Refine
Has Fulltext
- yes (121) (remove)
Is part of the Bibliography
- yes (121)
Year of publication
Document Type
- Journal article (121) (remove)
Keywords
- metabolism (8)
- apoptosis (7)
- SARS-CoV-2 (5)
- cytokinins (5)
- COVID-19 (4)
- leukemic cells (4)
- regulation (4)
- transcriptome (4)
- Candida albicans (3)
- bioinformatics (3)
- cisplatin (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)
- cytotoxicity (2)
- dendritic cells (2)
- differentiation (2)
- drug repurposing (2)
- engineering (2)
- evolution (2)
- infection (2)
- lung cancer (2)
- machine learning (2)
- metabolic modeling (2)
- molecular dynamics (2)
- mouse (2)
- oncolytic virus (2)
- oxidative stress (2)
- platelet (2)
- positive selection (2)
- recombination (2)
- resistance (2)
- synthetic biology (2)
- transcriptional regulation (2)
- 3D lung tumor model (1)
- 3D lung tumor tissue models (1)
- 3D tissue models (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)
- Berberine (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)
- 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)
- EMT (1)
- ERK signaling (1)
- Echinococcosis (1)
- Echinococcus (1)
- Einfluss (1)
- Embryonic induction (1)
- Enterobacteriaceae (1)
- Enzyme kinetics (1)
- Enzyme metabolism (1)
- Enzyme regulation (1)
- Enzymes (1)
- Epicardium-derived cells (1)
- Evaluation (1)
- FLS2 receptor (1)
- Functional modules (1)
- GPVI (1)
- Gene expression vectors (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)
- ICEP (1)
- ICP27 (1)
- IGFBP2 (1)
- Insulin (1)
- Integrated network analysis (1)
- IronChip Evaluation Package (1)
- KRAS biomarker signatures (1)
- KRAS mutation signature (1)
- Ki67 (1)
- Kinase inhibitor (1)
- Klimaneutralität (1)
- Klimapflanzen (1)
- Klimawandel (1)
- LEDs (1)
- LS-MIDA (1)
- MHC I (1)
- MHC II (1)
- MITE (1)
- Metabolic profiles (1)
- Microarray (1)
- Mikroarray (1)
- Milnesium tardigradum (1)
- Mycoplasma (1)
- Neuromuscular junctions (1)
- Olea (1)
- Phylogenie (1)
- PknB (1)
- Polymerase chain reaction (1)
- Predictive toxicology (1)
- Prognose (1)
- Proteasen (1)
- Proteine (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)
- 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)
- 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)
- 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)
- 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)
- 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)
- culture (1)
- cycle (1)
- cyclic nucleotide signaling (1)
- cytokines (1)
- cytokinin (1)
- cytokinin kinetin (1)
- data mining/methods (1)
- data storage (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 diagnosis (1)
- efficient intervention points (1)
- elementary body (1)
- elementary modes (1)
- encapsulation (1)
- encephalitis dementia (1)
- enteric pathogens (1)
- enterica serovar Typhimurium (1)
- enzyme (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)
- 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)
- 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 vivo toxicity (1)
- in-vitro (1)
- infected-cell protein (1)
- infection biology (1)
- infection spread (1)
- inflammation (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)
- 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)
- 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)
- olive (1)
- omics (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)
- pharmacology (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)
- pyrazolo[3,4-d]pyrimidine (1)
- radiation (1)
- rational drug design (1)
- re-annotation (1)
- receptor (1)
- regulatory networks (1)
- relA (1)
- reliability (1)
- reproductive toxicity (1)
- response regulator (1)
- reticulate body (1)
- ribosomal RNA (1)
- riboswitch (1)
- richtersius coronifer (1)
- scaffold search (1)
- secondary structure (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)
- tumors (1)
- type 1 (1)
- uptake (1)
- variable regions (1)
- variants (1)
- virulenceregulatory evolution (1)
- viruses (1)
- vitellogenin (1)
- water stress (1)
- wrong labelling (1)
- xanthurenic acid (1)
- yvcK/glmR operon (1)
Institute
- Theodor-Boveri-Institut für Biowissenschaften (119)
- Klinik und Poliklinik für Anästhesiologie (ab 2004) (6)
- Lehrstuhl für Tissue Engineering und Regenerative Medizin (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)
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.
