@phdthesis{Schlossarek2023,
  author    = {Schlossarek, Dennis},
  title     = {Identification of dynamic protein-metabolite complexes in saccharomyces cerevisiae using co-fractionation mass spectrometry},
  doi       = {10.25932/publishup-58282},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-582826},
  school      = {Universit{\"a}t Potsdam},
  pages     = {123},
  year      = {2023},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Bulut2023,
  author    = {Bulut, Mustafa},
  title     = {Assessing the genetic architecture underlying systemic responses to variable environments in crops using multi-omics},
  school      = {Universit{\"a}t Potsdam},
  pages     = {180, IV},
  year      = {2023},
  abstract  = {Plant metabolism serves as the primary mechanism for converting assimilated carbon into essential compounds crucial for plant growth and ultimately, crop yield. This renders it a focal point of research with significant implications. Despite notable strides in comprehending the genetic principles underpinning metabolism and yield, there remains a dearth of knowledge regarding the genetic factors responsible for trait variation under varying environmental conditions. Given the burgeoning global population and the advancing challenges posed by climate change, unraveling the intricacies of metabolic and yield responses to water scarcity became increasingly important in safeguarding food security. Our research group has recently started to work on the genetic resources of legume species. To this end, the study presented here investigates the metabolic diversity across five different legume species at a tissue level, identifying species-specific biosynthesis of alkaloids as well as iso-/flavonoids with diverse functional groups, namely prenylation, phenylacylation as well as methoxylation, to create a resource for follow up studies investigation the metabolic diversity in natural diverse populations of legume species. Following this, the second study investigates the genetic architecture of drought-induced changes in a global common bean population. Here, a plethora of quantitative trait loci (QTL) associated with various traits are identified by performing genome-wide association studies (GWAS), including for lipid signaling. On this site, overexpression of candidates highlighted the induction of several oxylipins reported to be pivotal in coping with harsh environmental conditions such as water scarcity. Diverging from the common bean and GWAS, the following study focuses on identifying drought-related QTL in tomato using a bi-parental breeding population. This descriptive study highlights novel multi-omic QTL, including metabolism, photosynthesis as well as fruit setting, some of which are uniquely assigned under drought. Compared to conventional approaches using the bi-parental IL population, the study presented improves the resolution by assessing further backcrossed ILs, named sub-ILs. In the final study, a photosynthetic gene, namely a PetM subunit of the cytochrome b6f complex encoding gene, involved in electron flow is characterized in an horticultural important crop. While several advances have been made in model organisms, this study highlights the transition of this fundamental knowledge to horticultural important crops, such as tomato, and investigates its function under differing light conditions. Overall, the presented thesis combines different strategies in unveiling the genetic components in multi-omic traits under drought using conventional breeding populations as well as a diverse global population. To this end, it allows a comparison of either approach and highlights their strengths and weaknesses.},
  language  = {en}
}
@phdthesis{LopesFernando2023,
  author    = {Lopes Fernando, Raquel Sofia},
  title     = {The impact of aging on proteolytic systems, transcriptome and metabolome of slow and fast muscle fiber types},
  doi       = {10.25932/publishup-60579},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XI, 125},
  year      = {2023},
  abstract  = {Aging is a complex process characterized by several factors, including loss of genetic and epigenetic information, accumulation of chronic oxidative stress, protein damage and aggregates and it is becoming an emergent drug target. Therefore, it is the utmost importance to study aging and agerelated diseases, to provide treatments to develop a healthy aging process. Skeletal muscle is one of the earliest tissues affected by age-related changes with progressive loss of muscle mass and function from 30 years old, effect known as sarcopenia. Several studies have shown the accumulation of protein aggregates in different animal models, as well as in humans, suggesting impaired proteostasis, a hallmark of aging, especially