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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.
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.
Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.
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.
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.
Synaptic vesicles (SVs) are a key component of neuronal signaling and fulfil different roles depending on their composition. In electron micrograms of neurites, two types of vesicles can be distinguished by morphological criteria, the classical “clear core” vesicles (CCV) and the typically larger “dense core” vesicles (DCV), with differences in electron density due to their diverse cargos. Compared to CCVs, the precise function of DCVs is less defined. DCVs are known to store neuropeptides, which function as neuronal messengers and modulators [1]. In C. elegans, they play a role in locomotion, dauer formation, egg-laying, and mechano- and chemosensation [2]. Another type of DCVs, also referred to as granulated vesicles, are known to transport Bassoon, Piccolo and further constituents of the presynaptic density in the center of the active zone (AZ), and therefore are important for synaptogenesis [3].
To better understand the role of different types of SVs, we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using image-based features. Analysis of the underlying ground truth data shows an increased fraction of DCVs as well as a higher mean distance between DCVs and AZs in dauer larvae compared to young adult hermaphrodites. Our machine learning based tools are adaptable and can be applied to study properties of different synaptic vesicle pools in electron tomograms of diverse model organisms.
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.
Die NFκB-Signalwege, Apoptose und Nekroptose sind essentielle Prozesse in der Immunantwort. Außerdem sind diese Signalwege Teil der Regulation von Zelldifferenzierung, -proliferation, -tod und Entzündungsreaktionen. Dabei wird zuerst der Rezeptor (TNFR1 oder TRAILR 1/2) aktiviert, die rekrutierten DD-Adapterproteine TRADD, FADD und RIPK1 leiten dann die entsprechende Signalkaskade weiter und bestimmen durch ihre Zusammenwirkung, ob der NFκB-Signalweg, Apoptose oder Nekroptose induziert wird.
TNFR1 und TRAILR 1/2 benötigen die DD-Adapterproteine TRADD, FADD und RIPK1 für die Zelltodinduktion, deren konkrete Bedeutung in Bezug auf Rezeptor-Spezifität, Zusammenwirken und Relevanz allerdings noch unklar ist. Um das Zusammenspiel dieser Proteine besser zu verstehen, wurden in dieser Arbeit Nekroptose-kompetente RIPK3-exprimierende HeLa-Zellen verwendet, bei denen die DD-Adapterproteine FADD, TRADD und RIPK1 einzeln oder in Kombination von zweien ausgeknockt wurden. Es stellte sich heraus, dass RIPK1 essentiell für die TNFR1- und TRAILR 1/2-vermittelte Nekroptose-Induktion ist, doch RIPK1 alleine, d.h. ohne FADD- oder TRADD-Mitbeteiligung, nur bei der TNFR1-Nekroptose-Induktion ausreicht. Wiederum inhibiert TRADD die TNFR1- und TRAILR 1/2-induzierte Nekroptose. RIPK1 und TRADD sind aber unverzichtbar für die NFκB-Aktivierung durch TNFR1 oder TRAILR 1/2 und spielen eine wichtige Rolle bei TNFR1-induzierter Apoptose. Andererseits ist FADD alleine ausreichend für die TRAILR 1/2-bezogene Caspase-8 Aktivierung. Zudem ist FADD notwendig für die TRAIL-induzierte NFκB-Signalaktivierung. In Abwesenheit von FADD und TRADD vermittelt RIPK1 die TNF-induzierte Caspase-8 Aktivierung. FADD wird für die TRAIL-induzierte Nekroptose benötigt, aber gegenläufig wirkt die TNF-induzierte Nektroptose in einer Caspase-8 abhängigen und unabhängigen Weise. Zudem sensitiviert TWEAK die TNF- und TRAIL-induzierte Nekroptose.
Zusammenfassend wurde in dieser Arbeit die Auswirkung von TNFR1 und TRAILR 1/2 auf die Aktivierung der unterschiedlichen Signalkaskaden untersucht. Des Weiteren wurde gezeigt, in welcher Weise sich das Zusammenspiel von TRADD, FADD und RIPK1 auf die Induktion von NFκB, Apoptose und Nekroptose auswirkt.
How genomic and ecological traits shape island biodiversity - insights from individual-based models
(2020)
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.
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.
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.
White Blood Cell (WBC) Leukaemia is caused by excessive production of leukocytes in the bone marrow, and image-based detection of malignant WBCs is important for its detection. Convolutional Neural Networks (CNNs) present the current state-of-the-art for this type of image classification, but their computational cost for training and deployment can be high. We here present an improved hybrid approach for efficient classification of WBC Leukemia. We first extract features from WBC images using VGGNet, a powerful CNN architecture, pre-trained on ImageNet. The extracted features are then filtered using a statistically enhanced Salp Swarm Algorithm (SESSA). This bio-inspired optimization algorithm selects the most relevant features and removes highly correlated and noisy features. We applied the proposed approach to two public WBC Leukemia reference datasets and achieve both high accuracy and reduced computational complexity. The SESSA optimization selected only 1 K out of 25 K features extracted with VGGNet, while improving accuracy at the same time. The results are among the best achieved on these datasets and outperform several convolutional network models. We expect that the combination of CNN feature extraction and SESSA feature optimization could be useful for many other image classification tasks.
