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Shared behavioural impairments in visual perception and place avoidance across different autism models are driven by periaqueductal grey hypoexcitability in Setd5 haploinsufficient mice [1]

['Laura E. Burnett', 'Institute Of Science', 'Technology Austria', 'Klosterneuburg', 'Peter Koppensteiner', 'Olga Symonova', 'Tomás Masson', 'Tomas Vega-Zuniga', 'Ximena Contreras', 'Thomas Rülicke']

Date: 2024-06 

Despite the diverse genetic origins of autism spectrum disorders (ASDs), affected individuals share strikingly similar and correlated behavioural traits that include perceptual and sensory processing challenges. Notably, the severity of these sensory symptoms is often predictive of the expression of other autistic traits. However, the origin of these perceptual deficits remains largely elusive. Here, we show a recurrent impairment in visual threat perception that is similarly impaired in 3 independent mouse models of ASD with different molecular aetiologies. Interestingly, this deficit is associated with reduced avoidance of threatening environments—a nonperceptual trait. Focusing on a common cause of ASDs, the Setd5 gene mutation, we define the molecular mechanism. We show that the perceptual impairment is caused by a potassium channel (Kv1)-mediated hypoexcitability in a subcortical node essential for the initiation of escape responses, the dorsal periaqueductal grey (dPAG). Targeted pharmacological Kv1 blockade rescued both perceptual and place avoidance deficits, causally linking seemingly unrelated trait deficits to the dPAG. Furthermore, we show that different molecular mechanisms converge on similar behavioural phenotypes by demonstrating that the autism models Cul3 and Ptchd1, despite having similar behavioural phenotypes, differ in their functional and molecular alteration. Our findings reveal a link between rapid perception controlled by subcortical pathways and appropriate learned interactions with the environment and define a nondevelopmental source of such deficits in ASD.

Deficits in sensation are seemingly separate phenomena from the more studied cognitive impairments in ASD, believed to arise from cortical malfunctions [ 15 – 17 ]. However, their close association indicates the possibility of a common underlying thread, particularly because their severity is associated with the strength and expression of other ASD traits [ 18 – 20 ]. Such evidence has recently implicated SC pathway impairments with atypical sensory and cognitive processing [ 6 , 14 ]. Given this evidence, we decided to directly explore the relationship between sensation and cognition in ASD within an innate, robust, and reproducible sensorimotor transformation known to be mediated by the SC—the looming escape response (LER) [ 21 , 22 ]. Using Setd5 [ 23 ] as a case study, we show that the LER is impaired, not due to direct sensory or motor deficits. While all animals immediately detect the looming stimulus and can respond vigorously to a threat, they require longer to initiate the escape response than their wild-type siblings and do not form an appropriate aversion to the threat area. These 2 behavioural traits are highly correlated. The stronger the perceptual deficits, the weaker the avoidance. We further show that these behavioural correlations are also present in aetiologically distinct ASD mouse models (Cul3 [ 24 ] and Ptchd1 [ 25 ]), indicating that these various molecular dysfunctions converge on common behavioural deficits. In Setd5, these deficits emerge through changes in the intrinsic excitability due to an increased potassium channel conductance of neurons in the dorsal periaqueductal grey (dPAG), a structure known for commanding escape responses and receiving direct input from the SC [ 26 , 27 ]. Rescuing this hypoexcitability phenotype in the dPAG in adult mice recovers both the perceptual and place avoidance deficits, linking a sensorimotor disorder via a defined molecular mechanism to cognitive dysfunction. Our results show that dPAG dysfunctions can be instrumental in the emergence of symptoms associated with ASDs and that some of the dysfunctions are not developmental, opening a path for their targeted treatment.

Autism spectrum disorders (ASDs) are conditions characterised by challenges with social interactions and repetitive behaviours, reflected in inadequate responses to others’ mental states and emotions [ 1 , 2 ]. These alterations in social cognition co-occur with disordered sensory processing [ 3 ], a widespread yet often overlooked feature across ASD observed in every sensory modality [ 4 ]. In the visual domain, affected individuals frequently exhibit difficulties with visual attention and hyper- or hyposensitivity to visual stimuli [ 5 ], and mounting evidence suggests that the circuits involved in visual information processing are disrupted [ 6 ]. For example, atypical neuronal responses to faces and looming stimuli have been observed in individuals with ASD [ 7 – 9 ]. Such sensory deficits directly affect visually guided behaviours such as gaze control, which emerges at a few months of age [ 10 ] and forms a prominent diagnostic feature [ 11 ]. Changes in gaze dynamics to subliminal stimuli [ 12 ] indicate that neuronal circuits mediating subconscious visual responses are affected, pointing towards subcortical circuits, particularly the superior colliculus (SC) [ 6 , 13 , 14 ].

