Publications by authors named "Markus Ralser"

105 Publications

Genetic architecture of host proteins involved in SARS-CoV-2 infection.

Nat Commun 2020 12 16;11(1):6397. Epub 2020 Dec 16.

MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.

Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).
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http://dx.doi.org/10.1038/s41467-020-19996-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744536PMC
December 2020

Ribosome profiling reveals ribosome stalling on tryptophan codons and ribosome queuing upon oxidative stress in fission yeast.

Nucleic Acids Res 2021 01;49(1):383-399

Department of Biochemistry, University of Cambridge, UK.

Translational control is essential in response to stress. We investigated the translational programmes launched by the fission yeast Schizosaccharomyces pombe upon five environmental stresses. We also explored the contribution of defence pathways to these programmes: The Integrated Stress Response (ISR), which regulates translation initiation, and the stress-response MAPK pathway. We performed ribosome profiling of cells subjected to each stress, in wild type cells and in cells with the defence pathways inactivated. The transcription factor Fil1, a functional homologue of the yeast Gcn4 and the mammalian Atf4 proteins, was translationally upregulated and required for the response to most stresses. Moreover, many mRNAs encoding proteins required for ribosome biogenesis were translationally downregulated. Thus, several stresses trigger a universal translational response, including reduced ribosome production and a Fil1-mediated transcriptional programme. Surprisingly, ribosomes stalled on tryptophan codons upon oxidative stress, likely due to a decrease in charged tRNA-Tryptophan. Stalling caused ribosome accumulation upstream of tryptophan codons (ribosome queuing/collisions), demonstrating that stalled ribosomes affect translation elongation by other ribosomes. Consistently, tryptophan codon stalling led to reduced translation elongation and contributed to the ISR-mediated inhibition of initiation. We show that different stresses elicit common and specific translational responses, revealing a novel role in Tryptophan-tRNA availability.
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http://dx.doi.org/10.1093/nar/gkaa1180DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7797079PMC
January 2021

Extraction and Integration of Genetic Networks from Short-Profile Omic Data Sets.

Metabolites 2020 Oct 29;10(11). Epub 2020 Oct 29.

Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK.

Mass spectrometry technologies are widely used in the fields of ionomics and metabolomics to simultaneously profile the intracellular concentrations of, e.g., amino acids or elements in genome-wide mutant libraries. These molecular or sub-molecular features are generally non-Gaussian and their covariance reveals patterns of correlations that reflect the system nature of the cell biochemistry and biology. Here, we introduce two similarity measures, the Mahalanobis cosine and the hybrid Mahalanobis cosine, that enforce information from the empirical covariance matrix of omics data from high-throughput screening and that can be used to quantify similarities between the profiled features of different mutants. We evaluate the performance of these similarity measures in the task of inferring and integrating genetic networks from short-profile ionomics/metabolomics data through an analysis of experimental data sets related to the ionome and the metabolome of the model organism . The study of the resulting ionome-metabolome multilayer genetic network, which encodes multiple omic-specific levels of correlations between genes, shows that the proposed measures can provide an alternative description of relations between biological processes when compared to the commonly used Pearson's correlation coefficient and have the potential to guide the construction of novel hypotheses on the function of uncharacterised genes.
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http://dx.doi.org/10.3390/metabo10110435DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7693762PMC
October 2020

Slow Growth and Increased Spontaneous Mutation Frequency in Respiratory Deficient Yeast Suppressed by a Dominant Mutation in .

G3 (Bethesda) 2020 Dec 3;10(12):4637-4648. Epub 2020 Dec 3.

