@article{DannhaeuserMrestaniGundelachetal.2022, author = {Dannh{\"a}user, Sven and Mrestani, Achmed and Gundelach, Florian and Pauli, Martin and Komma, Fabian and Kollmannsberger, Philip and Sauer, Markus and Heckmann, Manfred and Paul, Mila M.}, title = {Endogenous tagging of Unc-13 reveals nanoscale reorganization at active zones during presynaptic homeostatic potentiation}, series = {Frontiers in Cellular Neuroscience}, volume = {16}, journal = {Frontiers in Cellular Neuroscience}, issn = {1662-5102}, doi = {10.3389/fncel.2022.1074304}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299440}, year = {2022}, abstract = {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.}, language = {en} } @article{KaltdorfSchulzeHelmprobstetal.2017, author = {Kaltdorf, Kristin Verena and Schulze, Katja and Helmprobst, Frederik and Kollmannsberger, Philip and Dandekar, Thomas and Stigloher, Christian}, title = {Fiji macro 3D ART VeSElecT: 3D automated reconstruction tool for vesicle structures of electron tomograms}, series = {PLoS Computational Biology}, volume = {13}, journal = {PLoS Computational Biology}, number = {1}, doi = {10.1371/journal.pcbi.1005317}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172112}, year = {2017}, abstract = {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.}, language = {en} } @article{TrinklKaluzaWallaceetal.2020, author = {Trinkl, Moritz and Kaluza, Benjamin F. and Wallace, Helen and Heard, Tim A. and Keller, Alexander and Leonhardt, Sara D.}, title = {Floral Species Richness Correlates with Changes in the Nutritional Quality of Larval Diets in a Stingless Bee}, series = {Insects}, volume = {11}, journal = {Insects}, number = {2}, issn = {2075-4450}, doi = {10.3390/insects11020125}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-200605}, pages = {125}, year = {2020}, abstract = {Bees need food of appropriate nutritional quality to maintain their metabolic functions. They largely obtain all required nutrients from floral resources, i.e., pollen and nectar. However, the diversity, composition and nutritional quality of floral resources varies with the surrounding environment and can be strongly altered in human-impacted habitats. We investigated whether differences in plant species richness as found in the surrounding environment correlated with variation in the floral diversity and nutritional quality of larval provisions (i.e., mixtures of pollen, nectar and salivary secretions) composed by the mass-provisioning stingless bee Tetragonula carbonaria (Apidae: Meliponini). We found that the floral diversity of larval provisions increased with increasing plant species richness. The sucrose and fat (total fatty acid) content and the proportion and concentration of the omega-6 fatty acid linoleic acid decreased, whereas the proportion of the omega-3 fatty acid linolenic acid increased with increasing plant species richness. Protein (total amino acid) content and amino acid composition did not change. The protein to fat (P:F) ratio, known to affect bee foraging, increased on average by more than 40\% from plantations to forests and gardens, while the omega-6:3 ratio, known to negatively affect cognitive performance, decreased with increasing plant species richness. Our results suggest that plant species richness may support T. carbonaria colonies by providing not only a continuous resource supply (as shown in a previous study), but also floral resources of high nutritional quality.}, language = {en} } @article{BerberichKurzReinhardetal.2021, author = {Berberich, Andreas and Kurz, Andreas and Reinhard, Sebastian and Paul, Torsten Johann and Burd, Paul Ray and Sauer, Markus and Kollmannsberger, Philip}, title = {Fourier Ring Correlation and anisotropic kernel density estimation improve deep learning based SMLM reconstruction of microtubules}, series = {Frontiers in Bioinformatics}, volume = {1}, journal = {Frontiers in Bioinformatics}, doi = {10.3389/fbinf.2021.752788}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-261686}, year = {2021}, abstract = {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.}, language = {en} } @article{GuptaOsmanogluMinochaetal.2022, author = {Gupta, Shishir K. and Osmanoglu, {\"O}zge and Minocha, Rashmi and Bandi, Sourish Reddy and Bencurova, Elena and Srivastava, Mugdha and Dandekar, Thomas}, title = {Genome-wide scan for potential CD4+ T-cell vaccine candidates in Candida auris by exploiting reverse vaccinology and evolutionary information}, series = {Frontiers in Medicine}, volume = {9}, journal = {Frontiers in Medicine}, issn = {2296-858X}, doi = {10.3389/fmed.2022.1008527}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-293953}, year = {2022}, abstract = {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.