570 Biowissenschaften; Biologie
Refine
Document Type
- Doctoral Thesis (6)
- Article (4)
- Postprint (4)
Language
- English (14)
Is part of the Bibliography
- yes (14)
Keywords
- metabolomics (14) (remove)
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.
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.
Background/Aims: Impaired birth outcomes, like low birth weight, have consistently been associated with increased disease susceptibility to hypertension in later life. Alterations in the maternal or fetal metabolism might impact on fetal growth and influence birth outcomes. Discerning associations between the maternal and fetal metabolome and surrogate parameters of fetal growth could give new insight into the complex relationship between intrauterine conditions, birth outcomes, and later life disease susceptibility. Methods: Using flow injection tandem mass spectrometry, targeted metabolomics was performed in serum samples obtained from 226 mother/child pairs at delivery. Associations between neonatal birth weight and concentrations of 163 maternal and fetal metabolites were analyzed. Results: After FDR adjustment using the Benjamini-Hochberg procedure lysophosphatidylcholines (LPC) 14:0, 16:1, and 18:1 were strongly positively correlated with birth weight. In a stepwise linear regression model corrected for established confounding factors of birth weight, LPC 16: 1 showed the strongest independent association with birth weight (CI: 93.63 - 168.94; P = 6.94x10(-11)). The association with birth weight was stronger than classical confounding factors such as offspring sex (CI: - 258.81- -61.32; P = 0.002) and maternal smoking during pregnancy (CI: -298.74 - -29.51; P = 0.017). Conclusions: After correction for multiple testing and adjustment for potential confounders, LPC 16:1 showed a very strong and independent association with birth weight. The underlying molecular mechanisms linking fetal LPCs with birth weight need to be addressed in future studies. (c) 2018 The Author(s) Published by S. Karger AG, Basel
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.
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.
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.
Background/Aims: Gestational diabetes (GDM) might be associated with alterations in the metabolomic profile of affected mothers and their offspring. Until now, there is a paucity of studies that investigated both, the maternal and the fetal serum metabolome in the setting of GDM. Mounting evidence suggests that the fetus is not just passively affected by gestational disease but might play an active role in it. Metabolomic studies performed in maternal blood and fetal cord blood could help to better discern distinct fetal from maternal disease interactions. Methods: At the time of birth, serum samples from mothers and newborns (cord blood samples) were collected and screened for 163 metabolites utilizing tandem mass spectrometry. The cohort consisted of 412 mother/child pairs, including 31 cases of maternal GDM. Results: An initial non-adjusted analysis showed that eight metabolites in the maternal blood and 54 metabolites in the cord blood were associated with GDM. After Benjamini-Hochberg (BH) procedure and adjustment for confounding factors for GDM, fetal phosphatidylcholine acyl-alkyl C 32:1 and proline still showed an independent association with GDM. Conclusions: This study found metabolites in cord blood which were associated with GDM, even after adjustment for established risk factors of GDM. To the best of our knowledge, this is the first study demonstrating an independent association between fetal serum metabolites and maternal GDM. Our findings might suggest a potential effect of the fetal metabolome on maternal GDM. (c) 2018 The Author(s) Published by S. Karger AG, Basel
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.