@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{Fischer2022,
  author    = {Fischer, Axel},
  title     = {Investigating the impact of genomic compartments contributing to non-Mendelian inheritance based on high throughput sequencing data},
  doi       = {10.25932/publishup-54900},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-549001},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vii, 122},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@phdthesis{Gottmann2019,
  author    = {Gottmann, Pascal},
  title     = {In silico Analyse zur Kl{\"a}rung der Beteiligung von micro-RNAs, die in QTL lokalisiert sind, an den metabolischen Erkrankungen Adipositas und Typ-2-Diabetes mit Hilfe von Mausmodellen},
  school      = {Universit{\"a}t Potsdam},
  pages     = {XIII, 106},
  year      = {2019},
  language  = {de}
}
@phdthesis{Tong2019,
  author    = {Tong, Hao},
  title     = {Dissection of genetic architecture of intermediate phenotypes and predictions in plants},
  school      = {Universit{\"a}t Potsdam},
  pages     = {127},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@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}
}