regarding degradation systems. Thus, different publications have explored the role of the main proteolytic systems in skeletal muscle from rodents and humans, like ubiquitin proteasomal system (UPS) and autophagy lysosomal system (ALS), however with contradictory results. Yet, most of the published studies are performed in muscles that comprise more than one fiber type, that means, muscles composed by slow and fast fibers. These fiber types, exhibit different metabolism and contraction speed; the slow fibers or type I display an oxidative metabolism, while fast fibers function towards a glycolytic metabolism ranging from fast oxidative to fast glycolytic fibers. To this extent, the aim of this thesis sought to understand on how aging impacts both fiber types not only regarding proteostasis but also at a metabolome and transcriptome network levels. Therefore, the first part of this thesis, presents the differences between slow oxidative (from Soleus muscle) and fast glycolytic fibers (Extensor digitorum longus, EDL) in terms of degradation systems and how they cope with oxidative stress during aging, while the second part explores the differences between young and old EDL muscle transcriptome and metabolome, unraveling molecular features. More specifically, the results from the present work show that slow oxidative muscle performs better at maintaining the function of UPS and ALS during aging than EDL muscle, which is clearly affected, accounting for the decline in the catalytic activity rates and accumulation of autophagy-related proteins. Strinkingly, transcriptome and metabolome analyses reveal that fast glycolytic muscle evidences significant downregulation of mitochondrial related processes and damaged mitochondria morphology during aging, despite of having a lower oxidative metabolism compared to oxidative fibers. Moreover, predictive analyses reveal a negative association between aged EDL gene signature and lifespan extending interventions such as caloric restriction (CR). Although, CR intervention does not alter the levels of mitochondrial markers in aged EDL muscle, it can reverse the higher mRNA levels of muscle damage markers. Together, the results from this thesis give new insights about how different metabolic muscle fibers cope with age-related changes and why fast glycolytic fibers are more susceptible to aging than slow oxidative fibers.},
  language  = {en}
}
@phdthesis{Schaarschmidt2021,
  author    = {Schaarschmidt, Stephanie},
  title     = {Evaluation and application of omics approaches to characterize molecular responses to abiotic stresses in plants},
  doi       = {10.25932/publishup-50963},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-509630},
  school      = {Universit{\"a}t Potsdam},
  pages     = {viii, 117},
  year      = {2021},
  abstract  = {Aufgrund des globalen Klimawandels ist die Gew{\"a}hrleistung der Ern{\"a}hrungssicherheit f{\"u}r eine wachsende Weltbev{\"o}lkerung eine große Herausforderung. Insbesondere abiotische Stressoren wirken sich negativ auf Ernteertr{\"a}ge aus. Um klimaangepasste Nutzpflanzen zu entwickeln, ist ein umfassendes Verst{\"a}ndnis molekularer Ver{\"a}nderungen in der Reaktion auf unterschiedlich starke Umweltbelastungen erforderlich. Hochdurchsatz- oder "Omics"-Technologien k{\"o}nnen dazu beitragen, Schl{\"u}sselregulatoren und Wege abiotischer Stressreaktionen zu identifizieren. Zus{\"a}tzlich zur Gewinnung von Omics-Daten m{\"u}ssen auch Programme und statistische Analysen entwickelt und evaluiert werden, um zuverl{\"a}ssige biologische Ergebnisse zu erhalten. Ich habe diese Problemstellung in drei verschiedenen Studien behandelt und daf{\"u}r zwei Omics-Technologien benutzt. In der ersten Studie wurden Transkript-Daten von den beiden polymorphen Arabidopsis thaliana Akzessionen Col-0 und N14 verwendet, um sieben Programme hinsichtlich ihrer F{\"a}higkeit zur Positionierung und Quantifizierung von Illumina RNA Sequenz-Fragmenten („Reads") zu evaluieren. Zwischen 92\% und 99\% der Reads konnten an die Referenzsequenz positioniert werden und die ermittelten Verteilungen waren hoch korreliert f{\"u}r alle Programme. Bei der Durchf{\"u}hrung einer differentiellen Genexpressionsanalyse zwischen Pflanzen, die bei 20 °C oder 4 °C (K{\"a}lteakklimatisierung) exponiert wurden, ergab sich eine große paarweise {\"U}berlappung zwischen den Programmen. In der zweiten Studie habe ich die Transkriptome von zehn verschiedenen Oryza sativa (Reis) Kultivaren sequenziert. Daf{\"u}r wurde die PacBio Isoform Sequenzierungstechnologie benutzt. Die de novo Referenztranskriptome hatten zwischen 38.900 bis 54.500 hoch qualitative Isoformen pro Sorte. Die Isoformen wurden kollabiert, um die Sequenzredundanz zu verringern und danach evaluiert