A multiplexed approach for quantitative profiling of the translatome using bioorthogonal non-canonical amino acids

One of the major goals of systems biology is understanding how a cell changes from a healthy state to a diseased state. Entire fields of cell biology have been built around studying how changes in the type and abundance of specific biomolecules affect disease status. However, one major knowledge gap in systems biology is the quantification of protein synthesis rate at any given time (i.e the Translatome). At this time, measurements of protein synthesis rates are limited to methods that use mRNA abundance as a proxy; however, there are regulatory steps on the level of translation that can confound correlation between mRNA abundance and protein synthesis rates. Here, I improve upon a proteomics based method for measuring newly translated proteins, biorthogonal non-canonical amino acid tagging (BONCAT), and adapt it for robust quantitative multiplexing analysis. In the BONCAT method, cells are pulsed with azidohomoalanine (Aha), a methionine analog that contains an azide functional group, such that proteins synthesized for the duration of the pulse incorporate Aha. By coupling pulsed stable isotope labeling of amino acids in cell culture (pSILAC) and Aha metabolic labeling of newly synthesized proteins with strain-promoted azide-alkyne cycloaddition and tandem mass tag (TMT) labeling, I am able to quantitatively interrogate the translatome in a multiplexed manner with high sensitivity and high temporal resolution. The multiplexed BONCAT protocol was applied to observe changes in temporal protein synthesis during the unfolded protein response (UPR) and epidermal growth factor (EGF) stimulation. Eliciting the UPR by blocking N-glycosylation results in a global downregulation of protein translation, but upregulation of several key protein-folding chaperones. Furthermore, protein translation machinery (ribosomal proteins, initiation factors, and elongation factors) are downregulated to a much greater extent. In contrast to the UPR stress response, pro-growth EGF stimulation resulted in the upregulation of protein translation machinery. EGF stimulation also resulted in waves of temporally distinct protein synthesis, beginning with immediate and delayed early genes and followed by late response genes that determine cell fate. By sampling protein synthesis at both 30 minute and 15 minute intervals, I was able to further elucidate the order of protein synthesis with high temporal resolution. Comparison of protein translation with RNA sequencing and ribosome footprinting revealed tight correlations between RNA, ribosome occupancy, and protein synthesis. This comparison also allowed the distinction between protein synthesis driven by an increase in transcription versus that driven by an increase in translation. Interestingly, temporal delays between ribosome occupancy and protein synthesis were observed in many genes. These genes also demonstrated a unique codon bias compared to the average codon usage of the genome. An analysis of codon frequency revealed changes in global codon usage over time following EGF stimulation. Changes in chemical modifications of tRNA isoacceptors were also observed which may play a role in regulating protein translation. Finally, our multiplexed BONCAT method was leveraged to compare the translation response between MEK inhibitor resistant (MelJuso) and sensitive (MM415) melanoma cell lines. Using partial least squares regression (PLSR) and gene set enrichment analysis (GSEA), upregulation of melanoma lineage-dependent transcription factor MITF and MITF targets was observed in MM415s after binimetinib treatment, with no such response in the MelJuso cells. Using a small molecule inhibitor against MITF, we found that MITF inhibitions results in a protective effect against binimetinib in MM415 cells. However, the MM415 cells were resistant to siRNA-mediated knockdown of MITF. Further work needs to be done to characterize the role of MITF in the context of binimetinib sensitivity.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Biological Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Rothenberg, Daniel Abram

Advisor dc:contributor.advisor

• Forest M. White.

• Biological Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/113962

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/113962