Refine
Has Fulltext
- yes (138) (remove)
Is part of the Bibliography
- yes (138)
Year of publication
Document Type
- Journal article (119)
- Preprint (9)
- Review (8)
- Conference Proceeding (1)
- Working Paper (1)
Language
- English (138) (remove)
Keywords
- metabolism (8)
- apoptosis (7)
- cosmology (6)
- crystallization (6)
- SARS-CoV-2 (5)
- cytokinins (5)
- qubit (5)
- COVID-19 (4)
- decoherence (4)
- evolution (4)
- leukemic cells (4)
- regulation (4)
- transcriptome (4)
- Candida albicans (3)
- bioinformatics (3)
- cisplatin (3)
- cytotoxicity (3)
- database (3)
- expression (3)
- identification (3)
- molecular docking (3)
- mutation (3)
- systems biology (3)
- virulence (3)
- "-omics" (2)
- Aspergillus fumigatus (2)
- Bioinformatik (2)
- Biology (2)
- C-60 fullerene (2)
- Caenorhabditis elegans (2)
- DNA (2)
- DNA storage (2)
- Metabolic pathways (2)
- RNA (2)
- Salmonella-containing vacuole (SCV) (2)
- Staphylococcus aureus (2)
- adaptation (2)
- algorithm (2)
- alignment (2)
- annotation (2)
- cellular signalling networks (2)
- complexity (2)
- dendritic cells (2)
- differentiation (2)
- drug repurposing (2)
- emergent time (2)
- engineering (2)
- infection (2)
- loop quantum gravity (2)
- lung cancer (2)
- machine learning (2)
- metabolic modeling (2)
- modified inflation (2)
- molecular dynamics (2)
- mouse (2)
- oncolytic virus (2)
- oxidative stress (2)
- phase transition (2)
- platelet (2)
- positive selection (2)
- protein folding (2)
- qubit interaction (2)
- recombination (2)
- resistance (2)
- synthetic biology (2)
- transcriptional regulation (2)
- 1st-line treatment (1)
- 3D lung tumor model (1)
- 3D lung tumor tissue models (1)
- 3D tissue models (1)
- 5-Fluorouracil (1)
- 6-benzylaminopurine (1)
- A2a-R receptor (1)
- ACKR4 (1)
- AIModules (1)
- AKT (1)
- Abstandsmessung (1)
- Aspergillus (1)
- Aspergillus fumigalus (1)
- Axl tyrosine kinase (1)
- B-cell (1)
- BRAF mutation (1)
- Bedeutung (1)
- Beobachter (1)
- Berberine (1)
- Berechnungskomplexität (1)
- Beta-catenin (1)
- Biologie (1)
- Boolean function (1)
- Boolean signaling network (1)
- Boolean tree (1)
- C60 fullerene (1)
- CD95 (1)
- CETCH cycle (1)
- CLAVATA3 (1)
- CLV3p (1)
- CO2-sequestration (1)
- C\(_{60}\) fullerene (1)
- Camponotus floridanus (1)
- Carcinoma cells (1)
- Cell surface proteomics (1)
- Cestode (1)
- Chagas diagnosis (1)
- Chagas disease (1)
- Chagas monitoring (1)
- Chagas real time PCR (1)
- Chlamydia trachomatis (1)
- Computer modelling (1)
- Computer software (1)
- Cushing’s disease (1)
- DLS and AFM measurements (1)
- DNA damage (1)
- Doxorubicin (1)
- E8 symmetry (1)
- EMT (1)
- ERK signaling (1)
- Echinococcosis (1)
- Echinococcus (1)
- Einfluss (1)
- Embryonic induction (1)
- Enterobacteriaceae (1)
- Entscheidung (1)
- Entscheidungen (1)
- Enzyme kinetics (1)
- Enzyme metabolism (1)
- Enzyme regulation (1)
- Enzymes (1)
- Epicardium-derived cells (1)
- Evaluation (1)
- Evolution (1)
- FLS2 receptor (1)
- Fibroblasts (1)
- Fluorouracil (1)
- Functional modules (1)
- Fundamentalkonstante (1)
- GPVI (1)
- Gene expression vectors (1)
- Gödel (1)
- H7N9 influenza virus (1)
- HGPS (1)
- HIV (1)
- HPLC-ESI-MS (1)
- HSP90 inhibitor (1)
- HeLa cells (1)
- Host-parasite interaction (1)
- Human atrial stromal cells (1)
- Hurwitz theorem (1)
- Hurwitz-Theorem (1)
- ICEP (1)
- ICP27 (1)
- IGFBP2 (1)
- Insulin (1)
- Integrated network analysis (1)
- Invasion (1)
- IronChip Evaluation Package (1)
- KRAS biomarker signatures (1)
- KRAS mutation signature (1)
- Ki67 (1)
- Kinase inhibitor (1)
- Kolmogorov-Komplexität (1)
- Komplex <Algebra> (1)
- Komplexität (1)
- Kosmologie (1)
- LEDs (1)
- LS-MIDA (1)
- Lee Smolin (1)
- MHC I (1)
- MHC II (1)
- MITE (1)
- Matrix (1)
- Metabolic profiles (1)
- Microarray (1)
- Mikroarray (1)
- Milnesium tardigradum (1)
- Multicenter randomized-trial (1)
- Mycoplasma (1)
- Natur (1)
- Nature constants (1)
- Naturgesetz (1)
- Natürliche Auslese (1)
- Neuromuscular junctions (1)
- Olea (1)
