Markus S. Schröder

Genus-Wide Comparative Genomics of Malassezia Delineates Its Phylogeny, Physiology, and Niche Adaptation on Human Skin

Malassezia is a unique lipophilic genus in class Malasseziomycetes in Ustilaginomycotina, (Basidiomycota, fungi) that otherwise consists almost exclusively of plant pathogens. Malassezia are typically isolated from warm-blooded animals, are dominant members of the human skin mycobiome and are associated with common skin disorders. To characterize the genetic basis of the unique phenotypes of Malassezia spp., we sequenced the genomes of all 14 accepted species and used comparative genomics against a broad panel of fungal genomes to comprehensively identify distinct features that define the Malassezia gene repertoire: gene gain and loss; selection signatures; and lineage-specific gene family expansions. Our analysis revealed key gene gain events (64) with a single gene conserved across all Malassezia but absent in all other sequenced Basidiomycota. These likely horizontally transferred genes provide intriguing gain-of-function events and prime candidates to explain the emergence of Malassezia. A larger set of genes (741) were lost, with enrichment for glycosyl hydrolases and carbohydrate metabolism, concordant with adaptation to skin’s carbohydrate-deficient environment. Gene family analysis revealed extensive turnover and underlined the importance of secretory lipases, phospholipases, aspartyl proteases, and other peptidases. Combining genomic analysis with a re-evaluation of culture characteristics, we establish the likely lipid-dependence of all Malassezia. Our phylogenetic analysis sheds new light on the relationship between Malassezia and other members of Ustilaginomycotina, as well as phylogenetic lineages within the genus. Overall, our study provides a unique genomic resource for understanding Malassezia niche-specificity and potential virulence, as well as their abundance and distribution in the environment and on human skin.

Comparative Genome Analysis and Gene Finding in Candida Species Using CGOB

The Candida Gene Order Browser (CGOB) was developed as a tool to visualize and analyze synteny relationships in multiple Candida species, and to provide an accurate, manually curated set of orthologous Candida genes for evolutionary analyses. Here, we describe major improvements to CGOB. The underlying structure of the database has been changed significantly. Genomic features are now based directly on genome annotations rather than on protein sequences, which allows non-protein features such as centromere locations in Candida albicans and tRNA genes in all species to be included. The data set has been expanded to 13 species, including genomes of pathogens (C. albicans, C. parapsilosis, C. tropicalis, and C. orthopsilosis), and those of xylose-degrading species with important biotechnological applications (C. tenuis, Scheffersomyces stipitis, and Spathaspora passalidarum). Updated annotations of C. parapsilosis, C. dubliniensis, and Debaryomyces hansenii have been incorporated. We discovered more than 1,500 previously unannotated genes among the 13 genomes, ranging in size from 29 to 3,850 amino acids. Poorly conserved and rapidly evolving genes were also identified. Re-analysis of the mating type loci of the xylose degraders suggests that C. tenuis is heterothallic, whereas both Spa. passalidarum and S. stipitis are homothallic. As well as hosting the browser, the CGOB website (http://cgob.ucd.ie) gives direct access to all the underlying genome annotations, sequences, and curated orthology data.

Comparative Phenotypic Analysis of the Major Fungal Pathogens Candida parapsilosis and Candida albicans

Candida parapsilosis and Candida albicans are human fungal pathogens that belong to the CTG clade in the Saccharomycotina. In contrast to C. albicans, relatively little is known about the virulence properties of C. parapsilosis, a pathogen particularly associated with infections of premature neonates. We describe here the construction of C. parapsilosis strains carrying double allele deletions of 100 transcription factors, protein kinases and species-specific genes. Two independent deletions were constructed for each target gene. Growth in >40 conditions was tested, including carbon source, temperature, and the presence of antifungal drugs. The phenotypes were compared to C. albicans strains with deletions of orthologous transcription factors. We found that many phenotypes are shared between the two species, such as the role of Upc2 as a regulator of azole resistance, and of CAP1 in the oxidative stress response. Others are unique to one species. For example, Cph2 plays a role in the hypoxic response in C. parapsilosis but not in C. albicans. We found extensive divergence between the biofilm regulators of the two species. We identified seven transcription factors and one protein kinase that are required for biofilm development in C. parapsilosis. Only three (Efg1, Bcr1 and Ace2) have similar effects on C. albicans biofilms, whereas Cph2, Czf1, Gzf3 and Ume6 have major roles in C. parapsilosis only. Two transcription factors (Brg1 and Tec1) with well-characterized roles in biofilm formation in C. albicans do not have the same function in C. parapsilosis. We also compared the transcription profile of C. parapsilosis and C. albicans biofilms. Our analysis suggests the processes shared between the two species are predominantly metabolic, and that Cph2 and Bcr1 are major biofilm regulators in C. parapsilosis.

Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer.

UNLABELLEDnBreast cancer is one of the most frequent cancers among women. Extensive studies into the molecular heterogeneity of breast cancer have produced a plethora of molecular subtype classification and prognosis prediction algorithms, as well as numerous gene expression signatures. However, reimplementation of these algorithms is a tedious but important task to enable comparison of existing signatures and classification models between each other and with new models. Here, we present the genefu R/Bioconductor package, a multi-tiered compendium of bioinformatics algorithms and gene signatures for molecular subtyping and prognostication in breast cancer.nnnAVAILABILITY AND IMPLEMENTATIONnThe genefu package is available from Bioconductor. http://www.bioconductor.org/packages/devel/bioc/html/genefu.html Source code is also available on Github https://github.com/bhklab/genefunnnCONTACTnbhaibeka@uhnresearch.cannnSUPPLEMENTARY INFORMATIONnSupplementary data are available at Bioinformatics online.

Proteogenomics produces comprehensive and highly accurate protein-coding gene annotation in a complete genome assembly of Malassezia sympodialis

Abstract Complete and accurate genome assembly and annotation is a crucial foundation for comparative and functional genomics. Despite this, few complete eukaryotic genomes are available, and genome annotation remains a major challenge. Here, we present a complete genome assembly of the skin commensal yeast Malassezia sympodialis and demonstrate how proteogenomics can substantially improve gene annotation. Through long-read DNA sequencing, we obtained a gap-free genome assembly for M. sympodialis (ATCC 42132), comprising eight nuclear and one mitochondrial chromosome. We also sequenced and assembled four M. sympodialis clinical isolates, and showed their value for understanding Malassezia reproduction by confirming four alternative allele combinations at the two mating-type loci. Importantly, we demonstrated how proteomics data could be readily integrated with transcriptomics data in standard annotation tools. This increased the number of annotated protein-coding genes by 14% (from 3612 to 4113), compared to using transcriptomics evidence alone. Manual curation further increased the number of protein-coding genes by 9% (to 4493). All of these genes have RNA-seq evidence and 87% were confirmed by proteomics. The M. sympodialis genome assembly and annotation presented here is at a quality yet achieved only for a few eukaryotic organisms, and constitutes an important reference for future host-microbe interaction studies.

Multiple Origins of the Pathogenic Yeast Candida orthopsilosis by Separate Hybridizations between Two Parental Species

Mating between different species produces hybrids that are usually asexual and stuck as diploids, but can also lead to the formation of new species. Here, we report the genome sequences of 27 isolates of the pathogenic yeast Candida orthopsilosis. We find that most isolates are diploid hybrids, products of mating between two unknown parental species (A and B) that are 5% divergent in sequence. Isolates vary greatly in the extent of homogenization between A and B, making their genomes a mosaic of highly heterozygous regions interspersed with homozygous regions. Separate phylogenetic analyses of SNPs in the A- and B-derived portions of the genome produces almost identical trees of the isolates with four major clades. However, the presence of two mutually exclusive genotype combinations at the mating type locus, and recombinant mitochondrial genomes diagnostic of inter-clade mating, shows that the species C. orthopsilosis does not have a single evolutionary origin but was created at least four times by separate interspecies hybridizations between parents A and B. Older hybrids have lost more heterozygosity. We also identify two isolates with homozygous genomes derived exclusively from parent A, which are pure non-hybrid strains. The parallel emergence of the same hybrid species from multiple independent hybridization events is common in plant evolution, but is much less documented in pathogenic fungi.