Department of Biosciences, University of Salzburg, Austria

A yeast deletion mutation in the nuclear-encoded gene, , which codes for a mitochondrial ribosomal protein, led to slow growth on glucose, the inability to grow on glycerol or ethanol, and loss of mitochondrial DNA and respiration. We noticed that yeast readily obtains secondary mutations that suppress aspects of this phenotype, including its growth defect. We characterized and identified a dominant missense suppressor mutation in the gene. Comparing isogenic slowly growing rho-zero and rapidly growing suppressed strains under carefully controlled fermentation conditions showed that energy charge was not significantly different between strains and was not causal for the observed growth properties. Surprisingly, in a wild-type background, the dominant suppressor allele of still allowed respiratory growth but increased the petite frequency. Similarly, a slow-growing respiratory deficient strain displayed an about twofold increase in spontaneous frequency of point mutations (comparable to the rho-zero strain) while the suppressed strain showed mutation frequency comparable to the respiratory-competent WT strain. We conclude, that phenotypes that result from are mostly explained by rapidly emerging mutations that compensate for the slow growth that typically follows respiratory deficiency.
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http://dx.doi.org/10.1534/g3.120.401537DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7718765PMC
December 2020

Amino Acids Whose Intracellular Levels Change Most During Aging Alter Chronological Life Span of Fission Yeast.

J Gerontol A Biol Sci Med Sci 2021 Jan;76(2):205-210

Institute of Healthy Ageing, Department of Genetics, Evolution & Environment, University College London, UK.

Amino acid deprivation or supplementation can affect cellular and organismal life span, 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 nondividing 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 life span of wild-type but not of pka1Δ cells. Supplementation of aspartate, on the other hand, shortens the life span 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 life span.
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http://dx.doi.org/10.1093/gerona/glaa246DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7812441PMC
January 2021

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

EMBO Rep 2020 11 7;21(11):e50845. Epub 2020 Sep 7.

Institute of Healthy Ageing and Research Department of Genetics, Evolution & Environment, University College London, London, UK.

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.
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http://dx.doi.org/10.15252/embr.202050845DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7645267PMC
November 2020

Genetic architecture of host proteins interacting with SARS-CoV-2.

bioRxiv 2020 Jul 1. Epub 2020 Jul 1.

MRC Epidemiology Unit, University of Cambridge, Cambridge, UK.

Strategies to develop therapeutics for SARS-CoV-2 infection may be informed by experimental identification of viral-host protein interactions in cellular assays and measurement of host response proteins in COVID-19 patients. Identification of genetic variants that influence the level or activity of these proteins in the host could enable rapid 'in silico' assessment in human genetic studies of their causal relevance as molecular targets for new or repurposed drugs to treat COVID-19. We integrated large-scale genomic and aptamer-based plasma proteomic data from 10,708 individuals to characterize the genetic architecture of 179 host proteins reported to interact with SARS-CoV-2 proteins or to participate in the host response to COVID-19. We identified 220 host DNA sequence variants acting in (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links, evidence that putative viral interaction partners such as MARK3 affect immune response, and establish the first link between a recently reported variant for respiratory failure of COVID-19 patients at the locus and hypercoagulation, i.e. maladaptive host response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and dynamic and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).
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http://dx.doi.org/10.1101/2020.07.01.182709DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337378PMC
July 2020

Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection.

Cell Syst 2020 07 2;11(1):11-24.e4. Epub 2020 Jun 2.

The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK; Charité Universitätsmedizin, Department of Biochemistry, 10117 Berlin, Germany. Electronic address:

The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.
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http://dx.doi.org/10.1016/j.cels.2020.05.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7264033PMC
July 2020

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

Elife 2020 06 16;9. Epub 2020 Jun 16.

University College London, Institute of Healthy Ageing, Department of Genetics, Evolution and Environment, London, United Kingdom.

Microbial fitness screens are a key technique in functional genomics. We present an all-in-one solution, , for automating and improving data analysis pipelines associated with large-scale fitness screens, including image acquisition and quantification, data normalisation, and statistical analysis. 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 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. is user-friendly, open-source and fully documented, illustrated by applications to diverse fitness analysis scenarios.
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http://dx.doi.org/10.7554/eLife.55160DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297533PMC
June 2020

Pyruvate kinase variant of fission yeast tunes carbon metabolism, cell regulation, growth and stress resistance.

Mol Syst Biol 2020 04;16(4):e9270

Department of Genetics, Evolution & Environment, Institute of Healthy Ageing, University College London, London, UK.

