Trends Biochem Sci

The metabolic network has a modular architecture, is robust to perturbations, and responds to biological stimuli and environmental conditions. Through monitoring by metabolite responsive macromolecules, metabolic pathways interact with the transcriptome and proteome. Whereas pathway interconnecting cofactors and substrates report on the overall state of the network, specialised intermediates measure the activity of individual functional units. Transitions in the network affect many of these regulatory metabolites, facilitating the parallel regulation of the timing and control of diverse biological processes. The metabolic network controls its own balance, chromatin structure and the biosynthesis of molecular cofactors; moreover, metabolic shifts are crucial in the response to oxidative stress and play a regulatory role in cancer

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

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

J Mol Biol

Spinocerebellar ataxia type 2 (SCA2) is a hereditary neurodegenerative disorder caused by a trinucleotide expansion in the SCA2 gene, encoding a polyglutamine stretch in the gene product ataxin-2 (ATX2), whose cellular function is unknown. However, ATX2 interacts with A2BP1, a protein containing an RNA-recognition motif, and the existence of an interaction motif for the C-terminal domain of the poly(A)-binding protein (PABC) as well as an Lsm (Like Sm) domain in ATX2 suggest that ATX2 like its yeast homolog Pbp1 might be involved in RNA metabolism. Here, we show that, similar to Pbp1, ATX2 suppresses the petite (pet−) phenotype of Δmrs2 yeast strains lacking mitochondrial group II introns. This finding points to a close functional relationship between the two homologs. To gain insight into potential functions of ATX2, we also generated a comprehensive protein interaction network for Pbp1 from publicly available databases, which implicates Pbp1 in diverse RNA-processing pathways. The functional relationship of ATX2 and Pbp1 is further corroborated by the experimental confirmation of the predicted interaction of ATX2 with the cytoplasmic poly(A)-binding protein 1 (PABP) using yeast-2-hybrid analysis as well as co-immunoprecipitation experiments. Immunofluorescence studies revealed that ATX2 and PABP co-localize in mammalian cells, remarkably, even under conditions in which PABP accumulates in distinct cytoplasmic foci representing sites of mRNA triage

The Saccharomyces cerevisiae W303-K6001 cross-platform genome sequence: insights into ancestry and physiology of a laboratory mutt

Saccharomyces cerevisiae strain W303 is a widely used model organism. However, little is known about its genetic origins, as it was created in the 1970s from crossing yeast strains of uncertain genealogy. To obtain insights into its ancestry and physiology, we sequenced the genome of its variant W303-K6001, a yeast model of ageing research. The combination of two next-generation sequencing (NGS) technologies (Illumina and Roche/454 sequencing) yielded an 11.8 Mb genome assembly at an N50 contig length of 262 kb. Although sequencing was substantially more precise and sensitive than whole-genome tiling arrays, both NGS platforms produced a number of false positives. At a 378x average coverage, only 74 per cent of called differences to the S288c reference genome were confirmed by both techniques. The consensus W303-K6001 genome differs in 8133 positions from S288c, predicting altered amino acid sequence in 799 proteins, including factors of ageing and stress resistance. The W303-K6001 (85.4%) genome is virtually identical (less than equal to 0.5 variations per kb) to S288c, and thus originates in the same ancestor. Non-S288c regions distribute unequally over the genome, with chromosome XVI the most (99.6%) and chromosome XI the least (54.5%) S288c-like. Several of these clusters are shared with Sigma1278B, another widely used S288c-related model, indicating that these strains share a second ancestor. Thus, the W303-K6001 genome pictures details of complex genetic relationships between the model strains that date back to the early days of experimental yeast genetics. Moreover, this study underlines the necessity of combining multiple NGS and genome-assembling techniques for achieving accurate variant calling in genomic studies

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

Warburg effect and translocation-induced genomic instability: two yeast models for cancer cells

Yeast has been established as an efficient model system to study biological principles underpinning human health. In this review we focus on yeast models covering two aspects of cancer formation and progression (i) the activity of pyruvate kinase (PK), which recapitulates metabolic features of cancer cells, including the Warburg effect, and (ii) chromosome bridge-induced translocation (BIT) mimiking genome instability in cancer. Saccharomyces cerevisiae is an excellent model to study cancer cell metabolism, as exponentially growing yeast cells exhibit many metabolic similarities with rapidly proliferating cancer cells. The metabolic reconfiguration includes an increase in glucose uptake and fermentation, at the expense of respiration and oxidative phosphorylation (the Warburg effect), and involves a broad reconfiguration of nucleotide and amino acid metabolism. Both in yeast and humans, the regulation of this process seems to have a central player, PK, which is up-regulated in cancer, and to occur mostly on a post-transcriptional and post-translational basis. Furthermore, BIT allows to generate selectable translocation-derived recombinants ("translocants"), between any two desired chromosomal locations, in wild-type yeast strains transformed with a linear DNA cassette carrying a selectable marker flanked by two DNA sequences homologous to different chromosomes. Using the BIT system, targeted non-reciprocal translocations in mitosis are easily inducible. An extensive collection of different yeast translocants exhibiting genome instability and aberrant phenotypes similar to cancer cells has been produced and subjected to analysis. In this review, we hence provide an overview upon two yeast cancer models, and extrapolate general principles for mimicking human disease mechanisms in yeast

Saccharomyces cerevisiae single-copy plasmids for auxotrophy compensation, multiple marker selection, and for designing metabolically cooperating communities

Auxotrophic markers are useful tools in cloning and genome editing, enable a large spectrum of genetic techniques, as well as facilitate the study of metabolite exchange interactions in microbial communities. If unused background auxotrophies are left uncomplemented however, yeast cells need to be grown in nutrient supplemented or rich growth media compositions, which precludes the analysis of biosynthetic metabolism, and which leads to a profound impact on physiology and gene expression. Here we present a series of 23 centromeric plasmids designed to restore prototrophy in typical Saccharomyces cerevisiae laboratory strains. The 23 single-copy plasmids complement for deficiencies in HIS3, LEU2, URA3, MET17 or LYS2 genes and in their combinations, to match the auxotrophic background of the popular functional-genomic yeast libraries that are based on the S288c strain. The plasmids are further suitable for designing self-establishing metabolically cooperating (SeMeCo) communities, and possess a uniform multiple cloning site to exploit multiple parallel selection markers in protein expression experiments

Histaminylation of glutamine residues is a novel posttranslational modification implicated in G-protein signaling

Posttranslational modifications (PTM) have been shown to be essential for protein function and signaling. Here we report the identification of a novel modification, protein transfer of histamine, and provide evidence for its function in G protein signaling. Histamine, known as neurotransmitter and mediator of the inflammatory response, was found incorporated into mastocytoma proteins. Histaminylation was dependent on transglutaminase II. Mass spectrometry confirmed histamine modification of the small and heterotrimeric G proteins Cdc42, Galphao1 and Galphaq. The modification was specific for glutamine residues in the catalytic core, and triggered their constitutive activation. TGM2-mediated histaminylation is thus a novel PTM that functions in G protein signaling. Protein alphamonoaminylations, thus including histaminylation, serotonylation, dopaminylation and norepinephrinylation, hence emerge as a novel class of regulatory PTMs

Designing and interpreting 'multi-omic' experiments that may change our understanding of biology.

Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment