TY  - JOUR
A1  - Jose Clemente-Moreno, Maria
A1  - Omranian, Nooshin
A1  - Saez, Patricia
A1  - Maria Figueroa, Carlos
A1  - Del-Saz, Nestor
A1  - Elso, Mhartyn
A1  - Poblete, Leticia
A1  - Orf, Isabel
A1  - Cuadros-Inostroza, Alvaro
A1  - Cavieres, Lohengrin
A1  - Bravo, Leon
A1  - Fernie, Alisdair R.
A1  - Ribas-Carbo, Miquel
A1  - Flexas, Jaume
A1  - Nikoloski, Zoran
A1  - Brotman, Yariv
A1  - Gago, Jorge
T1  - Cytochrome respiration pathway and sulphur metabolism sustain stress tolerance to low temperature in the Antarctic species Colobanthus quitensis
JF  - New phytologist : international journal of plant science
N2  - 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.
KW  - Antarctica
KW  - antioxidant capacity
KW  - low temperature
KW  - photosynthesis
KW  - respiration
KW  - stress tolerance
KW  - sulphur metabolism
Y1  - 2019
U6  - https://doi.org/10.1111/nph.16167
SN  - 0028-646X
SN  - 1469-8137
VL  - 225
IS  - 2
SP  - 754
EP  - 768
PB  - Wiley
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - Omranian, Nooshin
A1  - Eloundou-Mbebi, Jeanne Marie Onana
A1  - Müller-Röber, Bernd
A1  - Nikoloski, Zoran
T1  - Gene regulatory network inference using fused LASSO on multiple data sets
JF  - Scientific reports
N2  - 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.
Y1  - 2016
U6  - https://doi.org/10.1038/srep20533
SN  - 2045-2322
VL  - 6
PB  - Nature Publ. Group
CY  - London
ER  - 
TY  - JOUR
A1  - Nunes-Nesi, Adriano
A1  - Alseekh, Saleh
A1  - de Oliveira Silva, Franklin Magnum
A1  - Omranian, Nooshin
A1  - Lichtenstein, Gabriel
A1  - Mirnezhad, Mohammad
A1  - Romero Gonzalez, Roman R.
A1  - Sabio y Garcia, Julia
A1  - Conte, Mariana
A1  - Leiss, Kirsten A.
A1  - Klinkhamer, Peter Gerardus Leonardus
A1  - Nikoloski, Zoran
A1  - Carrari, Fernando
A1  - Fernie, Alisdair R.
T1  - Identification and characterization of metabolite quantitative trait loci in tomato leaves and comparison with those reported for fruits and seeds
JF  - Metabolomics
N2  - 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.
KW  - Metabolite QTL
KW  - Tomato
KW  - Leaf metabolism
KW  - Metabolite network
Y1  - 2019
U6  - https://doi.org/10.1007/s11306-019-1503-8
SN  - 1573-3882
SN  - 1573-3890
VL  - 15
IS  - 46
PB  - Springer
CY  - New York
ER  - 
TY  - JOUR
A1  - Alseekh, Saleh
A1  - Tohge, Takayuki
A1  - Wendenberg, Regina
A1  - Scossa, Federico
A1  - Omranian, Nooshin
A1  - Li, Jie
A1  - Kleessen, Sabrina
A1  - Giavalisco, Patrick
A1  - Pleban, Tzili
A1  - Müller-Röber, Bernd
A1  - Zamir, Dani
A1  - Nikoloski, Zoran
A1  - Fernie, Alisdair R.
T1  - Identification and Mode of Inheritance of Quantitative Trait Loci for Secondary Metabolite Abundance in Tomato
JF  - The plant cell
N2  - 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.
Y1  - 2015
U6  - https://doi.org/10.1105/tpc.114.132266
SN  - 1040-4651
SN  - 1532-298X
VL  - 27
IS  - 3
SP  - 485
EP  - 512
PB  - American Society of Plant Physiologists
CY  - Rockville
ER  - 
TY  - JOUR
A1  - Omranian, Nooshin
A1  - Klie, Sebastian
A1  - Müller-Röber, Bernd
A1  - Nikoloski, Zoran
T1  - Network-based segmentation of biological multivariate time series
JF  - PLoS one
N2  - 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.
Y1  - 2013
U6  - https://doi.org/10.1371/journal.pone.0062974
SN  - 1932-6203
VL  - 8
IS  - 5
PB  - PLoS
CY  - San Fransisco
ER  - 
TY  - JOUR
A1  - Omranian, Nooshin
A1  - Müller-Röber, Bernd
A1  - Nikoloski, Zoran
T1  - PageRank-based identification of signaling crosstalk from transcriptomics data the case of Arabidopsis thaliana
JF  - Molecular BioSystems
N2  - 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.
Y1  - 2012
U6  - https://doi.org/10.1039/c2mb05365a
SN  - 1742-206X
VL  - 8
IS  - 4
SP  - 1121
EP  - 1127
PB  - Royal Society of Chemistry
CY  - Cambridge
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  - Schwahn, Kevin
A1  - Beleggia, Romina
A1  - Omranian, Nooshin
A1  - Nikoloski, Zoran
T1  - Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data
JF  - Frontiers in plant science
N2  - 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.
KW  - metabolism
KW  - systems biology
KW  - maximal correlation
KW  - correlation analysis
KW  - domestication
Y1  - 2017
U6  - https://doi.org/10.3389/fpls.2017.02152
SN  - 1664-462X
VL  - 8
PB  - Frontiers Research Foundation
CY  - Lausanne
ER  -