TY - JOUR A1 - Figueiredo, Ludmilla A1 - Krauss, Jochen A1 - Steffan-Dewenter, Ingolf A1 - Cabral, Juliano Sarmento T1 - Understanding extinction debts: spatio-temporal scales, mechanisms and a roadmap for future research JF - Ecography N2 - Extinction debt refers to delayed species extinctions expected as a consequence of ecosystem perturbation. Quantifying such extinctions and investigating long‐term consequences of perturbations has proven challenging, because perturbations are not isolated and occur across various spatial and temporal scales, from local habitat losses to global warming. Additionally, the relative importance of eco‐evolutionary processes varies across scales, because levels of ecological organization, i.e. individuals, (meta)populations and (meta)communities, respond hierarchically to perturbations. To summarize our current knowledge of the scales and mechanisms influencing extinction debts, we reviewed recent empirical, theoretical and methodological studies addressing either the spatio–temporal scales of extinction debts or the eco‐evolutionary mechanisms delaying extinctions. Extinction debts were detected across a range of ecosystems and taxonomic groups, with estimates ranging from 9 to 90% of current species richness. The duration over which debts have been sustained varies from 5 to 570 yr, and projections of the total period required to settle a debt can extend to 1000 yr. Reported causes of delayed extinctions are 1) life‐history traits that prolong individual survival, and 2) population and metapopulation dynamics that maintain populations under deteriorated conditions. Other potential factors that may extend survival time such as microevolutionary dynamics, or delayed extinctions of interaction partners, have rarely been analyzed. Therefore, we propose a roadmap for future research with three key avenues: 1) the microevolutionary dynamics of extinction processes, 2) the disjunctive loss of interacting species and 3) the impact of multiple regimes of perturbation on the payment of debts. For their ability to integrate processes occurring at different levels of ecological organization, we highlight mechanistic simulation models as tools to address these knowledge gaps and to deepen our understanding of extinction dynamics. KW - Anthropocene KW - biotic interaction KW - extinction dynamics KW - mechanistic modelling KW - time lag KW - transient dynamics Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-204859 VL - 42 IS - 12 ER - TY - JOUR A1 - Lichter, Katharina A1 - Paul, Mila Marie A1 - Pauli, Martin A1 - Schoch, Susanne A1 - Kollmannsberger, Philip A1 - Stigloher, Christian A1 - Heckmann, Manfred A1 - Sirén, Anna-Leena T1 - Ultrastructural analysis of wild-type and RIM1α knockout active zones in a large cortical synapse JF - Cell Reports N2 - Rab3A-interacting molecule (RIM) is crucial for fast Ca\(^{2+}\)-triggered synaptic vesicle (SV) release in presynaptic active zones (AZs). We investigated hippocampal giant mossy fiber bouton (MFB) AZ architecture in 3D using electron tomography of rapid cryo-immobilized acute brain slices in RIM1α\(^{−/−}\) and wild-type mice. In RIM1α\(^{−/−}\), AZs are larger with increased synaptic cleft widths and a 3-fold reduced number of tightly docked SVs (0–2 nm). The distance of tightly docked SVs to the AZ center is increased from 110 to 195 nm, and the width of their electron-dense material between outer SV membrane and AZ membrane is reduced. Furthermore, the SV pool in RIM1α\(^{−/−}\) is more heterogeneous. Thus, RIM1α, besides its role in tight SV docking, is crucial for synaptic architecture and vesicle pool organization in MFBs. KW - active zone KW - acute brain slices KW - CA3 KW - electron tomography KW - high-pressure freezing KW - hippocampal mossy fiber bouton KW - RIM1α KW - SV pool KW - synaptic ultrastructure KW - presynaptic Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-300913 VL - 40 IS - 12 ER - TY - JOUR A1 - Bemm, Felix A1 - Becker, Dirk A1 - Larisch, Christina A1 - Kreuzer, Ines A1 - Escalante-Perez, Maria A1 - Schulze, Waltraud X. A1 - Ankenbrand, Markus A1 - Van de Weyer, Anna-Lena A1 - Krol, Elzbieta A1 - Al-Rasheid, Khaled A. A1 - Mithöfer, Axel A1 - Weber, Andreas P. A1 - Schultz, Jörg A1 - Hedrich, Rainer T1 - Venus flytrap carnivorous lifestyle builds on herbivore defense strategies JF - Genome Research N2 - Although the concept of botanical carnivory has been known since Darwin's time, the molecular mechanisms that allow animal feeding remain unknown, primarily due to a complete lack of genomic information. Here, we show that the transcriptomic landscape of the Dionaea