One of the seminal achievements of biological research was the discovery of the flow of information from DNA to RNA via transcription and RNA to proteins via translation - called the Central Dogma of Molecular Biology. While the DNA is essentially the same for all the cells in our body, their protein content varies drastically, e.g., the cells in our brain, despite containing the same DNA, are very different from those in our liver. Proteins are typically the macromolecules closest to the phenotype, and differential protein abundances predominantly drive the phenotypes. Cells achieve differential protein expression by regulating the transcription levels, translation levels, or protein degradation.
Despite its importance, comprehensive genome-wide quantification of the regulatory processes that determine and alter protein levels is lacking. We still poorly understand how cells set the protein levels and subsequently modulate them to adapt to changing environments. Thanks to advancements in DNA sequencing, we can now measure transcription and translation rates globally. However, measuring protein turnover remains technologically challenging. Currently, even for the arguably best-studied model organism, E. coli, we still do know how the central dogma parameters change to adjust the abundance levels to various conditions.
In my dissertation, I make strides to close this gap and develop technology to decipher how cells modulate the central dogma rates of transcription, translation, and protein degradation to adjust protein abundances to different environments. I have developed experimental and statistical technologies to improve quantitative proteomics for E. coli (chapter 1), assign confidence to proteomic measurements (chapter 2), and establish methods for measuring protein degradation rates on a proteome-wide scale and apply it to E. coli under various growth conditions (chapter 3).
Leveraging these technologies, we discovered that the canonical model bacterium Escherichia coli (E. coli) eats its cytoplasmic proteins when subjected to limited Nitrogen. Furthermore, we show that protein degradation rates are generally independent of cell division rates. We find that none of the known ATP dependent proteases is responsible for the observed cytoplasmic protein degradation in nitrogen limitation using knockout experiments to assign substrates to the known ATP-dependent proteases. This suggests that a major proteolysis pathway in E. coli remains to be discovered.
Thus, this work significantly advances the field of proteomics by providing broadly applicable technology. Additionally, we provide a rich resource of protein turnover measurements in E. coli. In the future, this work can be expanded to more complex cells and systems. Hence, this work lays the groundwork to decipher how cells compute at a molecular level.