ERC FUNDED PROJECTS

Epigenetic mechanisms play an important role in regulating and maintaining the functionality of cells and have been implicated in a wide range of human diseases. Histone proteins that form the protein core of nucleosomes are subject to a bewildering array of covalent and structural modifications, which can repress, permit, or promote transcription. These modifications can be added and removed by specialized complexes that are recruited by other covalent modifications, by transcription factors, or by the transcriptional machinery. Advances in genomics led to comprehensive mapping of the ``epigenome'' in a range of tissues and organisms. These maps established the tight connection between histone modifications and transcription programs. These static charts, however, are less successful at uncovering the underlying mechanisms, logic, and function of histone modifications in establishing and maintaining transcriptional programs. Our premise is that we can answer these basic questions by observing the effect of genetic perturbations on the dynamics of both chromatin state and transcriptional activity. We aim to dissect the chromatin-transcription system in a systematic manner by building on our extensive experience in modeling and analysis, and a unique high-throughput experimental system we established in my lab. We plan to use the budding yeast model organism, which allows for efficient genetic and experimental manipulations. We will combine two technologies: (1) high-throughput measurements of single-cell transcriptional output using fluorescence reporters; and (2) high-throughput immunoprecipitation sequencing assays to map chromatin state. Measuring with these the dynamics of response to stimuli under different genetic backgrounds and using advanced stochastic network models, we will chart detailed mechanisms that are opaque to current approaches and elucidate the general principles that govern the interplay between chromatin and transcription.

2 396 450 €

Start date: 2014-01-01, End date: 2018-12-31

Within a decade, advances in single-cell genomics would allow humanity to embark on a coordinated international effort to discover the human cell lineage tree. The goal of LineageDiscovery is to lay the biological, computational and architectural foundations for this envisioned project and demonstrate its feasibility and value. An organismal cell lineage tree is a rooted, labelled binary tree where nodes represent organism cells, edges represent progeny relations and labels capture cell state. The tree of an adult human has about 100 trillion nodes. Many fundamental open questions in biology and medicine are about the structure, dynamics and variance of the human cell lineage tree in development, health, ageing and disease. E.g., which cancer cells give rise to metastases? Do beta cells renew? Which progeny do brain stem cells produce in development, maintenance and ageing? LineageDiscovery is based on a decade of research on this challenge by Shapiro’s lab and others. It will develop an efficient biological-computational cell lineage discovery workflow that starts with sampled cells and ends with knowledge of their cell lineage tree; and a scalable architecture for the collaborative development and the distributed deployment of this workflow. The workflow will be based on emerging single-cell technologies and will include novel algorithms to analyse single-cell data, to reconstruct cell lineage trees, and to infer ancestral cell type and state dynamics. A programmable meta-system will be developed and used for workflow optimization and evaluation. The workflow and architecture will be deployed and tested in a broad range of proof-of-concept human cell lineage discovery experiments with self-funded collaborators. Successful execution of this research plan coupled with expected advances in single-cell genomics would establish both the feasibility and the value of the envisioned large-scale human cell lineage discovery project, ideally leading to its launch.

2 250 000 €

Start date: 2015-09-01, End date: 2020-07-31

Cells process information using biochemical circuits of interacting proteins and genes. We wish to define principles guiding the design of those circuits. The interplay between variability and robustness is of key interest to us. Bio-molecular processes are stochastic, environmental conditions fluctuate, and sequence polymorphisms are abundant. How is variability buffered to maintain reproducible outcomes? Can variability enhance computational abilities? What is the impact of variability on bio-molecular circuit design? We will explore those fundamental questions in three contexts: Source of variability in Gene expression: We previously examined the mechanistic basis of expression variability, defining promoter structures associated with low vs. high variability. We will now address the more challenging question: what evolutionary pressures shape the expression program? On the network level, we will define mutual effects of selection for increased expression and for optimal growth. On the metabolic level, we will define which aspect of the expression process is limiting and the genomic consequences of this limitation. Role of expression variability in Nutrient homeostasis: We recently reported that repression of high affinity transporter in rich nutrient (the ‘dual-transporter’ motif) enables advanced preparation to nutrient depletion. We will now validate an additional predicted property of this motif: cells become committed to the starvation program, escaping it due to expression noise only. To this end, we will introduce a novel method for modulating expression noise while maintaining mean abundance. Buffering variability in Embryonic patterning: Buffering fluctuations is essential in embryonic patterning. We previously established that the embryonic DV axis of Drosophila is robustly patterned through the newly defined shuttling mechanism. We will quantify the ability of this system to buffer size variations (scaling), and reveal the underlying scaling mechanism.

