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Modern lifestyle is often at odds with endogenously driven rhythmicity, which can lead to circadian disruption and metabolic syndrome. One signature for circadian disruption is a reduced or altered metabolite cycling in the circulating tissue reflecting the current metabolic status. Drosophila is a well-established model in chronobiology, but day-time dependent variations of transport metabolites in the fly circulation are poorly characterized. Here, we sampled fly hemolymph throughout the day and analyzed diacylglycerols (DGs), phosphoethanolamines (PEs) and phosphocholines (PCs) using LC-MS. In wild-type flies kept on sugar-only medium under a light-dark cycle, all transport lipid species showed a synchronized bimodal oscillation pattern with maxima at the beginning and end of the light phase which were impaired in period01 clock mutants. In wild-type flies under constant dark conditions, the oscillation became monophasic with a maximum in the middle of the subjective day. In strong support of clock-driven oscillations, levels of the targeted lipids peaked once in the middle of the light phase under time-restricted feeding independent of the time of food intake. When wild-type flies were reared on full standard medium, the rhythmic alterations of hemolymph lipid levels were greatly attenuated. Our data suggest that the circadian clock aligns daily oscillations of DGs, PEs, and PCs in the hemolymph to the anabolic siesta phase, with a strong influence of light on phase and modality.
Copy number variations (CNVs) of the KITLG gene seem to be involved in the oncogenesis of digital squamous cell carcinoma (dSCC). The aims of this study were (1) to investigate KITLG CNV in giant (GS), standard (SS), and miniature (MS) schnauzers and (2) to compare KITLG CNV between black GS with and without dSCC. Blood samples from black GS (22 with and 17 without dSCC), black SS (18 with and 4 without dSSC; 5 unknown), and 50 MS (unknown dSSC status and coat colour) were analysed by digital droplet PCR. The results are that (1) most dogs had a copy number (CN) value > 4 (range 2.5–7.6) with no significant differences between GS, SS, and MS, and (2) the CN value in black GS with dSCC was significantly higher than in those without dSCC (p = 0.02). CN values > 5.8 indicate a significantly increased risk for dSCC, while CN values < 4.7 suggest a reduced risk for dSCC (grey area: 4.7–5.8). Diagnostic testing for KITLG CNV may sensitise owners to the individual risk of their black GS for dSCC. Further studies should investigate the relevance of KITLG CNV in SS and the protective effects in MS, who rarely suffer from dSCC.
Natural DNA storage allows cellular differentiation, evolution, the growth of our children and controls all our ecosystems. Here, we discuss the fundamental aspects of DNA storage and recent advances in this field, with special emphasis on natural processes and solutions that can be exploited. We point out new ways of efficient DNA and nucleotide storage that are inspired by nature. Within a few years DNA-based information storage may become an attractive and natural complementation to current electronic data storage systems. We discuss rapid and directed access (e.g. DNA elements such as promotors, enhancers), regulatory signals and modulation (e.g. lncRNA) as well as integrated high-density storage and processing modules (e.g. chromosomal territories). There is pragmatic DNA storage for use in biotechnology and human genetics. We examine DNA storage as an approach for synthetic biology (e.g. light-controlled nucleotide processing enzymes). The natural polymers of DNA and RNA offer much for direct storage operations (read-in, read-out, access control). The inbuilt parallelism (many molecules at many places working at the same time) is important for fast processing of information. Using biology concepts from chromosomal storage, nucleic acid processing as well as polymer material sciences such as electronical effects in enzymes, graphene, nanocellulose up to DNA macramé , DNA wires and DNA-based aptamer field effect transistors will open up new applications gradually replacing classical information storage methods in ever more areas over time (decades).
Most of the studies in cell biology primarily focus on models from the opisthokont group of eukaryotes. However, opisthokonts do not encompass the full diversity of eukaryotes. Thus, it is necessary to broaden the research focus to other organisms to gain a comprehensive understanding of basic cellular processes shared across the tree of life. In this sense, Trypanosoma brucei, a unicellular eukaryote, emerges as a viable alternative. The collaborative efforts in genome sequencing and protein tagging over the past two decades have significantly expanded our knowledge on this organism and have provided valuable tools to facilitate a more detailed analysis of this parasite. Nevertheless, numerous questions still remain.