Cells balance glycolysis with respiration to support their metabolic needs in different environmental or physiological contexts. With abundant glucose, many cells prefer to grow by aerobic glycolysis or fermentation. Using 161 natural isolates of fission yeast, we investigated the genetic basis and phenotypic effects of the fermentation-respiration balance. The laboratory and a few other strains depended more on respiration. This trait was associated with a single nucleotide polymorphism in a conserved region of Pyk1, the sole pyruvate kinase in fission yeast. This variant reduced Pyk1 activity and glycolytic flux. Replacing the "low-activity" pyk1 allele in the laboratory strain with the "high-activity" allele was sufficient to increase fermentation and decrease respiration. This metabolic rebalancing triggered systems-level adjustments in the transcriptome and proteome and in cellular traits, including increased growth and chronological lifespan but decreased resistance to oxidative stress. Thus, low Pyk1 activity does not lead to a growth advantage but to stress tolerance. The genetic tuning of glycolytic flux may reflect an adaptive trade-off in a species lacking pyruvate kinase isoforms.
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http://dx.doi.org/10.15252/msb.20199270DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175467PMC
April 2020

Marmota marmota.

Trends Genet 2020 05 3;36(5):383-384. Epub 2020 Feb 3.

Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1AT, UK; Department of Biochemistry, Charité, 10117, Berlin, Germany. Electronic address:

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http://dx.doi.org/10.1016/j.tig.2020.01.006DOI Listing
May 2020

DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput.

Nat Methods 2020 01 25;17(1):41-44. Epub 2019 Nov 25.

The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK.

We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods.
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http://dx.doi.org/10.1038/s41592-019-0638-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6949130PMC
January 2020

Self-Establishing Communities: A Yeast Model to Study the Physiological Impact of Metabolic Cooperation in Eukaryotic Cells.

Methods Mol Biol 2019 ;2049:263-282

The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.

All biosynthetically active cells are able to export and import metabolites, the small molecule intermediaries of metabolism. In dense cell populations, this hallmark of cells results in the intercellular exchange of a wide spectrum of metabolites. Such metabolite exchange enables metabolic specialization of individual cells, leading to far reaching biological implications, as a consequence of the intrinsic connection between metabolism and cell physiology. In this chapter, we discuss methods on how to study metabolite exchange interactions by using self-establishing metabolically cooperating communities (SeMeCos) in the budding yeast Saccharomyces cerevisiae. SeMeCos exploit the stochastic segregation of episomes to progressively increase the number of essential metabolic interdependencies in a community that grows out from an initially prototrophic cell. By coupling genotype to metabotype, SeMeCos allow for the tracking of cells while they specialize metabolically and hence the opportunity to study their progressive change in physiology.
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http://dx.doi.org/10.1007/978-1-4939-9736-7_16DOI Listing
June 2020

Lysine harvesting is an antioxidant strategy and triggers underground polyamine metabolism.

Nature 2019 08 31;572(7768):249-253. Epub 2019 Jul 31.

Department of Biochemistry, University of Cambridge, Cambridge, UK.

Both single and multicellular organisms depend on anti-stress mechanisms that enable them to deal with sudden changes in the environment, including exposure to heat and oxidants. Central to the stress response are dynamic changes in metabolism, such as the transition from the glycolysis to the pentose phosphate pathway-a conserved first-line response to oxidative insults. Here we report a second metabolic adaptation that protects microbial cells in stress situations. The role of the yeast polyamine transporter Tpo1p in maintaining oxidant resistance is unknown. However, a proteomic time-course experiment suggests a link to lysine metabolism. We reveal a connection between polyamine and lysine metabolism during stress situations, in the form of a promiscuous enzymatic reaction in which the first enzyme of the polyamine pathway, Spe1p, decarboxylates lysine and forms an alternative polyamine, cadaverine. The reaction proceeds in the presence of extracellular lysine, which is taken up by cells to reach concentrations up to one hundred times higher than those required for growth. Such extensive harvest is not observed for the other amino acids, is dependent on the polyamine pathway and triggers a reprogramming of redox metabolism. As a result, NADPH-which would otherwise be required for lysine biosynthesis-is channelled into glutathione metabolism, leading to a large increase in glutathione concentrations, lower levels of reactive oxygen species and increased oxidant tolerance. Our results show that nutrient uptake occurs not only to enable cell growth, but when the nutrient availability is favourable it also enables cells to reconfigure their metabolism to preventatively mount stress protection.
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http://dx.doi.org/10.1038/s41586-019-1442-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6774798PMC
August 2019

Low catalytic activity is insufficient to induce disease pathology in triosephosphate isomerase deficiency.