Mining biomedical images towards valuable information retrieval in biomedical and life sciences
(2016)
Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.
The composition of stable-isotope labelled isotopologues/isotopomers in metabolic products can be measured by mass spectrometry and supports the analysis of pathways and fluxes. As a prerequisite, the original mass spectra have to be processed, managed and stored to rapidly calculate, analyse and compare isotopomer enrichments to study, for instance, bacterial metabolism in infection. For such applications, we provide here the database application ‘Isotopo’. This software package includes (i) a database to store and process isotopomer data, (ii) a parser to upload and translate different data formats for such data and (iii) an improved application to process and convert signal intensities from mass spectra of \(^{13}C\)-labelled metabolites such as tertbutyldimethylsilyl-derivatives of amino acids. Relative mass intensities and isotopomer distributions are calculated applying a partial least square method with iterative refinement for high precision data. The data output includes formats such as graphs for overall enrichments in amino acids. The package is user-friendly for easy and robust data management of multiple experiments.
Protein-protein interaction (PPI) studies are gaining momentum these days due to the plethora of various high-throughput experimental methods available for detecting PPIs. Proteins create complexes and networks by functioning in harmony with other proteins and here in silico network biology hold the promise to reveal new functionality of genes as it is very difficult and laborious to carry out experimental high-throughput genetic screens in living organisms. We demonstrate this approach by computationally screening C. elegans conserved homologs of already reported human tumor suppressor and aging associated genes. We select by this nhr-6, vab-3 and gst-23 as predicted longevity genes for RNAi screen. The RNAi results demonstrated the pro-longevity effect of these genes. Nuclear hormone receptor nhr-6 RNAi inhibition resulted in a C. elegans phenotype of 23.46% lifespan reduction. Moreover, we show that nhr-6 regulates oxidative stress resistance in worms and does not affect the feeding behavior of worms. These findings imply the potential of nhr-6 as a common therapeutic target for aging and cancer ailments, stressing the power of in silico PPI network analysis coupled with RNAi screens to describe gene function.
The olive tree is a venerable Mediterranean plant and often used in traditional medicine. The main aim of the present study was to evaluate the effect of Olea europaea L. cv. Arbosana leaf extract (OLE) and its encapsulation within a spanlastic dosage form on the improvement of its pro-oxidant and antiproliferative activity against HepG-2, MCF-7, and Caco-2 human cancer cell lines. The LC-HRESIMS-assisted metabolomic profile of OLE putatively annotated 20 major metabolites and showed considerable in vitro antiproliferative activity against HepG-2, MCF-7, and Caco-2 cell lines with IC\(_{50}\) values of 9.2 ± 0.8, 7.1 ± 0.9, and 6.5 ± 0.7 µg/mL, respectively. The encapsulation of OLE within a (spanlastic) nanocarrier system, using a spraying method and Span 40 and Tween 80 (4:1 molar ratio), was successfully carried out (size 41 ± 2.4 nm, zeta potential 13.6 ± 2.5, and EE 61.43 ± 2.03%). OLE showed enhanced thermal stability, and an improved in vitro antiproliferative effect against HepG-2, MCF-7, and Caco-2 (IC\(_{50}\) 3.6 ± 0.2, 2.3 ± 0.1, and 1.8 ± 0.1 µg/mL, respectively) in comparison to the unprocessed extract. Both preparations were found to exhibit pro-oxidant potential inside the cancer cells, through the potential inhibitory activity of OLE against glutathione reductase and superoxide dismutase (IC\(_{50}\) 1.18 ± 0.12 and 2.33 ± 0.19 µg/mL, respectively). These inhibitory activities were proposed via a comprehensive in silico study to be linked to the presence of certain compounds in OLE. Consequently, we assume that formulating such a herbal extract within a suitable nanocarrier would be a promising improvement of its therapeutic potential.
Background:
Commensal bacteria like Neisseria meningitidis sometimes cause serious disease. However, genomic comparison of hyperinvasive and apathogenic lineages did not reveal unambiguous hints towards indispensable virulence factors. Here, in a systems biological approach we compared gene expression of the invasive strain MC58 and the carriage strain α522 under different ex vivo conditions mimicking commensal and virulence compartments to assess the strain-specific impact of gene regulation on meningococcal virulence.
