TY - THES A1 - Anwar, Ammarah T1 - Natural variation of gene regulatory networks in \(Arabidopsis\) \(thaliana\) T1 - Natürliche Variation genregulatorischer Netzwerke in \(Arabidopsis\) \(thaliana\) N2 - Understanding the causal relationship between genotype and phenotype is a major objective in biology. The main interest is in understanding trait architecture and identifying loci contributing to the respective traits. Genome-wide association mapping (GWAS) is one tool to elucidate these relationships and has been successfully used in many different species. However, most studies concentrate on marginal marker effects and ignore epistatic and gene-environment interactions. These interactions are problematic to account for, but are likely to make major contributions to many phenotypes that are not regulated by independent genetic effects, but by more sophisticated gene-regulatory networks. Further complication arises from the fact that these networks vary in different natural accessions. However, understanding the differences of gene regulatory networks and gene-gene interactions is crucial to conceive trait architecture and predict phenotypes. The basic subject of this study – using data from the Arabidopsis 1001 Genomes Project – is the analysis of pre-mature stop codons. These have been incurred in nearly one-third of the ~ 30k genes. A gene-gene interaction network of the co-occurrence of stop codons has been built and the over and under representation of different pairs has been statistically analyzed. To further classify the significant over and under- represented gene-gene interactions in terms of molecular function of the encoded proteins, gene ontology terms (GO-SLIM) have been applied. Furthermore, co- expression analysis specifies gene clusters that co-occur over different genetic and phenotypic backgrounds. To link these patterns to evolutionary constrains, spatial location of the respective alleles have been analyzed as well. The latter shows clear patterns for certain gene pairs that indicate differential selection. N2 - Das Verständnis des kausalen Zusammenhangs zwischen Genotyp und Phänotyp ist ein wichtiges Ziel in der Biologie. Das Hauptinteresse liegt darin, die Merkmalsarchitektur zu verstehen und Loci zu identifizieren, die zu den jeweiligen Merkmalen beitragen. Genome-wide association mapping (GWAS) ist ein Werkzeug, um diese Zusammenhänge aufzuklären und wurde erfolgreich in vielen verschiedenen Arten eingesetzt. Die meisten Studien konzentrieren sich jedoch auf marginale Markereffekte und ignorieren epistatische und Gen-Umwelt-Interaktionen. Diese Wechselwirkungen sind problematisch zu erklären, werden aber wahrscheinlich einen wichtigen Beitrag zu vielen Phänotypen leisten, die nicht durch unabhängige genetische Effekte, sondern durch ausgefeiltere genregulatorische Netzwerke reguliert werden. Eine weitere Komplikation ergibt sich aus der Tatsache, dass sich diese Netzwerke in verschiedenen natürlichen Akzessionen unterscheiden. Das Verständnis der Unterschiede zwischen genregulatorischen Netzwerken und Gen-Gen- Interaktionen ist jedoch entscheidend, um die Merkmalsarchitektur zu konzipieren und Phänotypen vorherzusagen. Das grundlegende Thema dieser Studie – unter Verwendung von Daten aus dem Arabidopsis 1001 Genomes Project – ist die Analyse von vorzeitigen Stop-Codons. Diese sind in fast einem Drittel der ~ 30k-Gene aufgetreten. Ein Gen-Gen- Interaktionsnetzwerk des gleichzeitigen Auftretens von Stop-Codons wurde aufgebaut und die Über- und Unterrepräsentation verschiedener Paare wurde statistisch analysiert. Um die signifikante über- und unterrepräsentierte Gen-Gen-Interaktion in Bezug auf den biologischen Prozess der kodierten Proteine weiter zu klassifizieren, wurden genonkologische Begriffe (GO-SLIM) verwendet. Darüber hinaus spezifiziert die Koexpressionsanalyse Gencluster, die über verschiedene genetische und phänotypische Hintergründe hinweg gleichzeitig auftreten. Um diese Muster mit evolutionären Einschränkungen in Verbindung zu bringen, wurde auch die räumliche Lage der jeweiligen Allele analysiert. Letzteres zeigt klare Muster für bestimmte Genepaare, die auf eine differentielle Selektion hinweisen. KW - Arabidopsis thaliana KW - Co-occurrence matrix KW - co-expression coefficient KW - gene