Down syndrome is a genetic disorder caused by the presence of a third copy of chromosome 21. Affected individuals show delayed growth, characteristic facial features, altered brain development; with mild to severe intellectual disability. The exact mechanisms underlying the intellectual disability in Down syndrome are unclear, although studies in mice have provided clues. Drugs that reduce the inhibitory activity in the brain improve cognition in a mouse model of Down syndrome. This suggests that excessive inhibitory activity may contribute to the cognitive impairments.

Many different neural circuits generate inhibitory activity in the brain. These circuits contain cells called interneurons. Sub-types of interneurons act via different mechanisms to reduce the activity of neurons. Identifying the interneurons that are affected in Down syndrome would thus improve our understanding of the brain basis of the disorder.

Zorrilla de San Martin et al. compared mice with Down syndrome to unaffected control mice. The results revealed an increased activity in two types of inhibitory brain circuits in Down syndrome. The first contains interneurons called Martinotti cells. These help the brain to combine inputs from different sources. The second contains interneurons called parvalbumin-positive basket cells. These help different areas of the brain to synchronize their activity, which in turn makes it easier for those areas to exchange information.

By mapping the changes in inhibitory circuits in Down syndrome, Zorrilla de San Martin et al. have provided new insights into the biological basis of the disorder. Future studies should examine whether targeting specific circuits with pharmacological treatments could ultimately help reduce the associated impairments.

Down syndrome (DS) is a condition caused by full or partial trisomy of human chromosome 21, characterized by various physical and neurological features including mild to severe intellectual disability (Antonarakis et al., 2020). Individuals with DS present important deficits in cognitive tasks known to depend on the anatomical and functional integrity of the frontal lobe (Lee et al., 2015). Moreover, DS is also associated with other CNS-mediated phenotypes, including an ultra-high risk for developing Alzheimer’s disease and high rates of autism for which the mechanisms are unknown (DiGuiseppi et al., 2010; Wiseman et al., 2015). Interventions to ameliorate DS-mediated cognitive dysfunctions are limited. The development of interventions for this vulnerable group of individuals can be achieved through a better understanding of the mechanisms underlying a core feature of DS, such as intellectual disability.

Importantly, mouse models of DS recapitulate several cognitive deficits of this condition (Herault et al., 2017; Olmos-Serrano et al., 2016b). One of the best-characterized mouse models is the Ts65Dn mouse line (herein referred to as Ts), which carries a partial trisomy of a segment of the mouse chromosome 16 (Davisson et al., 1993). Ts mice recapitulate several dysfunctions present in DS individuals, such as reduced birthweight, male sterility, abnormal facial appearance and several cognitive impairments, including executive functions, such as working memory and cognitive flexibility (Olmos-Serrano et al., 2016b).

Recovery of behavioral and neurophysiological deficits underlying cognitive impairments using GABA_A receptor blockers led to the hypothesis that intellectual deficits in DS are produced by an excessive activity of inhibitory circuits (Fernandez et al., 2007; Zorrilla de San Martin et al., 2018). Nonetheless, direct evidence for over-inhibition in DS is lacking. Moreover, given the anatomical, molecular and functional diversity of cortical inhibitory neurons (Tremblay et al., 2016), the functional implications of this hypothesis at the network level, as well as the involvement of specific GABAergic circuits remain obscure.

Executive functions depend on the integrity of the prefrontal cortex (PFC), which plays an essential role in the synchronization of task-relevant, large-scale neuronal activity (Helfrich and Knight, 2016). An important network correlate of this synchronization is represented by neuronal oscillations: rhythmic fluctuations of the electrical activity of single neurons, local neuronal populations and multiple neuronal assemblies, distributed across different brain regions (Buzsáki and Wang, 2012). Oscillations are the result of a balanced and coordinated activity of excitatory pyramidal neurons (PNs) and a rich diversity of inhibitory neurons that use γ-aminobutiric acid (GABA) as neurotransmitter. In particular, parvalbumin (PV)-positive inhibitory interneurons form synapses onto the perisomatic region of PNs. PV cells thus tightly control PN spiking activity and drive network oscillations in the γ-frequency range (30–100 Hz) (Buzsáki and Wang, 2012). γ-Oscillations are necessary for several PFC cognitive functions, such as sustained attention (Kim et al., 2016b) and cognitive flexibility (Cho et al., 2015). Conversely, Martinotti cells (MCs) are somatostatin (SST)-positive interneurons that inhibit distal dendrites of PNs, thereby controlling the integration of distal dendritic glutamatergic synaptic inputs originating from different regions of the brain (Tremblay et al., 2016). Dendritic integration of multi-pathway inputs is necessary for working memory (Abbas et al., 2018; Kim et al., 2016a). Therefore, PV interneurons and MCs represent two major cortical inhibitory circuits, characterized by a precise division of labor during cortical activity. Both forms of inhibition were shown to be involved in the entrainment of network oscillations (Cardin et al., 2009; Chen et al., 2017; Sohal et al., 2009; Veit et al., 2017) and in the cognitive performance during medial (m)PFC-dependent tasks (Abbas et al., 2018; Cho et al., 2015; Cummings and Clem, 2020). In particular, inhibition from SST interneurons plays a crucial role in mPFC-dependent memory (Abbas et al., 2018; Cummings and Clem, 2020).

