Population genomics shows no distinction between pathogenic Candida krusei and environmental Pichia kudriavzevii: One species, four names.

We investigated genomic diversity of a yeast species that is both an opportunistic pathogen and an important industrial yeast. Under the name Candida krusei, it is responsible for about 2% of yeast infections caused by Candida species in humans. Bloodstream infections with C. krusei are problematic because most isolates are fluconazole-resistant. Under the names Pichia kudriavzevii, Issatchenkia orientalis and Candida glycerinogenes, the same yeast, including genetically modified strains, is used for industrial-scale production of glycerol and succinate. It is also used to make some fermented foods. Here, we sequenced the type strains of C. krusei (CBS573T) and P. kudriavzevii (CBS5147T), as well as 30 other clinical and environmental isolates. Our results show conclusively that they are the same species, with collinear genomes 99.6% identical in DNA sequence. Phylogenetic analysis of SNPs does not segregate clinical and environmental isolates into separate clades, suggesting that C. krusei infections are frequently acquired from the environment. Reduced resistance of strains to fluconazole correlates with the presence of one gene instead of two at the ABC11-ABC1 tandem locus. Most isolates are diploid, but one-quarter are triploid. Loss of heterozygosity is common, including at the mating-type locus. Our PacBio/Illumina assembly of the 10.8 Mb CBS573T genome is resolved into 5 complete chromosomes, and was annotated using RNAseq support. Each of the 5 centromeres is a 35 kb gene desert containing a large inverted repeat. This species is a member of the genus Pichia and family Pichiaceae (the methylotrophic yeasts clade), and so is only distantly related to other pathogenic Candida species.

The Methylotroph Gene Order Browser (MGOB) reveals conserved synteny and ancestral centromere locations in the yeast family Pichiaceae.

The yeast family Pichiaceae, also known as the 'methylotrophs clade', is a relatively little studied group of yeasts despite its economic and clinical relevance. To explore the genome evolution and synteny relationships within this family, we developed the Methylotroph Gene Order Browser (MGOB, http://mgob.ucd.ie) similar to our previous gene order browsers for other yeast families. The dataset contains genome sequences from nine Pichiaceae species, including our recent reference sequence of Pichia kudriavzevii. As an example, we demonstrate the conservation of synteny around the MOX1 locus among species both containing and lacking the MOX1 gene for methanol assimilation. We found ancient clusters of genes that are conserved as adjacent between Pichiaceae and Saccharomycetaceae. Surprisingly, we found evidence that the locations of some centromeres have been conserved among Pichiaceae species, and between Pichiaceae and Saccharomycetaceae, even though the centromeres fall into different structural categories-point centromeres, inverted repeats and retrotransposon cluster centromeres.

Comparative genomics of biotechnologically important yeasts.

Ascomycete yeasts are metabolically diverse, with great potential for biotechnology. Here, we report the comparative genome analysis of 29 taxonomically and biotechnologically important yeasts, including 16 newly sequenced. We identify a genetic code change, CUG-Ala, in Pachysolen tannophilus in the clade sister to the known CUG-Ser clade. Our well-resolved yeast phylogeny shows that some traits, such as methylotrophy, are restricted to single clades, whereas others, such as l-rhamnose utilization, have patchy phylogenetic distributions. Gene clusters, with variable organization and distribution, encode many pathways of interest. Genomics can predict some biochemical traits precisely, but the genomic basis of others, such as xylose utilization, remains unresolved. Our data also provide insight into early evolution of ascomycetes. We document the loss of H3K9me2/3 heterochromatin, the origin of ascomycete mating-type switching, and panascomycete synteny at the MAT locus. These data and analyses will facilitate the engineering of efficient biosynthetic and degradative pathways and gateways for genomic manipulation.

Plasmodium P-Type Cyclin CYC3 Modulates Endomitotic Growth during Oocyst Development in Mosquitoes.