trap is dramatically shifted toward signal transduction and nutrient transport upon insect feeding, with touch hormone signaling and protein secretion prevailing. At the same time, a massive induction of general defense responses is accompanied by the repression of cell death-related genes/processes. We hypothesize that the carnivory syndrome of Dionaea evolved by exaptation of ancient defense pathways, replacing cell death with nutrient acquisition. KW - Dionaea-muscipula ellis KW - Plant utricularia-gibba KW - Programmed cell-death KW - Genomics data sets KW - RNA-SEQ data KW - Arabidopsis-thaliana KW - Jasmonate perception KW - Action potentials KW - Stress responses KW - Wonderful plants Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-188799 VL - 26 IS - 6 ER - TY - JOUR A1 - Schilcher, Felix A1 - Hilsmann, Lioba A1 - Ankenbrand, Markus J. A1 - Krischke, Markus A1 - Mueller, Martin J. A1 - Steffan-Dewenter, Ingolf A1 - Scheiner, Ricarda T1 - Honeybees are buffered against undernourishment during larval stages JF - Frontiers in Insect Science N2 - The negative impact of juvenile undernourishment on adult behavior has been well reported for vertebrates, but relatively little is known about invertebrates. In honeybees, nutrition has long been known to affect task performance and timing of behavioral transitions. Whether and how a dietary restriction during larval development affects the task performance of adult honeybees is largely unknown. We raised honeybees in-vitro, varying the amount of a standardized diet (150 µl, 160 µl, 180 µl in total). Emerging adults were marked and inserted into established colonies. Behavioral performance of nurse bees and foragers was investigated and physiological factors known to be involved in the regulation of social organization were quantified. Surprisingly, adult honeybees raised under different feeding regimes did not differ in any of the behaviors observed. No differences were observed in physiological parameters apart from weight. Honeybees were lighter when undernourished (150 µl), while they were heavier under the overfed treatment (180 µl) compared to the control group raised under a normal diet (160 µl). These data suggest that dietary restrictions during larval development do not affect task performance or physiology in this social insect despite producing clear effects on adult weight. We speculate that possible effects of larval undernourishment might be compensated during the early period of adult life. KW - nutrition KW - juvenile hormone KW - nurse bees KW - foragers KW - triglycerides KW - undernourishment KW - task allocation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-304646 SN - 2673-8600 VL - 2 ER - TY - JOUR A1 - Schilcher, Felix A1 - Hilsmann, Lioba A1 - Rauscher, Lisa A1 - Değirmenci, Laura A1 - Krischke, Markus A1 - Krischke, Beate A1 - Ankenbrand, Markus A1 - Rutschmann, Benjamin A1 - Mueller, Martin J. A1 - Steffan-Dewenter, Ingolf A1 - Scheiner, Ricarda T1 - In vitro rearing changes social task performance and physiology in honeybees JF - Insects N2 - In vitro rearing of honeybee larvae is an established method that enables exact control and monitoring of developmental factors and allows controlled application of pesticides or pathogens. However, only a few studies have investigated how the rearing method itself affects the behavior of the resulting adult honeybees. We raised honeybees in vitro according to a standardized protocol: marking the emerging honeybees individually and inserting them into established colonies. Subsequently, we investigated the behavioral performance of nurse bees and foragers and quantified the physiological factors underlying the social organization. Adult honeybees raised in vitro differed from naturally reared honeybees in their probability of performing social tasks. Further, in vitro-reared bees foraged for a shorter duration in their life and performed fewer foraging trips. Nursing behavior appeared to be unaffected by rearing condition. Weight was also unaffected by rearing condition. Interestingly, juvenile hormone titers, which normally increase strongly around the time when a honeybee becomes a forager, were significantly lower in three- and four-week-old in vitro bees. The effects of the rearing environment on individual sucrose responsiveness and lipid levels were rather minor. These data suggest that larval rearing conditions can affect the task performance and physiology of adult bees despite equal weight, pointing to an important role of the colony environment for these factors. Our observations of behavior and metabolic pathways offer important novel