Broad-spectrum GABA_AR antagonists are not clinically viable, as they can yield undesired seizure-like activity and/or anxiety. Interestingly, however, treatment of Ts mice with selective and partial negative allosteric modulators of α5-containing GABA_ARs (α5 inverse agonist or α5IA) reverse cognitive behavioral and long-term synaptic plasticity deficits in DS mice (Braudeau et al., 2011; Duchon et al., 2020; Martínez-Cué et al., 2013; Schulz et al., 2019). Importantly, neocortical dendritic synaptic inhibition of PNs from MCs relies on α5-containing GABA_ARs (Ali and Thomson, 2008). The preference for this specific GABA_AR subunit was also recently demonstrated at the equivalent hippocampal dendritic inhibitory circuit (Schulz et al., 2019; Schulz et al., 2018), raising the question of whether dendritic inhibition is specifically altered in DS.

Here we found that the dendritic synaptic inhibitory loop formed by MCs and PNs was strongly potentiated in Ts mice, with no alteration of either cell-type excitability. Conversely, the perisomatic synaptic inhibitory loop from PV cells onto PN cell bodies was unaffected in Ts mice. Strikingly, however, PV-cell excitability was strongly altered: these interneurons did not display their typical fast-spiking behavior and exhibited enhanced excitability. At the network level in vivo, these inhibitory microcircuit-specific alterations resulted in significant reduction of putative PN firing, which in turn was more tuned to β- and low γ-oscillations (10–60 Hz). These results confirm over-inhibition in DS, and reveal unexpected functional alterations of specific GABAergic circuits in this condition.

In this study, we analyzed synaptic and intrinsic properties of two major inhibitory circuits of the prefrontal cortex in a relevant mouse model of DS. We found specific alterations of distinct GABAergic circuits. In particular, we demonstrate that the dendritic inhibitory synaptic loop involving MCs and PNs was strongly potentiated in Ts, as compared to euploid control mice. In contrast, the perisomatic synaptic inhibitory control of PNs by PV cells was not affected in Ts mice. Strikingly, however, the excitability of PV cells was profoundly altered. These GABAergic circuit-specific alterations correlate with reduced PN spiking, and enhanced coupling with network β-γ-activity in Ts mice in vivo.

We analyzed MCs, which are a subset of SST interneurons, specialized in inhibiting the distal portion of apical dendrites of PNs, by ascending their axons in L1 (Kawaguchi and Kubota, 1998), where PN dendrites integrate top-down input originating from distal brain areas. In the hippocampus, dendritic inhibition strongly regulates PN dendrite electrogenesis and supra-linearity (Lovett-Barron et al., 2012), likely modulating the emergence of burst firing (Royer et al., 2012). Interestingly, α5–mediated dendritic inhibition in the hippocampus, known to strongly control dendritic integration and action potential firing (Schulz et al., 2018), is also enhanced in Ts65Dn mice and strongly control NMDAR activation, nonlinear dendritic integration, and AP firing (Schulz et al., 2019). In the neocortex, integration of top-down information in L1 and consequent dendrite-dependent generation of burst activity was hypothesized to underlie the encoding of context-rich, salient information (Larkum, 2013). Our results indicate a specific synaptic strengthening of the dendritic inhibitory loop involving MCs in Ts mice, suggesting a major impact on PN dendritic integration and electrogenesis.

Enhanced dendritic inhibition by MCs in DS could underlie the deficits of long-term plasticity of glutamatergic synapses similar to those observed in the hippocampus of Ts mice. Indeed, these LTP deficits can be recovered by treatment of allosteric modulators of α5-GABA_ARs (Duchon et al., 2020; Martínez-Cué et al., 2013; Schulz et al., 2019). Likewise, the enhanced MC-PN inhibitory loop in Ts mice shown here can provide a mechanism for the rescue of cognitive deficits in Ts mice, operated by selective pharmacology of α5-GABA_ARs (Braudeau et al., 2011; Duchon et al., 2020; Martínez-Cué et al., 2013). This subunit of GABA_ARs, whose synaptic vs. extrasynaptic expression is still debated (Ali and Thomson, 2008; Botta et al., 2015; Glykys and Mody, 2010; Hannan et al., 2020; Hausrat et al., 2015; Schulz et al., 2018; Serwanski et al., 2006), mediate dendritic synaptic inhibition from neocortical MCs (Ali and Thomson, 2008) and their hippocampal counterparts (the oriens-lacunosum moleculare or O-LM interneurons, Schulz et al., 2018). Here, we confirmed that α5-containing GABA_ARs are majorly responsible for MC-PN dendritic synaptic inhibition, due to the overall block of α5IA, which was close to the max potency of this drug (Dawson et al., 2006).

The enhancement of the dendritic inhibitory loop involving MCs and PNs in Ts mice could not be attributed to alterations of axonal and/or dendritic arborizations in the mutant mice. On the other hand, it could be due to a combination of pre- and postsynaptic mechanisms, including alterations of release probability, number of release sites or quantal size at either GABAergic or glutamatergic synapses involved in this circuit. The strong increase of synaptic charge at MC-PN GABAergic synapses could be attributed to alterations of uIPSC waveform in Ts mice. Although the small change of uIPSC rise time can in principle account for, at least in part, an increase in synaptic charge, it is unlikely to account for the 5-fold increase of synaptic charge, with no changes in uIPSC decay.