The survival of T. brucei within the mammalian host is intricately linked to the endo-lysosomal system, which plays a critical role in surface glycoprotein recycling, antibody clearance, and plasma membrane homeostasis. However, the dynamics of the duplication of the endo-lysosomal system during T. brucei proliferation and its potential relationship with plasma membrane growth remain poorly understood. Thus, as the primary objective, this thesis explores the endo-lysosomal system of T. brucei in the context of the cell cycle, providing insights on cell surface growth, endosome duplication, and clathrin recruitment. In addition, the study revisits ferritin endocytosis to provide quantitative data on the involvement of TbRab proteins (TbRab5A, TbRab7, and TbRab11) and the different endosomal subpopulations (early, late, and recycling endosomes, respectively) in the transport of this fluid-phase marker. Notably, while these subpopulations function as distinct compartments, different TbRabs can be found within the same region or structure, suggesting a potential physical connection between the endosomal subpopulations. The potential physical connection of endosomes is further explored within the context of the cell cycle and, finally, the duplication and morphological plasticity of the lysosome are also investigated. Overall, these findings provide insights into the dynamics of plasma membrane growth and the coordinated duplication of the endo-lysosomal system during T. brucei proliferation. The early duplication of endosomes suggests their potential involvement in plasma membrane growth, while the late duplication of the lysosome indicates a reduced role in this process. The recruitment of clathrin and TbRab GTPases to the site of endosome formation supports the assumption that the newly formed endosomal system is active during cell division and, consequently, indicates its potential role in plasma membrane homeostasis.
Furthermore, considering the vast diversity within the Trypanosoma genus, which includes ~500 described species, the macroevolution of the group was investigated using the combined information of the 18S rRNA gene sequence and structure. The sequence-structure analysis of T. brucei and other 42 trypanosome species was conducted in the context of the diversity of Trypanosomatida, the order in which trypanosomes are placed. An additional analysis focused on Trypanosoma highlighted key aspects of the group’s macroevolution. To explore these aspects further, additional trypanosome species were included, and the changes in the Trypanosoma tree topology were analyzed. The sequence-structure phylogeny confirmed the independent evolutionary history of the human pathogens T. brucei and Trypanosoma cruzi, while also providing insights into the evolution of the Aquatic clade, paraphyly of groups, and species classification into subgenera.
Recently, we have shown that C6-ceramides efficiently suppress viral replication by trapping the virus in lysosomes. Here, we use antiviral assays to evaluate a synthetic ceramide derivative α-NH2-ω-N3-C6-ceramide (AKS461) and to confirm the biological activity of C6-ceramides inhibiting SARS-CoV-2. Click-labeling with a fluorophore demonstrated that AKS461 accumulates in lysosomes. Previously, it has been shown that suppression of SARS-CoV-2 replication can be cell-type specific. Thus, AKS461 inhibited SARS-CoV-2 replication in Huh-7, Vero, and Calu-3 cells up to 2.5 orders of magnitude. The results were confirmed by CoronaFISH, indicating that AKS461 acts comparable to the unmodified C6-ceramide. Thus, AKS461 serves as a tool to study ceramide-associated cellular and viral pathways, such as SARS-CoV-2 infections, and it helped to identify lysosomes as the central organelle of C6-ceramides to inhibit viral replication.
Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal explanation. For the feature selection task, the principal feature analysis is utilized which reduces redundant information while selecting genes that carry the information for separating the phenotypes. In this context, the presented framework shows explainability of unsupervised learning as it reveals cell-type specific signatures. Apart from a Seurat preprocessing tool and the PFA script, the pipeline uses mutual information to balance accuracy and size of the gene set if desired. A validation part to evaluate the gene selection for their information content regarding the separation of the phenotypes is provided as well, binary and multiclass classification of 3 or 4 groups are studied. Results from different single-cell data are presented. In each, only about ten out of more than 30000 genes are identified as carrying the relevant information. The code is provided in a GitHub repository at https://github.com/AC-PHD/Seurat_PFA_pipeline.