- Phylogenie (1)
- PknB (1)
- Polymerase chain reaction (1)
- Predictive toxicology (1)
- Prognose (1)
- Proteasen (1)
- Proteine (1)
- Quantenschleifen-Gravitation (1)
- Qubits (1)
- R0 (1)
- RNA secondary structure (1)
- RNA sequencing (1)
- RNA-SEQ (1)
- RNAi (1)
- Receptor kinase (1)
- Salmonella enterica (1)
- Salmonella-containing vacuole (1)
- Sanger sequencing (1)
- Septins (1)
- Shotgun method (1)
- Spumaviren (1)
- Stp (1)
- Strukturanalyse (1)
- Synapses (1)
- Synaptic vesicles (1)
- Synthetic biology (1)
- T-cell (1)
- T-cell epitope (1)
- Tapeworm (1)
- Tissue (1)
- Toxicity (1)
- Transcriptional control (1)
- Trend test (1)
- Trypanosoma (1)
- Trypanosoma cruzi (1)
- UV–Vis (1)
- V1–V9 (1)
- V4 (1)
- V7/V8 (1)
- VASP (1)
- Vasodilatator-stimuliertes Phosphoprotein (1)
- Verschränkung (1)
- Vesicles (1)
- Virtual sequencing (1)
- WNT (1)
- Yolk protein (1)
- Zebrafish (1)
- Zika virus (1)
- abscisic acid (ABA) (1)
- accumulation (1)
- adaption (1)
- agent-based model (1)
- aging (1)
- air-liquid interface (1)
- alveolar fibrosis (1)
- alveolar regeneration (1)
- alzheimer's disease (1)
- alzheimers disease (1)
- animals (1)
- anti-cancer drug-like molecules (1)
- anti-thrombotic therapies (1)
- anticancer activity (1)
- antimicrobial peptides (1)
- antimycotics (1)
- antioxidants (1)
- antiproliferative (1)
- approved drugs (1)
- arabidopsis thaliana (1)
- arabidpsis thaliana (1)
- artificial membrane-permeability (1)
- asthmatic bronchial epithelium (1)
- auxin (1)
- bacterial invasion (1)
- bacterial pathogens (1)
- bacteriology (1)
- beta-lactamase inhibition (1)
- binary decision diagram (1)
- binding pocket (1)
- binding protein (1)
- biofilm formation (1)
- biofuel (1)
- bioinformatics and computational biology (1)
- biological activities (1)
- biomanufacturing (1)
- biomarker (1)
- biomaterial surfaces (1)
- bioreactor culture (1)
- bit (1)
- blood platelets (1)
- boolean in silico models (1)
- boolean modeling (1)
- brain (1)
- caenorhabditis elegans (1)
- calcium (1)
- calcium signaling pathway (1)
- camponotus floridanus (1)
- cancer therapy (1)
- candida genome database (1)
- carboxylation (1)
- carpenter ant (1)
- cascade (1)
- caspase-3 (1)
- cell death (1)
- cell staining (1)
- cell wall (1)
- centrality (1)
- cerebral ischemia (1)
- chemical similarity (1)
- chemoresistance (1)
- chondrosarcoma (1)
- cluster (1)
- collagen (1)
- colony-stimulating factor (1)
- colorectal cancer (1)
- combinatorial drug predictions (1)
- comparative sequence analysis (1)
- complex networks (1)
- computational (1)
- computational biology and bioinformatics (1)
- computational modelling (1)
- computational prediction (1)
- computational systems biology (1)
- computer modelling (1)
- computer-assisted (1)
- connector (1)
- control group (1)
- control profiles (1)
- controllability (1)
- corticotropin-releasing hormone (1)
- crosstalk (1)
- cryptic (1)
- crystal growth (1)
- culture (1)
- cycle (1)
- cyclic nucleotide signaling (1)
- cytokines (1)
- cytokinin (1)
- cytokinin kinetin (1)
- data mining/methods (1)
- data storage (1)
- decision (1)
- defense and evasion strategies (1)
- defense signaling (1)
- defenses (1)
- design (1)
- deubiquitinases (1)
- differentially expressed genes (1)
- discovery (1)
- disease (1)
- docking (1)
- domain (1)
- doxorubicin (1)
- drosophila (1)
- drug design (1)
- drug release (1)
- drug resistance (1)
- drug-minded protein (1)
- dynamic protein-protein interactions (1)
- early cosmology (1)
- early diagnosis (1)
- efficient intervention points (1)
- elementary body (1)
- elementary modes (1)
- embryonic stem cells (1)
- emergent gravity (1)
- encapsulation (1)
- encephalitis dementia (1)
- entanglement (1)
- enteric pathogens (1)
- enterica serovar Typhimurium (1)
- enzyme (1)
- epithelial cell culture (1)
- epitope mapping (1)
- epitope prediction (1)
- error (1)
- error-transfer (1)
- eugenol (1)
- evolutionary (1)
- exome (1)
- explainability of machine learning (1)