The robust increase of synaptic charge in Ts mice is likely due to one or a combination of different non-linearities that dynamically emerge during a spike train. These include the recruitment of peri- or extrasynaptic GABA_ARs, possible alterations of release probability or quantal size during trains and differences in postsynaptic dendritic filtering in the two genotypes. These complex interplays between these factors occur at distal dendrites; therefore, they are difficult to tease out from somatic recordings as they are characterized by poor dendritic voltage control due to space-clamping constraints. The enhanced short-term depression of MC-PN uIPSCs that we report here suggests that release probability at dendritic GABAergic synapses from MCs is increased in Ts mice. This is consistent with increased short-term depression of inhibition reported in the dentate gyrus (Kleschevnikov et al., 2012), but not with unaltered short-term plasticity shown in CA1 (Mitra et al., 2012). These discrepancies could be ascribed to differences between brain regions. Moreover, they could also be a consequence of non-specific recruitment of presynaptic axons by global extracellular stimulation, in contrast to the isolation of unitary synaptic responses by dual intracellular recordings as reported here. The lack of short-term plasticity alterations at PN-MC glutamatergic synapses suggests that increased excitatory recruitment of MCs is due to alterations at postsynaptic sites. Future studies will be necessary to pinpoint the exact biophysical and anatomical alterations underlying the prominent increase of dendritic inhibition operated by MCs in DS.

Potentiation of glutamatergic synapses in Ts mice seems to be specific for PN-MC connections, as PN-PV synapses were similar in both genotypes, suggesting that presynaptic terminals of local glutamatergic synapses can undergo target-specific modulation of their strength. Intriguingly, it has been recently shown that an increase of the excitatory drive of hippocampal interneurons (due to triplication of GluR5 kainate receptor expression) could explain excess of inhibition received by pyramidal neurons in the Ts2Cje Down syndrome mouse model (Valbuena et al., 2019). Therefore, a similar gene overdose of kainate receptors can boost the recruitment of specific interneurons in the PFC of Ts mice.

The strong increase of membrane resistance of PV cells in Ts mice underlies the augmented intrinsic excitability and can produce early ectopic bouts of activity, thus contributing to network over-inhibition. Enhanced membrane resistance could result from alterations in the expression of TWIK1 and TASK1 leak channels. Indeed, these channels underlie the developmental decrease of membrane resistance in PV cells (Okaty et al., 2009). In contrast, the inability of PV cells of Ts mice to sustain high frequency firing could prevent these interneurons from generating high-frequency bursts of action potentials and therefore have a detrimental effect on the temporal coding of these interneurons.

Interestingly, however, despite the dramatic alterations of intrinsic excitability in Ts PV cells, their output synaptic perisomatic inhibition was similar in both genotypes. This, despite the widening of single PV-cell action potentials, which could, in principle change the presynaptic Ca²⁺ dynamics and thus alter release probability. Since action potentials are recorded in the soma, the lack of effect at PV-cell synapses could be due to soma-specific alterations. Alternatively, since neocortical PV-PN GABAergic synapses exhibit high release probability (Deleuze et al., 2019; Kawaguchi and Kubota, 1998), alterations of spike width might not be enough to produce additional increases. Increase of action potential width in Ts mice could be due to changes in the expression Kv3.1b potassium channels, known to underlie the fast repolarization of action potentials and fast-spiking behavior of PV cells (Erisir et al., 1999). Future experiments will be necessary to reveal the exact molecular mechanism underlying the altered firing properties of PV cells, which, in Ts mice, have lost their characteristic fast-spiking signature.

The aberrant active and passive properties of PV cells in Ts mice could be due to a delayed development of these interneurons. Indeed, during development, neocortical PV cells display marked accelerations of single action potentials and increased firing frequency, accompanied by decreased input resistance (Okaty et al., 2009). Although we cannot rule out the possibility of a delayed development of PV cells in Ts mice, these interneurons showed abnormal passive and active properties within the entire age range studied here. It will be interesting to determine whether these profound alterations of PV-cell firing are present with the same incidence and magnitude along the entire life span of Ts mice. Although previous reports have shown higher number of inhibitory interneurons in the hippocampus (Hernández-González et al., 2015) and somatosensory cortex (Aziz et al., 2018; Chakrabarti et al., 2010) we failed to detect significant differences in the density of both SST- and PV-positive interneurons in the mPFC. This could be due to differences between brain regions. A systematic comparative analysis will be required to better understand the consequences of DS neurodevelopmental alterations of the cellular composition in different brain regions.

Both potentiation of the dendritic inhibitory loop and increased PV-cell excitability are consistent with the alterations of PN spiking activity that we recorded in vivo. Indeed, reduced spike rates and increased tuning in the β-γ-frequency range are both consistent with increased activity of inhibitory neurons (Atallah et al., 2012; Cardin et al., 2009; Chen et al., 2017; Sohal et al., 2009; Veit et al., 2017). The overall LFP power spectra observed in mPFC is similar in both Ts and Eu mice, consistently with a recent report (Chang et al., 2020). However, close examination of stLFP revealed a strong increase in the power of β- and low γ-frequency bands during periods of spiking activity. This likely reflects the consequences of alterations at the level of local microcircuits.

