Refine
Has Fulltext
- no (88) (remove)
Year of publication
Language
- English (88)
Is part of the Bibliography
- yes (88)
Keywords
- Arabidopsis thaliana (4)
- Network clustering (4)
- respiration (3)
- Coherent partition (2)
- Graph partitions (2)
- GxE interaction (2)
- Metabolic networks (2)
- Protein complexes (2)
- Species comparison (2)
- Zea mays (2)
Institute
- Institut für Biochemie und Biologie (88) (remove)
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.
Coherent network partitions
(2019)
Graph clustering is widely applied in the analysis of cellular networks reconstructed from large-scale data or obtained from experimental evidence. Here we introduce a new type of graph clustering based on the concept of coherent partition. A coherent partition of a graph G is a partition of the vertices of G that yields only disconnected subgraphs in the complement of G. The coherence number of G is then the size of the smallest edge cut inducing a coherent partition. A coherent partition of G is optimal if the size of the inducing edge cut is the coherence number of G. Given a graph G, we study coherent partitions and the coherence number in connection to (bi)clique partitions and the (bi)clique cover number. We show that the problem of finding the coherence number is NP-hard, but is of polynomial time complexity for trees. We also discuss the relation between coherent partitions and prominent graph clustering quality measures.
Coherent network partitions
(2021)
We continue to study coherent partitions of graphs whereby the vertex set is partitioned into subsets that induce biclique spanned subgraphs. The problem of identifying the minimum number of edges to obtain biclique spanned connected components (CNP), called the coherence number, is NP-hard even on bipartite graphs. Here, we propose a graph transformation geared towards obtaining an O (log n)-approximation algorithm for the CNP on a bipartite graph with n vertices. The transformation is inspired by a new characterization of biclique spanned subgraphs. In addition, we study coherent partitions on prime graphs, and show that finding coherent partitions reduces to the problem of finding coherent partitions in a prime graph. Therefore, these results provide future directions for approximation algorithms for the coherence number of a given graph.
Integrative studies of plant growth require spatially and temporally resolved information from high-throughput imaging systems. However, analysis and interpretation of conventional two-dimensional images is complicated by the three-dimensional nature of shoot architecture and by changes in leaf position over time, termed hyponasty. To solve this problem, Phytotyping(4D) uses a light-field camera that simultaneously provides a focus image and a depth image, which contains distance information about the object surface. Our automated pipeline segments the focus images, integrates depth information to reconstruct the three-dimensional architecture, and analyses time series to provide information about the relative expansion rate, the timing of leaf appearance, hyponastic movement, and shape for individual leaves and the whole rosette. Phytotyping(4D) was calibrated and validated using discs of known sizes, and plants tilted at various orientations. Information from this analysis was integrated into the pipeline to allow error assessment during routine operation. To illustrate the utility of Phytotyping(4D), we compare diurnal changes in Arabidopsis thaliana wild-type Col-0 and the starchless pgm mutant. Compared to Col-0, pgm showed very low relative expansion rate in the second half of the night, a transiently increased relative expansion rate at the onset of light period, and smaller hyponastic movement including delayed movement after dusk, both at the level of the rosette and individual leaves. Our study introduces light-field camera systems as a tool to accurately measure morphological and growth-related features in plants.
Significance Statement Phytotyping(4D) is a non-invasive and accurate imaging system that combines a 3D light-field camera with an automated pipeline, which provides validated measurements of growth, movement, and other morphological features at the rosette and single-leaf level. In a case study in which we investigated the link between starch and growth, we demonstrated that Phytotyping(4D) is a key step towards bridging the gap between phenotypic observations and the rich genetic and metabolic knowledge.
The tricarboxylic acid (TCA) cycle is a crucial component of respiratory metabolism in both photosynthetic and heterotrophic plant organs. All of the major genes of the tomato TCA cycle have been cloned recently, allowing the generation of a suite of transgenic plants in which the majority of the enzymes in the pathway are progressively decreased. Investigations of these plants have provided an almost complete view of the distribution of control in this important pathway. Our studies suggest that citrate synthase, aconitase, isocitrate dehydrogenase, succinyl CoA ligase, succinate dehydrogenase, fumarase and malate dehydrogenase have control coefficients flux for respiration of -0.4, 0.964, -0.123, 0.0008, 0.289, 0.601 and 1.76, respectively; while 2-oxoglutarate dehydrogenase is estimated to have a control coefficient of 0.786 in potato tubers. These results thus indicate that the control of this pathway is distributed among malate dehydrogenase, aconitase, fumarase, succinate dehydrogenase and 2-oxoglutarate dehydrogenase. The unusual distribution of control estimated here is consistent with specific non-cyclic flux mode and cytosolic bypasses that operate in illuminated leaves. These observations are discussed in the context of known regulatory properties of the enzymes and some illustrative examples of how the pathway responds to environmental change are given.
