for C4_06590W/CCJ1 in cell cycle. This tool will allow for a better characterization of under-annotated genes in pathogenic yeasts.