@article{MbebiBreitlerBordeauxetal.2022, author = {Mbebi, Alain J. and Breitler, Jean-Christophe and Bordeaux, M'elanie and Sulpice, Ronan and McHale, Marcus and Tong, Hao and Toniutti, Lucile and Castillo, Jonny Alonso and Bertrand, Benoit and Nikoloski, Zoran}, title = {A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids}, series = {G3: Genes, genomes, genetics}, volume = {12}, journal = {G3: Genes, genomes, genetics}, number = {9}, publisher = {Genetics Soc. of America}, address = {Pittsburgh, PA}, issn = {2160-1836}, doi = {10.1093/g3journal/jkac170}, pages = {11}, year = {2022}, abstract = {Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.}, language = {en} } @article{CordobaTongBurgosetal.2023, author = {C{\´o}rdoba, Sandra Correa and Tong, Hao and Burgos, Asdrubal and Zhu, Feng and Alseekh, Saleh and Fernie, Alisdair R. and Nikoloski, Zoran}, title = {Identification of gene function based on models capturing natural variability of Arabidopsis thaliana lipid metabolism}, series = {Nature Communications}, volume = {14}, journal = {Nature Communications}, number = {1}, publisher = {Springer Nature}, address = {London}, issn = {2041-1723}, doi = {10.1038/s41467-023-40644-9}, pages = {12}, year = {2023}, abstract = {The use of automated tools to reconstruct lipid metabolic pathways is not warranted in plants. Here, the authors construct Plant Lipid Module for Arabidopsis rosette using constraint-based modeling, demonstrate its integration in other plant metabolic models, and use it to dissect the genetic architecture of lipid metabolism. Lipids play fundamental roles in regulating agronomically important traits. Advances in plant lipid metabolism have until recently largely been based on reductionist approaches, although modulation of its components can have system-wide effects. However, existing models of plant lipid metabolism provide lumped representations, hindering detailed study of component modulation. Here, we present the Plant Lipid Module (PLM) which provides a mechanistic description of lipid metabolism in the Arabidopsis thaliana rosette. We demonstrate that the PLM can be readily integrated in models of A. thaliana Col-0 metabolism, yielding accurate predictions (83\%) of single lethal knock-outs and 75\% concordance between measured transcript and predicted flux changes under extended darkness. Genome-wide associations with fluxes obtained by integrating the PLM in diel condition- and accession-specific models identify up to 65 candidate genes modulating A. thaliana lipid metabolism. Using mutant lines, we validate up to 40\% of the candidates, paving the way for identification of metabolic gene function based on models capturing natural variability in metabolism.}, language = {en} } @article{ArendZimmerXuetal.2023, author = {Arend, Marius and Zimmer, David and Xu, Rudan and Sommer, Frederik and M{\"u}hlhaus, Timo and Nikoloski, Zoran}, title = {Proteomics and constraint-based modelling reveal enzyme kinetic properties of Chlamydomonas reinhardtii on a genome scale}, series = {Nature Communications}, volume = {14}, journal = {Nature Communications}, number = {1}, publisher = {Springer Nature}, address = {London}, issn = {2041-1723}, doi = {10.1038/s41467-023-40498-1}, pages = {9}, year = {2023}, abstract = {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.}, language = {en} } @article{KuekenTrevesNikoloski2023, author = {K{\"u}ken, Anika and Treves, Haim and Nikoloski, Zoran}, title = {A simulation-free constrained regression approach for flux estimation in isotopically nonstationary metabolic flux analysis with applications in microalgae}, series = {Frontiers in plant science : FPLS}, volume = {14}, journal = {Frontiers in plant science : FPLS}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {1664-462X}, doi = {10.3389/fpls.2023.1140829}, pages = {12}, year = {2023}, abstract = {Introduction Flux phenotypes from different organisms and growth conditions allow better understanding of differential metabolic networks functions. Fluxes of metabolic reactions represent the integrated outcome of transcription, translation, and post-translational modifications, and directly affect growth and fitness. However, fluxes of intracellular metabolic reactions cannot be directly measured, but are estimated via metabolic flux analysis (MFA) that integrates data on isotope labeling patterns of metabolites with metabolic models. While the application of metabolomics technologies in photosynthetic organisms have resulted in unprecedented data from 13CO2-labeling experiments, the bottleneck in flux estimation remains the application of isotopically nonstationary MFA (INST-MFA). INST-MFA entails fitting a (large) system of coupled ordinary differential equations, with metabolite pools and reaction fluxes as parameters. Here, we focus on the Calvin-Benson cycle (CBC) as a key pathway for carbon fixation in photosynthesizing organisms and ask if approaches other than classical INST-MFA can provide reliable estimation of fluxes for reactions comprising this pathway. Methods First, we show that flux estimation with the labeling patterns of all CBC intermediates can be formulated as a single constrained regression problem, avoiding the need for repeated simulation of time-resolved labeling patterns. Results We then compare the flux estimates of the simulation-free constrained regression approach with those obtained from the classical INST-MFA based on labeling patterns of metabolites from the microalgae Chlamydomonas reinhardtii, Chlorella sorokiniana and Chlorella ohadii under different growth conditions. Discussion Our findings indicate that, in data-rich scenarios, simulation-free regression-based approaches provide a suitable alternative for flux estimation from classical INST-MFA since we observe a high qualitative agreement (rs=0.89) to predictions obtained from INCA, a state-of-the-art tool for INST-MFA.}, language = {en} } @article{LangaryKuekenNikoloski2023, author = {Langary, Damoun and K{\"u}ken, Anika and Nikoloski, Zoran}, title = {The effective deficiency of biochemical networks}, series = {Scientific reports}, volume = {13}, journal = {Scientific reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {10.1038/s41598-023-41767-1}, pages = {12}, year = {2023}, abstract = {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.}, language = {en} }