The algorithms used by the genome to interpret neural activity

During development and throughout the adult life of an animal, experience-dependent changes inneuronal activity lead to transcriptional induction of several hundred genes. This activity-regulated geneprogram in turn regulates neural morphology and the strengths of synapses and is required for normalbrain development. The relevance of this process to developmental disability is highlighted by thesignificant number of genes that are both required for activity-dependent transcription and thatwhen mutated also result in developmental disability.The genome interprets electrical signals over timescales of seconds to minutes, making decisions thatlast a lifetime. Our understanding of how these decisions are made has been long been stymied by themismatch between the size and complexity of the activity-regulated gene program on the one hand and theone-gene-at-a-time techniques for studying it on the other. Genome-scale transcriptomics has recentlybroken this barrier, and we have identified “parts lists” of genes and regulatory elements that comprise theactivity-regulated gene program. Unfortunately, these parts list have yet to lead to an understanding howelectrical signals are processed by the genome to make long-term decisions: the algorithms used by thegenome to interpret neural activity quantitatively remain almost entirely undefined.The goal of this project is to define the algorithms by which the genome interprets a diversity of neuralactivity patterns, as well as to identify mechanisms by which these algorithms are encoded in cis- andtrans-regulatory logic. In the proposed work, we will test the hypothesis that different patterns of neuralactivity activate different complements of transcription factors, in turn driving distinct subprograms ofactivity-regulated genes. In support of this hypothesis, our preliminary experiments suggest that distinctclasses of genes interpret neural activity using stereotyped, gene class-specific algorithms.Our first aim is to determine the algorithms used by the genome to interpret neural activity. Ourapproach will be first to measure the induction of each gene across a range of neural firing rates andpatterns that span the full spectrum of neural physiology. We will use programmable LED arrays and optogenetic stimulation to control neural activity, along with high-throughput microfluidic quantitative PCR to quantify the mRNA levels of 250 activity-regulated genes across hundreds of optogenetically stimulated samples. A second aim will be to functionally evaluate thousands of enhancers and promoters to identify cis-regulatory mechanisms that allow different genes to interpret activity differently. We recently discovered 12,000 neural activity-regulated enhancers and 250 activity-regulated promoters. To identify specific regulatory sequences within these regions that distinguish patterns of neural activity, we are developing and adopting new technologies for Parallel Reporter Assays (PRAs). In these assays, the transcriptional activity of many regulatory sequences is assessed in a single experiment, via coupling of each regulatory sequence to a unique reporter “barcode” whose expression is monitored using RNA sequencing.