‘Domino’ systems biology and the ‘A’ of ATP

AbstractWe develop a strategic ‘domino’ approach that starts with one key feature of cell function and the main process providing for it, and then adds additional processes and components only as necessary to explain provoked experimental observations. The approach is here applied to the energy metabolism of yeast in a glucose limited chemostat, subjected to a sudden increase in glucose. The puzzles addressed include (i) the lack of increase in Adenosine triphosphate (ATP) upon glucose addition, (ii) the lack of increase in Adenosine diphosphate (ADP) when ATP is hydrolyzed, and (iii) the rapid disappearance of the ‘A’ (adenine) moiety of ATP. Neither the incorporation of nucleotides into new biomass, nor steady de novo synthesis of Adenosine monophosphate (AMP) explains. Cycling of the ‘A’ moiety accelerates when the cell's energy state is endangered, another essential domino among the seven required for understanding of the experimental observations. This new domino analysis shows how strategic experimental design and observations in tandem with theory and modeling may identify and resolve important paradoxes. It also highlights the hitherto unexpected role of the ‘A’ component of ATP

SALARECON connects the Atlantic salmon genome to growth and feed efficiency

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MEMOTE for standardized genome-scale metabolic model testing

Supplementary information is available for this paper at https://doi.org/10.1038/s41587-020-0446-yReconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with metadata can be built using standard reconstruction protocols2, and guidelines have been put in place for tracking provenance and enabling interoperability, but a standardized means of quality control for GEMs is lacking3. Here we report a community effort to develop a test suite named MEMOTE (for metabolic model tests) to assess GEM quality.We acknowledge D. Dannaher and A. Lopez for their supporting work on the Angular parts of MEMOTE; resources and support from the DTU Computing Center; J. Cardoso, S. Gudmundsson, K. Jensen and D. Lappa for their feedback on conceptual details; and P. D. Karp and I. Thiele for critically reviewing the manuscript. We thank J. Daniel, T. Kristjánsdóttir, J. Saez-Saez, S. Sulheim, and P. Tubergen for being early adopters of MEMOTE and for providing written testimonials. J.O.V. received the Research Council of Norway grants 244164 (GenoSysFat), 248792 (DigiSal) and 248810 (Digital Life Norway); M.Z. received the Research Council of Norway grant 244164 (GenoSysFat); C.L. received funding from the Innovation Fund Denmark (project “Environmentally Friendly Protein Production (EFPro2)”); C.L., A.K., N. S., M.B., M.A., D.M., P.M, B.J.S., P.V., K.R.P. and M.H. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 686070 (DD-DeCaF); B.G.O., F.T.B. and A.D. acknowledge funding from the US National Institutes of Health (NIH, grant number 2R01GM070923-13); A.D. was supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections; N.E.L. received funding from NIGMS R35 GM119850, Novo Nordisk Foundation NNF10CC1016517 and the Keck Foundation; A.R. received a Lilly Innovation Fellowship Award; B.G.-J. and J. Nogales received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no 686585 for the project LIAR, and the Spanish Ministry of Economy and Competitivity through the RobDcode grant (BIO2014-59528-JIN); L.M.B. has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement 633962 for project P4SB; R.F. received funding from the US Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research as part of the Scientific Discovery Through Advanced Computing program, grant DE-SC0010429; A.M., C.Z., S.L. and J. Nielsen received funding from The Knut and Alice Wallenberg Foundation, Advanced Computing program, grant #DE-SC0010429; S.K.’s work was in part supported by the German Federal Ministry of Education and Research (de.NBI partner project “ModSim” (FKZ: 031L104B)); E.K. and J.A.H.W. were supported by the German Federal Ministry of Education and Research (project “SysToxChip”, FKZ 031A303A); M.K. is supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054); J.A.P. and G.L.M. acknowledge funding from US National Institutes of Health (T32-LM012416, R01-AT010253, R01-GM108501) and the Wagner Foundation; G.L.M. acknowledges funding from a Grand Challenges Exploration Phase I grant (OPP1211869) from the Bill & Melinda Gates Foundation; H.H. and R.S.M.S. received funding from the Biotechnology and Biological Sciences Research Council MultiMod (BB/N019482/1); H.U.K. and S.Y.L. received funding from the Technology Development Program to Solve Climate Changes on Systems Metabolic Engineering for Biorefineries (grants NRF-2012M1A2A2026556 and NRF-2012M1A2A2026557) from the Ministry of Science and ICT through the National Research Foundation (NRF) of Korea; H.U.K. received funding from the Bio & Medical Technology Development Program of the NRF, the Ministry of Science and ICT (NRF-2018M3A9H3020459); P.B., B.J.S., Z.K., B.O.P., C.L., M.B., N.S., M.H. and A.F. received funding through Novo Nordisk Foundation through the Center for Biosustainability at the Technical University of Denmark (NNF10CC1016517); D.-Y.L. received funding from the Next-Generation BioGreen 21 Program (SSAC, PJ01334605), Rural Development Administration, Republic of Korea; G.F. was supported by the RobustYeast within ERA net project via SystemsX.ch; V.H. received funding from the ETH Domain and Swiss National Science Foundation; M.P. acknowledges Oxford Brookes University; J.C.X. received support via European Research Council (666053) to W.F. Martin; B.E.E. acknowledges funding through the CSIRO-UQ Synthetic Biology Alliance; C.D. is supported by a Washington Research Foundation Distinguished Investigator Award. I.N. received funding from National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) (grant P20GM125503).info:eu-repo/semantics/publishedVersio

Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

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Sensitivity analysis of a pathway with respect to fast and small temperature change

Temperature affects all enzymes simultaneously in a metabolic system. The enzyme concentration in a biochemical system can be considered as invariant under fast and small temperature change. Therefore, the total sensitivity of a steady state flux through a pathway with respect to the temperature can be expressed as: the apparent activation energy of a steady state pathway flux equals the sum of weighted activation energies of the individual reactions contributing to the flux, where the weighting factors are the flux control coefficients of these reactions in the context of the network. Correspondingly, since the elasticity of any enzyme with respect to temperature is always nonzero, only the reactions with a nonzero flux control coefficient contribute accordingly to the temperature sensitivity of the pathway.publishedVersio

Metabolic efficiency in yeast Saccharomyces cerevisiae in relation to temperature dependent growth and biomass yield

Canonized view on temperature effects on growth rate of microorganisms is based on assumption of protein denaturation, which is not confirmed experimentally so far. We develop an alternative concept, which is based on view that limits of thermal tolerance are based on imbalance of cellular energy allocation. Therefore, we investigated growth suppression of yeast Saccharomyces cerevisiae in the supraoptimal temperature range (30–40 °C), i.e. above optimal temperature (ToptTopt). The maximal specific growth rate (μmaxμmax) of biomass, its concentration and yield on glucose (Yx/glcYx/glc) were measured across the whole thermal window (5–40 °C) of the yeast in batch anaerobic growth on glucose. Specific rate of glucose consumption, specific rate of glucose consumption for maintenance (mglcmglc), true biomass yield on glucose (View the MathML sourceYx/glctrue), fractional conservation of substrate carbon in product and ATP yield on glucose (Yatp/glcYatp/glc) were estimated from the experimental data. There was a negative linear relationship between ATP, ADP and AMP concentrations and specific growth rate at any growth conditions, whilst the energy charge was always high (~0.83). There were two temperature regions where mglcmglc differed 12-fold, which points to the existence of a ‘low’ (within 5–31 °C) and a ‘high’ (within 33–40 °C) metabolic mode regarding maintenance requirements. The rise from the low to high mode occurred at 31–32 °C in step-wise manner and it was accompanied with onset of suppression of μmaxμmax. High mglcmglc at supraoptimal temperatures indicates a significant reduction of scope for growth, due to high maintenance cost. Analysis of temperature dependencies of product formation efficiency and Yatp/glcYatp/glc revealed that the efficiency of energy metabolism approaches its lower limit at 26–31 °C. This limit is reflected in the predetermined combination of View the MathML sourceYx/glctrue, elemental biomass composition and degree of reduction of the growth substrate. Approaching the limit implies a reduction of the safety margin of metabolic efficiency. We hypothesize that a temperature increase above ToptTopt (e.g. >31 °C) triggers both an increment in mglcmglc and suppression of μmaxμmax, which together contribute to an upshift of Yatp/glcYatp/glc from the lower limit and thus compensate for the loss of the safety margin. This trade-off allows adding 10 more degrees to ToptTopt and extends the thermal window up to 40 °C, sustaining survival and reproduction in supraoptimal temperatures. Deeper understanding of the limits of thermal tolerance can be practically exploited in biotechnological applications

Additional file 1: Figure S1. of Metabolic model of central carbon and energy metabolisms of growing Arabidopsis thaliana in relation to sucrose translocation

Stoichiometric model of Arabidopsis thaliana. The complete stoichiometric model consisted of three tissues-compartments (mesophyll, phloem, root) with two sub-compartments (mitochondrion, plastid). The reconstructed network contained 400 transformers, 413 balanced metabolites and 742 ORFs associated with corresponding reactions. The image was generated with Insilico Discovery™ (Insilico Biotechnology AG, Stuttgart, Germany). (JPG 18396 kb