Summary
RNA Binding Proteins (RBPs) are key regulators of gene expression. The mammalian genome encodes well
over 2,000 RBPs, exceeding the number of transcriptional regulators, and most of these RBPs are highly
conserved. RBPs are central to regulating all the stages of the RNA life cycle (e.g. transcription, processing,
transport, translation, degradation, etc.) and their regulatory function is important to all biological processes.
Moreover, RBPs form complex regulatory networks, where one RBP can regulate up to hundreds of target RNAs
and also several RBPs can interact with the same target RNA. This coordinated regulation of RNAs by RBPs in
time and space is thought to control many biological processes, such as cell fate determination and synaptic
plasticity. Therefore, in order to fully understand post-transcriptional gene expression regulation, it is crucial to
understand the molecular interactions of RBPs and how these interactions change throughout a physiological or
pathological process. Specifically, two kinds of molecular interactions define the functions of a given RBP: (1)
the set of RNAs that are targeted by a given RBP, and (2) other RBPs that function together with it by direct and
indirect (RNA-mediated) interactions.
As RBPs form this complex regulatory network, we need methods that allow us to measure the molecular
interactions of a large number of RBPs in parallel. However, current methods to characterize RBPs only map
the interaction of one RBP at a time, which make the effort of truly understanding their interplay and their impact
on post-transcriptional regulation challenging. Here I propose to use a set of novel approaches that my lab
recently developed, which will provide functional insight into molecular interactions of dozens to hundreds of
RBPs and their dynamic changes at a multi-lab consortia scale, but within a single lab, while preserving the data
quality of existing single gene approaches. We will: (1) Globally map protein protein interactions (PPI) for
hundreds of proteins at several time points during embryonic stem cell (ESC) differentiation at sub-complex
resolution. (2) Determine which of these PPIs are RNA-dependent. (3) Determine how post-translational
modifications (PTMs) drive specific PPI changes at global coverage. (4) Characterize protein-RNA interactions
for up to a hundred of RBPs at several time points during ESC differentiation. (5) Integrate these datasets, which
will – together with focused follow-up experiments – provide functional insight about the dynamics of post-
transcriptional gene expression regulation during ESC differentiation.
Taken together, the proposed work aims to develop and apply approaches that will allow us to characterize RBP
function in a highly parallel manner in order to define the post-transcriptional regulatory circuitry that underlies
ESC differentiation into neurons.