J Inherit Metab Dis 2019 09 11;42(5):839-849. Epub 2019 Jun 11.

The Molecular Biology of Metabolism Laboratory, Francis Crick Institute, London, UK.

Triosephosphate isomerase (TPI) deficiency is a fatal genetic disorder characterized by hemolytic anemia and neurological dysfunction. Although the enzyme defect in TPI was discovered in the 1960s, the exact etiology of the disease is still debated. Some aspects indicate the disease could be caused by insufficient enzyme activity, whereas other observations indicate it could be a protein misfolding disease with tissue-specific differences in TPI activity. We generated a mouse model in which exchange of a conserved catalytic amino acid residue (isoleucine to valine, Ile170Val) reduces TPI specific activity without affecting the stability of the protein dimer. TPI mice exhibit an approximately 85% reduction in TPI activity consistently across all examined tissues, which is a stronger average, but more consistent, activity decline than observed in patients or symptomatic mouse models that carry structural defect mutant alleles. While monitoring protein expression levels revealed no evidence for protein instability, metabolite quantification indicated that glycolysis is affected by the active site mutation. TPI mice develop normally and show none of the disease symptoms associated with TPI deficiency. Therefore, without the stability defect that affects TPI activity in a tissue-specific manner, a strong decline in TPI catalytic activity is not sufficient to explain the pathological onset of TPI deficiency.
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http://dx.doi.org/10.1002/jimd.12105DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887927PMC
September 2019

Ice-Age Climate Adaptations Trap the Alpine Marmot in a State of Low Genetic Diversity.

Curr Biol 2019 05 9;29(10):1712-1720.e7. Epub 2019 May 9.

Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK; Department of Biochemistry, Charitè, Am Chariteplatz 1, 10117 Berlin, Germany. Electronic address:

Some species responded successfully to prehistoric changes in climate [1, 2], while others failed to adapt and became extinct [3]. The factors that determine successful climate adaptation remain poorly understood. We constructed a reference genome and studied physiological adaptations in the Alpine marmot (Marmota marmota), a large ground-dwelling squirrel exquisitely adapted to the "ice-age" climate of the Pleistocene steppe [4, 5]. Since the disappearance of this habitat, the rodent persists in large numbers in the high-altitude Alpine meadow [6, 7]. Genome and metabolome showed evidence of adaptation consistent with cold climate, affecting white adipose tissue. Conversely, however, we found that the Alpine marmot has levels of genetic variation that are among the lowest for mammals, such that deleterious mutations are less effectively purged. Our data rule out typical explanations for low diversity, such as high levels of consanguineous mating, or a very recent bottleneck. Instead, ancient demographic reconstruction revealed that genetic diversity was lost during the climate shifts of the Pleistocene and has not recovered, despite the current high population size. We attribute this slow recovery to the marmot's adaptive life history. The case of the Alpine marmot reveals a complicated relationship between climatic changes, genetic diversity, and conservation status. It shows that species of extremely low genetic diversity can be very successful and persist over thousands of years, but also that climate-adapted life history can trap a species in a persistent state of low genetic diversity.
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http://dx.doi.org/10.1016/j.cub.2019.04.020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6538971PMC
May 2019

Evolthon: A community endeavor to evolve lab evolution.

PLoS Biol 2019 03 29;17(3):e3000182. Epub 2019 Mar 29.

Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel.

