Refine
Document Type
- Doctoral Thesis (5)
Is part of the Bibliography
- yes (5)
Keywords
- metabolomics (2)
- Arabidopsis thaliana (1)
- Bisulfit Sequenzierung (1)
- Chloroplasten (1)
- De novo Assemblierung (1)
- Differenzielle Genexpression (1)
- Interaktion (1)
- Interaktions Netzwerk (1)
- Metabolit (1)
- Metabolomik (1)
Institute
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.
More than a century ago the phenomenon of non-Mendelian inheritance (NMI), defined as any type of inheritance pattern in which traits do not segregate in accordance with Mendel’s laws, was first reported. In the plant kingdom three genomic compartments, the nucleus, chloroplast, and mitochondrion, can participate in such a phenomenon. High-throughput sequencing (HTS) proved to be a key technology to investigate NMI phenomena by assembling and/or resequencing entire genomes. However, generation, analysis and interpretation of such datasets remain challenging by the multi-layered biological complexity. To advance our knowledge in the field of NMI, I conducted three studies involving different HTS technologies and implemented two new algorithms to analyze them.
In the first study I implemented a novel post-assembly pipeline, called Semi-Automated Graph-Based Assembly Curator (SAGBAC), which visualizes non-graph-based assemblies as graphs, identifies recombinogenic repeat pairs (RRPs), and reconstructs plant mitochondrial genomes (PMG) in a semiautomated workflow. We applied this pipeline to assemblies of three Oenothera species resulting in a spatially folded and circularized model. This model was confirmed by PCR and Southern blot analyses and was used to predict a defined set of 70 PMG isoforms. With Illumina Mate Pair and PacBio RSII data, the stoichiometry of the RRPs was determined quantitatively differing up to three-fold.
In the second study I developed a post-multiple sequence alignment algorithm, called correlation mapping (CM), which correlates segment-wise numbers of nucleotide changes to a numeric ascertainable phenotype. We applied this algorithm to 14 wild type and 18 mutagenized plastome assemblies within the Oenothera genus and identified two genes, accD and ycf2 that may cause the competitive behavior of plastid genotypes as plastids can be biparental inherited in Oenothera. Moreover, lipid composition of the plastid envelope membrane is affected by polymorphisms within these two genes.
For the third study, I programmed a pipeline to investigate a NMI phenomenon, known as paramutation, in tomato by analyzing DNA and bisulfite sequencing data as well as microarray data. We identified the responsible gene (Solyc02g0005200) and were able to fully repress its caused phenotype by heterologous complementation with a paramutation insensitive transgene of the Arabidopsis thaliana orthologue. Additionally, a suppressor mutant shows a globally altered DNA methylation pattern and carries a large deletion leading to a gene fusion involving a histone deacetylase.
In conclusion, my developed and implemented algorithms and data analysis pipelines are suitable to investigate NMI and led to novel insights about such phenomena by reconstructing PMGs (SAGBAC) as a requirement to study mitochondria-associated phenotypes, by identifying genes (CM) causing interplastidial competition as well by applying a DNA/Bisulfite-seq analysis pipeline to shed light in a transgenerational epigenetic inheritance phenomenon.
Determining the relationship between genotype and phenotype is the key to understand the plasticity and robustness of phenotypes in nature. While the directly observable plant phenotypes (e.g. agronomic, yield and stress resistance traits) have been well-investigated, there is still a lack in our knowledge about the genetic basis of intermediate phenotypes, such as metabolic phenotypes. Dissecting the links between genotype and phenotype depends on suitable statistical models. The state-of-the-art models are developed for directly observable phenotypes, regardless the characteristics of intermediate phenotypes. This thesis aims to fill the gaps in understanding genetic architecture of intermediate phenotypes, and how they tie to composite traits, namely plant growth. The metabolite levels and reaction fluxes, as two aspects of metabolic phenotypes, are shaped by the interrelated chemical reactions formed in genome-scale metabolic network. Here, I attempt to answer the question: Can the knowledge of underlying genome-scale metabolic network improve the model performance for prediction of metabolic phenotypes and associated plant growth? To this end, two projects are investigated in this thesis. Firstly, we propose an approach that couples genomic selection with genome-scale metabolic network and metabolic profiles in Arabidopsis thaliana to predict growth. This project is the first integration of genomic data with fluxes predicted based on constraint-based modeling framework and data on biomass composition. We demonstrate that our approach leads to a considerable increase of prediction accuracy in comparison to the state-of-the-art methods in both within and across environment predictions. Therefore, our work paves the way for combining knowledge on metabolic mechanisms in the statistical approach underlying genomic selection to increase the efficiency of future plant breeding approaches. Secondly, we investigate how reliable is genomic selection for metabolite levels, and which single nucleotide polymorphisms (SNPs), obtained from different neighborhoods of a given metabolic network, contribute most to the accuracy of prediction. The results show that the local structure of first and second neighborhoods are not sufficient for predicting the genetic basis of metabolite levels in Zea mays. Furthermore, we find that the enzymatic SNPs can capture most the genetic variance and the contribution of non-enzymatic SNPs is in fact small. To comprehensively understand the genetic architecture of metabolic phenotypes, I extend my study to a local Arabidopsis thaliana population and their hybrids. We analyze the genetic architecture in primary and secondary metabolism as well as in growth. In comparison to primary metabolites, compounds from secondary metabolism were more variable and show more non-additive inheritance patterns which could be attributed to epistasis. Therefore, our study demonstrates that heterozygosity in local Arabidopsis thaliana population generates metabolic variation and may impact several tasks directly linked to metabolism. The studies in this thesis improve the knowledge of genetic architecture of metabolic phenotypes in both inbreed and hybrid population. The approaches I proposed to integrate genome-scale metabolic network with genomic data provide the opportunity to obtain mechanistic insights about the determinants of agronomically important polygenic traits.
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.