z.B. hinsichtlich des Vollst{\"a}ndigkeitsgrades (BUSCO), der Transkriptl{\"a}nge und der Anzahl einzigartiger Transkripte pro Genloci. F{\"u}r die hitze- und trockenheitstolerante Sorte N22 wurden ca. 650 einzigartige und neue Transkripte identifiziert, von denen 56 signifikant unterschiedlich in sich entwickelnden Samen unter kombiniertem Trocken- und Hitzestress exprimiert wurden. In der letzten Studie habe ich die Ver{\"a}nderungen in Metabolitprofilen von acht Reissorten gemessen und analysiert, die dem Stress hoher Nachttemperaturen (HNT) ausgesetzt waren und w{\"a}hrend der Trocken- und Regenzeit im Feld auf den Philippinen angebaut wurden. Es wurden jahreszeitlich bedingte Ver{\"a}nderungen im Metabolitspiegel sowie f{\"u}r agronomische Parameter identifiziert und m{\"o}gliche Stoffwechselwege, die einen Ertragsr{\"u}ckgang unter HNT-Bedingungen verursachen, vorgeschlagen. Zusammenfassend konnte ich zeigen, dass der Vergleich der RNA-seq Programme den Pflanzenwissenschaftler*innen helfen kann, sich f{\"u}r das richtige Werkzeug f{\"u}r ihre Daten zu entscheiden. Die de novo Transkriptom-Rekonstruktion von Reissorten ohne Genomsequenz bietet einen gezielten, kosteneffizienten Ansatz zur Identifizierung neuer Gene, die durch verschiedene Stressbedingungen reguliert werden unabh{\"a}ngig vom Organismus. Mit dem Metabolomik-Ansatz f{\"u}r HNT-Stress in Reis habe ich stress- und jahreszeitenspezifische Metabolite identifiziert, die in Zukunft als molekulare Marker f{\"u}r die Verbesserung von Nutzpflanzen verwendet werden k{\"o}nnten.},
  language  = {en}
}
@phdthesis{Stoessel2018,
  author    = {St{\"o}ßel, Daniel},
  title     = {Biomarker Discovery in Multiple Sclerosis and Parkinson's disease},
  school      = {Universit{\"a}t Potsdam},
  pages     = {135},
  year      = {2018},
  abstract  = {Neuroinflammatory and neurodegenerative diseases such as Parkinson's (PD) and multiple sclerosis (MS) often result in a severe impairment of the patient´s quality of life. Effective therapies for the treatment are currently not available, which results in a high socio-economic burden. Due to the heterogeneity of the disease subtypes, stratification is particularly difficult in the early phase of the disease and is mainly based on clinical parameters such as neurophysiological tests and central nervous imaging. Due to good accessibility and stability, blood and cerebrospinal fluid metabolite markers could serve as surrogates for neurodegenerative processes. This can lead to an improved mechanistic understanding of these diseases and further be used as "treatment response" biomarkers in preclinical and clinical development programs. Therefore, plasma and CSF metabolite profiles will be identified that allow differentiation of PD from healthy controls, association of PD with dementia (PDD) and differentiation of PD subtypes such as akinetic rigid and tremor dominant PD patients. In addition, plasma metabolites for the diagnosis of primary progressive MS (PPMS) should be investigated and tested for their specificity to relapsing-remitting MS (RRMS) and their development during PPMS progression. By applying untargeted high-resolution metabolomics of PD patient samples and in using random forest and partial least square machine learning algorithms, this study identified 20 plasma metabolites and 14 CSF metabolite biomarkers. These differentiate against healthy individuals with an AUC of 0.8 and 0.9 in PD, respectively. We also identify ten PDD specific serum metabolites, which differentiate against healthy individuals and PD patients without dementia with an AUC of 1.0, respectively. Furthermore, 23 akinetic-rigid specific plasma markers were identified, which differentiate against tremor-dominant PD patients with an AUC of 0.94 and against healthy individuals with an AUC of 0.98. These findings also suggest more severe disease pathology in the akinetic-rigid PD than in tremor dominant PD. In the analysis of MS patient samples a partial least square analysis yielded predictive models for the classification of PPMS and resulted in 20 PPMS specific metabolites. In another MS study unknown changes in human metabolism were identified after administration of the multiple sclerosis drug dimethylfumarate, which is used for the treatment of RRMS. These results allow to describe and understand the hitherto completely unknown mechanism of action of this new drug and to use these findings for the further development of new drugs and targets against RRMS. In conclusion, these results have the potential for improved diagnosis of these diseases and improvement of mechanistic understandings, as multiple deregulated pathways were identified. Moreover, novel Dimethylfumarate targets can be used to aid drug development and treatment efficiency. Overall, metabolite profiling in combination with machine learning identified as a promising approach for biomarker discovery and mode of action elucidation.},