- expressed sequence tag (1)
- extracellular matrix (1)
- facultatively intracellular pathogens (1)
- feature analysis (1)
- feature selection (1)
- fine-tuning (1)
- flg22 (1)
- fluorescence recovery after photobleaching (1)
- flux balance analysis (1)
- format (1)
- fostamatinib (1)
- free energy (1)
- functional modules (1)
- fungal pathogens (1)
- fungicide (1)
- gamma (1)
- gene expression (1)
- gene ontology (1)
- genes (1)
- genetic regulatory network (1)
- genetic variation (1)
- genetics (1)
- genome browser (1)
- genus Aspergillus (1)
- guard cells (1)
- heart (1)
- hepatotoxicity (1)
- heuristics (1)
- histidine kinase (1)
- homology modeling (1)
- host-pathogen adaption (1)
- host-pathogen interaction (1)
- human (1)
- human immune system (1)
- human immunodeficiency virus (1)
- human pathogenic fungi (1)
- human xenografted mouse models (1)
- humans (1)
- image processing (1)
- imaging (1)
- immune system (1)
- immune-informatics (1)
- immunity (1)
- immunocytochemistry (1)
- immunological cross-talk (1)
- immunotherapies (1)
- in silico simulation (1)
- in vitro (1)
- in vitro models (1)
- in vivo toxicity (1)
- in-vitro (1)
- infected-cell protein (1)
- infection biology (1)
- infection spread (1)
- inflammation (1)
- inflation (1)
- inhibitor (1)
- insilico drug screening too (1)
- interaction (1)
- interaction map (1)
- interaction networks (1)
- interactome (1)
- intermediate host (1)
- internal transcribed spacer 2 (1)
- interolog (1)
- interpolation (1)
- interspecies comparison (1)
- invasion (1)
- invasiveness (1)
- ion signaling (1)
- isotopolog profiling (1)
- kinase signaling (1)
- kinetin (1)
- lactate dehydrogenase (1)
- lethality rate (1)
- life-span regulation (1)
- light-gated proteins (1)
- lncRNAs (1)
- lymphocytes (1)
- lymphotoxicity (1)
- mammalian system (1)
- meaning (1)
- messenger RNA (1)
- meta-data (1)
- meta-transcriptome (1)
- metabolic flux (1)
- metabolic modelling (1)
- metabolic pathways (1)
- metabolomic profiling (1)
- miRNAs (1)
- microRNAs (1)
- microRNA–target interaction (1)
- microarray (1)
- microbes (1)
- model reduction (1)
- modeling (1)
- models (1)
- modular tumor tissue models (1)
- modulatory effects (1)
- molecular cloning (1)
- molecular dynamics simulation (1)
- molecular evolution (1)
- molecular modeling (1)
- molecular systematics (1)
- mortality (1)
- mosquito (1)
- mouse model (1)
- multiverse (1)
- myocardial infarction (1)
- myocardium (1)
- nanocarrier (1)
- nanocellulose (1)
- nanocomplex (1)
- natural language processing (1)
- natural processing (1)
- neisseria meningitidis (1)
- network (1)
- network analysis (1)
- network biology (1)
- network inference (1)
- network simulation (1)
- neuraminidase (1)
- neutrophils (1)
- next generation sequencing (1)
- nitric oxide (1)
- non-invasive biomarkers (1)
- noncovalent complex (1)
- noncovalent nanocomplex (1)
- nucleic acid motifs (1)
- observer (1)
- olive (1)
- omics (1)
- on-a-chip (1)
- optimal drug combination (1)
- optimal drug targeting (1)
- optimal pharmacological modulation (1)
- optimal treatment strategies (1)
- optogenetics (1)
- organogenesis (1)
- origin (1)
- pH (1)
- pangolin (1)
- parasite (1)
- pathogen-host interaction (PHI) (1)
- pathogenesis (1)
- pathogenicity (1)
- pathways (1)
- patient data (1)
- permeability (1)
- pharmacology (1)
- phase space (1)
- phosphoproteome (1)
- phosphorylation (1)
- photodanamic therapy (1)
- photodynamic chemotherapy (1)
- photorespiration (1)
- phylogenetic analysis (1)
- phylogenetic tree (1)
- phylogenetics (1)
- phylogeny (1)
- phytohormones (1)
- plant hormones (1)
- plant system (1)
- pollen tube (1)
- population coverage (1)
- potential role (1)
- principal (1)
- pro-oxidant (1)
- progeria (1)
- promoter (1)
- protease; Indinavir; lead expansion; docking; pharmacophore (1)
- protein (1)
- protein analysis (1)
- protein chip (1)
- protein familiy (1)
- protein interaction database (1)
- protein-protein interaction (1)
- protein-protein interaction network (1)