}, language = {en} } @article{LopezArboledaReinertNordborgetal.2021, author = {Lopez-Arboleda, William Andres and Reinert, Stephan and Nordborg, Magnus and Korte, Arthur}, title = {Global genetic heterogeneity in adaptive traits}, series = {Molecular Biology and Evolution}, volume = {38}, journal = {Molecular Biology and Evolution}, number = {11}, doi = {10.1093/molbev/msab208}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-270410}, pages = {4822-4831}, year = {2021}, abstract = {Understanding the genetic architecture of complex traits is a major objective in biology. The standard approach for doing so is genome-wide association studies (GWAS), which aim to identify genetic polymorphisms responsible for variation in traits of interest. In human genetics, consistency across studies is commonly used as an indicator of reliability. However, if traits are involved in adaptation to the local environment, we do not necessarily expect reproducibility. On the contrary, results may depend on where you sample, and sampling across a wide range of environments may decrease the power of GWAS because of increased genetic heterogeneity. In this study, we examine how sampling affects GWAS in the model plant species Arabidopsis thaliana. We show that traits like flowering time are indeed influenced by distinct genetic effects in local populations. Furthermore, using gene expression as a molecular phenotype, we show that some genes are globally affected by shared variants, whereas others are affected by variants specific to subpopulations. Remarkably, the former are essentially all cis-regulated, whereas the latter are predominately affected by trans-acting variants. Our result illustrate that conclusions about genetic architecture can be extremely sensitive to sampling and population structure.}, language = {en} } @article{SchilcherHilsmannAnkenbrandetal.2022, author = {Schilcher, Felix and Hilsmann, Lioba and Ankenbrand, Markus J. and Krischke, Markus and Mueller, Martin J. and Steffan-Dewenter, Ingolf and Scheiner, Ricarda}, title = {Honeybees are buffered against undernourishment during larval stages}, series = {Frontiers in Insect Science}, volume = {2}, journal = {Frontiers in Insect Science}, issn = {2673-8600}, doi = {10.3389/finsc.2022.951317}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-304646}, year = {2022}, abstract = {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.}, language = {en} } @phdthesis{Leidinger2020, author = {Leidinger, Ludwig Klaus Theodor}, title = {How genomic and ecological traits shape island biodiversity - insights from individual-based models}, doi = {10.25972/OPUS-20730}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207300}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2020}, abstract = {Life on oceanic islands provides a playground and comparably easy\-/studied basis for the understanding of biodiversity in general. Island biota feature many fascinating patterns: endemic species, species radiations and species with peculiar trait syndromes. However, classic and current island biogeography theory does not yet consider all the factors necessary to explain many of these patterns. In response to this, there is currently a shift in island biogeography research to systematically consider species traits and thus gain a more functional perspective. Despite this recent development, a set of species characteristics remains largely ignored in island biogeography, namely genomic traits. Evidence suggests that genomic factors could explain many of the speciation and adaptation patterns found in nature and thus may be highly informative to explain the fascinating and iconic phenomena known for oceanic islands, including species radiations and susceptibility to biotic invasions. Unfortunately, the current lack of comprehensive meaningful data makes studying these factors challenging. Even with paleontological data and space-for-time rationales, data is bound to be incomplete due to the very environmental processes taking place on oceanic islands, such as land slides and volcanism, and lacks causal information due to the focus on correlative approaches. As promising alternative, integrative mechanistic models can explicitly consider essential underlying eco\-/evolutionary mechanisms. In fact, these models have shown to be applicable to a variety of different systems and study questions. In this thesis, I therefore examined present mechanistic island models to identify how they might be used to address some of the current open questions in island biodiversity research. Since none of the models simultaneously considered speciation and adaptation at a genomic level, I developed a new genome- and niche-explicit, individual-based model. I used this model to address three different phenomena of island biodiversity: environmental variation, insular species radiations and species invasions. Using only a single model I could show that small-bodied species with flexible genomes are successful under environmental variation, that a complex combination of dispersal abilities, reproductive strategies and genomic traits affect the occurrence of species radiations and that invasions are primarily driven by the intensity of introductions and the trait characteristics of invasive species. This highlights how the consideration of functional traits can promote the understanding of some of the understudied phenomena in island biodiversity. The results presented in this thesis exemplify the generality of integrative models which are built on first principles. Thus, by applying such models to various complex study questions, they are able to unveil multiple biodiversity dynamics and patterns. The combination of several models such as the one I developed to an eco\-/evolutionary model ensemble could further help to identify fundamental eco\-/evolutionary principles. I conclude the thesis with an outlook on how to use and extend my developed model to investigate geomorphological dynamics in archipelagos and to allow dynamic genomes, which would further increase the model's generality.