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Tre6P synthesis by TPS1 is essential for embryogenesis and postembryonic growth in Arabidopsis, and appropriate Suc signaling by Tre6P is dependent on the noncatalytic domains of TPS1. In Arabidopsis (Arabidopsis thaliana), TREHALOSE-6-PHOSPHATE SYNTHASE1 (TPS1) catalyzes the synthesis of the sucrose-signaling metabolite trehalose 6-phosphate (Tre6P) and is essential for embryogenesis and normal postembryonic growth and development. To understand its molecular functions, we transformed the embryo-lethal tps1-1 null mutant with various forms of TPS1 and with a heterologous TPS (OtsA) from Escherichia coli, under the control of the TPS1 promoter, and tested for complementation. TPS1 protein localized predominantly in the phloem-loading zone and guard cells in leaves, root vasculature, and shoot apical meristem, implicating it in both local and systemic signaling of Suc status. The protein is targeted mainly to the nucleus. Restoring Tre6P synthesis was both necessary and sufficient to rescue the tps1-1 mutant through embryogenesis. However, postembryonic growth and the sucrose-Tre6P relationship were disrupted in some complementation lines. A point mutation (A119W) in the catalytic domain or truncating the C-terminal domain of TPS1 severely compromised growth. Despite having high Tre6P levels, these plants never flowered, possibly because Tre6P signaling was disrupted by two unidentified disaccharide-monophosphates that appeared in these plants. The noncatalytic domains of TPS1 ensure its targeting to the correct subcellular compartment and its catalytic fidelity and are required for appropriate signaling of Suc status by Tre6P.
Tre6P synthesis by TPS1 is essential for embryogenesis and postembryonic growth in Arabidopsis, and appropriate Suc signaling by Tre6P is dependent on the noncatalytic domains of TPS1. In Arabidopsis (Arabidopsis thaliana), TREHALOSE-6-PHOSPHATE SYNTHASE1 (TPS1) catalyzes the synthesis of the sucrose-signaling metabolite trehalose 6-phosphate (Tre6P) and is essential for embryogenesis and normal postembryonic growth and development. To understand its molecular functions, we transformed the embryo-lethal tps1-1 null mutant with various forms of TPS1 and with a heterologous TPS (OtsA) from Escherichia coli, under the control of the TPS1 promoter, and tested for complementation. TPS1 protein localized predominantly in the phloem-loading zone and guard cells in leaves, root vasculature, and shoot apical meristem, implicating it in both local and systemic signaling of Suc status. The protein is targeted mainly to the nucleus. Restoring Tre6P synthesis was both necessary and sufficient to rescue the tps1-1 mutant through embryogenesis. However, postembryonic growth and the sucrose-Tre6P relationship were disrupted in some complementation lines. A point mutation (A119W) in the catalytic domain or truncating the C-terminal domain of TPS1 severely compromised growth. Despite having high Tre6P levels, these plants never flowered, possibly because Tre6P signaling was disrupted by two unidentified disaccharide-monophosphates that appeared in these plants. The noncatalytic domains of TPS1 ensure its targeting to the correct subcellular compartment and its catalytic fidelity and are required for appropriate signaling of Suc status by Tre6P.
Cells are built from a variety of macromolecules and metabolites. Both, the proteome and the metabolome are highly dynamic and responsive to environmental cues and developmental processes. But it is not their bare numbers, but their interactions that enable life. The protein-protein (PPI) and protein-metabolite interactions (PMI) facilitate and regulate all aspects of cell biology, from metabolism to mitosis. Therefore, the study of PPIs and PMIs and their dynamics in a cell-wide context is of great scientific interest. In this dissertation, I aim to chart a map of the dynamic PPIs and PMIs across metabolic and cellular transitions. As a model system, I study the shift from the fermentative to the respiratory growth, known as the diauxic shift, in the budding yeast Saccharomyces cerevisiae. To do so, I am applying a co-fractionation mass spectrometry (CF-MS) based method, dubbed protein metabolite interactions using size separation (PROMIS). PROMIS, as well as comparable methods, will be discussed in detail in chapter 1.