(A ) Timeline of in vivo α -DTX cannula experiments. ( B ) Confocal micrograph of a coronal section of the location of the cannula above the dPAG as well as the expression of neurobiotin that was infused into the cannula at the end of the experiments. Scale bar: 500 μm. ( C ) Raster plots of mouse speed in response to the looming stimuli for Setd5 +/+ before (top, n = 6, 10 trials) and after (bottom, n = 6, 11 trials) infusion of α -DTX (500 nL, 500 nM) sorted by reaction time. ( D ) As for ( C ) but for Setd5 +/− before (top, n = 6, 15 trials) and after (bottom, n = 6, 13 trials) animals after infusion of α -DTX (500 nL, 500 nM). ( E ) Effect of α -DTX on reaction time. Before α -DTX (Setd5 +/+ versus Setd5 +/− ; P = 0.017), effect of α -DTX on Setd5 +/+ (saline versus α -DTX; p = 0.104) and Setd5 +/− (saline versus α -DTX; p = 0.014) and after α -DTX (Setd5 +/+ versus Setd5 +/− ; P = 0.571). ( F ) Effect of α -DTX on escape vigour. Before α -DTX (Setd5 +/+ versus Setd5 +/− ; P = 0.052), effect of α -DTX on Setd5 +/+ (saline versus α -DTX; p = 0.760) and Setd5 +/− (saline versus α -DTX; p = 0.649) and after α -DTX (Setd5 +/+ versus Setd5 +/− ; P = 0.075). ( G ) Shelter exit behaviour during the first LER trial when the mice are injected with saline (Setd5 +/+ , black, top left; Setd5 +/− , red, bottom left) or α -DTX (Setd5 +/+ , blue, top right; Setd5 +/− , purple, bottom right). Each row represents 1 animal, filled and open dots represent exits crossed into the threat zone or not, respectively. (H ) Effect of α -DTX on shelter exits. Before α -DTX (Setd5 +/+ versus Setd5 +/− ; P = 0.012), effect of α -DTX on Setd5 +/+ (saline versus α -DTX; p = 0.038) and Setd5 +/− (saline versus α -DTX; p = 0.003) and after α -DTX (Setd5 +/+ versus Setd5 +/− ; P = 0.495). Markers represent the average values across all trials for individual animals. P-values: Wilcoxon’s test, p-values: paired t test. The data underlying this figure can be found in S8 Data .

(A ) Volcano plot for differential protein levels between adult Setd5 +/+ and Setd5 +/− mice (n = 6 samples per genotype, cyan dots represent proteins annotated as ion-channels and purple dots represent Kv1.1, Kv1.2, and Kv1.6. Lines and shaded areas, mean ± SEM, respectively. Box-and-whisker plots show the median, IQR, and range. P-values are Wilcoxon’s rank sum test. p-values are two-way repeated measures ANOVA. Volcano plot horizontal dashed line represents the significance threshold (P-value < 0.1) from a two-sided moderated t test, while the vertical dashed lines indicate fold change values greater or lower than 0.4 between Setd5 +/+ and Setd5 +/− . ( B, C ) Tissue-specific western blots of Kv1.1 protein content in the dorsal periaqueductal grey (dPAG) for Setd5 +/+ and Setd5 +/− mice and ( C ) their quantification (dPAG: Setd5 +/+ , 0.259; Setd5 +/− , 0.157, P = 0.090). ( D ) Schematic of SC and PAG regions of interest. ( E, F ) Antibody staining for Kv1.1 in ( E ) Setd5 +/+ and ( F ) Setd5 +/− ( E , 55 μm projection; F , 30 μm projection). Arrowheads indicate somas stained for Kv1.1. Scale bar: D: 200 μm; E : 50 μm; F : 100 μm. P-values are two-tailed Wilcoxon’s signed-rank test. The data underlying this figure can be found in S6 and S7 Data.