expression networks KW - non-sense mutations KW - phenotype KW - local adaptation KW - variations in genome KW - Ackerschmalwand Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-291549 ER - TY - JOUR A1 - Faist, Hanna A1 - Ankenbrand, Markus J. A1 - Sickel, Wiebke A1 - Hentschel, Ute A1 - Keller, Alexander A1 - Deeken, Rosalia T1 - Opportunistic bacteria of grapevine crown galls are equipped with the genomic repertoire for opine utilization JF - Genome Biology and Evolution N2 - Young grapevines (Vitis vinifera) suffer and eventually can die from the crown gall disease caused by the plant pathogen Allorhizobium vitis (Rhizobiaceae). Virulent members of A. vitis harbor a tumor-inducing plasmid and induce formation of crown galls due to the oncogenes encoded on the transfer DNA. The expression of oncogenes in transformed host cells induces unregulated cell proliferation and metabolic and physiological changes. The crown gall produces opines uncommon to plants, which provide an important nutrient source for A. vitis harboring opine catabolism enzymes. Crown galls host a distinct bacterial community, and the mechanisms establishing a crown gall–specific bacterial community are currently unknown. Thus, we were interested in whether genes homologous to those of the tumor-inducing plasmid coexist in the genomes of the microbial species coexisting in crown galls. We isolated 8 bacterial strains from grapevine crown galls, sequenced their genomes, and tested their virulence and opine utilization ability in bioassays. In addition, the 8 genome sequences were compared with 34 published bacterial genomes, including closely related plant-associated bacteria not from crown galls. Homologous genes for virulence and opine anabolism were only present in the virulent Rhizobiaceae. In contrast, homologs of the opine catabolism genes were present in all strains including the nonvirulent members of the Rhizobiaceae and non-Rhizobiaceae. Gene neighborhood and sequence identity of the opine degradation cluster of virulent and nonvirulent strains together with the results of the opine utilization assay support the important role of opine utilization for cocolonization in crown galls, thereby shaping the crown gall community. KW - Vitis vinifera KW - bacterial community KW - Agrobacterium KW - Allorhizobium vitis KW - Ti plasmids KW - de novo sequenced genomes Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-350172 VL - 15 IS - 12 ER - TY - JOUR A1 - Imhoff, Johannes F. A1 - Rahn, Tanja A1 - Künzel, Sven A1 - Keller, Alexander A1 - Neulinger, Sven C. T1 - Osmotic adaptation and compatible solute biosynthesis of phototrophic bacteria as revealed from genome analyses JF - Microorganisms N2 - Osmotic adaptation and accumulation of compatible solutes is a key process for life at high osmotic pressure and elevated salt concentrations. Most important solutes that can protect cell structures and metabolic processes at high salt concentrations are glycine betaine and ectoine. The genome analysis of more than 130 phototrophic bacteria shows that biosynthesis of glycine betaine is common among marine and halophilic phototrophic Proteobacteria and their chemotrophic relatives, as well as in representatives of Pirellulaceae and Actinobacteria, but are also found in halophilic Cyanobacteria and Chloroherpeton thalassium. This ability correlates well with the successful toleration of extreme salt concentrations. Freshwater bacteria in general lack the possibilities to synthesize and often also to take up these compounds. The biosynthesis of ectoine is found in the phylogenetic lines of phototrophic Alpha- and Gammaproteobacteria, most prominent in the Halorhodospira species and a number of Rhodobacteraceae. It is also common among Streptomycetes and Bacilli. The phylogeny of glycine-sarcosine methyltransferase (GMT) and diaminobutyrate-pyruvate aminotransferase (EctB) sequences correlate well with otherwise established phylogenetic groups. Most significantly, GMT sequences of cyanobacteria form two major phylogenetic branches and the branch of Halorhodospira species is distinct from all other Ectothiorhodospiraceae. A variety of transport systems for osmolytes are present in the studied bacteria. KW - genomes of photosynthetic bacteria KW - glycine betaine biosynthesis