During brain development, synapse assembly and maturation are critical processes involving several families of adhesion molecules, among which the neurexin-neuroligin complex has been one of the most extensively studied (Bemben et al., 2015b; Chanda et al., 2017; Letellier et al., 2018; Südhof, 2017; Wu et al., 2019). Neuroligins (NLGNs) are post-synaptic proteins that comprise four members in rodents (NLGN1-4), NLGN1 being primarily localized at excitatory synapses, NLGN2 and NLGN4 at inhibitory synapses, and NLGN3 at both types of synapses (Budreck and Scheiffele, 2007; Varoqueaux et al., 2004). At the structural level, NLGNs form both homo- and hetero-dimers through cis-interactions between their acetylcholinesterase (AchE)-like domains (Araç et al., 2007; Chen et al., 2008; Dean et al., 2003; Fabrichny et al., 2007; Poulopoulos et al., 2012). NLGNs also contain a stalk region that can be cleaved by proteases (Peixoto et al., 2012; Suzuki et al., 2012), a single pass transmembrane domain, and a conserved intracellular tail whose binding to post-synaptic scaffolding molecules can be modulated by phosphorylation and thereby influence AMPA receptor (AMPAR) recruitment (Antonelli et al., 2014; Bemben et al., 2014; Bemben et al., 2015a; Giannone et al., 2013; Letellier et al., 2020; Letellier et al., 2018; Poulopoulos et al., 2009). Post-synaptic NLGNs bind pre-synaptic neurexins (NRXNs) through extracellular interactions, thus making a bridge between sub-micron adhesive modules across the synaptic cleft that precisely position AMPARs (Chamma et al., 2016a; Haas et al., 2018; Trotter et al., 2019).

Apart from NRXNs, few direct NLGN binding partners have been identified (Südhof, 2017). Recently, MAM-domain GPI-anchored molecules (MDGAs) were reported to bind in cis to NLGNs with high affinity and compete with their binding to NRXNs (Connor et al., 2019). In the co-culture assay, the expression of MDGAs together with NLGNs in heterologous cells impairs the synapse-inducing activity of NLGNs on contacting axons (Elegheert et al., 2017; Lee et al., 2013; Pettem et al., 2013). Crystal structures of MDGA-NLGN complexes revealed that MDGAs bind through their first Ig1-Ig2 domains to the two lobes of the NLGN extracellular dimer, at sites which overlap with the NRXN binding interface (Elegheert et al., 2017; Gangwar et al., 2017; Kim et al., 2017). Manipulations of MDGA1 protein levels by over-expression (OE), knock-down (KD), or knock-out (KO) in neurons have led to the common view that MDGA1 selectively inhibits inhibitory synapse formation by primarily repressing NLGN2-NRXN interactions (Connor et al., 2017; Lee et al., 2013; Loh et al., 2016; Pettem et al., 2013). Similar studies performed on MDGA2 have led to more debated results, i.e. some reports showing that MDGA2 KO specifically inhibits excitatory synapse formation in vivo (Connor et al., 2016), while others showing no effect of MDGA2 KD on either excitatory or inhibitory synapses in culture (Loh et al., 2016).

Notwithstanding the foregoing, there are still a number of critical questions that need to be answered in order to get a more complete picture of the role of MDGAs in synapse differentiation and function (Connor et al., 2019; Thoumine and Marchot, 2017). 1 / What is the surface dynamics and nanoscale localization of endogenous MDGAs at the neuronal membrane? Indeed, in the absence of highly specific and efficient antibodies to MDGAs allowing reliable immunostaining in tissue, over-expression approaches have yielded contrasting results about the presence of MDGA1 and MDGA2 at excitatory versus inhibitory synapses (Loh et al., 2016; Pettem et al., 2013). Given the absence of an intracellular domain with potential synaptic retention motifs, MDGAs are in fact expected to display fast membrane diffusion and not accumulate at synapses. 2 / Considering that the binding of MDGAs and NRXNs to NLGNs is mutually exclusive, what is the effect of MDGAs on the dynamic distribution of NLGN in dendrites and on the NLGN-dependent phosphotyrosine signaling pathway known to regulate post-synaptic differentiation (Giannone et al., 2013; Letellier et al., 2018)? 3 / Consequently, what is the impact of MDGAs on AMPAR surface dynamics and synaptic function, which were shown to be tightly regulated by NLGN1 (Czöndör et al., 2013; Haas et al., 2018; Letellier et al., 2020; Mondin et al., 2011)?