In the fast-evolving landscape of biomedical research, the emergence of big data has presented researchers with extraordinary opportunities to explore biological complexities. In biomedical research, big data imply also a big responsibility. This is not only due to genomics data being sensitive information but also due to genomics data being shared and re-analysed among the scientific community. This saves valuable resources and can even help to find new insights in silico. To fully use these opportunities, detailed and correct metadata are imperative. This includes not only the availability of metadata but also their correctness. Metadata integrity serves as a fundamental determinant of research credibility, supporting the reliability and reproducibility of data-driven findings. Ensuring metadata availability, curation, and accuracy are therefore essential for bioinformatic research. Not only must metadata be readily available, but they must also be meticulously curated and ideally error-free. Motivated by an accidental discovery of a critical metadata error in patient data published in two high-impact journals, we aim to raise awareness for the need of correct, complete, and curated metadata. We describe how the metadata error was found, addressed, and present examples for metadata-related challenges in omics research, along with supporting measures, including tools for checking metadata and software to facilitate various steps from data analysis to published research.
Highlights
• Data awareness and data integrity underpins the trustworthiness of results and subsequent further analysis.
• Big data and bioinformatics enable efficient resource use by repurposing publicly available RNA-Sequencing data.
• Manual checks of data quality and integrity are insufficient due to the overwhelming volume and rapidly growing data.
• Automation and artificial intelligence provide cost-effective and efficient solutions for data integrity and quality checks.
• FAIR data management, various software solutions and analysis tools assist metadata maintenance.
Dark-haired dogs are predisposed to the development of digital squamous cell carcinoma (DSCC). This may potentially suggest an underlying genetic predisposition not yet completely elucidated. Some authors have suggested a potential correlation between the number of copies KIT Ligand (KITLG) and the predisposition of dogs to DSCC, containing a higher number of copies in those affected by the neoplasm. In this study, the aim was to evaluate a potential correlation between the number of copies of the KITLG and the histological grade of malignancy in dogs with DSCC. For this, 72 paraffin-embedded DSCCs with paired whole blood samples of 70 different dogs were included and grouped according to their haircoat color as follow: Group 0/unknown haircoat color (n = 11); Group 1.a/black non-Schnauzers (n = 15); group 1.b/black Schnauzers (n = 33); group 1.c/black and tan dogs (n = 7); group 2/tan animals (n = 4). The DSCCs were histologically graded. Additionally, KITLG Copy Number Variation (CNV) was determined by ddPCR. A significant correlation was observed between KITLG copy number and the histological grade and score value. This finding may suggest a possible factor for the development of canine DSCC, thus potentially having an impact on personalized veterinary oncological strategies and breeding programs.
Grading, immunohistochemistry and c-kit mutation status are criteria for assessing the prognosis and therapeutic options of canine cutaneous mast cell tumours (MCTs). As a subset, canine digital MCTs have rarely been explored in this context. Therefore, in this retrospective study, 68 paraffin-embedded canine digital MCTs were analysed, and histological grading was assessed according to Patnaik and Kiupel. The immunohistochemical markers KIT and Ki67 were used, as well as polymerase chain reaction (PCR) for mutational screening in c-kit exons 8, 9, 11 and 14. Patnaik grading resulted in 22.1% grade I, 67.6% grade II and 10.3% grade III tumours. Some 86.8% of the digital MCTs were Kiupel low-grade. Aberrant KIT staining patterns II and III were found in 58.8%, and a count of more than 23 Ki67-positive cells in 52.3% of the cases. Both parameters were significantly associated with an internal tandem duplication (ITD) in c-kit exon 11 (12.7%). French Bulldogs, which tend to form well-differentiated cutaneous MCTs, had a higher proportion of digital high-grade MCTs and ITD in c-kit exon 11 compared with mongrels. Due to its retrospective nature, this study did not allow for an analysis of survival data. Nevertheless, it may contribute to the targeted characterisation of digital MCTs.
