Refine
Has Fulltext
- yes (119) (remove)
Is part of the Bibliography
- yes (119)
Year of publication
Document Type
- Journal article (119) (remove)
Keywords
- metabolism (8)
- apoptosis (6)
- SARS-CoV-2 (5)
- cytokinins (5)
- COVID-19 (4)
- regulation (4)
- transcriptome (4)
- Candida albicans (3)
- bioinformatics (3)
- cisplatin (3)
- database (3)
- expression (3)
- identification (3)
- leukemic cells (3)
- molecular docking (3)
- mutation (3)
- systems biology (3)
- virulence (3)
- "-omics" (2)
- Aspergillus fumigatus (2)
- Bioinformatik (2)
- Biology (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 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 tree (1)
- C-60 fullerene (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)
- 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)
- Ki67 (1)
- Kinase inhibitor (1)
- Klimaneutralität (1)
- Klimapflanzen (1)
- Klimawandel (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)
- 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)
- 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)
- 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)
- 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 (117)
- Klinik und Poliklinik für Anästhesiologie (ab 2004) (6)
- Institut für Molekulare Infektionsbiologie (5)
- Lehrstuhl für Tissue Engineering und Regenerative Medizin (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.
Since ancient times aging has also been regarded as a disease, and humankind has always strived to extend the natural lifespan. Analyzing the genes involved in aging and disease allows for finding important indicators and biological markers for pathologies and possible therapeutic targets. An example of the use of omics technologies is the research regarding aging and the rare and fatal premature aging syndrome progeria (Hutchinson-Gilford progeria syndrome, HGPS). In our study, we focused on the in silico analysis of differentially expressed genes (DEGs) in progeria and aging, using a publicly available RNA-Seq dataset (GEO dataset GSE113957) and a variety of bioinformatics tools. Despite the GSE113957 RNA-Seq dataset being well-known and frequently analyzed, the RNA-Seq data shared by Fleischer et al. is far from exhausted and reusing and repurposing the data still reveals new insights. By analyzing the literature citing the use of the dataset and subsequently conducting a comparative analysis comparing the RNA-Seq data analyses of different subsets of the dataset (healthy children, nonagenarians and progeria patients), we identified several genes involved in both natural aging and progeria (KRT8, KRT18, ACKR4, CCL2, UCP2, ADAMTS15, ACTN4P1, WNT16, IGFBP2). Further analyzing these genes and the pathways involved indicated their possible roles in aging, suggesting the need for further in vitro and in vivo research. In this paper, we (1) compare “normal aging” (nonagenarians vs. healthy children) and progeria (HGPS patients vs. healthy children), (2) enlist genes possibly involved in both the natural aging process and progeria, including the first mention of IGFBP2 in progeria, (3) predict miRNAs and interactomes for WNT16 (hsa-mir-181a-5p), UCP2 (hsa-mir-26a-5p and hsa-mir-124-3p), and IGFBP2 (hsa-mir-124-3p, hsa-mir-126-3p, and hsa-mir-27b-3p), (4) demonstrate the compatibility of well-established R packages for RNA-Seq analysis for researchers interested but not yet familiar with this kind of analysis, and (5) present comparative proteomics analyses to show an association between our RNA-Seq data analyses and corresponding changes in protein expression.
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.
Virotherapy on the basis of oncolytic vaccinia virus (VACV) strains is a promising approach for cancer therapy. Recently, we showed that the oncolytic vaccinia virus GLV-1h68 has a therapeutic potential in treating human prostate and hepatocellular carcinomas in xenografted mice. In this study, we describe the use of dynamic boolean modeling for tumor growth prediction of vaccinia virus-injected human tumors. Antigen profiling data of vaccinia virus GLV-1h68-injected human xenografted mice were obtained, analyzed and used to calculate differences in the tumor growth signaling network by tumor type and gender. Our model combines networks for apoptosis, MAPK, p53, WNT, Hedgehog, the T-killer cell mediated cell death, Interferon and Interleukin signaling networks. The in silico findings conform very well with in vivo findings of tumor growth. Similar to a previously published analysis of vaccinia virus-injected canine tumors, we were able to confirm the suitability of our boolean modeling for prediction of human tumor growth after virus infection in the current study as well. In summary, these findings indicate that our boolean models could be a useful tool for testing of the efficacy of VACV-mediated cancer therapy already before its use in human patients.
