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

TORC1 signaling inhibition by rapamycin and caffeine affect lifespan, global gene expression, and cell proliferation of fission yeast.

Target of rapamycin complex 1 (TORC1) is implicated in growth control and aging from yeast to humans. Fission yeast is emerging as a popular model organism to study TOR signaling, although rapamycin has been thought to not affect cell growth in this organism. Here, we analyzed the effects of rapamycin and caffeine, singly and combined, on multiple cellular processes in fission yeast. The two drugs led to diverse and specific phenotypes that depended on TORC1 inhibition, including prolonged chronological lifespan, inhibition of global translation, inhibition of cell growth and division, and reprograming of global gene expression mimicking nitrogen starvation. Rapamycin and caffeine differentially affected these various TORC1-dependent processes. Combined drug treatment augmented most phenotypes and effectively blocked cell growth. Rapamycin showed a much more subtle effect on global translation than did caffeine, while both drugs were effective in prolonging chronological lifespan. Rapamycin and caffeine did not affect the lifespan via the pH of the growth media. Rapamycin prolonged the lifespan of nongrowing cells only when applied during the growth phase but not when applied after cells had stopped proliferation. The doses of rapamycin and caffeine strongly correlated with growth inhibition and with lifespan extension. This comprehensive analysis will inform future studies into TORC1 function and cellular aging in fission yeast and beyond

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

Pyphe, a python toolbox for assessing microbial growth and cell viability in high-throughput colony screens

Microbial fitness screens are a key technique in functional genomics. We present an allin-one solution, pyphe, for automating and improving data analysis pipelines associated with largescale fitness screens, including image acquisition and quantification, data normalisation, and statistical analysis. Pyphe is versatile and processes fitness data from colony sizes, viability scores from phloxine B staining or colony growth curves, all obtained with inexpensive transilluminating flatbed scanners. We apply pyphe to show that the fitness information contained in late endpoint measurements of colony sizes is similar to maximum growth slopes from time series. We phenotype gene-deletion strains of fission yeast in 59,350 individual fitness assays in 70 conditions, revealing that colony size and viability provide complementary, independent information. Viability scores obtained from quantifying the redness of phloxine-stained colonies accurately reflect the fraction of live cells within colonies. Pyphe is user-friendly, open-source and fully documented, illustrated by applications to diverse fitness analysis scenarios

Amino acids whose intracellular levels change most during aging alter chronological lifespan of fission yeast

Amino acid deprivation or supplementation can affect cellular and organismal lifespan, but we know little about the role of concentration changes in free, intracellular amino acids during aging. Here, we determine free amino-acid levels during chronological aging of non-dividing fission yeast cells. We compare wild-type with long-lived mutant cells that lack the Pka1 protein of the protein kinase A signalling pathway. In wild-type cells, total amino-acid levels decrease during aging, but much less so in pka1 mutants. Two amino acids strongly change as a function of age: glutamine decreases, especially in wild-type cells, while aspartate increases, especially in pka1 mutants. Supplementation of glutamine is sufficient to extend the chronological lifespan of wild-type but not of pka1Δ cells. Supplementation of aspartate, on the other hand, shortens the lifespan of pka1Δ but not of wild-type cells. Our results raise the possibility that certain amino acids are biomarkers of aging, and their concentrations during aging can promote or limit cellular lifespan

Global network analysis in Schizosaccharomyces pombe reveals three distinct consequences of the common 1-kb deletion causing juvenile CLN3 disease