  language  = {en}
}
@phdthesis{Schwahn2018,
  author    = {Schwahn, Kevin},
  title     = {Data driven approaches to infer the regulatory mechanism shaping and constraining levels of metabolites in metabolic networks},
  doi       = {10.25932/publishup-42324},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-423240},
  school      = {Universit{\"a}t Potsdam},
  pages     = {109},
  year      = {2018},
  abstract  = {Systems biology aims at investigating biological systems in its entirety by gathering and analyzing large-scale data sets about the underlying components. Computational systems biology approaches use these large-scale data sets to create models at different scales and cellular levels. In addition, it is concerned with generating and testing hypotheses about biological processes. However, such approaches are inevitably leading to computational challenges due to the high dimensionality of the data and the differences in the dimension of data from different cellular layers. This thesis focuses on the investigation and development of computational approaches to analyze metabolite profiles in the context of cellular networks. This leads to determining what aspects of the network functionality are reflected in the metabolite levels. With these methods at hand, this thesis aims to answer three questions: (1) how observability of biological systems is manifested in metabolite profiles and if it can be used for phenotypical comparisons; (2) how to identify couplings of reaction rates from metabolic profiles alone; and (3) which regulatory mechanism that affect metabolite levels can be distinguished by integrating transcriptomics and metabolomics read-outs. I showed that sensor metabolites, identified by an approach from observability theory, are more correlated to each other than non-sensors. The greater correlations between sensor metabolites were detected both with publicly available metabolite profiles and synthetic data simulated from a medium-scale kinetic model. I demonstrated through robustness analysis that correlation was due to the position of the sensor metabolites in the network and persisted irrespectively of the experimental conditions. Sensor metabolites are therefore potential candidates for phenotypical comparisons between conditions through targeted metabolic analysis. Furthermore, I demonstrated that the coupling of metabolic reaction rates can be investigated from a purely data-driven perspective, assuming that metabolic reactions can be described by mass action kinetics. Employing metabolite profiles from domesticated and wild wheat and tomato species, I showed that the process of domestication is associated with a loss of regulatory control on the level of reaction rate coupling. I also found that the same metabolic pathways in Arabidopsis thaliana and Escherichia coli exhibit differences in the number of reaction rate couplings. I designed a novel method for the identification and categorization of transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approach determines the partial correlation of metabolites with control by the principal components of the transcript levels. The principle components contain the majority of the transcriptomic information allowing to partial out the effect of the transcriptional layer from the metabolite profiles. Depending whether the correlation between metabolites persists upon controlling for the effect of the transcriptional layer, the approach allows us to group metabolite pairs into being associated due to post-transcriptional or transcriptional regulation, respectively. I showed that the classification of metabolite pairs into those that are associated due to transcriptional or post-transcriptional regulation are in agreement with existing literature and findings from a Bayesian inference approach. The approaches developed, implemented, and investigated in this thesis open novel ways to jointly study metabolomics and transcriptomics data as well as to place metabolic profiles in the network context. The results from these approaches have the potential to provide further insights into the regulatory machinery in a biological system.},
  language  = {en}
}
@phdthesis{Wittenbecher2017,
  author    = {Wittenbecher, Clemens},
  title     = {Linking whole-grain bread, coffee, and red meat to the risk of type 2 diabetes},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-404592},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XII, 194, ii},
  year      = {2017},