In experimental evolution, scientists evolve organisms in the lab, typically by challenging them to new environmental conditions. How best to evolve a desired trait? Should the challenge be applied abruptly, gradually, periodically, sporadically? Should one apply chemical mutagenesis, and do strains with high innate mutation rate evolve faster? What are ideal population sizes of evolving populations? There are endless strategies, beyond those that can be exposed by individual labs. We therefore arranged a community challenge, Evolthon, in which students and scientists from different labs were asked to evolve Escherichia coli or Saccharomyces cerevisiae for an abiotic stress-low temperature. About 30 participants from around the world explored diverse environmental and genetic regimes of evolution. After a period of evolution in each lab, all strains of each species were competed with one another. In yeast, the most successful strategies were those that used mating, underscoring the importance of sex in evolution. In bacteria, the fittest strain used a strategy based on exploration of different mutation rates. Different strategies displayed variable levels of performance and stability across additional challenges and conditions. This study therefore uncovers principles of effective experimental evolutionary regimens and might prove useful also for biotechnological developments of new strains and for understanding natural strategies in evolutionary arms races between species. Evolthon constitutes a model for community-based scientific exploration that encourages creativity and cooperation.
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http://dx.doi.org/10.1371/journal.pbio.3000182DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440615PMC
March 2019

Reply to 'Do sulfate radicals really enable a non-enzymatic Krebs cycle precursor?'

Nat Ecol Evol 2019 02;3(2):139-140

Department of Biochemistry, University of Cambridge, Cambridge, UK.

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http://dx.doi.org/10.1038/s41559-018-0792-zDOI Listing
February 2019

The next decade of metabolism.

Nat Metab 2019 01;1(1):2-4

Institute of Diabetes and Regeneration Research, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.

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http://dx.doi.org/10.1038/s42255-018-0022-7DOI Listing
January 2019

A mouse model for intellectual disability caused by mutations in the X-linked 2'‑O‑methyltransferase Ftsj1 gene.

Biochim Biophys Acta Mol Basis Dis 2019 09 14;1865(9):2083-2093. Epub 2018 Dec 14.

Department of Functional Genomics, Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, Felix-Hausdorff-Strasse 8, 17489 Greifswald, Germany.

Mutations in the X chromosomal tRNA 2'‑O‑methyltransferase FTSJ1 cause intellectual disability (ID). Although the gene is ubiquitously expressed affected individuals present no consistent clinical features beyond ID. In order to study the pathological mechanism involved in the aetiology of FTSJ1 deficiency-related cognitive impairment, we generated and characterized an Ftsj1 deficient mouse line based on the gene trapped stem cell line RRD143. Apart from an impaired learning capacity these mice presented with several statistically significantly altered features related to behaviour, pain sensing, bone and energy metabolism, the immune and the hormone system as well as gene expression. These findings show that Ftsj1 deficiency in mammals is not phenotypically restricted to the brain but affects various organ systems. Re-examination of ID patients with FTSJ1 mutations from two previously reported families showed that several features observed in the mouse model were recapitulated in some of the patients. Though the clinical spectrum related to Ftsj1 deficiency in mouse and man is variable, we suggest that an increased pain threshold may be more common in patients with FTSJ1 deficiency. Our findings demonstrate novel roles for Ftsj1 in maintaining proper cellular and tissue functions in a mammalian organism.
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http://dx.doi.org/10.1016/j.bbadis.2018.12.011DOI Listing
September 2019

Changes of Cell Biochemical States Are Revealed in Protein Homomeric Complex Dynamics.

Cell 2018 11 25;175(5):1418-1429.e9. Epub 2018 Oct 25.

Département de Biochimie, Université de Montréal, C.P. 6128, Succursale Centre-ville, Montréal, QC H3C 3J7, Canada; Centre Robert-Cedergren, Bio-Informatique et Génomique, Université de Montréal, C.P. 6128, Succursale centre-ville, Montréal, QC H3C 3J7, Canada. Electronic address:

We report here a simple and global strategy to map out gene functions and target pathways of drugs, toxins, or other small molecules based on "homomer dynamics" protein-fragment complementation assays (hdPCA). hdPCA measures changes in self-association (homomerization) of over 3,500 yeast proteins in yeast grown under different conditions. hdPCA complements genetic interaction measurements while eliminating the confounding effects of gene ablation. We demonstrate that hdPCA accurately predicts the effects of two longevity and health span-affecting drugs, the immunosuppressant rapamycin and the type 2 diabetes drug metformin, on cellular pathways. We also discovered an unsuspected global cellular response to metformin that resembles iron deficiency and includes a change in protein-bound iron levels. This discovery opens a new avenue to investigate molecular mechanisms for the prevention or treatment of diabetes, cancers, and other chronic diseases of aging.
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http://dx.doi.org/10.1016/j.cell.2018.09.050DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6242466PMC
November 2018

Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts.