Metabolic engineering of microalgae offers a promising solution for sustainable biofuel production, and rational design of engineering strategies can be improved by employing metabolic models that integrate enzyme turnover numbers. However, the coverage of turnover numbers for Chlamydomonas reinhardtii, a model eukaryotic microalga accessible to metabolic engineering, is 17-fold smaller compared to the heterotrophic cell factory Saccharomyces cerevisiae. Here we generate quantitative protein abundance data of Chlamydomonas covering 2337 to 3708 proteins in various growth conditions to estimate in vivo maximum apparent turnover numbers. Using constrained-based modeling we provide proxies for in vivo turnover numbers of 568 reactions, representing a 10-fold increase over the in vitro data for Chlamydomonas. Integration of the in vivo estimates instead of in vitro values in a metabolic model of Chlamydomonas improved the accuracy of enzyme usage predictions. Our results help in extending the knowledge on uncharacterized enzymes and improve biotechnological applications of Chlamydomonas.
The photosynthetic carbon metabolism, including the Calvin-Benson cycle, is the primary pathway in C-3-plants, producing starch and sucrose from CO2. Understanding the interplay between regulation and efficiency of this pathway requires the development of mathematical models which would explain the observed dynamics of metabolic transformations. Here, we address this question by casting the existing models of Calvin-Benson cycle and the end-product processes into an analysis framework which not only facilitates the comparison of the different models, but also allows for their ranking with respect to chosen criteria, including stability, sensitivity, robustness and/or compliance with experimental data. The importance of the photosynthetic carbon metabolism for the increase of plant biomass has resulted in many models with various levels of detail. We provide the largest compendium of 15 existing, well-investigated models together with a comprehensive classification as well as a ranking framework to determine the best-performing models for metabolic engineering and planning of in silica experiments. The classification can be additionally used, based on the model structure, as a tool to identify the models which match best the experimental design. The provided ranking is just one alternative to score models and, by changing the weighting factor, this framework also could be applied for selection of other criteria of interest.
The Calvin-Benson cycle (CBC) provides the precursors for biomass synthesis necessary for plant growth. The dynamic behavior and yield of the CBC depend on the environmental conditions and regulation of the cellular state. Accurate quantitative models hold the promise of identifying the key determinants of the tightly regulated CBC function and their effects on the responses in future climates. We provide an integrative analysis of the largest compendium of existing models for photosynthetic processes. Based on the proposed ranking, our framework facilitates the discovery of best-performing models with regard to metabolomics data and of candidates for metabolic engineering.
Motivation: Network-centered studies in systems biology attempt to integrate the topological properties of biological networks with experimental data in order to make predictions and posit hypotheses. For any topology-based prediction, it is necessary to first assess the significance of the analyzed property in a biologically meaningful context. Therefore, devising network null models, carefully tailored to the topological and biochemical constraints imposed on the network, remains an important computational problem.
Results: We first review the shortcomings of the existing generic sampling scheme-switch randomization-and explain its unsuitability for application to metabolic networks. We then devise a novel polynomial-time algorithm for randomizing metabolic networks under the (bio)chemical constraint of mass balance. The tractability of our method follows from the concept of mass equivalence classes, defined on the representation of compounds in the vector space over chemical elements. We finally demonstrate the uniformity of the proposed method on seven genome-scale metabolic networks, and empirically validate the theoretical findings. The proposed method allows a biologically meaningful estimation of significance for metabolic network properties.
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.
Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein-protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, such as small average path length, large clustering coefficient, heavy-tail degree distribution and hierarchical organization, viewed as requirements for efficient and robust system architectures. However, for biological networks, it is unclear to what extent these properties reflect the evolutionary history of the represented systems. Here, we show that the salient structural properties of six metabolic networks from all kingdoms of life may be inherently related to the evolution and functional organization of metabolism by employing network randomization under mass balance constraints. Contrary to the results from the common Markov-chain switching algorithm, our findings suggest the evolutionary importance of the small-world hypothesis as a fundamental design principle of complex networks. The approach may help us to determine the biologically meaningful properties that result from evolutionary pressure imposed on metabolism, such as the global impact of local reaction knockouts. Moreover, the approach can be applied to test to what extent novel structural properties can be used to draw biologically meaningful hypothesis or predictions from structure alone.
Background: Reconstruction of genome-scale metabolic networks has resulted in models capable of reproducing experimentally observed biomass yield/growth rates and predicting the effect of alterations in metabolism for biotechnological applications. The existing studies rely on modifying the metabolic network of an investigated organism by removing or inserting reactions taken either from evolutionary similar organisms or from databases of biochemical reactions (e.g., KEGG). A potential disadvantage of these knowledge-driven approaches is that the result is biased towards known reactions, as such approaches do not account for the possibility of including novel enzymes, together with the reactions they catalyze.
Results: Here, we explore the alternative of increasing biomass yield in three model organisms, namely Bacillus subtilis, Escherichia coil, and Hordeum vulgare, by applying small, chemically feasible network modifications. We use the predicted and experimentally confirmed growth rates of the wild-type networks as reference values and determine the effect of inserting mass-balanced, thermodynamically feasible reactions on predictions of growth rate by using flux balance analysis.
Conclusions: While many replacements of existing reactions naturally lead to a decrease or complete loss of biomass production ability, in all three investigated organisms we find feasible modifications which facilitate a significant increase in this biological function. We focus on modifications with feasible chemical properties and a significant increase in biomass yield. The results demonstrate that small modifications are sufficient to substantially alter biomass yield in the three organisms. The method can be used to predict the effect of targeted modifications on the yield of any set of metabolites (e.g., ethanol), thus providing a computational framework for synthetic metabolic engineering.
Analysis of biological networks requires assessing the statistical significance of network-based predictions by using a realistic null model. However, the existing network null model, switch randomization, is unsuitable for metabolic networks, as it does not include physical constraints and generates unrealistic reactions. We present JMassBalance, a tool for mass-balanced randomization and analysis of metabolic networks. The tool allows efficient generation of large sets of randomized networks under the physical constraint of mass balance. In addition, various structural properties of the original and randomized networks can be calculated, facilitating the identification of the salient properties of metabolic networks with a biologically meaningful null model.
The actin cytoskeleton is an essential intracellular filamentous structure that underpins cellular transport and cytoplasmic streaming in plant cells. However, the system-level properties of actin-based cellular trafficking remain tenuous, largely due to the inability to quantify key features of the actin cytoskeleton. Here, we developed an automated image-based, network-driven framework to accurately segment and quantify actin cytoskeletal structures and Golgi transport. We show that the actin cytoskeleton in both growing and elongated hypocotyl cells has structural properties facilitating efficient transport. Our findings suggest that the erratic movement of Golgi is a stable cellular phenomenon that might optimize distribution efficiency of cell material. Moreover, we demonstrate that Golgi transport in hypocotyl cells can be accurately predicted from the actin network topology alone. Thus, our framework provides quantitative evidence for system-wide coordination of cellular transport in plant cells and can be readily applied to investigate cytoskeletal organization and transport in other organisms.
