Filtern
Volltext vorhanden
- ja (46)
Gehört zur Bibliographie
- ja (46)
Dokumenttyp
Schlagworte
- machine learning (5)
- active zone (4)
- Caenorhabditis elegans (2)
- bee decline (2)
- dSTORM (2)
- deep learning (2)
- evolution (2)
- foraging (2)
- genetic architecture (2)
- juvenile hormone (2)
- nutrition (2)
- phenotype (2)
- triglycerides (2)
- 3D reconstruction (1)
- AI (1)
- Ackerschmalwand (1)
- Action potentials (1)
- Acute lymphocytic leukaemia (1)
- Agrobacterium (1)
- Allorhizobium vitis (1)
- Anthropocene (1)
- Arabidopsis thaliana (1)
- Arabidopsis-thaliana (1)
- Artenreichtum (1)
- Bacillus (1)
- Biodiversität (1)
- Biologisches Modell (1)
- Biology (1)
- Bruchpilot (1)
- CA2+ channels (1)
- CA3 (1)
- CA3 pyrimidal cells (1)
- CMR (1)
- COVID-19 (1)
- Caenorhabditis elegans (C. elegans) (1)
- Co-occurrence matrix (1)
- Computer science (1)
- Computer software (1)
- Deep learning (1)
- Dionaea-muscipula ellis (1)
- Ecology (1)
- FIB-SEM (1)
- FLIMbee (1)
- GWAS (1)
- Gasaustausch (1)
- Genetik (1)
- Genom (1)
- Genomics data sets (1)
- Genomweite Assoziationstudie (GWAS) (1)
- Germany (1)
- Global change (1)
- HDBSCAN (1)
- ICM cells (1)
- IL2 branching (1)
- Image processing (1)
- Inselbiogeografie (1)
- Jacobian matrix (1)
- Jasmonate perception (1)
- Keras (1)
- Klimaänderung (1)
- Macrophytes (1)
- Makrophyten (1)
- Mechanistic model (1)
- MiMIC (1)
- Mikroevolution (1)
- Myocardial infarction (1)
- Neuromuscular junctions (1)
- Osmia bicornis (1)
- PER (1)
- Paenibacillus (1)
- Phänotyp (1)
- Plant utricularia-gibba (1)
- Programmed cell-death (1)
- RIM-binding protein (1)
- RIM1α (1)
- RNA-SEQ data (1)
- Rezeptoren (1)
- SARS-CoV-2 (1)
- SMLM (1)
- SNP (1)
- STORM (1)
- SV channel (1)
- SV pool (1)
- Scar (1)
- Segmentation (1)
- Septins (1)
- Signaltransduktion (1)
- Simulation (1)
- Species richness (1)
- Sporosarcina (1)
- Stress responses (1)
- Synapses (1)
- Synaptic vesicles (1)
- T-cell epitope (1)
- TNFR1 (1)
- TPC1 (1)
- TRAIL (1)
- Taita Hills (1)
- Ti plasmids (1)
- Todesdomäne (1)
- UMAP (1)
- Unc-13 (1)
- Vesicles (1)
- Vitis vinifera (1)
- Water Framework Directive (1)
- Wonderful plants (1)
- Zebrafish (1)
- Zosterops silvanus (1)
- acute brain slices (1)
- adaptation (1)
- adapterprotein (1)
- adrenocortical carcinoma (1)
- alveolar gas exchange (1)
- alveolarer Gasaustausch (1)
- amphids (1)
- angiogenesis (1)
- aquaporin (1)
- aquatic plants (1)
- artificial rearing (1)
- bacterial community (1)
- bacterial transmission (1)
- behavior (1)
- biodiversity (1)
- biodiversity exploratories (1)
- biodiversity gradients (1)
- biodiversity hypotheses (1)
- bioinformatic clustering (1)
- biomarker prediction (1)
- biotic interaction (1)
- breeding (1)
- cancer (1)
- cell differentiation patterns (1)
- cellular physiology (1)
- co-expression coefficient (1)
- community trait analysis (1)
- compaction (1)
- comparative sequence analysis (1)
- compressed sensing (1)
- connectome (1)
- data-driven in silico modeling (1)
- datengesteuerte in silico Modellierung (1)
- dauer (1)
- de novo sequenced genomes (1)
- deep lakes (1)
- deep learning–artificial neural network (DL-ANN) (1)
- developmental biology (1)
- eco-evolutionary feedbacks (1)
- eco-metabolomics (1)
- ecological modelling (1)
- ectoine biosynthesis (1)
- electron tomography (1)
- endometriosis (1)
- epitope prediction (1)
- evolutionary genomics (1)
- evolutionary rescue (1)
- expression (1)
- extinction dynamics (1)
- floral resources (1)
- foragers (1)
- gene expression networks (1)
- generation (1)
- genomes of photosynthetic bacteria (1)
- genomic selection (1)
- genotype (1)
- glycine betaine biosynthesis (1)
- green systems biology (1)
- habitat change (1)
- high-pressure freezing (1)
- hippocampal (1)
- hippocampal mossy fiber bouton (1)
- homeostasis (1)
- honeybee (1)
- hub genes (1)
- immune infiltration (1)
- immune-informatics (1)
- in silico analysis (1)
- in situ analysis (1)
- in vitro (1)
- individual-based modelling (1)
- individual-based models (1)
- individual‐based model (1)
- interactive simulation (1)
- interaktive Simulation (1)
- intermediate host (1)
- introgressive hybridization (1)
- island biogeography (1)
- island plant communities (1)
- kidney cancer (1)
- local adaptation (1)
- local cell neighborhood (1)
- localization microscopy (1)
- mRNA (1)
- mTOR (1)
- mass spectrometry (1)
- mechanisms (1)
- mechanistic modelling (1)
- mechanistic models (1)
- metabarcoding (1)
- metabolic modeling (1)
- metabolomics (1)
- metastasis (1)
- miRNA (1)
- microbiome (1)
- microtubule cytoskeleton (1)
- mitochondrial DNA (1)
- model complexity (1)
- model organism (1)
- module (1)
- mossy fiber synapses (1)
- mouse blastocysts (1)
- mouse embryonic stem cells (1)
- mtDNA (1)
- multilayer perceptron model (1)
- mutation (1)
- nanoarchitecture (1)
- natural variation (1)
- neuroanatomy (1)
- nnU-net (1)
- non-sense mutations (1)
- nurse bees (1)
- nursing (1)
- origin (1)
- osmotic adaptation (1)
- osmotic stress (1)
- osteocyte network (1)
- ouf mutants (1)
- pan-RCC (1)
- pangolin (1)
- pathogen (1)
- phenotype prediction (1)
- phylogeny of osmolyte biosynthesis (1)
- plant-insect interactions (1)
- plasticity (1)
- platform (1)
- pollen nutrients (1)
- pollen provisions (1)
- positive selection (1)
- presynaptic (1)
- presynaptic homeostasis (1)
- presynaptic plasticity (1)
- propagule pressure (1)
- proteins (1)
- recognition (1)
- recombination (1)
- reconstruction (1)
- regulation of gene expression (1)
- release (1)
- research software engineering (1)
- reveals (1)
- salt-and-pepper pattern (1)
- secondary invader (1)
- selection (1)
- shape (1)
- signaling (1)
- single molecule localization microscopy (1)
- software complexity (1)
- software development (1)
- solitary bee (1)
- solitary bees (1)
- species invasions (1)
- super-resolution (1)
- super-resolution microscopy (1)
- synaptic ultrastructure (1)
- synaptic vesicles (1)
- t-SNE (1)
- task allocation (1)
- time lag (1)
- transcriptome (1)
- transcriptomic analysis (1)
- transient dynamics (1)
- transmission (1)
- tumor microenvironment (1)
- ubiquitination (1)
- undernourishment (1)
- variations in genome (1)
- visual clustering (1)
- weighted gene co-expression network (1)
- Ökologie (1)
Institut
- Center for Computational and Theoretical Biology (46) (entfernen)
EU-Projektnummer / Contract (GA) number
- 2020010013 (1)
- 250194-Carnivorom (1)
- 835102) (1)
Although many genes have been identified using high throughput technologies in endometriosis (ES), only a small number of individual genes have been analyzed functionally. This is due to the complexity of the disease that has different stages and is affected by various genetic and environmental factors. Many genes are upregulated or downregulated at each stage of the disease, thus making it difficult to identify key genes. In addition, little is known about the differences between the different stages of the disease. We assumed that the study of the identified genes in ES at a system-level can help to better understand the molecular mechanism of the disease at different stages of the development. We used publicly available microarray data containing archived endometrial samples from women with minimal/mild endometriosis (MMES), mild/severe endometriosis (MSES) and without endometriosis. Using weighted gene co-expression analysis (WGCNA), functional modules were derived from normal endometrium (NEM) as the reference sample. Subsequently, we tested whether the topology or connectivity