The first question we asked here was whether CCK is involved in trace fear memory formation. We studied transgenic Cck^-/- mice (Cck-CreER, strain #012710, Jackson Laboratory), which lack CCK expression (Chen et al., 2019). We subjected Cck^-/- and wildtype (WT) control (C57BL/6) mice to trace fear conditioning using two training protocols: long-trace interval and short-trace interval training.

We performed the trace fear conditioning experiment by collecting baseline readouts on pre-conditioning day, training with the appropriate CS-US pairings on conditioning days, and testing the conditioned fear responses on post-conditioning/testing day. In the long trace protocol, mice sequentially received a 10 s pure tone (as the CS), a 20 s gap (trace interval), and a 0.5 s foot shock (as the US) (Figure 1a). We calculated the percentage of time frames where mice displayed a freezing response as the measure of fear memory. Freezing percentages were compared before (baseline) and after (post-training) trace fear conditioning as well as before (Figure 1b) and after (Figure 1c) presentation of the CS. The after-CS freezing percentage was calculated within the time window that includes the duration of CS (10 s) and the trace interval (20 s). For before-CS freezing percentage, we selected the time window with same length (30 s) just before the presentation of CS. At baseline, Cck^-/- (N = 10/2 cages) and WT (N = 14/3 cages) mice showed similarly low freezing percentages both before (Figure 1b) and after (Figure 1c) the CS (Figure 1b, two-way repeated-measures analysis of variance [RM ANOVA], significant interaction, F[1,22] = 10.85, p = 0.003 < 0.01; pairwise comparison, WT vs. Cck^-/- before CS, 7.0% ± 1.0% vs. 5.9% ± 1.1%; 95% confidence interval [CI], [5.0%, 9.0%] vs. [3.6%, 8.3%]; Bonferroni test, p = 0.482 > 0.05; Figure 1c, two-way RM ANOVA, significant interaction, F[1,22] = 8.94, p = 0.007 < 0.01; pairwise comparison, WT vs. Cck^-/- after CS, 9.9% ± 1.5% vs. 9.6% ± 1.8%; 95% CI, [6.8–13.0%] vs. [5.9–13.3%]; Bonferroni test, p = 0.911 > 0.05). After conditioning, Cck^-/- mice showed significantly lower freezing percentages (39.3% ± 5.3%, 95% CI, [28.3%, 50.2%]) than WT mice after receiving the CS (61.6% ± 4.5%, 95% CI, [52.4%, 70.9%]; pairwise comparison, p = 0.004 < 0.01), indicating poor performance in associating the CS with the US (Figure 1c, Videos 1 and 2). This effect was not due to elevated basal freezing levels caused by training in WT animals. Instead, we found that Cck^-/- mice (23.0% ± 2.1%, 95% CI, [18.6%, 27.4%]) had higher freezing percentages than WT mice (14.0% ± 1.8%, 95% CI, [10.3%, 17.7%]) in the absence of the CS (Figure 1b, pairwise comparison, p = 0.003 < 0.01). Together, these results suggest that trace fear conditioning results in elevated conditioned freezing percentages in WT mice, which are primarily elicited by the CS, and that loss of CCK impairs the freezing response to the CS. Furthermore, we defined an empirical threshold of moving velocity and converted the moving velocity to a binary freezing score plot, in which value 1 represents active state, and value 0 represents freezing state (see Materials and methods). Using this method, we were able to assess the freezing response of the animal as it occurred during the CS presentation. Again, we found that WT mice obtained higher average freezing scores than Cck^-/- mice during and after the presentation of the CS (Figure 1d, two-way RM ANOVA with a Greenhouse-Geisser correction, interaction significant, F(8.214, 180.716) = 2.149, p = 0.032 < 0.05; post hoc Bonferroni multiple pairwise comparisons between two groups in each bin, *p = 0.00015, 0.00036, 0.031, 0.015, 0.022 < 0.05 at time point 6–8, 8–10, 20–22, 26–28, and 28–30 s referred to the onset of CS, respectively).

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Summary of freezing percentage in long and short trace fear conditioning.

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In addition to the long-trace interval, we also investigated freezing responses of mice during a short-trace fear conditioning paradigm. Mice were presented a 3 s CS followed by a 2 s trace interval and a 0.5 s electrical foot shock (Figure 1e). Same as above, freezing percentage in the after-CS period was calculated from the time window that includes duration of the CS (3 s) and the trace interval (2 s), and before-CS freezing percentage was from a 5-s-long time window right before the presentation of the CS. Before training, WT (N = 11/3 cages) and Cck^-/- (N = 14/4 cages) mice showed similarly low freezing percentages both before (Figure 1f) and after (Figure 1g) presentation of the CS (Figure 1g, two-way RM ANOVA, significant interaction, F[1,23] = 5.18, p = 0.032 < 0.05; pairwise comparison, WT vs. Cck^-/- in the baseline session, 20.4% ± 3.3% vs. 13.9% ± 2.9%; 95% CI, [13.7%, 27.2%] vs. [8.0%, 19.9%]; p = 0.150 > 0.05; Figure 1f, two-way RM ANOVA, interaction not significant, F[1,23] = 1.99, p = 0.17 > 0.05; pairwise comparison, WT vs. Cck^-/- in the baseline session, 10.3% ± 1.8% vs. 8.2% ± 1.6%; 95% CI, [6.5%, 14.1%] vs. [4.8%, 11.6%]; p = 0.402 > 0.05). Consistent with results from the long-trace paradigm, Cck^-/- mice showed an impaired freezing response (41.0% ± 5.1%) to the CS after training compared to WT mice (66.3% ± 5.2%; 95% CI, [54.3%, 78.3%]; pairwise comparison, p = 0.003 < 0.01, Figure 1g, Videos 3–4). Additionally, we observed no significant difference between fear conditioned WT and Cck^-/- mice prior to the presentation of the CS (Figure 1f, pairwise comparison, WT vs. Cck^-/- in the post-training session, 12.4% ± 2.3% vs. 16.0% ± 2.0%; 95% CI, [7.7%, 17.2%] vs. [11.8%, 20.2%]; p = 0.253 > 0.05). Finally, we found significant differences in freezing scores between WT and Cck^-/- mice when presented the CS (Figure 1h, two-way RM ANOVA with a Greenhouse-Geisser correction, interaction significant, F(8.093, 186.145) = 2.499, p = 0.013 < 0.05; post hoc Bonferroni multiple comparisons in each bin, *p = 0.034, 0.001 < 0.05 at time point 5–6 and 8–9 s referred to the onset of the CS, respectively).

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We conducted the innate hearing and fear expression examinations to rule out a potential inherent deficit derived from genome editing in Cck^-/- transgenic mice. To evaluate hearing, we recorded the open-field auditory brainstem response (ABR) in anesthetized animals. We observed five peaks in both WT and Cck^-/- mice at sound intensities above 50 dB of sound pressure level (dB SPL) (Figure 1—figure supplement 1b), and we did not observe any remarkable differences between the waveforms. Compared to WT mice, Cck^-/- mice had better hearing (40.0 ± 1.2 dB in Cck^-/- mice, N = 15/3 cages, vs. 47.3 ± 2.1 dB in WT mice, N = 11/3 cages, two-sample t-test, t(24) = 3.238, p = 0.003 < 0.01, Figure 1—figure supplement 1c). Thus, auditory perception does not account for the deficient trace fear memory formation of Cck^-/- mice.