Since PROMIS was developed originally for Arabidopsis thaliana, in chapter 2, I will describe the adaptation of PROMIS to S. cerevisiae. Here, the obtained results demonstrated a wealth of protein-metabolite interactions, and experimentally validated 225 previously predicted PMIs. Applying orthogonal, targeted approaches to validate the interactions of a proteogenic dipeptide, Ser-Leu, five novel protein-interactors were found. One of those proteins, phosphoglycerate kinase, is inhibited by Ser-Leu, placing the dipeptide at the regulation of glycolysis.
In chapter 3, I am presenting PROMISed, a novel web-tool designed for the analysis of PROMIS- and other CF-MS-datasets. Starting with raw fractionation profiles, PROMISed enables data pre-processing, profile deconvolution, scores differences in fractionation profiles between experimental conditions, and ultimately charts interaction networks. PROMISed comes with a user-friendly graphic interface, and thus enables the routine analysis of CF-MS data by non-computational biologists.
Finally, in chapter 4, I applied PROMIS in combination with the isothermal shift assay to the diauxic shift in S. cerevisiae to study changes in the PPI and PMI landscape across this metabolic transition. I found a major rewiring of protein-protein-metabolite complexes, exemplified by the disassembly of the proteasome in the respiratory phase, the loss of interaction of an enzyme involved in amino acid biosynthesis and its cofactor, as well as phase and structure specific interactions between dipeptides and enzymes of central carbon metabolism.
In chapter 5, I am summarizing the presented results, and discuss a strategy to unravel the potential patterns of dipeptide accumulation and binding specificities. Lastly, I recapitulate recently postulated guidelines for CF-MS experiments, and give an outlook of protein interaction studies in the near future.
Plant metabolism is the main process of converting assimilated carbon to different crucial compounds for plant growth and therefore crop yield, which makes it an important research topic. Although major advances in understanding genetic principles contributing to metabolism and yield have been made, little is known about the genetics responsible for trait variation or canalization although the concepts have been known for a long time. In light of a growing global population and progressing climate change, understanding canalization of metabolism and yield seems ever-more important to ensure food security. Our group has recently found canalization metabolite quantitative trait loci (cmQTL) for tomato fruit metabolism, showing that the concept of canalization applies on metabolism. In this work two approaches to investigate plant metabolic canalization and one approach to investigate yield canalization are presented.
In the first project, primary and secondary metabolic data from Arabidopsis thaliana and Phaseolus vulgaris leaf material, obtained from plants grown under different conditions was used to calculate cross-environment coefficient of variations or fold-changes of metabolite levels per genotype and used as input for genome wide association studies. While primary metabolites have lower CV across conditions and show few and mostly weak associations to genomic regions, secondary metabolites have higher CV and show more, strong metabolite to genome associations. As candidate genes, both potential regulatory genes as well as metabolic genes, can be found, albeit most metabolic genes are rarely directly related to the target metabolites, suggesting a role for both potential regulatory mechanisms as well as metabolic network structure for canalization of metabolism.
In the second project, candidate genes of the Solanum lycopersicum cmQTL mapping are selected and CRISPR/Cas9-mediated gene-edited tomato lines are created, to validate the genes role in canalization of metabolism. Obtained mutants appeared to either have strong aberrant developmental phenotypes or appear wild type-like. One phenotypically inconspicuous mutant of a pantothenate kinase, selected as candidate for malic acid canalization shows a significant increase of CV across different watering conditions. Another such mutant of a protein putatively involved in amino acid transport, selected as candidate for phenylalanine canalization shows a similar tendency to increased CV without statistical significance. This potential role of two genes involved in metabolism supports the hypothesis of structural relevance of metabolism for its own stability.