Background: Xenobiotics represent an environmental stress and as such are a source for antibiotics, including the isoquinoline (IQ) compound IQ-143. Here, we demonstrate the utility of complementary analysis of both host and pathogen datasets in assessing bacterial adaptation to IQ-143, a synthetic analog of the novel type N,C-coupled naphthyl-isoquinoline alkaloid ancisheynine. Results: Metabolite measurements, gene expression data and functional assays were combined with metabolic modeling to assess the effects of IQ-143 on Staphylococcus aureus, Staphylococcus epidermidis and human cell lines, as a potential paradigm for novel antibiotics. Genome annotation and PCR validation identified novel enzymes in the primary metabolism of staphylococci. Gene expression response analysis and metabolic modeling demonstrated the adaptation of enzymes to IQ-143, including those not affected by significant gene expression changes. At lower concentrations, IQ-143 was bacteriostatic, and at higher concentrations bactericidal, while the analysis suggested that the mode of action was a direct interference in nucleotide and energy metabolism. Experiments in human cell lines supported the conclusions from pathway modeling and found that IQ-143 had low cytotoxicity. Conclusions: The data suggest that IQ-143 is a promising lead compound for antibiotic therapy against staphylococci. The combination of gene expression and metabolite analyses with in silico modeling of metabolite pathways allowed us to study metabolic adaptations in detail and can be used for the evaluation of metabolic effects of other xenobiotics.
No abstract available
Background
The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution.
Results
The open-source software “Least Square Mass Isotopomer Analyzer” (LS-MIDA) is presented that processes experimental mass spectrometry (MS) data on the basis of metabolite information such as the number of atoms in the compound, mass to charge ratio (m/e or m/z) values of the compounds and fragments under study, and the experimental relative MS intensities reflecting the enrichments of isotopomers in 13C- or 15 N-labelled compounds, in comparison to the natural abundances in the unlabelled molecules. The software uses Brauman’s least square method of linear regression. As a result, global isotope enrichments of the metabolite or fragment under study and the molar abundances of each isotopomer are obtained and displayed.
Conclusions
The new software provides an open-source platform that easily and rapidly converts experimental MS patterns of labelled metabolites into isotopomer enrichments that are the basis for subsequent observation-driven analysis of pathways and fluxes, as well as for model-driven metabolic flux calculations.
No abstract available
No abstract available
Climate plants are critical to prevent global warming as all efforts to save carbon dioxide are too slow and climate disasters on the rise. For best carbon dioxide harvesting we compare algae, trees and crop plants and use metagenomic analysis of environmental samples. We compare different pathways, carbon harvesting potentials of different plants as well as synthetic modifications including carbon dioxide flux balance analysis. For implementation, agriculture and modern forestry are important.
The human-pathogenic bacterium Salmonella enterica adjusts and adapts to different environments while attempting colonization. In the course of infection nutrient availabilities change drastically. New techniques, “-omics” data and subsequent integration by systems biology improve our understanding of these changes. We review changes in metabolism focusing on amino acid and carbohydrate metabolism. Furthermore, the adaptation process is associated with the activation of genes of the Salmonella pathogenicity islands (SPIs). Anti-infective strategies have to take these insights into account and include metabolic and other strategies. Salmonella infections will remain a challenge for infection biology.