To address those questions, we examined the surface localization and dynamics of MDGAs, using both custom-made antibodies to endogenous MDGA1 as well as replacement strategies with recombinant MDGAs bearing small tags and labelled with monomeric fluorescent probes. Using a combination of single-molecule imaging and electrophysiology, we also assessed the effects of single-cell MDGA knock-down or knock-out on NLGN1 and AMPAR membrane diffusion and localization in relation to synapse maturation, as well as on synaptic transmission. Finally, we examined the impact of MDGA knock-down on endogenous NLGN phosphotyrosine levels by biochemistry. Together, our data indicate that MDGAs are highly mobile and homogeneously distributed molecules, that alter both NLGN1 and AMPAR dynamics, localization, and function, thereby significantly delaying the differentiation of excitatory post-synapses.

In this study, we characterized the membrane localization of MDGAs and their role on the dynamics and signaling of their direct binding partner, NLGN1, as well as associated effects on synaptic differentiation and the recruitment of AMPARs. We demonstrate that both MDGA1 and MDGA2 are essentially non-synaptically enriched molecules that exhibit fast diffusion in the dendritic membrane. Moreover, the knock-down of MDGAs selectively increases excitatory synapse density and as a consequence reduces the surface mobility of both NLGN1 and AMPARs, increases AMPAR-mediated mEPSC frequency and evoked EPSC amplitude, as well as NLGN1 phosphosignaling. Thus, by shielding a fraction of NLGN1 from binding to pre-synaptic NRXNs, MDGAs negatively regulates NLGN function in excitatory synaptic differentiation (Figure 10).

Figure 10

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We first examined the localization of MDGAs in hippocampal tissue and dissociated cultures. The generation of specific antibodies allowed us to examine the distribution of native MDGA1, which showed strong expression levels in the neuropil of the CA region of the hippocampus, confirming previously shown results of in situ hybridization of MDGA1 mRNAs and staining of β-galactosidase activity expressed from the Mdga1 locus (Connor et al., 2017; Connor et al., 2016; Lee et al., 2013). MDGA1 immunostaining in dissociated cultures at DIV 14 showed that a large fraction of excitatory synapses did not contain MDGA1. Recombinant MDGA1 and MDGA2 expressed by rescuing endogenous MDGA levels also showed no preferential retention at Homer1c-positive puncta, in contrast with the positive controls NLGN1 and LRRTM2 labeled similarly and showing strong post-synaptic accumulation, as previously described (Chamma et al., 2016a). Previous studies also reported a small colocalization extent of either YFP-MDGA1 or HRP-MDGA2 with excitatory synaptic markers (Loh et al., 2016; Pettem et al., 2013). Our results show that individual MDGAs often localized as transient sub-micron clusters at the periphery of Homer1c puncta, representing the confinement area of a small number of labeled molecules. The nature of these domains is unclear, but might represent a transition zone where NLGN1 could switch between MDGA-bound and NRXN-bound states, with the potential existence of mixed NLGN1 dimers that would exhibit specific mobility properties. In any case, the sum of this MDGA-rich peri-synaptic compartment plus the diffusive pool of MDGAs in synapses might represent enough material to be detected in synaptosomes (this study) and in the synaptic cleft proteome (Loh et al., 2016). When sampled at saturating labeling density using dSTORM, MDGAs showed a rather diffuse localization in the shaft and at synapses both at DIV 10 and 14, with modest synaptic enrichment as the negative control GFP-GPI. As compared to uPAINT, which gives information on the membrane dynamics of a subset of sparsely labeled molecules, dSTORM performed after saturating live labeling provides a snapshot of the whole population of MDGAs that integrates over time many transient confinement areas into a single 2D projection image. Thus the localization of molecules observed in dSTORM seems naturally more homogeneous, as previously reported for NLGN1 (Chamma et al., 2016a).

