TY - JOUR A1 - Pandey, Prashant K. A1 - Yu, Jing A1 - Omranian, Nooshin A1 - Alseekh, Saleh A1 - Vaid, Neha A1 - Fernie, Alisdair R. A1 - Nikoloski, Zoran A1 - Laitinen, Roosa A. E. T1 - Plasticity in metabolism underpins local responses to nitrogen in Arabidopsis thaliana populations JF - Plant Direct N2 - Nitrogen (N) is central for plant growth, and metabolic plasticity can provide a strategy to respond to changing N availability. We showed that two local A. thaliana populations exhibited differential plasticity in the compounds of photorespiratory and starch degradation pathways in response to three N conditions. Association of metabolite levels with growth-related and fitness traits indicated that controlled plasticity in these pathways could contribute to local adaptation and play a role in plant evolution. KW - Arabidopsis thaliana KW - natural variation KW - nitrogen availability KW - photorespiration KW - plasticity Y1 - 2019 U6 - https://doi.org/10.1002/pld3.186 SN - 2475-4455 VL - 3 IS - 11 PB - John Wiley & sonst LTD CY - Chichester ER - TY - JOUR A1 - Calderan-Rodrigues, Maria Juliana A1 - Luzarowski, Marcin A1 - Monte-Bello, Carolina Cassano A1 - Minen, Romina Ines A1 - Zühlke, Boris M. A1 - Nikoloski, Zoran A1 - Skirycz, Aleksandra A1 - Caldana, Camila T1 - Proteogenic dipeptides are characterized by diel fluctuations and target of rapamycin complex-signaling dependency in the model plant Arabidopsis thaliana JF - Frontiers in plant science : FPLS N2 - As autotrophic organisms, plants capture light energy to convert carbon dioxide into ATP, nicotinamide adenine dinucleotide phosphate (NADPH), and sugars, which are essential for the biosynthesis of building blocks, storage, and growth. At night, metabolism and growth can be sustained by mobilizing carbon (C) reserves. In response to changing environmental conditions, such as light-dark cycles, the small-molecule regulation of enzymatic activities is critical for reprogramming cellular metabolism. We have recently demonstrated that proteogenic dipeptides, protein degradation products, act as metabolic switches at the interface of proteostasis and central metabolism in both plants and yeast. Dipeptides accumulate in response to the environmental changes and act via direct binding and regulation of critical enzymatic activities, enabling C flux distribution. Here, we provide evidence pointing to the involvement of dipeptides in the metabolic rewiring characteristics for the day-night cycle in plants. Specifically, we measured the abundance of 13 amino acids and 179 dipeptides over short- (SD) and long-day (LD) diel cycles, each with different light intensities. Of the measured dipeptides, 38 and eight were characterized by day-night oscillation in SD and LD, respectively, reaching maximum accumulation at the end of the day and then gradually falling in the night. Not only the number of dipeptides, but also the amplitude of the oscillation was higher in SD compared with LD conditions. Notably, rhythmic dipeptides were enriched in the glucogenic amino acids that can be converted into glucose. Considering the known role of Target of Rapamycin (TOR) signaling in regulating both autophagy and metabolism, we subsequently investigated whether diurnal fluctuations of dipeptides levels are dependent on the TOR Complex (TORC). The Raptor1b mutant (raptor1b), known for the substantial reduction of TOR kinase activity, was characterized by the augmented accumulation of dipeptides, which is especially pronounced under LD conditions. We were particularly intrigued by the group of 16 dipeptides, which, based on their oscillation under SD conditions and accumulation in raptor1b, can be associated with limited C availability or photoperiod. By mining existing protein-metabolite interaction data, we delineated putative protein interactors for a representative dipeptide Pro-Gln. The obtained list included enzymes of C and amino acid metabolism, which are also linked to the TORC-mediated metabolic network. Based on the obtained results, we speculate that the diurnal accumulation of dipeptides contributes to its metabolic adaptation in response to changes in C availability. We hypothesize that dipeptides would act as alternative respiratory substrates and by directly modulating the activity of the focal enzymes. KW - dipeptide KW - diel cycle KW - metabolism KW - TOR signaling KW - protein-metabolite KW - interactions KW - carbon limitation KW - amino acid Y1 - 2021 U6 - https://doi.org/10.3389/fpls.2021.758933 SN - 1664-462X VL - 12 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Arend, Marius A1 - Zimmer, David A1 - Xu, Rudan A1 - Sommer, Frederik A1 - Mühlhaus, Timo A1 - Nikoloski, Zoran T1 - Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale JF - Nature Communications N2 - Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas. KW - Computational models KW - Enzymes KW - Proteomics Y1 - 2023 U6 - https://doi.org/10.1038/s41467-023-40498-1 SN - 2041-1723 VL - 14 IS - 1 PB - Springer Nature CY - London ER - TY - JOUR A1 - Seep, Lea A1 - Razaghi-Moghadam, Zahra A1 - Nikoloski, Zoran T1 - Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis JF - Scientific reports N2 - Thermodynamic metabolic flux analysis (TMFA) can narrow down the space of steady-state flux distributions, but requires knowledge of the standard Gibbs free energy for the modelled reactions. The latter are often not available due to unknown Gibbs free energy change of formation ,Delta fG0, of metabolites. To optimize the usage of data on thermodynamics in constraining a model, reaction lumping has been proposed to eliminate metabolites with unknown Delta fG0. However, the lumping procedure has not been formalized nor implemented for systematic identification of lumped reactions. Here, we propose, implement, and test a combined procedure for reaction lumping, applicable