In the third project, a mutant for a putative disulfide isomerase, important for thylakoid biogenesis, is characterized by a multi-omics approach. The mutant was characterized previously in a yield stability screening and showed a variegated leaf phenotype, ranging from green leaves with wild type levels of chlorophyll over differently patterned variegated to completely white leaves almost completely devoid of photosynthetic pigments. White mutant leaves show wild type transcript levels of photosystem assembly factors, with the exception of ELIP and DEG orthologs indicating a stagnation at an etioplast to chloroplast transition state. Green mutant leaves show an upregulation of these assembly factors, possibly acting as overcompensation for partially defective disulfide isomerase, which seems sufficient for proper chloroplast development as confirmed by a wild type-like proteome. Likely as a result of this phenotype, a general stress response, a shift to a sink-like tissue and abnormal thylakoid membranes, strongly alter the metabolic profile of white mutant leaves. As the severity and pattern of variegation varies from plant to plant and may be effected by external factors, the effect on yield instability, may be a cause of a decanalized ability to fully exploit the whole leaf surface area for photosynthetic activity.
Stable isotopes represent a unique approach to provide insights into the ecology of organisms. δ13C and δ15N have specifically been used to obtain information on the trophic ecology and food-web interactions. Trophic discrimination factors (TDF, Δ13C and Δ15N) describe the isotopic fractionation occurring from diet to consumer tissue, and these factors are critical for obtaining precise estimates within any application of δ13C and δ15N values. It is widely acknowledged that metabolism influences TDF, being responsible for different TDF between tissues of variable metabolic activity (e.g., liver vs. muscle tissue) or species body size (small vs. large). However, the connection between the variation of metabolism occurring within a single species during its ontogeny and TDF has rarely been considered. Here, we conducted a 9-month feeding experiment to report Δ13C and Δ15N of muscle and liver tissues for several weight classes of Eurasian perch (Perca fluviatilis), a widespread teleost often studied using stable isotopes, but without established TDF for feeding on a natural diet. In addition, we assessed the relationship between the standard metabolic rate (SMR) and TDF by measuring the oxygen consumption of the individuals. Our results showed a significant negative relationship of SMR with Δ13C, and a significant positive relationship of SMR with Δ15N of muscle tissue, but not with TDF of liver tissue. SMR varies inversely with size, which translated into a significantly different TDF of muscle tissue between size classes. In summary, our results emphasize the role of metabolism in shaping-specific TDF (i.e., Δ13C and Δ15N of muscle tissue) and especially highlight the substantial differences between individuals of different ontogenetic stages within a species. Our findings thus have direct implications for the use of stable isotope data and the applications of stable isotopes in food-web studies.
Stable isotopes represent a unique approach to provide insights into the ecology of organisms. δ13C and δ15N have specifically been used to obtain information on the trophic ecology and food-web interactions. Trophic discrimination factors (TDF, Δ13C and Δ15N) describe the isotopic fractionation occurring from diet to consumer tissue, and these factors are critical for obtaining precise estimates within any application of δ13C and δ15N values. It is widely acknowledged that metabolism influences TDF, being responsible for different TDF between tissues of variable metabolic activity (e.g., liver vs. muscle tissue) or species body size (small vs. large). However, the connection between the variation of metabolism occurring within a single species during its ontogeny and TDF has rarely been considered. Here, we conducted a 9-month feeding experiment to report Δ13C and Δ15N of muscle and liver tissues for several weight classes of Eurasian perch (Perca fluviatilis), a widespread teleost often studied using stable isotopes, but without established TDF for feeding on a natural diet. In addition, we assessed the relationship between the standard metabolic rate (SMR) and TDF by measuring the oxygen consumption of the individuals. Our results showed a significant negative relationship of SMR with Δ13C, and a significant positive relationship of SMR with Δ15N of muscle tissue, but not with TDF of liver tissue. SMR varies inversely with size, which translated into a significantly different TDF of muscle tissue between size classes. In summary, our results emphasize the role of metabolism in shaping-specific TDF (i.e., Δ13C and Δ15N of muscle tissue) and especially highlight the substantial differences between individuals of different ontogenetic stages within a species. Our findings thus have direct implications for the use of stable isotope data and the applications of stable isotopes in food-web studies.