In addition, AP-MDGA1 and AP-MDGA2 exhibited fast membrane diffusion throughout dendrites, even within post-synaptic sites, indicating no particular molecular retention at synapses. These observations are compatible with the fact that MDGAs lack an intracellular C-terminal domain such as those present in NLGN1 or AMPAR auxiliary subunits, which bind PDZ domain-containing scaffolding proteins that stabilize them at synapses (Bats et al., 2007; Irie et al., 1997; Mondin et al., 2011). A mild confinement of MDGAs outside synapses was observed upon neuronal maturation, which might be due to the fact that a fraction of MDGAs bind to extra-synaptic NLGN clusters (Gerrow et al., 2006) or to another unknown protein, e.g. through their Ig4-6 domains (Lee et al., 2013). These domains might also be due to interactions of the GPI anchor of MDGAs to the lipid microenvironment present in some membrane microdomains such as lipid rafts (Díaz-López et al., 2005; Renner et al., 2009). By comparing side by side the distributions of diffusion coefficients for MDGAs and NLGN1, we estimate that around 12% of the highly mobile extra-synaptic MDGAs are not bound to NLGN1, otherwise MDGAs would naturally adopt the slower diffusion of NLGN1. Furthermore, the fact that MDGAs do not accumulate at synapses like NLGN1 over neuronal development (from DIV 10–14) suggests that synaptic NLGN1 is not bound to MDGAs, but instead to NRXNs which display high local concentration at pre-synapses (Chamma et al., 2016a; Chamma et al., 2016b). Thus, by preventing dendritic NLGN1 from aberrantly binding to the fraction of freely-diffusing NRXNs at the surface of contacting axons (Chamma et al., 2016a; Neupert et al., 2015), native MDGAs seem to protect neurons from forming synapses too quickly.

Using either previously reported shRNAs or newly generated CRIPSR/Cas9 constructs, we showed that MDGA2 delays excitatory synaptic development and AMPAR-dependent transmission, consistent with the in vivo KO of Mdga2 (Connor et al., 2016). These effects were accompanied by a global decrease of NLGN1 diffusion, supporting the idea that by losing its MDGA partner, NLGN1 is more available to bind axonal NRXNs and thereby accelerates synapse formation. Indeed, the effects of MDGA2 KD and KO were more prominent at DIV 10 during the active phase of synaptogenesis, and barely detectable at DIV 14, in agreement with the lack of effect of shMDGA2 previously seen on excitatory synapse density in DIV 15 neurons (Loh et al., 2016). Upon MDGA2 knock-down, there was no significant increase in the synaptic enrichment of either NRXN1β or NLGN1, suggesting that synapses can be newly assembled using a fixed number of those trans-synaptic adhesion molecules. We found more contrasted results with MDGA1, that is the CRISPR/Cas9 strategy significantly increased excitatory post-synaptic density and AMPAR-mediated mEPSC frequency at DIV 10 in dissociated neurons, as well as evoked AMPAR-mediated EPSCs in organotypic slices, while shRNA to MDGA1 had little effect at both DIV 10 and DIV 14, in agreement with previous reports (Pettem et al., 2013). The discrepancy might be due to the fact that CRISPR/Cas9 represses MDGA1 expression more strongly than shMDGA1. Indeed, in previous studies, effects of MDGAs on excitatory and inhibitory synapse development were seen only when both MDGA1 and MDGA2 were knocked down (Lee et al., 2013; Loh et al., 2016), suggesting that MDGA1 and MDGA2 might compensate for each other’s loss such that the important parameter in these experiments is the overall MDGA level. The selective increase in inhibitory - but not excitatory - synapses in hippocampal CA1 neurons of full Mdga1 KO mice (Connor et al., 2017) might come from the fact that MDGA1 is silenced throughout the whole animal lifetime, which might lead to compensatory effects. Furthermore, MDGA1 knock-out affects all cell types, including both pyramidal neurons and astrocytes that also express MDGA1 and NLGNs (Stogsdill et al., 2017), which might overall cause mixed effects on hippocampal circuitry. A difference in the results obtained by global gene knock-out in mice and sparse single-neuron silencing was also seen for NLGN1 (Chih et al., 2005; Varoqueaux et al., 2006), suggesting that adhesion-dependent competition between neurons regulates synaptogenesis (Kwon et al., 2012), although sparsely knocking out NLGN1 did not reduce synapse number in another study (Chanda et al., 2017). Strikingly, the phenotypes we saw with single cell MDGA2 KO (e.g. increases in post-synapse numbers and AMPAR-mediated mEPSC frequency in dissociated neurons) resembles the ones we previously observed by over-expressing NLGN1, thereby reinforcing the view that neurons compete for synapse formation through NRXN-NLGN adhesion. MDGA1 KO had more subtle effects, that is it significantly increased AMPAR-mediated mEPSC frequency but not the density of VGlut1 or GluA1 puncta, which might be due to the fact that electrophysiology is a more sensitive technique than immunostaining to detect AMPAR-dependent synaptic changes (Letellier et al., 2018). In any case, crystal structures and affinity measurements support the concept of a stable MDGA1-NLGN1 complex (Elegheert et al., 2017; Gangwar et al., 2017; Kim et al., 2017), which should be compatible with the fact that endogenous MDGA1 can interact with NLGN1 as strongly as MDGA2 to impair excitatory synapse formation.