Fear expression is the behavioral output of fear conditioning. We wondered if Cck^-/- mice suffered from a deficit in fear expression, which is observed in Klüver-Bucy syndrome and other diseases (Lilly et al., 1983). To test whether the Cck^-/- mice have a deficit in fear expression, we presented a loud (90 dB SPL) white noise and quantified sound-driven innate freezing. We found no statistical difference between WT (46.1% ± 5.5%, N = 11/3 cages) and Cck^-/- mice (46.5% ± 6.6%, N = 14/3 cages, two-sample t-test, t(23) = 0.046, p = 0.964 > 0.05, Figure 1—figure supplement 1d), indicating that Cck^-/- mice can express passive defensive behaviors such as freezing. Thus, the deficiency in trace fear memory formation of Cck^-/- is not due to a deficit in fear expression. It may be due to a deficit in establishing an association between the CS and the US.

In summary, Cck^-/- mice display deficient trace fear memory formations in both short- and long-trace models that are not caused by inherent hearing or fear expression abnormalities.

As neural plasticity in the LA is widely regarded as the basis of fear memory formation (Rogan et al., 1997; LeDoux, 2000; Nabavi et al., 2014; Kim and Cho, 2017), we examined LTP in the LA of WT and Cck^-/- mice by in vivo recording (Figure 2a). First, we successfully recorded the auditory evoked potential (AEP) in the LA of anesthetized WT and Cck^-/- mice (Figure 2b–e). Then, we used theta-burst electrical stimulation to induce LTP of AEP (AEP-LTP) (Figure 2f). Interestingly, AEP-LTP was effectively induced in WT mice (N = 15/6 cages) but was not in Cck^-/- mice (N = 12/4 cages). WT mice demonstrated remarkable potentiation (Figure 2g, two-way RM ANOVA, significant interaction, F[1,25] = 6.775, p = 0.015 < 0.05; pairwise comparison, after vs. before induction, 142.7% ± 12.6% vs. 99.1% ± 3.4%, p = 0.003 < 0.01), whereas Cck^-/- mice showed no potentiation (pairwise comparison, after vs. before induction, 98.0% ± 11.3% vs. 100.6% ± 3.0%, p = 0.824 > 0.05). These results suggest that Cck^-/- mice have a deficit in neural plasticity in the LA that may contribute to their reduced response to trace fear conditioning.

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AEP-LTP induction in WT and CCK-KO mice.

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Although the translation and release of CCK are disrupted in Cck^-/- mice, we found that the predominant CCK receptor, CCKBR, was expressed normally in both WT and Cck^-/- mice (Figure 2h). Therefore, we hypothesized that exogenous stimulation of CCKBR might rescue trace fear memory deficits in Cck^-/- mice. CCKBR can be stimulated by several agonists, including CCK octapeptide sulfated (CCK-8s) and CCK tetrapeptide (CCK-4). As CCK-8s is a potent agonist of both CCKAR and CCKBR, we selected CCK-4, which is a preferred CCKBR agonist (Berna et al., 2007). To monitor CCK signaling in vivo, we expressed a G protein-coupled receptor (GPCR) activation-based CCK sensor (GRAB_CCK, AAV-hSyn-CCK2.0) in the LA of WT mice (Jing et al., 2019). Using this model, binding of the GPCR CCKBR with endogenous or exogenous CCK results in increased fluorescence intensity, which we measured by fiber photometry in the LA (Figure 2—figure supplement 1a). We first confirmed that intraperitoneal (i.p.) administration of CCK-4 penetrated the blood-brain barrier (BBB) and activated the CCK2.0 sensor. Moreover, we demonstrated that the administration of CCK-4 evoked an apparent and long-term increase in the fluorescent signal (Figure 2—figure supplement 1b-c). Together, these data verify that CCK-4 passes through the BBB and binds with CCKBR in the LA.

After validating our model, we conducted short-trace fear conditioning in Cck^-/- mice on 2 consecutive days just after intraperitoneal administration of CCK-4 or the corresponding vehicle (VEH) (Figure 2i–j). We collected data during the 2 conditioning days to monitor the learning curve of mice as conditioning progressed. The learning curves were plotted as the freezing percentages of CCK-4- or VEH-treated Cck^-/- mice during the six training trials (Figure 2k–l). We did not observe any statistical differences between the two groups during the first three trials on the first conditioning day and even in the fourth trial on the second conditioning day. We found that CCK-4-treated mice had significantly higher freezing levels than VEH-treated mice during the fifth training trials conducted on the second conditioning day (Figure 2l, two-way RM ANOVA, interaction significant, F[5, 65] = 3.45, p = 0.008 < 0.01; Bonferroni pairwise comparison, 84.2% ± 8.4% in the CCK-4 group [N = 7/2 cages] vs. 48.4% ± 11.5% in the VEH group [N = 8/2 cages] in the fifth trial, p = 0.029 < 0.05; 84.4% ± 7.3% in the CCK-4 group vs. 52.9% ± 13.0% in the VEH group in the sixth trial, p = 0.064). In support of this evidence, we did not find a statistical difference between the two groups prior to CS presentation during the fifth or sixth trials (Figure 2k, two-way RM ANOVA, F[5, 65] = 0.696, p = 0.628 > 0.05; Bonferroni pairwise comparison, 53.8% ± 11.5% in the CCK-4 group vs. 52.5% ± 11.8% in the VEH group in the fifth trial, p = 0.938 > 0.05; 56.0% ± 10.8% in the CCK-4 group vs. 47.8% ± 11.8% in the VEH group in the sixth trial, p = 0.622 > 0.05). Together, these data suggest that mice in the CCK-4- and VEH-treated groups showed similar baseline freezing levels and that CCK-4 treatment improved trace fear conditioning learning responses in Cck^-/- mice.

We examined the conditioned fear response in CCK-4- and VEH-treated Cck^-/- mice 2 days after training compared to fear responses at baseline before training (Figure 2m–n). We found that CCK-4-treated mice showed remarkably higher freezing levels than VEH-treated mice post-training, whereas no significant difference was detected at baseline (Figure 2n, two-way RM ANOVA, significant interaction, F[1,11] = 6.40, p = 0.028 < 0.05; pairwise comparison, CCK-4 vs. VEH at baseline, 10.4% ± 2.2% vs. 7.0% ± 2.0%; 95% CI, [5.6%, 15.2%] vs. [2.5%, 11.5%]; p = 0.278 > 0.05; CCK-4 vs. VEH post-training, 54.3% ± 4.8% vs. 30.4% ± 4.4%; 95% CI, [43.8%, 64.8%] vs. [20.6%, 40.1%]; p = 0.004 < 0.05; Videos 5–6). There was no statistical difference between the two groups before the presentation of the CS (Figure 2m, two-way RM ANOVA, interaction not significant, F[1, 11] = 0.174, p = 0.684 > 0.05; the main effect of drug application [CCK-4 vs. VEH] on freezing percentage was not significant, F[1,11] = 0.15, p = 0.706 > 0.05). Additionally, CCK-4-treated mice had significantly higher freezing scores than VEH-treated mice (Figure 2o, two-way RM ANOVA with a Huynh-Feldt correction, interaction significant, F(17.22, 189.417) = 1.932, p = 0.017 < 0.05; post hoc Bonferroni multiple comparisons, *p = 0.025, 0.014, 0.005 < 0.05 at time point 0–1, 2–3, 9–10 s referred to the onset of the CS, respectively). These results indicate that CCK-4 treatment effectively improved learning response to trace fear conditioning in Cck^-/- mice. Moreover, this rescue was not an artifact caused by reduced locomotion after drug application and fear conditioning training, as there was no difference between the two groups in the freezing percentage prior to presentation of the CS (Figure 2m). Therefore, the exogenous application of a CCKBR agonist activated endogenous CCKBR and improved the fear memory formation of Cck^-/- mice after trace fear conditioning.