Cell Syst 2018 09 5;7(3):269-283.e6. Epub 2018 Sep 5.

The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Department of Biochemistry, Charité Universitaetsmedizin Berlin, Berlin, Germany. Electronic address:

A challenge in solving the genotype-to-phenotype relationship is to predict a cell's metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.
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http://dx.doi.org/10.1016/j.cels.2018.08.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167078PMC
September 2018

Freeing Yeast from Alcohol Addiction (Just) to Make (It) Fat Instead.

Cell 2018 09;174(6):1342-1344

The Francis Crick Institute, London, UK; Department of Biochemistry, Charitè University Medicine, Berlin, Germany. Electronic address:

Synthetically re-designing eukaryotic metabolism has proven immensely challenging, raising the question of whether evolution has metabolically hardwired eukaryotic cells. Yu et al. now report that, through orchestrating multiple genetic changes and laboratory evolution, Saccharomyces metabolism can be reprogrammed from its evolutionary objective of producing ethanol to produce large amounts of free fatty acids.
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http://dx.doi.org/10.1016/j.cell.2018.08.024DOI Listing
September 2018

An appeal to magic? The discovery of a non-enzymatic metabolism and its role in the origins of life.

Authors:
Markus Ralser

Biochem J 2018 08 30;475(16):2577-2592. Epub 2018 Aug 30.

Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, 80 Tennis Court Rd, Cambridge CB2 1GA, U.K.

Until recently, prebiotic precursors to metabolic pathways were not known. In parallel, chemistry achieved the synthesis of amino acids and nucleotides only in reaction sequences that do not resemble metabolic pathways, and by using condition step changes, incompatible with enzyme evolution. As a consequence, it was frequently assumed that the topological organisation of the metabolic pathway has formed in a Darwinian process. The situation changed with the discovery of a non-enzymatic glycolysis and pentose phosphate pathway. The suite of metabolism-like reactions is promoted by a metal cation, (Fe(II)), abundant in Archean sediment, and requires no condition step changes. Knowledge about metabolism-like reaction topologies has accumulated since, and supports non-enzymatic origins of gluconeogenesis, the -adenosylmethionine pathway, the Krebs cycle, as well as CO fixation. It now feels that it is only a question of time until essential parts of metabolism can be replicated non-enzymatically. Here, I review the 'accidents' that led to the discovery of the non-enzymatic glycolysis, and on the example of a chemical network based on hydrogen cyanide, I provide reasoning why metabolism-like non-enzymatic reaction topologies may have been missed for a long time. Finally, I discuss that, on the basis of non-enzymatic metabolism-like networks, one can elaborate stepwise scenarios for the origin of metabolic pathways, a situation that increasingly renders the origins of metabolism a tangible problem.
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http://dx.doi.org/10.1042/BCJ20160866DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6117946PMC
August 2018

Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition.

Sci Rep 2018 03 12;8(1):4346. Epub 2018 Mar 12.

Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, 80 Tennis Court Rd, Cambridge, CB2 1GA, UK.

Quantitative proteomics is key for basic research, but needs improvements to satisfy an increasing demand for large sample series in diagnostics, academia and industry. A switch from nanoflowrate to microflowrate chromatography can improve throughput and reduce costs. However, concerns about undersampling and coverage have so far hampered its broad application. We used a QTOF mass spectrometer of the penultimate generation (TripleTOF5600), converted a nanoLC system into a microflow platform, and adapted a SWATH regime for large sample series by implementing retention time- and batch correction strategies. From 3 µg to 5 µg of unfractionated tryptic digests that are obtained from proteomics-typical amounts of starting material, microLC-SWATH-MS quantifies up to 4000 human or 1750 yeast proteins in an hour or less. In the acquisition of 750 yeast proteomes, retention times varied between 2% and 5%, and quantified the typical peptide with 5-8% signal variation in replicates, and below 20% in samples acquired over a five-months period. Providing precise quantities without being dependent on the latest hardware, our study demonstrates that the combination of microflow chromatography and data-independent acquisition strategies has the potential to overcome current bottlenecks in academia and industry, enabling the cost-effective generation of precise quantitative proteomes in large scale.
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http://dx.doi.org/10.1038/s41598-018-22610-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5847575PMC
March 2018

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

Curr Opin Syst Biol 2017 Dec;6:37-45

The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1AT, United Kingdom.