We cannot directly link the inhibitory circuit-specific alterations that we detected in slices with the increased synchronization of β-γ-activity that we measured in vivo. However, a large body of literature indicates that oscillations in this frequency range strongly depends on the activity of PV cells (Buzsáki and Wang, 2012; Sohal et al., 2009; Cardin et al., 2009). More recently, also SST interneurons were shown to control PN phase coupling with low frequency (30 Hz) neocortical γ-oscillations (Chen et al., 2017; Veit et al., 2017). It is therefore tempting to speculate that increased dendritic inhibition from SST-expressing MCs modulates phase coupling of PNs with β- and low γ-oscillations. On the other hand, the increased phase coupling of PN spikes with high-frequency γ-activity could result from the augmented excitability of PV cells. The differential phase coupling of these two interneuron types at distinct frequencies is consistent with the peculiar fast and slower recruitment and biophysical properties of PV interneurons and MCs, respectively. Future experiments involving chemogenetic alterations of PV-interneuron excitability and/or pharmacological manipulations of MC-PN synapses in Ts mice will help decipher the role played by each inhibitory cell subtype in controlling the temporal dynamics of PN firing during different rhythmic cortical network activities. Alternatively, our results could be interpreted as differences in long-range functional connectivity in the two genotypes, possibly due to alterations in myelination and conduction velocity in Ts vs. Eu mice (Olmos-Serrano et al., 2016a). Nevertheless, a reduction of axonal conduction velocity would produce a temporal shift in spike-to-phase association, rather than increased phase locking.

The increase in β-γ-band power and enhanced neural synchronization in these frequency ranges in Ts mice is consistent with recent evidence indicating augmented hippocampal-PFC synchronization and LFP γ-band power during natural non-REM sleep in Ts65Dn mice (Alemany-González et al., 2020). This suggests that the alterations of γ-oscillations observed here could play a role in the pathophysiology of sleep disruptions reported in DS children (Fernandez et al., 2017) and adults (Giménez et al., 2018).

In sum, here we report direct evidence for over-inhibition of mPFC circuits in a mouse model of DS. However, over-inhibition was not due to a generic increase of GABAergic signaling, but emerged from highly specific synaptic and intrinsic alterations of dendritic and somatic inhibitory circuits, respectively. Future experiments are necessary to reveal whether other inhibitory neuron types are also affected in DS. Likewise, it will be fundamental to assess whether specific dysfunctions of individual GABAergic circuits underlie different aspects of cognitive deficits (e.g. impaired memory and flexibility, autistic traits), which affect individuals with DS.

Experimental procedures followed national and European (2010/63/EU) guidelines, and have been approved by the authors' institutional review boards and national authorities. All efforts were made to minimize suffering and reduce the number of animals. Experiments were performed on >4 week old and 18- to 25-day-old mice for in vivo and ex vivo recordings, respectively. We used B6EiC3Sn a/A-Ts(17 ¹⁶)65Dn/J (also known as Ts65Dn) mice (The Jackson Laboratory, Bar Harbor, Maine; stock #: 001924). For ex vivo slice experiments to label SST-positive MCs, Ts65Dn mice were crossed with Tg(Gad1-EGFP)98Agmo/J, also known as GAD67-GFP X98 or GFP-X98 (Jackson Laboratory). To label PV-INs, Ts65Dn mice were crossed with C57BL/6-Tg(Pvalb-tdTomato)15Gfng/J (Pvalb-tdTomato, Jackson Laboratory). Mice used in this study were of both sexes.

Source data files have been provided for: Figure 1, Figure 1–figure supplement 2, Figure 1–figure supplement 3, Figure 2, Figure 2–figure supplement 1, Figure 2–figure supplement 2, Figure 3, Figure 3–figure supplement 1, Figure 4, Figure 4–figure supplement 1 and Figure 5.

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Acceptance summary:

Down Syndrome is a developmental disorder due to a chromosomal abnormality which can be modeled in mice. Recent work has suggested that the intellectual disability results from excess inhibition in brain circuits. The authors use elegant physiological methods to provide direct evidence for the proposed over-inhibition in a mouse model of Down Syndrome and identify specific changes differently affecting two well studied classes of GABAergic interneurons.

Decision letter after peer review:

Thank you for submitting your article "Alterations of specific cortical GABAergic circuits underlie abnormal network activity in a mouse model of Down syndrome" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Sacha B Nelson as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Gary Westbrook as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Josef Bischofberger (Reviewer #2). The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.

Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in eLife, either of which would be linked to the original paper.

Summary:

The concept of "E/I balance" has dominated discussions of the etiology of developmental disorders involving intellectual disability and Autism Spectrum Disorders. But many have rightly complained that this concept is poorly defined and poorly specified. The disorder Down Syndrome (DS) is especially interesting in this regard, both because it is the most common genetic syndrome causing intellectual disability and because pharmacological studies in mice have argued for enhanced inhibition as an important cause. The present study presents the first direct evidence for enhanced inhibition in mice that model the trisomy underlying DS. The authors use a rigorous approach involving paired recordings between identified types of neurons and identify specific synaptic and intrinsic changes in two important subpopulations of cortical interneurons; the Sst-positive Martinotti cells, and Pvalb-expressing basket cells.

Revisions for this paper:

The authors should make textual changes to address the key points raised by the three reviewers. Specifically,

1) Clarify and better discuss the limitations and caveats to the experiments involving pharmacological manipulation of enhanced dendritic inhibition.

2) Provide further analyses, clarification and discussion of the alterations in Pvalb-neuron excitability, along the lines suggested by reviewers #2 and #3 below.

3) Enhance the discussion of the final figure to better integrate these results into the rest of the paper.

The logic of the requested textual revisions is explained in the concerns of the reviewers and so those are included below.

Revisions expected in follow-up work:

If available, follow up recordings in older animals would add to the impact of the work as would follow up pharmacology studies.