- proteins (1)
- proteome (1)
- pseudomas-syringae (1)
- pulmonary drug-delivery (1)
- pyrazolo[3,4-d]pyrimidine (1)
- quantum computing (1)
- radiation (1)
- rational drug design (1)
- re-annotation (1)
- receptor (1)
- reconstructed human epidermis (1)
- regulatory networks (1)
- relA (1)
- reliability (1)
- reproductive toxicity (1)
- respiratory syncytial virus (1)
- response regulator (1)
- reticulate body (1)
- ribosomal RNA (1)
- riboswitch (1)
- richtersius coronifer (1)
- scaffold search (1)
- secondary structure (1)
- selection (1)
- sensor (1)
- sequence (1)
- sequence alignment (1)
- shoot apical meristem (1)
- signaling (1)
- signaling network (1)
- signaling pathway (1)
- signalling (1)
- signalling pathways (1)
- silico model (1)
- simulation (1)
- single cell analysis (1)
- single-electron transistors (1)
- sky kinases (1)
- software (1)
- somatic mutations (1)
- spanlastic (1)
- splicing factors (1)
- stable state (1)
- stable-isotope (1)
- stem cell niche (1)
- stem-cell-triggered immunity (1)
- stemness (1)
- stratification (1)
- stringent response (1)
- structure (1)
- structure-activity relationship (1)
- sun exposure (1)
- superoxide-dismutase (1)
- synaptic vesicles (1)
- synergistic effect (1)
- synthetic pathways (1)
- system inference (1)
- tardigrada (1)
- target (1)
- targeted combination therapy (1)
- targeted therapy (1)
- targets (1)
- therapeutic strategy (1)
- thrombosis (1)
- tolerance (1)
- tools overview (1)
- transcription (1)
- translation (1)
- transmission (1)
- transport studies (1)
- tumors (1)
- type 1 (1)
- unified theories (1)
- uptake (1)
- variable regions (1)
- variants (1)
- vesicle-based barrier (1)
- virulenceregulatory evolution (1)
- viruses (1)
- vitellogenin (1)
- water stress (1)
- wrong labelling (1)
- xanthurenic acid (1)
- yeast U3 localization (1)
- yvcK/glmR operon (1)
Institute
- Theodor-Boveri-Institut für Biowissenschaften (134)
- Lehrstuhl für Tissue Engineering und Regenerative Medizin (8)
- Klinik und Poliklinik für Anästhesiologie (ab 2004) (6)
- Institut für Molekulare Infektionsbiologie (5)
- Center for Computational and Theoretical Biology (4)
- Institut für Experimentelle Biomedizin (4)
- Institut für Pharmakologie und Toxikologie (4)
- Institut für Virologie und Immunbiologie (4)
- Kinderklinik und Poliklinik (3)
- Institut für Humangenetik (2)
Sonstige beteiligte Institutionen
EU-Project number / Contract (GA) number
- 031A408B (1)
- CoG 721016–HERPES (1)
- ESF-ZDEX 4.0 (1)
PRO-Simat is a simulation tool for analysing protein interaction networks, their dynamic change and pathway engineering. It provides GO enrichment, KEGG pathway analyses, and network visualisation from an integrated database of more than 8 million protein-protein interactions across 32 model organisms and the human proteome. We integrated dynamical network simulation using the Jimena framework, which quickly and efficiently simulates Boolean genetic regulatory networks. It enables simulation outputs with in-depth analysis of the type, strength, duration and pathway of the protein interactions on the website. Furthermore, the user can efficiently edit and analyse the effect of network modifications and engineering experiments. In case studies, applications of PRO-Simat are demonstrated: (i) understanding mutually exclusive differentiation pathways in Bacillus subtilis, (ii) making Vaccinia virus oncolytic by switching on its viral replication mainly in cancer cells and triggering cancer cell apoptosis and (iii) optogenetic control of nucleotide processing protein networks to operate DNA storage. Multilevel communication between components is critical for efficient network switching, as demonstrated by a general census on prokaryotic and eukaryotic networks and comparing design with synthetic networks using PRO-Simat. The tool is available at https://prosimat.heinzelab.de/ as a web-based query server.
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.
