Quantitative Analysis of the Proteomic Response of Human Tumor Cell Lines to Sirtinol

Baykal, Betül (2015). Quantitative Analysis of the Proteomic Response of Human Tumor Cell Lines to Sirtinol. PhD thesis The Open University.

DOI: https://doi.org/10.21954/ou.ro.0000ef8c

Abstract

My thesis develops and evaluates a novel, hybrid method for mass spectrometry based proteomics, with the goal of increasing the number of protein identifications, while also allowing quantitative analysis to be performed. The hybrid approach I developed uses classical SILAC labeling coupled to a “novel” scheme for enriching cysteic acid containing peptides which are generated by oxidizing the cysteine and cystine residues of proteins. The resultant acidic side chains are used for the selective enrichment of these peptides. As a result, I get the ease of labeling and enhanced quantitative performance of SILAC protocols with the added benefit of the reduction in sample complexity seen with ICAT protocols.

The hybrid method enabled me to study the proteomic profiles of HI229 cancer cells following sirtinol treatment. Sirtinol, which targets members of the sirtuin family, has been proposed as a treatment for neurodegeneration and cancer. In fact, sirtinol induces senescence in H1299 cells, and I was able to observe the long term therapeutic effects of sirtinol at the proteome level.

The optimized hybrid protocol yielded approximately 5 times the proteins identifications from H1299 cells, when compared to the standard protocol. Quantification of this data revealed that 140 proteins are downregulated and 88 proteins are upregulated following sirtinol treatment of H1299 cells. Moreover, an additional benefit of the protocol was an increase in the total number of post translational modifications (PTMs) identified using the hybrid approach. This work will give a better understanding of the mode of action of these compounds, as well as revealing the biological mechanisms of their effects.

In conclusion, the hybrid method is useful for increasing the number of protein identifications from complex samples. It is an accurate and straightforward method for comparative studies. This method is especially useful for drug analysis studies or other binary comparisons.

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