Results:
Despite indistinguishable ex vivo phenotypes, both strains differed in the expression of over 500 genes under infection mimicking conditions. These differences comprised in particular metabolic and information processing genes as well as genes known to be involved in host-damage such as the nitrite reductase and numerous LOS biosynthesis genes. A model based analysis of the transcriptomic differences in human blood suggested ensuing metabolic flux differences in energy, glutamine and cysteine metabolic pathways along with differences in the activation of the stringent response in both strains. In support of the computational findings, experimental analyses revealed differences in cysteine and glutamine auxotrophy in both strains as well as a strain and condition dependent essentiality of the (p)ppGpp synthetase gene relA and of a short non-coding AT-rich repeat element in its promoter region.
Conclusions:
Our data suggest that meningococcal virulence is linked to transcriptional buffering of cryptic genetic variation in metabolic genes including global stress responses. They further highlight the role of regulatory elements for bacterial virulence and the limitations of model strain approaches when studying such genetically diverse species as N. meningitidis.
Stapylococcus aureus colonises the nose of healthy individuals but can also cause a wide range of infections. Amino acid (AA) synthesis and their availability is crucial to adapt to conditions encountered in vivo. Most S. aureus genomes comprise all genes required for AA biosynthesis. Nevertheless, different strains require specific sets of AAs for growth. In this study we show that regulation inactivates pathways under certain conditions which result in these observed auxotrophies. We analyzed in vitro and modeled in silico in a Boolean semiquantitative model (195 nodes, 320 edges) the regulatory impact of stringent response (SR) on AA requirement in S. aureus HG001 (wild-type) and in mutant strains lacking the metabolic regulators RSH, CodY and CcpA, respectively. Growth in medium lacking single AAs was analyzed. Results correlated qualitatively to the in silico predictions of the final model in 92% and quantitatively in 81%. Remaining gaps in our knowledge are evaluated and discussed. This in silico model is made fully available and explains how integration of different inputs is achieved in SR and AA metabolism of S. aureus. The in vitro data and in silico modeling stress the role of SR and central regulators such as CodY for AA metabolisms in S. aureus.
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.
Comparison of the central human and mouse platelet signaling cascade by systems biological analysis
(2020)
Background
Understanding the molecular mechanisms of platelet activation and aggregation is of high interest for basic and clinical hemostasis and thrombosis research. The central platelet protein interaction network is involved in major responses to exogenous factors. This is defined by systemsbiological pathway analysis as the central regulating signaling cascade of platelets (CC).
Results
The CC is systematically compared here between mouse and human and major differences were found. Genetic differences were analysed comparing orthologous human and mouse genes. We next analyzed different expression levels of mRNAs. Considering 4 mouse and 7 human high-quality proteome data sets, we identified then those major mRNA expression differences (81%) which were supported by proteome data. CC is conserved regarding genetic completeness, but we observed major differences in mRNA and protein levels between both species. Looking at central interactors, human PLCB2, MMP9, BDNF, ITPR3 and SLC25A6 (always Entrez notation) show absence in all murine datasets. CC interactors GNG12, PRKCE and ADCY9 occur only in mice. Looking at the common proteins, TLN1, CALM3, PRKCB, APP, SOD2 and TIMP1 are higher abundant in human, whereas RASGRP2, ITGB2, MYL9, EIF4EBP1, ADAM17, ARRB2, CD9 and ZYX are higher abundant in mouse. Pivotal kinase SRC shows different regulation on mRNA and protein level as well as ADP receptor P2RY12.
Conclusions
Our results highlight species-specific differences in platelet signaling and points of specific fine-tuning in human platelets as well as murine-specific signaling differences.
To improve and focus preclinical testing, we combine tumor models based on a decellularized tissue matrix with bioinformatics to stratify tumors according to stage-specific mutations that are linked to central cancer pathways. We generated tissue models with BRAF-mutant colorectal cancer (CRC) cells (HROC24 and HROC87) and compared treatment responses to two-dimensional (2D) cultures and xenografts. As the BRAF inhibitor vemurafenib is—in contrast to melanoma—not effective in CRC, we combined it with the EGFR inhibitor gefitinib. In general, our 3D models showed higher chemoresistance and in contrast to 2D a more active HGFR after gefitinib and combination-therapy. In xenograft models murine HGF could not activate the human HGFR, stressing the importance of the human microenvironment. In order to stratify patient groups for targeted treatment options in CRC, an in silico topology with different stages including mutations and changes in common signaling pathways was developed. We applied the established topology for in silico simulations to predict new therapeutic options for BRAF-mutated CRC patients in advanced stages. Our in silico tool connects genome information with a deeper understanding of tumor engines in clinically relevant signaling networks which goes beyond the consideration of single drivers to improve CRC patient stratification.
