Georgia Tsiliki, NTUA
(The National Technical University of Athens, Greece)
29th October 2015, 4 PM (Central European Time) / 11 AM (US Eastern Time)
You can download slides by Georgia Tsiliki >> here <<
The eNanoMapper project is working towards developing a modular and extensible infrastructure for data sharing, data analysis, and building computational toxicology models for ENMs. A number of services are now available for calculating ENM-specific descriptors, developing nanoQSAR models and automated workflows for model selection or validation.
Along these lines, we present a methodology to incorporate biological information with omics data and specifically proteomics data originating from protein corona fingerprinting, which has been reported to efficiently predict biological responses such as cellular uptake, signalling, and toxicity.
Our findings suggest that there is scope for further enhancement of protein corona data with biological information to allow for different protein weights according to their biological plausibility.
License: CC-BY 4.0
Georgia Tsiliki firstname.lastname@example.org
eNanoMapper (Grant Agreement no. 604134) is a project supported by the European Commission through the Seventh Framework Programme (FP7).
1. Walkey CD, Olsenb JB, Songa F, Liug R, Guob H, et al. (2014) Protein Corona Fingerprinting Predicts the Cellular Interaction of Gold Nanoparticles. ACS Nano, 8 (3); 2439–2455, DOI: 10.1021/nn406018q.
2. Liu R, Jiang W, Walkey CD, Chan WC, Cohen Y (2015) Prediction of nanoparticles-cell association based on corona proteins and physicochemical descriptors. Nanoscale, 7(21): 9664-75
3. Mistry, M., and Pavlidis, P. (2008). Gene ontology term overlap as a measure of gene functional similarity. BMC Bioinformatics, 9:327.
4. Frohlich, H., Speer, N., and Zell, A. (2006). Kernel based functional gene grouping. In Proc. Int. Joint Conf. Neural Networks, pages 6886 -6891.
5. Ritz S, Schöttler S, Kotman N, Baier G, Musyanovych A, et al. (2015) Protein Corona of Nanoparticles: Distinct Proteins Regulate the Cellular Uptake Biomacromolecules 2015 16 (4), 1311-1321, DOI: 10.1021/acs.biomac.5b00108
6. Balbin OA, Prensner JR, Sahu A, Yocum A, Shankar S, et al (2013) Reconstructing targetable pathways in lung cancer by integrating diverse omics data. Nat. Commun. 4, 2617 DOI: 10.1038/ncomms3617.
7. Yang X, Regan K, Huang Y, Zhang Q, Li J, et al. (2012) Single sample expression-anchored mechanisms predict survival in head and neck cancer. PLoS Comput. Biol. 8, DOI: 10.1371/journal.pcbi.1002350.
8. Saber AT, Lamson JS, Jacobsen NR, Ravn-Haren G, Hougaard KS, et al. (2013) Particle-Induced Pulmonary Acute Phase Response Correlates with Neutrophil Influx Linking Inhaled Particles and Cardiovascular Risk. PLoS ONE 8(7): e69020. doi: 10.1371/journal.pone.0069020
9. Higashisaka K, Yoshioka Y, Yamashita K, Morishita Y, Fujimura M, et al. (2011) Acute phase proteins as biomarkers for predicting the exposure and toxicity of nanomaterials.. Biomaterials 32, no. 1 (2011): 3-9.