TY - JOUR A1 - de Abreu e Lima, Francisco Anastacio A1 - Willmitzer, Lothar A1 - Nikoloski, Zoran T1 - Classification-driven framework to predict maize hybrid field performance from metabolic profiles of young parental roots JF - PLoS one N2 - Maize (Zea mays L.) is a staple food whose production relies on seed stocks that largely comprise hybrid varieties. Therefore, knowledge about the molecular determinants of hybrid performance (HP) in the field can be used to devise better performing hybrids to address the demands for sustainable increase in yield. Here, we propose and test a classification-driven framework that uses metabolic profiles from in vitro grown young roots of parental lines from the Dent x Flint maize heterotic pattern to predict field HP. We identify parental analytes that best predict the metabolic inheritance patterns in 328 hybrids. We then demonstrate that these analytes are also predictive of field HP (0.64 >= r >= 0.79) and discriminate hybrids of good performance (accuracy of 87.50%). Therefore, our approach provides a cost-effective solution for hybrid selection programs. Y1 - 2018 U6 - https://doi.org/10.1371/journal.pone.0196038 SN - 1932-6203 VL - 13 IS - 4 PB - PLoS CY - San Fransisco ER - TY - JOUR A1 - Schwahn, Kevin A1 - Nikoloski, Zoran T1 - Data reduction approaches for dissecting transcriptional effects on metabolism JF - Frontiers in plant science N2 - The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coil, Saccharomycies cerevisiae, and Arabidopsis thaliana. KW - E. coil KW - S. cerevisiae KW - A. thaliana KW - partial correlation KW - principal component analysis KW - metabolomics KW - data reduction KW - regulation Y1 - 2018 U6 - https://doi.org/10.3389/fpls.2018.00538 SN - 1664-462X VL - 9 PB - Frontiers Research Foundation CY - Lausanne ER - TY - JOUR A1 - Küken, Anika A1 - Sommer, Frederik A1 - Yaneva-Roder, Liliya A1 - Mackinder, Luke C. M. A1 - Hoehne, Melanie A1 - Geimer, Stefan A1 - Jonikas, Martin C. A1 - Schroda, Michael A1 - Stitt, Mark A1 - Nikoloski, Zoran A1 - Mettler-Altmann, Tabea T1 - Effects of microcompartmentation on flux distribution and metabolic pools in Chlamydomonas reinhardtii chloroplasts JF - eLife N2 - Cells and organelles are not homogeneous but include microcompartments that alter the spatiotemporal characteristics of cellular processes. The effects of microcompartmentation on metabolic pathways are however difficult to study experimentally. The pyrenoid is a microcompartment that is essential for a carbon concentrating mechanism (CCM) that improves the photosynthetic performance of eukaryotic algae. Using Chlamydomonas reinhardtii, we obtained experimental data on photosynthesis, metabolites, and proteins in CCM-induced and CCM-suppressed cells. We then employed a computational strategy to estimate how fluxes through the Calvin-Benson cycle are compartmented between the pyrenoid and the stroma. Our model predicts that ribulose-1,5-bisphosphate (RuBP), the substrate of Rubisco, and 3-phosphoglycerate (3PGA), its product, diffuse in and out of the pyrenoid, respectively, with higher fluxes in CCM-induced cells. It also indicates that there is no major diffusional barrier to metabolic flux between the pyrenoid and stroma. Our computational approach represents a stepping stone to understanding microcompartmentalized CCM in other organisms. Y1 - 2018 U6 - https://doi.org/10.7554/eLife.37960 SN - 2050-084X VL - 7 PB - eLife Sciences Publications CY - Cambridge ER - TY - JOUR A1 - Scheunemann, Michael A1 - Brady, Siobhan M. A1 - Nikoloski, Zoran T1 - Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models JF - Scientific reports N2 - Plant organs consist of multiple cell types that do not operate in isolation, but communicate with each other to maintain proper functions. Here, we extract models specific to three developmental stages of eight root cell types or tissue layers in Arabidopsis thaliana based on a state-of-the-art constraint-based modeling approach with all publicly available transcriptomics and metabolomics data from this system to date. We integrate these models into a multi-cell