Next-generation sequencing: applications beyond genomes.

The development of DNA sequencing more than 30 years ago has profoundly impacted biological research. In the last couple of years, remarkable technological innovations have emerged that allow the direct and cost-effective sequencing of complex samples at unprecedented scale and speed. These next-generation technologies make it feasible to sequence not only static genomes, but also entire transcriptomes expressed under different conditions. These and other powerful applications of next-generation sequencing are rapidly revolutionizing the way genomic studies are carried out. Below, we provide a snapshot of these exciting new approaches to understanding the properties and functions of genomes. Given that sequencing-based assays may increasingly supersede microarray-based assays, we also compare and contrast data obtained from these distinct approaches

Identifying genes required for respiratory growth of fission yeast

We have used both auxotroph and prototroph versions of the latest deletion-mutant library to identify genes required for respiratory growth on solid glycerol medium in fission yeast. This data set complements and enhances our recent study on functional and regulatory aspects of energy metabolism by providing additional proteins that are involved in respiration. Most proteins identified in this mutant screen have not been implicated in respiration in budding yeast. We also provide a protocol to generate a prototrophic mutant library, and data on technical and biological reproducibility of colony-based high-throughput screens

Fission yeast SWI/SNF and RSC complexes show compositional and functional differences from budding yeast.

SWI/SNF chromatin-remodeling complexes have crucial roles in transcription and other chromatin-related processes. The analysis of the two members of this class in Saccharomyces cerevisiae, SWI/SNF and RSC, has heavily contributed to our understanding of these complexes. To understand the in vivo functions of SWI/SNF and RSC in an evolutionarily distant organism, we have characterized these complexes in Schizosaccharomyces pombe. Although core components are conserved between the two yeasts, the compositions of S. pombe SWI/SNF and RSC differ from their S. cerevisiae counterparts and in some ways are more similar to metazoan complexes. Furthermore, several of the conserved proteins, including actin-like proteins, are markedly different between the two yeasts with respect to their requirement for viability. Finally, phenotypic and microarray analyses identified widespread requirements for SWI/SNF and RSC on transcription including strong evidence that SWI/SNF directly represses iron-transport genes

Co-Expression Network Models Suggest that Stress Increases Tolerance to Mutations

Network models are a well established tool for studying the robustness of complex systems, including modelling the effect of loss of function mutations in protein interaction networks. Past work has concentrated on average damage caused by random node removal, with little attention to the shape of the damage distribution. In this work, we use fission yeast co-expression networks before and after exposure to stress to model the effect of stress on mutational robustness. We find that exposure to stress decreases the average damage from node removal, suggesting stress induces greater tolerance to loss of function mutations. The shape of the damage distribution is also changed upon stress, with a greater incidence of extreme damage after exposure to stress. We demonstrate that the change in shape of the damage distribution can have considerable functional consequences, highlighting the need to consider the damage distribution in addition to average behaviour

High-Throughput, High-Precision Colony Phenotyping with Pyphe

Colony fitness screens are powerful approaches for functional genomics and genetics. This protocol describes experimental and computational procedures for assaying the fitness of thousands of microbial strains in numerous conditions in parallel. Data analysis is based on pyphe, an all-in-one bioinformatics toolbox for scanning, image analysis, data normalization, and interpretation. We describe a standard protocol where endpoint colony areas are used as fitness proxy and two variations on this, one using colony growth curves and one using colony viability staining with phloxine B. Different strategies for experimental design, normalization and quality control are discussed. Using these approaches, it is possible to collect hundreds of thousands of data points, with low technical noise levels around 5%, in an experiment typically lasting 2 weeks or less

Gene Function Prediction from Functional Association Networks Using Kernel Partial Least Squares Regression

With the growing availability of large-scale biological datasets, automated methods of extracting functionally meaningful information from this data are becoming increasingly important. Data relating to functional association between genes or proteins, such as co-expression or functional association, is often represented in terms of gene or protein networks. Several methods of predicting gene function from these networks have been proposed. However, evaluating the relative performance of these algorithms may not be trivial: concerns have been raised over biases in different benchmarking methods and datasets, particularly relating to non-independence of functional association data and test data. In this paper we propose a new network-based gene function prediction algorithm using a commute-time kernel and partial least squares regression (Compass). We compare Compass to GeneMANIA, a leading network-based prediction algorithm, using a number of different benchmarks, and find that Compass outperforms GeneMANIA on these benchmarks. We also explicitly explore problems associated with the non-independence of functional association data and test data. We find that a benchmark based on the Gene Ontology database, which, directly or indirectly, incorporates information from other databases, may considerably overestimate the performance of algorithms exploiting functional association data for prediction