KW - ectoine biosynthesis KW - osmotic adaptation KW - phylogeny of osmolyte biosynthesis Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-220161 SN - 2076-2607 VL - 9 IS - 1 ER - TY - JOUR A1 - Marquardt, André A1 - Hartrampf, Philipp A1 - Kollmannsberger, Philip A1 - Solimando, Antonio G. A1 - Meierjohann, Svenja A1 - Kübler, Hubert A1 - Bargou, Ralf A1 - Schilling, Bastian A1 - Serfling, Sebastian E. A1 - Buck, Andreas A1 - Werner, Rudolf A. A1 - Lapa, Constantin A1 - Krebs, Markus T1 - Predicting microenvironment in CXCR4- and FAP-positive solid tumors — a pan-cancer machine learning workflow for theranostic target structures JF - Cancers N2 - (1) Background: C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Methods: Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database — representing n = 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in n = 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3) Results: We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4) Conclusions: CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets. KW - machine learning KW - tumor microenvironment KW - immune infiltration KW - angiogenesis KW - mRNA KW - miRNA KW - transcriptome Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-305036 SN - 2072-6694 VL - 15 IS - 2 ER - TY - JOUR A1 - Vedder, Daniel A1 - Leidinger, Ludwig A1 - Sarmento Cabral, Juliano T1 - Propagule pressure and an invasion syndrome determine invasion success in a plant community model JF - Ecology and Evolution N2 - The success of species invasions depends on multiple factors, including propagule pressure, disturbance, productivity, and the traits of native and non-native species. While the importance of many of these determinants has already been investigated in relative isolation, they are rarely studied in combination. Here, we address this shortcoming by exploring the effect of the above-listed factors on the success of invasions using an individual-based mechanistic model. This approach enables us to explicitly control environmental factors (temperature as surrogate for productivity, disturbance, and propagule pressure) as well as to monitor whole-community trait distributions of environmental adaptation, mass, and dispersal abilities. We simulated introductions of plant individuals to an oceanic island to assess which factors and species traits contribute to invasion success. We found that the most influential factors were higher propagule pressure and a particular set of traits. This invasion trait syndrome was characterized by a relative similarity in functional traits of invasive to native species, while invasive species had on average higher environmental adaptation, higher body mass, and increased dispersal distances, that is, had greater competitive and dispersive abilities. Our results highlight the importance in management practice of reducing the import of alien species, especially those that display this trait syndrome and come from similar habitats as those being managed. KW - community trait analysis KW - individual-based modelling KW - island plant communities KW - propagule pressure KW - species invasions Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259107 VL - 11 IS - 23 ER - TY - JOUR A1 - Dirk, Robin A1 - Fischer, Jonas L. A1 - Schardt, Simon A1 - Ankenbrand, Markus J. A1 - Fischer, Sabine C. T1 - Recognition and reconstruction of cell differentiation patterns with deep learning JF - PLoS Computational Biology N2 - Abstract Cell lineage decisions occur in three-dimensional spatial patterns that are difficult to identify by eye. There is an ongoing effort to replicate such patterns using mathematical modeling. One approach uses long ranging cell-cell communication to replicate common spatial arrangements like checkerboard and engulfing patterns. In this model, the cell-cell communication has been implemented as a signal that disperses throughout the tissue. On the other hand, machine learning models have been developed for pattern recognition and pattern reconstruction tasks. We combined synthetic data generated by the mathematical model with spatial summary statistics and deep learning algorithms to recognize and reconstruct cell fate patterns in organoids of mouse embryonic stem cells. Application of Moran’s index