Candida albicans commensalism in the oral mucosa is favoured by limited virulence and metabolic adaptation.

As part of the human microbiota, the fungus Candida albicans colonizes the oral cavity and other mucosal surfaces of the human body. Commensalism is tightly controlled by complex interactions of the fungus and the host to preclude fungal elimination but also fungal overgrowth and invasion, which can result in disease. As such, defects in antifungal T cell immunity render individuals susceptible to oral thrush due to interrupted immunosurveillance of the oral mucosa. The factors that promote commensalism and ensure persistence of C. albicans in a fully immunocompetent host remain less clear. Using an experimental model of C. albicans oral colonization in mice we explored fungal determinants of commensalism in the oral cavity. Transcript profiling of the oral isolate 101 in the murine tongue tissue revealed a characteristic metabolic profile tailored to the nutrient poor conditions in the stratum corneum of the epithelium where the fungus resides. Metabolic adaptation of isolate 101 was also reflected in enhanced nutrient acquisition when grown on oral mucosa substrates. Persistent colonization of the oral mucosa by C. albicans also correlated inversely with the capacity of the fungus to induce epithelial cell damage and to elicit an inflammatory response. Here we show that these immune evasive properties of isolate 101 are explained by a strong attenuation of a number of virulence genes, including those linked to filamentation. De-repression of the hyphal program by deletion or conditional repression of NRG1 abolished the commensal behaviour of isolate 101, thereby establishing a central role of this factor in the commensal lifestyle of C. albicans in the oral niche of the host.

Function Analysis of MBF1, a Factor Involved in the Response to Amino Acid Starvation and Virulence in Candida albicans.

Candida albicans is a commensal of human mucosae, but also one of the most common fungal pathogens of humans. Systemic infections caused by this fungus, mostly affecting immunocompromised patients, are associated to fatality rates as high as 50% despite the available treatments. In order to improve this situation, it is necessary to fully understand how C. albicans is able to cause disease and how it copes with the host defenses. Our previous studies have revealed the importance of the C. albicans gene MBF1 in virulence and ability to colonize internal organs of mammalian and insect hosts. MBF1 encodes a putative transcriptional regulator, and as such it likely has an impact in the regulation of C. albicans gene expression during host infection. Here, recent advances in RNA-seq technologies were used to obtain a detailed analysis of the impact of MBF1 on C. albicans gene expression both in vitro and during infection. MBF1 was involved in the regulation of several genes with a role in glycolysis and response to stress, particularly to nutritional stress. We also investigated whether an interaction existed between MBF1 and GCN4, a master regulator of response to starvation, and found that both genes were needed for resistance to amino acid starvation, suggesting some level of interaction between the two. Reinforcing this idea, we showed that the proteins encoded by both genes could interact. Consistent with the role of MBF1 in virulence, we also established that GCN4 was necessary for virulence in the mouse model of systemic infection as well as in the Galleria mellonella infection model.

Condition-specific series of metabolic sub-networks and its application for gene set enrichment analysis.

MOTIVATION: Genome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organism's metabolism, yet their integration to achieve biological insight remains challenging. RESULTS: We investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO. AVAILABILITY AND IMPLEMENTATION: The metaboGSE R package is available at https://CRAN.R-project.org/package=metaboGSE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RNA Enrichment Method for Quantitative Transcriptional Analysis of Pathogens In Vivo Applied to the Fungus Candida albicans.

