@article{JoseClementeMorenoOmranianSaezetal.2019, author = {Jose Clemente-Moreno, Maria and Omranian, Nooshin and Saez, Patricia and Maria Figueroa, Carlos and Del-Saz, Nestor and Elso, Mhartyn and Poblete, Leticia and Orf, Isabel and Cuadros-Inostroza, Alvaro and Cavieres, Lohengrin and Bravo, Leon and Fernie, Alisdair R. and Ribas-Carbo, Miquel and Flexas, Jaume and Nikoloski, Zoran and Brotman, Yariv and Gago, Jorge}, title = {Cytochrome respiration pathway and sulphur metabolism sustain stress tolerance to low temperature in the Antarctic species Colobanthus quitensis}, series = {New phytologist : international journal of plant science}, volume = {225}, journal = {New phytologist : international journal of plant science}, number = {2}, publisher = {Wiley}, address = {Hoboken}, issn = {0028-646X}, doi = {10.1111/nph.16167}, pages = {754 -- 768}, year = {2019}, abstract = {Understanding the strategies employed by plant species that live in extreme environments offers the possibility to discover stress tolerance mechanisms. We studied the physiological, antioxidant and metabolic responses to three temperature conditions (4, 15, and 23 degrees C) of Colobanthus quitensis (CQ), one of the only two native vascular species in Antarctica. We also employed Dianthus chinensis (DC), to assess the effects of the treatments in a non-Antarctic species from the same family. Using fused LASSO modelling, we associated physiological and biochemical antioxidant responses with primary metabolism. This approach allowed us to highlight the metabolic pathways driving the response specific to CQ. Low temperature imposed dramatic reductions in photosynthesis (up to 88\%) but not in respiration (sustaining rates of 3.0-4.2 mu mol CO2 m(-2) s(-1)) in CQ, and no change in the physiological stress parameters was found. Its notable antioxidant capacity and mitochondrial cytochrome respiratory activity (20 and two times higher than DC, respectively), which ensure ATP production even at low temperature, was significantly associated with sulphur-containing metabolites and polyamines. Our findings potentially open new biotechnological opportunities regarding the role of antioxidant compounds and respiratory mechanisms associated with sulphur metabolism in stress tolerance strategies to low temperature.}, language = {en} } @article{OmranianEloundouMbebiMuellerRoeberetal.2016, author = {Omranian, Nooshin and Eloundou-Mbebi, Jeanne Marie Onana and M{\"u}ller-R{\"o}ber, Bernd and Nikoloski, Zoran}, title = {Gene regulatory network inference using fused LASSO on multiple data sets}, series = {Scientific reports}, volume = {6}, journal = {Scientific reports}, publisher = {Nature Publ. Group}, address = {London}, issn = {2045-2322}, doi = {10.1038/srep20533}, pages = {14}, year = {2016}, abstract = {Devising computational methods to accurately reconstruct gene regulatory networks given gene expression data is key to systems biology applications. Here we propose a method for reconstructing gene regulatory networks by simultaneous consideration of data sets from different perturbation experiments and corresponding controls. The method imposes three biologically meaningful constraints: (1) expression levels of each gene should be explained by the expression levels of a small number of transcription factor coding genes, (2) networks inferred from different data sets should be similar with respect to the type and number of regulatory interactions, and (3) relationships between genes which exhibit similar differential behavior over the considered perturbations should be favored. We demonstrate that these constraints can be transformed in a fused LASSO formulation for the proposed method. The comparative analysis on transcriptomics time-series data from prokaryotic species, Escherichia coli and Mycobacterium tuberculosis, as well as a eukaryotic species, mouse, demonstrated that the proposed method has the advantages of the most recent approaches for regulatory network inference, while obtaining better performance and assigning higher scores to the true regulatory links. The study indicates that the combination of sparse regression techniques with other biologically meaningful constraints is a promising framework for gene regulatory network reconstructions.}, language = {en} } @article{NunesNesiAlseekhdeOliveiraSilvaetal.2019, author = {Nunes-Nesi, Adriano and Alseekh, Saleh and de Oliveira Silva, Franklin Magnum and Omranian, Nooshin and Lichtenstein, Gabriel and Mirnezhad, Mohammad and Romero Gonzalez, Roman R. and Sabio y Garcia, Julia and Conte, Mariana and Leiss, Kirsten A. and Klinkhamer, Peter Gerardus Leonardus and Nikoloski, Zoran and Carrari, Fernando and Fernie, Alisdair R.}, title = {Identification and characterization of metabolite quantitative trait loci in tomato leaves and comparison with those reported for fruits and seeds}, series = {Metabolomics}, volume = {15}, journal = {Metabolomics}, number = {46}, publisher = {Springer}, address = {New York}, issn = {1573-3882}, doi = {10.1007/s11306-019-1503-8}, pages = {13}, year = {2019}, abstract = {IntroductionTo date, most studies of natural variation and metabolite quantitative trait loci (mQTL) in tomato have focused on fruit metabolism, leaving aside the identification of genomic regions involved in the regulation of leaf metabolism.ObjectiveThis study was conducted to identify leaf mQTL in tomato and to assess the association of leaf metabolites and physiological traits with the metabolite levels from other tissues.MethodsThe analysis of components of leaf metabolism was performed by