Every human cell carries the same DNA, yet a skin cell and a liver cell produce wildly different amounts of the same proteins—and cancer cells scramble those rules entirely. This project aims to quantify exactly how cells decide which proteins to make and in what quantity, a process that remains surprisingly poorly understood since Francis Crick first described the “central dogma” of molecular biology 60 years ago. The core problem is that scientists know DNA, mRNA, and protein levels are linked, but not how cells choose between making more mRNA, translating it faster, or slowing its degradation to adjust protein output. In cancer, this matters acutely: tumour cells often have extra or missing gene copies, yet somehow buffer most protein levels back toward normal—except for certain oncogenes, whose protein excess may drive tumour growth. Understanding this buffering mechanism could reveal new therapeutic targets. This is fundamentally curiosity-driven research into a basic biological process. However, similar fundamental work on gene regulation has previously underpinned breakthroughs in targeted cancer drugs and mRNA vaccines. By systematically measuring DNA, mRNA, and protein across human cells using sequencing and mass spectrometry, and by using machine learning to identify control factors, the work may also uncover thousands of previously overlooked small proteins produced by cancer cells—potential flags for immunotherapies that attack tumour-specific molecules.
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60 years ago, Francis Crick famously described the "central dogma" of molecular biology. It explains that genetic information flows from DNA over mRNA to proteins, which are the bioactive molecules through which genes exert their function. However, the quantitative relationship between these three layers of gene expression remains poorly understood. For example, if a cell needs to increase the amount of a particular protein, will it increase the rate of transcription (mRNA synthesis) or that of translation (protein synthesis)? Or will it reduce the rate at which either mRNA or protein are degraded? Ultimately, to answer these and many related questions, it is necessary to establish a quantitative, integrated perspective of the entire gene expression process. To understand how genotypes lead to phenotypes, we need to quantify the central dogma. My work will be an important step in this direction. My goal is to systematically quantify how DNA, mRNA and protein levels relate to each other and, importantly, to understand which mechanisms control and regulate these relationships. Using next-generation sequencing and mass spectrometry I will quantify DNA, mRNA and protein levels in a range of human cells and assess how they change between different conditions. Using high-performance computing and machine-learning I will look to understand which factors control the levels of mRNAs and proteins. This topic is particularly important for cancer biology. Most types of cancer show wide-spread chromosome abnormalities. Normal cells have two copies of each gene, but in cancer cells some of these gene copies can be deleted or multiplied. Recent research has shown that this has a direct impact on mRNA levels produced by such genes, whereas protein levels are generally buffered towards normal levels. However, this does not work for all proteins: amplification of some oncogenes leads to increased protein levels and this could drive or sustain the growth of cancer cells. Therefore, understanding how protein levels are buffered against changes at the DNA and mRNA level will help us to understand cellular processes that lead to cancer and may offer new therapeutic strategies. Another intriguing aspect of the central dogma is that not all human DNA produces mRNAs, and not all mRNAs produce proteins. In fact, traditionally it is assumed that 98% of the human genome does not encode for proteins. Modern genomics methods have challenged this view. For example, recent evidence suggests that cancer cells (and stressed cells in general) may produce thousands of small proteins from mRNA regions that were so far thought not to be non-coding. My proposed work will identify many of these and predict their potential functions. Again, this could have important implications for cancer therapy, because such unusual proteins may be specific for cancer cells and therefore present promising therapeutic targets, especially for immunotherapy. In summary, I expect that my research will answer long-standing biological questions about how protein levels are regulated, and make a direct contribution towards understanding cancer and how to fight it.
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