@article{MrestaniPauliKollmannsbergeretal.2021, author = {Mrestani, Achmed and Pauli, Martin and Kollmannsberger, Philip and Repp, Felix and Kittel, Robert J. and Eilers, Jens and Doose, S{\"o}ren and Sauer, Markus and Sir{\´e}n, Anna-Leena and Heckmann, Manfred and Paul, Mila M.}, title = {Active zone compaction correlates with presynaptic homeostatic potentiation}, series = {Cell Reports}, volume = {37}, journal = {Cell Reports}, number = {1}, doi = {10.1016/j.celrep.2021.109770}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-265497}, pages = {109770}, year = {2021}, abstract = {Neurotransmitter release is stabilized by homeostatic plasticity. Presynaptic homeostatic potentiation (PHP) operates on timescales ranging from minute- to life-long adaptations and likely involves reorganization of presynaptic active zones (AZs). At Drosophila melanogaster neuromuscular junctions, earlier work ascribed AZ enlargement by incorporating more Bruchpilot (Brp) scaffold protein a role in PHP. We use localization microscopy (direct stochastic optical reconstruction microscopy [dSTORM]) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) to study AZ plasticity during PHP at the synaptic mesoscale. We find compaction of individual AZs in acute philanthotoxin-induced and chronic genetically induced PHP but unchanged copy numbers of AZ proteins. Compaction even occurs at the level of Brp subclusters, which move toward AZ centers, and in Rab3 interacting molecule (RIM)-binding protein (RBP) subclusters. Furthermore, correlative confocal and dSTORM imaging reveals how AZ compaction in PHP translates into apparent increases in AZ area and Brp protein content, as implied earlier.}, language = {en} } @article{KaltdorfTheissMarkertetal.2018, author = {Kaltdorf, Kristin Verena and Theiss, Maria and Markert, Sebastian Matthias and Zhen, Mei and Dandekar, Thomas and Stigloher, Christian and Kollmannsberger, Philipp}, title = {Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning}, series = {PLoS ONE}, volume = {13}, journal = {PLoS ONE}, number = {10}, doi = {10.1371/journal.pone.0205348}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176831}, pages = {e0205348}, year = {2018}, abstract = {Synaptic vesicles (SVs) are a key component of neuronal signaling and fulfil different roles depending on their composition. In electron micrograms of neurites, two types of vesicles can be distinguished by morphological criteria, the classical "clear core" vesicles (CCV) and the typically larger "dense core" vesicles (DCV), with differences in electron density due to their diverse cargos. Compared to CCVs, the precise function of DCVs is less defined. DCVs are known to store neuropeptides, which function as neuronal messengers and modulators [1]. In C. elegans, they play a role in locomotion, dauer formation, egg-laying, and mechano- and chemosensation [2]. Another type of DCVs, also referred to as granulated vesicles, are known to transport Bassoon, Piccolo and further constituents of the presynaptic density in the center of the active zone (AZ), and therefore are important for synaptogenesis [3]. To better understand the role of different types of SVs, we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using image-based features. Analysis of the underlying ground truth data shows an increased fraction of DCVs as well as a higher mean distance between DCVs and AZs in dauer larvae compared to young adult hermaphrodites. Our machine learning based tools are adaptable and can be applied to study properties of different synaptic vesicle pools in electron tomograms of diverse model organisms.}, language = {en} } @article{PaulKollmannsberger2020, author = {Paul, Torsten Johann and Kollmannsberger, Philip}, title = {Biological network growth in complex environments: A computational framework}, series = {PLoS Computational Biology}, volume = {16}, journal = {PLoS Computational Biology}, number = {11}, doi = {10.1371/journal.pcbi.1008003}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-231373}, year = {2020}, abstract = {Spatial biological networks are abundant on all scales of life, from single cells to ecosystems, and perform various important functions including signal transmission and nutrient transport. These biological functions depend on the architecture of the