Overexpression of Dyrk1A, a Down Syndrome Candidate, Decreases Excitability and Impairs Gamma Oscillations in the Prefrontal Cortex

Abstract

The dual-specificity tyrosine phosphorylation-regulated kinase DYRK1A is a serine/threonine kinase involved in neuronal differentiation and synaptic plasticity and a major candidate of Down syndrome brain alterations and cognitive deficits. DYRK1A is strongly expressed in the cerebral cortex, and its overexpression leads to defective cortical pyramidal cell morphology, synaptic plasticity deficits, and altered excitation/inhibition balance. These previous observations, however, do not allow predicting how the behavior of the prefrontal cortex (PFC) network and the resulting properties of its emergent activity are affected. Here, we integrate functional, anatomical, and computational data describing the prefrontal network alterations in transgenic mice overexpressingDyrk1A(TgDyrk1A). Usingin vivoextracellular recordings, we show decreased firing rate and gamma frequency power in the prefrontal network of anesthetized and awakeTgDyrk1Amice. Immunohistochemical analysis identified a selective reduction of vesicular GABA transporter punctae on parvalbumin positive neurons, without changes in the number of cortical GABAergic neurons in the PFC ofTgDyrk1Amice, which suggests that selective disinhibition of parvalbumin interneurons would result in an overinhibited functional network. Using a conductance-based computational model, we quantitatively demonstrate that this alteration could explain the observed functional deficits including decreased gamma power and firing rate. Our results suggest that dysfunction of cortical fast-spiking interneurons might be central to the pathophysiology of Down syndrome.

Significance statement: DYRK1Ais a major candidate gene in Down syndrome. Its overexpression results into altered cognitive abilities, explained by defective cortical microarchitecture and excitation/inhibition imbalance. An open question is how these deficits impact the functionality of the prefrontal cortex network. Combining functional, anatomical, and computational approaches, we identified decreased neuronal firing rate and deficits in gamma frequency in the prefrontal cortices of transgenic mice overexpressingDyrk1A We also identified a reduction of vesicular GABA transporter punctae specifically on parvalbumin positive interneurons. Using a conductance-based computational model, we demonstrate that this decreased inhibition on interneurons recapitulates the observed functional deficits, including decreased gamma power and firing rate. Our results suggest that dysfunction of cortical fast-spiking interneurons might be central to the pathophysiology of Down syndrome.

Keywords: DYRK1A; Down syndrome; gamma oscillations; prefrontal cortex; transgenic mouse model.

Copyright © 2016 the authors 0270-6474/16/363649-12$15.00/0.

Figures

Figure 1.

Quantification of slow oscillatory activity in the PFC of anesthetized TgDyrk1A and WT mice. A, B, Top, Raw traces of the slow oscillation recorded in WT and TgDyrk1A mice, respectively. Bottom, Autocorrelations of the raw traces in both genotypes. C–G, Temporal parameters of the slow oscillations resulting from the analysis of MUA in both groups of mice. A significant difference (p < 0.05) was found in the coefficient of variation of UP state durations. H–K, Parameters regarding the firing properties of PFC network of both genotypes. H, I, Example cases illustrating the methodology and the most significant differences between TgDyrk1A and WT mice. Left, Raster plots of 100 UP states. Relative firing rates are color coded [log(MUA); see Materials and Methods]. Right, Average peristimulus histogram of log(MUA) from the raster plot on the left. The shades correspond to the SD (WT, n = 15; TgDyrk1A, n = 16). J, Comparison of the population “relative firing rate” [log(MUA); see Materials and Methods] during UP states. K, Comparison of the population upward transition slopes of log(MUA) from DOWN to UP states. Filled box plots depict the first and third quartiles with the median (center black line). Center white circles represent mean values. Whiskers extend to extreme values, excluding outliers (crosses). Outliers are data values exceeding the nearest quartile by 1.5 times the distance between first and third quartiles. *p < 0.05, Student's t test.

Figure 2.

Decrease in gamma frequencies in the prefrontal cortex of TgDyrk1A mice. A, Representative example of a WT (blue) case. Left, The power spectral density in UP (continuous line) and DOWN (discontinuous line) states. Right, The relative power (UP/DOWN). B, Representative example of a TgDyrk1A (red) case. Left, Power spectral density in UP (continuous line) and DOWN (discontinuous line) states. Right, Relative power (UP/DOWN). C, Population average of the power spectrum; shades depict SEM. Left, Power spectral density in UP (continuous line) and DOWN (discontinuous line) states in both WT (blue) and TgDyrk1A (red) mice. Right, Relative power (UP/DOWN) in WT (blue) and TgDyrk1A (red) mice. D, Box plots of the relative power in beta (20–30 Hz) and gamma (30–90 Hz) bands in both genotypes (WT, n = 15; TgDyrk1A, n = 16 mice). Filled box plots depict the first and third quartiles with the median (center black line). *p < 0.05, Mann–Whitney U test.

Figure 3.

