Genetic and behavioral adaptation of Candida parapsilosis to the microbiome of hospitalized infants revealed by in situ genomics, transcriptomics, and proteomics

Abstract

Background Candida parapsilosis is a common cause of invasive candidiasis, especially in newborn infants, and infections have been increasing over the past two decades. C. parapsilosis has been primarily studied in pure culture, leaving gaps in understanding of its function in a microbiome context. Results Here, we compare five unique C. parapsilosis genomes assembled from premature infant fecal samples, three of which are newly reconstructed, and analyze their genome structure, population diversity, and in situ activity relative to reference strains in pure culture. All five genomes contain hotspots of single nucleotide variants, some of which are shared by strains from multiple hospitals. A subset of environmental and hospital-derived genomes share variants within these hotspots suggesting derivation of that region from a common ancestor. Four of the newly reconstructed C. parapsilosis genomes have 4 to 16 copies of the gene RTA3, which encodes a lipid translocase and is implicated in antifungal resistance, potentially indicating adaptation to hospital antifungal use. Time course metatranscriptomics and metaproteomics on fecal samples from a premature infant with a C. parapsilosis blood infection revealed highly variable in situ expression patterns that are distinct from those of similar strains in pure cultures. For example, biofilm formation genes were relatively less expressed in situ, whereas genes linked to oxygen utilization were more highly expressed, indicative of growth in a relatively aerobic environment. In gut microbiome samples, C. parapsilosis co-existed with Enterococcus faecalis that shifted in relative abundance over time, accompanied by changes in bacterial and fungal gene expression and proteome composition. Conclusions The results reveal potentially medically relevant differences in Candida function in gut vs. laboratory environments, and constrain evolutionary processes that could contribute to hospital strain persistence and transfer into premature infant microbiomes.

Echinocandin heteroresistance causes prophylaxis failure and facilitates breakthroughCandida parapsilosisinfection

“…The grids on the grey dash line indicate self-comparisons of each C. parapsilosis strain. Color bar indicates the scale of popANI, white color represents 99.999% identity, as a common threshold of “identical strains” 53 . Patient 1 and patient 2 in this figure were patient 2 and patient 3, respectively, in a previous study 17 .…”

Inferring early-life host and microbiome functions by mass spectrometry-based metaproteomics and metabolomics

“…A case study recently demonstrated how low abundance microbial taxa, fungal species in this example, affect microbiome dynamics in a preterm infant [77] . Using a strategy of two bioinformatics pipelines for deriving eukaryotic and prokaryotic metagenomes, and creating a custom-built database composed of the concatenated metagenome-derived predicted proteomes, the authors described unique interactions between the fungus Candida parapsilosis and the bacterium Enterococcus faecalis within the infant gut microbiome.…”

“…Time course metaproteomics had also been applied to specific actions of microbial eukaryotes within the gut microbiome [77] . This case study of low abundance microbial taxa characterised fecal samples from a premature infant with a documented Candida blood infection with the aim to describe the behaviour of the fungi in the human gut.…”

Considerations for constructing a protein sequence database for metaproteomics

“…Additionally, none of the evaluations mentioned in this paragraph processed their assembled metagenomes into MAGs, thus they were not able to evaluate whether MAGs improved taxonomic assignment or functional annotations. Benefits such as improved taxonomic classification, assignment of complete pathways to individual organisms, and the ability to analyze genes in their genomic context have led to MAGs being a critical component of the many studies that have investigated function with metaproteomics at the species and genome level [6] , [7] , [9] , [25] , [26] , [90] , [101] . Therefore, the use of genome-resolved metagenomes in metaproteomics databases deserves more careful future evaluations.…”

“…A workflow for retrieving eukaryotic contigs using machine learning, and applying binning methods to assemble eukaryotic MAGs, was proposed by West et al and benchmarked using a variety of data sets [135] . West et al further used this approach to identify proteins of the yeast Candida in preterm infants using metaproteomics [101] . The BUSCO tool can be used to classify bins as eukaryotic, bacterial, or archaeal, and provides gene predictions and completion versus contamination metrics for those bins [124] .…”