( A ) Schematic of the experimental approach, in vitro patch clamp recordings (top) and micrograph of VGluT2 + dmSC projections to the dPAG infected with AAV9-ires-DIO-ChR2 (green) with dPAG biocytin filled and recorded cells (red and arrow heads, scale bar: 100 μm). ( B ) Top, whole-cell voltage clamp traces of example Setd5 +/+ and Setd5 +/− cells (black and red, respectively) responding to 10 Hz light stimulation (blue ticks). Amplitude (middle, p = 0.078) and relative EPSC amplitude (bottom, p = 0.565) of responses to sequential light pulses in a 10-Hz train. ( C ) Intrinsic properties of Setd5 +/+ (n = 6, 11 cells) and Setd5 +/− (n = 7, 14 cells) dPAG cells. Input resistance (P = 0.756, top left), membrane constant tau (P = 0.436, top right), membrane capacitance (P > 0.995, bottom left) and resting membrane potential (P = 0.213, bottom right). ( D ) Summary of the relationship between current injection and action potential firing for putative glutamatergic cells showing a strong reduction in firing (p < 0.001). Inset, representative example traces to a 50-pA current injection. Grey area indicates the current injection values that significantly differ between Setd5 +/+ cells (black) and Setd5 +/− cells (red) found by a multiple comparisons analysis with Tukey correction. ( E ) Average shape and ( F ) phase plane analysis of the action potentials generated in the rheobase sweep (Setd5 +/+ , 13 cells, 42 spikes; Setd5 +/− , 14 cells, 72 spikes). ( G ) Summary of the relationship between current injection and action potential firing for all Setd5 +/+ cells (black, n = 18) and Setd5 +/− cells (red, n = 20) before and after (Setd5 +/+ : blue, n = 12 cells; Setd5 +/− : purple, n = 13 cells) application of α -Dendrotoxin ( α -DTX, 100 nM, p > 0.995 and p = 0.0147 for the effect of α -DTX on Setd5 +/+ and Setd5 +/− firing, respectively). Inset, representative example traces from Setd5 +/+ cells (blue) and Setd5 +/− (purple) cells after α -DTX application to a 50-pA current injection. ( H ) Effect of α -DTX on firing in response to 120 pA current injection. Before α -DTX (Setd5 +/+ versus Setd5 +/− ; p = 0.0017), effect of α -DTX on Setd5 +/+ (before versus after α -DTX; P > 0.995) and Setd5 +/− (before versus after α -DTX; P < 0.001, Setd5 +/+ before versus Setd5 +/− after α -DTX; P > 0.995). Multiple comparison analysis after rm-ANOVA with Tukey correction. ( I ) α -DTX-sensitive current densities in Setd5 +/+ and Setd5 +/− dPAG neurons (p < 0.001). Inset, example of α -DTX-sensitive traces (Setd5 +/+ : black, n = 7 cells; Setd5 +/− : red, n = 6 cells). Grey area indicates the current values that significantly differ between Setd5 +/+ cells (black) and Setd5 +/− cells (red) found by a multiple comparisons analysis with Tukey correction. (J ) Action potential shape and ( K ) phase plane analysis of the action potentials generated in the rheobase current in Setd5 +/+ cells (without α -DTX, black, n = 19; with α -DTX, blue, n = 12), Setd5 +/− cells (without α -DTX, red, n = 21; with α DTX, purple, n = 13). The data underlying this figure can be found in S5 Data .

( A ) Timeline of the experimental protocol for optogenetic activation of the dmSC. ( B ) Video frame during an optogenetics trial. ( C ) Confocal micrograph of AAV-ChR2 expression in dmSC and optic fibre location reconstruction, scale bar: 200 μm. ( D ) Raster plot of mouse speed in response to optogenetic activation, sorted by laser intensity and ( E ) mean speed responses at increasing laser intensities for Setd5 +/+ (n = 4, 312 trials). ( F, G ) As ( D, E ) but for Setd5 +/− mice (n = 4, 291 trials). Blue-shaded areas represent the laser stimulation. ( H ) Subplots of trials in ( D, F ) showing the immediate change in speed upon light activation at different laser intensities for Setd5 +/+ (n = 4, paired t tests) and Setd5 +/− (n = 4, paired Wilcoxon’s tests) optogenetics trials. S at is the mean speed of the animal ±50 ms of laser onset, and S im is the mean speed of the animal 300–800 ms after laser onset. ( I ) Proportion of trials at different laser intensities that either show an increase (white), decrease (black), or no change (grey) in speed upon light activation for Setd5 +/+ (top) and Setd5 +/− (bottom). 0.01 mW mm −2 : 35 trials, p = 0.649; 0.5 mW mm −2 : 111 trials, p = 0.193; 5 mW mm −2 : 50 trials, p < 0.001; 10 mW mm −2 : 49 trials, p < 0.001; 15 mW mm −2 : 61 trials, p = 0.004; 20 mW mm −2 : 49 trials, p < 0.001, X 2 test of independence. ( J ) Schematic of the behavioural divergence between genotypes with increasing stimulus intensity (laser or loom). ( K ) Summary of mean ± SEM of reaction time to the LER paradigm at different stimulus contrasts (p = 0.021 for the interaction between genotype and contrast, Setd5 +/+ , n = 13, 68 trials; Setd5 +/− , n = 13, 59 trials, repeated measures ANOVA. p = 0.018 for 98% contrast, with multiple comparisons and Bonferroni correction. Setd5 +/+ , 24 trials; Setd5 +/− , 22 trials). ( l ) Proportion of trials at different contrast looms that show an increase (white), decrease (black), or no change (grey) in speed upon light activation (20%: p = 0.698; 50%, p = 0.026; 98%, p = 0.021, X 2 test of independence). The data underlying this figure can be found in S4 Data .