The CgHaa1-Regulon Mediates Response and Tolerance to Acetic Acid Stress in the Human Pathogen Candida glabrata

To thrive in the acidic vaginal tract, Candida glabrata has to cope with high concentrations of acetic acid. The mechanisms underlying C. glabrata tolerance to acetic acid at low pH remain largely uncharacterized. In this work, the essential role of the CgHaa1 transcription factor (encoded by ORF CAGL0L09339g) in the response and tolerance of C. glabrata to acetic acid is demonstrated. Transcriptomic analysis showed that CgHaa1 regulates, directly or indirectly, the expression of about 75% of the genes activated under acetic acid stress. CgHaa1-activated targets are involved in multiple physiological functions including membrane transport, metabolism of carbohydrates and amino acids, regulation of the activity of the plasma membrane H+-ATPase, and adhesion. Under acetic acid stress, CgHaa1 increased the activity and the expression of the CgPma1 proton pump and contributed to increased colonization of vaginal epithelial cells by C. glabrata. CgHAA1, and two identified CgHaa1-activated targets, CgTPO3 and CgHSP30, are herein demonstrated to be determinants of C. glabrata tolerance to acetic acid. The protective effect of CgTpo3 and of CgHaa1 was linked to a role of these proteins in reducing the accumulation of acetic acid inside C. glabrata cells. In response to acetic acid stress, marked differences were found in the regulons controlled by CgHaa1 and by its S. cerevisiae ScHaa1 ortholog, demonstrating a clear divergent evolution of the two regulatory networks. The results gathered in this study significantly advance the understanding of the molecular mechanisms underlying the success of C. glabrata as a vaginal colonizer.

Ensemble approach combining multiple methods improves human transcription start site prediction

BackgroundThe computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets.ResultsWe demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier (Profisi Ensemble) using predictions from 7 programs, along with 2 other data sources. Support vector machines using full and reduced data sets are combined in an either/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool.ConclusionsSupervised learning methods are a useful way to combine predictions from diverse sources.

Identification of Non-Coding RNAs in the Candida parapsilosis Species Group

The Candida CTG clade is a monophyletic group of fungal species that translates CTG as serine, and includes the pathogens Candida albicans and Candida parapsilosis. Research has typically focused on identifying protein-coding genes in these species. Here, we use bioinformatic and experimental approaches to annotate known classes of non-coding RNAs in three CTG-clade species, Candida parapsilosis, Candida orthopsilosis and Lodderomyces elongisporus. We also update the annotation of ncRNAs in the C. albicans genome. The majority of ncRNAs identified were snoRNAs. Approximately 50% of snoRNAs (including most of the C/D box class) are encoded in introns. Most are within mono- and polycistronic transcripts with no protein coding potential. Five polycistronic clusters of snoRNAs are highly conserved in fungi. In polycistronic regions, splicing occurs via the classical pathway, as well as by nested and recursive splicing. We identified spliceosomal small nuclear RNAs, the telomerase RNA component, signal recognition particle, RNase P RNA component and the related RNase MRP RNA component in all three genomes. Stem loop IV of the U2 spliceosomal RNA and the associated binding proteins were lost from the ancestor of C. parapsilosis and C. orthopsilosis, following the divergence from L. elongisporus. The RNA component of the MRP is longer in C. parapsilosis, C. orthopsilosis and L. elongisporus than in S. cerevisiae, but is substantially shorter than in C. albicans.

Using RNA-seq for Analysis of Differential Gene Expression in Fungal Species.

The ability to extract, identify and annotate large amounts of biological data is a key feature of the omics era, and has led to an explosion in the amount of data available. One pivotal advance is the use of Next-Generation Sequencing (NGS) techniques such as RNA-sequencing (RNA-seq). RNA-seq uses data from millions of small mRNA transcripts or reads which are aligned to a reference genome. Comparative transcriptomics analyses using RNA-seq can provide the researcher with a comprehensive view of the cells response to a given environment or stimulus.Here, we describe the NGS techniques (based on Illumina technology) that are routinely used for comparative transcriptome analysis of fungal species. We describe the entire process from isolation of RNA to computational identification of differentially expressed genes. We provide instructions to allow the beginner to implement packages in R such as Bioconductor. The methods described are not limited to yeast, and can also be applied to other eukaryotic organisms.