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We next examined the source of endogenous CCK that signals to the LA. We injected a potent retrograde neuronal tracer cholera toxin subunit B (CTB) conjugated to a fluorescent tag Alexa-647 (CTB-647) into the LA and dissected the upstream anatomical brain regions that project to the LA (Figure 3a). In addition to regions that are canonically involved in fear circuitry, including the AC and the medial geniculate body (MGB), we found that EC was also densely labeled with retrograde CTB-647, suggesting that the EC is connected with the LA (Figure 3b–e). We next injected a Cre-dependent retrograde AAV (retroAAV-hSyn-FLEX-jGcamp7s) into the LA of CCK-ires-Cre (CCK-Cre) mice to label CCK-positive neurons that project into the LA, further to confirm the above observation (Figure 3f–g). In the CCK-ires-Cre mouse line, Cre expression was restricted to the CCK-expressing neurons, where the Cre-mediated recombination took place and the Cre-dependent green fluorescent protein jGcamp7s was expressed (Figure 3f). Fluorescent signal was detected in the AC and the EC, but not in the MGB (Figure 3h–j), which suggests that CCK may originate from these two brain regions during trace fear memory formation. Immunofluorescent staining revealed that most CCK-positive neurons in the EC that project to the LA are glutamatergic (Figure 3k–l), which is consistent with our previous findings in CCK-positive neurons in the EC (Chen et al., 2019).

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Summary of colocalization between Retro-Gcamp and CamKIIα or GAD67.

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Considering the potential tropism of retroAAV that may cause the absence of AAV expression in the MGB, we injected a Cre-expressing retroAAV (retroAAV-hSyn-Cre) into the LA of the Cre-dependent tdTomato reporter Ai14 mice (N = 3/1 cage). Besides the AC and EC, we also found the tdTomato-positive neurons in the MGB suggesting retroAAV does not have the tropism to avoid expression in the MGB (Figure 3—figure supplement 1). However, based on our ongoing studies, we cannot exclude the possible scenario that MGB may originate some CCK-positive projection to LA during some stages of development.

Interestingly, the EC is involved in the formation of trace fear memory but is not a component of canonical delay fear memory (Esclassan et al., 2009). This selectivity suggests that the EC may be a component of the neural circuit underlying trace fear memory formation. To evaluate a requirement for the EC in trace fear memory, we utilized a designer receptors exclusively activated by designer drugs (DREADD) system to silence EC neurons (Armbruster et al., 2007). Specifically, the inhibitory receptor hM4Di was expressed in the EC of WT mice (Figure 4a) and was activated by administrating the designer drug clozapine (CLZ). Activation of hM4Di by CLZ induces membrane hyperpolarization, effectively silencing neurons. We verified EC neuron silencing by in vivo electrophysiological recording (Figure 4b–d and Figure 4—figure supplement 1). We found that a low dose of CLZ (0.5 mg/kg) effectively suppressed both instant and long-term neuronal firing. Of note, we used CLZ instead of the canonical DREADD ligand clozapine-N-oxide (CNO). A recent study identified CLZ as the active metabolite of CNO (Gomez et al., 2017), and CLZ more effectively penetrates the BBB and binds with DREADD receptors compared to CNO. As a result, a much lower dose of CLZ can elicit similar behavioral effects as higher doses of CNO (Gomez et al., 2017). Therefore, we used a low dose of CLZ (0.5 mg/kg) in our experiments.

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Summary of freezing percentage in mice with chemogenetic inhibition of the EC and EC-CCK neurons.

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Six weeks after injection of AAV9-hSyn-hM4Di-EGFP or AAV9-hSyn-EGFP, we administered CLZ by intraperitoneal injection and conducted short-trace fear conditioning 30 min later. We repeated the CLZ treatment and trace fear conditioning the following day and tested conditioned fear responses 2 days after that. As expected, mice expressing hM4Di (hM4Di, N = 7/2 cages) showed significantly lower freezing percentages in response to the CS than those expressing the control virus (EGFP, N = 7/2 cages) post-training (Figure 4f, two-way RM ANOVA, significant interaction, F[1,12] = 6.58, p = 0.025 < 0.05; EGFP vs. hM4Di post-training, 68.1% ± 8.1% vs. 38.9% ± 8.1%, p = 0.026 < 0.05; Videos 7–8). No significant differences were observed between the two groups at baseline (Figure 4f, pairwise comparison, EGFP vs. hM4Di at baseline, 12.0% ± 3.2% vs. 15.0% ± 3.2%, p = 0.530 > 0.05) or prior to the CS (Figure 4e, two-way RM ANOVA, interaction not significant, F[1, 12] = 0.029, p = 0.869 > 0.05; pairwise comparison, EGFP vs. hM4Di post-training, 16.0% ± 4.3% vs. 16.4% ± 4.3%, p = 0.952 > 0.05).

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As we have shown that CCK-positive neural projections extend from the EC to the LA, we transfected CCK-expressing neurons in the EC with a Cre-dependent hM4Di in CCK-Cre mice (Figure 4h–j). These mice received an i.p. injection of CLZ (N = 10/3 cages) or VEH (N = 10/3 cages) prior to long-trace fear conditioning. After training, mice injected with CLZ showed significantly lower freezing percentages than those injected with the VEH, whereas no statistical differences were observed at baseline or prior to the CS (Figure 4l, two-way RM ANOVA, significant interaction, F[1,18] = 5.904, p = 0.026 < 0.05; pairwise comparison, CLZ vs. VEH at baseline, 12.9% ± 2.0% vs. 14.2% ± 2.0%, p = 0.644 > 0.05; CLZ vs. VEH post-training, 48.4% ± 5.8% vs. 27.1% ± 5.8%, p = 0.019 < 0.05; Figure 4k, two-way RM ANOVA, interaction not significant, F[1, 18] = 0.043, p = 0.838 > 0.05; pairwise comparison, CLZ vs. VEH at baseline, 10.2 % ± 1.9 vs. 9.4% ± 1.9%, p = 0.784 > 0.05; CLZ vs. VEH post-training, 18.0% ± 3.3% vs. 18.3% ± 3.3%, p = 0.949 > 0.05; Videos 9–10). These results mirror those observed in Cck^-/- mice and suggest that trace fear memory formation relies on intact and functional CCK-positive neurons in the EC.