In a modified inflation scenario we replace the “big bang” by a condensation event in an eternal all-compassing big ocean of free qubits in our modified cosmology. Interactions of qubits in the qubit ocean are rare. If they happen, they provide a nucleus for a new universe as the qubits become decoherent and freeze-out into defined bit ensembles. Second, we replace inflation by a crystallization event triggered by the nucleus of interacting qubits to which rapidly more and more qubits attach (like in everyday crystal growth) – the crystal unit cell guarantees same symmetries everywhere. Hence, the textbook inflation scenario to explain the same laws of nature in our domain is replaced by the crystal unit cell of the crystal formed. We give here only the perspective or outline of this modified inflation theory, as the detailed mathematical physics behind this has still to be formulated and described.
Interacting qubits solidify, quantum entropy decreases (but increases in the ocean around). The interacting qubits form a rapidly growing domain where the n**m states become separated ensemble states, rising long-range forces stop ultimately further growth. After that very early events, standard cosmology with the hot fireball model takes over. Our theory agrees well with lack of inflation traces in cosmic background measurements, but more importantly can explain well by such a type of cosmological crystallization instead of inflation the early creation of large-scale structure of voids and filaments, supercluster formation, galaxy formation, and the dominance of matter: no annihilation of antimatter necessary, rather the unit cell of our crystal universe has a matter handedness avoiding anti-matter.
We prove a triggering of qubit interactions can only be 1,2,4 or 8-dimensional (agrees with E8 symmetry of our universe). Repulsive forces at ultrashort distances result from quantization, long-range forces limit crystal growth. Crystals come and go in the qubit ocean. This selects for the ability to lay seeds for new crystals, for self-organization and life-friendliness.
The phase space of the crystal agrees with the standard model of the basic four forces for n quanta. It includes all possible ensemble combinations of their quantum states m, a total of n**m states. Neighbor states reach according to transition possibilities (S-matrix) with emergent time from entropic ensemble gradients. However, this means that in our four dimensions there is only one bit overlap to neighbor states left (almost solid, only below h dash liquidity left). However, the E8 symmetry of heterotic string theory has six rolled-up, small dimensions which help to keep the qubit crystal together and will never expand.
Finally, we give first energy estimates for free qubits vs bound qubits, misplacements in the qubit crystal and entropy increase during qubit decoherence / crystal formation. Scalar fields for color interaction and gravity derive from the permeating qubit-interaction field in the crystal. Hence, vacuum energy gets low inside the qubit crystal. Condensed mathematics may advantageously help to model free (many states denote the same qubit) and bound qubits in phase space.
The phase space for the standard model of the basic four forces for n quanta includes all possible ensemble combinations of their quantum states m, a total of n**m states. Neighbor states reach according to transition possibilities (S-matrix) with emergent time from entropic ensemble gradients.
We replace the “big bang” by a condensation event (interacting qubits become decoherent) and inflation by a crystallization event – the crystal unit cell guarantees same symmetries everywhere. Interacting qubits solidify and form a rapidly growing domain where the n**m states become separated ensemble states, rising long-range forces stop ultimately further growth. After that very early events, standard cosmology with the hot fireball model takes over. Our theory agrees well with lack of inflation traces in cosmic background measurements, large-scale structure of voids and filaments, supercluster formation, galaxy formation, dominance of matter and life-friendliness.
We prove qubit interactions to be 1,2,4 or 8 dimensional (agrees with E8 symmetry of our universe). Repulsive forces at ultrashort distances result from quantization, long-range forces limit crystal growth. Crystals come and go in the qubit ocean. This selects for the ability to lay seeds for new crystals, for self-organization and life-friendliness.
We give energy estimates for free qubits vs bound qubits, misplacements in the qubit crystal and entropy increase during qubit decoherence / crystal formation. Scalar fields for color interaction and gravity derive from the permeating qubit-interaction field. Hence, vacuum energy gets low only inside the qubit crystal. Condensed mathematics may advantageously model free / bound qubits in phase space.
Protein folding achieves a clear solution structure in a huge parameter space (the so-called protein folding problem). Proteins fold in water, and get by this a highly ordered structure. Finally, inside a protein crystal for structure resolution, you have everywhere the same symmetries as there is everywhere the same unit cell. We apply this to qubit interactions to do fundamental physics:
in a modified cosmology, we replace the big bang by a condensation event in an eternal all-encompassing ocean of free qubits. Interactions of qubits in the qubit ocean are quite rare but provide a nucleus or seed for a new universe (domain) as the qubits become decoherent and freeze-out into defined bit ensembles. Second, we replace inflation by a crystallization event triggered by the nucleus of interacting qubits to which rapidly more and more qubits attach (like in everyday crystal growth). The crystal unit cell guarantees same symmetries everywhere inside the crystal. The textbook inflation scenario to explain the same laws of nature in our domain is replaced by the unit cell of the crystal formed.