We also demonstrated a selective increase in the phosphotyrosine level of NLGNs upon MDGA2 knock-down. This observation relates to our previous findings that NLGN1 can be phosphorylated at a unique intracellular tyrosine residue (Y782), and that the NLGN1 phosphotyrosine level regulates the assembly of excitatory post-synaptic scaffolds in a NRXN-dependent fashion (Giannone et al., 2013). Moreover, using either the expression of NLGN1 point mutants or the optogenetic stimulation of endogenous NLGN1 phosphorylation, we showed that a high NLGN1 phosphotyrosine level is associated with the selective increase in excitatory synapse number and AMPAR-mediated synaptic transmission (Letellier et al., 2020; Letellier et al., 2018). Strikingly, single-cell MDGA KO in CA1 neurons selectively enhanced AMPAR- (but not NMDAR-) mediated synaptic transmission, further implicating NLGN in the recruitment of AMPARs at nascent synapses. We thus propose the model that by occupying NLGN1, MDGAs inhibit the NRXN-induced phosphotyrosine signaling pathway associated with NLGN1 and thereby delay the assembly of functional excitatory synapses containing AMPARs. In this context, we were surprised not to observe any impact of MDGA KD on the global diffusion or pre-synaptic accumulation of GFP-NRXN1β in axons contacting neurons expressing shMDGA2, but these effects might have been obscured by the limited number of contacts between these cells or by NRXN1β over-expression. Future single-molecule tracking experiments on NRXN1-α or -β expressed at endogenous levels may allow one to clarify this issue (Klatt et al., 2021; Neupert et al., 2015). The enhancement of NLGN tyrosine phosphorylation caused by MDGA2 KD might be linked to the increased number of synapses, leading to enhanced local concentration of receptor tyrosine kinases such as Trk family members that are able to phosphorylate native NLGNs (Letellier et al., 2018). Since NLGN3 can also be tyrosine phosphorylated in neurons (Letellier et al., 2018), a contribution of NLGN3 to the increase in phosphotyrosine level seen upon MDGA2 KD is possible given that we used a pan NLGN antibody to reach efficient immunoprecipitation. However, the fact that MDGA binds 10-fold more weakly to NLGN3 than to NLGN1 in vitro (Elegheert et al., 2017), and that the effects of NLGN tyrosine phosphorylation on post-synaptic differentiation are not seen in cultures from NLGN1 KO mice (Letellier et al., 2020) suggest that NLGN3 might only play a minor role in this process.