As autotrophic organisms, plants capture light energy to convert carbon dioxide into ATP, nicotinamide adenine dinucleotide phosphate (NADPH), and sugars, which are essential for the biosynthesis of building blocks, storage, and growth. At night, metabolism and growth can be sustained by mobilizing carbon (C) reserves. In response to changing environmental conditions, such as light-dark cycles, the small-molecule regulation of enzymatic activities is critical for reprogramming cellular metabolism. We have recently demonstrated that proteogenic dipeptides, protein degradation products, act as metabolic switches at the interface of proteostasis and central metabolism in both plants and yeast. Dipeptides accumulate in response to the environmental changes and act via direct binding and regulation of critical enzymatic activities, enabling C flux distribution. Here, we provide evidence pointing to the involvement of dipeptides in the metabolic rewiring characteristics for the day-night cycle in plants. Specifically, we measured the abundance of 13 amino acids and 179 dipeptides over short- (SD) and long-day (LD) diel cycles, each with different light intensities. Of the measured dipeptides, 38 and eight were characterized by day-night oscillation in SD and LD, respectively, reaching maximum accumulation at the end of the day and then gradually falling in the night. Not only the number of dipeptides, but also the amplitude of the oscillation was higher in SD compared with LD conditions. Notably, rhythmic dipeptides were enriched in the glucogenic amino acids that can be converted into glucose. Considering the known role of Target of Rapamycin (TOR) signaling in regulating both autophagy and metabolism, we subsequently investigated whether diurnal fluctuations of dipeptides levels are dependent on the TOR Complex (TORC). The Raptor1b mutant (raptor1b), known for the substantial reduction of TOR kinase activity, was characterized by the augmented accumulation of dipeptides, which is especially pronounced under LD conditions. We were particularly intrigued by the group of 16 dipeptides, which, based on their oscillation under SD conditions and accumulation in raptor1b, can be associated with limited C availability or photoperiod. By mining existing protein-metabolite interaction data, we delineated putative protein interactors for a representative dipeptide Pro-Gln. The obtained list included enzymes of C and amino acid metabolism, which are also linked to the TORC-mediated metabolic network. Based on the obtained results, we speculate that the diurnal accumulation of dipeptides contributes to its metabolic adaptation in response to changes in C availability. We hypothesize that dipeptides would act as alternative respiratory substrates and by directly modulating the activity of the focal enzymes.
Young Genes out of the Male: An Insight from Evolutionary Age Analysis of the Pollen Transcriptome
(2015)
The birth of new genes in genomes is an important evolutionary event. Several studies reveal that new genes in animals tend to be preferentially expressed in male reproductive tissues such as testis (Betran et al., 2002; Begun et al., 2007; Dubruille et al., 2012), and thus an "out of testis' hypothesis for the emergence of new genes has been proposed (Vinckenbosch et al., 2006; Kaessmann, 2010). However, such phenomena have not been examined in plant species. Here, by employing a phylostratigraphic method, we dated the origin of protein-coding genes in rice and Arabidopsis thaliana and observed a number of young genes in both species. These young genes tend to encode short extracellular proteins, which may be involved in rapid evolving processes, such as reproductive barriers, species specification, and antimicrobial processes. Further analysis of transcriptome age indexes across different tissues revealed that male reproductive cells express a phylogenetically younger transcriptome than other plant tissues. Compared with sporophytic tissues, the young transcriptomes of the male gametophyte displayed greater complexity and diversity, which included a higher ratio of anti-sense and inter-genic transcripts, reflecting a pervasive transcription state that facilitated the emergence of new genes. Here, we propose that pollen may act as an "innovation incubator' for the birth of de novo genes. With cases of male-biased expression of young genes reported in animals, the "new genes out of the male' model revealed a common evolutionary force that drives reproductive barriers, species specification, and the upgrading of defensive mechanisms against pathogens.
The use of automated tools to reconstruct lipid metabolic pathways is not warranted in plants. Here, the authors construct Plant Lipid Module for Arabidopsis rosette using constraint-based modeling, demonstrate its integration in other plant metabolic models, and use it to dissect the genetic architecture of lipid metabolism.
Lipids play fundamental roles in regulating agronomically important traits. Advances in plant lipid metabolism have until recently largely been based on reductionist approaches, although modulation of its components can have system-wide effects. However, existing models of plant lipid metabolism provide lumped representations, hindering detailed study of component modulation. Here, we present the Plant Lipid Module (PLM) which provides a mechanistic description of lipid metabolism in the Arabidopsis thaliana rosette. We demonstrate that the PLM can be readily integrated in models of A. thaliana Col-0 metabolism, yielding accurate predictions (83%) of single lethal knock-outs and 75% concordance between measured transcript and predicted flux changes under extended darkness. Genome-wide associations with fluxes obtained by integrating the PLM in diel condition- and accession-specific models identify up to 65 candidate genes modulating A. thaliana lipid metabolism. Using mutant lines, we validate up to 40% of the candidates, paving the way for identification of metabolic gene function based on models capturing natural variability in metabolism.
Bridging metabolomics with plant phenotypic responses is challenging. Multivariate analyses account for the existing dependencies among metabolites, and regression models in particular capture such dependencies in search for association with a given trait. However, special care should be undertaken with metabolomics data. Here we propose a modeling workflow that considers all caveats imposed by such large data sets.