pattern of the modules was preserved in MMES and/or MSES. Common and specific hub genes were identified in non-preserved modules. Accordingly, hub genes were detected in the non-preserved modules at each stage. We identified sixteen co-expression modules. Of the 16 modules, nine were non-preserved in both MMES and MSES whereas five were preserved in NEM, MMES, and MSES. Importantly, two non-preserved modules were found in either MMES or MSES, highlighting differences between the two stages of the disease. Analyzing the hub genes in the non-preserved modules showed that they mostly lost or gained their centrality in NEM after developing the disease into MMES and MSES. The same scenario was observed, when the severeness of the disease switched from MMES to MSES. Interestingly, the expression analysis of the new selected gene candidates including CC2D2A, AEBP1, HOXB6, IER3, and STX18 as well as IGF-1, CYP11A1 and MMP-2 could validate such shifts between different stages. The overrepresented gene ontology (GO) terms were enriched in specific modules, such as genetic disposition, estrogen dependence, progesterone resistance and inflammation, which are known as endometriosis hallmarks. Some modules uncovered novel co-expressed gene clusters that were not previously discovered.
Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.
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.
The prediction of breeding values and phenotypes is of central importance for both livestock and crop breeding. In this study, we analyze the use of artificial neural networks (ANN) and, in particular, local convolutional neural networks (LCNN) for genomic prediction, as a region-specific filter corresponds much better with our prior genetic knowledge on the genetic architecture of traits than traditional convolutional neural networks. Model performances are evaluated on a simulated maize data panel (n = 10,000; p = 34,595) and real Arabidopsis data (n = 2,039; p = 180,000) for a variety of traits based on their predictive ability. The baseline LCNN, containing one local convolutional layer (kernel size: 10) and two fully connected layers with 64 nodes each, is outperforming commonly proposed ANNs (multi layer perceptrons and convolutional neural networks) for basically all considered traits. For traits with high heritability and large training population as present in the simulated data, LCNN are even outperforming state-of-the-art methods like genomic best linear unbiased prediction (GBLUP), Bayesian models and extended GBLUP, indicated by an increase in predictive ability of up to 24%. However, for small training populations, these state-of-the-art methods outperform all considered ANNs. Nevertheless, the LCNN still outperforms all other considered ANNs by around 10%. Minor improvements to the tested baseline network architecture of the LCNN were obtained by increasing the kernel size and of reducing the stride, whereas the number of subsequent fully connected layers and their node sizes had neglectable impact. Although gains in predictive ability were obtained for large scale data sets by using LCNNs, the practical use of ANNs comes with additional problems, such as the need of genotyping all considered individuals, the lack of estimation of heritability and reliability. Furthermore, breeding values are additive by design, whereas ANN-based estimates are not. However, ANNs also comes with new opportunities, as networks can easily be extended to account for additional inputs (omics, weather etc.) and outputs (multi-trait models), and computing time increases linearly with the number of individuals. With advances in high-throughput phenotyping and cheaper genotyping, ANNs can become a valid alternative for genomic prediction.