Background: Tardigrades are multicellular organisms, resistant to extreme environmental changes such as heat, drought, radiation and freezing. They outlast these conditions in an inactive form (tun) to escape damage to cellular structures and cell death. Tardigrades are apparently able to prevent or repair such damage and are therefore a crucial model organism for stress tolerance. Cultures of the tardigrade Milnesium tardigradum were dehydrated by removing the surrounding water to induce tun formation. During this process and the subsequent rehydration, metabolites were measured in a time series by GC-MS. Additionally expressed sequence tags are available, especially libraries generated from the active and inactive state. The aim of this integrated analysis is to trace changes in tardigrade metabolism and identify pathways responsible for their extreme resistance against physical stress. Results: In this study we propose a novel integrative approach for the analysis of metabolic networks to identify modules of joint shifts on the transcriptomic and metabolic levels. We derive a tardigrade-specific metabolic network represented as an undirected graph with 3,658 nodes (metabolites) and 4,378 edges (reactions). Time course metabolite profiles are used to score the network nodes showing a significant change over time. The edges are scored according to information on enzymes from the EST data. Using this combined information, we identify a key subnetwork (functional module) of concerted changes in metabolic pathways, specific for de- and rehydration. The module is enriched in reactions showing significant changes in metabolite levels and enzyme abundance during the transition. It resembles the cessation of a measurablemetabolism (e.g. glycolysis and amino acid anabolism) during the tun formation, the production of storage metabolites and bioprotectants, such as DNA stabilizers, and the generation of amino acids and cellular components from monosaccharides as carbon and energy source during rehydration. Conclusions: The functional module identifies relationships among changed metabolites (e.g. spermidine) and reactions and provides first insights into important altered metabolic pathways. With sparse and diverse data available, the presented integrated metabolite network approach is suitable to integrate all existing data and analyse it in a combined manner.
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).
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.
The rapid development of green and sustainable materials opens up new possibilities in the field of applied research. Such materials include nanocellulose composites that can integrate many components into composites and provide a good chassis for smart devices. In our study, we evaluate four approaches for turning a nanocellulose composite into an information storage or processing device: 1) nanocellulose can be a suitable carrier material and protect information stored in DNA. 2) Nucleotide-processing enzymes (polymerase and exonuclease) can be controlled by light after fusing them with light-gating domains; nucleotide substrate specificity can be changed by mutation or pH change (read-in and read-out of the information). 3) Semiconductors and electronic capabilities can be achieved: we show that nanocellulose is rendered electronic by iodine treatment replacing silicon including microstructures. Nanocellulose semiconductor properties are measured, and the resulting potential including single-electron transistors (SET) and their properties are modeled. Electric current can also be transported by DNA through G-quadruplex DNA molecules; these as well as classical silicon semiconductors can easily be integrated into the nanocellulose composite. 4) To elaborate upon miniaturization and integration for a smart nanocellulose chip device, we demonstrate pH-sensitive dyes in nanocellulose, nanopore creation, and kinase micropatterning on bacterial membranes as well as digital PCR micro-wells. Future application potential includes nano-3D printing and fast molecular processors (e.g., SETs) integrated with DNA storage and conventional electronics. This would also lead to environment-friendly nanocellulose chips for information processing as well as smart nanocellulose composites for biomedical applications and nano-factories.
Background
The metacestode of the tapeworm Echinococcus multilocularis is the causative agent of alveolar echinococcosis, a lethal zoonosis. Infections are initiated through establishment of parasite larvae within the intermediate host’s liver, where high concentrations of insulin are present, followed by tumour-like growth of the metacestode in host organs. The molecular mechanisms determining the organ tropism of E. multilocularis or the influences of host hormones on parasite proliferation are poorly understood.
