@article{YangRajeeveRudeletal.2019, author = {Yang, Manli and Rajeeve, Karthika and Rudel, Thomas and Dandekar, Thomas}, title = {Comprehensive Flux Modeling of Chlamydia trachomatis Proteome and qRT-PCR Data Indicate Biphasic Metabolic Differences Between Elementary Bodies and Reticulate Bodies During Infection}, series = {Frontiers in Microbiology}, volume = {10}, journal = {Frontiers in Microbiology}, number = {2350}, issn = {1664-302X}, doi = {10.3389/fmicb.2019.02350}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-189434}, year = {2019}, abstract = {Metabolic adaptation to the host cell is important for obligate intracellular pathogens such as Chlamydia trachomatis (Ct). Here we infer the flux differences for Ct from proteome and qRT-PCR data by comprehensive pathway modeling. We compare the comparatively inert infectious elementary body (EB) and the active replicative reticulate body (RB) systematically using a genome-scale metabolic model with 321 metabolites and 277 reactions. This did yield 84 extreme pathways based on a published proteomics dataset at three different time points of infection. Validation of predictions was done by quantitative RT-PCR of enzyme mRNA expression at three time points. Ct's major active pathways are glycolysis, gluconeogenesis, glycerol-phospholipid (GPL) biosynthesis (support from host acetyl-CoA) and pentose phosphate pathway (PPP), while its incomplete TCA and fatty acid biosynthesis are less active. The modeled metabolic pathways are much more active in RB than in EB. Our in silico model suggests that EB and RB utilize folate to generate NAD(P)H using independent pathways. The only low metabolic flux inferred for EB involves mainly carbohydrate metabolism. RB utilizes energy -rich compounds to generate ATP in nucleic acid metabolism. Validation data for the modeling include proteomics experiments (model basis) as well as qRT-PCR confirmation of selected metabolic enzyme mRNA expression differences. The metabolic modeling is made fully available here. Its detailed insights and models on Ct metabolic adaptations during infection are a useful modeling basis for future studies.}, language = {en} } @article{BencurovaGuptaSarukhanyanetal.2018, author = {Bencurova, Elena and Gupta, Shishir K. and Sarukhanyan, Edita and Dandekar, Thomas}, title = {Identification of antifungal targets based on computer modeling}, series = {Journal of Fungi}, volume = {4}, journal = {Journal of Fungi}, number = {3}, issn = {2309-608X}, doi = {10.3390/jof4030081}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197670}, pages = {81}, year = {2018}, abstract = {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.}, language = {en} } @article{KaltdorfTheissMarkertetal.2018, author = {Kaltdorf, Kristin Verena and Theiss, Maria and Markert, Sebastian Matthias and Zhen, Mei and Dandekar, Thomas and Stigloher, Christian and Kollmannsberger, Philipp}, title = {Automated classification of synaptic vesicles in electron tomograms of C. elegans using machine learning}, series = {PLoS ONE}, volume = {13}, journal = {PLoS ONE}, number = {10}, doi = {10.1371/journal.pone.0205348}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176831}, pages = {e0205348}, year = {2018}, abstract = {Synaptic vesicles (SVs) are a key component of neuronal signaling and fulfil different roles depending on their composition. In electron micrograms of neurites, two types of vesicles can be distinguished by morphological criteria, the classical "clear core" vesicles (CCV) and the typically larger "dense core" vesicles (DCV), with differences in electron density due to their diverse cargos. Compared to CCVs, the precise function of DCVs is less defined. DCVs are known to store neuropeptides, which function as neuronal messengers and modulators [1]. In C. elegans, they play a role in locomotion, dauer formation, egg-laying, and mechano- and chemosensation [2]. Another type of DCVs, also referred to as granulated vesicles, are known to transport Bassoon, Piccolo and further constituents of the presynaptic density in the center of the active zone (AZ), and therefore are important for synaptogenesis [3]. To better understand the role of different types of SVs, we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using image-based features. Analysis of the underlying ground truth data shows an increased fraction of DCVs as well as a higher mean distance between DCVs and AZs in dauer larvae compared to young adult hermaphrodites. Our machine learning based tools are adaptable and can be applied to study properties of different synaptic vesicle pools in electron tomograms of diverse model organisms.