- Rienk A. Rienksma (1)
- Niels A. Zondervan (1)
- Vitor A.P. Martins Dos Santos (1)
- Vitor A.P. Martins dos Santos (1)
- Jesse C.J. Dam van (1)
- Peter J. Schaap (1)
- Erno Lindfors (1)
- Carolyn Ming Chi Lam (1)
- R.A. Rienksma (1)
- Maria Suarez-Diez (2)
Modeling Host-Pathogen Interaction to Elucidate the Metabolic Drug Response of Intracellular Mycobacterium tuberculosis
Rienksma, Rienk A. ; Schaap, Peter J. ; Martins Dos Santos, Vitor A.P. ; Suarez-Diez, Maria - \ 2019
Frontiers in Cellular and Infection Microbiology 9 (2019). - ISSN 2235-2988 - 1 p.
antibiotics - drug response - flux balance analysis - host-pathogen interaction - metabolic model - Mycobacterium tuberculosis
Little is known about the metabolic state of Mycobacterium tuberculosis (Mtb) inside the phagosome, a compartment inside phagocytes for killing pathogens and other foreign substances. We have developed a combined model of Mtb and human metabolism, sMtb-RECON and used this model to predict the metabolic state of Mtb during infection of the host. Amino acids are predicted to be used for energy production as well as biomass formation. Subsequently we assessed the effect of increasing dosages of drugs targeting metabolism on the metabolic state of the pathogen and predict resulting metabolic adaptations and flux rerouting through various pathways. In particular, the TCA cycle becomes more important upon drug application, as well as alanine, aspartate, glutamate, proline, arginine and porphyrin metabolism, while glycine, serine, and threonine metabolism become less important. We modeled the effect of 11 metabolically active drugs. Notably, the effect of eight could be recreated and two major profiles of the metabolic state were predicted. The profiles of the metabolic states of Mtb affected by the drugs BTZ043, cycloserine and its derivative terizidone, ethambutol, ethionamide, propionamide, and isoniazid were very similar, while TMC207 is predicted to have quite a different effect on metabolism as it inhibits ATP synthase and therefore indirectly interferes with a multitude of metabolic pathways.
Rienksma, R.A. - \ 2018
model - Mycobacterium tuberculosis
SyNDI : Synchronous network data integration framework
Lindfors, Erno ; Dam, Jesse C.J. van; Lam, Carolyn Ming Chi ; Zondervan, Niels A. ; Martins dos Santos, Vitor A.P. ; Suarez-Diez, Maria - \ 2018
BMC Bioinformatics 19 (2018)1. - ISSN 1471-2105
Cytoscape - Galaxy - Mycobacterium tuberculosis - Network biology - Staphylococcus aureus - Synchronous network visualization - Systems biology - Workflow
Background: Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights. Results: In order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified. Conclusions: Our SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.