The human-pathogenic bacterium Salmonella enterica adjusts and adapts to different environments while attempting colonization. In the course of infection nutrient availabilities change drastically. New techniques, "-omics" data and subsequent integration by systems biology improve our understanding of these changes. We review changes in metabolism focusing on amino acid and carbohydrate metabolism. Furthermore, the adaptation process is associated with the activation of genes of the Salmonella pathogenicity islands (SPIs). Anti-infective strategies have to take these insights into account and include metabolic and other strategies. Salmonella infections will remain a challenge for infection biology.
The infectious intracellular lifestyle of Salmonella enterica relies on the adaptation to nutritional conditions within the Salmonella-containing vacuole (SCV) in host cells. We summarize latest results on metabolic requirements for Salmonella during infection. This includes intracellular phenotypes of mutant strains based on metabolic modeling and experimental tests, isotopolog profiling using (13)C-compounds in intracellular Salmonella, and complementation of metabolic defects for attenuated mutant strains towards a comprehensive understanding of the metabolic requirements of the intracellular lifestyle of Salmonella. Helpful for this are also genomic comparisons. We outline further recent studies and which analyses of intracellular phenotypes and improved metabolic simulations were done and comment on technical required steps as well as progress involved in the iterative refinement of metabolic flux models, analyses of mutant phenotypes, and isotopolog analyses. Salmonella lifestyle is well-adapted to the SCV and its specific metabolic requirements. Salmonella metabolism adapts rapidly to SCV conditions, the metabolic generalist Salmonella is quite successful in host infection.
An essential topic for synthetic biologists is to understand the structure and function of biological processes and involved proteins and plan experiments accordingly. Remarkable progress has been made in recent years towards this goal. However, efforts to collect and present all information on processes and functions are still cumbersome. The database tool GoSynthetic provides a new, simple and fast way to analyse biological processes applying a hierarchical database. Four different search modes are implemented. Furthermore, protein interaction data, cross-links to organism-specific databases (17 organisms including six model organisms and their interactions), COG/KOG, GO and IntAct are warehoused. The built in connection to technical and engineering terms enables a simple switching between biological concepts and concepts from engineering, electronics and synthetic biology. The current version of GoSynthetic covers more than one million processes, proteins, COGs and GOs. It is illustrated by various application examples probing process differences and designing modifications.
The diploid, polymorphic yeast Candida albicans is one of the most important human pathogenic fungi. C. albicans can grow, proliferate and coexist as a commensal on or within the human host for a long time. However, alterations in the host environment can render C. albicans virulent. In this review, we describe the immunological cross-talk between C. albicans and the human innate immune system. We give an overview in form of pairs of human defense strategies including immunological mechanisms as well as general stressors such as nutrient limitation, pH, fever etc. and the corresponding fungal response and evasion mechanisms. Furthermore, Computational Systems Biology approaches to model and investigate these complex interactions are highlighted with a special focus on game-theoretical methods and agent-based models. An outlook on interesting questions to be tackled by Systems Biology regarding entangled defense and evasion mechanisms is given.
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.
Though several previous studies reported the in vitro and in vivo antioxidant effect of kinetin (Kn), details on its action in cisplatin-induced toxicity are still scarce. In this study we evaluated, for the first time, the effects of kinetin in cisplatin (cp)- induced liver and lymphocyte toxicity in rats. Wistar male albino rats were divided into nine groups: (i) the control (C), (ii) groups 2,3 and 4, which received 0.25, 0.5 and 1 mg/kg kinetin for 10 days; (iii) the cisplatin (cp) group, which received a single intraperitoneal injection of CP (7.0 mg/kg); and (iv) groups 6, 7, 8 and 9, which received, for 10 days, 0.25, 0.5 and 1 mg/kg kinetin or 200 mg/kg vitamin C, respectively, and Cp on the fourth day. CP-injected rats showed a significant impairment in biochemical, oxidative stress and inflammatory parameters in hepatic tissue and lymphocytes. PCR showed a profound increase in caspase-3, and a significant decline in AKT gene expression. Intriguingly, Kn treatment restored the biochemical, redox status and inflammatory parameters. Hepatic AKT and caspase-3 expression as well as CD95 levels in lymphocytes were also restored. In conclusion, Kn mitigated oxidative imbalance, inflammation and apoptosis in CP-induced liver and lymphocyte toxicity; therefore, it can be considered as a promising therapy.