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To further demonstrate that afferents to the amygdala originate from CCK-expressing neurons in the EC, we locally injected a Cre-dependent color-switching virus (AAV-CAG-DO-mCherry-DIO-EGFP) in the EC of CCK-Cre mice (N = 2; Figure 5a–b). With this combination, CCK-positive neurons express EGFP, and CCK-negative neurons express mCherry (Saunders et al., 2012). We found that EGFP+ (i.e., CCK+) neurons made up a slightly higher proportion of labeled neurons than mCherry+ (i.e., CCK–) neurons (Figure 5c–d, EGFP vs. mCherry, 58.9% ± 4.8% vs. 38.6% ± 5.0%, one-way RM ANOVA, Wilks’ lambda = 0.58, F[1,6] = 4.34, p = 0.0822 > 0.05). Interestingly, we found that CCK + neural projections from the EC to the LA were densely labeled with EGFP, whereas mCherry labeling of CCK– projections was dramatically weaker. Quantitative analysis revealed that the projection intensity of the EC^CCK+ was threefold higher than the EC^CCK− (35.6% ± 9.5%). In other words, CCK-positive afferents constituted approximately 75% of total afferents from the EC to the LA (Figure 5e–f).

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Quantification of viral expression and projection strength.

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To determine if the fluorescent reporter proteins interfered with projection strength, we inverted the color combination by combining two AAVs: AAV-hSyn-DIO-mCherry and AAV-EF1α-FAS-EGFP (Saunders et al., 2012). These Cre-dependent AAVs were injected into the EC of CCK-Cre mice. In CCK-Cre mice, AAV-hSyn-DIO-mCherry induces Cre-ON mCherry expression in CCK+ neurons, and AAV-EF1α-FAS-EGFP induces Cre-OFF EGFP expression in CCK– neurons (Figure 5g–h). With the mixed AAVs, we labeled approximately 50% CCK– EGFP+ neurons, 41% CCK+ mCherry + neurons, and 8.9% double-positive neurons (Figure 5i–j). The higher percentage of double-positive neurons present in this system indicates a higher probability of off-target effects compared to the previous color-switching AAV (8.9% ± 2.7% vs. 2.5% ± 1.1%). Consistent with the previous color-switching AAV, we observed that CCK+ (mCherry+) projections were predominant. Specifically, the intensity of the EC^CCK+ was approximately fourfold higher than the EC^CCK− (24.0% ± 5.6%). Altogether, our results suggest that the EC^CCK+ is the predominant subpopulation of projections, and that these projections are of functional significance in the EC-LA pathway.

Furthermore, we asked whether CCK-positive projections from the EC modulate neural plasticity in the LA. First, we expressed a Cre-dependent high-frequency-responsive channelrhodopsin (ChR2) variant E123T (ChETA) under control of the universal EF1α promoter in CCK-Cre mice (Figure 6a). Then, we inserted optic fibers targeting the LA to illuminate EC^CCK+ projections and electrodes to conduct in vivo electrophysiological recording as before (Figure 6b). Post hoc anatomical analysis confirmed the distribution of ChETA in the EC-LA axon terminals (Figure 6c). Terminals of these CCK-positive projections were colocalized with CCKBR in the LA (Figure 6d), implying that CCK-positive projections from the EC may innervate with CCKBR in the LA. Finally, we recorded AEP and visual evoked potential (VEP) in the LA of anesthetized mice (Figure 6e–g). Although AEP and VEP had similar waveforms, the latency of AEP was much shorter than VEP (Figure 6e–f, peak latency: 38.9 ± 3.2 ms for AEP, N = 13, vs. 89.5 ± 3.1 ms for VEP, N = 11, two-sample t-test, t (22) = 11.376, p = 1.1E-10 < 0.001). This observation implies that the auditory and visual signal transmission pathway to the LA has different features. We applied high-frequency laser stimulation (HFLS, Figure 6h) of the EC-LA axons before the AS to trigger AEP-LTP in the LA. After induction, the AEP slope in the ChETA-expressing group (n = 10) increased significantly, whereas the VEP slope did not change (Figure 6i–j, two-way RM ANOVA, significant interaction, F[1,9] = 14.46, p = 0.004 < 0.01; pairwise comparison, AEP before vs. after pairing, 97.8% ± 5.5% vs. 187.6% ± 15.6%; 95% CI, [85.3%, 110.3%] vs. [152.4%, 222.8]; p = 0.000258 < 0.001; VEP before vs. after pairing, 96.3% ± 4.9% vs. 120.7% ± 9.1%; 95% CI, [85.2%, 107.3%] vs. [100.1%, 141.3%], p = 0.091 > 0.05). Additionally, we injected a non-opsin expressing control AAV (AAV- EF1α-DIO-EYFP, n = 22) and the AEP-LTP was not induced with the same protocol (two-way RM ANOVA between CHETA and EYFP, F[1,30] = 46.65, p = 1.41E-7 < 0.001; pairwise comparison, before vs. after pairing in the EYFP group, 102.9% ± 2.7% vs. 106.7% ± 7.0%; 95% CI, [97.3%, 108.5%] vs. [92.4%, 120.9%]; p = 0.591 > 0.05, Figure 6h–i) These results suggest that high-frequency activation of EC^CCK+ switches the AEP-LTP in the LA.

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AEP-LTP induction with HFLS on CCK+ projection from the EC to the LA.

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In the next experiment, we examined the possibility of other neuroactive molecules co-released with CCK and contributing to HFLS-induced AEP-LTP. We adopted an RNA interference technique to knock down the Cck expression in the EC specifically. We accomplished this by injecting a Cre-dependent AAV cassette carrying a ChR2 variant (E123T/T159C) and a short hairpin RNA (shRNA) targeting Cck (anti-Cck) or a nonsense sequence (anti-Scramble) into the EC of CCK-Cre mice (Figure 7a–c). The knockdown efficiency on Cck expression was quantitatively verified by real-time PCR (Figure 7d). Meanwhile, we injected this virus in WT mice and found ChR2 was not expressed in the injected area, indicating a reliable Cre dependency of this AAV (Figure 7—figure supplement 1). The inclusion of laser-responsive ChR2 allowed us to induce the above AEP-LTP by specifically stimulating the ${\text{EC}}^{\text{CCK+}}\text{→LA}$ pathway. We applied our HFLS pairing protocol in these mice and found that AEP-LTP could not be induced in the anti-Cck group but could be successfully induced in the anti-Scramble group (Figure 7e–h, two-way RM ANOVA, significant interaction, F[1,31] = 14.94, p = 0.00053 < 0.001; pairwise comparison, before vs. after pairing in the anti-Cck group, 101.5% ± 2.8% vs. 98.0% ± 6.5%; 95% CI, [95.7%, 107.4%] vs. [84.6%, 111.3%]; p = 0.594 > 0.05; before vs. after pairing in the anti-Scramble group, 103.0% ± 3.3% vs. 138.8% ± 7.6%; 95% CI, [96.2%, 109.8%] vs. [123.3%, 154.4%]; p = 0.000062 < 0.001). This observation implies that CCK alone is responsible for HFLS-induced AEP-LTP.