UNLABELLED: In vivo transcriptional analyses of microbial pathogens are often hampered by low proportions of pathogen biomass in host organs, hindering the coverage of full pathogen transcriptome. We aimed to address the transcriptome profiles of Candida albicans, the most prevalent fungal pathogen in systemically infected immunocompromised patients, during systemic infection in different hosts. We developed a strategy for high-resolution quantitative analysis of the C. albicans transcriptome directly from early and late stages of systemic infection in two different host models, mouse and the insect Galleria mellonella. Our results show that transcriptome sequencing (RNA-seq) libraries were enriched for fungal transcripts up to 1,600-fold using biotinylated bait probes to capture C. albicans sequences. This enrichment biased the read counts of only ~3% of the genes, which can be identified and removed based on a priori criteria. This allowed an unprecedented resolution of C. albicans transcriptome in vivo, with detection of over 86% of its genes. The transcriptional response of the fungus was surprisingly similar during infection of the two hosts and at the two time points, although some host- and time point-specific genes could be identified. Genes that were highly induced during infection were involved, for instance, in stress response, adhesion, iron acquisition, and biofilm formation. Of the in vivo-regulated genes, 10% are still of unknown function, and their future study will be of great interest. The fungal RNA enrichment procedure used here will help a better characterization of the C. albicans response in infected hosts and may be applied to other microbial pathogens. IMPORTANCE: Understanding the mechanisms utilized by pathogens to infect and cause disease in their hosts is crucial for rational drug development. Transcriptomic studies may help investigations of these mechanisms by determining which genes are expressed specifically during infection. This task has been difficult so far, since the proportion of microbial biomass in infected tissues is often extremely low, thus limiting the depth of sequencing and comprehensive transcriptome analysis. Here, we adapted a technology to capture and enrich C. albicans RNA, which was next used for deep RNA sequencing directly from infected tissues from two different host organisms. The high-resolution transcriptome revealed a large number of genes that were so far unknown to participate in infection, which will likely constitute a focus of study in the future. More importantly, this method may be adapted to perform transcript profiling of any other microbes during host infection or colonization.

Examining the virulence of Candida albicans transcription factor mutants using Galleria mellonella and mouse infection models.

The aim of the present study was to identify Candida albicans transcription factors (TFs) involved in virulence. Although mice are considered the gold-standard model to study fungal virulence, mini-host infection models have been increasingly used. Here, barcoded TF mutants were first screened in mice by pools of strains and fungal burdens (FBs) quantified in kidneys. Mutants of unannotated genes which generated a kidney FB significantly different from that of wild-type were selected and individually examined in Galleria mellonella. In addition, mutants that could not be detected in mice were also tested in G. mellonella. Only 25% of these mutants displayed matching phenotypes in both hosts, highlighting a significant discrepancy between the two models. To address the basis of this difference (pool or host effects), a set of 19 mutants tested in G. mellonella were also injected individually into mice. Matching FB phenotypes were observed in 50% of the cases, highlighting the bias due to host effects. In contrast, 33.4% concordance was observed between pool and single strain infections in mice, thereby highlighting the bias introduced by the "pool effect." After filtering the results obtained from the two infection models, mutants for MBF1 and ZCF6 were selected. Independent marker-free mutants were subsequently tested in both hosts to validate previous results. The MBF1 mutant showed impaired infection in both models, while the ZCF6 mutant was only significant in mice infections. The two mutants showed no obvious in vitro phenotypes compared with the wild-type, indicating that these genes might be specifically involved in in vivo adapt.

Novel Approaches for Fungal Transcriptomics from Host Samples.

Candida albicans adaptation to the host requires a profound reprogramming of the fungal transcriptome as compared to in vitro laboratory conditions. A detailed knowledge of the C. albicans transcriptome during the infection process is necessary in order to understand which of the fungal genes are important for host adaptation. Such genes could be thought of as potential targets for antifungal therapy. The acquisition of the C. albicans transcriptome is, however, technically challenging due to the low proportion of fungal RNA in host tissues. Two emerging technologies were used recently to circumvent this problem. One consists of the detection of low abundance fungal RNA using capture and reporter gene probes which is followed by emission and quantification of resulting fluorescent signals (nanoString). The other is based first on the capture of fungal RNA by short biotinylated oligonucleotide baits covering the C. albicans ORFome permitting fungal RNA purification. Next, the enriched fungal RNA is amplified and subjected to RNA sequencing (RNA-seq). Here we detail these two transcriptome approaches and discuss their advantages and limitations and future perspectives in microbial transcriptomics from host material.