network, which emerges as the result of a dynamic, feedback-driven developmental process. While cell behavior during growth can be genetically encoded, the resulting network structure depends on spatial constraints and tissue architecture. Since network growth is often difficult to observe experimentally, computer simulations can help to understand how local cell behavior determines the resulting network architecture. We present here a computational framework based on directional statistics to model network formation in space and time under arbitrary spatial constraints. Growth is described as a biased correlated random walk where direction and branching depend on the local environmental conditions and constraints, which are presented as 3D multilayer grid. To demonstrate the application of our tool, we perform growth simulations of a dense network between cells and compare the results to experimental data from osteocyte networks in bone. Our generic framework might help to better understand how network patterns depend on spatial constraints, or to identify the biological cause of deviations from healthy network function. Author summary We present a novel modeling approach and computational implementation to better understand the development of spatial biological networks under the influence of external signals. Our tool allows us to study the relationship between local biological growth parameters and the emerging macroscopic network function using simulations. This computational approach can generate plausible network graphs that take local feedback into account and provide a basis for comparative studies using graph-based methods.}, language = {en} } @article{VedderAnkenbrandSarmentoCabral2021, author = {Vedder, Daniel and Ankenbrand, Markus and Sarmento Cabral, Juliano}, title = {Dealing with software complexity in individual-based models}, series = {Methods in Ecology and Evolution}, volume = {12}, journal = {Methods in Ecology and Evolution}, number = {12}, doi = {10.1111/2041-210X.13716}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-258214}, pages = {2324-2333}, year = {2021}, abstract = {Individual-based models are doubly complex: as well as representing complex ecological systems, the software that implements them is complex in itself. Both forms of complexity must be managed to create reliable models. However, the ecological modelling literature to date has focussed almost exclusively on the biological complexity. Here, we discuss methods for containing software complexity. Strategies for containing complexity include avoiding, subdividing, documenting and reviewing it. Computer science has long-established techniques for all of these strategies. We present some of these techniques and set them in the context of IBM development, giving examples from published models. Techniques for avoiding software complexity are following best practices for coding style, choosing suitable programming languages and file formats and setting up an automated workflow. Complex software systems can be made more tractable by encapsulating individual subsystems. Good documentation needs to take into account the perspectives of scientists, users and developers. Code reviews are an effective way to check for errors, and can be used together with manual or automated unit and integration tests. Ecological modellers can learn from computer scientists how to deal with complex software systems. Many techniques are readily available, but must be disseminated among modellers. There is a need for further work to adapt software development techniques to the requirements of academic research groups and individual-based modelling.}, language = {en} } @article{LewerentzHoffmannSarmentoCabral2021, author = {Lewerentz, Anne and Hoffmann, Markus and Sarmento Cabral, Juliano}, title = {Depth diversity gradients of macrophytes: Shape, drivers, and recent shifts}, series = {Ecology and Evolution}, volume = {11}, journal = {Ecology and Evolution}, number = {20}, doi = {10.1002/ece3.8089}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-260280}, pages = {13830-13845}, year = {2021}, abstract = {Investigating diversity gradients helps to understand biodiversity drivers and threats. However, one diversity gradient is rarely assessed, namely how plant species distribute along