The metabolic state of an organism reflects the entire phenotype that is jointly affected by genetic and environmental changes. Due to the complexity of metabolism, system-level modelling approaches have become indispensable tools to obtain new insights into biological functions. In particular, simulation and analysis of metabolic networks using constraint-based modelling approaches have helped the analysis of metabolic fluxes. However, despite ongoing improvements in prediction of reaction flux through a system, approaches to directly predict metabolite concentrations from large-scale metabolic networks remain elusive. In this thesis, we present a computational approach for inferring concentration ranges from genome-scale metabolic models endowed with mass action kinetics. The findings specify a molecular mechanism underling facile control of concentration ranges for components in large-scale metabolic networks. Most importantly, an extended version of the approach can be used to predict concentration ranges without knowledge of kinetic parameters, provided measurements of concentrations in a reference state. We show that the approach is applicable with large-scale kinetic and stoichiometric metabolic models of organisms from different kingdoms of life. By challenging the predictions of concentration ranges in the genome-scale metabolic network of Escherichia coli with real-world data sets, we further demonstrate the prediction power and limitations of the approach. To predict concentration ranges in other species, e.g. model plant species Arabidopsis thaliana, we would rely on estimates of kinetic parameters (i.e. enzyme catalytic rates) since plant-specific enzyme catalytic rates are poorly documented. Using the constraint-based approach of Davidi et al. for estimation of enzyme catalytic rates, we obtain values for 168 plant enzymes. The approach depends on quantitative proteomics data and flux estimates obtained from constraint-based model of plant leaf metabolism integrating maximal rates of selected enzymes, plant-specific constraints on fluxes through canonical pathways, and growth measurements from Arabidopsis thaliana rosette under ten conditions. We demonstrate a low degree of plant enzyme saturation, supported by the agreement between concentrations of nicotinamide adenine dinucleotide, adenosine triphosphate, and glyceraldehyde 3-phosphate, based on our maximal in vivo catalytic rates, and available quantitative metabolomics data. Hence, our results show genome-wide estimation for plant-specific enzyme catalytic rates is feasible. These can now be readily employed to study resource allocation, to predict enzyme and metabolite concentrations using recent constrained-based modelling approaches. Constraint-based methods do not directly account for kinetic mechanisms and corresponding parameters. Therefore, a number of workflows have already been proposed to approximate reaction kinetics and to parameterize genome-scale kinetic models. We present a systems biology strategy to build a fully parameterized large-scale model of Chlamydomonas reinhardtii accounting for microcompartmentalization in the chloroplast stroma. Eukaryotic algae comprise a microcompartment, the pyrenoid, essential for the carbon concentrating mechanism (CCM) that improves their photosynthetic performance. Since the experimental study of the effects of microcompartmentation on metabolic pathways is challenging, we employ our model to investigate compartmentation of fluxes through the Calvin-Benson cycle between pyrenoid and stroma. Our model predicts that ribulose-1,5-bisphosphate, the substrate of Rubisco, and 3-phosphoglycerate, its product, diffuse in and out of the pyrenoid. We also find that there is no major diffusional barrier to metabolic flux between the pyrenoid and stroma. Therefore, our computational approach represents a stepping stone towards understanding of microcompartmentalized CCM in other organisms. This thesis provides novel strategies to use genome-scale metabolic networks to predict and integrate metabolite concentrations. Therefore, the presented approaches represent an important step in broadening the applicability of large-scale metabolic models to a range of biotechnological and medical applications.
Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data
(2017)
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.