Reviewer #1:

The authors provide direct evidence for the proposed over-inhibition in a mouse model of Down Syndrome and identify specific changes differently affecting two well studied classes of GABAergic interneurons. The results are robust and well-illustrated and the experiments are rigorous and well designed.

I have mainly minor suggestions for improving the manuscript. The most significant concern is that the English usage would benefit from careful editing.

Reviewer #2:

The new paper “Alterations of specific cortical GABAergic circuits underlie abnormal network activity in a mouse model of Down Syndrome” investigates how altered synaptic transmission in prefrontal cortex (PFC) might impact on cortical network activity in Down Syndrome. Using elegant electrophysiological paired recordings of synaptically coupled Martinotti-cells, PV-interneurons and pyramidal cells, the authors nicely show that dendritic inhibition is enhanced in the PFC. By contrast, peri-somatic inhibition is relatively normal. Furthermore, it is claimed that the excitability of PV-basket cells is increased affecting cortical γ-oscillations in anesthetized mice. To support these later claims, some more analysis is necessary.

1) Increased dendritic inhibition. This first part is very convincing in general. However, I was wondering about the 5-fold increase in synaptic charge, while peak amplitudes are only 3-times larger (Figure 1C). Is the decay time course slower in TS-mice? Furthermore, I could not find any information about the used GABA receptor modulator (a5IA). Is it L-822179 (PubChem CID 6918451)? There are many inverse agonists/modulators of GABAa5 receptors available. It is important that the authors precisely specify which drug and from what source. Finally, the authors should refer correctly to Schulz et al., 2019 and discuss that increased dendritic inhibition via GABAa5 receptors was also found in CA1 pyramidal cells of Ts65Dn mice.

2) PV-firing. Firstly, I am surprised by the control data of PV-cells in Figure 4. It seems that there is no defined rheobase in Figure 4B in contrast to the traces shown in Figure 4A black. Did the authors record form separate populations of PV-interneurons which distorts the average FI-curve? It would be important to analyze the FI-curves of different cells separately (as for example performed in Schulz et al., 2019). This will allow to calculate mean values (and medians) for rheobase and gain. A histogram (or cumulative distribution) of rheobase and gain will show the presence of different populations of cells in control and in Ts65Dn. To do so, the formula in subsection “Analysis of slices electrophysiological recordings” should be replaced by a more appropriate function, which includes a rheobase and without a base firing which is zero anyway (for example F = gain*ln(I/rheobase)). Second, the mice for slices are rather young (P18-P25). In adult animals, cortical PV-tdTom cells appear to show a mean Rin of 118 MOhm, an AP half duration of 0.5 ms and a taum of 7.2 ms (Kaiser et al., 2016). The control animals in the present study show consistently larger values. The values in Ts65Dn mice are further increased. Is it possible that PV cells of TS animals are just delayed in development? A first indication could be a correlative analysis plotting AP with, Rin, rheobase, gain and max firing frequency against animal age (P18-P25) to see whether there are significant correlations. The best would be slice recordings using older animals (> 4 weeks), to see whether the differences disappear.

3) in vivo field potentials. The field potential analysis is only loosely connected to the rest of the paper. How do we know whether “alterations in cortical GABA circuits underlie the abnormal network activity” and increased γ-power in the in vivo LFPs? Furthermore, the authors discuss these data in the context of temporal coding and cognition. The slow waves during urethane anesthesia (1-4Hz) resembles slow wave sleep, which is relevant for memory consolidation. The authors should discuss the new paper by Alemany-Gonzalez et al., 2020 (PNAS 117:11788-11798), which show disturbed slow wave sleep oscillations in Ts65Dn mice. Similar to the present study they find increased γ power in PFC during slow wave sleep (Alemany-Gonzalez, Figure 2B), which could be related to the well-known sleep disturbances in DS patients.

Reviewer #3:

The authors present a clearly written, technically sound and very interesting study in which they demonstrate substantial changes in the prefrontal cortex inhibitory circuitry of a well-characterized transgenic Down Syndrome model. They show an aberrant dendritic inhibitory circuitry caused by an increased in synaptic transmission from and to Martinotti cells in the medial prefrontal cortex and changes in excitability of perisomatic inhibitory interneurons without apparent changes in synaptic properties. Finally, they demonstrate a strong decrease in principal cell activity on the medial prefrontal cortex of urethane anesthetized Ts mice and increase coupling to γ oscillations.

I find the study interesting and suitable for publication in eLife. It gives a detailed description of the inhibitory circuitry in the Ts mice and provides new in vivo evidence supporting the theory of over-inhibition as a main cause for cognitive deficits in DS. Nevertheless, there are some issues I think should be addressed first.

1) There is a mismatch between the age of animals used for in vitro and in vivo recordings. Down syndrome is a developmental disease, and it could be that interneuron development might take more time than in WT animals. PV positive interneurons are known to develop in the hippocampus during the first 4 postnatal weeks, bringing the question if the abnormalities observed in this study remain during adulthood. I suggest performing some of the main in vitro experiments for parvalbumin and somatostatin interneurons in slices from adult animals.

2) Another concern relates to the final conclusion of the paper, which correlates the in vitro observed abnormalities in inhibition with the decreased activity of principal cells. Although this might be the most rational assumption, I would appreciate evidence to make this hypothesis more likely, considering that DS mice also show changes in spine density, NMDA receptor expression in principal cells and increase threshold for firing as shown in this study. This could be performed by:

– Evaluating the effect of α5IA injection on principal cell activity in Eu and Ts mice.

– Performing recordings from identified interneurons in vivo in Eu and Ts mice.