(A ) Raster plot of mouse speed in response to the looming stimuli for Ptchd1 Y/+ (upper, n = 9, 49 trials) and Ptchd1 Y/− (lower, n = 9, 259 trials), sorted by reaction time. Bottom, distribution of reaction times for all Ptchd1 Y/+ (black) and Ptchd1 Y/− (orange) trials. ( B ) Left, example trials based on whether the mouse responds within 1 of the 5-loom stimuli, after the fifth (>5), or not at all (NR, no response) from 1 Ptchd1 Y/− mouse. Right, proportion of escape to loom presentations. Trials where mice escaped within the first loom: Ptchd1 Y/+ , 0.826, Ptchd1 Y/− , 0.374, p = 0.004. ( C ) Average reaction time and ( D ) maximum escape speed per animal, for all trials where the mice escaped (reaction time; Ptchd1 Y/+ , 0.411 s, Ptchd1 Y/− , 1.41 s, p = 0.002; maximum escape speed; Ptchd1 Y/+ , 69.1 cm s −1 , Ptchd1 Y/− , 54.2 cm s −1 , p = 0.005). ( E ) Average total looms triggered per genotype across 5 days of testing (Ptchd1 Y/+ , 5.44 looms; Ptchd1 Y/− , 28.8 looms, P = 0.004). ( F ) Average total shelter exits across the 5 test days (Ptchd1 Y/+ , 15; Ptchd1 Y/− , 53, P = 0.025). ( G ) Ethogram of shelter exits during the prestimulus acclimatisation, first and last trial of the LER paradigm. Each row represents one animal, filled and open dots represent exits that crossed into the threat zone or not, respectively. ( H ) Relationship between the number of shelter exits and the average reaction time per animal (Ptchd1 Y/+ , p = 0.126; Ptchd1 Y/− , r = 0.76, p = 0.012, Pearson’s correlation). (I-P ) Same as ( A-H) but for Cul3. ( I) Cul3 +/+ (top, n = 10, 61 trials) and Cul3 +/− (bottom, n = 10, 112 trials). Bottom, distribution of reaction times (p < 0.001, two-way KS test). ( J ) Cul3 +/+ , 0.892, Cul3 +/− , 0.347, p < 0.001, two-way KS test). ( K ) Reaction time; Cul3 +/+ , 0.464 s, Cul3 +/− , 1.11 s, P < 0.001. ( I) Maximum escape speed; Cul3 +/+ , 62.7 cm s −1 , Cul3 +/− , 46.8 cm s −1 , P < 0.001). ( M ) (Cul3 +/+ , 5.60 looms; Cul3 +/− , 12.9 looms, P = 0.023). ( N ) (Cul3 +/+ , 47; Cul3 +/− , 89, P = 0.006). ( P ) (Cul3 +/+ , p = 0.078; Cul3 +/− , p = 0.571, Pearson’s correlation). Box-and-whisker plots show median, IQR, and range. Shaded areas represent SEM. Lines are shaded areas, mean ± SEM, respectively. P-values are Wilcoxon’s test, p-values: two-sample Kolmogorov–Smirnov test, unless specified. The data underlying this figure can be found in S3 Data .