The decrease in global AMPAR diffusion observed upon MDGA2 knock-down can be linked to the parallel increase in the number of synaptic puncta. Indeed, a similar decrease in AMPAR diffusion was seen across neuronal development in culture or upon the over-expression of NLGN1, which both enhance the number of post-synapses that act as trapping elements for surface diffusing AMPARs (Czöndör et al., 2012; Mondin et al., 2011). Upon MDGA2 knock-down, a transient immobilization of surface-diffusing AMPARs is expected to occur at newly formed synapses enriched in NLGN1, resembling what was previously observed at micro-patterned dots coated with NRXN1β-Fc (Czöndör et al., 2013). Interestingly, the actual content of AMPARs per post-synapse did not seem to be modified by MDGA KO in dissociated neurons, since both the synaptic AMPAR enrichment and the amplitude of AMPAR-mediated mEPSCs remained similar to control conditions. However, the density of synaptic puncta as well as the frequency of AMPAR-mediated mEPSCs were significantly enhanced by MDGA KO, suggesting that the new synapses that had appeared contained functional AMPARs. This situation is similar to NLGN1 over-expression that increases the number of synapses and the frequency, but not the amplitude, of AMPAR-mediated mEPSCs (Letellier et al., 2018; Levinson et al., 2005). In both cases (MDGA knock-down or NLGN overexpression), AMPARs seem to be inserted in novel synapses as individual units or modules, most likely influenced by the presence of NLGN1 (Haas et al., 2018; Hruska et al., 2018; MacGillavry et al., 2013; Nair et al., 2013). In organotypic slices, MDGA KO selectively enhanced the AMPA/NMDA ratio in CA1 neurons but did not affect NMDAR-mediated EPSCs, indicating no net change in synapse number but instead an unsilencing mechanism (Kerchner and Nicoll, 2008). This process might also occur in dissociated neurons where the increase in the frequency of AMPAR-mediated mEPSCs upon MDGA KD (3-fold) was much larger than the enhancement in synapse number (50%). In any case, the robust recruitment of AMPARs through synaptogenic and/or unsilencing mechanisms ressembles the effects previously observed upon direct manipulation of NLGN1 expression or signaling (Letellier et al., 2020; Letellier et al., 2018; Mondin et al., 2011), suggesting that MDGAs act through a regulation of NLGN1 function.

Given the strong effects caused by MDGA loss-of-function on synaptic differentiation, the next challenge would be to determine which biological processes regulate endogenous MDGA levels in specific neuron types across development. One interesting factor might be constitutive neuronal activity that can influence synaptic protein expression levels, for example by modulating microRNAs (Dubes et al., 2019; Letellier et al., 2014). Indeed, MDGA1 transcripts were recently found to be regulated in response to chronic synaptic activity blockade (Silva et al., 2019). Moreover, the action of MDGAs might be finely tuned by other proteins associated to the NRXN-NLGN trans-synaptic complex, including hevin and SPARC that are secreted by astrocytes (Fan et al., 2021). Interestingly, the presence of NLGNs in astrocytes offers an additional level of regulation of synapse development through such a network of proteins (Stogsdill et al., 2017). Finally, genetic mutations identified in patients with autism and leading to alterations in MDGA levels (Bucan et al., 2009), are expected to cause profound changes in synapse differentiation such as the ones shown here.

All data generated or analysed during this study are included in the manuscript and supporting file; source data files are provided.

1. 1. Antonelli R
   2. Pizzarelli R
   3. Pedroni A
   4. Fritschy JM
   5. Del Sal G
   6. Cherubini E
   7. Zacchi P

   (2014) Pin1-dependent signalling negatively affects GABAergic transmission by modulating neuroligin2/gephyrin interaction

   Nature Communications 5:5066.

   https://doi.org/10.1038/ncomms6066

   • PubMed
   • Google Scholar
2. 1. Araç D
   2. Boucard AA
   3. Ozkan E
   4. Strop P
   5. Newell E
   6. Südhof TC
   7. Brunger AT

   (2007) Structures of neuroligin-1 and the neuroligin-1/neurexin-1 beta complex reveal specific protein-protein and protein-Ca2+ interactions

   Neuron 56:992–1003.

   https://doi.org/10.1016/j.neuron.2007.12.002

   • PubMed
   • Google Scholar
3. 1. Bats C
   2. Groc L
   3. Choquet D

   (2007) The interaction between Stargazin and PSD-95 regulates AMPA receptor surface trafficking

   Neuron 53:719–734.

