Infections by the pathogenic yeasts Candida albicans and Candida glabrata are among the most common fungal diseases. The success of these species as human pathogens is contingent on their ability to resist antifungal therapy and thrive within the human host. C. glabrata is especially resilient to azole antifungal treatment, while C. albicans is best known for its wide array of virulence features. The core mechanisms that underlie antifungal resistance and virulence in these pathogens has been continuously addressed, but the investigation on how such mechanisms evolve according to each environment is scarcer. This review aims to explore current knowledge on microevolution experiments to several treatment and host-associated conditions in C. albicans and C. glabrata. The analysis of adaptation strategies that evolve over time will allow to better understand the mechanisms by which Candida species are able to achieve stable phenotypes in real-life scenarios, which are the ones that should constitute the most interesting drug targets.

DNA microarrays are highly sensitive tools to evaluate the gene expression status of organismic samples and standardized array formats exist for many different sample types. Differential expression studies usually utilize the strongest up- or downregulated genes to generate networks visualizing the relationships among these genes. To include all yeast genes in one analysis and to get broader information on all cellular responses, we test a priori input of predefined genome-wide expression cliques and subsequent statistical analysis of the expression data. To this end, we generate a set of 72 co-regulation cliques using the information from 3196 microarray experiments. The obtained cliques performed highly significant in gene ontology and transcription factor enrichment analyses. We then tested the clique set on individual microarray experiments reporting on responses to pheromone, glycerol versus glucose based growth and the cellular response to heat. In all cases a highly significant determination of affected expression cliques was possible based on their average expression differences, the positions of their genes within hit rankings (UpRegScore) or the enrichment of the Top200 hits in certain cliques. The 72 cliques were finally used to compare experiments, which reported on the transcriptional response to polyglutamine proteins of different lengths. Using the predefined clique set it is possible to identify with high sensitivity and good significance sample and condition specific changes to gene expression. We thus conclude that an analysis, starting with these 72 preformed expression cliques, can complement traditional microarray analyses by visualizing the entire response on a static genome-wide gene set.

Pelvic inflammatory disease (PID) is a female upper genital tract inflammatory disorder that arises after sexually transmitted bacterial infections (STI). Factors modulating risk for reproductive sequelae include co-infection, microbiota, host genetics and physiology. In a pilot study of cervical samples obtained from women at high risk for STIs, we examined the potential for unbiased characterization of host, pathogen and microbiome interactions using whole transcriptome sequencing analysis of ribosomal RNA-depleted total RNAs (Total RNA-Seq). Only samples from women with STI infection contained pathogen-specific sequences (3 to 38% transcriptome coverage). Simultaneously, we identified and quantified their active microbial communities. After integration with host-derived reads from the same data, we detected clustering of host transcriptional profiles that reflected microbiome differences and STI infection. Together, our study suggests that total RNA profiling will advance understanding of the interplay of pathogen, host and microbiota during natural infection and may reveal novel, outcome-relevant biomarkers.