Fungal infections are a major global health burden where Candida albicans is among the most common fungal pathogen in humans and is a common cause of invasive candidiasis. Fungal phenotypes, such as those related to morphology, proliferation and virulence are mainly driven by gene expression, which is primarily regulated by kinase signaling cascades. Serine-arginine (SR) protein kinases are highly conserved among eukaryotes and are involved in major transcriptional processes in human and S. cerevisiae. Candida albicans harbors two SR protein kinases, while Sky2 is important for metabolic adaptation, Sky1 has similar functions as in S. cerevisiae. To investigate the role of these SR kinases for the regulation of transcriptional responses in C. albicans, we performed RNA sequencing of sky1Δ and sky2Δ and integrated a comprehensive phosphoproteome dataset of these mutants. Using a Systems Biology approach, we study transcriptional regulation in the context of kinase signaling networks. Transcriptomic enrichment analysis indicates that pathways involved in the regulation of gene expression are downregulated and mitochondrial processes are upregulated in sky1Δ. In sky2Δ, primarily metabolic processes are affected, especially for arginine, and we observed that arginine-induced hyphae formation is impaired in sky2Δ. In addition, our analysis identifies several transcription factors as potential drivers of the transcriptional response. Among these, a core set is shared between both kinase knockouts, but it appears to regulate different subsets of target genes. To elucidate these diverse regulatory patterns, we created network modules by integrating the data of site-specific protein phosphorylation and gene expression with kinase-substrate predictions and protein-protein interactions. These integrated signaling modules reveal shared parts but also highlight specific patterns characteristic for each kinase. Interestingly, the modules contain many proteins involved in fungal morphogenesis and stress response. Accordingly, experimental phenotyping shows a higher resistance to Hygromycin B for sky1Δ. Thus, our study demonstrates that a combination of computational approaches with integration of experimental data can offer a new systems biological perspective on the complex network of signaling and transcription. With that, the investigation of the interface between signaling and transcriptional regulation in C. albicans provides a deeper insight into how cellular mechanisms can shape the phenotype.
Rational drug design of Axl tyrosine kinase type I inhibitors as promising candidates against cancer
(2020)
The high level of Axl tyrosine kinase expression in various cancer cell lines makes it an attractive target for the development of anti-cancer drugs. In this study, we carried out several sets of in silico screening for the ATP-competitive Axl kinase inhibitors based on different molecular docking protocols. The best drug-like candidates were identified, after parental structure modifications, by their highest affinity to the target protein. We found that our newly designed compound R5, a derivative of the R428 patented analog, is the most promising inhibitor of the Axl kinase according to the three molecular docking algorithms applied in the study. The molecular docking results are in agreement with the molecular dynamics simulations using the MM-PBSA/GBSA implicit solvation models, which confirm the high affinity of R5 toward the protein receptor. Additionally, the selectivity test against other kinases also reveals a high affinity of R5 toward ABL1 and Tyro3 kinases, emphasizing its promising potential for the treatment of malignant tumors.
Dendritic cells (DCs) are antigen presenting cells which serve as a passage between the innate and the acquired immunity. Aspergillosis is a major lethal condition in immunocompromised patients caused by the adaptable saprophytic fungus Aspergillus fumigatus. The healthy human immune system is capable to ward off A. fumigatus infections however immune-deficient patients are highly vulnerable to invasive aspergillosis. A. fumigatus can persist during infection due to its ability to survive the immune response of human DCs. Therefore, the study of the metabolism specific to the context of infection may allow us to gain insight into the adaptation strategies of both the pathogen and the immune cells. We established a metabolic model of A. fumigatus central metabolism during infection of DCs and calculated the metabolic pathway (elementary modes; EMs). Transcriptome data were used to identify pathways activated when A. fumigatus is challenged with DCs. In particular, amino acid metabolic pathways, alternative carbon metabolic pathways and stress regulating enzymes were found to be active. Metabolic flux modeling identified further active enzymes such as alcohol dehydrogenase, inositol oxygenase and GTP cyclohydrolase participating in different stress responses in A. fumigatus. These were further validated by qRT-PCR from RNA extracted under these different conditions. For DCs, we outlined the activation of metabolic pathways in response to the confrontation with A. fumigatus. We found the fatty acid metabolism plays a crucial role, along with other metabolic changes. The gene expression data and their analysis illuminate additional regulatory pathways activated in the DCs apart from interleukin regulation. In particular, Toll-like receptor signaling, NOD-like receptor signaling and RIG-I-like receptor signaling were active pathways. Moreover, we identified subnetworks and several novel key regulators such as UBC, EGFR, and CUL3 of DCs to be activated in response to A. fumigatus. In conclusion, we analyze the metabolic and regulatory responses of A. fumigatus and DCs when confronted with each other.