   https://doi.org/10.1016/j.neuron.2007.01.030

   • PubMed
   • Google Scholar
4. 1. Bemben MA
   2. Shipman SL
   3. Hirai T
   4. Herring BE
   5. Li Y
   6. Badger JD
   7. Nicoll RA
   8. Diamond JS
   9. Roche KW

   (2014) CaMKII phosphorylation of neuroligin-1 regulates excitatory synapses

   Nature Neuroscience 17:56–64.

   https://doi.org/10.1038/nn.3601

   • PubMed
   • Google Scholar
5. 1. Bemben MA
   2. Nguyen QA
   3. Wang T
   4. Li Y
   5. Nicoll RA
   6. Roche KW

   (2015a) Autism-associated mutation inhibits protein kinase C-mediated neuroligin-4X enhancement of excitatory synapses

   PNAS 112:2551–2556.

   https://doi.org/10.1073/pnas.1500501112

   • PubMed
   • Google Scholar
6. 1. Bemben MA
   2. Shipman SL
   3. Nicoll RA
   4. Roche KW

   (2015b) The cellular and molecular landscape of neuroligins

   Trends in Neurosciences 38:496–505.

   https://doi.org/10.1016/j.tins.2015.06.004

   • PubMed
   • Google Scholar
7. 1. Bucan M
   2. Abrahams BS
   3. Wang K
   4. Glessner JT
   5. Herman EI
   6. Sonnenblick LI
   7. Alvarez Retuerto AI
   8. Imielinski M
   9. Hadley D
   10. Bradfield JP
   11. Kim C
   12. Gidaya NB
   13. Lindquist I
   14. Hutman T
   15. Sigman M
   16. Kustanovich V
   17. Lajonchere CM
   18. Singleton A
   19. Kim J
   20. Wassink TH
   21. McMahon WM
   22. Owley T
   23. Sweeney JA
   24. Coon H
   25. Nurnberger JI
   26. Li M
   27. Cantor RM
   28. Minshew NJ
   29. Sutcliffe JS
   30. Cook EH
   31. Dawson G
   32. Buxbaum JD
   33. Grant SFA
   34. Schellenberg GD
   35. Geschwind DH
   36. Hakonarson H

   (2009) Genome-wide analyses of exonic copy number variants in a family-based study point to novel autism susceptibility genes

   PLOS Genetics 5:e1000536.

   https://doi.org/10.1371/journal.pgen.1000536

   • PubMed
   • Google Scholar
8. 1. Budreck EC
   2. Scheiffele P

   (2007) Neuroligin-3 is a neuronal adhesion protein at GABAergic and glutamatergic synapses

   The European Journal of Neuroscience 26:1738–1748.

   https://doi.org/10.1111/j.1460-9568.2007.05842.x

   • PubMed
   • Google Scholar
9. 1. Cadwell CR
   2. Palasantza A
   3. Jiang X
   4. Berens P
   5. Deng Q
   6. Yilmaz M
   7. Reimer J
   8. Shen S
   9. Bethge M
   10. Tolias KF
   11. Sandberg R
   12. Tolias AS

   (2016) Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq

   Nature Biotechnology 34:199–203.

   https://doi.org/10.1038/nbt.3445

   • PubMed
   • Google Scholar
10. 1. Chamma I
    2. Letellier M
    3. Butler C
    4. Tessier B
    5. Lim KH
    6. Gauthereau I
    7. Choquet D
    8. Sibarita JB
    9. Park S
    10. Sainlos M
    11. Thoumine O

    (2016a) Mapping the dynamics and nanoscale organization of synaptic adhesion proteins using monomeric streptavidin

    Nature Communications 7:10773.

    https://doi.org/10.1038/ncomms10773

    • PubMed
    • Google Scholar
11. 1. Chamma I
    2. Levet F
    3. Sibarita JB
    4. Sainlos M
    5. Thoumine O

    (2016b) Nanoscale organization of synaptic adhesion proteins revealed by single-molecule localization microscopy

    Neurophotonics 3:041810.

    https://doi.org/10.1117/1.NPh.3.4.041810

    • PubMed
    • Google Scholar
12. 1. Chamma I
    2. Rossier O
    3. Giannone G
    4. Thoumine O
    5. Sainlos M