}, language = {en} } @article{SarukhanyanShityakovDandekar2018, author = {Sarukhanyan, Edita and Shityakov, Sergey and Dandekar, Thomas}, title = {In silico designed Axl receptor blocking drug candidates against Zika virus infection}, series = {ACS Omega}, volume = {3}, journal = {ACS Omega}, number = {5}, doi = {10.1021/acsomega.8b00223}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176739}, pages = {5281-5290}, year = {2018}, abstract = {After a large outbreak in Brazil, novel drugs against Zika virus became extremely necessary. Evaluation of virus-based pharmacological strategies concerning essential host factors brought us to the idea that targeting the Axl receptor by blocking its dimerization function could be critical for virus entry. Starting from experimentally validated compounds, such as RU-301, RU-302, warfarin, and R428, we identified a novel compound 2′ (R428 derivative) to be the most potent for this task amongst a number of alternative compounds and leads. The improved affinity of compound 2′ was confirmed by molecular docking as well as molecular dynamics simulation techniques using implicit solvation models. The current study summarizes a new possibility for inhibition of the Axl function as a potential target for future antiviral therapies.}, language = {en} } @article{KaltdorfSchulzeHelmprobstetal.2017, author = {Kaltdorf, Kristin Verena and Schulze, Katja and Helmprobst, Frederik and Kollmannsberger, Philip and Dandekar, Thomas and Stigloher, Christian}, title = {Fiji macro 3D ART VeSElecT: 3D automated reconstruction tool for vesicle structures of electron tomograms}, series = {PLoS Computational Biology}, volume = {13}, journal = {PLoS Computational Biology}, number = {1}, doi = {10.1371/journal.pcbi.1005317}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172112}, year = {2017}, abstract = {Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial.}, language = {en} } @article{TemmeFriebeSchmidtetal.2017, author = {Temme, Sebastian and Friebe, Daniela and Schmidt, Timo and Poschmann, Gereon and Hesse, Julia and Steckel, Bodo and St{\"u}hler, Kai and Kunz, Meik and Dandekar, Thomas and Ding, Zhaoping and Akhyari, Payam and Lichtenberg, Artur and Schrader, J{\"u}rgen}, title = {Genetic profiling and surface proteome analysis of human atrial stromal cells and rat ventricular epicardium-derived cells reveals novel insights into their cardiogenic potential}, series = {Stem Cell Research}, volume = {25}, journal = {Stem Cell Research}, doi = {10.1016/j.scr.2017.11.006}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-172716}, pages = {183-190}, year = {2017}, abstract = {Epicardium-derived cells (EPDC) and atrial stromal cells (ASC) display cardio-regenerative potential, but the molecular details are still unexplored. Signals which induce activation, migration and differentiation of these cells are largely unknown. Here we have isolated rat ventricular EPDC and rat/human ASC and performed genetic and proteomic profiling. EPDC and ASC expressed epicardial/mesenchymal markers (WT-1, Tbx18, CD73,CD90, CD44, CD105), cardiac markers (Gata4, Tbx5, troponin T) and also contained phosphocreatine. We used cell surface biotinylation to isolate plasma membrane proteins of rEPDC and hASC, Nano-liquid chromatography with subsequent mass spectrometry and bioinformatics analysis identified 396 rat and 239 human plasma membrane proteins with 149 overlapping proteins. Functional GO-term analysis revealed several significantly enriched categories related to extracellular matrix (ECM), cell migration/differentiation, immunology or angiogenesis. We identified receptors for ephrin and growth factors (IGF, PDGF, EGF, anthrax toxin) known to be involved in cardiac repair and regeneration. Functional category enrichment identified clusters around integrins, PI3K/Akt-signaling and various cardiomyopathies. Our study indicates that EPDC and ASC have a similar molecular phenotype related to cardiac healing/regeneration. The cell surface proteome repository will help to further unravel the molecular details of their cardio-regenerative potential and their role in cardiac diseases.