Nucleic acid motifs consist of conserved and variable nucleotide regions. For functional action, several motifs are combined to modules. The tool AIModules allows identification of such motifs including combinations of them and conservation in several nucleic acid stretches. AIModules recognizes conserved motifs and combinations of motifs (modules) allowing a number of interesting biological applications such as analysis of promoter and transcription factor binding sites (TFBS), identification of conserved modules shared between several gene families, e.g. promoter regions, but also analysis of shared and conserved other DNA motifs such as enhancers and silencers, in mRNA (motifs or regulatory elements e.g. for polyadenylation) and lncRNAs. The tool AIModules presented here is an integrated solution for motif analysis, offered as a Web service as well as downloadable software. Several nucleotide sequences are queried for TFBSs using predefined matrices from the JASPAR DB or by using one’s own matrices for diverse types of DNA or RNA motif discovery. Furthermore, AIModules can find TFBSs common to two or more sequences. Demanding high or low conservation, AIModules outperforms other solutions in speed and finds more modules (specific combinations of TFBS) than alternative available software. The application also searches RNA motifs such as polyadenylation site or RNA–protein binding motifs as well as DNA motifs such as enhancers as well as user-specified motif combinations (https://bioinfo-wuerz.de/aimodules/; alternative entry pages: https://aimodules.heinzelab.de or https://www.biozentrum.uni-wuerzburg.de/bioinfo/computing/aimodules). The application is free and open source whether used online, on-site, or locally.
Cisplatin is a commonly used chemotherapeutic agent; however, its potential side effects, including gonadotoxicity and infertility, are a critical problem. Oxidative stress has been implicated in the pathogenesis of cisplatin-induced testicular dysfunction. We investigated whether kinetin use at different concentrations could alleviate gonadal injury associated with cisplatin treatment, with an exploration of the involvement of its antioxidant capacity. Kinetin was administered in different doses of 0.25, 0.5, and 1 mg/kg, alone or along with cisplatin for 10 days. Cisplatin toxicity was induced via a single IP dose of 7 mg/kg on day four. In a dose-dependent manner, concomitant administration of kinetin with cisplatin significantly restored testicular oxidative stress parameters, corrected the distorted sperm quality parameters and histopathological changes, enhanced levels of serum testosterone and testicular StAR protein expression, as well as reduced the up-regulation of testicular TNF-α, IL-1β, Il-6, and caspase-3, caused by cisplatin. It is worth noting that the testicular protective effect of the highest kinetin dose was comparable/more potent and significantly higher than the effects of vitamin C and the lowest kinetin dose, respectively. Overall, these data indicate that kinetin may offer a promising approach for alleviating cisplatin-induced reproductive toxicity and organ damage, via ameliorating oxidative stress and reducing inflammation and apoptosis.
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.
Tumor models based on cancer cell lines cultured two-dimensionally (2D) on plastic lack histological complexity and functionality compared to the native microenvironment. Xenogenic mouse tumor models display higher complexity but often do not predict human drug responses accurately due to species-specific differences. We present here a three-dimensional (3D) in vitro colon cancer model based on a biological scaffold derived from decellularized porcine jejunum (small intestine submucosa+mucosa, SISmuc). Two different cell lines were used in monoculture or in coculture with primary fibroblasts. After 14 days of culture, we demonstrated a close contact of human Caco2 colon cancer cells with the preserved basement membrane on an ultrastructural level as well as morphological characteristics of a well-differentiated epithelium. To generate a tissue-engineered tumor model, we chose human SW480 colon cancer cells, a reportedly malignant cell line. Malignant characteristics were confirmed in 2D cell culture: SW480 cells showed higher vimentin and lower E-cadherin expression than Caco2 cells. In contrast to Caco2, SW480 cells displayed cancerous characteristics such as delocalized E-cadherin and nuclear location of beta-catenin in a subset of cells. One central drawback of 2D cultures-especially in consideration of drug testing-is their artificially high proliferation. In our 3D tissue-engineered tumor model, both cell lines showed decreased numbers of proliferating cells, thus correlating more precisely with observations of primary colon cancer in all stages (UICC I-IV). Moreover, vimentin decreased in SW480 colon cancer cells, indicating a mesenchymal to epithelial transition process, attributed to metastasis formation. Only SW480 cells cocultured with fibroblasts induced the formation of tumor-like aggregates surrounded by fibroblasts, whereas in Caco2 cocultures, a separate Caco2 cell layer was formed separated from the fibroblast compartment beneath. To foster tissue generation, a bioreactor was constructed for dynamic culture approaches. This induced a close tissue-like association of cultured tumor cells with fibroblasts reflecting tumor biopsies. Therapy with 5-fluorouracil (5-FU) was effective only in 3D coculture. In conclusion, our 3D tumor model reflects human tissue-related tumor characteristics, including lower tumor cell proliferation. It is now available for drug testing in metastatic context-especially for substances targeting tumor-stroma interactions.
The present study reports the synthesis of new purine bioisosteres comprising a pyrazolo[3,4-d]pyrimidine scaffold linked to mono-, di-, and trimethoxy benzylidene moieties through hydrazine linkages. First, in silico docking experiments of the synthesized compounds against Bax, Bcl-2, Caspase-3, Ki67, p21, and p53 were performed in a trial to rationalize the observed cytotoxic activity for the tested compounds. The anticancer activity of these compounds was evaluated in vitro against Caco-2, A549, HT1080, and Hela cell lines. Results revealed that two (5 and 7) of the three synthesized compounds (5, 6, and 7) showed high cytotoxic activity against all tested cell lines with IC50 values in the micro molar concentration. Our in vitro results show that there is no significant apoptotic effect for the treatment with the experimental compounds on the viability of cells against A549 cells. Ki67 expression was found to decrease significantly following the treatment of cells with the most promising candidate: drug 7. The overall results indicate that these pyrazolopyrimidine derivatives possess anticancer activity at varying doses. The suggested mechanism of action involves the inhibition of the proliferation of cancer cells.