  abstract  = {Background: Consumption of whole-grain, coffee, and red meat were consistently related to the risk of developing type 2 diabetes in prospective cohort studies, but potentially underlying biological mechanisms are not well understood. Metabolomics profiles were shown to be sensitive to these dietary exposures, and at the same time to be informative with respect to the risk of type 2 diabetes. Moreover, graphical network-models were demonstrated to reflect the biological processes underlying high-dimensional metabolomics profiles. Aim: The aim of this study was to infer hypotheses on the biological mechanisms that link consumption of whole-grain bread, coffee, and red meat, respectively, to the risk of developing type 2 diabetes. More specifically, it was aimed to consider network models of amino acid and lipid profiles as potential mediators of these risk-relations. Study population: Analyses were conducted in the prospective EPIC-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2731, including 692 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Concentrations of 126 metabolites (acylcarnitines, phosphatidylcholines, sphingomyelins, amino acids) were determined in baseline-serum samples. Incident type 2 diabetes cases were assed and validated in an active follow-up procedure. The median follow-up time was 6.6 years. Analytical design: The methodological approach was conceptually based on counterfactual causal inference theory. Observations on the network-encoded conditional independence structure restricted the space of possible causal explanations of observed metabolomics-data patterns. Given basic directionality assumptions (diet affects metabolism; metabolism affects future diabetes incidence), adjustment for a subset of direct neighbours was sufficient to consistently estimate network-independent direct effects. Further model-specification, however, was limited due to missing directionality information on the links between metabolites. Therefore, a multi-model approach was applied to infer the bounds of possible direct effects. All metabolite-exposure links and metabolite-outcome links, respectively, were classified into one of three categories: direct effect, ambiguous (some models indicated an effect others not), and no-effect. Cross-sectional and longitudinal relations were evaluated in multivariable-adjusted linear regression and Cox proportional hazard regression models, respectively. Models were comprehensively adjusted for age, sex, body mass index, prevalence of hypertension, dietary and lifestyle factors, and medication. Results: Consumption of whole-grain bread was related to lower levels of several lipid metabolites with saturated and monounsaturated fatty acids. Coffee was related to lower aromatic and branched-chain amino acids, and had potential effects on the fatty acid profile within lipid classes. Red meat was linked to lower glycine levels and was related to higher circulating concentrations of branched-chain amino acids. In addition, potential marked effects of red meat consumption on the fatty acid composition within the investigated lipid classes were identified. Moreover, potential beneficial and adverse direct effects of metabolites on type 2 diabetes risk were detected. Aromatic amino acids and lipid metabolites with even-chain saturated (C14-C18) and with specific polyunsaturated fatty acids had adverse effects on type 2 diabetes risk. Glycine, glutamine, and lipid metabolites with monounsaturated fatty acids and with other species of polyunsaturated fatty acids were classified as having direct beneficial effects on type 2 diabetes risk. Potential mediators of the diet-diabetes links were identified by graphically overlaying this information in network models. Mediation analyses revealed that effects on lipid metabolites could potentially explain about one fourth of the whole-grain bread effect on type 2 diabetes risk; and that effects of coffee and red meat consumption on amino acid and lipid profiles could potentially explain about two thirds of the altered type 2 diabetes risk linked to these dietary exposures. Conclusion: An algorithm was developed that is capable to integrate single external variables (continuous exposures, survival time) and high-dimensional metabolomics-data in a joint graphical model. Application to the EPIC-Potsdam cohort study revealed that the observed conditional independence patterns were consistent with the a priori mediation hypothesis: Early effects on lipid and amino acid metabolism had the potential to explain large parts of the link between three of the most widely discussed diabetes-related dietary exposures and the risk of developing type 2 diabetes.},
  language  = {en}
}
@phdthesis{Boelling2006,
  author    = {B{\"o}lling, Christian},
