Genome-Wide Scale-Free Network Inference for Candida albicans

Abstract

Discovery of essential genes in pathogenic organisms is an important step in the development of new medication. Despite a growing number of genome data available, little is known about C. albicans, a major fungal pathogen. Most of the human population carries C. albicans as commensal, but it can cause systemic infection that may lead to the death of the host if the immune system has deteriorated. In many organisms central nodes in the interaction network (hubs) play a crucial role for information and energy transport. Knock-outs of such hubs often lead to lethal phenotypes making them interesting drug targets. To identify these central genes via topological analysis, we inferred gene regulatory networks that are sparse and scale-free. We collected information from various sources to complement the limited expression data available. We utilized a linear regression algorithm to infer genome-wide gene regulatory interaction networks. To evaluate the predictive power of our approach, we used an automated text-mining system that scanned full-text research papers for known interactions. With the help of the compendium of known interactions, we also optimize the influence of the prior knowledge and the sparseness of the model to achieve the best results. We compare the results of our approach with those of other state-of-the-art network inference methods and show that we outperform those methods. Finally we identify a number of hubs in the genome of the fungus and investigate their biological relevance.

Network-assisted genetic dissection of pathogenicity and drug resistance in the opportunistic human pathogenic fungus Cryptococcus neoformans

“…Although a few molecular networks for pathogenic fungi have been reported over the past several years, these networks were either small protein-protein interaction networks 15 16 17 or transcriptional regulatory networks relevant to specific cellular conditions 18 19 . More recently, a genome-wide scale-free network of Candida albicans has been reported 20 , but its quality has been assessed by only a few network hub genes with no experimental validation. The network edge information and analysis tools for hypothesis generation are not available to the public for any of these networks, however, and therefore neither the reassessment nor the reuse of these networks is possible.…”

Construction and verification of the transcriptional regulatory response network of Streptococcus mutansupon treatment with the biofilm inhibitor carolacton

“…Although non-exhaustive due to the lack of sufficient biological information, genome wide transcriptional regulatory networks have nevertheless been constructed from heterogeneous data for a few organisms such as Escherichia coli [20], Saccharomyces cerevisiae [21], Bacillus subtilis [17], Candida albicans [22], Streptomyces coelicolor [18], Halobacterium NRC-1 [19], Pseudomonas aeruginosa [23] and others. Despite the advances made in the field of genome-wide regulatory network reconstruction, most of these attempts have been carried out for model or standard organisms (for which ample biological information exists) such as E. coli, S. cerevisiae, B. subtilis, P. aeruginosa [17, 20, 23, 24].…”

Role of Genomics and RNA-seq in Studies of Fungal Virulence

“…Tierney et al [47••] restricted their analyses to a small number of genes supported by previous experimental evidence, and focused on transcription factor "hubs". While a similar approach has been used to identify networks associated with drug resistance in Candida [51], this method is only successful when previous experimental analyses are available and emphasizes the need for supporting laboratory-based research.…”