TY - JOUR A1 - Mbebi, Alain J. A1 - Breitler, Jean-Christophe A1 - Bordeaux, M'elanie A1 - Sulpice, Ronan A1 - McHale, Marcus A1 - Tong, Hao A1 - Toniutti, Lucile A1 - Castillo, Jonny Alonso A1 - Bertrand, Benoit A1 - Nikoloski, Zoran T1 - A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids JF - G3: Genes, genomes, genetics N2 - 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. KW - genomic prediction KW - phenomic prediction KW - 3-way coffee hybrids KW - chlorophyll a fluorescence KW - GenPred KW - Shared Data Resource Y1 - 2022 U6 - https://doi.org/10.1093/g3journal/jkac170 SN - 2160-1836 VL - 12 IS - 9 PB - Genetics Soc. of America CY - Pittsburgh, PA ER - TY - JOUR A1 - Küken, Anika A1 - Wendering, Philipp A1 - Langary, Damoun A1 - Nikoloski, Zoran T1 - A structural property for reduction of biochemical networks JF - Scientific reports N2 - Large-scale biochemical models are of increasing sizes due to the consideration of interacting organisms and tissues. Model reduction approaches that preserve the flux phenotypes can simplify the analysis and predictions of steady-state metabolic phenotypes. However, existing approaches either restrict functionality of reduced models or do not lead to significant decreases in the number of modelled metabolites. Here, we introduce an approach for model reduction based on the structural property of balancing of complexes that preserves the steady-state fluxes supported by the network and can be efficiently determined at genome scale. Using two large-scale mass-action kinetic models of Escherichia coli, we show that our approach results in a substantial reduction of 99% of metabolites. Applications to genome-scale metabolic models across kingdoms of life result in up to 55% and 85% reduction in the number of metabolites when arbitrary and mass-action kinetics is assumed, respectively. We also show that predictions of the specific growth rate from the reduced models match those based on the original models. Since steady-state flux phenotypes from the original model are preserved in the reduced, the approach paves the way for analysing other metabolic phenotypes in large-scale biochemical networks. Y1 - 2021 U6 - https://doi.org/10.1038/s41598-021-96835-1 SN - 2045-2322 VL - 11 IS - 1 PB - Macmillan Publishers Limited, part of Springer Nature CY - London ER - TY - JOUR A1 - Basler, Georg A1 - Fernie, Alisdair R. A1 - Nikoloski, Zoran T1 - Advances in metabolic flux analysis toward genome-scale profiling of higher organisms JF - Bioscience reports : communications and reviews in molecular and cellular biology N2 - Methodological and technological advances have recently paved the way for metabolic flux profiling in higher organisms, like plants. However, in comparison with omics technologies, flux profiling has yet to provide comprehensive differential flux maps at a genome-scale and in different cell types, tissues, and organs. Here we highlight the recent advances in technologies to gather metabolic labeling patterns and flux profiling approaches. We provide an opinion of how recent local flux profiling approaches can be used in conjunction with the constraint-based modeling framework to arrive at genome-scale flux maps. In addition, we point at approaches which use metabolomics data without introduction of label to predict either non-steady state fluxes in a time-series experiment or flux changes in different experimental scenarios. The combination of these developments allows an experimentally feasible approach for flux-based large-scale systems biology studies. Y1 - 2018 U6 - https://doi.org/10.1042/BSR20170224 SN - 0144-8463 SN - 1573-4935 VL - 38 PB - Portland Press (London) CY - London ER - 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 - Wendering, Philipp A1 - Nikoloski, Zoran T1 - COMMIT BT - Consideration of metabolite leakage and community composition improves microbial community reconstructions JF - PLoS Computational Biology : a new community journal / publ. by the Public Library of Science (PLoS) in association with the International Society for Computational Biology (ISCB) N2 - Composition and functions of microbial communities affect important traits in diverse hosts, from crops to humans. Yet, mechanistic understanding of how metabolism of individual microbes is affected by the community composition and metabolite leakage is lacking. Here, we first show that the consensus of automatically generated metabolic reconstructions improves the quality of the draft reconstructions, measured by comparison to reference models. We then devise an approach for gap filling, termed COMMIT, that considers metabolites for secretion based on their permeability and the composition of the community. By applying COMMIT with two soil communities from the Arabidopsis thaliana culture collection, we could significantly reduce the gap-filling solution in comparison to filling gaps in individual reconstructions without affecting the genomic support. Inspection of the metabolic interactions in the soil communities allows us to identify microbes with community roles of helpers and beneficiaries. Therefore, COMMIT offers a versatile fully automated solution for large-scale modelling of microbial communities for diverse biotechnological applications.
