No abstract

SummaryNervous systems are constructed from a deep repertoire of neuron types but the underlying gene expression programs that specify individual neuron identities are poorly understood. To address this deficit, we have produced an expression profile of all 302 neurons of the C. elegans nervous system that matches the single cell resolution of its anatomy and wiring diagram. Our results suggest that individual neuron classes can be solely identified by combinatorial expression of specific gene families. For example, each neuron class expresses unique codes of ∼23 neuropeptide-encoding genes and ∼36 neuropeptide receptors thus pointing to an expansive “wireless” signaling network. To demonstrate the utility of this uniquely comprehensive gene expression catalog, we used computational approaches to (1) identify cis-regulatory elements for neuron-specific gene expression across the nervous system and (2) reveal adhesion proteins with potential roles in synaptic specificity and process placement. These data are available at cengen.org and can be interrogated at the web application CengenApp. We expect that this neuron-specific directory of gene expression will spur investigations of underlying mechanisms that define anatomy, connectivity and function throughout the C. elegans nervous system.

Decoding post-transcriptional regulatory programs in RNA is a critical step in the larger goal to develop predictive dynamical models of cellular behavior. Despite recent efforts1–3, the vast landscape of RNA regulatory elements remain largely uncharacterized. A longstanding obstacle is the contribution of local RNA secondary structure in defining interaction partners in a variety of regulatory contexts, including but not limited to transcript stability3, alternative splicing4 and localization3. There are many documented instances where the presence of a structural regulatory element dictates alternative splicing patterns (e.g. human cardiac troponin T) or affects other aspects of RNA biology5. Thus, a full characterization of post-transcriptional regulatory programs requires capturing information provided by both local secondary structures and the underlying sequence3,6. We have developed a computational framework based on context-free grammars3,7 and mutual information2 that systematically explores the immense space of small structural elements and reveals motifs that are significantly informative of genome-wide measurements of RNA behavior. The application of this framework to genome-wide mammalian mRNA stability data revealed eight highly significant elements with substantial structural information, for the strongest of which we showed a major role in global mRNA regulation. Through biochemistry, mass-spectrometry, and in vivo binding studies, we identified HNRPA2B1 as the key regulator that binds this element and stabilizes a large number of its target genes. Ultimately, we created a global post-transcriptional regulatory map based on the identity of the discovered linear and structural cis-regulatory elements, their regulatory interactions and their target pathways. This approach can also be employed to reveal the structural elements that modulate other aspects of RNA behavior.