( A ) Left, graphic depicting exits where the mouse enters (dotted line, filled dot) or does not enter (dashed line, open dot) the threat zone. Right, ethogram of exploratory shelter exit behaviour during the prestimulus acclimatisation, the first and last test of the LER paradigm. Each row represents one animal. ( B-D) Adaptation in the number of shelter exits, average number of looms triggered and reaction times across days ( B , Setd5 +/+ : p = 0.2189, Setd5 +/− : p = 0.4974; C , Setd5 +/+ , p = 0.0087; Setd5 +/− , p = 0.2192; D , Setd5 +/+ , p = 0.890; Setd5 +/− , p < 0.001). ( E ) Relationship between the number of shelter exits and the average reaction time per animal (Setd5 +/+ , p = 0.626; Setd5 +/− , p = 0.002). ( F ) As ( E ) but for maximum escape speed per animal (Setd5 +/+ , p = 0.547; Setd5 +/− , p = 0.053). ( G ) Left, reward trial example showing the location of the food reward within the threat zone. Right, example trajectories during the reward trials show the mouse’s position for the 3 s before triggering the loom (light grey) and the 6 s following the stimulus start (black or red). Filled dots represent the position of the mouse when the stimulus was triggered, grey square represents the shelter, and the yellow star shows the position of the food reward. ( H ) Number of looms triggered during the reward trial (Setd5 +/+ , 1 bout; Setd5 +/− , 8 bouts, P = 0.005). ( I ) Ethograms of exits, as in ( A ), during the reward trials show an increased probability of Setd5 +/− mice leaving the shelter during a trial (number of exits, 2.12 for Setd5 +/+ , 6.88 for Setd5 +/− , p = 0.057). ( J ) Reaction time (top panel, Setd5 +/− , 44 trials, r = 0.828, p = 0.0001) and escape vigour (bottom panel, Setd5 +/− , 44 trials, r = 0.184, p = 0.5116) during repeated presentations of the loom. Trials when the animal was interacting with the reward were excluded. P-values: Wilcoxon’s test, p-values: Pearson’s correlation test, unless specified. Plotted linear fits depict the statistically significant correlations. The data underlying this figure can be found in S2 Data .

Although the innate LER does not require learning, repeated loom presentations cause adaptive behavioural changes, for example, the emergence of place avoidance of the threat zone [ 29 ]. Given that Setd5 +/− mice triggered 3 times more looming events and shelter exits than their WT siblings ( Fig 1E and 1F ), we explored if altered adaptation to repeated presentations could account for the observed difference in average reaction time and vigour ( Fig 1G ). For that purpose, we compared the intrinsic behavioural characteristics and the effect of the LER on the exploration strategies and behavioural adaptations across days ( Fig 2A–2D ). Before stimulus exposure, both cohorts had similar exploratory strategies ( S1A–S1G Fig ), indicating that their innate exploration strategies, and, thus, intrinsic levels of anxiety, cannot account for the differences in reaction time and vigour. After stimulus exposure, WT animals showed expected reductions in their exploratory behaviour following the initial exposure to the looming stimulus, making fewer exits than during the prestimulus exploration time ( Fig 2B ), eliciting fewer looms in total ( Fig 2C ) with relatively constant reaction time ( Fig 2D ). Strikingly, Setd5 +/− consistently triggered more looms and had no signs of sensitisation upon repeated exposures ( Fig 2A–2D ). After the initial decrease in exploration following the first stimulus presentation, they showed no further consolidation of place avoidance to the threat zone. The reaction time and vigour of the escape response remained longer and slower across days ( Fig 2D ), respectively, indicating that Setd5 +/− do not sensitise as their WT siblings do to the LER paradigm. We next tested if the delayed perceptual decisions ( Fig 1 ) are related to the consolidation of place avoidance, 2 seemingly independent behaviours. We analysed the total number of shelter exits for each animal and compared them with their average reaction times and vigour ( Fig 2E and 2F ). Surprisingly, the strength of the place avoidance was a strong predictor of the reaction times, but not vigour, suggesting that the timing of the perceptual decision and the formation of place avoidance are intrinsically linked. To assess the limits of response adaptation and to ascertain if the presence of a reward would overcome the fear, we conducted the same behavioural experiment but with the presence of a food reward on the far side of the arena within the threat zone ( Fig 2G–2J ) and with no interstimulus interval restriction. Although WT siblings did make some attempts to retrieve the food reward, they quickly sensitised, rarely leaving the shelter and were unsuccessful in obtaining the food reward. Setd5 +/− , on the other hand, increased their shelter-leaving events, often persisting until reaching the reward ( Fig 2G–2I and S2 Video ). Despite the rapid and repeated exposure, Setd5 +/− mice continued to escape to the shelter upon stimulus presentation, for >10 consecutive presentations ( Fig 2H–2J ). Their reaction times showed a mild adaptation ( Fig 2J , top), while vigour remained largely constant ( Fig 2J , bottom). These results show that 2 behavioural traits, LER and place avoidance, strongly correlate, suggesting they share a common neuronal pathway.