phenotypying 76 tomato ILs with chromosome segments of the wild species Solanum pennellii in the genetic background of a cultivated tomato (S. lycopersicum) variety M82. The plants were cultivated in two different environments in independent years and samples were harvested from mature leaves of non-flowering plants at the middle of the light period. The non-targeted metabolite profiling was obtained by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). With the data set obtained in this study and already published metabolomics data from seed and fruit, we performed QTL mapping, heritability and correlation analyses.ResultsChanges in metabolite contents were evident in the ILs that are potentially important with respect to stress responses and plant physiology. By analyzing the obtained data, we identified 42 positive and 76 negative mQTL involved in carbon and nitrogen metabolism.ConclusionsOverall, these findings allowed the identification of S. lycopersicum genome regions involved in the regulation of leaf primary carbon and nitrogen metabolism, as well as the association of leaf metabolites with metabolites from seeds and fruits.}, language = {en} } @article{AlseekhTohgeWendenbergetal.2015, author = {Alseekh, Saleh and Tohge, Takayuki and Wendenberg, Regina and Scossa, Federico and Omranian, Nooshin and Li, Jie and Kleessen, Sabrina and Giavalisco, Patrick and Pleban, Tzili and M{\"u}ller-R{\"o}ber, Bernd and Zamir, Dani and Nikoloski, Zoran and Fernie, Alisdair R.}, title = {Identification and Mode of Inheritance of Quantitative Trait Loci for Secondary Metabolite Abundance in Tomato}, series = {The plant cell}, volume = {27}, journal = {The plant cell}, number = {3}, publisher = {American Society of Plant Physiologists}, address = {Rockville}, issn = {1040-4651}, doi = {10.1105/tpc.114.132266}, pages = {485 -- 512}, year = {2015}, abstract = {A large-scale metabolic quantitative trait loci (mQTL) analysis was performed on the well-characterized Solanum pennellii introgression lines to investigate the genomic regions associated with secondary metabolism in tomato fruit pericarp. In total, 679 mQTLs were detected across the 76 introgression lines. Heritability analyses revealed that mQTLs of secondary metabolism were less affected by environment than mQTLs of primary metabolism. Network analysis allowed us to assess the interconnectivity of primary and secondary metabolism as well as to compare and contrast their respective associations with morphological traits. Additionally, we applied a recently established real-time quantitative PCR platform to gain insight into transcriptional control mechanisms of a subset of the mQTLs, including those for hydroxycinnamates, acyl-sugar, naringenin chalcone, and a range of glycoalkaloids. Intriguingly, many of these compounds displayed a dominant-negative mode of inheritance, which is contrary to the conventional wisdom that secondary metabolite contents decreased on domestication. We additionally performed an exemplary evaluation of two candidate genes for glycolalkaloid mQTLs via the use of virus-induced gene silencing. The combined data of this study were compared with previous results on primary metabolism obtained from the same material and to other studies of natural variance of secondary metabolism.}, language = {en} } @article{OmranianKlieMuellerRoeberetal.2013, author = {Omranian, Nooshin and Klie, Sebastian and M{\"u}ller-R{\"o}ber, Bernd and Nikoloski, Zoran}, title = {Network-based segmentation of biological multivariate time series}, series = {PLoS one}, volume = {8}, journal = {PLoS one}, number = {5}, publisher = {PLoS}, address = {San Fransisco}, issn = {1932-6203}, doi = {10.1371/journal.pone.0062974}, pages = {10}, year = {2013}, abstract = {Molecular phenotyping technologies (e.g., transcriptomics, proteomics, and metabolomics) offer the possibility to simultaneously obtain multivariate time series (MTS) data from different levels of information processing and metabolic conversions in biological systems. As a result, MTS data capture the dynamics of biochemical processes and components whose couplings may involve different scales and exhibit temporal changes. Therefore, it is important to develop methods for determining the time segments in MTS data, which may correspond to critical biochemical events reflected in the coupling of the system's components. Here we provide a novel network-based formalization of the MTS segmentation problem based on temporal dependencies and the covariance structure of the data. We demonstrate that the problem of partitioning MTS data into k segments to maximize a distance function, operating on polynomially computable network properties, often used in analysis of biological network, can be efficiently solved. To enable biological interpretation, we also propose a breakpoint-penalty (BP-penalty) formulation for determining MTS segmentation which combines a distance function with the number/length of segments. Our empirical analyses of synthetic benchmark data as well as time-resolved transcriptomics data from the metabolic and cell cycles of Saccharomyces cerevisiae demonstrate that the proposed method accurately infers the phases in the temporal compartmentalization of biological processes. In addition, through comparison on the same data sets, we show that the results from the proposed formalization of the MTS segmentation problem match biological knowledge and provide more rigorous statistical support in comparison to the contending state-of-the-art methods.