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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 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.
Due to global climate change providing food security for an increasing world population is a big challenge. Especially abiotic stressors have a strong negative effect on crop yield. To develop climate-adapted crops a comprehensive understanding of molecular alterations in the response of varying levels of environmental stresses is required. High throughput or ‘omics’ technologies can help to identify key-regulators and pathways of abiotic stress responses. In addition to obtain omics data also tools and statistical analyses need to be designed and evaluated to get reliable biological results.
To address these issues, I have conducted three different studies covering two omics technologies. In the first study, I used transcriptomic data from the two polymorphic Arabidopsis thaliana accessions, namely Col-0 and N14, to evaluate seven computational tools for their ability to map and quantify Illumina single-end reads. Between 92% and 99% of the reads were mapped against the reference sequence. The raw count distributions obtained from the different tools were highly correlated. Performing a differential gene expression analysis between plants exposed to 20 °C or 4°C (cold acclimation), a large pairwise overlap between the mappers was obtained. In the second study, I obtained transcript data from ten different Oryza sativa (rice) cultivars by PacBio Isoform sequencing that can capture full-length transcripts. De novo reference transcriptomes were reconstructed resulting in 38,900 to 54,500 high-quality isoforms per cultivar. Isoforms were collapsed to reduce sequence redundancy and evaluated, e.g. for protein completeness level (BUSCO), transcript length, and number of unique transcripts per gene loci. For the heat and drought tolerant aus cultivar N22, I identified around 650 unique and novel transcripts of which 56 were significantly differentially expressed in developing seeds during combined drought and heat stress. In the last study, I measured and analyzed the changes in metabolite profiles of eight rice cultivars exposed to high night temperature (HNT) stress and grown during the dry and wet season on the field in the Philippines. Season-specific changes in metabolite levels, as well as for agronomic parameters, were identified and metabolic pathways causing a yield decline at HNT conditions suggested.
In conclusion, the comparison of mapper performances can help plant scientists to decide on the right tool for their data. The de novo reconstruction of rice cultivars without a genome sequence provides a targeted, cost-efficient approach to identify novel genes responding to stress conditions for any organism. With the metabolomics approach for HNT stress in rice, I identified stress and season-specific metabolites which might be used as molecular markers for crop improvement in the future.
We recently demonstrated that the sympathetic nervous system can be voluntarily activated following a training program consisting of cold exposure, breathing exercises, and meditation. This resulted in profound attenuation of the systemic inflammatory response elicited by lipopolysaccharide (LPS) administration. Herein, we assessed whether this training program affects the plasma metabolome and if these changes are linked to the immunomodulatory effects observed. A total of 224 metabolites were identified in plasma obtained from 24 healthy male volunteers at six timepoints, of which 98 were significantly altered following LPS administration. Effects of the training program were most prominent shortly after initiation of the acquired breathing exercises but prior to LPS administration, and point towards increased activation of the Cori cycle. Elevated concentrations of lactate and pyruvate in trained individuals correlated with enhanced levels of anti-inflammatory interleukin (IL)-10. In vitro validation experiments revealed that co-incubation with lactate and pyruvate enhances IL-10 production and attenuates the release of pro-inflammatory IL-1 beta and IL-6 by LPS-stimulated leukocytes. Our results demonstrate that practicing the breathing exercises acquired during the training program results in increased activity of the Cori cycle. Furthermore, this work uncovers an important role of lactate and pyruvate in the anti-inflammatory phenotype observed in trained subjects.
We recently demonstrated that the sympathetic nervous system can be voluntarily activated following a training program consisting of cold exposure, breathing exercises, and meditation. This resulted in profound attenuation of the systemic inflammatory response elicited by lipopolysaccharide (LPS) administration. Herein, we assessed whether this training program affects the plasma metabolome and if these changes are linked to the immunomodulatory effects observed. A total of 224 metabolites were identified in plasma obtained from 24 healthy male volunteers at six timepoints, of which 98 were significantly altered following LPS administration. Effects of the training program were most prominent shortly after initiation of the acquired breathing exercises but prior to LPS administration, and point towards increased activation of the Cori cycle. Elevated concentrations of lactate and pyruvate in trained individuals correlated with enhanced levels of anti-inflammatory interleukin (IL)-10. In vitro validation experiments revealed that co-incubation with lactate and pyruvate enhances IL-10 production and attenuates the release of pro-inflammatory IL-1 beta and IL-6 by LPS-stimulated leukocytes. Our results demonstrate that practicing the breathing exercises acquired during the training program results in increased activity of the Cori cycle. Furthermore, this work uncovers an important role of lactate and pyruvate in the anti-inflammatory phenotype observed in trained subjects.
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.
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.
The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coil, Saccharomycies cerevisiae, and Arabidopsis thaliana.
Metabolites and lipids are the final products of enzymatic processes, distinguishing the different cellular functions and activities of single cells or whole tissues. Understanding these cellular functions within a well-established model system requires a systemic collection of molecular and physiological information. In the current report, the green alga Chlamydomonas reinhardtii was selected to establish a comprehensive workflow for the detailed multi-omics analysis of a synchronously growing cell culture system. After implementation and benchmarking of the synchronous cell culture, a two-phase extraction method was adopted for the analysis of proteins, lipids, metabolites and starch from a single sample aliquot of as little as 10-15million Chlamydomonas cells. In a proof of concept study, primary metabolites and lipids were sampled throughout the diurnal cell cycle. The results of these time-resolved measurements showed that single compounds were not only coordinated with each other in different pathways, but that these complex metabolic signatures have the potential to be used as biomarkers of various cellular processes. Taken together, the developed workflow, including the synchronized growth of the photoautotrophic cell culture, in combination with comprehensive extraction methods and detailed metabolic phenotyping has the potential for use in in-depth analysis of complex cellular processes, providing essential information for the understanding of complex biological systems.
Leaf senescence is a developmentally controlled process, which is additionally modulated by a number of adverse environmental conditions. Nitrogen shortage is a well-known trigger of precocious senescence in many plant species including crops, generally limiting biomass and seed yield. However, leaf senescence induced by nitrogen starvation may be reversed when nitrogen is resupplied at the onset of senescence. Here, the transcriptomic, hormonal, and global metabolic rearrangements occurring during nitrogen resupply-induced reversal of senescence in Arabidopsis thaliana were analysed. The changes induced by senescence were essentially in keeping with those previously described; however, these could, by and large, be reversed. The data thus indicate that plants undergoing senescence retain the capacity to sense and respond to the availability of nitrogen nutrition. The combined data are discussed in the context of the reversibility of the senescence programme and the evolutionary benefit afforded thereby. Future prospects for understanding and manipulating this process in both Arabidopsis and crop plants are postulated.