Given that cortical malfunctions have been suggested as the cause of sensory differences in autism [ 15 – 17 ] and that the visual cortex has been shown to modulate the response magnitude of looming sensitive cells in the SC [ 30 ], we tested whether the behavioural responses are affected in cortex and hippocampus-specific conditional Setd5 animals (Setd5 +/fl ; Emx1-Cre, S2 Fig ). These animals showed no behavioural differences in their reaction times and vigour, or response kinetics ( S2C–S2F Fig ), in line with previous studies that show that subcortical pathways [ 29 , 31 ], namely, the SC and PAG, and not altered top-down modulation from cortical areas, are required for this behaviour.

To test whether the delay in initiating an escape to the loom was due to the mutant mice simply detecting the stimulus later than their WT siblings, we examined the immediate change in the speed of the animals from the time of the first loom, selecting only the trials where the mice performed an escape to the shelter at any point during the looming events. For trials where the animals generated an escape within the first loom presentation, both WT and mutant animals increased their speed significantly upon stimulus onset ( Fig 1I ). For trials where the mice did not generate an escape within the first loom, which was the majority of trials for mutant animals but only a small fraction of trials for WT animals ( Fig 1D ), the mice significantly reduced their speed in the time immediately following the stimulus onset ( Fig 1J ). This demonstrates that Setd5 +/− animals detect and respond to the stimulus within a similar time frame as their WT siblings but preferentially perform a locomotor arrest instead of an escape response (see S1 – S3 Videos ). This arrest behaviour is more reminiscent of risk assessment behaviour as previously shown for loom stimuli of different contrast [ 29 ] rather than the defensive freezing response characterised by the sustained cessation of all movement, since the animals were still performing small movements of their head and upper body. Indicating that the animals are using this time to perform ongoing threat evaluation. This suggests that the delay in LER arises from difficulties in either evaluating the threat level of the stimulus or initiating an appropriate response.

(A ) LER paradigm showing the shelter’s location and the threat zone (left) and LER example (right). (B) Paradigm schematic. Day 0 (D0) was used for acclimatisation. D1-D5 consisted of an acclimatisation period (grey) followed by 3 LER tests (red). The looming stimulus consisted of 5 consecutive looms (right). ( C ) Raster plot of mouse speed during LER (white, dotted vertical lines denote the start of each loom; solid white line denotes the end of the stimulus) for Setd5 +/+ (upper, n = 14, 60 trials) and Setd5 +/− (lower, n = 14, 198 trials), sorted by reaction time. Bottom, distribution of reaction times for all Setd5 +/+ (black) and Setd5 +/− (red) trials (p < 0.001, two-sample Kolmogorov–Smirnov test). ( D ) Left, example trials based on whether the mouse responds within 1 of the 5-loom stimuli, after the fifth (>5), or not at all (NR, no response) from one Setd5 +/− mouse. Grey shaded areas represent the frames used to calculate the speed at stimulus onset (S at ) and the immediate response speed (S im ). Right, proportion of escapes to loom presentations. ( E ) Total looms triggered across the 5 test days (Setd5 +/+ , 4.36 looms; Setd5 +/− , 16 looms, P = 0.013). ( F ) Total shelter exits across the 5 test days (Setd5 +/+ , 6.0; Setd5 +/− , 24.5, P = 0.019). ( G ) Average reaction time (left) and maximum escape speed (right) per animal (reaction time, P = 0.001; max. escape speed, P < 0.001). ( H ) As ( G ), but only for the very first loom presentation (reaction time, P = 0.046; max. escape speed, P = 0.166). ( I ) Average immediate speed change following the stimulus presentation for all trials where the mice escape within the first loom presentation (left, Setd5 +/+ , n = 14, black, p < 0.001; right, Setd5 +/− , n = 14, red, p < 0.001). S at is the mean speed of the animal ±50 ms of stimulus onset, and S im is the mean speed of the animal 300–800 ms after stimulus onset. ( J ) As ( I ) but for trials where the mice escape during or after the second loom (left, Setd5 +/+ , n = 3, black, p = 0.007; right, Setd5 +/− , n = 11, red, p < 0.001). ( K ) Proportion of response types per genotype (X 2 = 103.9, p < 0.001, X 2 test of independence). P-values: Wilcoxon’s test, p-values: paired t test, unless specified. The data underlying this figure can be found in S1 Data .

