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Asset Tag: Yeast genome

July 7, 2019  |  

TeloPCR-seq: a high-throughput sequencing approach for telomeres.

We have developed a high-throughput sequencing approach that enables us to determine terminal telomere sequences from tens of thousands of individual Schizosaccharomyces pombe telomeres. This method provides unprecedented coverage of telomeric sequence complexity in fission yeast. S. pombe telomeres are composed of modular degenerate repeats that can be explained by variation in usage of the TER1 RNA template during reverse transcription. Taking advantage of this deep sequencing approach, we find that ‘like’ repeat modules are highly correlated within individual telomeres. Moreover, repeat module preference varies with telomere length, suggesting that existing repeats promote the incorporation of like repeats and/or that specific conformations of the telomerase holoenzyme efficiently and/or processively add repeats of like nature. After the loss of telomerase activity, this sequencing and analysis pipeline defines a population of telomeres with altered sequence content. This approach will be adaptable to study telomeric repeats in other organisms and also to interrogate repetitive sequences throughout the genome that are inaccessible to other sequencing methods.© 2016 Federation of European Biochemical Societies.

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July 7, 2019  |  

Complete genomic and transcriptional landscape analysis using third-generation sequencing: a case study of Saccharomyces cerevisiae CEN.PK113-7D.

Completion of eukaryal genomes can be difficult task with the highly repetitive sequences along the chromosomes and short read lengths of second-generation sequencing. Saccharomyces cerevisiae strain CEN.PK113-7D, widely used as a model organism and a cell factory, was selected for this study to demonstrate the superior capability of very long sequence reads for de novo genome assembly. We generated long reads using two common third-generation sequencing technologies (Oxford Nanopore Technology (ONT) and Pacific Biosciences (PacBio)) and used short reads obtained using Illumina sequencing for error correction. Assembly of the reads derived from all three technologies resulted in complete sequences for all 16 yeast chromosomes, as well as the mitochondrial chromosome, in one step. Further, we identified three types of DNA methylation (5mC, 4mC and 6mA). Comparison between the reference strain S288C and strain CEN.PK113-7D identified chromosomal rearrangements against a background of similar gene content between the two strains. We identified full-length transcripts through ONT direct RNA sequencing technology. This allows for the identification of transcriptional landscapes, including untranslated regions (UTRs) (5′ UTR and 3′ UTR) as well as differential gene expression quantification. About 91% of the predicted transcripts could be consistently detected across biological replicates grown either on glucose or ethanol. Direct RNA sequencing identified many polyadenylated non-coding RNAs, rRNAs, telomere-RNA, long non-coding RNA and antisense RNA. This work demonstrates a strategy to obtain complete genome sequences and transcriptional landscapes that can be applied to other eukaryal organisms.

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July 7, 2019  |  

Tracing the de novo origin of protein-coding genes in yeast.

De novo genes are very important for evolutionary innovation. However, how these genes originate and spread remains largely unknown. To better understand this, we rigorously searched for de novo genes in Saccharomyces cerevisiae S288C and examined their spread and fixation in the population. Here, we identified 84 de novo genes in S. cerevisiae S288C since the divergence with their sister groups. Transcriptome and ribosome profiling data revealed at least 8 (10%) and 28 (33%) de novo genes being expressed and translated only under specific conditions, respectively. DNA microarray data, based on 2-fold change, showed that 87% of the de novo genes are regulated during various biological processes, such as nutrient utilization and sporulation. Our comparative and evolutionary analyses further revealed that some factors, including single nucleotide polymorphism (SNP)/indel mutation, high GC content, and DNA shuffling, contribute to the birth of de novo genes, while domestication and natural selection drive the spread and fixation of these genes. Finally, we also provide evidence suggesting the possible parallel origin of a de novo gene between S. cerevisiae and Saccharomyces paradoxus Together, our study provides several new insights into the origin and spread of de novo genes.IMPORTANCE Emergence of de novo genes has occurred in many lineages during evolution, but the birth, spread, and function of these genes remain unresolved. Here we have searched for de novo genes from Saccharomyces cerevisiae S288C using rigorous methods, which reduced the effects of bad annotation and genomic gaps on the identification of de novo genes. Through this analysis, we have found 84 new genes originating de novo from previously noncoding regions, 87% of which are very likely involved in various biological processes. We noticed that 10% and 33% of de novo genes were only expressed and translated under specific conditions, therefore, verification of de novo genes through transcriptome and ribosome profiling, especially from limited expression data, may underestimate the number of bona fide new genes. We further show that SNP/indel mutation, high GC content, and DNA shuffling could be involved in the birth of de novo genes, while domestication and natural selection drive the spread and fixation of these genes. Finally, we provide evidence suggesting the possible parallel origin of a new gene. Copyright © 2018 Wu and Knudson.