Interacting qubits solidify, quantum entropy decreases (but increases in the ocean around). In a modified inflation scenario, the interacting qubits form a rapidly growing domain where the n**m states become separated ensemble states, rising long-range forces stop ultimately further growth. Then standard cosmology with the hot fireball model takes over. Our theory agrees well with lack of inflation traces in cosmic background measurements. We explain by cosmological crystallization instead of inflation: early creation of large-scale structure of voids and filaments, supercluster formation, galaxy formation, and the dominance of matter: the unit cell of our crystal universe has a matter handedness avoiding anti-matter.
We prove initiation of qubit interactions can only be 1,2,4 or 8-dimensional (agrees with E8 symmetry of our universe). Repulsive forces at ultrashort distances result from quantization, long-range forces limit crystal growth. Crystals come and go in the qubit ocean. This selects for the ability to lay seeds for new crystals, for self-organization and life-friendliness.
The phase space of the crystal agrees with the standard model of the basic four forces for n quanta. It includes all possible ensemble combinations of their quantum states m, a total of n**m states. Neighbor states reach according to transition possibilities (S-matrix) with emergent time from entropic ensemble gradients. However, in our four dimensions there is only one bit overlap to neighbor states left (almost solid, only below Planck quantum there is liquidity left). The E8 symmetry of heterotic string theory has six curled-up, small dimensions which help to keep the qubit crystal together and will never expand.
Mathematics focusses on the Hurwitz proof applied to qubit interaction, a toy model of qubit interaction and repulsive forces of qubits. Vacuum energy gets appropriate low inside the crystal. We give first energy estimates for free qubits vs bound qubits, misplacements in the qubit crystal and entropy increase during qubit decoherence / crystal formation. Scalar fields for color interaction/confinement and gravity are derived from the qubit-interaction field.
Agricultural abandonment is one of the main land-use changes in Europe, and its consequences on biodiversity are context- and taxa-dependent. While several studies have worked on this topic, few have focused on traditional orchards, especially in different landscapes and under a Mediterranean climate. In this context, we aimed to determine the effects of almond orchard abandonment on the communities of three groups of beneficial arthropods and the role of the landscape context in modulating these effects. Between February and September 2019, four samplings were carried out in twelve almond orchards (three abandoned and three traditional (active orchards under traditional agricultural management) located in simple landscapes as well as three abandoned and three traditional in complex landscapes). Abandoned and traditional almond orchards harbor different arthropod communities and diversity metrics that are strongly conditioned by seasonality. Abandoned orchards can favor pollinators and natural enemies, providing alternative resources in simple landscapes. However, the role that abandoned orchards play in simple landscapes disappears as the percentage of semi-natural habitats in the landscape increases. Our results show that landscape simplification, through the loss of semi-natural habitats, has negative consequences on arthropod biodiversity, even in traditional farming landscapes with small fields and high crop diversity.
In the eusocial insect honeybee (Apis mellifera), many sterile worker bees live together with a reproductive queen in a colony. All tasks of the colony are performed by the workers, undergoing age-dependent division of labor. Beginning as hive bees, they take on tasks inside the hive such as cleaning or the producing of larval food, later developing into foragers. With that, the perception of sweetness plays a crucial role for all honeybees whether they are sitting on the honey stores in the hive or foraging for food. Their ability to sense sweetness is undoubtedly necessary to develop and evaluate food sources. Many of the behavioral decisions in honeybees are based on sugar perception, either on an individual level for ingestion, or for social behavior such as the impulse to collect or process nectar. In this context, honeybees show a complex spectrum of abilities to perceive sweetness on many levels. They are able to perceive at least seven types of sugars and decide to collect them for the colony. Further, they seem to distinguish between these sugars or at least show clear preferences when collecting them. Additionally, the perception of sugar is not rigid in honeybees. For instance, their responsiveness towards sugar changes during the transition from in-hive bees (e.g. nurses) to foraging and is linked to the division of labor. Other direct or immediate factors changing responsiveness to sugars are stress, starvation or underlying factors, such as genotype.