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AEP-LTP induction in mice with knockdown of Cck expression in the EC.

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We employed optogenetics to dissect the real-time behavioral dependency of the trace fear memory formation on the ${\text{EC}}^{\text{CCK+}}\text{→LA}$ pathway. We expressed a red-shifted inhibitory opsin Jaws (AAV8-hSyn-FLEX-Jaws-GFP) (Chuong et al., 2014) or mCherry control (AAV8-hSyn-DIO-mCherry) in the EC of CCK-Cre mice. We also implanted optical fibers targeting the bilateral LA in these mice and then subjected them to the long-trace fear conditioning (Figure 8a–e). During trace fear conditioning, EC^CCK+ were stimulated by a 635 nm red laser at a frequency of 5 Hz (i.e., 100 ms illumination +100 ms interval) through the optic fibers for the duration of the CS and trace interval, as indicated in Figure 8c. Freezing percentage to the CS was measured before (baseline) and after (post-training) this long-trace fear conditioning (Figure 8e). We found that mice expressed Jaws (Exp, N = 8/3 cages) had a prominent lower freezing percentage than mice expressed mCherry control (Ctrl, N = 9/3 cages), while in baseline session, there is no statistical difference between these groups (Figure 8e, two-way RM ANOVA, significant interaction, F[1,15] = 5.59, p = 0.032 < 0.05; in baseline session, Exp vs. Ctrl, 7.8 ± 2.1 % vs. 11.6 ± 2.0%; 95% CI, [3.4%, 12.2%] vs. [7.4%, 15.7%]; p = 0.208 > 0.05; in post-training session, Exp vs. Ctrl, 33.3% ± 5.3 % vs. 51.9% ± 5.0 %; 95% CI, [22.1%, 44.5%] vs. [41.4%, 62.5%]; p = 0.021 < 0.05; Videos 11–12). Also, we quantified the freezing percentage before the CS presentation in the baseline and post-training session to evaluate the basal freezing level without the CS (Figure 8d). We found no difference between the two groups (two-way RM ANOVA with Bonferroni pairwise comparison). From the freezing score plot in test day (Figure 8c), we found the control group had a higher freezing score than the experimental group after the CS presentation (two-way RM ANOVA with a Huynh Feldt correction, F[23.6, 354.3] = 0.971, p = 0.503 > 0.05; Bonferroni multiple pairwise comparisons between two groups in each time point, *p = 0.048, 0.016 < 0.05 at time point 10–12 and 18–20 s referred to the onset of CS, respectively). The results indicate that opto-inhibition of the ${\text{EC}}^{\text{CCK+}}\text{→LA}$ during the training session of fear conditioning can impair the formation of long-trace fear memory.

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Summary of freezing percentage in mice with opto-inhibition in long trace and long delay conditioning.

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To test the specificity of the CCK pathway from EC to LA, we applied a long-delay fear conditioning paradigm, in which CS was 30-s-long to cover the whole trace interval and co-terminated with the US (Figure 8f). We did the same optogenetic manipulation on CCK-positive terminals in a new batch of mice (Exp, N = 11/3 cages; Ctrl, N = 10/3 cages). Interestingly, we found that after this long-delay conditioning, both groups of mice can obtain a high and similar freezing level in response to the CS presentation (Figure 8h, two-way RM ANOVA, interaction not significant, F[1, 19] = 1.12, p = 0.304 > 0.05; in baseline session, Exp vs. Ctrl, 8.6 ± 2.3 % vs. 7.2 ± 2.4%; 95% CI, [3.8%, 13.3%] vs. [2.2%, 12.2%]; p = 0.676 > 0.05; in post-training session, Exp vs. Ctrl, 80.0 ± 3.6 % vs. 72.2 ± 3.7%; 95% CI, [72.5%, 87.5%] vs. [64.3%, 80.0%]; p = 0.145 > 0.05; Videos 13–14). From the freezing score plot on the test day (Figure 8f), we observed a similar response curve to the CS, with some time points, the experimental group had a higher freezing score than the control group (two-way RM ANOVA with a Huynh Feldt correction, F[22.0, 418.8] = 1.56, p = 0.051 > 0.05; Bonferroni multiple pairwise comparisons between two groups in each time point, *p = 0.026, 0.003 < 0.05 at time point 24–26 and 26–28 s referred to the onset of CS, respectively).

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We also test the effect of real-time optogenetic inhibition on EC^CCK+ in the short-trace fear conditioning in a head-fixed setup (Figure 8—figure supplement 1). We expressed the inhibitory opsin eNpHR3.0 (AAV-EF1α-DIO-eNpHR3.0-mCherry) or GFP control (AAV-hSyn-FLEX-GFP) in the EC of CCK-Cre mice. Same as above, optic fibers were implanted to target bilateral LA in these mice. During the short-trace fear conditioning, mice were positioned in a head-fixed setup on a movable surface, and an electrical tail shock was given as the US. EC^CCK+ was inhibited by a 561 nm laser illumination at a frequency of 5 Hz (i.e., 100 ms illumination +100 ms interval) for the duration of the CS and trace interval, as indicated in Figure 8—figure supplement 1a. After administration of the US, we most commonly observed flight (running). Interestingly, we found that after a few training trials, some GFP control mice (3/6 animals, data not shown) began running before the US was given, suggesting that GFP mice associate the CS with the US and make predictions in subsequent training trials (Video 15). In contrast, we observe much fewer conditioned defensive responses in the eNpHR group throughout the training process (1/8 animals and 2/40 observed training trials, data not shown, Video 16). Additionally, we recorded the freezing percentages in response to the CS before and after head-fixed fear conditioning (Figure 8—figure supplement 1c–d). We found that mice in the eNpHR group showed impaired freezing percentages post-training compared to mice in the GFP group (Figure 8—figure supplement 1d, two-way RM ANOVA, significant interaction, F[1,12] = 19.20, p = 8.93E-4 < 0.001; pairwise comparison, GFP vs. eNpHR post-training, 39.1% ± 3.7% vs. 12.2% ± 3.2%; 95% CI, [31.3%, 46.8%] vs. [5.6%, 18.9%]; p = 8.39E-4 < 0.001; Videos 17–18). We did not observe any differences between the two groups at baseline (Figure 8—figure supplement 1d, pairwise comparison, GFP vs. eNpHR at baseline, 12.7% ± 2.8% vs. 16.1% ± 2.5%; 95% CI, [6.5%, 18.9%] vs. [10.7%, 21.4%]; p = 0.389 > 0.05) or prior to the CS (Figure 8—figure supplement 1c, two-way RM ANOVA, interaction not significant, F[1, 12] = 0.67, p = 0.43 > 0.05; pairwise comparison, GFP vs. eNpHR post-training, 19.3% ± 5.4% vs. 17.8% ± 4.7%; 95% CI, [7.5%, 31.1%] vs. [7.6%, 28.0%]; p = 0.835 > 0.05). Altogether, our results suggest that short-trace fear memory formation is also disturbed by real-time inhibition of the EC^CCK+ pathway.

