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Biology subjects

Gao, R. D.

Publications and source records attributed to Gao, R. D..

2 recordsLinked to original sources

Cell-Based Sensor for Extracellular DNA

Detection of molecules with cell-based sensors allows for conversion of binding events into gene expression outputs. Here, we present a cell-based sensor that can detect extracellular double-stranded DNA. This sensor is based on an engineered receptor which we call Luminescent Ultrasensitive Nucleic Acid Reporter, or LUNAR. LUNAR is based on a recently developed Programmable Antigen-gated G-protein-coupled Engineered Receptor (PAGER). PAGERs are a genetic fusion of an auto-inhibitory peptide, a protein-binding domain, and a modified kappa opioid receptor. PAGERs are gated by two binding events. First, a protein ligand displaces an intramolecular inhibitor, Arodyn, then a second ligand activates the receptor. By replacing the protein-binding domain with a DNA binding zinc finger protein (ZFP) we could detect extracellular DNA in a dose-dependent fashion. Here, we show that first-generation LUNAR constructs can detect both oligonucleotides and plasmid double-stranded DNA with nanomolar sensitivity in mammalian cells. Future work will focus on improving sensitivity, fold-change, and multiplexing capabilities for sequence-specific DNA detection.

synthetic biology↗

Field Potential Reflects the Balance of Synaptic Excitation and Inhibition

Neural circuits sit in a dynamic balance between excitation (E) and inhibition (I). Fluctuations in this E:I balance have been shown to influence neural computation, working memory, and information processing. While more drastic shifts and aberrant E:I patterns are implicated in numerous neurological and psychiatric disorders, current methods for measuring E:I dynamics require invasive procedures that are difficult to perform in behaving animals, and nearly impossible in humans. This has limited the ability to examine the full impact that E:I shifts have in neural computation and disease. In this study, we develop a computational model to show that E:I ratio can be estimated from the power law exponent (slope) of the electrophysiological power spectrum, and validate this relationship using previously published datasets from two species (rat local field potential and macaque electrocorticography). This simple method--one that can be applied retrospectively to existing data--removes a major hurdle in understanding a currently difficult to measure, yet fundamental, aspect of neural computation.

neuroscience↗