Juvenile CLN3 disease is a recessively inherited paediatric neurodegenerative disorder, with most patients homozygous for a 1-kb intragenic deletion in CLN3. The btn1 gene is the Schizosaccharomyces pombe orthologue of CLN3. Here, we have extended the use of synthetic genetic array (SGA) analyses to delineate functional signatures for two different disease-causing mutations in addition to complete deletion of btn1. We show that genetic-interaction signatures can differ for mutations in the same gene, which helps to dissect their distinct functional effects. The mutation equivalent to the minor transcript arising from the 1-kb deletion (btn1102–208del) shows a distinct interaction pattern. Taken together, our results imply that the minor 1-kb deletion transcript has three consequences for CLN3: to both lose and retain some inherent functions and to acquire abnormal characteristics. This has particular implications for the therapeutic development of juvenile CLN3 disease. In addition, this proof of concept could be applied to conserved genes for other mendelian disorders or any gene of interest, aiding in the dissection of their functional domains, unpacking the global consequences of disease pathogenesis, and clarifying genotype–phenotype correlations. In doing so, this detail will enhance the goals of personalised medicine to improve treatment outcomes and reduce adverse events

Predicting the Fission Yeast Protein Interaction Network

A systems-level understanding of biological processes and information flow requires the mapping of cellular component interactions, among which protein–protein interactions are particularly important. Fission yeast (Schizosaccharomyces pombe) is a valuable model organism for which no systematic protein-interaction data are available. We exploited gene and protein properties, global genome regulation datasets, and conservation of interactions between budding and fission yeast to predict fission yeast protein interactions in silico. We have extensively tested our method in three ways: first, by predicting with 70–80% accuracy a selected high-confidence test set; second, by recapitulating interactions between members of the well-characterized SAGA co-activator complex; and third, by verifying predicted interactions of the Cbf11 transcription factor using mass spectrometry of TAP-purified protein complexes. Given the importance of the pathway in cell physiology and human disease, we explore the predicted sub-networks centered on the Tor1/2 kinases. Moreover, we predict the histidine kinases Mak1/2/3 to be vital hubs in the fission yeast stress response network, and we suggest interactors of argonaute 1, the principal component of the siRNA-mediated gene silencing pathway, lost in budding yeast but preserved in S. pombe. Of the new high-quality interactions that were discovered after we started this work, 73% were found in our predictions. Even though any predicted interactome is imperfect, the protein network presented here can provide a valuable basis to explore biological processes and to guide wet-lab experiments in fission yeast and beyond. Our predicted protein interactions are freely available through PInt, an online resource on our website (www.bahlerlab.info/PInt)

Global transcriptional responses of fission and budding yeast to changes in copper and iron levels: a comparative study

Analysis of genome-wide responses to changing copper and iron levels in budding and fission yeast reveals conservation of only a small core set of genes and remarkable differences in the responses of the two yeasts to excess copper

Barcode sequencing and a high-throughput assay for chronological lifespan uncover ageing-associated genes in fission yeast

Ageing-related processes are largely conserved, with simple organisms remaining the main platform to discover and dissect new ageing-associated genes. Yeasts provide potent model systems to study cellular ageing owing their amenability to systematic functional assays under controlled conditions. Even with yeast cells, however, ageing assays can be laborious and resource-intensive. Here we present improved experimental and computational methods to study chronological lifespan in Schizosaccharomyces pombe. We decoded the barcodes for 3206 mutants of the latest gene-deletion library, enabling the parallel profiling of ~700 additional mutants compared to previous screens. We then applied a refined method of barcode sequencing (Bar-seq), addressing technical and statistical issues raised by persisting DNA in dead cells and sampling bottlenecks in aged cultures, to screen for mutants showing altered lifespan during stationary phase. This screen identified 341 long-lived mutants and 1246 short-lived mutants which point to many previously unknown ageing-associated genes, including 46 conserved but entirely uncharacterized genes. The ageing-associated genes showed coherent enrichments in processes also associated with human ageing, particularly with respect to ageing in non-proliferative brain cells. We also developed an automated colony-forming unit assay to facilitate medium- to high-throughput chronological-lifespan studies by saving time and resources compared to the traditional assay. Results from the Bar-seq screen showed good agreement with this new assay. This study provides an effective methodological platform and identifies many new ageing-associated genes as a framework for analysing cellular ageing in yeast and beyond