Whereas the role of calcium ions (Ca\(^{2+}\)) in plant signaling is well studied, the physiological significance of pH‐changes remains largely undefined.
Here we developed CapHensor, an optimized dual‐reporter for simultaneous Ca\(^{2+}\) and pH ratio‐imaging and studied signaling events in pollen tubes (PTs), guard cells (GCs), and mesophyll cells (MCs). Monitoring spatio‐temporal relationships between membrane voltage, Ca\(^{2+}\)‐ and pH‐dynamics revealed interconnections previously not described.
In tobacco PTs, we demonstrated Ca\(^{2+}\)‐dynamics lag behind pH‐dynamics during oscillatory growth, and pH correlates more with growth than Ca\(^{2+}\). In GCs, we demonstrated abscisic acid (ABA) to initiate stomatal closure via rapid cytosolic alkalization followed by Ca2+ elevation. Preventing the alkalization blocked GC ABA‐responses and even opened stomata in the presence of ABA, disclosing an important pH‐dependent GC signaling node. In MCs, a flg22‐induced membrane depolarization preceded Ca2+‐increases and cytosolic acidification by c. 2 min, suggesting a Ca\(^{2+}\)/pH‐independent early pathogen signaling step. Imaging Ca2+ and pH resolved similar cytosol and nuclear signals and demonstrated flg22, but not ABA and hydrogen peroxide to initiate rapid membrane voltage‐, Ca\(^{2+}\)‐ and pH‐responses.
We propose close interrelation in Ca\(^{2+}\)‐ and pH‐signaling that is cell type‐ and stimulus‐specific and the pH having crucial roles in regulating PT growth and stomata movement.
Circadian endogenous clocks of eukaryotic organisms are an established and rapidly developing research field. To investigate and simulate in an effective model the effect of external stimuli on such clocks and their components we developed a software framework for download and simulation. The application is useful to understand the different involved effects in a mathematical simple and effective model. This concerns the effects of Zeitgebers, feedback loops and further modifying components. We start from a known mathematical oscillator model, which is based on experimental molecular findings. This is extended with an effective framework that includes the impact of external stimuli on the circadian oscillations including high dose pharmacological treatment. In particular, the external stimuli framework defines a systematic procedure by input-output-interfaces to couple different oscillators. The framework is validated by providing phase response curves and ranges of entrainment. Furthermore, Aschoffs rule is computationally investigated. It is shown how the external stimuli framework can be used to study biological effects like points of singularity or oscillators integrating different signals at once. The mathematical framework and formalism is generic and allows to study in general the effect of external stimuli on oscillators and other biological processes. For an easy replication of each numerical experiment presented in this work and an easy implementation of the framework the corresponding Mathematica files are fully made available. They can be downloaded at the following link: https://www.biozentrum.uni-wuerzburg.de/bioinfo/computing/circadian/.
Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have consumed a significant amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA), European Medicines Agency (EMA), or Worldwide for another indication is a more rapid and less expensive option. Therefore, we apply the scaffold searching approach based on known amyloid-beta (Aβ) inhibitor tramiprosate to screen the DrugCentral database (n = 4,642) of clinically tested drugs. As a result, menadione bisulfite and camphotamide substances with protrombogenic and neurostimulation/cardioprotection effects were identified as promising Aβ inhibitors with an improved binding affinity (ΔGbind) and blood-brain barrier permeation (logBB). Finally, the data was also confirmed by molecular dynamics simulations using implicit solvation, in particular as Molecular Mechanics Generalized Born Surface Area (MM-GBSA) model. Overall, the proposed in silico pipeline can be implemented through the early stage rational drug design to nominate some lead candidates for AD, which will be further validated in vitro and in vivo, and, finally, in a clinical trial.
A precise and rapid adjustment of fluxes through metabolic pathways is crucial for organisms to prevail in changing environmental conditions. Based on this reasoning, many guiding principles that govern the evolution of metabolic networks and their regulation have been uncovered. To this end, methods from dynamic optimization are ideally suited since they allow to uncover optimality principles behind the regulation of metabolic networks. We used dynamic optimization to investigate the influence of toxic intermediates in connection with the efficiency of enzymes on the regulation of a linear metabolic pathway. Our results predict that transcriptional regulation favors the control of highly efficient enzymes with less toxic upstream intermediates to reduce accumulation of toxic downstream intermediates. We show that the derived optimality principles hold by the analysis of the interplay between intermediate toxicity and pathway regulation in the metabolic pathways of over 5000 sequenced prokaryotes. Moreover, using the lipopolysaccharide biosynthesis in Escherichia coli as an example, we show how knowledge about the relation of regulation, kinetic efficiency and intermediate toxicity can be used to identify drug targets, which control endogenous toxic metabolites and prevent microbial growth. Beyond prokaryotes, we discuss the potential of our findings for the development of antifungal drugs.