    (2017) Optimized labeling of membrane proteins for applications to super-resolution imaging in confined cellular environments using monomeric streptavidin

    Nature Protocols 12:748–763.

    https://doi.org/10.1038/nprot.2017.010

    • PubMed
    • Google Scholar
13. 1. Chanda S
    2. Hale WD
    3. Zhang B
    4. Wernig M
    5. Südhof TC

    (2017) Unique versus Redundant Functions of Neuroligin Genes in Shaping Excitatory and Inhibitory Synapse Properties

    The Journal of Neuroscience 37:6816–6836.

    https://doi.org/10.1523/JNEUROSCI.0125-17.2017

    • PubMed
    • Google Scholar
14. 1. Chen X
    2. Liu H
    3. Shim AHR
    4. Focia PJ
    5. He X

    (2008) Structural basis for synaptic adhesion mediated by neuroligin-neurexin interactions

    Nature Structural & Molecular Biology 15:50–56.

    https://doi.org/10.1038/nsmb1350

    • PubMed
    • Google Scholar
15. 1. Chen S
    2. Zhou Y
    3. Chen Y
    4. Gu J

    (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor

    Bioinformatics (Oxford, England) 34:i884–i890.

    https://doi.org/10.1093/bioinformatics/bty560

    • PubMed
    • Google Scholar
16. 1. Chih B
    2. Engelman H
    3. Scheiffele P

    (2005) Control of excitatory and inhibitory synapse formation by neuroligins

    Science 307:1324–1328.

    https://doi.org/10.1126/science.1107470

    • PubMed
    • Google Scholar
17. 1. Condomitti G
    2. Wierda KD
    3. Schroeder A
    4. Rubio SE
    5. Vennekens KM
    6. Orlandi C
    7. Martemyanov KA
    8. Gounko NV
    9. Savas JN
    10. de Wit J

    (2018) An Input-Specific Orphan Receptor GPR158-HSPG Interaction Organizes Hippocampal Mossy Fiber-CA3 Synapses

    Neuron 100:201–215.

    https://doi.org/10.1016/j.neuron.2018.08.038

    • PubMed
    • Google Scholar
18. 1. Connor SA
    2. Ammendrup-Johnsen I
    3. Chan AW
    4. Kishimoto Y
    5. Murayama C
    6. Kurihara N
    7. Tada A
    8. Ge Y
    9. Lu H
    10. Yan R
    11. LeDue JM
    12. Matsumoto H
    13. Kiyonari H
    14. Kirino Y
    15. Matsuzaki F
    16. Suzuki T
    17. Murphy TH
    18. Wang YT
    19. Yamamoto T
    20. Craig AM

    (2016) Altered Cortical Dynamics and Cognitive Function upon Haploinsufficiency of the Autism-Linked Excitatory Synaptic Suppressor MDGA2

    Neuron 91:1052–1068.

    https://doi.org/10.1016/j.neuron.2016.08.016

    • PubMed
    • Google Scholar
19. 1. Connor SA
    2. Ammendrup-Johnsen I
    3. Kishimoto Y
    4. Karimi Tari P
    5. Cvetkovska V
    6. Harada T
    7. Ojima D
    8. Yamamoto T
    9. Wang YT
    10. Craig AM

    (2017) Loss of Synapse Repressor MDGA1 Enhances Perisomatic Inhibition, Confers Resistance to Network Excitation, and Impairs Cognitive Function

    Cell Reports 21:3637–3645.

    https://doi.org/10.1016/j.celrep.2017.11.109

    • PubMed
    • Google Scholar
20. 1. Connor SA
    2. Elegheert J
    3. Xie Y
    4. Craig AM

    (2019) Pumping the brakes: suppression of synapse development by MDGA-neuroligin interactions

    Current Opinion in Neurobiology 57:71–80.

    https://doi.org/10.1016/j.conb.2019.01.002

    • PubMed
    • Google Scholar
21. 1. Czöndör K
    2. Mondin M
    3. Garcia M
    4. Heine M
    5. Frischknecht R
    6. Choquet D
    7. Sibarita JB
    8. Thoumine OR

    (2012) Unified quantitative model of AMPA receptor trafficking at synapses

    PNAS 109:3522–3527.

