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 - Winck, Flavia Vischi A1 - Arvidsson, Samuel Janne A1 - Mauricio Riano-Pachon, Diego A1 - Hempel, Sabrina A1 - Koseska, Aneta A1 - Nikoloski, Zoran A1 - Urbina Gomez, David Alejandro A1 - Rupprecht, Jens A1 - Müller-Röber, Bernd T1 - Genome-wide identification of regulatory elements and reconstruction of gene regulatory networks of the green alga chlamydomonas reinhardtii under carbon deprivation JF - PLoS one N2 - The unicellular green alga Chlamydomonas reinhardtii is a long-established model organism for studies on photosynthesis and carbon metabolism-related physiology. Under conditions of air-level carbon dioxide concentration [CO2], a carbon concentrating mechanism (CCM) is induced to facilitate cellular carbon uptake. CCM increases the availability of carbon dioxide at the site of cellular carbon fixation. To improve our understanding of the transcriptional control of the CCM, we employed FAIRE-seq (formaldehyde-assisted Isolation of Regulatory Elements, followed by deep sequencing) to determine nucleosome-depleted chromatin regions of algal cells subjected to carbon deprivation. Our FAIRE data recapitulated the positions of known regulatory elements in the promoter of the periplasmic carbonic anhydrase (Cah1) gene, which is upregulated during CCM induction, and revealed new candidate regulatory elements at a genome-wide scale. In addition, time series expression patterns of 130 transcription factor (TF) and transcription regulator (TR) genes were obtained for cells cultured under photoautotrophic condition and subjected to a shift from high to low [CO2]. Groups of co-expressed genes were identified and a putative directed gene-regulatory network underlying the CCM was reconstructed from the gene expression data using the recently developed IOTA (inner composition alignment) method. Among the candidate regulatory genes, two members of the MYB-related TF family, Lcr1 (Low-CO2 response regulator 1) and Lcr2 (Low-CO2 response regulator 2), may play an important role in down-regulating the expression of a particular set of TF and TR genes in response to low [CO2]. The results obtained provide new insights into the transcriptional control of the CCM and revealed more than 60 new candidate regulatory genes. Deep sequencing of nucleosome-depleted genomic regions indicated the presence of new, previously unknown regulatory elements in the C. reinhardtii genome. Our work can serve as a basis for future functional studies of transcriptional regulator genes and genomic regulatory elements in Chlamydomonas. Y1 - 2013 U6 - https://doi.org/10.1371/journal.pone.0079909 SN - 1932-6203 VL - 8 IS - 11 PB - PLoS CY - San Fransisco 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 - Mettler, Tabea A1 - Mühlhaus, Timo A1 - Hemme, Dorothea A1 - Schöttler, Mark Aurel A1 - Rupprecht, Jens A1 - Idoine, Adam A1 - Veyel, Daniel A1 - Pal, Sunil Kumar A1 - Yaneva-Roder, Liliya A1 - Winck, Flavia Vischi A1 - Sommer, Frederik A1 - Vosloh, Daniel A1 - Seiwert, Bettina A1 - Erban, Alexander A1 - Burgos, Asdrubal A1 - Arvidsson, Samuel Janne A1 - Schoenfelder, Stephanie A1 - Arnold, Anne A1 - Guenther, Manuela A1 - Krause, Ursula A1 - Lohse, Marc A1 - Kopka, Joachim A1 - Nikoloski, Zoran A1 - Müller-Röber, Bernd A1 - Willmitzer, Lothar A1 - Bock, Ralph A1 - Schroda, Michael A1 - Stitt, Mark T1 - Systems analysis of the response of photosynthesis, metabolism, and growth to an increase in irradiance in the photosynthetic model organism chlamydomonas reinhardtii JF - The plant cell N2 - We investigated the systems response of metabolism and growth after an increase in irradiance in the nonsaturating range in the algal model Chlamydomonas reinhardtii. In a three-step process, photosynthesis and the levels of metabolites increased immediately, growth increased after 10 to 15 min, and transcript and protein abundance responded by 40 and 120 to 240 min, respectively. In the first phase, starch and metabolites provided a transient buffer for carbon until growth increased. This uncouples photosynthesis from growth in a fluctuating light environment. In the first and second phases, rising metabolite levels and increased polysome loading drove an increase in fluxes. Most Calvin-Benson cycle (CBC) enzymes were substrate-limited in vivo, and strikingly, many were present at higher concentrations than their substrates, explaining how rising metabolite levels stimulate CBC flux. Rubisco, fructose-1,6-biosphosphatase, and seduheptulose-1,7-bisphosphatase were close to substrate saturation in vivo, and flux was increased by posttranslational activation. In the third phase, changes in abundance of particular proteins, including increases in plastidial ATP synthase and some CBC enzymes, relieved potential bottlenecks and readjusted protein allocation between different processes. Despite reasonable overall agreement between changes in transcript and protein abundance (R-2 = 0.24), many proteins, including those in photosynthesis, changed independently of transcript abundance. Y1 - 2014 U6 - https://doi.org/10.1105/tpc.114.124537 SN - 1040-4651 SN - 1532-298X VL - 26 IS - 6 SP - 2310 EP - 2350 PB - American Society of Plant Physiologists CY - Rockville 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 - 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 - 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 -