Mitochondrial respiration is required to provide amino acids during fermentative proliferation of fission yeast

When glucose is available, many organisms repress mitochondrial respiration in favour of aerobic glycolysis, or fermentation in yeast, that suffices for ATP production. Fission yeast cells, however, rely partially on respiration for rapid proliferation under fermentative conditions. Here, we determined the limiting factors that require respiratory function during fermentation. When inhibiting the electron transport chain, supplementation with arginine was necessary and sufficient to restore rapid proliferation. Accordingly, a systematic screen for mutants growing poorly without arginine identified mutants defective in mitochondrial oxidative metabolism. Genetic or pharmacological inhibition of respiration triggered a drop in intracellular levels of arginine and amino acids derived from the Krebs cycle metabolite alpha-ketoglutarate: glutamine, lysine and glutamic acid. Conversion of arginine into these amino acids was required for rapid proliferation when blocking the respiratory chain. The respiratory block triggered an immediate gene expression response diagnostic of TOR inhibition, which was muted by arginine supplementation or without the AMPK-activating kinase Ssp1. The TOR-controlled proteins featured biased composition of amino acids reflecting their shortage after respiratory inhibition. We conclude that respiration supports rapid proliferation in fermenting fission yeast cells by boosting the supply of Krebs cycle-derived amino acids

urg1: a uracil-regulatable promoter system for fission yeast with short induction and repression times.

BACKGROUND: The fission yeast Schizosaccharomyces pombe is a popular genetic model organism with powerful experimental tools. The thiamine-regulatable nmt1 promoter and derivatives, which take >15 hours for full induction, are most commonly used for controlled expression of ectopic genes. Given the short cell cycle of fission yeast, however, a promoter system that can be rapidly regulated, similar to the GAL system for budding yeast, would provide a key advantage for many experiments. METHODOLOGY/PRINCIPAL FINDINGS: We used S. pombe microarrays to identify three neighbouring genes (urg1, urg2, and urg3) whose transcript levels rapidly and strongly increased in response to uracil, a condition which otherwise had little effect on global gene expression. We cloned the promoter of urg1 (uracil-regulatable gene) to create several PCR-based gene targeting modules for replacing native promoters with the urg1 promoter (Purg1) in the normal chromosomal locations of genes of interest. The kanMX6 and natMX6 markers allow selection under urg1 induced and repressed conditions, respectively. Some modules also allow N-terminal tagging of gene products placed under urg1 control. Using pom1 as a proof-of-principle, we observed a maximal increase of Purg1-pom1 transcripts after uracil addition within less than 30 minutes, and a similarly rapid decrease after uracil removal. The induced and repressed transcriptional states remained stable over 24-hour periods. RT-PCR comparisons showed that both induced and repressed Purg1-pom1 transcript levels were lower than corresponding P3nmt1-pom1 levels (wild-type nmt1 promoter) but higher than P81nmt1-pom1 levels (weak nmt1 derivative). CONCLUSIONS/SIGNIFICANCE: We exploited the urg1 promoter system to rapidly induce pom1 expression at defined cell-cycle stages, showing that ectopic pom1 expression leads to cell branching in G2-phase but much less so in G1-phase. The high temporal resolution provided by the urg1 promoter should facilitate experimental design and improve the genetic toolbox for the fission yeast community

Preparation of Total RNA from Fission Yeast

Treatment with hot phenol breaks open fission yeast cells and begins to strip away bound proteins from RNA. Deproteinization is completed by multiple extractions with chloroform/isoamyl alcohol and separation of the aqueous and organic phases using MaXtract gel, an inert material that acts as a physical barrier between the phases. The final step is concentration of the RNA by ethanol precipitation. The protocol can be used to prepare RNA from several cultures grown in parallel, but it is important not to process too many samples at once because delays can be detrimental to RNA quality. A reasonable number of samples to process at once would be three to four for microarray or RNA sequencing analyses and six for preliminary investigations of mutants implicated in RNA metabolism