Theodor-Boveri-Institut für Biowissenschaften
Refine
Has Fulltext
- yes (19)
Is part of the Bibliography
- yes (19)
Document Type
- Journal article (18)
- Doctoral Thesis (1)
Language
- English (19)
Keywords
- active zone (4)
- Caenorhabditis elegans (2)
- bee decline (2)
- dSTORM (2)
- evolution (2)
- foraging (2)
- juvenile hormone (2)
- nutrition (2)
- triglycerides (2)
- 3D reconstruction (1)
Institute
- Center for Computational and Theoretical Biology (19) (remove)
EU-Project number / Contract (GA) number
- 250194-Carnivorom (1)
- 835102) (1)
Ultrastructural analysis of wild-type and RIM1α knockout active zones in a large cortical synapse
(2022)
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.
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.
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.
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.
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.
Candida auris is a globally emerging fungal pathogen responsible for causing nosocomial outbreaks in healthcare associated settings. It is known to cause infection in all age groups and exhibits multi-drug resistance with high potential for horizontal transmission. Because of this reason combined with limited therapeutic choices available, C. auris infection has been acknowledged as a potential risk for causing a future pandemic, and thus seeking a promising strategy for its treatment is imperative. Here, we combined evolutionary information with reverse vaccinology approach to identify novel epitopes for vaccine design that could elicit CD4+ T-cell responses against C. auris. To this end, we extensively scanned the family of proteins encoded by C. auris genome. In addition, a pathogen may acquire substitutions in epitopes over a period of time which could cause its escape from the immune response thus rendering the vaccine ineffective. To lower this possibility in our design, we eliminated all rapidly evolving genes of C. auris with positive selection. We further employed highly conserved regions of multiple C. auris strains and identified two immunogenic and antigenic T-cell epitopes that could generate the most effective immune response against C. auris. The antigenicity scores of our predicted vaccine candidates were calculated as 0.85 and 1.88 where 0.5 is the threshold for prediction of fungal antigenic sequences. Based on our results, we conclude that our vaccine candidates have the potential to be successfully employed for the treatment of C. auris infection. However, in vivo experiments are imperative to further demonstrate the efficacy of our design.
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.
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.
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.
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.