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Collectively, with the real-time opto-inhibition on CCK projections from the EC to the LA, we found the specific involvement of the EC^CCK+ in the trace fear memory formation.

In summary, the release of the neuropeptide CCK from the EC neurons switches neural plasticity in the LA and facilitates the formation of trace fear memory. Dysfunction in any part of this pathway impairs the formation of trace fear memory in mice. These results extend our understanding of learning and memory formation and have important implications for fear-related mental disorders.

Adult male and female C57BL/6, Cck^-/- (CCK-CreER), and CCK-Cre (CCK-ires-Cre) mice were used in experiments. For behavioral experiments, only adult male mice were used. Mice were housed in a 12 hr shift of the reversed light-dark cycle and were provided food and water ad libitum. All behavioral experiments were conducted in the dark cycle. All experimental procedures were approved by the Animal Subjects Ethics Sub-Committee of the City University of Hong Kong.

For surgical procedures when doing virus injection and optic fiber implantation, mice were anesthetized with pentobarbital sodium (80 mg/kg, i.p., 20% Dorminal, Alfasan International B.V., Woerden, The Netherlands). For acute electrophysiological recording, mice were anesthetized with pentobarbital sodium (80 mg/kg, i.p.) or urethane sodium (2 g/kg, i.p., Sigma-Aldrich, St. Louis, MO). Both anesthetics were periodically supplemented during the experiment to maintain anesthesia. Mice were fixed in a stereotaxic device, and the scalp was incised. A local anesthetic (xylocaine, 2%) was applied to the incision site for analgesia. After skull levelling, craniotomies were performed with varying parameters based on the region of the brain being accessed.

AS, including pure tones and white noise, were digitally generated by a specialized auditory processor (RZ6 from Tucker-Davis Technologies [TDT], Alachua, FL). For behavioral experiments, AS were delivered via a free-field magnetic speaker (MF-1, TDT) mounted 60 cm above the animal. The sound intensity was adjusted by a condenser microphone (Center Technology, Taipei) to ∼70 dB when it reached the animal. For in vivo recording, AS were delivered via a close-field speaker placed contralaterally to the recording side. The sound intensity that induced 50–70% of the maximum response was selected. Visual stimuli were generated by a direct current-driven torch bulb via the analog voltage output of the TDT workstation. Light intensity was roughly quantified as the value of the trigger voltage. For in vivo recording, the light intensity that induced 50–70% of the maximum response was selected.

Mice were anesthetized with pentobarbital sodium (80 mg/kg, i.p.) and placed on a clean and warm blanket in a soundproof chamber. A free-field magnetic speaker (MF-1, TDT) was placed 10 cm away from the right ear of mice. Recording, reference, and ground needle electrodes (Spes Medica, Genova, Italy) were subcutaneously inserted below the forehead, right ear, and left ear, respectively. AS (wide spectrum clicks, 0.1 ms) were presented to the mouse with a decreasing level from 80 to 20 dB with an interval of 5 dB. For each level of click stimulus, total 512 times of presentations were given at a frequency of 21 Hz. ABR signals were collected via a specialized processor (RZ6, TDT) and digitalized with a bandpass filter from 100 Hz to 5 kHz. Stimuli generation and data processing were performed with software BioSigRZ (TDT).

On the pre-conditioning day, each mouse was placed into the testing context (acrylic box with white wallpaper measuring 25 cm × 25 cm × 25 cm) for habituation and baseline recording. After 3 min of habituation, a CS (2.7 or 8.2 kHz pure tone, 70 dB SPL, 3 s for the short-trace paradigm and 10 s for the long-trace paradigm) was given three times within 20 min.

On conditioning day, the mouse was placed into the fear conditioning context (acrylic box with brown wallpaper measuring 18 cm wide ×18 cm long ×30 cm high and equipped with foot shock stainless steel grid floor). After 3 min of habituation, a CS-US pairing was given. In the short-trace interval paradigm, an US (0.5 mA foot shock, 0.5 s) was given 2 s after a 3-s-long CS. Three trials were given on each training day, and the interval between trials was 10–15 min. Totally two training days were given. The mouse was kept in the fear conditioning context for a 10 min consolidation period after the last training trial. In the long-trace interval paradigm, an US was given 20 s after a 10-s-long CS. Eight training trials were given each training day, and the interval between trials was 2–3 min. The mouse was kept in the fear conditioning context for a 5 min consolidation period after the last training trial. After training, each animal was kept in a temporary cage and returned to its home cage after all individuals finished training.

On post-conditioning day (test day), the mouse was placed into the testing context. After 3 min of habituation, a CS was presented to the animal twice with a 2-min-long interval between stimuli. Two minutes after the last trial, the animal was transferred to a temporary cage and returned to its home cage after all individuals in its cage finished testing.

All contexts were cleaned thoroughly with 75% ethanol after each individual session. All of the above procedures were conducted in a soundproof chamber, and all videos (baseline, training, and testing) were recorded with a webcam (Logitech C270) set in the ceiling of the chamber. Videos were analyzed with a custom program based on an open-source platform (Lopes et al., 2015) (https://bonsai-rx.org). Briefly, the centroid of the animal was extracted from the videos. By comparing the coordinates of the centroid frame by frame, we then calculated the distance moved between two frames. The instant velocity of the animal was calculated by dividing this distance by the time span between two adjacent frames. The freezing percentage was defined as the percentage of frames with an instant velocity lower than the threshold of all frames in an observed time window. We compared the output of this program to results observed by the naked eye. Finally, we selected 0.1 (pixel²/s) as the appropriate moving threshold to define freezing. Freezing score was defined as the binary value (0 or 1) of time frame with instant velocity higher (0, ‘not freezing’) or lower (1, ‘freezing’) than the threshold. For the freezing score plot shown in Figures 1, 2 and 4, freezing scores from all test sessions were averaged per second for data visualization.

Mice were subjected to the surgical procedures described above. Tracheotomy was conducted to facilitate breathing and to prevent asphyxia caused by tracheal secretions during the experiment. Craniotomy was performed 1.0–2.0 mm posterior and 3.0–4.0 mm lateral to the bregma to target the LA. Dura mater was partially opened using a metal hook made of a 29 G syringe needle. Tungsten recording electrodes (0.5–3.0 MΩ, FHC, Bowdoin, ME) were slowly inserted into the LA (approximately 3.5 mm from the brain surface). For laser stimulation experiments, another craniotomy was performed at the temporal lobe (1.0–2.0 mm posterior to the bregma) to expose the lateral rhinal vein. One optic fiber (200 µm diameter, 0.22 NA, Thorlabs, Newton, NJ) was inserted below the rhinal vein and forwarded till 1.0–1.5 mm from the surface. The angle of the optic fiber was approximately 75° from the vertical reference. Responses were recorded and passed to a pre-amplifier (PZ5, TDT) and an acquisition system (RZ5D, TDT). Signals were filtered for field potential or spikes with respective bandwidth ranges of 10–500 and 1–5000 Hz. All recordings were stored using TDT software (OpenEx, TDT). The maximum sound intensity was defined as the intensity that elicited a saturated AEP. The AEP baseline was recorded with 50% of the maximum sound intensity at a 5 s intertrial interval for 20 min. For high-frequency electrical stimulation experiments, we used ∼ 70% of the maximum sound intensity and a 150 µA electrical stimulation current. For HFLS experiments, we used >10 mW laser power to ensure activation of transfected axons. After AEP-LTP induction, we recorded the AEP for another 20 min.