and pair correlation functions for in vitro and synthetic data from the model showed local clustering and radial segregation. To assess the patterns as a whole, a graph neural network was developed and trained on synthetic data from the model. Application to in vitro data predicted a low signal dispersion value. To test this result, we implemented a multilayer perceptron for the prediction of a given cell fate based on the fates of the neighboring cells. The results show a 70% accuracy of cell fate imputation based on the nine nearest neighbors of a cell. Overall, our approach combines deep learning with mathematical modeling to link cell fate patterns with potential underlying mechanisms. Author summary Mammalian embryo development relies on organized differentiation of stem cells into different lineages. Particularly at the early stages of embryogenesis, cells of different fates form three-dimensional spatial patterns that are difficult to identify by eye. Pattern quantification and mathematical modeling have produced first insights into potential mechanisms for the cell fate arrangements. However, these approaches have relied on classifications of the patterns such as inside-out or random, or used summary statistics such as pair correlation functions or cluster radii. Deep neural networks allow characterizing patterns directly. Since the tissue context can be readily reproduced by a graph, we implemented a graph neural network to characterize the patterns of embryonic stem cell organoids as a whole. In addition, we implemented a multilayer perceptron model to reconstruct the fate of a given cell based on its neighbors. To train and test the models, we used synthetic data generated by our mathematical model for cell-cell communication. This interplay of deep learning and mathematical modeling in combination with summary statistics allowed us to identify a potential mechanism for cell fate determination in mouse embryonic stem cells. Our results agree with a mechanism with a dispersion of the intercellular signal that links a cell’s fate to those of the local neighborhood. KW - recognition KW - reconstruction KW - cell differentiation patterns KW - deep learning KW - mouse embryonic stem cells KW - multilayer perceptron model Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-350167 VL - 19 IS - 10 ER - TY - JOUR A1 - Reinhard, Sebastian A1 - Helmerich, Dominic A. A1 - Boras, Dominik A1 - Sauer, Markus A1 - Kollmannsberger, Philip T1 - ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy JF - BMC Bioinformatics N2 - Background Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method. Results Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality. Conclusions Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility. KW - compressed sensing KW - AI KW - SMLM KW - FLIMbee KW - dSTORM Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-299768 VL - 23 IS - 1 ER - TY - JOUR A1 - Gupta, Shishir K. A1 - Minocha, Rashmi A1 - Thapa, Prithivi Jung A1 - Srivastava, Mugdha A1 - Dandekar, Thomas T1 - Role of the pangolin in origin of SARS-CoV-2: an evolutionary perspective JF - International Journal of Molecular Sciences N2 - After the recent emergence of SARS-CoV-2 infection, unanswered questions remain related to its evolutionary history, path of transmission or divergence and role of recombination. There is emerging evidence on amino acid substitutions occurring in key residues of the receptor-binding domain of the spike glycoprotein in coronavirus isolates from bat and pangolins. In this article, we summarize our current knowledge on the origin of SARS-CoV-2. We also analyze the host ACE2-interacting residues of the receptor-binding domain of spike glycoprotein in SARS-CoV-2 isolates from bats, and compare it to pangolin SARS-CoV-2 isolates collected from Guangdong province (GD Pangolin-CoV) and Guangxi autonomous regions (GX Pangolin-CoV) of South China. Based on our comparative analysis, we support the view that the Guangdong Pangolins are the intermediate hosts that adapted the SARS-CoV-2 and represented a significant evolutionary link in the path of transmission of SARS-CoV-2 virus. We also discuss the role of intermediate hosts in the origin of Omicron. KW - COVID-19 KW - SARS-CoV-2 KW - origin KW - evolution KW - intermediate