2 311 000 €

Start date: 2014-02-01, End date: 2019-01-31

Lack of replicability of scientific discoveries has surfaced too often in recent years, and even reached the attention of the general public. An ignored cause is the inappropriate statistical treatment of two statistical problems: (1) selective inference, manifested in selecting few promising leads following the statistical analysis of the potential many, where ignoring the selection process on estimates, confidence intervals and observed significance; (2) using too optimistic a yardstick of variation with which confidence intervals set and statistical significance of the potential discovery is judged, as a result of ignoring the variability between laboratories and subjects. The first problem becomes more serious as the pool of potential discoveries increases, the second paradoxically becomes more serious as measuring ability improves, which explain why the two problems are more prominent in recent years. Both problems have statistical solutions, but the solutions are not practical as they burden the analysis to a point where the power to discover new findings is exceedingly low. Therefore, unless required by regulating agencies, scientists tend to avoid using these solutions. I propose to develop methods that address such replicablity problems specific to medical research, epidemiology, genomics, brain research, and behavioral neuroscience. The methods include (a) new hierarchical weighted procedures, and model selection methods, that control the false discovery rate in testing; (b) shorter confidence intervals that offer false coverage-statement rate for the selected, both addressing the concern about selective inference; and (c) a compromise between using random effects models for the laboratories and subjects and treating them as fixed, to be aided by multiple laboratory database in behavior genetics and neuroscience. By serving the exact needs of scientists, while avoiding excessive protection, I expect the offered methodologies to become widely adapted.

1 933 200 €

Start date: 2012-03-01, End date: 2018-02-28

Small RNAs (sRNAs) are major regulators of gene expression in bacteria, exerting their regulation in trans by base pairing with target RNAs. Traditionally, sRNAs were considered post-transcriptional regulators, mainly regulating translation by blocking or exposing the ribosome binding site. However, accumulating evidence suggest that sRNAs can exploit the base pairing to manipulate their targets in different ways, assisting or interfering with various molecular processes involving the target RNA. Currently there are a few examples of these alternative regulation modes, but their extent and implications in the cellular circuitry have not been assessed. Here we propose to take advantage of the power of RNA-seq-based technologies to develop innovative approaches to address these challenges transcriptome-wide. These approaches will enable us to map the regulatory mechanism a sRNA employs per target through its effect on a certain molecular process. For feasibility we propose studying three processes: RNA cleavage by RNase E, pre-mature Rho-dependent transcription termination, and transcription elongation pausing. Finding targets regulated by sRNA manipulation of the two latter processes would be especially intriguing, as it would suggest that sRNAs can function as gene-specific transcription regulators (alluded to by our preliminary results). As a basis of our research we will use the network of ~2400 sRNA-target pairs in Escherichia coli, deciphered by RIL-seq (a method we recently developed for global in vivo detection of sRNA targets). Revealing the regulatory mechanism(s) employed per target will shed light on the principles underlying the integration of distinct sRNA regulation modes in specific regulatory circuits and cellular contexts, with direct implications to synthetic biology and pathogenic bacteria. Our study may change the way sRNAs are perceived, from post-transcriptional to versatile regulators that apply different regulation modes to different targets.

2 278 125 €

Start date: 2019-09-01, End date: 2024-08-31