  title     = {Comprehensive metabolite analysis in Chlamydomonas reinhardtii : method development and application to the study of environmental and genetic perturbations},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-11329},
  school      = {Universit{\"a}t Potsdam},
  year      = {2006},
  abstract  = {This study introduces a method for multiparallel analysis of small organic compounds in the unicellular green alga Chlamydomonas reinhardtii, one of the premier model organisms in cell biology. The comprehensive study of the changes of metabolite composition, or metabolomics, in response to environmental, genetic or developmental signals is an important complement of other functional genomic techniques in the effort to develop an understanding of how genes, proteins and metabolites are all integrated into a seamless and dynamic network to sustain cellular functions. The sample preparation protocol was optimized to quickly inactivate enzymatic activity, achieve maximum extraction capacity and process large sample quantities. As a result of the rapid sampling, extraction and analysis by gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF) more than 800 analytes from a single sample can be measured, of which over a 100 could be positively identified. As part of the analysis of GC-TOF raw data, aliquot ratio analysis to systematically remove artifact signals and tools for the use of principal component analysis (PCA) on metabolomic datasets are proposed. Cells subjected to nitrogen (N), phosphorus (P), sulfur (S) or iron (Fe) depleted growth conditions develop highly distinctive metabolite profiles with metabolites implicated in many different processes being affected in their concentration during adaptation to nutrient deprivation. Metabolite profiling allowed characterization of both specific and general responses to nutrient deprivation at the metabolite level. Modulation of the substrates for N-assimilation and the oxidative pentose phosphate pathway indicated a priority for maintaining the capability for immediate activation of N assimilation even under conditions of decreased metabolic activity and arrested growth, while the rise in 4-hydroxyproline in S deprived cells could be related to enhanced degradation of proteins of the cell wall. The adaptation to sulfur deficiency was analyzed with greater temporal resolution and responses of wild-type cells were compared with mutant cells deficient in SAC1, an important regulator of the sulfur deficiency response. Whereas concurrent metabolite depletion and accumulation occurs during adaptation to S deprivation in wild-type cells, the sac1 mutant strain is characterized by a massive incapability to sustain many processes that normally lead to transient or permanent accumulation of the levels of certain metabolites or recovery of metabolite levels after initial down-regulation. For most of the steps in arginine biosynthesis in Chlamydomonas mutants have been isolated that are deficient in the respective enzyme activities. Three strains deficient in the activities of N-acetylglutamate-5-phosphate reductase (arg1), N2 acetylornithine-aminotransferase (arg9), and argininosuccinate lyase (arg2), respectively, were analyzed with regard to activation of endogenous arginine biosynthesis after withdrawal of externally supplied arginine. Enzymatic blocks in the arginine biosynthetic pathway could be characterized by precursor accumulation, like the amassment of argininosuccinate in arg2 cells, and depletion of intermediates occurring downstream of the enzymatic block, e.g. N2-acetylornithine, ornithine, and argininosuccinate depletion in arg9 cells. The unexpected finding of substantial levels of the arginine pathway intermediates N-acetylornithine, citrulline, and argininosuccinate downstream the enzymatic block in arg1 cells provided an explanation for the residual growth capacity of these cells in the absence of external arginine sources. The presence of these compounds, together with the unusual accumulation of N-Acetylglutamate, the first intermediate that commits the glutamate backbone to ornithine and arginine biosynthesis, in arg1 cells suggests that alternative pathways, possibly involving the activity of ornithine aminotransferase, may be active when the default reaction sequence to produce ornithine via acetylation of glutamate is disabled.},
  language  = {en}
}
@phdthesis{Zhang2005,
  author    = {Zhang, Baichen},
  title     = {Dissection of phloem transport in cucurbitaceae by metabolomic analysis},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-6644},
  school      = {Universit{\"a}t Potsdam},
  year      = {2005},