Slow wave propagation across frontal cortex of WT and TgDyrk1A mice. A, Matrix of time lags δn of UP state onsets simultaneously detected in 16 aligned electrodes from an example recording in motor cortex. Matrix rows are different DOWN to UP transitions, and colors code for δn. Principal component analysis is used to sort DOWN–UP transitions, bringing together similar modes of slow wave propagation. B, UP state onsets are pooled in five equally sized groups of transitions (dashed lines in A), and group averaged δn are plotted for each of them (gray levels as in vertical color bar of A). C, Statistics of inverse local speeds in the five propagation modes in B. For each mode, the 15 inverse local speeds are the differences between average δn in nearby electrodes divided by their distance. Black circles with a dot are means of inverse speeds. Thick bars represent first and third quartiles, thin lines represent extreme values, and gray circles represent outliers (as in Fig. 1). D, E, Mean inverse speeds in all groups of transitions detected in motor (D) and prefrontal (E) cortex of both WT (blue, n = 18) and TgDyrk1A (red, n = 20) mice. Shaded areas show first and third quartiles of inverse local speeds in each group of transitions. Propagation modes are sorted by inverse speeds. Symbols representing means are white filled if the inverse speed of the propagation mode is significantly different from 0 (Wilcoxon's test, p < 0.05).

Figure 4.

PFC spontaneous activity in the awake TgDyrk1A and WT mice. A, Raw signal from chronically implanted electrodes are recorded in different experimental sessions from consecutive days. Awake animals were placed in transparent boxes without performing any particular task (see Materials and Methods). Spectrograms of the unfiltered field potentials are shown on the right for representative WT (left) and TgDyrk1A (right) mice. Vertical dotted lines mark daily recording sessions. Left, Power spectra P(ω) resulting from the average in time of the spectrograms. Red and blue curves are for TgDyrk1A and WT mice, respectively. B, Box plot of the mean log (MUA), a value proportional to the local network firing rate, was measured in all recording sessions (WT, n = 58; Tg, n = 73, average of 8.2 sessions per animal). In WT mice, the firing rate was significantly larger than the activity measured in TgDyrk1A mice (p < 0.05). C, Grand average P(ω) across animals, solid curves. Shaded strips show the SEM. The legend shows the included numbers of animals. The gray region is the frequency band where the average spectra are significantly different (p < 0.05). D, Relative power spectra computed as the ratio between P(ω) in the WT and the average spectrum P(ω) in TgDyrk1A mice. *p < 0.05, Mann–Whitney U test.

Figure 5.

Histological analysis of the PFCs of TgDyrk1A mice. A, Density of the different inhibitory neuronal populations represented by the parvalbumin (PV), calretinin (CR), and somatostatin (SOM) positive cells in the PFC. B Ratio of the vesicular VGLUT1 (excitatory) versus VGAT (inhibitory) presynaptic markers as an index of the excitatory–inhibitory balance. C, Number of presynaptic inhibitory and excitatory punctae in the PFC. D, Confocal images showing VGLUT1 (green) and VGAT (red) punctae in WT (top) and TgDyrk1A (bottom) PFC. E, F, Number of postsynaptic inhibitory (Gephyrin; E) and excitatory (PSD-95; F) sites. G, Density of excitatory and inhibitory terminals contacting with excitatory pyramidal neurons. H, Single confocal images illustrating inhibitory terminals (red) contacting the soma of pyramidal neurons (green) in WT (top) and TgDyrk1A (bottom) PFC. I, Density of excitatory and inhibitory terminals contacting with parvalbumin positive neurons. J, Representative confocal images of inhibitory perisomatic contacts (red) on parvalbumin positive neurons (green) of WT (top) and TgDyrk1A (bottom) PFC. Blue bars represent WT mice, and red bars represent TgDyrk1A mice. Scale bars: D, 5 μm; H, J, 1 μm. Arrows show colocalization of VGAT puncta with neuronal soma. Data are represented as mean ± SE. *p < 0.05, two-tailed Student's t test.

Figure 6.

Neural network model. A, Population-based diagram of the neural network model. The overinhibited network (red) corresponds to a 35% reduction change of the inhibitory–inhibitory coupling with respect to the control network (blue) described by Compte et al. (2003). E, Excitatory; I, inhibitory. B, Simulated LFP traces considering 50 excitatory neurons showing the slow oscillatory dynamics. An UP state is enlarged for better resolution of the faster gamma fluctuations. C, Simulated MUA traces averaged around the UP states within 200 ms. Here MUA is defined as the number of spikes triggered in one time bin of 10 ms. D, Relative power of the LFP computed during the UP states with respect to the power in the DOWN states for the control (blue) and overinhibited (red) networks. The gray and black curves show, respectively, the relative power of the LFP for a network with a 1.25-fold increase in the excitatory to inhibitory connections and a network with a twofold increase in the amplitude of the external excitatory input onto the inhibitory population. The dashed and solid blue and red lines are the same lines. Note that the gray curve produces a shift of the gamma peak and a decrease of power at lower frequencies than the peak, hence showing a different effect than the red curve.