Understanding extinction debts: spatio-temporal scales, mechanisms and a roadmap for future research
(2019)
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.
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.
In the framework of the presented doctoral thesis, the plant ubiquitous, non-selective vacuolar cation channel TPC1/SV was electrophysiologically studied in Arabidopsis thaliana mesophyll vacuoles to further enlighten its physiological role in plant stress responses. For this, the hyperactive channel version fou2 (D454N), gaining a non-functional vacuolar calcium sensor, strong retarded growth phenotype and upregulated JA signalling pathway, and eight fou2 reverting WT-like ouf mutants were used. Except of ouf4, all other seven ouf mutants carried a 2nd mutation in the TPC1 gene. Therefore, the TPC1 electrical features of all ouf mutants were electrophysiologically characterized with the patch clamp method and compared with fou2 and WT.
Due to a missense mutation, ouf1 and ouf7 mutants harboured a truncated TPC1 channel protein, resulting in an impaired protein integrity and in turn loss of TPC1 channel activity. Accordingly, ouf1 and ouf7 mimicked the tpc1-2 null mutant with a WT- rather fou2-like phenotype. The ouf2 (G583D D454N) mutant exhibited inactive TPC1 channels, probably because the G583D mutation located in luminal part of the S11 helix caused (i) a shift of the activation threshold to much more positive voltages (i.e. to more than +110 mV) (ii) or channel blockage. As a result of the TPC1 channel inactivity, the ouf2 mutant also imitates the WT-like phenotype of the tpc1-2 null mutant. In the ouf6 mutant (A669V D454N) the 2nd reverting mutation selectively influenced fou2-like SV channel features. Both, the fast activation kinetics and reduced luminal calcium sensitivity were similar in ouf6 and fou2. However, deviations in both, the relative and absolute open channel probability, resulted in strongly reduced (80 %) current density at 0 mM and channel inactivity in the voltage range between -30 mV to +40 mV compared to fou2 and WT. Furthermore, the TPC1 channels in ouf6 exhibited a higher susceptibility to inhibitory luminal Ca2+ than fou2. As a result of these different effects, the TPC1 channel activity almost vanished at high luminal Ca2+ loads, what is very likely the reason that ouf6 lost the fou2-like phenotype. The ouf4 mutation did not change the fou2 TPC1-channel features like fast channel activation, single channel conductance and voltage-dependent gating behaviour. Nevertheless, the TPC1 current density was 80% less in ouf4 than in fou2. Since the TPC1 gene was not the target of the 2nd mutation, it can be assumed that it is modulated via external, yet unknown factor. In the ouf8 mutant the TPC1 channels additionally possess M629I mutation within the selectivity filter II resulting in a 50% decrease in the TPC1 unitary conductance. However, the slightly increased relative open channel probability of the TPC1 channels in ouf8 compared to fou2 appeared to be sufficient to compensate the reduced transport capacity of individual TPC1 channels. As a result, a similar macroscopic outward current density of ouf8 and fou2 was detected in the absence of vacuolar Ca2+. Furthermore, ouf8 mutation did not drastically change the typical fou2 TPC1 channel features such as fast activation, vacuolar calcium insensitivity and voltage dependency. However, a reversible block of the cytosol-directed potassium efflux at increased vacuolar calcium concentration in ouf8 mutant was found. Further inspection of transiently expressed TPC1 channel variants (M629I, M629T) on the single channel level suggest that Met629 of AtTPC1 in the channel pore region is crucial for the unitary channel conductance.
