Pınar Pir

The Automation of Science

The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist “Adam,” which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adams conclusions through manual experiments. To describe Adams research, we have developed an ontology and logical language. The resulting formalization involves over 10,000 different research units in a nested treelike structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge.

Further developments towards a genome-scale metabolic model of yeast

BackgroundTo date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity.ResultsWe have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites - significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions.ConclusionsWe report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/.

A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes

We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a “cycle of knowledge” strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom‐up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.

A prototrophic deletion mutant collection for yeast metabolomics and systems biology

Auxotrophic markers - mutations within genes encoding enzymes in pathways for the biosynthesis of metabolic building blocks, such as an amino acid or nucleotide, are used as selection markers in the vast majority of yeast genetics and genomics experiments 1-3. The nutritional deficiency caused by the mutation (auxotrophy) can be compensated by supplying the required nutrient in the growth medium. This compensation, however, is not necessarily quantitative because such mutations influence a number of physiological parameters and may act in combination 2,4,5 . The construction of a prototrophic derivative of the parent strain of the widely used genome-scale yeast deletion collection1 has confirmed the need to remove auxotrophic markers in order to reduce bias in physiological and metabolic studies 2 . Moreover, flux balance analyses using a genome-wide metabolic model (Yeast 5)6 indicate that the activity status of some 200-300 reactions changes between different auxotrophic strains and the wild type. To alleviate this bias we have constructed a version of the haploid deletant collection restoring prototrophy in the genetic background, such that influence of auxotrophy to the phenotype of a given gene deletion is prevented. This new deletant library facilitates the exploitation of yeast in both functional genomics and quantitative systems biology. The physiological impact of auxotrophy was assessed by monitoring the growth of 16 yeast strains carrying all possible combinations of the markers (histidine (his3Δ1), leucine (leu2Δ), methionine (met15Δ) and uracil (ura3Δ) used in the MATa version of the yeast deletion collection1. All markers and their combinations affected yeast growth, but without altering the adenylate (ATP, ADP, AMP) energy charge (Fig 1 a). As the most critical phenotypic quantity, the maximum specific growth rate (μmax) varied between 0.125 h−1 (leu2Δ) and 0.20 h−1 (his3Δmet15Δura3Δ), rendering quantitative comparisons between these strains impossible (Fig 1 a, Suppl. Table 1). These growth differences were not explained by the different media supplementations, as i) prototrophic yeast exhibited a different and substantially less diverse growth pattern in the 16 minimal media (Fig 1c, left panel; media recipes are given in the Supplementary material); and ii) growth differences where altered, but not abrogated, when other proteogenic amino acids were supplemented as well (synthetic complete (SC) medium; Fig 1b). Importantly, on both types of media, complex interactions between all auxotrophic mutations were observed. For instance, restoring MET15 in leu2Δura3Δhis3Δmet15Δ (0.185 h−1 → 0.164 h−1) or leu2Δura3Δmet15Δ (0.162 h−1 → 0.149 h−1) had a negative effect on μmax, but surprisingly promoted growth in leu2Δhis3Δmet15Δ (0.136 h−1 → 0.173 h−1) (Fig 1a); restoring LEU2 in leu2Δura3Δhis3Δ (0.164 h−1 → 0.185 h−1) or leu2Δura3Δmet15Δ (0.136 h−1 → 0.161 h−1) had a positive effect, but not in leu2Δura3Δhis3Δmet15Δ (0.185 h−1 → 0.186 h−1) (Fig 1a; Suppl. Table 1). Thus, although blocking different pathways, all markers influence each other, indicating that they have a wide-ranging and combinatorial influence on the metabolic network. Figure 1 The combinatorial impact of yeast auxotrophic markers In batch culture experiments, further problems arise from the unequal consumption of amino acid supplements resulting in cultivation phase-dependent starvation. Growth of BY4741 (the auxotrophic parent of the standard yeast gene-deletion collection 1) in SC media depleted nutrients in a way they became first limiting for met15Δ, then for leu2Δ, his3Δ1 and finally for ura3Δ auxotrophic yeast (Fig 1d). This effect could not be compensated by increasing amino acid supplementation(s), as this inhibited cell growth (Fig 1c, right panel). Chronological lifespan (CLS) is a phenotype that is profoundly influenced by both nutrient supplementation and growth rate. Indeed, we observed an increase in stationary phase survival in YPD media upon restoring prototrophy. In a competitive growth experiment, auxotrophic cells had lost their colony-forming capacities within 10 days, but their prototrophic counterparts were perfectly viable for more than 20 days (Fig 1e). Longer CLS of prototrophic versus auxotrophic yeast was also reported for other backgrounds, and in synthetic media nutrient starvation shortened the lifespan of auxotrophic cells 7,8. Restoring protrotrophy is thus, to our knowledge, one of the most powerful genetic modifications for extending CLS. Hence, as auxotrophic markers have substantial and combinatorial influences on fundamental biological parameters such as growth and ageing, auxotrophic genome resources introduce bias for analyzing physiological parameters and even more to quantitative studies addressing the metabolic network. We would thus encourage the yeast community to switch, where possible, to prototrophic yeast for experiments in transcriptomics, proteomics, and metabolomics. To create a prototrophic resource for genome-scale experiments, we re-introduced auxotrophic markers into the MATa versions of the S288c-based deletion collection (5185 strains)1 and the titratable promoter essential collection (839 strains)3. These strains were transformed with a centromere-containing single-copy vector (minichromosome), containing the chromosome VI centromere, the autonomous replication sequence of HHF1 (ARSH4)9, and the marker genes HIS3, URA3, LEU2, and MET15 under control of their endogenous promoter sequences (pHLUM; Suppl Fig 1, Addgene ID 40276). Under non-selective conditions, the vector was transmitted in 99.15% of cell divisions (0.85% segregation mean over 20 generations). After 20 days, all cells were found prototrophic due to their positive selection (Fig 1e), facilitating screens on both selective and non-selective media. Furthermore, pHLUM- transformed BY4741 derivates wild type for HIS3, LEU2, MET15 or URA3 grew similar as BY4741 pHLUM (Suppl. Fig 2), indicating that the minichromosome fully restored prototrophy. The titratable-promoter essential collection3 was exploited to demonstrate screening capacities. By replicating original and prototrophic strains onto doxycycline-containing media, we found that 13 of the 370 lethal phenotypes were compensated (Fig 1 f, Suppl. Table 2). Thus, auxotrophic markers do not only influence physiological parameters, they are also responsible for a number of essential phenotypes. Since all strains possess a native metabolic network, the new library reduces bias from the use of auxotrophic markers in functional genomics and metabolic systems biology. Based on the pHLUM minichromosome, which is counterselectable, the new resource retains full compatibility with the popular S288c knock-out and essential collections 1,3. However, the use of a plasmid will introduce confounding factors to those mutants which have deficits in plasmid stability and segregation. The library is distributed as arrayed on 96-well plates (Euroscarf, Frankfurt), and contains a deep-red coloured and counter-selectable mutant (ade12Δ) on both universal and plate-specific positions, which simplifies plate orientation and identification, and can serve as replicate-control in quantitative metabolomics experiments (Suppl. Fig 3).

