title: RNA Enrichment Method for Quantitative Transcriptional Analysis of Pathogens In Vivo Applied to the Fungus Candida albicans.
abstract: 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.
  
  
  OBJECTIVE
  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.
authors.affiliation: Institute of Microbiology, University Hospital Lausanne, and University Hospital Center, Lausanne, Switzerland.
authors.forename: Sara
authors.initials: S
authors.lastname: Amorim-Vaz
authors.affiliation: Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
authors.forename: Van Du T
authors.initials: Vdu T
authors.lastname: Tran
authors.affiliation: Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland Genomic Technologies Facility, University of Lausanne, Lausanne, Switzerland.
authors.forename: Sylvain
authors.initials: S
authors.lastname: Pradervand
authors.affiliation: Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
authors.forename: Marco
authors.initials: M
authors.lastname: Pagni
authors.affiliation: Institute of Microbiology, University Hospital Lausanne, and University Hospital Center, Lausanne, Switzerland.
authors.forename: Alix T
authors.initials: AT
authors.lastname: Coste
authors.affiliation: Institute of Microbiology, University Hospital Lausanne, and University Hospital Center, Lausanne, Switzerland Dominique.Sanglard@chuv.ch.
authors.forename: Dominique
authors.identifier: http://orcid.org/0000-0002-5244-4178
authors.initials: D
authors.lastname: Sanglard
mesh_terms: D000818:Animals; D002176:Candida albicans / Q000235:genetics* / Q000254:growth & development*; D002177:Candidiasis / Q000382:microbiology*; D004195:Disease Models, Animal; D020869:Gene Expression Profiling / Q000379:methods*; D007313:Insecta; D051379:Mice; D017423:Sequence Analysis, RNA; D013997:Time Factors