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July 7, 2019  |  

Rationally designed perturbation factor drives evolution in Saccharomyces cerevisiae for industrial application.

Saccharomyces cerevisiae strains with favorable characteristics are preferred for application in industries. However, the current ability to reprogram a yeast cell on the genome scale is limited due to the complexity of yeast ploids. In this study, a method named genome replication engineering-assisted continuous evolution (GREACE) was proved efficient in engineering S. cerevisiae with different ploids. Through iterative cycles of culture coupled with selection, GREACE could continuously improve the target traits of yeast by accumulating beneficial genetic modification in genome. The application of GREACE greatly improved the tolerance of yeast against acetic acid compared with their parent strain. This method could also be employed to improve yeast aroma profile and the phenotype could be stably inherited to the offspring. Therefore, GREACE method was efficient in S. cerevisiae engineering and it could be further used to evolve yeast with other specific characteristics.

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July 7, 2019  |  

Traditional Norwegian kveik are a genetically distinct group of domesticated Saccharomyces cerevisiae brewing yeasts.

The widespread production of fermented food and beverages has resulted in the domestication of Saccharomyces cerevisiae yeasts specifically adapted to beer production. While there is evidence beer yeast domestication was accelerated by industrialization of beer, there also exists a farmhouse brewing culture in western Norway which has passed down yeasts referred to as kveik for generations. This practice has resulted in ale yeasts which are typically highly flocculant, phenolic off flavor negative (POF-), and exhibit a high rate of fermentation, similar to previously characterized lineages of domesticated yeast. Additionally, kveik yeasts are reportedly high-temperature tolerant, likely due to the traditional practice of pitching yeast into warm (>28°C) wort. Here, we characterize kveik yeasts from 9 different Norwegian sources via PCR fingerprinting, whole genome sequencing of selected strains, phenotypic screens, and lab-scale fermentations. Phylogenetic analysis suggests that kveik yeasts form a distinct group among beer yeasts. Additionally, we identify a novel POF- loss-of-function mutation, as well as SNPs and CNVs potentially relevant to the thermotolerance, high ethanol tolerance, and high fermentation rate phenotypes of kveik strains. We also identify domestication markers related to flocculation in kveik. Taken together, the results suggest that Norwegian kveik yeasts are a genetically distinct group of domesticated beer yeasts with properties highly relevant to the brewing sector.

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July 7, 2019  |  

Approximate, simultaneous comparison of microbial genome architectures via syntenic anchoring of quiver representations

Motivation A long-standing limitation in comparative genomic studies is the dependency on a reference genome, which hinders the spectrum of genetic diversity that can be identified across a population of organisms. This is especially true in the microbial world where genome architectures can significantly vary. There is therefore a need for computational methods that can simultaneously analyze the architectures of multiple genomes without introducing bias from a reference. Results In this article, we present Ptolemy: a novel method for studying the diversity of genome architectures—such as structural variation and pan-genomes—across a collection of microbial assemblies without the need of a reference. Ptolemy is a ‘top-down’ approach to compare whole genome assemblies. Genomes are represented as labeled multi-directed graphs—known as quivers—which are then merged into a single, canonical quiver by identifying ‘gene anchors’ via synteny analysis. The canonical quiver represents an approximate, structural alignment of all genomes in a given collection encoding structural variation across (sub-) populations within the collection. We highlight various applications of Ptolemy by analyzing structural variation and the pan-genomes of different datasets composing of Mycobacterium, Saccharomyces, Escherichia and Shigella species. Our results show that Ptolemy is flexible and can handle both conserved and highly dynamic genome architectures. Ptolemy is user-friendly—requires only FASTA-formatted assembly along with a corresponding GFF-formatted file—and resource-friendly—can align 24 genomes in ~10 mins with four CPUs and <2 GB of RAM.

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