Cell-cycle progression and cell division in eukaryotes are governed in part by the cyclin family and their regulation of cyclin-dependent kinases (CDKs). Cyclins are very well characterised in model systems such as yeast and human cells, but surprisingly little is known about their number and role in Plasmodium, the unicellular protozoan parasite that causes malaria. Malaria parasite cell division and proliferation differs from that of many eukaryotes. During its life cycle it undergoes two types of mitosis: endomitosis in asexual stages and an extremely rapid mitotic process during male gametogenesis. Both schizogony (producing merozoites) in host liver and red blood cells, and sporogony (producing sporozoites) in the mosquito vector, are endomitotic with repeated nuclear replication, without chromosome condensation, before cell division. The role of specific cyclins during Plasmodium cell proliferation was unknown. We show here that the Plasmodium genome contains only three cyclin genes, representing an unusual repertoire of cyclin classes. Expression and reverse genetic analyses of the single Plant (P)-type cyclin, CYC3, in the rodent malaria parasite, Plasmodium berghei, revealed a cytoplasmic and nuclear location of the GFP-tagged protein throughout the lifecycle. Deletion of cyc3 resulted in defects in size, number and growth of oocysts, with abnormalities in budding and sporozoite formation. Furthermore, global transcript analysis of the cyc3-deleted and wild type parasites at gametocyte and ookinete stages identified differentially expressed genes required for signalling, invasion and oocyst development. Collectively these data suggest that cyc3 modulates oocyst endomitotic development in Plasmodium berghei.

Development of a syndromic surveillance system for Irish dairy cattle using milk recording data.

In the last decade and a half, emerging vector-borne diseases have become a substantial threat to cattle across Europe. To mitigate the impact of the emergence of new diseases, outbreaks must be detected early. However, the clinical signs associated with many diseases may be nonspecific. Furthermore, there is often a delay in the development of new diagnostic tests for novel pathogens which limits the ability to detect emerging disease in the initial stages. Syndromic Surveillance has been proposed as an additional surveillance method that could augment traditional methods by detecting aberrations in non-specific disease indicators. The aim of this study was to develop a syndromic surveillance system for Irish dairy herds based on routinely collected milk recording and meteorological data. We sought to determine whether the system would have detected the 2012 Schmallenberg virus (SBV) incursion into Ireland earlier than conventional surveillance methods. Using 7,743,138 milk recordings from 730,724 cows in 7037 herds between 2007 and 2012, linear mixed-effects models were developed to predict milk yield and alarms generated with temporally clustered deviations from predicted values. Additionally, hotspot spatial analyses were conducted at corresponding time points. Using a range of thresholds, our model generated alarms throughout September 2012, between 4 and 6 weeks prior to the first laboratory confirmation of SBV in Ireland. This system for monitoring milk yield represents both a potentially useful tool for early detection of disease, and a valuable foundation for developing similar tools using other metrics.

Coverage-Versus-Length Plots, a Simple Quality Control Step for de Novo Yeast Genome Sequence Assemblies.

Illumina sequencing has revolutionized yeast genomics, with prices for commercial draft genome sequencing now below $200. The popular SPAdes assembler makes it simple to generate a de novo genome assembly for any yeast species. However, whereas making genome assemblies has become routine, understanding what they contain is still challenging. Here, we show how graphing the information that SPAdes provides about the length and coverage of each scaffold can be used to investigate the nature of an assembly, and to diagnose possible problems. Scaffolds derived from mitochondrial DNA, ribosomal DNA, and yeast plasmids can be identified by their high coverage. Contaminating data, such as cross-contamination from other samples in a multiplex sequencing run, can be identified by its low coverage. Scaffolds derived from the bacteriophage PhiX174 and Lambda DNAs that are frequently used as molecular standards in Illumina protocols can also be detected. Assemblies of yeast genomes with high heterozygosity, such as interspecies hybrids, often contain two types of scaffold: regions of the genome where the two alleles assembled into two separate scaffolds and each has a coverage level C, and regions where the two alleles co-assembled (collapsed) into a single scaffold that has a coverage level 2C Visualizing the data with Coverage-vs.-Length (CVL) plots, which can be done using Microsoft Excel or Google Sheets, provides a simple method to understand the structure of a genome assembly and detect aberrant scaffolds or contigs. We provide a Python script that allows assemblies to be filtered to remove contaminants identified in CVL plots.