Membrane transporter engineering in industrial biotechnology and whole cell biocatalysis

Because they mainly do not involve chemical changes, membrane transporters have been a Cinderella subject in the biotechnology of small molecule production, but this is a serious oversight. Influx transporters contribute significantly to the flux towards product, and efflux transporters ensure the accumulation of product in the much greater extracellular space of fermentors. Programmes for improving biotechnological processes might therefore give greater consideration to transporters than may have been commonplace. Strategies for identifying important transporters include expression profiling, genome-wide knockout studies, stress-based selection, and the use of inhibitors. In addition, modern methods of directed evolution and synthetic biology, especially those effecting changes in energy coupling, offer huge opportunities for increasing the flux towards extracellular product formation by transporter engineering.

Nutrient control of eukaryote cell growth: a systems biology study in yeast

BackgroundTo elucidate the biological processes affected by changes in growth rate and nutrient availability, we have performed a comprehensive analysis of the transcriptome, proteome and metabolome responses of chemostat cultures of the yeast, Saccharomyces cerevisiae, growing at a range of growth rates and in four different nutrient-limiting conditions.ResultsWe find significant changes in expression for many genes in each of the four nutrient-limited conditions tested. We also observe several processes that respond differently to changes in growth rate and are specific to each nutrient-limiting condition. These include carbohydrate storage, mitochondrial function, ribosome synthesis, and phosphate transport. Integrating transcriptome data with proteome measurements allows us to identify previously unrecognized examples of post-transcriptional regulation in response to both nutrient and growth-rate signals.ConclusionsOur results emphasize the unique properties of carbon metabolism and the carbon substrate, the limitation of which induces significant changes in gene regulation at the transcriptional and post-transcriptional level, as well as altering how many genes respond to growth rate. By comparison, the responses to growth limitation by other nutrients involve a smaller set of genes that participate in specific pathways.See associated commentary http://www.biomedcentral.com/1741-7007/8/62

Absolute Quantification of the Glycolytic Pathway in Yeast: DEPLOYMENT OF A COMPLETE QconCAT APPROACH

The availability of label-free data derived from yeast cells (based on the summed intensity of the three strongest, isoform-specific peptides) permitted a preliminary assessment of protein abundances for glycolytic proteins. Following this analysis, we demonstrate successful application of the QconCAT technology, which uses recombinant DNA techniques to generate artificial concatamers of large numbers of internal standard peptides, to the quantification of enzymes of the glycolysis pathway in the yeast Saccharomyces cerevisiae. A QconCAT of 88 kDa (59 tryptic peptides) corresponding to 27 isoenzymes was designed and built to encode two or three analyte peptides per protein, and after stable isotope labeling of the standard in vivo, protein levels were determined by LC-MS, using ultra high performance liquid chromatography-coupled mass spectrometry. We were able to determine absolute protein concentrations between 14,000 and 10 million molecules/cell. Issues such as efficiency of extraction and completeness of proteolysis are addressed, as well as generic factors such as optimal quantotypic peptide selection and expression. In addition, the same proteins were quantified by intensity-based label-free analysis, and both sets of data were compared with other quantification methods.