– Test the effects of specific inhibition of parvalbumin or somatostatin interneurons on the activity of pyramidal cells in Eu and Ts mice.

A more comprehensive discussion addressing these issues might also be sufficient in case experiments are not feasible in a reasonable time frame.

3) The concentration of α5IA of 100 nm seems not to provide the highest specificity of the drug, having a milder but significant effect in receptors containing the α3 and α1 subunits (Dawson et al., 2005). Considering the general importance that this strong and very specific effect may have for the community, and the sparse literature using this drug in vitro I would appreciate a better characterization of this particular experiment.

– Does α5IA changes the kinetics of the GABAergic response?

– Are PV-mediated IPSCs affected by this concentration of α5IA?

– Does the α5IA dependent response shows voltage-dependency as has been reported (Schultz et al., 2019)

Reviewer #1:

The authors provide direct evidence for the proposed over-inhibition in a mouse model of Down Syndrome and identify specific changes differently affecting two well studied classes of GABAergic interneurons. The results are robust and well-illustrated and the experiments are rigorous and well designed.

I have mainly minor suggestions for improving the manuscript. The most significant concern is that the English usage would benefit from careful editing.

We have revised the English language throughout the manuscript and we asked a native speaker to read it. We hope the revised text is much clearer than the previous version.

Reviewer #2:

The new paper “Alterations of specific cortical GABAergic circuits underlie abnormal network activity in a mouse model of Down Syndrome” investigates how altered synaptic transmission in prefrontal cortex (PFC) might impact on cortical network activity in Down Syndrome. Using elegant electrophysiological paired recordings of synaptically coupled Martinotti-cells, PV-interneurons and pyramidal cells, the authors nicely show that dendritic inhibition is enhanced in the PFC. By contrast, peri-somatic inhibition is relatively normal. Furthermore, it is claimed that the excitability of PV-basket cells is increased affecting cortical γ-oscillations in anesthetized mice. To support these later claims, some more analysis is necessary.

1) Increased dendritic inhibition. This first part is very convincing in general. However, I was wondering about the 5-fold increase in synaptic charge, while peak amplitudes are only 3-times larger (Figure 1C). Is the decay time course slower in TS-mice?

Whereas the amplitude was calculated on the first synaptic response, the charge was measured over the entire train. We have measured the kinetics of MC-PN uIPSCs. We found no significant differences in isolated uIPSC decay time constant in Eu vs. Ts mice (see the new Figure 1—figure supplement 2). We found a significant increase of uIPSC rise-times in Eu vs Ts. This could be ascribed to a more distal location of MC-PN synapses on dendrites of Ts mice. Although this change could in principle account for, at least in part, an increase in synaptic charge, the magnitude of this change is unlikely to account for a 5-fold increase.

We speculate that a 5-fold increase of synaptic charge is due to one or a combination of different non-linearities that are dynamically emerging during a spike train: (i) recruitment of peri- or extrasynaptic GABA_ARs due to enhanced release of GABA; (ii) changes in postsynaptic receptors occupation and desensitization. Complex interplays between these factors occur at distal dendrites, where we have poor voltage control due to space-clamping constraints. Future studies will be necessary to pinpoint the exact biophysical and anatomical alterations underlying the prominent increase of dendritic inhibition operated by MCs in DS.

We have generated a new figure supplement to Figure 1 (Figure 1—figure supplement 2), detailing this finding. We have described (in subsection “Synaptic enhancement of dendritic inhibition in DS”) and integrated this new analysis to the discussion in the revised manuscript.

Furthermore, I could not find any information about the used GABA receptor modulator (a5IA). Is it L-822179 (PubChem CID 6918451)? There are many inverse agonists/modulators of GABAa5 receptors available. It is important that the authors precisely specify which drug and from what source.

The α5IA, an inverse antagonist of α5-containing GABAA receptors used in our study corresponds to the molecule ID:4095 (IUPHAR/BPS), also named L-822179, or CID 6918451 in PubChem. This has been detailed in the Materials and methods section of the revised manuscript, with the appropriate references.

Finally, the authors should refer correctly to Schulz et al., 2019 and discuss that increased dendritic inhibition via GABAa5 receptors was also found in CA1 pyramidal cells of Ts65Dn mice.

We thank the reviewer for pointing this out. We have correctly addressed this paper in the revised manuscript (Discussions section).

2) PV-firing. Firstly, I am surprised by the control data of PV-cells in Figure 4. It seems that there is no defined rheobase in Figure 4B in contrast to the traces shown in Figure 4A black. Did the authors record form separate populations of PV-interneurons which distorts the average FI-curve? It would be important to analyze the FI-curves of different cells separately (as for example performed in Schulz et al., 2019). This will allow to calculate mean values (and medians) for rheobase and gain. A histogram (or cumulative distribution) of rheobase and gain will show the presence of different populations of cells in control and in Ts65Dn. To do so, the formula in subsection “Analysis of slices electrophysiological recordings” should be replaced by a more appropriate function, which includes a rheobase and without a base firing which is zero anyway (for example F = gain*ln(I/rheobase)).

We have re-analyzed f-i curves as suggested by the reviewer. The new Figure 4 includes two representative examples of f-i curves in the two genotypes, fitted with the function suggested by the reviewer (panel b). Moreover, population data relative to rheobase and gain are shown in box-plot panels (left panel in c). This new analysis is consistent with a strong alteration of PV-cell excitability and firing dynamics in Ts mice. We have detailed this new analysis in the revised manuscript (subsection “Excitability of PV cells, and not their perisomatic control of PNs, is strongly altered in Ts mice”).