Author summaryMicrobial communities are important in ecology, human health, and crop productivity. However, detailed information on the interactions within natural microbial communities is hampered by the community size, lack of detailed information on the biochemistry of single organisms, and the complexity of interactions between community members. Metabolic models are comprised of biochemical reaction networks based on the genome annotation, and can provide mechanistic insights into community functions. Previous analyses of microbial community models have been performed with high-quality reference models or models generated using a single reconstruction pipeline. However, these models do not contain information on the composition of the community that determines the metabolites exchanged between the community members. In addition, the quality of metabolic models is affected by the reconstruction approach used, with direct consequences on the inferred interactions between community members. Here, we use fully automated consensus reconstructions from four approaches to arrive at functional models with improved genomic support while considering the community composition. We applied our pipeline to two soil communities from the Arabidopsis thaliana culture collection, providing only genome sequences. Finally, we show that the obtained models have 90% genomic support and demonstrate that the derived interactions are corroborated by independent computational predictions. Y1 - 2022 U6 - https://doi.org/10.1371/journal.pcbi.1009906 SN - 1553-734X SN - 1553-7358 VL - 18 IS - 3 PB - Public Library of Science CY - San Fransisco 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 - Omranian, Sara A1 - Nikoloski, Zoran A1 - Grimm, Dominik G. T1 - Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward BT - present state, challenges, and the way forward JF - Computational and structural biotechnology journal N2 - Physically interacting proteins form macromolecule complexes that drive diverse cellular processes. Advances in experimental techniques that capture interactions between proteins provide us with protein-protein interaction (PPI) networks from several model organisms. These datasets have enabled the prediction and other computational analyses of protein complexes. Here we provide a systematic review of the state-of-the-art algorithms for protein complex prediction from PPI networks proposed in the past two decades. The existing approaches that solve this problem are categorized into three groups, including: cluster-quality-based, node affinity-based, and network embedding-based approaches, and we compare and contrast the advantages and disadvantages. We further include a comparative analysis by computing the performance of eighteen methods based on twelve well-established performance measures on four widely used benchmark protein-protein interaction networks. Finally, the limitations and drawbacks of both, current data and approaches, along with the potential solutions in this field are discussed, with emphasis on the points that pave the way for future research efforts in this field. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/). KW - Protein Complex Prediction KW - Protein-Protein interaction network KW - Network KW - Clustering Algorithms KW - Network embedding Y1 - 2022 U6 - https://doi.org/10.1016/j.csbj.2022.05.049 SN - 2001-0370 VL - 20 SP - 2699 EP - 2712 PB - Research Network of Computational and Structural Biotechnology (RNCSB) CY - Gotenburg ER - TY - JOUR A1 - Omranian, Sara A1 - Nikoloski, Zoran T1 - CUBCO+: prediction of protein complexes based on min-cut network partitioning into biclique spanned subgraphs JF - Applied Network Science N2 - High-throughput proteomics approaches have resulted in large-scale protein–protein interaction (PPI) networks that have been employed for the prediction of protein complexes. However, PPI networks contain false-positive as well as false-negative PPIs that affect the protein complex prediction algorithms. To address this issue, here we propose an algorithm called CUBCO+ that: (1) employs GO semantic similarity to retain only biologically relevant interactions with a high similarity score, (2) based on link prediction approaches, scores the false-negative edges, and (3) incorporates the resulting scores to predict protein complexes. Through comprehensive analyses with PPIs from Escherichia coli, Saccharomyces cerevisiae, and Homo sapiens, we show that CUBCO+ performs as well as the approaches that predict protein complexes based on recently introduced graph partitions into biclique spanned subgraphs and outperforms the other state-of-the-art approaches. Moreover, we illustrate that in combination with GO semantic similarity, CUBCO+ enables us to predict more accurate protein complexes in 36% of the cases in comparison to CUBCO as its predecessor. KW - Protein complexes KW - Protein–protein interaction KW - Network clustering KW - Species comparison Y1 - 2022 U6 - https://doi.org/10.1007/s41109-022-00508-5 SN - 2364-8228 VL - 7 PB - Springer International Publishing CY - Cham 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 -