Integrative investigation of metabolic and transcriptomic data

BackgroundNew analysis methods are being developed to integrate data from transcriptome, proteome, interactome, metabolome, and other investigative approaches. At the same time, existing methods are being modified to serve the objectives of systems biology and permit the interpretation of the huge datasets currently being generated by high-throughput methods.ResultsTranscriptomic and metabolic data from chemostat fermentors were collected with the aim of investigating the relationship between these two data sets. The variation in transcriptome data in response to three physiological or genetic perturbations (medium composition, growth rate, and specific gene deletions) was investigated using linear modelling, and open reading-frames (ORFs) whose expression changed significantly in response to these perturbations were identified. Assuming that the metabolic profile is a function of the transcriptome profile, expression levels of the different ORFs were used to model the metabolic variables via Partial Least Squares (Projection to Latent Structures – PLS) using PLS toolbox in Matlab.ConclusionThe experimental design allowed the analyses to discriminate between the effects which the growth medium, dilution rate, and the deletion of specific genes had on the transcriptome and metabolite profiles. Metabolite data were modelled as a function of the transcriptome to determine their congruence. The genes that are involved in central carbon metabolism of yeast cells were found to be the ORFs with the most significant contribution to the model.

The genetic control of growth rate: a systems biology study in yeast

BackgroundControl of growth rate is mediated by tight regulation mechanisms in all free-living organisms since long-term survival depends on adaptation to diverse environmental conditions. The yeast, Saccharomyces cerevisiae, when growing under nutrient-limited conditions, controls its growth rate via both nutrient-specific and nutrient-independent gene sets. At slow growth rates, at least, it has been found that the expression of the genes that exert significant control over growth rate (high flux control or HFC genes) is not necessarily regulated by growth rate itself. It has not been determined whether the set of HFC genes is the same at all growth rates or whether it is the same in conditions of nutrient limitation or excess.ResultsHFC genes were identified in competition experiments in which a population of hemizygous diploid yeast deletants were grown at, or close to, the maximum specific growth rate in either nutrient-limiting or nutrient-sufficient conditions. A hemizygous mutant is one in which one of any pair of homologous genes is deleted in a diploid, These HFC genes divided into two classes: a haploinsufficient (HI) set, where the hemizygous mutants grow slower than the wild type, and a haploproficient (HP) set, which comprises hemizygotes that grow faster than the wild type. The HI set was found to be enriched for genes involved in the processes of gene expression, while the HP set was enriched for genes concerned with the cell cycle and genome integrity.ConclusionA subset of growth-regulated genes have HFC characteristics when grown in conditions where there are few, or no, external constraints on the rate of growth that cells may attain. This subset is enriched for genes that participate in the processes of gene expression, itself (i.e. transcription and translation). The fact that haploproficiency is exhibited by mutants grown at the previously determined maximum rate implies that the control of growth rate in this simple eukaryote represents a trade-off between the selective advantages of rapid growth and the need to maintain the integrity of the genome.

Functional expression of parasite drug targets and their human orthologs in yeast.

Background The exacting nutritional requirements and complicated life cycles of parasites mean that they are not always amenable to high-throughput drug screening using automated procedures. Therefore, we have engineered the yeast Saccharomyces cerevisiae to act as a surrogate for expressing anti-parasitic targets from a range of biomedically important pathogens, to facilitate the rapid identification of new therapeutic agents. Methodology/Principal Findings Using pyrimethamine/dihydrofolate reductase (DHFR) as a model parasite drug/drug target system, we explore the potential of engineered yeast strains (expressing DHFR enzymes from Plasmodium falciparum, P. vivax, Homo sapiens, Schistosoma mansoni, Leishmania major, Trypanosoma brucei and T. cruzi) to exhibit appropriate differential sensitivity to pyrimethamine. Here, we demonstrate that yeast strains (lacking the major drug efflux pump, Pdr5p) expressing yeast (ScDFR1), human (HsDHFR), Schistosoma (SmDHFR), and Trypanosoma (TbDHFR and TcDHFR) DHFRs are insensitive to pyrimethamine treatment, whereas yeast strains producing Plasmodium (PfDHFR and PvDHFR) DHFRs are hypersensitive. Reassuringly, yeast strains expressing field-verified, drug-resistant mutants of P. falciparum DHFR (Pfdhfr 51I,59R,108N) are completely insensitive to pyrimethamine, further validating our approach to drug screening. We further show the versatility of the approach by replacing yeast essential genes with other potential drug targets, namely phosphoglycerate kinases (PGKs) and N-myristoyl transferases (NMTs). Conclusions/Significance We have generated a number of yeast strains that can be successfully harnessed for the rapid and selective identification of urgently needed anti-parasitic agents.