Second, the mice for slices are rather young (P18-P25). In adult animals, cortical PV-tdTom cells appear to show a mean Rin of 118 MOhm, an AP half duration of 0.5 ms and a taum of 7.2 ms (Kaiser et al., 2016). The control animals in the present study show consistently larger values. The values in Ts65Dn mice are further increased. Is it possible that PV cells of TS animals are just delayed in development? A first indication could be a correlative analysis plotting AP with, Rin, rheobase, gain and max firing frequency against animal age (P18-P25) to see whether there are significant correlations. The best would be slice recordings using older animals (> 4 weeks), to see whether the differences disappear.

The reviewer raises an excellent point. Indeed, active and passive properties of PV cells of Ts mice are consistent with an immature phenotype (Okaty et al., 2009). We performed a correlation analysis of the passive properties and excitability with age as suggested. In Eu mice, we found that input resistance, tau membrane and AP half width change along this period of development into values similar to those reported by Okaty et al., 2009 and Kaiser et al., 2016. We have added a figure supplement to Figure 4 illustrating this developmental profile (see Figure 4—figure supplement 2).

Importantly, if we confine our analysis in the older time window of our recordings (P23-25), we found that differences in AP width, input resistance and membrane time constant remain starkly different. We include Author response image 1 and Author response table 1 illustrating this finding at this age. We also include the values of the Kaiser paper obtained in the somatosensory cortex of adult mice. Overall, this analysis excludes potential effect of a bias in the age composition of our sample and validates our interpretation.

Author response image 1

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*Two-sided Mann Whitney U test.

^§The age of mice in this paper was P45-90 and measurements were done in S1.

However, the reviewer is right in pointing that in vivo recordings were obtained in adult animals. Moreover, we agree with the reviewer that it is important to determine whether this phenotype is transitory or if it lasts into adulthood. We shared this view, and indeed, we had started to record from adult animals, but we were forced to suspend all experiments and sacrifice our mouse colonies due to the current pandemic crisis in France. We are in the process of obtaining new colonies specifically for these experiments. In the following months, we will test the hypothesis of delayed development to be exploited in a future study that will include cross-disciplinary approaches.

3) in vivo field potentials. The field potential analysis is only loosely connected to the rest of the paper. How do we know whether “alterations in cortical GABA circuits underlie the abnormal network activity” and increased γ-power in the in vivo LFPs? Furthermore, the authors discuss these data in the context of temporal coding and cognition. The slow waves during urethane anesthesia (1-4Hz) resembles slow wave sleep, which is relevant for memory consolidation. The authors should discuss the new paper by Alemany-Gonzalez et al., 2020 (PNAS 117:11788-11798), which show disturbed slow wave sleep oscillations in Ts65Dn mice. Similar to the present study they find increased γ power in PFC during slow wave sleep (Alemany-Gonzalez, Figure 2B), which could be related to the well-known sleep disturbances in DS patients.

The reviewer raises a series of good points. We cannot directly link the inhibitory circuit-specific alterations that we detect in slices with the increased synchronization of β-γ-activity we see in vivo. However, a large body of literature indicates that oscillations in this frequency range strongly depends on the activity of PV cells (Buzsaki and Wang, 2012; Sohal et al., 2009; Cardin et al., 2009). More recently, also SST interneurons were shown to be involved in low frequency (30 Hz) neocortical γ-oscillations (Veit et al., 2017). Here, we find evidence for over-inhibition in Ts mice in vivo, accompanied by LFP power increases in β-γ-frequency ranges. These observations are consistent with increased interneuron activity (Cardin et al., 2009; Sohal et al., 2009; Atallah et al., 2012). Future studies will be necessary to identify the specific roles played by PV interneurons and MCs in controlling PN spiking in vivo its temporal correlation with network oscillations in Ts mice. These studies will be lengthy, as they will require controlling PV or MC firing using pharmacogenetics. We have better discussed this in the revised manuscript (Discussion section).

We agree with the reviewer that our results are more pertinent with slow-wave sleep and memory consolidation. We have revised our interpretation throughout the manuscript. We have also cited the recent report by Alemany-González et al., 2020, which showed increased low β- and γ-band during sleep and disrupted functional connectivity between the PFC and the hippocampus during learning in Ts65Dn mice.

Reviewer #3:

The authors present a clearly written, technically sound and very interesting study in which they demonstrate substantial changes in the prefrontal cortex inhibitory circuitry of a well-characterized transgenic Down Syndrome model. They show an aberrant dendritic inhibitory circuitry caused by an increased in synaptic transmission from and to Martinotti cells in the medial prefrontal cortex and changes in excitability of perisomatic inhibitory interneurons without apparent changes in synaptic properties. Finally, they demonstrate a strong decrease in principal cell activity on the medial prefrontal cortex of urethane anesthetized Ts mice and increase coupling to γ oscillations.

I find the study interesting and suitable for publication in eLife. It gives a detailed description of the inhibitory circuitry in the Ts mice and provides new in vivo evidence supporting the theory of over-inhibition as a main cause for cognitive deficits in DS. Nevertheless, there are some issues I think should be addressed first.

1) There is a mismatch between the age of animals used for in vitro and in vivo recordings. Down syndrome is a developmental disease, and it could be that interneuron development might take more time than in WT animals. PV positive interneurons are known to develop in the hippocampus during the first 4 postnatal weeks, bringing the question if the abnormalities observed in this study remain during adulthood. I suggest performing some of the main in vitro experiments for parvalbumin and somatostatin interneurons in slices from adult animals.