root model which we investigate with respect to network structure, distribution of fluxes, and concordance to transcriptomics and proteomics data. From a methodological point, we show that the coupling of tissue-specific models in a multi-tissue model yields a higher specificity of the interconnected models with respect to network structure and flux distributions. We use the extracted models to predict and investigate the flux of the growth hormone indole-3-actetate and its antagonist, trans-Zeatin, through the root. While some of predictions are in line with experimental evidence, constraints other than those coming from the metabolic level may be necessary to replicate the flow of indole-3-actetate from other simulation studies. Therefore, our work provides the means for data-driven multi-tissue metabolic model extraction of other Arabidopsis organs in the constraint-based modeling framework. Y1 - 2018 U6 - https://doi.org/10.1038/s41598-018-26232-8 SN - 2045-2322 VL - 8 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Smith, Sarah R. A1 - Dupont, Chris L. A1 - McCarthy, James K. A1 - Broddrick, Jared T. A1 - Obornik, Miroslav A1 - Horak, Ales A1 - Füssy, Zoltán A1 - Cihlar, Jaromir A1 - Kleessen, Sabrina A1 - Zheng, Hong A1 - McCrow, John P. A1 - Hixson, Kim K. A1 - Araujo, Wagner L. A1 - Nunes-Nesi, Adriano A1 - Fernie, Alisdair R. A1 - Nikoloski, Zoran A1 - Palsson, Bernhard O. A1 - Allen, Andrew E. T1 - Evolution and regulation of nitrogen flux through compartmentalized metabolic networks in a marine diatom JF - Nature Communications N2 - Diatoms outcompete other phytoplankton for nitrate, yet little is known about the mechanisms underpinning this ability. Genomes and genome-enabled studies have shown that diatoms possess unique features of nitrogen metabolism however, the implications for nutrient utilization and growth are poorly understood. Using a combination of transcriptomics, proteomics, metabolomics, fluxomics, and flux balance analysis to examine short-term shifts in nitrogen utilization in the model pennate diatom in Phaeodactylum tricornutum, we obtained a systems-level understanding of assimilation and intracellular distribution of nitrogen. Chloroplasts and mitochondria are energetically integrated at the critical intersection of carbon and nitrogen metabolism in diatoms. Pathways involved in this integration are organelle-localized GS-GOGAT cycles, aspartate and alanine systems for amino moiety exchange, and a split-organelle arginine biosynthesis pathway that clarifies the role of the diatom urea cycle. This unique configuration allows diatoms to efficiently adjust to changing nitrogen status, conferring an ecological advantage over other phytoplankton taxa. KW - Biochemistry KW - Computational biology and bioinformatics KW - Evolution KW - Microbiology KW - Molecular biology Y1 - 2019 U6 - https://doi.org/10.1038/s41467-019-12407-y SN - 2041-1723 VL - 10 PB - Nature Publ. Group CY - London ER - TY - JOUR A1 - Ferrari, Camilla A1 - Proost, Sebastian A1 - Janowski, Marcin Andrzej A1 - Becker, Jörg A1 - Nikoloski, Zoran A1 - Bhattacharya, Debashish A1 - Price, Dana A1 - Tohge, Takayuki A1 - Bar-Even, Arren A1 - Fernie, Alisdair R. A1 - Stitt, Mark A1 - Mutwil, Marek T1 - Kingdom-wide comparison reveals the evolution of diurnal gene expression in Archaeplastida JF - Nature Communications N2 - Plants have adapted to the diurnal light-dark cycle by establishing elaborate transcriptional programs that coordinate many metabolic, physiological, and developmental responses to the external environment. These transcriptional programs have been studied in only a few species, and their function and conservation across algae and plants is currently unknown. We performed a comparative transcriptome analysis of the diurnal cycle of nine members of Archaeplastida, and we observed that, despite large phylogenetic distances and dramatic differences in morphology and lifestyle, diurnal transcriptional programs of these organisms are similar. Expression of genes related to cell division and the majority of biological pathways depends on the time of