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 - 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 - THES A1 - Omranian, Nooshin T1 - Inferring gene regulatory networks and cellular phases from time-resolved transcriptomics data Y1 - 2014 ER - TY - JOUR A1 - Omranian, Nooshin A1 - Kleessen, Sabrina A1 - Tohge, Takayuki A1 - Klie, Sebastian A1 - Basler, Georg A1 - Müller-Röber, Bernd A1 - Fernie, Alisdair R. A1 - Nikoloski, Zoran T1 - Differential metabolic and coexpression networks of plant metabolism JF - Trends in plant science N2 - Recent analyses have demonstrated that plant metabolic networks do not differ in their structural properties and that genes involved in basic metabolic processes show smaller coexpression than genes involved in specialized metabolism. By contrast, our analysis reveals differences in the structure of plant metabolic networks and patterns of coexpression for genes in (non)specialized metabolism. Here we caution that conclusions concerning the organization of plant metabolism based on network-driven analyses strongly depend on the computational approaches used. KW - plant specialized metabolism KW - metabolic networks KW - gene coexpression KW - differential network analysis Y1 - 2015 U6 - https://doi.org/10.1016/j.tplants.2015.02.002 SN - 1360-1385 VL - 20 IS - 5 SP - 266 EP - 268 PB - Elsevier CY - London 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 - Müller-Röber, Bernd A1 - Nikoloski, Zoran T1 - Segmentation of biological multivariate time-series data JF - Scientific reports N2 - Time-series data from multicomponent systems capture the dynamics of the ongoing processes and reflect the interactions between the components. The progression of processes in such systems usually involves check-points and events at which the relationships between the components are altered in response to stimuli. Detecting these events together with the implicated components can help understand the temporal aspects of complex biological systems. Here we propose a regularized regression-based approach for identifying breakpoints and corresponding segments from multivariate time-series data. In combination with techniques from clustering, the approach also allows estimating the significance of the determined breakpoints as well as the key components implicated in the emergence of the breakpoints. Comparative analysis with the existing alternatives demonstrates the power of the approach to identify biologically meaningful breakpoints in diverse time-resolved transcriptomics data sets from the yeast Saccharomyces cerevisiae and the diatom Thalassiosira pseudonana. Y1 - 2015 U6 - https://doi.org/10.1038/srep08937 SN - 2045-2322 VL - 5 PB - Nature Publ. Group CY - London 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 - 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 - TY - JOUR A1 - Janowski, Marcin Andrzej A1 - Zoschke, Reimo A1 - Scharff, Lars B. A1 - Jaime, Silvia Martinez A1 - Ferrari, Camilla A1 - Proost, Sebastian A1 - Xiong, Jonathan Ng Wei A1 - Omranian, Nooshin A1 - Musialak-Lange, Magdalena A1 - Nikoloski, Zoran A1 - Graf, Alexander A1 - Schoettler, Mark Aurel A1 - Sampathkumar, Arun A1 - Vaid, Neha A1 - Mutwil, Marek T1 - AtRsgA from Arabidopsis thaliana is important for maturation of the small subunit of the chloroplast ribosome JF - The plant journal N2 - Plastid ribosomes are very similar in structure and function to the ribosomes of their bacterial ancestors. Since ribosome biogenesis is not thermodynamically favorable under biological conditions it requires the activity of many assembly factors. Here we have characterized a homolog of bacterial RsgA in Arabidopsis thaliana and show that it can complement the bacterial homolog. Functional characterization of a strong mutant in Arabidopsis revealed that the protein is essential for plant viability, while a weak mutant produced dwarf, chlorotic plants that incorporated immature pre-16S ribosomal RNA into translating ribosomes. Physiological analysis of the mutant plants revealed smaller, but more numerous, chloroplasts in the mesophyll cells, reduction of chlorophyll a and b, depletion of proplastids from the rib meristem and decreased photosynthetic electron transport rate and efficiency. Comparative RNA sequencing and proteomic analysis of the weak mutant and wild-type plants revealed that various biotic stress-related, transcriptional regulation and post-transcriptional modification pathways were repressed in the mutant. Intriguingly, while nuclear- and chloroplast-encoded photosynthesis-related proteins were less abundant in the mutant, the corresponding transcripts were increased, suggesting an elaborate compensatory mechanism, potentially via differentially active retrograde signaling pathways. To conclude, this study reveals a chloroplast ribosome assembly factor and outlines the transcriptomic and proteomic responses of the compensatory mechanism activated during decreased chloroplast function. Significance Statement AtRsgA is an assembly factor necessary for maturation of the small subunit of the chloroplast ribosome. Depletion of AtRsgA leads to dwarfed, chlorotic plants, a decrease of mature 16S rRNA and smaller, but more numerous, chloroplasts. Large-scale transcriptomic and proteomic analysis revealed that chloroplast-encoded and -targeted proteins were less abundant, while the corresponding transcripts were increased in the mutant. We analyze the transcriptional responses of several retrograde signaling pathways to suggest the mechanism underlying this compensatory response. KW - ribosome assembly KW - chloroplast ribosome KW - assembly factor KW - 30S subunit KW - RsgA KW - Arabidopsis thaliana Y1 - 2018 U6 - https://doi.org/10.1111/tpj.14040 SN - 0960-7412 SN - 1365-313X VL - 96 IS - 2 SP - 404 EP - 420 PB - Wiley CY - Hoboken 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 -