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.
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http://dx.doi.org/10.1016/j.coisb.2017.08.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477987PMC
December 2017

Partitioning of One-Carbon Units in Folate and Methionine Metabolism Is Essential for Neural Tube Closure.

Cell Rep 2017 Nov;21(7):1795-1808

Developmental Biology & Cancer Programme, UCL Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK. Electronic address:

Abnormal folate one-carbon metabolism (FOCM) is implicated in neural tube defects (NTDs), severe malformations of the nervous system. MTHFR mediates unidirectional transfer of methyl groups from the folate cycle to the methionine cycle and, therefore, represents a key nexus in partitioning one-carbon units between FOCM functional outputs. Methionine cycle inhibitors prevent neural tube closure in mouse embryos. Similarly, the inability to use glycine as a one-carbon donor to the folate cycle causes NTDs in glycine decarboxylase (Gldc)-deficient embryos. However, analysis of Mthfr-null mouse embryos shows that neither S-adenosylmethionine abundance nor neural tube closure depend on one-carbon units derived from embryonic or maternal folate cycles. Mthfr deletion or methionine treatment prevents NTDs in Gldc-null embryos by retention of one-carbon units within the folate cycle. Overall, neural tube closure depends on the activity of both the methionine and folate cycles, but transfer of one-carbon units between the cycles is not necessary.
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http://dx.doi.org/10.1016/j.celrep.2017.10.072DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5699646PMC
November 2017

Yeast Creates a Niche for Symbiotic Lactic Acid Bacteria through Nitrogen Overflow.

Cell Syst 2017 10 27;5(4):345-357.e6. Epub 2017 Sep 27.

European Molecular Biology Laboratory, Heidelberg 69117, Germany. Electronic address:

Many microorganisms live in communities and depend on metabolites secreted by fellow community members for survival. Yet our knowledge of interspecies metabolic dependencies is limited to few communities with small number of exchanged metabolites, and even less is known about cellular regulation facilitating metabolic exchange. Here we show how yeast enables growth of lactic acid bacteria through endogenous, multi-component, cross-feeding in a readily established community. In nitrogen-rich environments, Saccharomyces cerevisiae adjusts its metabolism by secreting a pool of metabolites, especially amino acids, and thereby enables survival of Lactobacillus plantarum and Lactococcus lactis. Quantity of the available nitrogen sources and the status of nitrogen catabolite repression pathways jointly modulate this niche creation. We demonstrate how nitrogen overflow by yeast benefits L. plantarum in grape juice, and contributes to emergence of mutualism with L. lactis in a medium with lactose. Our results illustrate how metabolic decisions of an individual species can benefit others.
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http://dx.doi.org/10.1016/j.cels.2017.09.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5660601PMC
October 2017

Metabolomics in Yeast.

Cold Spring Harb Protoc 2017 Sep 1;2017(9):pdb.top083576. Epub 2017 Sep 1.

Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge CB2 1GA, United Kingdom.

Budding yeast has from the beginning been a major eukaryotic model for the study of metabolic network structure and function. This is attributable to both its genetic and biochemical capacities and its role as a workhorse in food production and biotechnology. New inventions in analytical technologies allow accurate, simultaneous detection and quantification of metabolites, and a series of recent findings have placed the metabolic network at center stage in the physiology of the cell. For example, metabolism might have facilitated the origin of life, and in modern organisms it not only provides nutrients to the cell but also serves as a buffer to changes in the cellular environment, a regulator of cellular processes, and a requirement for cell growth. These findings have triggered a rapid and massive renaissance in this important field. Here, we provide an introduction to analysis of metabolomics in yeast.
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http://dx.doi.org/10.1101/pdb.top083576DOI Listing
September 2017