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.
Conventional anticancer chemotherapy is limited because of severe side effects as well as a quickly evolving multidrug resistance of the tumor cells. To address this problem, we have explored a C\(_{60}\) fullerene-based nanosized system as a carrier for anticancer drugs for an optimized drug delivery to leukemic cells.Here, we studied the physicochemical properties and anticancer activity of C\(_{60}\) fullerene noncovalent complexes with the commonly used anticancer drug doxorubicin. C\(_{60}\)-Doxorubicin complexes in a ratio 1:1 and 2:1 were characterized with UV/Vis spectrometry, dynamic light scattering, and high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). The obtained analytical data indicated that the 140-nm complexes were stable and could be used for biological applications. In leukemic cell lines (CCRF-CEM, Jurkat, THP1 and Molt-16), the nanocomplexes revealed 3.5 higher cytotoxic potential in comparison with the free drug in a range of nanomolar concentrations. Also, the intracellular drug's level evidenced C\(_{60}\) fullerene considerable nanocarrier function.The results of this study indicated that C\(_{60}\) fullerene-based delivery nanocomplexes had a potential value for optimization of doxorubicin efficiency against leukemic cells.
Aspergillus is an important fungal genus containing economically important species, as well as pathogenic species of animals and plants. Using eighteen fungal species of the genus Aspergillus, we conducted a comprehensive investigation of conserved genes and their evolution. This also allows us to investigate the selection pressure driving the adaptive evolution in the pathogenic species A. fumigatus. Among single-copy orthologs (SCOs) for A. fumigatus and the closely related species A. fischeri, we identified 122 versus 50 positively selected genes (PSGs), respectively. Moreover, twenty conserved genes of unknown function were established to be positively selected and thus important for adaption. A. fumigatus PSGs interacting with human host proteins show over-representation of adaptive, symbiosis-related, immunomodulatory and virulence-related pathways, such as the TGF-β pathway, insulin receptor signaling, IL1 pathway and interfering with phagosomal GTPase signaling. Additionally, among the virulence factor coding genes, secretory and membrane protein-coding genes in multi-copy gene families, 212 genes underwent positive selection and also suggest increased adaptation, such as fungal immune evasion mechanisms (aspf2), siderophore biosynthesis (sidD), fumarylalanine production (sidE), stress tolerance (atfA) and thermotolerance (sodA). These genes presumably contribute to host adaptation strategies. Genes for the biosynthesis of gliotoxin are shared among all the close relatives of A. fumigatus as an ancient defense mechanism. Positive selection plays a crucial role in the adaptive evolution of A. fumigatus. The genome-wide profile of PSGs provides valuable targets for further research on the mechanisms of immune evasion, antimycotic targeting and understanding fundamental virulence processes.
Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host–pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.
Synergy of chemo- and photodynamic therapies with C\(_{60}\) Fullerene-Doxorubicin nanocomplex
(2019)
A nanosized drug complex was explored to improve the efficiency of cancer chemotherapy, complementing it with nanodelivery and photodynamic therapy. For this, nanomolar amounts of a non-covalent nanocomplex of Doxorubicin (Dox) with carbon nanoparticle C\(_{60}\) fullerene (C\(_{60}\)) were applied in 1:1 and 2:1 molar ratio, exploiting C\(_{60}\) both as a drug-carrier and as a photosensitizer. The fluorescence microscopy analysis of human leukemic CCRF-CEM cells, in vitro cancer model, treated with nanocomplexes showed Dox’s nuclear and C\(_{60}\)'s extranuclear localization. It gave an opportunity to realize a double hit strategy against cancer cells based on Dox's antiproliferative activity and C\(_{60}\)'s photoinduced pro-oxidant activity. When cells were treated with 2:1 C\(_{60}\)-Dox and irradiated at 405 nm the high cytotoxicity of photo-irradiated C\(_{60}\)-Dox enabled a nanomolar concentration of Dox and C\(_{60}\) to efficiently kill cancer cells in vitro. The high pro-oxidant and pro-apoptotic efficiency decreased IC\(_{50}\) 16, 9 and 7 × 10\(^3\)-fold, if compared with the action of Dox, non-irradiated nanocomplex, and C\(_{60}\)'s photodynamic effect, correspondingly. Hereafter, a strong synergy of therapy arising from the combination of C\(_{60}\)-mediated Dox delivery and C\(_{60}\) photoexcitation was revealed. Our data indicate that a combination of chemo- and photodynamic therapies with C\(_{60}\)-Dox nanoformulation provides a promising synergetic approach for cancer treatment.