to genome-scale metabolic models. It is based on identification of groups of metabolites with unknown Delta fG0 whose elimination can be conducted independently of the others via: (1) group implementation, aiming to eliminate an entire such group, and, if this is infeasible, (2) a sequential implementation to ensure that a maximal number of metabolites with unknown Delta fG0 are eliminated. Our comparative analysis with genome-scale metabolic models of Escherichia coli, Bacillus subtilis, and Homo sapiens shows that the combined procedure provides an efficient means for systematic identification of lumped reactions. We also demonstrate that TMFA applied to models with reactions lumped according to the proposed procedure lead to more precise predictions in comparison to the original models. The provided implementation thus ensures the reproducibility of the findings and their application with standard TMFA. Y1 - 2021 U6 - https://doi.org/10.1038/s41598-021-87643-8 SN - 2045-2322 VL - 11 IS - 1 PB - Macmillan Publishers Limited, part of Springer Nature CY - London ER - TY - JOUR A1 - Razaghi-Moghadam, Zahra A1 - Nikoloski, Zoran T1 - Supervised learning of gene-regulatory networks based on graph distance profiles of transcriptomics data JF - npj Systems biology and applications N2 - Characterisation of gene-regulatory network (GRN) interactions provides a stepping stone to understanding how genes affect cellular phenotypes. Yet, despite advances in profiling technologies, GRN reconstruction from gene expression data remains a pressing problem in systems biology. Here, we devise a supervised learning approach, GRADIS, which utilises support vector machine to reconstruct GRNs based on distance profiles obtained from a graph representation of transcriptomics data. By employing the data fromEscherichia coliandSaccharomyces cerevisiaeas well as synthetic networks from the DREAM4 and five network inference challenges, we demonstrate that our GRADIS approach outperforms the state-of-the-art supervised and unsupervided approaches. This holds when predictions about target genes for individual transcription factors as well as for the entire network are considered. We employ experimentally verified GRNs fromE. coliandS. cerevisiaeto validate the predictions and obtain further insights in the performance of the proposed approach. Our GRADIS approach offers the possibility for usage of other network-based representations of large-scale data, and can be readily extended to help the characterisation of other cellular networks, including protein-protein and protein-metabolite interactions. Y1 - 2020 U6 - https://doi.org/10.1038/s41540-020-0140-1 SN - 2056-7189 VL - 6 IS - 1 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Langary, Damoun A1 - Küken, Anika A1 - Nikoloski, Zoran T1 - The effective deficiency of biochemical networks JF - Scientific reports N2 - The deficiency of a (bio)chemical reaction network can be conceptually interpreted as a measure of its ability to support exotic dynamical behavior and/or multistationarity. The classical definition of deficiency relates to the capacity of a network to permit variations of the complex formation rate vector at steady state, irrespective of the network kinetics. However, the deficiency is by definition completely insensitive to the fine details of the directionality of reactions as well as bounds on reaction fluxes. While the classical definition of deficiency can be readily applied in the analysis of unconstrained, weakly reversible networks, it only provides an upper bound in the cases where relevant constraints on reaction fluxes are imposed. Here we propose the concept of effective deficiency, which provides a more accurate assessment of the network’s capacity to permit steady state variations at the complex level for constrained networks of any reversibility patterns. The effective deficiency relies on the concept of nonstoichiometric balanced complexes, which we have already shown to be present in real-world biochemical networks operating under flux constraints. Our results demonstrate that the effective deficiency of real-world biochemical networks is smaller than the classical deficiency, indicating the effects of reaction directionality and flux bounds on the variation of the complex formation rate vector at steady state. Y1 - 2023 U6 - https://doi.org/10.1038/s41598-023-41767-1 SN - 2045-2322 VL - 13 PB - Springer Nature CY - London ER - TY - JOUR A1 - Küken, Anika A1 - Langary, Damoun A1 - Nikoloski, Zoran T1 - The hidden simplicity of metabolic networks is revealed by multireaction dependencies JF - Science Advances N2 - Understanding the complexity of metabolic networks has implications for manipulation of their functions. The complexity of metabolic networks can be characterized by identifying multireaction dependencies that are challenging to determine due to the sheer number of combinations to consider. Here, we propose the concept of concordant complexes that captures multireaction dependencies and can be efficiently determined from the algebraic structure and operational constraints of metabolic networks. The concordant complexes imply the existence of concordance modules based on which the apparent complexity of 12 metabolic networks of organisms from all kingdoms of life can be reduced by at least 78%. A comparative analysis against an ensemble of randomized metabolic networks shows that the metabolic network of Escherichia coli contains fewer concordance modules and is, therefore, more tightly coordinated than expected by chance. Together, our findings demonstrate that metabolic networks are considerably simpler than what can be perceived from their structure alone. Y1 - 2022 U6 - https://doi.org/10.1126/sciadv.abl6962 SN - 2375-2548 VL - 8 IS - 13 PB - American Assoc. for the Advancement of Science CY - Washington ER -