This is an excellent point that was similarly raised by reviewer #2. Please see the response to his point 2. We have re-analyzed our data, indicating that active and passive properties of PV cells in control animals were similar to those recorded in adults (Okaty et al., 2009 and Kaiser et al., 2016; see Author response table 1). However, we agree with the reviewer that it is important to determine whether the phenotypes observed are transitory or if they last throughout adulthood. We shared this view, and indeed, we had started to record from adult animals (X98 crossed with Ts65Dn and PvalbTdTomato crossed with Ts65Dn), but we were forced to suspend all experiments and sacrifice the mice due to the current pandemic crisis in France. We are in the process of obtaining new colonies specifically for these experiments in >P70 mice. In the following months, we will be able to test the hypothesis of delayed development of PV cells. Results will be exploited in a future study that will include cross-disciplinary approaches. We have discussed this possibility in the revised manuscript.

2) Another concern relates to the final conclusion of the paper, which correlates the in vitro observed abnormalities in inhibition with the decreased activity of principal cells. Although this might be the most rational assumption, I would appreciate evidence to make this hypothesis more likely, considering that DS mice also show changes in spine density, NMDA receptor expression in principal cells and increase threshold for firing as shown in this study. This could be performed by:

– Evaluating the effect of α5IA injection on principal cell activity in Eu and Ts mice.

– Performing recordings from identified interneurons in vivo in Eu and Ts mice.

– Test the effects of specific inhibition of parvalbumin or somatostatin interneurons on the activity of pyramidal cells in Eu and Ts mice.

A more comprehensive discussion addressing these issues might also be sufficient in case experiments are not feasible in a reasonable time frame.

The reviewer raises an excellent point. Please see our response to point 3 of Rev. #2, who raised a similar concern. In particular, we agree with the reviewer that what he/she proposes would be excellent experiments to complement and corroborate our conclusions. However, as detailed above, we are presently in a position that we cannot start even the easiest experiments before six months. In particular, 2-photon assisted recordings from identified interneurons cannot be performed in the mPFC, as it is too deep to allow 2P imaging. We would need to record from identified interneurons using either photo-tagging or GRIN-lens techniques, both of which will take several months to be implemented. Likewise, properly controlled chemogenetic manipulations of specific interneuron populations in vivo will require many months of work and substantial controls. We have therefore discussed more thoroughly the correlation between our in vivo and in vitro experiments in the revised manuscript, highlighting the limitations of our dataset.

3) The concentration of α5IA of 100 nm seems not to provide the highest specificity of the drug, having a milder but significant effect in receptors containing the α3 and α1 subunits (Dawson et al., 2005). Considering the general importance that this strong and very specific effect may have for the community, and the sparse literature using this drug in vitro I would appreciate a better characterization of this particular experiment.

– Does α5IA changes the kinetics of the GABAergic response?

– Are PV-mediated IPSCs affected by this concentration of α5IA?

The reviewer raises a good concern. However, we are confident that we are blocking α5-GABA_ARs selectively. Indeed, in Table 2 of Sternfeld et al., 2004, compound 16 corresponding to α5IA shows -29%, -4% and +4% efficacy at α5, α1 and α3-expressing GABA_ARs respectively. In addition, Figure 3 of Dawson et al. indicates that α5-IA at 100nM has less than 10% effect with recombinant α3-expressing GABA_ARs and around 15% effect for α1-containing GABA_ARs while the effect was above 40% for α5-expressing GABA_ARs. Moreover, PV-mediated IPSCs were not affected by this concentration of α5IA in control mice.

To assess a possible effect of the drug on the kinetics of synaptic responses we quantified rise and decay time constants (taus) in the presence and absence of α5IA in the bath. The drug did not affect uIPSC kinetics in both genotypes. Representative uIPSCs traces and population are shown in Author response image 2, and indicated in the revised manuscript (Results section).

Author response image 2

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– Does the α5IA dependent response shows voltage-dependency as has been reported (Schultz et al., 2019)

We did not test voltage-dependency of the α5IA, as this drug was already characterized at dendritic inhibitory synapses (e.g. Schulz et al., 2018; Ali and Thomson, 2008). The experiments with this drug were not central to the core findings of our manuscript (namely inhibitory circuit-specific alterations in Ts mice). In general, however, voltage-dependency on dendrite-targeting synaptic responses can be problematic due to known space-clamp issues.

Author details

1. Javier Zorrilla de San Martin

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   javier.zorrilla@icm-institute.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2848-7482

2. Cristina Donato

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Present address

   Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg

   Contribution

   Formal analysis, Investigation, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4078-0745

3. Jérémy Peixoto

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Present address

   Institut Pasteur, Paris, France

   Contribution

   Formal analysis, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6814-9747

4. Andrea Aguirre

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Formal analysis, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1176-8747

5. Vikash Choudhary

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Software, Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Angela Michela De Stasi

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

7. Joana Lourenço

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5550-9291

8. Marie-Claude Potier

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   marie-claude.potier@upmc.fr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2462-7150

9. Alberto Bacci

   Institut du Cerveau (ICM), CNRS UMR 7225 – Inserm U1127, Sorbonne Université, Paris, France

   Contribution

   Conceptualization, Resources, Software, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   alberto.bacci@icm-institute.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3355-5892

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