ERC FUNDED PROJECTS

Estrogen Receptor (ER) is the driving transcription factor in ~75% of all breast cancers. ER antagonists are routinely used for treatment, but significant variability exists in clinical response. We are interested in explaining this heterogeneity and exploiting the mechanistic insight. We have recently identified important, but previously uncharacterised cross-talk between ER and the progesterone receptor (PR) and androgen receptor (AR) pathways, both of which are commonly expressed in ER+ tumours. Recently, ER has been shown to be mutated in ~18-55% of metastatic breast cancers. In addition, two key ER-chromatin regulatory proteins, FoxA1 and GATA3, are mutated in primary ER+ disease. Finally we have discovered three previously unknown phosphorylation events on FoxA1. Aim 1: We will comprehensively explore the cross-talk that exists between ER and PR and AR pathways to determine the physiological effects on ER function. Aim 2: We will recapitulate the key mutations observed in ER, FoxA1 and GATA3, to assess the impact on ER-DNA interactions, ER transcriptional activity and cell growth and drug response. This will be explored under different hormonal contexts to identify how the mutational spectrum influences the cross-talk between ER and the parallel PR and AR pathways. Aim 3: We will identify upstream kinase pathways that influence FoxA1 and GATA3 function. Aim 4: We will establish a novel single locus chromatin purification method for isolation of specific chromatin loci, followed by Mass Spectrometry to characterise the potential role of PR and AR variants and to identify unknown regulatory factors. Given recent biological discoveries and technological advances, we are perfectly positioned to apply cutting-edge tools to glean mechanistic insight into the factors that determine variability within ER+ disease. This proposal aims to advance our understanding of ER+ tumour heterogeneity, revealing ways of exploiting this in a clinically meaningful manner.

1 987 274 €

Start date: 2015-06-01, End date: 2020-05-31

The provision of advanced functional materials in the area of regenerative medicine and discovery applications depends on many different factors to provide the appropriate targeted function. As adherent cells also read their environment through substrate interactions there is a great interest in developing such substrates in a predictable manner. Their first point of contact is through their focal adhesions and it is also though them that forces are applied allowing the cell to migrate and establish cytoskeletal tension which in turn regulates cell function. The objective of this project is to investigate the cell-substrate interaction at the nanoscale and correlate that to the surface topography for predictable biomaterials. Through the application of state-of-the-art nanofabrication we will fabricate precise surface topographies with length scales comparable to the structural units found in the focal adhesions. The aim is to map and understand the topographical influence in the architectural arrangement of the proteins in the adhesions. Aided by high resolution microscopy we will classify cell types on different nanotopographies. Combining that information with machine learning, we will be able to gain information about cell characteristics from the rule set. That information can also be used in reverse to identify cell types with the previously defined characteristic. This approach is similar to face recognition seen on cameras and mobile phones. The proposed research project will not only provide insight to an area of biomaterials not previously explored, yet aim to provide a blueprint for future design of biomaterials.

2 128 895 €

Start date: 2015-08-01, End date: 2021-07-31

Macroeconomic data are largely non-experimental. Thus, causal inference in macroeconomics is largely based on assumptions about what aspects of the variation in the data are exogenous. This presents two major challenges, which this research addresses directly. First, few such assumptions are generally accepted. Second, conditional on any set of assumptions, identification of causal effects is often weak because there is little relevant variation in the data. To tackle these challenges, I propose three lines of enquiry to explore new sources of identification and develop the requisite econometric methods. The first line will study the implications of the so-called ‘zero lower bound’ (ZLB) on nominal interest rates for identification. The key novel insight is that the ZLB causes monetary policy to be set at least in part exogenously. This can be thought of as a natural experiment that generates a new instrument to identify the underlying policy model. This insight applies more generally to policy functions subject to exogenous constraints. The informativeness of these constraints depends on the probability that they bind, so recent experience makes the ZLB a promising application of the idea. The second line will analyse new ways of using time-variation in some of the parameters of macroeconomic models, such as trend inflation or the volatility of shocks, to study important open questions in macro, such as the degree of forward versus backward-looking behaviour and the ‘good luck versus good policy’ debate. The third line will contribute to the on-going research on developing methods of inference that are robust to weak identification. This is a pervasive problem in macro that threatens the validity of structural inference under any identification scheme. The synergies among these three lines' methodological analyses will accelerate progress on each line well beyond what would be possible in a piecemeal approach.

1 312 383 €

Start date: 2015-09-01, End date: 2021-02-28

Many therapeutic applications of stem cells require accurate control of their differentiation. To this purpose there is a major ongoing effort in the development of advanced culture substrates to be used as “synthetic niches” for the cells, mimicking the native ones. The goal of this project is to use a synthetic niche cell culture model to test my revolutionary hypothesis that in stem cell differentiation, nuclear import of gene-regulating transcription factors is controlled by the stretch of the nuclear pore complexes. If verified, this idea could lead to a breakthrough in biomimetic approaches to engineering stem cell differentiation. I investigate this question specifically in mesenchymal stem cells (MSC), because they are adherent and highly mechano-sensitive to architectural cues of the microenvironment. To verify my hypothesis I will use a combined experimental-computational model of mechanotransduction. I will a) scale-up an existing three-dimensional synthetic niche culture substrate, fabricated by two-photon laser polymerization, b) characterize the effect of tridimensionality on the differentiation fate of MSC cultured in the niches, c) develop a multiphysics/multiscale computational model of nuclear import of transcription factors within differentially-spread cultured cells, and d) integrate the numerical predictions with experimentally-measured import of fluorescently-labelled transcription factors. This project requires the synergic combination of several advanced bioengineering technologies, including micro/nano fabrication and biomimetics. The use of two-photon laser polymerization for controlling the geometry of the synthetic cell niches is very innovative and will highly impact the fields of bioengineering and biomaterial technology. A successful outcome will lead to a deeper understanding of bioengineering methods to direct stem cell fate and have therefore a significant impact in tissue repair technologies and regenerative medicine.

1 903 330 €

Start date: 2015-05-01, End date: 2020-07-31