The highly conserved protein complex containing the Target of Rapamycin (TOR) kinase is known to integrate intra- and extra-cellular stimuli controlling nutrient allocation and cellular growth. This thesis describes three studies aimed to understand how TOR signaling pathway influences carbon and nitrogen metabolism in Chlamydomonas reinhardtii. The first study presents a time-resolved analysis of the molecular and physiological features across the diurnal cycle. The inhibition of TOR leads to 50% reduction in growth followed by nonlinear delays in the cell cycle progression. The metabolomics analysis showed that the growth repression is mainly driven by differential carbon partitioning between anabolic and catabolic processes. Furthermore, the high accumulation of nitrogen-containing compounds indicated that TOR kinase controls the carbon to nitrogen balance of the cell, which is responsible for biomass accumulation, growth and cell cycle progression. In the second study the cause of the high accumulation of amino acids is explained. For this purpose, the effect of TOR inhibition on Chlamydomonas was examined under different growth regimes using stable 13C- and 15N-isotope labeling. The data clearly showed that an increased nitrogen uptake is induced within minutes after the inhibition of TOR. Interestingly, this increased N-influx is accompanied by increased activities of nitrogen assimilating enzymes. Accordingly, it was concluded that TOR inhibition induces de-novo amino acid synthesis in Chlamydomonas. The recognition of this novel process opened an array of questions regarding potential links between central metabolism and TOR signaling. Therefore a detailed phosphoproteomics study was conducted to identify the potential substrates of TOR pathway regulating central metabolism. Interestingly, some of the key enzymes involved in carbon metabolism as well as amino acid synthesis exhibited significant changes in the phosphosite intensities immediately after TOR inhibition. Altogether, these studies provide a) detailed insights to metabolic response of Chlamydomonas to TOR inhibition, b) identification of a novel process causing rapid upshifts in amino acid levels upon TOR inhibition and c) finally highlight potential targets of TOR signaling regulating changes in central metabolism. Further biochemical and molecular investigations could confirm these observations and advance the understanding of growth signaling in microalgae.
Plants are unable to move away from unwanted environments and therefore have to locally adapt to changing conditions. Arabidopsis thaliana (Arabidopsis), a model organism in plant biology, has been able to rapidly colonize a wide spectrum of environments with different biotic and abiotic challenges. In recent years, natural variation in Arabidopsis has shown to be an excellent resource to study genes underlying adaptive traits and hybridization’s impact on natural diversity. Studies on Arabidopsis hybrids have provided information on the genetic basis of hybrid incompatibilities and heterosis, as well as inheritance patterns in hybrids. However, previous studies have focused mainly on global accessions and yet much remains to be known about variation happening within a local growth habitat. In my PhD, I investigated the impact of heterozygosity at a local collection site of Arabidopsis and its role in local adaptation. I focused on two different projects, both including hybrids among Arabidopsis individuals collected around Tübingen in Southern Germany. The first project sought to understand the impact of hybridization on metabolism and growth within a local Arabidopsis collection site. For this, the inheritance patterns in primary and secondary metabolism, together with rosette size of full diallel crosses among seven parents originating from Southern Germany were analyzed. In comparison to primary metabolites, compounds from secondary metabolism were more variable and showed pronounced non-additive inheritance patterns. In addition, defense metabolites, mainly glucosinolates, displayed the highest degree of variation from the midparent values and were positively correlated with a proxy for plant size.
In the second project, the role of ACCELERATED CELL DEATH 6 (ACD6) in the defense response pathway of Arabidopsis necrotic hybrids was further characterized. Allelic interactions of ACD6 have been previously linked to hybrid necrosis, both among global and local Arabidopsis accessions. Hence, I characterized the early metabolic and ionic changes induced by ACD6, together with marker gene expression assays of physiological responses linked to its activation. An upregulation of simple sugars and metabolites linked to non-enzymatic antioxidants and the TCA cycle were detected, together with putrescine and acids linked to abiotic stress responses. Senescence was found to be induced earlier in necrotic hybrids and cytoplasmic calcium signaling was unaffected in response to temperature. In parallel, GFP-tagged constructs of ACD6 were developed.
This work therefore gave novel insights on the role of heterozygosity in natural variation and adaptation and expanded our current knowledge on the physiological and molecular responses associated with ACD6 activation.