Osmotic adaptation and accumulation of compatible solutes is a key process for life at high osmotic pressure and elevated salt concentrations. Most important solutes that can protect cell structures and metabolic processes at high salt concentrations are glycine betaine and ectoine. The genome analysis of more than 130 phototrophic bacteria shows that biosynthesis of glycine betaine is common among marine and halophilic phototrophic Proteobacteria and their chemotrophic relatives, as well as in representatives of Pirellulaceae and Actinobacteria, but are also found in halophilic Cyanobacteria and Chloroherpeton thalassium. This ability correlates well with the successful toleration of extreme salt concentrations. Freshwater bacteria in general lack the possibilities to synthesize and often also to take up these compounds. The biosynthesis of ectoine is found in the phylogenetic lines of phototrophic Alpha- and Gammaproteobacteria, most prominent in the Halorhodospira species and a number of Rhodobacteraceae. It is also common among Streptomycetes and Bacilli. The phylogeny of glycine-sarcosine methyltransferase (GMT) and diaminobutyrate-pyruvate aminotransferase (EctB) sequences correlate well with otherwise established phylogenetic groups. Most significantly, GMT sequences of cyanobacteria form two major phylogenetic branches and the branch of Halorhodospira species is distinct from all other Ectothiorhodospiraceae. A variety of transport systems for osmolytes are present in the studied bacteria.
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.
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.
Spatial biological networks are abundant on all scales of life, from single cells to ecosystems, and perform various important functions including signal transmission and nutrient transport. These biological functions depend on the architecture of the network, which emerges as the result of a dynamic, feedback-driven developmental process. While cell behavior during growth can be genetically encoded, the resulting network structure depends on spatial constraints and tissue architecture. Since network growth is often difficult to observe experimentally, computer simulations can help to understand how local cell behavior determines the resulting network architecture. We present here a computational framework based on directional statistics to model network formation in space and time under arbitrary spatial constraints. Growth is described as a biased correlated random walk where direction and branching depend on the local environmental conditions and constraints, which are presented as 3D multilayer grid. To demonstrate the application of our tool, we perform growth simulations of a dense network between cells and compare the results to experimental data from osteocyte networks in bone. Our generic framework might help to better understand how network patterns depend on spatial constraints, or to identify the biological cause of deviations from healthy network function.
Author summary
We present a novel modeling approach and computational implementation to better understand the development of spatial biological networks under the influence of external signals. Our tool allows us to study the relationship between local biological growth parameters and the emerging macroscopic network function using simulations. This computational approach can generate plausible network graphs that take local feedback into account and provide a basis for comparative studies using graph-based methods.
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.
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.
Investigating diversity gradients helps to understand biodiversity drivers and threats. However, one diversity gradient is rarely assessed, namely how plant species distribute along the depth gradient of lakes. Here, we provide the first comprehensive characterization of depth diversity gradient (DDG) of alpha, beta, and gamma species richness of submerged macrophytes across multiple lakes. We characterize the DDG for additive richness components (alpha, beta, gamma), assess environmental drivers, and address temporal change over recent years. We take advantage of yet the largest dataset of macrophyte occurrence along lake depth (274 depth transects across 28 deep lakes) as well as of physiochemical measurements (12 deep lakes from 2006 to 2017 across Bavaria), provided publicly online by the Bavarian State Office for the Environment. We found a high variability in DDG shapes across the study lakes. The DDGs for alpha and gamma richness are predominantly hump-shaped, while beta richness shows a decreasing DDG. Generalized additive mixed-effect models indicate that the depth of the maximum richness (Dmax) is influenced by light quality, light quantity, and layering depth, whereas the respective maximum alpha richness within the depth gradient (Rmax) is significantly influenced by lake area only. Most observed DDGs seem generally stable over recent years. However, for single lakes we found significant linear trends for Rmax and Dmax going into different directions. The observed hump-shaped DDGs agree with three competing hypotheses: the mid-domain effect, the mean–disturbance hypothesis, and the mean–productivity hypothesis. The DDG amplitude seems driven by lake area (thus following known species–area relationships), whereas skewness depends on physiochemical factors, mainly water transparency and layering depth. Our results provide insights for conservation strategies and for mechanistic frameworks to disentangle competing explanatory hypotheses for the DDG.
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.