ERC FUNDED PROJECTS

It is now clear that many intergenic regions in eukaryotic genomes give rise to a range of processed and regulated transcripts that do not appear to code for functional proteins. A subset of these are long (>200 nt), capped and polyadenylated RNAs transcribed by RNA polymerase II and collectively called long intervening noncoding RNAs or lincRNAs. The recent estimates are that the human genome encodes >10,000 distinct lincRNAs, many of which show tissue-specific expression and are frequently dysregulated in human disease, including neurodegeneration. Given the growing number of lincRNAs implicated in human disease or required for proper development, fundamental questions that need to be addressed are: Which lincRNAs are functional? How is functional information encoded in the lincRNA sequence? Is this information interpreted in the context of the mature or the nascent RNA? What are the identities and functional roles of specific sequence domains within lincRNA genes? Our main hypothesis is that many lincRNA loci play key roles in gene regulation during cell differentiation, both via functionally important transcription events and post-transcriptionally, through the combined action of multiple short sequence domains. We will test this hypothesis using three complementary approaches – comparative genomics, detailed perturbations in mammalian cells followed by quantitative molecular phenotyping, and high-throughput screens for sequences able to carry out specific functions. We propose an interdisciplinary approach combining computational, molecular and stem cell biology. Our methodology will be scalable, allowing us to tackle completely uncharacterized long RNAs and eventually zoom in and study their individual bases. Upon successful accomplishment of the program, we will delineate modes of action of numerous lincRNAs, report sequence patches that are functionally important and understand how specific bases and structures act in concert to drive lincRNA function.

1 500 000 €

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

Carbon fixation is a prerequisite for accumulating biomass and storing energy in most of the living world. As such, it supplies our food and dominates land and water usage by humanity. In agriculture, where water and nutrients are abundant, the rate of carbon fixation often limits growth rate. Therefore increasing the rate of carbon fixation is of global importance towards agricultural and energetic sustainability. What are the limits on the possible rate of carbon fixation? Attempts to improve RuBisCO, the key enzyme in the Calvin-Benson cycle, have achieved only limited results. My lab focuses on trying to overcome this global challenge by building synthetic pathways for carbon fixation. We create a computational framework that designs and scores pathways and creates step-wise selection strategies for in-vivo experimental implementation. Our most promising synthetic carbon fixation pathways are found to utilize the highly effective carboxylating enzyme, PEP carboxylase. We experimentally test these pathways in the most genetically tractable context by constructing an E.coli strain that depends on atmospheric CO2 fixation. We will gradually incorporate the pathways, initially as essential reaction steps for biomass production, and finally with CO2 as sole carbon input of the cell. As a stepping-stone towards this challenging goal, we will construct an autotrophic E.coli strain that uses the Calvin-Benson cycle. We systematically convert this synthetic biology grand challenge into a gradual evolutionary ladder with independently selectable steps. We recently achieved key steps in the ladder, such as semi-autotrophic growth, serving as powerful proofs of concept. The proposed research will advance our basic-science understanding of evolutionary plasticity of metabolic pathways. It also paves the way for a hybrid rational-design/experimental-evolution approach to revisit and advance the capacity of metabolism for agricultural productivity and renewable energy storage.

1 999 843 €

Start date: 2016-01-01, End date: 2020-12-31

"In the proposed research we will explore the functional proteomic diversity of histologically-defined regions within human breast tumors, aiming to identify novel protein biomarkers of tumor aggressiveness. Once identified, these proteins will serve as potent diagnostic markers and therapeutic targets. Towards this aim we will perform genome-scale proteomic profiling on tumor regions displaying diverse histopathology. This will be followed by functional investigation of these cancer cell sub-populations to determine their tumorigenic potential, and search for microparticle-based proteomic biomarkers from serum samples towards identification of cancer aggressiveness in blood tests. Analysis of the proteomic diversity holds a promise to reveal yet unidentified regulators of the tumorigenic phenotype as quantitative protein profiling is expected to most faithfully predict cellular phenotypes. This will be accomplished using the 'super-SILAC' technology, which I developed during my post-doctoral research. Using this technology, we identified over 12,000 proteins in formalin-fixed paraffin embedded breast cancer tumors. In the current project we will take one large step further, namely, microdissect and analyze selected regions in breast tumors based on local histopathological characteristics, such as the expression of known markers, cancer cell density, the vicinity to blood vessels and to the tumor invasive front. This ""topological map"" of the proteome will be followed by functional in vitro and in vivo studies, directly probing the aggressiveness of these cell populations, manifested by an accelerated proliferation and invasive/metastatic capacity. Finally, proteins associated with tumor aggressiveness will serve as blood-based biomarkers for predicting the tumorigenic phenotype using non-invasive tests. This work will set the basis for quantitative probing of tumor heterogeneity, which is crucial for accurate diagnosis and effective therapy. "

1 699 261 €

Start date: 2015-07-01, End date: 2020-06-30

Prostate cancer (PCA) is a genetically heterogeneous disease. Advances in targeted hormonal therapy (second generation anti-androgens) have led to more effective management of castration-resistant prostate cancer (CRPC). Despite these highly potent drugs, disease recurs with new genomic and epigenetic alterations. In this ERC proposal, I will leverage my expertise in cancer genomics and a new computational methodology to unravel the landscape of lethal PCA, with a focus on determining the Achilles’ heel of these aggressive tumours. In Aim 1, I will take advantage of DNA sequencing data from over 1000 patient-derived tumour samples and use highly innovative mathematical algorithms to create a detailed evolution chart for each tumour and identify driver events leading to CRPC. After nominating candidate drivers, we propose testing 10 using in vitro gain- and loss-of-function validations experiments (i.e., CRISPR/Cas9, shRNA, and Tet-On assays) in PCA cell lines using migration, invasion, and cell cycle as readouts. In Aim 2, I will focus on genomic events that occur in recalcitrant CRPC, positing that genetic alterations occurring prior or secondary to treatment harbour clues into resistance. In vitro validations will be performed on the top 10 biomarkers. In Aim 3, I will nominate synthetic lethality combinations by mining CRPC genomic data taken from Stand Up 2 Cancer CRPC clinical trials. I will prioritize mutually exclusive genomic alterations in genes for which approved drugs exist. The top 5-10 candidates will be validated in a prostate lineage-specific manner. In summary, this ERC proposal will leverage my many years of expertise in PCA genomics and emerging public and private CRPC datasets to uncover driver mutations that will enhance our understanding of recalcitrant CRPC. Successful completion of this study should lead to novel treatment approaches for CRPC and to a computational model that may transform our approach to evaluating other cancers.

1 996 428 €

Start date: 2015-10-01, End date: 2020-09-30