insight into how the rearing environment affects adult honeybees. KW - honeybee KW - artificial rearing KW - behavior KW - in vitro KW - juvenile hormone KW - triglycerides KW - PER KW - foraging KW - nursing Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-252305 SN - 2075-4450 VL - 13 IS - 1 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 - Dannhäuser, Sven A1 - Mrestani, Achmed A1 - Gundelach, Florian A1 - Pauli, Martin A1 - Komma, Fabian A1 - Kollmannsberger, Philip A1 - Sauer, Markus A1 - Heckmann, Manfred A1 - Paul, Mila M. T1 - Endogenous tagging of Unc-13 reveals nanoscale reorganization at active zones during presynaptic homeostatic potentiation JF - Frontiers in Cellular Neuroscience N2 - Introduction Neurotransmitter release at presynaptic active zones (AZs) requires concerted protein interactions within a dense 3D nano-hemisphere. Among the complex protein meshwork the (M)unc-13 family member Unc-13 of Drosophila melanogaster is essential for docking of synaptic vesicles and transmitter release. Methods We employ minos-mediated integration cassette (MiMIC)-based gene editing using GFSTF (EGFP-FlAsH-StrepII-TEV-3xFlag) to endogenously tag all annotated Drosophila Unc-13 isoforms enabling visualization of endogenous Unc-13 expression within the central and peripheral nervous system. Results and discussion Electrophysiological characterization using two-electrode voltage clamp (TEVC) reveals that evoked and spontaneous synaptic transmission remain unaffected in unc-13\(^{GFSTF}\) 3rd instar larvae and acute presynaptic homeostatic potentiation (PHP) can be induced at control levels. Furthermore, multi-color structured-illumination shows precise co-localization of Unc-13\(^{GFSTF}\), Bruchpilot, and GluRIIA-receptor subunits within the synaptic mesoscale. Localization microscopy in combination with HDBSCAN algorithms detect Unc-13\(^{GFSTF}\) subclusters that move toward the AZ center during PHP with unaltered Unc-13\(^{GFSTF}\) protein levels. KW - active zone KW - Unc-13 KW - MiMIC KW - presynaptic homeostasis KW - nanoarchitecture KW - localization microscopy KW - STORM KW - HDBSCAN Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-299440 SN - 1662-5102 VL - 16 ER - TY - JOUR A1 - Kaltdorf, Kristin Verena A1 - Schulze, Katja A1 - Helmprobst, Frederik A1 - Kollmannsberger, Philip A1 - Dandekar, Thomas A1 - Stigloher, Christian T1 - Fiji macro 3D ART VeSElecT: 3D automated reconstruction tool for vesicle structures of electron tomograms JF - PLoS Computational Biology N2 - Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial. KW - Biology KW - Vesicles KW - Caenorhabditis elegans KW - Zebrafish KW - Septins KW - Synaptic vesicles KW - Neuromuscular junctions KW - Computer software KW - Synapses Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-172112 VL - 13 IS - 1 ER - 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 - Berberich, Andreas A1 - Kurz, Andreas A1 - Reinhard, Sebastian A1 - Paul, Torsten Johann A1 - Burd, Paul Ray A1 - Sauer, Markus A1 - Kollmannsberger, Philip T1 - Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules JF - Frontiers in Bioinformatics N2 - Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the localization precision of individual fluorescent molecules, but also by their density, which becomes a limiting factor e.g., in expansion microscopy. Artificial deep neural networks can learn to reconstruct dense super-resolved structures such as microtubules from a sparse, noisy set of data points. This approach requires a robust method to assess the quality of a predicted density image and to quantitatively compare it to a ground truth image. Such a quality measure needs to be differentiable to be applied as loss function in deep learning. We developed a new trainable quality measure based on Fourier Ring Correlation (FRC) and used it to train deep neural networks to map a small number of sampling points to an underlying density. Smooth ground truth images of microtubules were generated from localization coordinates using an anisotropic Gaussian kernel density estimator. We show that the FRC criterion ideally complements the existing state-of-the-art multiscale structural similarity index, since both are interpretable and there is no trade-off between them during optimization. The TensorFlow implementation of our FRC metric can easily be integrated into existing deep learning workflows. KW - dSTORM KW - deep learning–artificial neural network (DL-ANN) KW - single molecule localization microscopy KW - microtubule cytoskeleton KW - super-resolution Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-261686 VL - 1 ER -