Interestingly, the complexity in their sugar perception is in stark contrast to the fact that honeybees seem to have only three predicted sugar receptors.
In this work, we were able to characterize the three known sugar receptors (AmGr1, AmGr2 and AmGr3) of the honeybee fully and comprehensively in oocytes (Manuscript II, Chapter 3 and Manuscript III, Chapter 4). We could show that AmGr1 is a broad sugar receptor reacting to sucrose, glucose, maltose, melezitose and trehalose (which is the honeybees’ main blood sugar), but not fructose. AmGr2 acts as its co-receptor altering AmGr1’s specificity, AmGr3 is a specific fructose receptor and we proved the heterodimerization of all receptors. With my studies, I was able to reproduce and compare the ligand specificity of the sugar receptors in vivo by generating receptor mutants with CRISPR/Cas9. With this thesis, I was able to define AmGr1 and AmGr3 as the honeybees’ basis receptors already capable to detect all sugars of its known taste spectrum.
In the expression analysis of my doctoral thesis (Manuscript I, Chapter 2) I demonstrated that both basis receptors are expressed in the antennae and the brain of nurse bees and foragers. This thesis assumes that AmGr3 (like the Drosophila homologue) functions as a sensor for fructose, which might be the satiety signal, while AmGr1 can sense trehalose as the main blood sugar in the brain. Both receptors show a reduced expression in the brain of foragers when compared with nurse bees. These results may reflect the higher concentrated diet of nurse bees in the hive. The higher number of receptors in the brain may allow nurse bees to perceive hunger earlier and to consume the food their sitting on. Forager bees have to be more persistent to hunger, when they are foraging, and food is not so accessible. The findings of reduced expression of the fructose receptor AmGr3 in the antennae of nurse bees are congruent with my other result that nurse bees are also less responsive to fructose at the antennae when compared to foragers (Manuscript I, Chapter 2). This is possible, since nurse bees sit more likely on ripe honey which contains not only higher levels of sugars but also monosaccharides (such as fructose), while foragers have to evaluate less-concentrated nectar.
My investigations of the expression of AmGr1 in the antennae of honeybees found no differences between nurse bees and foragers, although foragers are more responsive to the respective sugar sucrose (Manuscript I, Chapter 2). Considering my finding that AmGr2 is the co-receptor of AmGr1, it can be assumed that AmGr1 and the mediated sucrose taste might not be directly controlled by its expression, but indirectly by its co-receptor. My thesis therefore clearly shows that sugar perception is associated with division of labor in honeybees and appears to be directly or indirectly regulated via expression.
The comparison with a characterization study using other bee breeds and thus an alternative protein sequence of AmGr1 shows that co-expression of different AmGr1 versions with AmGr2 alters the sugar response differently. Therefore, this thesis provides first important indications that alternative splicing could also represent an important regulatory mechanism for sugar perception in honeybees.
Further, I found out that the bitter compound quinine lowers the reward quality in learning experiments for honeybees (Manuscript IV, Chapter 5). So far, no bitter receptor has been found in the genome of honeybees and this thesis strongly assumes that bitter substances such as quinine inhibit sugar receptors in honeybees. With this finding, my work includes other molecules as possible regulatory mechanism in the honeybee sugar perception as well. We showed that the inhibitory effect is lower for fructose compared to sucrose. Considering that sugar signals might be processed as differently attractive in honeybees, this thesis concludes that the sugar receptor inhibition via quinine in honeybees might depend on the receptor (or its co-receptor), is concentration-dependent and based on the salience or attractiveness and concentration of the sugar present.
