Single genomic enhancers drive experience-dependent GABAergic plasticity to maintain sensory processing in the adult cortex

Experience-dependent plasticity of synapses modulates information processing in neural circuits and is essential for cognitive functions. The genome, via non-coding enhancers, was proposed to control information processing and circuit plasticity by regulating experience-induced transcription of genes that modulate specific sets of synapses. To test this idea, we analyze here the cellular and circuit functions of the genomic mechanisms that control the experience-induced transcription of Igf1 (insulin-like growth factor 1) in vasoactive intestinal peptide (VIP) interneurons (INs) in the visual cortex of adult mice. We find that two sensory-induced enhancers selectively and cooperatively drive the activity-induced transcription of Igf1 to thereby promote GABAergic inputs onto VIP INs and to homeostatically control the ratio between excitation and inhibition (E/I ratio)-in turn, this restricts neural activity in VIP INs and principal excitatory neurons and maintains spatial frequency tuning. Thus, enhancer-mediated activity-induced transcription maintains sensory processing in the adult cortex via homeostatic modulation of E/I ratio

Uncovering functional brain signature via random matrix theory

The brain is organized in a modular way, serving multiple functionalities. This multiplicity requires that both positive (e.g. excitatory, phase-coherent) and negative (e.g. inhibitory, phase-opposing) interactions take place across brain modules. Unfortunately, most methods to detect modules from time series either neglect or convert to positive any measured negative correlation. This may leave a significant part of the sign-dependent functional structure undetected. Here we present a novel method, based on random matrix theory, for the identification of sign-dependent modules in the brain. Our method filters out the joint effects of local (unit-specific) noise and global (system-wide) dependencies that empirically obfuscate such structure. The method is guaranteed to identify an optimally contrasted functional `signature', i.e. a partition into modules that are positively correlated internally and negatively correlated across. The method is purely data-driven, does not use any arbitrary threshold or network projection, and outputs only statistically significant structure. In measurements of neuronal gene expression in the biological clock of mice, the method systematically uncovers two otherwise undetectable, negatively correlated modules whose relative size and mutual interaction strength are found to depend on photoperiod. The neurons alternating between the two modules define a candidate region of functional plasticity for circadian modulation

Single-cell period variability (SD) shows regional differences, especially after exposure to long photoperiod.

<p>Regional averages of single-cell period variability binned from all recordings for six areas of the anterior (top panels) and posterior SCN (bottom panels), in long (LP, left panels) and short photoperiod (SP, right panels). As indicated by the color bar on the right, dark (blue-black) colors indicate small, and light (green-yellow) colors indicate larger period variability. Scale bar: 200 μm.</p

Peak time of PER2::LUC expression is similar in anterior and posterior SCN in both long and short photoperiod.

<p>(A) Overlays of brightfield images of anterior SCN cultures from short (SP) and long photoperiod (LP), with the corresponding bioluminescence image. Scale bar: 200 μm. (B) Intensity traces of PER2::LUC expression from single cells. Raw traces of bioluminescence intensity from the anterior SCN from SP (<i>n</i> = 183 cells; top panel), with corresponding smoothed traces (middle panel), and smoothed traces from the anterior SCN from LP (<i>n</i> = 177 cells; bottom panel). (C) Average peak time of PER2::LUC rhythms per slice, of the anterior and posterior SCN in LP (green squares, <i>n</i> = 5) and SP (red circles, <i>n</i> = 4) are plotted as external time (ExT). Grey background indicates projected dark period of light regime preceding the experiment. Black bars indicate mean ± SEM; * <i>p</i> < 0.05.</p

Exposure to long photoperiod increases peak time distribution.

<p>(A) Representative histograms of peak times of two individual slices from the anterior SCN in long (LP, <i>n</i> = 177 cells) and short photoperiod (SP, <i>n</i> = 183 cells) plotted in external time (ExT). (B) Phase distribution is defined as the standard deviation (SD) of peak time, of the first cycle <i>in vitro</i> (top panel). Phase distribution was calculated per slice, and is shown for the anterior and posterior SCN (bottom panel), in LP (green squares) and SP (red circles). Black bars indicate mean ± SEM; *** <i>p</i> < 0.001.</p

Single-cell period variability (SD) increases after exposure to long photoperiod.

<p>(A) Representative examples of the variability in single-cell cycle-to-cycle interval from anterior SCN slices in long (LP, <i>n</i> = 177; top panel) and in short photoperiod (SP, <i>n</i> = 183; bottom panel). Black traces show the cycle-to-cycle interval of individual cells for the first three cycles <i>in vitro</i>; colored traces show the average period per cycle for the presented slice. (B) Cycle interval is defined as the cycle-to-cycle time difference between the half-maximum of the rising edge of the PER2::LUC expression rhythm. The variability in period is defined as the standard deviation (SD) of the cycle interval of individual cells, calculated for the first three cycles <i>in vitro</i> (top panel). Single-cell period variability was averaged per slice and is shown for the anterior SCN, in LP (green squares) and SP (red circles; bottom panel). Black bars indicate mean ± SEM. (C) Linear regression of the relationship between single-cell period variability and peak time SD for all recordings (<i>n</i> = 18, <i>p</i> < 0.001).</p

Functional clusters show distinct spatial distribution and region specific rhythm characteristics.

<p>(A) Representative examples of the communities detected by an advanced, unsupervised method for correlation matrix analysis [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0168954#pone.0168954.ref015" target="_blank">15</a>]. The community detection method automatically divided the SCN in two distinct regions, a ventromedial (VM) and dorsolateral (DL) region in the anterior SCN (middle panel), and a medial (M) and lateral (L) region in the posterior SCN, in both long (LP; left panel), and short photoperiod (SP, right panel). (B) Peak times in external time (ExT) were averaged per region, per slice. Darkness and light of the previous light regime are represented by grey and white background respectively. (C) Phase distribution, defined as peak time standard deviation (SD), was calculated per region, per slice. (D) Single-cell period variability was averaged per region, per slice. All data are shown for the VM and DL region in the anterior, and M and L region in the posterior SCN, in LP (green squares) and SP (red circles). Black bars indicate mean ± SEM; * <i>p</i> < 0.01.</p