day in unicellular algae but we did not observe such patterns at the tissue level in multicellular land plants. Hence, our study provides evidence for the universality of diurnal gene expression and elucidates its evolutionary history among different photosynthetic eukaryotes. Y1 - 2019 U6 - https://doi.org/10.1038/s41467-019-08703-2 SN - 2041-1723 VL - 10 PB - Nature Publ. Group CY - London ER - 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 - Lyall, Rafe A1 - Nikoloski, Zoran A1 - Gechev, Tsanko T1 - Comparative analysis of ROS network genes in extremophile Eukaryotes JF - International journal of molecular sciences N2 - The reactive oxygen species (ROS) gene network, consisting of both ROS-generating and detoxifying enzymes, adjusts ROS levels in response to various stimuli. We performed a cross-kingdom comparison of ROS gene networks to investigate how they have evolved across all Eukaryotes, including protists, fungi, plants and animals. We included the genomes of 16 extremotolerant Eukaryotes to gain insight into ROS gene evolution in organisms that experience extreme stress conditions. Our analysis focused on ROS genes found in all Eukaryotes (such as catalases, superoxide dismutases, glutathione reductases, peroxidases and glutathione peroxidase/peroxiredoxins) as well as those specific to certain groups, such as ascorbate peroxidases, dehydroascorbate/monodehydroascorbate reductases in plants and other photosynthetic organisms. ROS-producing NADPH oxidases (NOX) were found in most multicellular organisms, although several NOX-like genes were identified in unicellular or filamentous species. However, despite the extreme conditions experienced by extremophile species, we found no evidence for expansion of ROS-related gene families in these species compared to other Eukaryotes. Tardigrades and rotifers do show ROS gene expansions that could be related to their extreme lifestyles, although a high rate of lineage-specific horizontal gene transfer events, coupled with recent tetraploidy in rotifers, could explain this observation. This suggests that the basal Eukaryotic ROS scavenging systems are sufficient to maintain ROS homeostasis even under the most extreme conditions. KW - ROS KW - extremotolerance KW - resurrection plants Y1 - 2020 U6 - https://doi.org/10.3390/ijms21239131 SN - 1422-0067 VL - 21 IS - 23 PB - Molecular Diversity Preservation International (MDPI) CY - Basel 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 - Tong, Hao A1 - Küken, Anika A1 - Nikoloski, Zoran T1 - Integrating molecular markers into metabolic models improves genomic selection for Arabidopsis growth JF - Nature Communications N2 - The current trends of crop yield improvements are not expected to meet the projected rise in demand. Genomic selection uses molecular markers and machine learning to identify superior genotypes with improved traits, such as growth. Plant growth directly depends on rates of metabolic reactions which transform nutrients into the building blocks of biomass. Here, we predict growth of Arabidopsis thaliana accessions by employing genomic prediction of reaction rates estimated from accession-specific metabolic models. We demonstrate that, comparing to classical genomic selection on the available data sets for 67 accessions, our approach improves the prediction accuracy for growth within and across nitrogen environments by 32.6% and 51.4%, respectively, and from optimal nitrogen to low carbon environment by 50.4%. Therefore, integration of molecular markers into metabolic models offers an approach to predict traits directly related to metabolism, and its usefulness in breeding can be examined by gathering matching datasets in crops. An increase in genomic selection (GS) accuracy can accelerate genetic gain by shortening the breeding cycles. Here, the authors introduce a network-based GS method that uses metabolic models and improves the prediction accuracy of Arabidopsis growth within and across environments. Y1 - 2020 U6 - https://doi.org/10.1038/s41467-020-16279-5 SN - 2041-1723 VL - 11 IS - 1 PB - Nature Publishing Group UK CY - London ER -