Taken together, current membrane recordings from ouf mutants revealed one common feature: All of them lacked or showed a strongly impaired ability for TPC1-mediated potassium release from the vacuole into the cytosol. Additionally, considering the detected dependence of the vacuolar membrane voltage on TPC1 activity, it thus seems that the TPC1-triggered vacuolar membrane depolarization caused by vacuolar K+ release plays a key role in generation of the fou2-like phenotype. Accordingly, one can conclude that TPC1-dependent vacuolar membrane depolarization and initiation of jasmonate production are likely linked. This statement is supported also by the complete restoration of WT-like plant phenotype and JA signalling in the ouf mutants. Finally, as a control element of the vacuolar membrane voltage TPC1 is probably upstream located in JA signalling pathway and therefore a perfect junction for linking multiple physiological stimuli and response to them.
Im Rahmen der vorgelegten Doktorarbeit wurde der in Pflanzen ubiquitär exprimierte, nicht-selektive vakuoläre Kationenkanal TPC1/SV elektrophysiologisch in Arabidopsis thaliana Mesophyllvakuolen untersucht, um seine physiologische Rolle in der pflanzlichen Stressantwort weiter aufzuklären. Hierfür wurde die hyperaktive Kanalvariante fou2 (D454N), die einen nicht-funktionalen vakuolären Calciumsensor, ein stark verzögertes Pflanzenwachstum und einen hochregulierten Jasmonsäure-Signalweg aufweist, sowie acht ouf Mutanten mit fou2-umkehrenden Phänotyp benutzt. Mit Ausnahme von ouf4 enthalten alle anderen ouf Mutanten eine weitere Mutation im TPC1-Gen. Daher wurden die elektrischen Eigenschaften von TPC1 in allen ouf Mutanten elektrophysiologisch mittels der Patch clamp Technik charakterisiert und mit fou2 und dem Wildtyp verglichen.
Aufgrund einer Missense-Mutation beinhalten die Mutanten ouf1 und ouf7 ein verkürztes TPC1 Protein, woraus eine gestörte Proteinintegrität resultiert und daraus wiederum ein Fehlen der TCP1-Kanalaktivität. Dementsprechend ähneln ouf1 und ouf7 der tpc1-2 Nullmutante mit einem WT- oder eher fou2-artigen Phänotyp. Wahrscheinlich weist die ouf2 (G583D D454N) Mutante einen inaktiven TPC1-Kanal auf, weil die G583D Mutation, die in einem luminalen Teil der S11 Helix sitzt, eine Verschiebung der Aktivierungsschwelle hin zu einer höheren Spannung (z. B. mehr als +110 mV) oder einen Kanalblock verursacht. Als Folge der TPC1 Kanal Inaktivität, ahmt die ouf2 Mutante auch den WT-ähnlichen Phänotyp der tpc1-2 Nullmutante nach. In der ouf6 Mutante (A669V D454N) beeinflusst die zweite Mutation selektiv die fou2-ähnlichen SV-Kanaleigenschaften. Sowohl die schnelle Aktivierungskinetik als auch die verringerte luminale Calciumsensitivität waren denen von ouf6 und fou2 ähnlich. Die Abweichungen in der relativen sowie der absoluten Offenwahrscheinlichkeit resultierten jedoch in einer stark reduzierten (80 %) Stromdichte bei 0 mM luminalem Calcium verglichen mit fou2 und dem WT, sowie einer Kanalinaktivität bei Spannungen zwischen -30 mV und +40 mV. Darüber hinaus zeigten die TPC1 Kanäle in ouf6 eine höhere Anfälligkeit für inhibitorisches, luminales Calcium als die in fou2. Das Ergebnis der beiden unterschiedlichen Effekte ist, dass die TPC1 Kanalaktivität bei einer hohen luminalen Calciumkonzentration fast verschwindet, woraus zu schließen ist, dass ouf6 den fou2-ähnlichen Phänotyp verlor. Die ouf4 Mutation veränderte nicht die fou2 TPC1 Kanaleigenschaften, wie die schnelle Kanalaktivierung, die