For recording in the EC, we applied the protocol from the Li I. Zhang Laboratory (Zhang et al., 2018). Craniotomy was performed at the juncture of the temporal, occipital, and interparietal bones and exposed the caudal rhinal vein and the transverse sinus (Figure 4—figure supplement 1). Electrodes were inserted approximately 1 mm below the dura mater.

All field potential data were extracted and processed in the MATLAB program, and all single-unit data were extracted from the TDT data tank to the Offline Sorter (Plexon) for spike sorting. Sorted data were forwarded to the Neuroexplorer (Plexon) for additional processing and visualization.

The sequence and cloning details of plasmid will be described elsewhere (Su et al., manuscript in preparation). In principle, we generated AAV vectors that allow Cre-controlled expression of shRNA and channelrhodopsin in neurons. For plasmid pAAV-Cre-ON-mU6-ShRNA-CAG-ChR2(E123T/T159C), shRNA was placed under the control of a mouse U6 (mU6) promoter inserted with a TATALox element (Ventura et al., 2004). CAG-DIO-ChR2(E123T/T159C) cassette was inserted following the mU6-TATAlox-ShRNA cassette.

In brief, the pAAV backbone was recovered after digesting pAAV-CAG-Flex-tdTomato (Addgene 28306) with NdeI and HindIII. Fragment 1 (pUC57-Cre-ON-mU6-shRNA) was acquired by digesting pUC57-Cre-ON-mU6(TATALox) with HpaI and XhoI and then ligating it with annealed oligos that targets the coding sequence of Cck mRNA (Anti-Cck) or nonsense sequence (Anti-Scramble). Fragment 2 was acquired by digesting pUC57-CAG-DIO-ChR2(E123T/T159C-Flag) with XhoI and HindIII. Fragment 3 was acquired by digesting pUC57-CAG-DIO-mCherry-EYFP (inverted) with EcoRI and HindIII. pAAV backbone, Fragment 1 and Fragment 2, was ligated to make pAAV-Cre-ON-mU6-ShRNA-CAG-DIO-ChR2 (E123T/T159C)-Flag. pAAV backbone, Fragment 1 without shRNA, Fragment 3, was ligated to make pAAV-CAG-DO-mCherry-DIO-EYFP. DNA templates and shRNA oligos mentioned above were acquired from Addgene or synthesized from BGI (Shenzhen, China) and verified by sequencing.

For AAV packaging (Xiong et al., 2015), HEK293T cells were seeded into five dishes (15 cm, poly-D-lysine coated) for one viral preparation 1 day before transfection. Standard medium (DMEM, + 10% FBS and antibiotics) were used for HEK293T cells. For PEI transfection, mix 35 μg AAV8 helper plasmid, 35 μg AAV vector, 100 μg pHGTI-adenol, 510 μL of PEI (1 μg/mL, Sigma) with DMEM (without FBS or antibiotics) to final volume of 25 mL. Incubate this mixture at room temperature for 15 min. Meanwhile, replace the media in dishes with DMEM + 10% NuSerum (Bio-gene)+ antibiotics (20 mL/plate). Then add 5 mL of transformation mix per plate. Twenty-four hours after transfection, change the culture media to DMEM + antibiotics without Serum; 72 hr after transfection, culture medium was collected and filtered to get rid of cell pellets. Collected medium was stirred at 4℃ for 1.5 hr, meanwhile mixed with NaCl (final concentration of 0.4 M) and PEG8000 (final concentration of 8.5% w/v). Virus were precipitated by centrifugation at 7000 g for 10 min. Supernatant was discarded and 10 mL lysis buffer (150 mM NaCl, 20 mM Tris pH = 8.0) was added to re-suspend the virus pellet. Virus was then concentrated and purified via iodixanol gradients (‘Optiprep’ Sigma D1556-250mL). Centrifuge the gradients for 90 min at 46,500 rpm at 16℃. The virus in 40% fraction was harvested and mixed with PBS and then transferred to an Amacon 100 K columns – UFC910008 to remove the iodixanol. Purity and titer of virus were then assessed by SDS-PAGE and SYPRO ruby staining (S-12000, Life Technologies, Carlsbad, CA).

Mice were subjected to the surgical procedures described above. For viral injection into the EC, the following rostral parameters were used: anterior-posterior (AP) = 3.25 mm, medial-lateral (ML) = 3.80 mm, dorsal-ventral (DV) = 3.60 mm from the surface, volume = 100 nL. Similarly, the following caudal parameters were used: AP = 4.25 mm, ML = 3.60 mm, DV = 2.60 mm from surface, volume = 200 nL. For injection of tracer or virus into the LA, we used the following parameters: AP = 1.70 mm, ML = 3.40 mm, DV = 3.70 mm from the surface, volume = 200 nL. Craniotomy was performed after skull levelling and partial opening of the dura mater using a syringe needle hook (29 G). We used the Nanoliter2000 system (World Precision Instruments, Sarasota County, FL) for all infusions. Viral or tracer infusions were slowly pumped into brain tissue trough a fine-tip glass pipette filled with silicon oil at a speed of no more than 50 nL/min. After infusion, the pipette was left in the injection site for an extra 5–10 min before slow withdrawal. After withdrawal of the pipette, the scalp was sutured, and a local anesthetic was applied. The animal was returned to its home cage after awaking. For axon stimulation (observation), the virus was expressed for at least 7 weeks, and for cell body stimulation (observation), the virus was expressed for at least 4 weeks. For CTB tracer labeling, we perfused animals after 7 days of viral expression.

To determine the expression of Cck after injecting our AAV carrying anti-Cck or anti-Scramble shRNA, real-time quantitative PCR (qPCR) was performed regarding the injection site (EC) and a reference site (contralateral AC). After expressing shRNA for at least 3 weeks, mice were deeply anesthetized with isoflurane (RWD, Shenzhen, China), and the brains were harvested. Tissue from target areas was collected and RNA from these tissues was first extracted by using Trizol (Cat# 15596018, Invitrogen, Waltham, MA) and then reverse-transcribed to cDNA with the PrimeScript RT Reagent Kit (Cat# RR037B, TaKaRA Bio Inc, Kusatsu, Shiga, Japan). Real-time PCR was performed by using TB Green Premix Ex Taq II (Cat# RR820A, TaKaRa). All samples were tested in triplicate. The primers used were listed in Table 1. The comparative cycle threshold (Ct) method (2^-ΔΔCt) was employed to calculate the relative level of gene expression. The housekeeping gene GAPDH was used to normalize the original Ct values in our current experiments.

Mice were subjected to the surgical procedures described above. Craniotomy was performed bilaterally to target the LA using the coordinates described above. Optic fibers (optic cannulae) were gently inserted into the LA (50–100 µm above the target area) and fixed with dental cement (mega PRESS NV + JET X, megadental GmbH, Büdingen, Germany). For head fixation, a long screw was fixed to the skull with dental cement at a 45° angle from the vertical axis.