}, subject = {Inselbiogeografie}, language = {en} } @article{VedderLensMartinetal.2022, author = {Vedder, Daniel and Lens, Luc and Martin, Claudia A. and Pellikka, Petri and Adhikari, Hari and Heiskanen, Janne and Engler, Jan O. and Sarmento Cabral, Juliano}, title = {Hybridization may aid evolutionary rescue of an endangered East African passerine}, series = {Evolutionary Applications}, volume = {15}, journal = {Evolutionary Applications}, number = {7}, doi = {10.1111/eva.13440}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-287264}, pages = {1177-1188}, year = {2022}, abstract = {Abstract Introgressive hybridization is a process that enables gene flow across species barriers through the backcrossing of hybrids into a parent population. This may make genetic material, potentially including relevant environmental adaptations, rapidly available in a gene pool. Consequently, it has been postulated to be an important mechanism for enabling evolutionary rescue, that is the recovery of threatened populations through rapid evolutionary adaptation to novel environments. However, predicting the likelihood of such evolutionary rescue for individual species remains challenging. Here, we use the example of Zosterops silvanus, an endangered East African highland bird species suffering from severe habitat loss and fragmentation, to investigate whether hybridization with its congener Zosterops flavilateralis might enable evolutionary rescue of its Taita Hills population. To do so, we employ an empirically parameterized individual-based model to simulate the species' behaviour, physiology and genetics. We test the population's response to different assumptions of mating behaviour and multiple scenarios of habitat change. We show that as long as hybridization does take place, evolutionary rescue of Z. silvanus is likely. Intermediate hybridization rates enable the greatest long-term population growth, due to trade-offs between adaptive and maladaptive introgressed alleles. Habitat change did not have a strong effect on population growth rates, as Z. silvanus is a strong disperser and landscape configuration is therefore not the limiting factor for hybridization. Our results show that targeted gene flow may be a promising avenue to help accelerate the adaptation of endangered species to novel environments, and demonstrate how to combine empirical research and mechanistic modelling to deliver species-specific predictions for conservation planning.}, language = {en} } @article{MarquardtLandwehrRonchietal.2021, author = {Marquardt, Andr{\´e} and Landwehr, Laura-Sophie and Ronchi, Cristina L. and di Dalmazi, Guido and Riester, Anna and Kollmannsberger, Philip and Altieri, Barbara and Fassnacht, Martin and Sbiera, Silviu}, title = {Identifying New Potential Biomarkers in Adrenocortical Tumors Based on mRNA Expression Data Using Machine Learning}, series = {Cancers}, volume = {13}, journal = {Cancers}, number = {18}, issn = {2072-6694}, doi = {10.3390/cancers13184671}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-246245}, year = {2021}, abstract = {Simple Summary Using a visual-based clustering method on the TCGA RNA sequencing data of a large adrenocortical carcinoma (ACC) cohort, we were able to classify these tumors in two distinct clusters largely overlapping with previously identified ones. As previously shown, the identified clusters also correlated with patient survival. Applying the visual clustering method to a second dataset also including benign adrenocortical samples additionally revealed that one of the ACC clusters is more closely located to the benign samples, providing a possible explanation for the better survival of this ACC cluster. Furthermore, the subsequent use of machine learning identified new possible biomarker genes with prognostic potential for this rare disease, that are significantly differentially expressed in the different survival clusters and should be further evaluated. Abstract Adrenocortical carcinoma (ACC) is a rare disease, associated with poor survival. Several "multiple-omics" studies characterizing ACC on a molecular level identified two different clusters correlating with patient survival (C1A and C1B). We here used the publicly available transcriptome data from the TCGA-ACC dataset (n = 79), applying machine learning (ML) methods to classify the ACC based on expression pattern in an unbiased manner. UMAP (uniform manifold approximation and projection)-based clustering resulted in two distinct groups, ACC-UMAP1 and ACC-UMAP2, that largely overlap with clusters C1B and C1A, respectively. However, subsequent use of random-forest-based learning revealed a set of new possible marker genes showing significant differential expression in the described clusters (e.g., SOAT1, EIF2A1). For validation purposes, we used a secondary dataset based on a previous study from our group, consisting of 4 normal adrenal glands and 52 benign and 7 malignant tumor samples. The results largely confirmed those obtained for the TCGA-ACC cohort. In addition, the ENSAT dataset showed a correlation between benign adrenocortical tumors and the good prognosis ACC cluster ACC-UMAP1/C1B. In conclusion, the use of ML approaches re-identified and redefined known prognostic ACC subgroups. On the other hand, the subsequent use of random-forest-based learning identified new possible prognostic marker genes for ACC.}, language = {en} }