Discussion

ASDs are widespread in the global population, but our understanding of the disorder’s origins remains limited. Advancements in genetic sequencing technologies have enabled the isolation of autism-risk genes, opening the door to several in-depth studies into the molecular mechanism of action of these genes [36]. However, there is a considerable mismatch between the observed wealth of molecular changes and our understanding of their roles in changing circuit function and, correspondingly, behaviour. The latter is particularly relevant since ASD is diagnosed by a combination of behavioural traits known as diagnostic criteria [2]. Although several behavioural paradigms are being studied across mouse models [37], the variability and experimental intricacies of behavioural studies make direct comparisons across models difficult. This has led to studies focusing on single models, defining particular changes but not general principles [38]. Furthermore, these studies have primarily centred on the intricacies of social interactions, communication, and repetitive behaviours—complex traits that present a formidable challenge in establishing a clear connection between neuronal dysfunction and behavioural manifestations. Here, we show that the study of innate defensive behaviours, a perceptual task, provides a convergent behavioural framework that enables the directed analysis of the underlying sensorimotor processes up to the molecular level, permitting comparative dissections of the neuronal dysfunctions across ASD models and a systematic understanding of the neuronal mechanisms underlying the co-occurrence of behavioural traits. This is particularly interesting as the severity of sensory and perceptual impairments has been strongly linked to the strength and expression of traditional, nonsensory ASD traits [18–20].

Defensive escape behaviours are among the most fundamental perceptual decisions performed by animals [22]. Their finely-tuned mechanisms are indispensable for survival, but also for properly interacting with the environment. Whereas some stimuli unambiguously signal an imminent threat and should instruct immediate action, others are ambiguous and require adaptive responses arbitrated by the current context and state to decide, e.g., between ignoring, freezing, fighting, or escaping [22]. In mice, behavioural escape decisions are known to be initiated by the dPAG [29], where appropriate escape decisions are thought to be determined. While all tested ASD models can, in principle, respond behaviourally as robustly as their WT siblings, they require longer and respond with less vigour once an action has been initiated, hampering their ability to develop an appropriate place avoidance to the threat zone (Figs 1–3). The underlying changes were rigorously dissected in the Setd5 haploinsufficient model, pointing to a specific misregulation of voltage-gated potassium channels in dPAG neurons that gives rise to a strong hypoexcitability phenotype (Figs 3–5), namely, Kv1.1, Kv1.2, and Kv1.6. Interestingly, it is not the level of expression of the channel that appears to be important, as no difference was found between Setd5+/− and Setd5+/+ at the protein level (Fig 6). This indicates that Kv1 channels may be generally inactive in WT animals, as evidenced by the lack of increase in excitability in Setd5+/+ animals during ɑ-DTX application, and suggests that changing the conductance of Kv channels may be a mechanism for behavioural adaptation to threatening stimuli, such as imminent suppression of escape [39]. Targeted pharmacological rescue of this hypoexcitability in vivo completely reverses the behavioural phenotype (Fig 7). This is an important finding since other brain areas are also known to be required for proper defensive responses, in particular, freezing, such as the pathway to the basolateral amygdala (BLA) via the lateral posterior thalamus (LP) [9,40]. Our results indicate a specific involvement of the dPAG and not the BLA via LP pathway.

In addition to the delayed LER, we observed a strong reduction in place avoidance to the threat zone that elicited maladapted repetitive behaviour (Fig 2 and S2 Video). This suggests that ASD mouse models either have deficits in fear memory formation or are intrinsically less fearful, thus incapable of appropriately interpreting the noxiousness of the threatening experience (Figs 1 and 2 and S1 and S2 Videos)—maladaptive behaviours that recapitulate some human ASD traits [41]. Interestingly, this reduction of place avoidance is directly linked with longer reaction times to the LER across ASD models (Figs 2E, 3H and 3P), and, thus, with the dPAG hypoexcitability phenotype (Fig 5D). We show that this correlation is causally linked, given that the target-specific pharmacological rescue reverses both, the delayed LER and place avoidance to WT levels (Fig 7 and S3 Video). Thus, maladaptive perceptual decisions, as with the delayed LER, can profoundly affect seemingly independent behavioural traits, such as LER, threat-induced place avoidance and the emergence of repetitive behaviours (S1 and S2 Videos). This is in line with studies in humans. Threat imminence has been shown to elicit PAG activation [42] and the electrical stimulation of midbrain structures elicited strong emotional reactions [43]. In rodents, the dPAG has been shown to support fear learning [44], particularly in contextual conditioning paradigms [45]. Overall, our results emphasise the role of subcortical pathways through the PAG in the altered perceptual abilities frequently described in ASD, and fear memory formation in general.