Einzelkanalleitfähigkeit und das spannungsabhängige Verhalten. Nichtsdestotrotz war die TCP1 Stromdichte in ouf4 um 80 % geringer als in fou2. Da das TPC1 Gen nicht das Ziel der zweiten Mutation war, kann angenommen werden, dass es durch äußere, bisher noch unbekannte Faktoren, reguliert wird. In der ouf8 Mutante haben die TPC1 Kanäle zusätzlich eine M629I Mutation innerhalb des zweiten Selektivitätsfilters, welche in einem 50 % Rückgang der TCP1 Einzelkanalleitfähigkeit resultiert. Jedoch scheint die leicht erhöhte Offenwahrscheinlichkeit der TCP1 Kanäle in ouf8, verglichen mit fou2, ausreichend zu sein, um die reduzierte Transportkapazität der individuellen TPC1 Kanäle zu kompensieren. Schlussfolgernd wurde eine ähnliche makroskopische auswärts gerichtete Stromdichte des ouf8 und des fou2 in Abwesenheit vakuolären Calciums entdeckt. Des Weiteren änderte eine ouf8 Mutation die fou2 TPC1 Kanaleigenschaften wie eine schnelle Aktivierung, vakuoläre Calciuminsensitivität und die Spannungsabhängigkeit nicht drastisch. Jedoch wurde ein reversibler Block des Zytosol-gerichteten Kalium Ausstroms bei erhöhten vakuolären Calcium Konzentrationen in ouf8 gefunden. Eine weitere Betrachtung transient exprimierter TPC1 Kanalvarianten (M629I, M629T) auf Einzelkanalebene weist darauf hin, dass das Met629 des AtTPC1 in der Kanalporenregion entscheidend ist für die Einzelkanalleitfähigkeit.
Zusammengefasst zeigt der über die Membran von ouf Mutanten gemessene Strom eine Gemeinsamkeit: Alle zeigten keinen oder einen stark beeinträchtigten TPC1-vermittelten Kaliumausstrom aus der Vakuole ins Zytosol. Unter Berücksichtigung der beobachteten Abhängigkeit der vakuolären Membranspannung von der TPC1 Aktivität, scheint es, als ob die durch TPC1 angeregte Depolarisation der Vakuolenmembran, welche durch die vakuoläre Kaliumfreisetzung bedingt wird, in der Ausbildung des fou2 Phänotyps eine Rolle spielt. Daraus lässt sich ableiten, dass die TPC1-abhängige Depolarisation der Vakuolenmembran und die Jasmonat Bildung vermutlich verbunden sind. Diese Behauptung wird auch gestützt durch die komplette Wiederherstellung des WT-ähnlichen Pflanzenphänotyps und des Jasmonsäure Signalwegs in den ouf Mutanten. Letztendlich ist TPC1 als kontrollierendes Element der vakuolären Membranspannung wahrscheinlich dem Jasmonsäure Signalweg vorgeschaltet und deswegen ein perfekter Knotenpunkt, der verschiedene physiologische Stimuli und ihre Antworten verbindet.
Summary
Embryos develop in a concerted sequence of spatiotemporal arrangements of cells. In the preimplantation mouse embryo, the distribution of the cells in the inner cell mass evolves from a salt-and-pepper pattern to spatial segregation of two distinct cell types. The exact properties of the salt-and-pepper pattern have not been analyzed so far. We investigate the spatiotemporal distribution of NANOG- and GATA6-expressing cells in the ICM of the mouse blastocysts with quantitative three-dimensional single-cell-based neighborhood analyses. A combination of spatial statistics and agent-based modeling reveals that the cell fate distribution follows a local clustering pattern. Using ordinary differential equations modeling, we show that this pattern can be established by a distance-based signaling mechanism enabling cells to integrate information from the whole inner cell mass into their cell fate decision. Our work highlights the importance of longer-range signaling to ensure coordinated decisions in groups of cells to successfully build embryos.