The commercial 1-site Fiber Photometry System (Doric Lenses Inc, Quebec, Canada) coupled with the RZ5D processor (TDT, Alachua, FL) was used in the current study. Excitation light at 470 and 405 nm was emitted from two fiber-coupled LEDs (M470F3 and M405FP1, Thorlabs) and sinusoidally modulated at 210 and 330 Hz, respectively. The intensity of the excitation light was controlled by an LED driver (LEDD1B, Thorlabs) connected with the RZ5D processor via the software Synapse. Excitation light was delivered to the animal through a dichroic mirror embedded in single fluorescence MiniCube (Doric Lenses, Quebec, QC, Canada) in a fiber-optic patch cord (200 μm, 0.37 NA, Inper, Hangzhou, China). The intensity of the excitation light at the tip of the patch cord was adjusted to less than 30 μW to avoid photobleaching. The emission fluorescence was collected and transmitted through a bandpass filtered by the MiniCube. The fluorescent signal was then detected, amplified, and converted to an analog signal by the photoreceiver (Doric Lenses). Finally, the analog signal was digitalized by the RZ5D processor and analyzed using Synapse software at 1 kHz with a 5 Hz low-pass filter.

Optical fiber implantation and fiber photometry were used to visualize CCK activity in vivo via a fluorescent sensor. Briefly, the GPCR activation-based CCK sensor (GRAB_CCK, AAV-hSyn-CCK2.0) was developed by inserting a circular-permutated green fluorescent protein (cpEGFP) into the intracellular domain of CCKBR (Jing et al., 2019). Binding of CCKBR with its endogenous or exogenous ligand (CCK) induces a conformational change in cpEGFP and results in increased fluorescence intensity, which we measured by fiber photometry.

Each animal (with DREADD virus injection) received CLZ (0.5 mg/kg, Sigma-Aldrich, dissolved with 0.1% DMSO) or vehicle (sterilized saline with 0.1% DMSO) by intraperitoneal injection. After injection, animals were kept in transfer cages for 30 min to allow the drug to penetrate the BBB and bind to the DREADD receptor (Gomez et al., 2017). Animals were then placed in conditioning boxes for further training.

CCK-Cre mice were injected with AAV-EF1α-DIO-eNpHR3.0-mCherry or control AAV-hSyn-FLEX-GFP. After 6 weeks, animals received bilateral optic fiber implantation as described above. Mice were allowed a 1 week recovery before being subject to the long-trace or long-delay fear conditioning. Baseline freezing percentages were recorded in the test context on the pre-conditioning day as described above. On the conditioning day, mice were connected with the optic cables, which are relayed by a rotatory joint (Inper, Hangzhou, China) then connected to a 635 nm laser source (Inper, Hangzhou, China). For long-trace fear conditioning paradigm, the training procedures were described as above while the laser illumination was applied from the onset of the CS to the onset of the US with a frequency of 5 Hz (100 ms illumination +100 ms interval, 12 mW at tip). For long-delay fear conditioning paradigm, a 30-s-long CS was co-terminated with a 0.5-s-long US. Laser illumination with same intensity and frequency covered the whole CS presentation. Three trials of CS-US pairing were conducted in each training day and animals totally received 2 days of training. After training, on post-conditioning day, the conditioned response of the animal was recorded in the test context. All activity was captured by a camera on the ceiling and analyzed with the previously described Bonsai program.

Animals were anesthetized with an overdose of pentobarbital sodium, perfused with ice-cold phosphate buffered saline (PBS, 0.01 M, Sigma-Aldrich), and fixed with paraformaldehyde solution (PFA, 4% in PBS, Santa Cruz Biotechnology, Dallas, TX). Animals were decapitated, and the brain was gently removed and submerged into 4% PFA solution for additional fixation (~48 hr). Brains were sectioned into 40-µm-thick slices on vibratome (Leica VT1000 S). To observe viral expression, neural tracer labeling, or electrode track verification, sections were counter-stained with DAPI (1:10000, Santa Cruz Biotechnology) for 10 min and mounted onto slides with 70% glycerol (Santa Cruz Biotechnology) in PBS. For immunohistochemistry, sections were washed with 0.01 M PBS three times for 7 min each and blocked with blocking solution (5% goat serum and 0.1% Triton X-100 in PBS) at room temperature for 1.5 hr. Each primary antibody was diluted to the appropriate concentration (Table 1) in blocking solution and incubated on sections overnight at 4°C. The next day, sections were washed with PBS three times for 7 min each and stained with secondary antibody, which was prepared in PBST (0.1% Triton X-100 in PBS). Each secondary antibody was incubated on sections at room temperature for 3 hr. After secondary incubation, the sections were washed with PBS three times for 7 min each and counter-stained with DAPI for 10 min. Finally, sections were washed three times with PBS and mounted onto slides with 70% glycerol mounting medium. Fluorescent images were captured with a Nikon Eclipse Ni-E upright fluorescence microscope and a Zeiss LSM880 confocal microscope.

Imaging signal analysis, including quantification of intensity and percent positivity, was conducted in Fiji (https://imagej.net/Fiji) (Schindelin et al., 2012). To quantify the number (percentage) of viral- or immunohistochemical-positive neurons, we used the Cell Counter plugin in Fiji. To quantify the projection intensity of viral-positive neural fibers, we used the FeatureJ plugin in Fiji. We applied Hessian filter to extract the fiber-like structures and converted the raw images to eigen images with smallest eigen values selected. Eigen images were then converted to binary image by applying a threshold in Fiji and pixel density was measured as the intensity of neural projection (Grider et al., 2006). To quantify the colocalization of the CCK+ terminal (CCK-EYFP and synaptophysin double positive) and the CCKBR-innervating CCK+ terminal (CCK-EYFP, synaptophysin, and CCKBR triple positive), we extracted the double-positive and triple-positive pixels in Fiji and adopted the pixel-based colocalization analysis algorithm from CellProfiler (https://cellprofiler.org/examples) (McQuin et al., 2018) to calculate the colocalization ratios.

Group data are shown as mean ± SEM (standard error of the mean) unless otherwise stated. Statistical analyses, including two sample t-tests, paired sample t-tests, one-way RM ANOVA, and two-way RM ANOVA, were conducted in Origin 2018 (OriginLab, Northampton, MA) and SPSS 26 (IBM, Armonk, NY). Statistical significance was defined as p < 0.05 by default.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Funding: The authors thank Eduardo Lau for administrative and technical assistance. This work was supported by Hong Kong Research Grants Council (T13-605/18W, 11102417M, 11101818M, 11103220), Natural Science Foundation of China (31671102), Health and Medical Research Fund (06172456 and 31571096), Innovation and Technology Fund (MRP/101/17X, MPF/053/18X, GHP_075_19GD). We also thank the following charitable foundations for their generous supports to JH: Wong Chun Hong Endowed Chair Professorship, Charlie Lee Charitable Foundation, and Fong Shu Fook Tong Foundation.

All experimental procedures were approved by the Animal Subjects Ethics Sub-Committee of the City University of Hong Kong (Reference number of animal ethics review: A-0529 and A-0282).

© 2021, Feng et al.

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