With my thesis, I was able to expand the knowledge on honeybee’s sugar perception and formulate a complex, comprehensive overview. Thereby, I demonstrated the multidimensional mechanism that regulates the sugar receptors and thus the sugar perception of honeybees. With this work, I defined AmGr1 and AmGr3 as the basis of sugar perception and enlarged these components to the co-receptor AmGr2 and the possible splice variants of AmGr1. I further demonstrated how those sugar receptor components function, interact and that they are clearly involved in the division of labor in honeybees. In summary, my thesis describes the mechanisms that enable honeybees to perceive sugar in a complex way, even though they inhere a limited number of sugar receptors. My data strongly suggest that honeybees overall might not only differentiate sugars and their diet by their general sweetness (as expected with only one main sugar receptor). The found sugar receptor mechanisms and their interplay further suggest that honeybees might be able to discriminate directly between monosaccharides and disaccharides or sugar molecules and with that their diet (honey and nectar).
Honeybees (Apis mellifera) need their fine sense of taste to evaluate nectar and pollen sources. Gustatory receptors (Grs) translate taste signals into electrical responses. In vivo experiments have demonstrated collective responses of the whole Gr-set. We here disentangle the contributions of all three honeybee sugar receptors (AmGr1-3), combining CRISPR/Cas9 mediated genetic knock-out, electrophysiology and behaviour. We show an expanded sugar spectrum of the AmGr1 receptor. Mutants lacking AmGr1 have a reduced response to sucrose and glucose but not to fructose. AmGr2 solely acts as co-receptor of AmGr1 but not of AmGr3, as we show by electrophysiology and using bimolecular fluorescence complementation. Our results show for the first time that AmGr2 is indeed a functional receptor on its own. Intriguingly, AmGr2 mutants still display a wildtype-like sugar taste. AmGr3 is a specific fructose receptor and is not modulated by a co-receptor. Eliminating AmGr3 while preserving AmGr1 and AmGr2 abolishes the perception of fructose but not of sucrose. Our comprehensive study on the functions of AmGr1, AmGr2 and AmGr3 in honeybees is the first to combine investigations on sugar perception at the receptor level and simultaneously in vivo. We show that honeybees rely on two gustatory receptors to sense all relevant sugars.
The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from −8% (WW) and −1.6% (OSR) and increase the R
2 by 14.3% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R
2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops.
Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km\(^2\)), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R\(^2\) of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R\(^2\) of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R\(^2\) of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R\(^2\) = 0.88) and OSR (R\(^2\) = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R\(^2\) of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.
The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km\(^2\)) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R\(^2\) = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R\(^2\) = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R\(^2\) = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R\(^2\) = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world.
Formation of the Aurora-A–MYCN complex increases levels of the oncogenic transcription factor MYCN in neuroblastoma cells by abrogating its degradation through the ubiquitin proteasome system. While some small-molecule inhibitors of Aurora-A were shown to destabilize MYCN, clinical trials have not been satisfactory to date. MYCN itself is considered to be `undruggable' due to its large intrinsically disordered regions. Targeting the Aurora-A–MYCN complex rather than Aurora-A or MYCN alone will open new possibilities for drug development and screening campaigns. To overcome the challenges that a ternary system composed of Aurora-A, MYCN and a small molecule entails, a covalently cross-linked construct of the Aurora-A–MYCN complex was designed, expressed and characterized, thus enabling screening and design campaigns to identify selective binders.
Precision-cut tumor slices (PCTS) maintain tissue heterogeneity concerning different cell types and preserve the tumor microenvironment (TME). Typically, PCTS are cultured statically on a filter support at an air–liquid interface, which gives rise to intra-slice gradients during culture. To overcome this problem, we developed a perfusion air culture (PAC) system that can provide a continuous and controlled oxygen medium, and drug supply. This makes it an adaptable ex vivo system for evaluating drug responses in a tissue-specific microenvironment. PCTS from mouse xenografts (MCF-7, H1437) and primary human ovarian tumors (primary OV) cultured in the PAC system maintained the morphology, proliferation, and TME for more than 7 days, and no intra-slice gradients were observed. Cultured PCTS were analyzed for DNA damage, apoptosis, and transcriptional biomarkers for the cellular stress response. For the primary OV slices, cisplatin treatment induced a diverse increase in the cleavage of caspase-3 and PD-L1 expression, indicating a heterogeneous response to drug treatment between patients. Immune cells were preserved throughout the culturing period, indicating that immune therapy can be analyzed. The novel PAC system is suitable for assessing individual drug responses and can thus be used as a preclinical model to predict in vivo therapy responses.