Life without Phd? – Phd Oxygen Sensor Proteins in Hypoxic Carcinoma Cell Survival

The microenvironment within the tumor plays a central role in cellular signaling. Rapidly proliferating cancer cells need building blocks for structures as well as nutrients and oxygen for energy production. In normal tissue, the vasculature effectively transports oxygen, nutrient and waste products, and maintains physiological pH. Within a tumor however, the vasculature is rarely sufficient for the needs of tumor cells. This causes the tumor to suffer from lack of oxygen (hypoxia) and nutrients as well as acidification, as the glycolytic end product lactate is accumulated. Cancer cells harbor mutations enabling survival in the rough microenvironment. One of the best characterized mutations is the inactivation of the von Hippel-Lindau protein (pVHL) in clear cell renal cell carcinoma (ccRCC). Inactivation causes constitutive activation of hypoxia-inducible factor HIF which is an important survival factor regulating glycolysis, neovascularization and apoptosis. HIFs are normally regulated by HIF prolyl hydroxylases (PHDs), which in the presence of oxygen target HIF α-subunit to ubiquitination by pVHL and degradation by proteasomes. In my thesis work, I studied the role of PHDs in the survival of carcinoma cells in hypoxia. My work revealed an essential role of PHD1 and PHD3 in cell cycle regulation through two cyclin-dependent kinase inhibitors (CKIs) p21 and p27. Depletion of PHD1 or PHD3 caused a cell cycle arrest and subjected the carcinoma cells to stress and impaired the survival.

Tissue Proteomes: Quantitative Mass Spectrometry of Murine Liver and Ovarian Endometrioma

A human genome contains more than 20 000 protein-encoding genes. A human proteome, instead, has been estimated to be much more complex and dynamic. The most powerful tool to study proteins today is mass spectrometry (MS). MS based proteomics is based on the measurement of the masses of charged peptide ions in a gas-phase. The peptide amino acid sequence can be deduced, and matching proteins can be found, using software to correlate MS-data with sequence database information. Quantitative proteomics allow the estimation of the absolute or relative abundance of a certain protein in a sample. The label-free quantification methods use the intrinsic MS-peptide signals in the calculation of the quantitative values enabling the comparison of peptide signals from numerous patient samples. In this work, a quantitative MS methodology was established to study aromatase overexpressing (AROM+) male mouse liver and ovarian endometriosis tissue samples. The workflow of label-free quantitative proteomics was optimized in terms of sensitivity and robustness, allowing the quantification of 1500 proteins with a low coefficient of variance in both sample types. Additionally, five statistical methods were evaluated for the use with label-free quantitative proteomics data. The proteome data was integrated with other omics datasets, such as mRNA microarray and metabolite data sets. As a result, an altered lipid metabolism in liver was discovered in male AROM+ mice. The results suggest a reduced beta oxidation of long chain phospholipids in the liver and increased levels of pro-inflammatory fatty acids in the circulation in these mice. Conversely, in the endometriosis tissues, a set of proteins highly specific for ovarian endometrioma were discovered, many of which were under the regulation of the growth factor TGF-β1. This finding supports subsequent biomarker verification in a larger number of endometriosis patient samples.