the depth gradient of lakes. Here, we provide the first comprehensive characterization of depth diversity gradient (DDG) of alpha, beta, and gamma species richness of submerged macrophytes across multiple lakes. We characterize the DDG for additive richness components (alpha, beta, gamma), assess environmental drivers, and address temporal change over recent years. We take advantage of yet the largest dataset of macrophyte occurrence along lake depth (274 depth transects across 28 deep lakes) as well as of physiochemical measurements (12 deep lakes from 2006 to 2017 across Bavaria), provided publicly online by the Bavarian State Office for the Environment. We found a high variability in DDG shapes across the study lakes. The DDGs for alpha and gamma richness are predominantly hump-shaped, while beta richness shows a decreasing DDG. Generalized additive mixed-effect models indicate that the depth of the maximum richness (Dmax) is influenced by light quality, light quantity, and layering depth, whereas the respective maximum alpha richness within the depth gradient (Rmax) is significantly influenced by lake area only. Most observed DDGs seem generally stable over recent years. However, for single lakes we found significant linear trends for Rmax and Dmax going into different directions. The observed hump-shaped DDGs agree with three competing hypotheses: the mid-domain effect, the mean-disturbance hypothesis, and the mean-productivity hypothesis. The DDG amplitude seems driven by lake area (thus following known species-area relationships), whereas skewness depends on physiochemical factors, mainly water transparency and layering depth. Our results provide insights for conservation strategies and for mechanistic frameworks to disentangle competing explanatory hypotheses for the DDG.}, language = {en} } @article{Korte2022, author = {Korte, Arthur}, title = {Der Zusammenhang zwischen Genom und Ph{\"a}notyp}, series = {BIOspektrum}, volume = {28}, journal = {BIOspektrum}, number = {3}, issn = {0947-0867}, doi = {10.1007/s12268-022-1765-y}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324231}, pages = {279-282}, year = {2022}, abstract = {Understanding the causal relationship between genotype and phenotype is a major objective in biology. Genome-wide association studies (GWAS) correlate genetic polymorphisms with trait variation and have already identified causative variants for various traits in many different organisms, from humans to plants. Importantly, many adaptive traits, like the regulation of flowering time in plants, are not regulated by distinct genetic effects, but by more sophisticated gene regulatory networks.}, language = {de} } @phdthesis{Fuellsack2019, author = {F{\"u}llsack, Simone Alexandra}, title = {Die Bedeutung von Todesdom{\"a}ne Adapterproteinen f{\"u}r die Signaltransduktion des TNFR1 und der TRAIL Todesrezeptoren}, doi = {10.25972/OPUS-18451}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-184518}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2019}, abstract = {Die NFκB-Signalwege, Apoptose und Nekroptose sind essentielle Prozesse in der Immunantwort. Außerdem sind diese Signalwege Teil der Regulation von Zelldifferenzierung, -proliferation, -tod und Entz{\"u}ndungsreaktionen. Dabei wird zuerst der Rezeptor (TNFR1 oder TRAILR 1/2) aktiviert, die rekrutierten DD-Adapterproteine TRADD, FADD und RIPK1 leiten dann die entsprechende Signalkaskade weiter und bestimmen durch ihre Zusammenwirkung, ob der NFκB-Signalweg, Apoptose oder Nekroptose induziert wird. TNFR1 und TRAILR 1/2 ben{\"o}tigen die DD-Adapterproteine TRADD, FADD und RIPK1 f{\"u}r die Zelltodinduktion, deren konkrete Bedeutung in Bezug auf Rezeptor-Spezifit{\"a}t, Zusammenwirken und Relevanz allerdings noch unklar ist. Um das Zusammenspiel dieser Proteine besser zu verstehen, wurden in dieser Arbeit Nekroptose-kompetente RIPK3-exprimierende HeLa-Zellen verwendet, bei denen die DD-Adapterproteine FADD, TRADD und RIPK1 einzeln oder in Kombination von zweien ausgeknockt wurden. Es stellte sich heraus, dass RIPK1 essentiell f{\"u}r die TNFR1- und TRAILR 1/2-vermittelte Nekroptose-Induktion ist, doch RIPK1 alleine, d.h. ohne FADD- oder TRADD-Mitbeteiligung, nur bei der TNFR1-Nekroptose-Induktion ausreicht. Wiederum inhibiert TRADD die TNFR1- und TRAILR 