host KW - pangolin KW - mutation KW - recombination KW - adaptation KW - transmission KW - comparative sequence analysis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-285995 SN - 1422-0067 VL - 23 IS - 16 ER - TY - JOUR A1 - Berger, Nathalie A1 - Demolombe, Vincent A1 - Hem, Sonia A1 - Rofidal, Valérie A1 - Steinmann, Laura A1 - Krouk, Gabriel A1 - Crabos, Amandine A1 - Nacry, Philippe A1 - Verdoucq, Lionel A1 - Santoni, Véronique T1 - Root membrane ubiquitinome under short-term osmotic stress JF - International Journal of Molecular Sciences N2 - Osmotic stress can be detrimental to plants, whose survival relies heavily on proteomic plasticity. Protein ubiquitination is a central post-translational modification in osmotic-mediated stress. In this study, we used the K-Ɛ-GG antibody enrichment method integrated with high-resolution mass spectrometry to compile a list of 719 ubiquitinated lysine (K-Ub) residues from 450 Arabidopsis root membrane proteins (58% of which are transmembrane proteins), thereby adding to the database of ubiquitinated substrates in plants. Although no ubiquitin (Ub) motifs could be identified, the presence of acidic residues close to K-Ub was revealed. Our ubiquitinome analysis pointed to a broad role of ubiquitination in the internalization and sorting of cargo proteins. Moreover, the simultaneous proteome and ubiquitinome quantification showed that ubiquitination is mostly not involved in membrane protein degradation in response to short osmotic treatment but that it is putatively involved in protein internalization, as described for the aquaporin PIP2;1. Our in silico analysis of ubiquitinated proteins shows that two E2 Ub-conjugating enzymes, UBC32 and UBC34, putatively target membrane proteins under osmotic stress. Finally, we revealed a positive role for UBC32 and UBC34 in primary root growth under osmotic stress. KW - aquaporin KW - mass spectrometry KW - osmotic stress KW - ubiquitination Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-284003 SN - 1422-0067 VL - 23 IS - 4 ER - TY - INPR A1 - Heidenreich, Julius F. A1 - Gassenmaier, Tobias A1 - Ankenbrand, Markus J. A1 - Bley, Thorsten A. A1 - Wech, Tobias T1 - Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction N2 - Purpose To fully automatically derive quantitative parameters from late gadolinium enhancement (LGE) cardiac MR (CMR) in patients with myocardial infarction and to investigate if phase sensitive or magnitude reconstructions or a combination of both results in best segmentation accuracy. Methods In this retrospective single center study, a convolutional neural network with a U-Net architecture with a self-configuring framework (“nnU-net”) was trained for segmentation of left ventricular myocardium and infarct zone in LGE-CMR. A database of 170 examinations from 78 patients with history of myocardial infarction was assembled. Separate fitting of the model was performed, using phase sensitive inversion recovery, the magnitude reconstruction or both contrasts as input channels. Manual labelling served as ground truth. In a subset of 10 patients, the performance of the trained models was evaluated and quantitatively compared by determination of the Sørensen-Dice similarity coefficient (DSC) and volumes of the infarct zone compared with the manual ground truth using Pearson’s r correlation and Bland-Altman analysis. Results The model achieved high similarity coefficients for myocardium and scar tissue. No significant difference was observed between using PSIR, magnitude reconstruction or both contrasts as input (PSIR and MAG; mean DSC: 0.83 ± 0.03 for myocardium and 0.72 ± 0.08 for scars). A strong correlation for volumes of infarct zone was observed between manual and model-based approach (r = 0.96), with a significant underestimation of the volumes obtained from the neural network. Conclusion The self-configuring nnU-net achieves predictions with strong agreement compared to manual segmentation, proving the potential as a promising tool to provide fully automatic quantitative evaluation of LGE-CMR. KW - Deep learning KW - CMR KW - Segmentation KW - Myocardial infarction KW - Scar KW - nnU-net Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323418 UR - https://doi.org/10.1016/j.ejrad.2021.109817 ET - accepted version ER -