Results
Using in vitro cultivation systems for parasite larvae we show that physiological concentrations (10 nM) of human insulin significantly stimulate the formation of metacestode larvae from parasite stem cells and promote asexual growth of the metacestode. Addition of human insulin to parasite larvae led to increased glucose uptake and enhanced phosphorylation of Echinococcus insulin signalling components, including an insulin receptor-like kinase, EmIR1, for which we demonstrate predominant expression in the parasite’s glycogen storage cells. We also characterized a second insulin receptor family member, EmIR2, and demonstrated interaction of its ligand binding domain with human insulin in the yeast two-hybrid system. Addition of an insulin receptor inhibitor resulted in metacestode killing, prevented metacestode development from parasite stem cells, and impaired the activation of insulin signalling pathways through host insulin.
Conclusions
Our data indicate that host insulin acts as a stimulant for parasite development within the host liver and that E. multilocularis senses the host hormone through an evolutionarily conserved insulin signalling pathway. Hormonal host-parasite cross-communication, facilitated by the relatively close phylogenetic relationship between E. multilocularis and its mammalian hosts, thus appears to be important in the pathology of alveolar echinococcosis. This contributes to a closer understanding of organ tropism and parasite persistence in larval cestode infections. Furthermore, our data show that Echinococcus insulin signalling pathways are promising targets for the development of novel drugs.
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/.
Mathematical optimization framework allows the identification of certain nodes within a signaling network. In this work, we analyzed the complex extracellular-signal-regulated kinase 1 and 2 (ERK1/2) cascade in cardiomyocytes using the framework to find efficient adjustment screws for this cascade that is important for cardiomyocyte survival and maladaptive heart muscle growth. We modeled optimal pharmacological intervention points that are beneficial for the heart, but avoid the occurrence of a maladaptive ERK1/2 modification, the autophosphorylation of ERK at threonine 188 (ERK\(^{Thr188}\) phosphorylation), which causes cardiac hypertrophy. For this purpose, a network of a cardiomyocyte that was fitted to experimental data was equipped with external stimuli that model the pharmacological intervention points. Specifically, two situations were considered. In the first one, the cardiomyocyte was driven to a desired expression level with different treatment strategies. These strategies were quantified with respect to beneficial effects and maleficent side effects and then which one is the best treatment strategy was evaluated. In the second situation, it was shown how to model constitutively activated pathways and how to identify drug targets to obtain a desired activity level that is associated with a healthy state and in contrast to the maleficent expression pattern caused by the constitutively activated pathway. An implementation of the algorithms used for the calculations is also presented in this paper, which simplifies the application of the presented framework for drug targeting, optimal drug combinations and the systematic and automatic search for pharmacological intervention points. The codes were designed such that they can be combined with any mathematical model given by ordinary differential equations.
Background: Because most human stroke victims are elderly, studies of experimental stroke in the aged rather than the young rat model may be optimal for identifying clinically relevant cellular responses, as well for pinpointing beneficial interventions.
Methodology/Principal Findings: We employed the Affymetrix platform to analyze the whole-gene transcriptome following temporary ligation of the middle cerebral artery in aged and young rats. The correspondence, heat map, and dendrogram analyses independently suggest a differential, age-group-specific behaviour of major gene clusters after stroke. Overall, the pattern of gene expression strongly suggests that the response of the aged rat brain is qualitatively rather than quantitatively different from the young, i.e. the total number of regulated genes is comparable in the two age groups, but the aged rats had great difficulty in mounting a timely response to stroke. Our study indicates that four genes related to neuropathic syndrome, stress, anxiety disorders and depression (Acvr1c, Cort, Htr2b and Pnoc) may have impaired response to stroke in aged rats. New therapeutic options in aged rats may also include Calcrl, Cyp11b1, Prcp, Cebpa, Cfd, Gpnmb, Fcgr2b, Fcgr3a, Tnfrsf26, Adam 17 and Mmp14. An unexpected target is the enzyme 3-hydroxy-3-methylglutaryl-Coenzyme A synthase 1 in aged rats, a key enzyme in the cholesterol synthesis pathway. Post-stroke axonal growth was compromised in both age groups.
Conclusion/Significance: We suggest that a multi-stage, multimodal treatment in aged animals may be more likely to produce positive results. Such a therapeutic approach should be focused on tissue restoration but should also address other aspects of patient post-stroke therapy such as neuropathic syndrome, stress, anxiety disorders, depression, neurotransmission and blood pressure.
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.