1/2-induzierte Nekroptose. RIPK1 und TRADD sind aber unverzichtbar f{\"u}r die NFκB-Aktivierung durch TNFR1 oder TRAILR 1/2 und spielen eine wichtige Rolle bei TNFR1-induzierter Apoptose. Andererseits ist FADD alleine ausreichend f{\"u}r die TRAILR 1/2-bezogene Caspase-8 Aktivierung. Zudem ist FADD notwendig f{\"u}r die TRAIL-induzierte NFκB-Signalaktivierung. In Abwesenheit von FADD und TRADD vermittelt RIPK1 die TNF-induzierte Caspase-8 Aktivierung. FADD wird f{\"u}r die TRAIL-induzierte Nekroptose ben{\"o}tigt, aber gegenl{\"a}ufig wirkt die TNF-induzierte Nektroptose in einer Caspase-8 abh{\"a}ngigen und unabh{\"a}ngigen Weise. Zudem sensitiviert TWEAK die TNF- und TRAIL-induzierte Nekroptose. Zusammenfassend wurde in dieser Arbeit die Auswirkung von TNFR1 und TRAILR 1/2 auf die Aktivierung der unterschiedlichen Signalkaskaden untersucht. Des Weiteren wurde gezeigt, in welcher Weise sich das Zusammenspiel von TRADD, FADD und RIPK1 auf die Induktion von NFκB, Apoptose und Nekroptose auswirkt.}, subject = {Signaltransduktion}, language = {de} } @article{PetersKellerLeonhardt2022, author = {Peters, Birte and Keller, Alexander and Leonhardt, Sara Diana}, title = {Diets maintained in a changing world: Does land-use intensification alter wild bee communities by selecting for flexible generalists?}, series = {Ecology and evolution}, volume = {12}, journal = {Ecology and evolution}, number = {5}, issn = {2045-7758}, doi = {10.1002/ece3.8919}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312786}, year = {2022}, abstract = {Biodiversity loss, as often found in intensively managed agricultural landscapes, correlates with reduced ecosystem functioning, for example, pollination by insects, and with altered plant composition, diversity, and abundance. But how does this change in floral resource diversity and composition relate to occurrence and resource use patterns of trap-nesting solitary bees? To better understand the impact of land-use intensification on communities of trap-nesting solitary bees in managed grasslands, we investigated their pollen foraging, reproductive fitness, and the nutritional quality of larval food along a land-use intensity gradient in Germany. We found bee species diversity to decrease with increasing land-use intensity irrespective of region-specific community compositions and interaction networks. Land use also strongly affected the diversity and composition of pollen collected by bees. Lack of suitable pollen sources likely explains the absence of several bee species at sites of high land-use intensity. The only species present throughout, Osmia bicornis (red mason bee), foraged on largely different pollen sources across sites. In doing so, it maintained a relatively stable, albeit variable nutritional quality of larval diets (i.e., protein to lipid (P:L) ratio). The observed changes in bee-plant pollen interaction patterns indicate that only the flexible generalists, such as O. bicornis, may be able to compensate the strong alterations in floral resource landscapes and to obtain food of sufficient quality through readily shifting to alternative plant sources. In contrast, other, less flexible, bee species disappear.}, language = {en} } @article{NaglerNaegeleGillietal.2018, author = {Nagler, Matthias and N{\"a}gele, Thomas and Gilli, Christian and Fragner, Lena and Korte, Arthur and Platzer, Alexander and Farlow, Ashley and Nordborg, Magnus and Weckwerth, Wolfram}, title = {Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field}, series = {Frontiers in Plant Science}, volume = {9}, journal = {Frontiers in Plant Science}, number = {1556}, issn = {1664-462X}, doi = {10.3389/fpls.2018.01556}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-189560}, year = {2018}, abstract = {Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.}, language = {en} } @article{SahlolKollmannsbergerEwees2020, author = {Sahlol, Ahmed T. and Kollmannsberger, Philip and Ewees, Ahmed A.}, title = {Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features}, series = {Scientific Reports}, volume = {10}, journal = {Scientific Reports}, number = {1}, doi = {10.1038/s41598-020-59215-9}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229398}, year = {2020}, abstract = {White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.}, language = {en} }