}, language = {en} } @article{AmpattuHagmannLiangetal.2017, author = {Ampattu, Biju Joseph and Hagmann, Laura and Liang, Chunguang and Dittrich, Marcus and Schl{\"u}ter, Andreas and Blom, Jochen and Krol, Elizaveta and Goesmann, Alexander and Becker, Anke and Dandekar, Thomas and M{\"u}ller, Tobias and Schoen, Christoph}, title = {Transcriptomic buffering of cryptic genetic variation contributes to meningococcal virulence}, series = {BMC Genomics}, volume = {18}, journal = {BMC Genomics}, number = {282}, doi = {10.1186/s12864-017-3616-7}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-157534}, year = {2017}, abstract = {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.}, language = {en} } @article{EwaldBartlDandekaretal.2017, author = {Ewald, Jan and Bartl, Martin and Dandekar, Thomas and Kaleta, Christoph}, title = {Optimality principles reveal a complex interplay of intermediate toxicity and kinetic efficiency in the regulation of prokaryotic metabolism}, series = {PLOS Computational Biology}, volume = {13}, journal = {PLOS Computational Biology}, number = {2}, doi = {10.1371/journal.pcbi.1005371}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180870}, pages = {19}, year = {2017}, abstract = {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.}, language = {en} } @article{KunzGoettlichWallesetal.2017, author = {Kunz, Meik and G{\"o}ttlich, Claudia and Walles, Thorsten and Nietzer, Sarah and Dandekar, Gudrun and Dandekar, Thomas}, title = {MicroRNA-21 versus microRNA-34: Lung cancer promoting and inhibitory microRNAs analysed in silico and in vitro and their clinical impact}, series = {Tumor Biology}, volume = {39}, journal = {Tumor Biology}, number = {7}, doi = {10.1177/1010428317706430}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-158399}, year = {2017}, abstract = {MicroRNAs are well-known strong RNA regulators modulating whole functional units in complex signaling networks. Regarding clinical application, they have potential as biomarkers for prognosis, diagnosis, and therapy. In this review, we focus on two microRNAs centrally involved in lung cancer progression. MicroRNA-21 promotes and microRNA-34 inhibits cancer progression. We elucidate here involved pathways and imbed these antagonistic microRNAs in a network of interactions, stressing their cancer microRNA biology, followed by experimental and bioinformatics analysis of such microRNAs and their targets. This background is then illuminated from a clinical perspective on microRNA-21 and microRNA-34 as general examples for the complex microRNA biology in lung cancer and its diagnostic value. Moreover, we discuss the immense potential that microRNAs such as microRNA-21 and microRNA-34 imply by their broad regulatory effects. These should be explored for novel therapeutic strategies in the clinic.}, language = {en} } @article{KunzLiangNillaetal.2016, author = {Kunz, Meik and Liang, Chunguang and Nilla, Santosh and Cecil, Alexander and Dandekar, Thomas}, title = {The drug-minded protein interaction database (DrumPID) for efficient target analysis and drug development}, series = {Database}, volume = {2016}, journal = {Database}, doi = {10.1093/database/baw041}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147369}, pages = {baw041}, year = {2016}, abstract = {The drug-minded protein interaction database (DrumPID) has been designed to provide fast, tailored information on drugs and their protein networks including indications, protein targets and side-targets. Starting queries include compound, target and protein interactions and organism-specific protein families. Furthermore, drug name, chemical structures and their SMILES notation, affected proteins (potential drug targets), organisms as well as diseases can be queried including various combinations and refinement of searches. Drugs and protein interactions are analyzed in detail with reference to protein structures and catalytic domains, related compound structures as well as potential targets in other organisms. DrumPID considers drug functionality, compound similarity, target structure, interactome analysis and organismic range for a compound, useful for drug development, predicting drug side-effects and structure-activity relationships.}, language = {en} }