  abstract  = {This thesis aimed to investigate several fundamental and perplexing questions relating to the phloem loading and transport mechanisms of Cucurbita maxima, by combining metabolomic analysis with cell biological techniques. This putative symplastic loading species has long been used for experiments on phloem anatomy, phloem biochemistry, phloem transport physiology and phloem signalling. Symplastic loading species have been proposed to use a polymer trapping mechanism to accumulate RFO (raffinose family oligosaccharides) sugars to build up high osmotic pressure in minor veins which sustains a concentration gradient that drives mass flow. However, extensive evidence indicating a low sugar concentration in their phloem exudates is a long-known problem that conflicts with this hypothesis. Previous metabolomic analysis shows the concentration of many small molecules in phloem exudates is higher than that of leaf tissues, which indicates an active apoplastic loading step. Therefore, in the view of the phloem metabolome, a symplastic loading mechanism cannot explain how small molecules other than RFO sugars are loaded into phloem. Most studies of phloem physiology using cucurbits have neglected the possible functions of vascular architecture in phloem transport. It is well known that there are two phloem systems in cucurbits with distinctly different anatomical features: central phloem and extrafascicular phloem. However, mistaken conclusions on sources of cucurbit phloem exudation from previous reports have hindered consideration of the idea that there may be important differences between these two phloem systems. The major results are summarized as below: 1) O-linked glycans in C.maxima were structurally identified as beta-1,3 linked glucose polymers, and the composition of glycans in cucurbits was found to be species-specific. Inter-species grafting experiments proved that these glycans are phloem mobile and transported uni-directionally from scion to stock. 2) As indicated by stable isotopic labelling experiments, a considerable amount of carbon is incorporated into small metabolites in phloem exudates. However, the incorporation of carbon into RFO sugars is much faster than for other metabolites. 3) Both CO2 labelling experiments and comparative metabolomic analysis of phloem exudates and leaf tissues indicated that metabolic processes other than RFO sugar metabolism play an important role in cucurbit phloem physiology. 4) The underlying assumption that the central phloem of cucurbits continuously releases exudates after physical incision was proved wrong by rigorous experiments including direct observation by normal microscopy and combined multiple-microscopic methods. Errors in previous experimental confirmation of phloem exudation in cucurbits are critically discussed. 5) Extrafascicular phloem was proved to be functional, as indicated by phloem-mobile carboxyfluorescein tracer studies. Commissural sieve tubes interconnect phloem bundles into a complete super-symplastic network. 6) Extrafascicular phloem represents the main source of exudates following physical incision. The major transported metabolites by these extrafacicular phloem are non-sugar compounds including amino acids, O-glycans, amines. 7) Central phloem contains almost exclusively RFO sugars, the estimated amount of which is up to 1 to 2 molar. The major RFO sugar present in central phloem is stachyose. 8) Cucurbits utilize two structurally different phloem systems for transporting different group of metabolites (RFO sugars and non-RFO sugar compounds). This implies that cucurbits may use spatially separated loading mechanisms (apoplastic loading for extrafascicular phloem and symplastic loading for central phloem) for supply of nutrients to sinks. 9) Along the transport systems, RFO sugars were mainly distributed within central phloem tissues. There were only small amounts of RFO sugars present in xylem tissues (millimolar range) and trace amounts of RFO sugars in cortex and pith. The composition of small molecules in external central phloem is very different from that in internal central phloem. 10) Aggregated P-proteins were manually dissected from central phloem and analysed by both SDS-PAGE and mass spectrometry. Partial sequences of peptides were obtained by QTOF de novo sequencing from trypsin digests of three SDS-PAGE bands. None of these partial sequences shows significant homology to known cucurbit phloem proteins or other plant proteins. This proves that these central phloem proteins are a completely new group of proteins different from those in extrafascicular phloem. The extensively analysed P-proteins reported in literature to date are therefore now shown to arise from extrafascicular phloem and not central phloem, and therefore do not appear to be involved in the occlusion processes in central phloem.},
  subject      = {phloem},
  language  = {en}
}