Eugenol is a phytochemical present in different plant products, e.g., clove oil. Traditionally, it is used against a number of different disorders and it was suggested to have anticancer activity. In this study, the activity of eugenol was evaluated in a human cervical cancer (HeLa) cell line and cell proliferation was examined after treatment with various concentrations of eugenol and different treatment durations. Cytotoxicity was tested using lactate dehydrogenase (LDH) enzyme leakage. In order to assess eugenol’s potential to act synergistically with chemotherapy and radiotherapy, cell survival was calculated after eugenol treatment in combination with cisplatin and X-rays. To elucidate its mechanism of action, caspase-3 activity was analyzed and the expression of various genes and proteins was checked by RT-PCR and western blot analyses. Eugenol clearly decreased the proliferation rate and increased LDH release in a concentration- and time-dependent manner. It showed synergistic effects with cisplatin and X-rays. Eugenol increased caspase-3 activity and the expression of Bax, cytochrome c (Cyt-c), caspase-3, and caspase-9 and decreased the expression of B-cell lymphoma (Bcl)-2, cyclooxygenase-2 (Cox-2), and interleukin-1 beta (IL-1β) indicating that eugenol mainly induced cell death by apoptosis. In conclusion, eugenol showed antiproliferative and cytotoxic effects via apoptosis and also synergism with cisplatin and ionizing radiation in the human cervical cancer cell line.
Nature is a rich source of biologically active novel compounds. Sixty years ago, the plant hormones cytokinins were first discovered. These play a major role in cell division and cell differentiation. They affect organogenesis in plant tissue cultures and contribute to many other physiological and developmental processes in plants. Consequently, the effect of cytokinins on mammalian cells has caught the attention of researchers. Many reports on the contribution and potential of cytokinins in the therapy of different human diseases and pathophysiological conditions have been published and are reviewed here. We compare cytokinin effects and pathways in plants and mammalian systems and highlight the most important biological activities. We present the strong profile of the biological actions of cytokinins and their possible therapeutic applications.
The Enterobacteriaceae comprise a large number of clinically relevant species with several individual subspecies. Overlapping virulence-associated gene pools and the high overall genome plasticity often interferes with correct enterobacterial strain typing and risk assessment. Array technology offers a fast, reproducible and standardisable means for bacterial typing and thus provides many advantages for bacterial diagnostics, risk assessment and surveillance. The development of highly discriminative broad-range microbial diagnostic microarrays remains a challenge, because of marked genome plasticity of many bacterial pathogens. Results: We developed a DNA microarray for strain typing and detection of major antimicrobial resistance genes of clinically relevant enterobacteria. For this purpose, we applied a global genome-wide probe selection strategy on 32 available complete enterobacterial genomes combined with a regression model for pathogen classification. The discriminative power of the probe set was further tested in silico on 15 additional complete enterobacterial genome sequences. DNA microarrays based on the selected probes were used to type 92 clinical enterobacterial isolates. Phenotypic tests confirmed the array-based typing results and corroborate that the selected probes allowed correct typing and prediction of major antibiotic resistances of clinically relevant Enterobacteriaceae, including the subspecies level, e.g. the reliable distinction of different E. coli pathotypes. Conclusions: Our results demonstrate that the global probe selection approach based on longest common factor statistics as well as the design of a DNA microarray with a restricted set of discriminative probes enables robust discrimination of different enterobacterial variants and represents a proof of concept that can be adopted for diagnostics of a wide range of microbial pathogens. Our approach circumvents misclassifications arising from the application of virulence markers, which are highly affected by horizontal gene transfer. Moreover, a broad range of pathogens have been covered by an efficient probe set size enabling the design of high-throughput diagnostics.