The dPAG hypoexcitability phenotype indicates that the integration and action initiation required for adequate perceptual decision-making is disrupted in Setd5 animals. This physiological phenotype is tightly linked with behavioural impairments. Substantial threat evidence, either by strong optogenetic activation of dmSC neurons or high-contrast visual looms, elicit a slower and less robust response in Setd5+/− compared to their WT siblings. On the other hand, limited threat evidence, either by weak optogenetic activation or low-contrast looms, instructs similar behavioural responses between genotypes (Fig 4). Accordingly, only strong current injections that match the expected dPAG drive caused by high-contrast loom [29] cause a Kv-channel induced hypoexcitability phenotype (Fig 5), indicating that the delayed response phenotype is due to a dPAG dysfunction. This stimulus strength relationship aligns with the coping difficulties of many individuals with ASD to salient sensory stimuli, such as bright lights or crowded places, but not to mellow sensory environments [6]. Recently, changes in dPAG excitability and the expression of defensive behaviours have also been identified in the Nlgn3+/− rat model of autism [42]. Notably, these rats exhibit the inverse behavioural and physiological phenotype to that observed in Setd5+/− mice, displaying stronger responses auditory fear conditioning, prolonged place avoidance and hyperexcitability in dPAG cells. These complementary results reflect the wide range of sensory sensitivities observed in the human population with ASD and reinforce the PAG as an important area to investigate disrupted sensory processing and its far-reaching effects in the core symptomatology associated with autism.

Given that the expression levels of Kv channels remain unaltered (Figs 5K and S8), the causes of the physiological hypoexcitability phenotype probably arise through homeostatic misregulation, either by altered cellular or subcellular channel localisation [33], posttranslational modifications [46], or interactions with auxiliary subunits [47]. Through these mechanisms, neuronal excitability could be modified by changing the effective number of Kv channels present in the membrane, or by modulating the gating or conductance of the channels, without changing the overall expression of the channels. Further experiments are required to determine the validity of these hypotheses. The link between potassium channels and autism is well established [48]. Genetic analyses of individuals with ASD uncovered deleterious mutations in potassium channels [48]. Notably, these mutations do not include Kv1.1. Nevertheless, autistic-like repetitive and social behaviours in the Scn2a haploinsufficiency mouse model have been rescued in a Kv1.1-deficient background [49], supporting our results that enhancing and not disrupting Kv1.1’s contribution can be a fundamental factor in ASD.

Sensory processing and perceptual abnormalities have been shown to co-occur with classical ASD diagnostic criteria and have been recently proposed as promising behavioural biomarkers of autism [4]. Here, we show that the innate LER provides a reliable and quantitative framework to systematically link behavioural traits with the underlying molecular changes. This is not a trivial task, as several possible changes could lead to delays in LER. The simplest explanation for these behavioural impairments is abnormalities in early visual processing. For example, if salient, high-contrast loom stimuli are relayed as low-contrast looms to dPAG neurons, these would be weakly activated, leading to delayed LERs [29]. Other mechanisms that could lead to weaker dPAG activation include changes in synaptic transmission, e.g., from dmSC to dPAG, imbalance in the excitatory–inhibitory ratio of the circuits involved [50], abnormal development of brain connectivity [51], or reduced excitability in dPAG neurons, among many others. Therefore, the exact molecular changes are likely to be model-dependent. We show that in all models tested, early visual processing (S4 and S5 Figs) and most intrinsic properties (S7 and S9 Figs) remain unaffected. The main difference observed is the dPAG hypoexcitability phenotype, which we were able to causally link to LER in the Setd5 model (Fig 5). In addition, we were able to correlate a similar hypoexcitability phenotype in the dPAG with LER in Ptchd1 animals, albeit through a different molecular mechanism. This already shows that different molecular perturbations lead to similar behavioural phenotypes, as further demonstrated by the weaker LER deficits in Cul3 animals that are independent of the intrinsic properties of the dPAG (S9 Fig). This level of dissection can lead to important insights that may reveal therapeutic targets, as envisioned by precision medicine approaches [36]. Specifically, our study shows that in the Setd5 haploinsufficient model, these behaviours are not necessarily developmental, as they can be pharmacologically ameliorated in adulthood.

In summary, our work links innate LER dysfunctions across diverse genetic mouse models of ASDs. Specifically, in the Setd5 haploinsufficient model, we found that a key behavioural node, the PAG [52], functions as an interface between sensory, limbic, and motor circuits that, when disrupted, causally affects seemingly unrelated behaviours. This appears to be related to observations in people with ASD, where the severity of sensory processing impairments is related to the severity of core symptoms associated with autism [18–20]. Future studies designed to dissect the causal relationships between innate sensorimotor deficits, such as LER, and core ASD behavioural symptoms, such as social and communication difficulties, will be a revealing avenue to build a comprehensive view of ASD.

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[1] Url: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3002668

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