}, language = {en} } @article{OmranianMuellerRoeberNikoloski2012, author = {Omranian, Nooshin and M{\"u}ller-R{\"o}ber, Bernd and Nikoloski, Zoran}, title = {PageRank-based identification of signaling crosstalk from transcriptomics data the case of Arabidopsis thaliana}, series = {Molecular BioSystems}, volume = {8}, journal = {Molecular BioSystems}, number = {4}, publisher = {Royal Society of Chemistry}, address = {Cambridge}, issn = {1742-206X}, doi = {10.1039/c2mb05365a}, pages = {1121 -- 1127}, year = {2012}, abstract = {The levels of cellular organization, from gene transcription to translation to protein-protein interaction and metabolism, operate via tightly regulated mutual interactions, facilitating organismal adaptability and various stress responses. Characterizing the mutual interactions between genes, transcription factors, and proteins involved in signaling, termed crosstalk, is therefore crucial for understanding and controlling cells' functionality. We aim at using high-throughput transcriptomics data to discover previously unknown links between signaling networks. We propose and analyze a novel method for crosstalk identification which relies on transcriptomics data and overcomes the lack of complete information for signaling pathways in Arabidopsis thaliana. Our method first employs a network-based transformation of the results from the statistical analysis of differential gene expression in given groups of experiments under different signal-inducing conditions. The stationary distribution of a random walk (similar to the PageRank algorithm) on the constructed network is then used to determine the putative transcripts interrelating different signaling pathways. With the help of the proposed method, we analyze a transcriptomics data set including experiments from four different stresses/signals: nitrate, sulfur, iron, and hormones. We identified promising gene candidates, downstream of the transcription factors (TFs), associated to signaling crosstalk, which were validated through literature mining. In addition, we conduct a comparative analysis with the only other available method in this field which used a biclustering-based approach. Surprisingly, the biclustering-based approach fails to robustly identify any candidate genes involved in the crosstalk of the analyzed signals. We demonstrate that our proposed method is more robust in identifying gene candidates involved downstream of the signaling crosstalk for species for which large transcriptomics data sets, normalized with the same techniques, are available. Moreover, unlike approaches based on biclustering, our approach does not rely on any hidden parameters.}, language = {en} } @article{PandeyYuOmranianetal.2019, author = {Pandey, Prashant K. and Yu, Jing and Omranian, Nooshin and Alseekh, Saleh and Vaid, Neha and Fernie, Alisdair R. and Nikoloski, Zoran and Laitinen, Roosa A. E.}, title = {Plasticity in metabolism underpins local responses to nitrogen in Arabidopsis thaliana populations}, series = {Plant Direct}, volume = {3}, journal = {Plant Direct}, number = {11}, publisher = {John Wiley \& sonst LTD}, address = {Chichester}, issn = {2475-4455}, doi = {10.1002/pld3.186}, pages = {6}, year = {2019}, abstract = {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.}, language = {en} } @article{SchwahnBeleggiaOmranianetal.2017, author = {Schwahn, Kevin and Beleggia, Romina and Omranian, Nooshin and Nikoloski, Zoran}, title = {Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data}, series = {Frontiers in plant science}, volume = {8}, journal = {Frontiers in plant science}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-462X}, doi = {10.3389/fpls.2017.02152}, pages = {12}, year = {2017}, abstract = {Recent advances in metabolomics technologies have resulted in high-quality (time-resolved) metabolic profiles with an increasing coverage of metabolic pathways. These data profiles represent read-outs from often non-linear dynamics of metabolic networks. Yet, metabolic profiles have largely been explored with regression-based approaches that only capture linear relationships, rendering it difficult to determine the extent to which the data reflect the underlying reaction rates and their couplings. Here we propose an approach termed Stoichiometric Correlation Analysis (SCA) based on correlation between positive linear combinations of log-transformed metabolic profiles. The log-transformation is due to the evidence that metabolic networks can be modeled by mass action law and kinetics derived from it. Unlike the existing approaches which establish a relation between pairs of metabolites, SCA facilitates the discovery of higherorder dependence between more than two metabolites. By using a paradigmatic model of the tricarboxylic acid cycle we show that the higher-order dependence reflects the coupling of concentration of reactant complexes, capturing the subtle difference between the employed enzyme kinetics. Using time-resolved metabolic profiles from Arabidopsis thaliana and Escherichia coli, we show that SCA can be used to quantify the difference in coupling of reactant complexes, and hence, reaction rates, underlying the stringent response in these model organisms. By using SCA with data from natural variation of wild and domesticated wheat and tomato accession, we demonstrate that the domestication is accompanied by loss of such couplings, in these species. Therefore, application of SCA to metabolomics data from natural variation in wild and domesticated populations provides a mechanistic way to understanding domestication and its relation to metabolic networks.}, language = {en} }