Highlights
• The local cell neighborhood and global ICM population composition correlate
• ICM cells show characteristics of local clustering in early and mid mouse blastocysts
• ICM patterning requires integration of signals from cells beyond the first neighbors
The abundance of high-quality genotype and phenotype data for the model organism Arabidopsis thaliana enables scientists to study the genetic architecture of many complex traits at an unprecedented level of detail using genome-wide association studies (GWAS). GWAS have been a great success in A. thaliana and many SNP-trait associations have been published. With the AraGWAS Catalog (https://aragwas.1001genomes.org) we provide a publicly available, manually curated and standardized GWAS catalog for all publicly available phenotypes from the central A. thaliana phenotype repository, AraPheno. All GWAS have been recomputed on the latest imputed genotype release of the 1001 Genomes Consortium using a standardized GWAS pipeline to ensure comparability between results. The catalog includes currently 167 phenotypes and more than 222 000 SNP-trait associations with P < 10\(^{-4}\), of which 3887 are significantly associated using permutation-based thresholds. The AraGWAS Catalog can be accessed via a modern web-interface and provides various features to easily access, download and visualize the results and summary statistics across GWAS.
Revealing the molecular organization of anatomically precisely defined brain regions is necessary for refined understanding of synaptic plasticity. Although three-dimensional (3D) single-molecule localization microscopy can provide the required resolution, imaging more than a few micrometers deep into tissue remains challenging. To quantify presynaptic active zones (AZ) of entire, large, conditional detonator hippocampal mossy fiber (MF) boutons with diameters as large as 10 mu m, we developed a method for targeted volumetric direct stochastic optical reconstruction microscopy (dSTORM). An optimized protocol for fast repeated axial scanning and efficient sequential labeling of the AZ scaffold Bassoon and membrane bound GFP with Alexa Fluor 647 enabled 3D-dSTORM imaging of 25 mu m thick mouse brain sections and assignment of AZs to specific neuronal substructures. Quantitative data analysis revealed large differences in Bassoon cluster size and density for distinct hippocampal regions with largest clusters in MF boutons. Pauli et al. develop targeted volumetric dSTORM in order to image large hippocampal mossy fiber boutons (MFBs) in brain slices. They can identify synaptic targets of individual MFBs and measured size and density of Bassoon clusters within individual untruncated MFBs at nanoscopic resolution.
Solitary bees are subject to a variety of pressures that cause severe population declines. Currently, habitat loss, temperature shifts, agrochemical exposure, and new parasites are identified as major threats. However, knowledge about detrimental bacteria is scarce, although they may disturb natural microbiomes, disturb nest environments, or harm the larvae directly. To address this gap, we investigated 12 Osmia bicornis nests with deceased larvae and 31 nests with healthy larvae from the same localities in a 16S ribosomal RNA (rRNA) gene metabarcoding study. We sampled larvae, pollen provisions, and nest material and then contrasted bacterial community composition and diversity in healthy and deceased nests. Microbiomes of pollen provisions and larvae showed similarities for healthy larvae, whilst this was not the case for deceased individuals. We identified three bacterial taxa assigned to Paenibacillus sp. (closely related to P. pabuli/amylolyticus/xylanexedens), Sporosarcina sp., and Bacillus sp. as indicative for bacterial communities of deceased larvae, as well as Lactobacillus for corresponding pollen provisions. Furthermore, we performed a provisioning experiment, where we fed larvae with untreated and sterilized pollens, as well as sterilized pollens inoculated with a Bacillus sp. isolate from a deceased larva. Untreated larval microbiomes were consistent with that of the pollen provided. Sterilized pollen alone did not lead to acute mortality, while no microbiome was recoverable from the larvae. In the inoculation treatment, we observed that larval microbiomes were dominated by the seeded bacterium, which resulted in enhanced mortality. These results support that larval microbiomes are strongly determined by the pollen provisions. Further, they underline the need for further investigation of the impact of detrimental bacterial acquired via pollens and potential buffering by a diverse pollen provision microbiome in solitary bees.
Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups.
Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256).
Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients.
Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.
At the end of the first larval stage, the nematode Caenorhabditis elegans developing in harsh environmental conditions is able to choose an alternative developmental path called the dauer diapause. Dauer larvae exhibit different physiology and behaviors from non-dauer larvae. Using focused ion beam-scanning electron microscopy (FIB-SEM), we volumetrically reconstructed the anterior sensory apparatus of C. elegans dauer larvae with unprecedented precision. We provide a detailed description of some neurons, focusing on structural details that were unknown or unresolved by previously published studies. They include the following: (1) dauer-specific branches of the IL2 sensory neurons project into the periphery of anterior sensilla and motor or putative sensory neurons at the sub-lateral cords; (2) ciliated endings of URX sensory neurons are supported by both ILso and AMso socket cells near the amphid openings; (3) variability in amphid sensory dendrites among dauers; and (4) somatic RIP interneurons maintain their projection into the pharyngeal nervous system. Our results support the notion that dauer larvae structurally expand their sensory system to facilitate searching for more favorable environments.
Spatiotemporal dynamics of freshwater macrophytes in Bavarian lakes under environmental change
(2022)
Macrophytes are key components of freshwater ecosystems because they provide habitat, food, and improve the water quality. Macrophyte are vulnerable to environmental change as their physiological processes depend on changing environmental factors, which themselves vary within a geographical region and along lake depth. Their spatial distribution is not well understood and their importance is publicly little-known. In this thesis, I have investigated the spatiotemporal dynamics of freshwater macrophytes in Bavarian lakes to understand their diversity pattern along different scales and to predict and communicate potential consequences of global change on their richness.
In the introduction (Chapter 1), I provide an overview of the current scientific knowledge of the species richness patterns of macrophytes in freshwater lakes, the influences of climate and land-use change on macrophyte growth, and different modelling approaches of macrophytes.
The main part of the thesis starts with a study about submerged and emergent macrophyte species richness in natural and artificial lakes of Bavaria (Chapter 2). By analysing publicly available monitoring data, I have found a higher species richness of submerged macrophytes in natural lakes than in artificial lakes. Furthermore, I showed that the richness of submerged species is better explained by physio-chemical lake parameters than the richness of emergent species. In Chapter 3, I considered that submerged macrophytes grow along a depth gradient that provides a sharp environmental gradient on a short spatial scale. This study is the first comparative assessment of the depth diversity gradient (DDG) of macrophytes. I have found a hump-shaped pattern of different diversity components. Generalised additive mixed-effect models indicate that the shape of the DDG is influenced mainly by light quality, light quantity, layering depth, and lake area. I could not identify a general trend of the DDG within recent years, but single lakes show trends leading into different directions. In Chapter 4, I used a mechanistic eco-physiological model to explore changes in the distribution of macrophyte species richness under different scenarios of environmental conditions across lakes and with depths. I could replicate the hump-shaped pattern of potential species richness along depth. Rising temperature leads to increased species richness in all lake types, and depths. The effect of turbidity and nutrient change depends on depth and lake type. Traits that characterise “loser species” under increased turbidity and nutrients are a high light consumption and a high sensibility to disturbances. “Winner species” can be identified by a high biomass production. In Chapter 5, I discuss the image problem of macrophytes. Unawareness, ignorance, and the poor accessibility of macrophytes can lead to conflicts of use. I assumed that an increased engagement and education could counteract this. Because computer games can transfer knowledge interactively while creating an immersive experience, I present in the chapter an interactive single-player game for children.
Finally, I discuss the findings of this thesis in the light of their implications for ecological theory, their implications for conservation, and future research ideas (Chapter 6). The findings help to understand the regional distribution and the drivers of macrophyte species richness. By applying eco-physiological models, multiple environmental shaping factors for species richness were tested and scenarios of climate and land-use change were explored.
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.
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.
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.
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.
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.
Propagule pressure and an invasion syndrome determine invasion success in a plant community model
(2021)
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.