    https://doi.org/10.1073/pnas.1109818109

    • PubMed
    • Google Scholar
22. 1. Czöndör K
    2. Garcia M
    3. Argento A
    4. Constals A
    5. Breillat C
    6. Tessier B
    7. Thoumine O

    (2013) Micropatterned substrates coated with neuronal adhesion molecules for high-content study of synapse formation

    Nature Communications 4:2252.

    https://doi.org/10.1038/ncomms3252

    • PubMed
    • Google Scholar
23. 1. Dagar S
    2. Gottmann K

    (2019) Differential Properties of the Synaptogenic Activities of the Neurexin Ligands Neuroligin1 and LRRTM2

    Frontiers in Molecular Neuroscience 12:1–11.

    https://doi.org/10.3389/fnmol.2019.00269

    • PubMed
    • Google Scholar
24. 1. Dani A
    2. Huang B
    3. Bergan J
    4. Dulac C
    5. Zhuang X

    (2010) Superresolution imaging of chemical synapses in the brain

    Neuron 68:843–856.

    https://doi.org/10.1016/j.neuron.2010.11.021

    • PubMed
    • Google Scholar
25. 1. Dean C
    2. Scholl FG
    3. Choih J
    4. DeMaria S
    5. Berger J
    6. Isacoff E
    7. Scheiffele P

    (2003) Neurexin mediates the assembly of presynaptic terminals

    Nature Neuroscience 6:708–716.

    https://doi.org/10.1038/nn1074

    • PubMed
    • Google Scholar
26. 1. Demonte D
    2. Drake EJ
    3. Lim KH
    4. Gulick AM
    5. Park S

    (2013) Structure-based engineering of streptavidin monomer with a reduced biotin dissociation rate

    Proteins 81:1621–1633.

    https://doi.org/10.1002/prot.24320

    • PubMed
    • Google Scholar
27. 1. Díaz-López A
    2. Rivas C
    3. Iniesta P
    4. Morán A
    5. García-Aranda C
    6. Megías D
    7. Sánchez-Pernaute A
    8. Torres A
    9. Díaz-Rubio E
    10. Benito M
    11. De Juan C

    (2005) Characterization of MDGA1, a novel human glycosylphosphatidylinositol-anchored protein localized in lipid rafts

    Experimental Cell Research 307:91–99.

    https://doi.org/10.1016/j.yexcr.2005.02.016

    • PubMed
    • Google Scholar
28. 1. Dobin A
    2. Davis CA
    3. Schlesinger F
    4. Drenkow J
    5. Zaleski C
    6. Jha S
    7. Batut P
    8. Chaisson M
    9. Gingeras TR

    (2013) STAR: ultrafast universal RNA-seq aligner

    Bioinformatics (Oxford, England) 29:15–21.

    https://doi.org/10.1093/bioinformatics/bts635

    • PubMed
    • Google Scholar
29. 1. Dubes S
    2. Favereaux A
    3. Thoumine O
    4. Letellier M

    (2019) miRNA-Dependent Control of Homeostatic Plasticity in Neurons

    Frontiers in Cellular Neuroscience 13:536.

    https://doi.org/10.3389/fncel.2019.00536

    • PubMed
    • Google Scholar
30. 1. Elegheert J
    2. Cvetkovska V
    3. Clayton AJ
    4. Heroven C
    5. Vennekens KM
    6. Smukowski SN
    7. Regan MC
    8. Jia W
    9. Smith AC
    10. Furukawa H
    11. Savas JN
    12. de Wit J
    13. Begbie J
    14. Craig AM
    15. Aricescu AR

    (2017) Structural Mechanism for Modulation of Synaptic Neuroligin-Neurexin Signaling by MDGA Proteins

    Neuron 95:896–913.

    https://doi.org/10.1016/j.neuron.2017.07.040

    • PubMed
    • Google Scholar
31. 1. Fabrichny IP
    2. Leone P
    3. Sulzenbacher G
    4. Comoletti D
    5. Miller MT
    6. Taylor P
    7. Bourne Y
    8. Marchot P

    (2007) Structural analysis of the synaptic protein neuroligin and its beta-neurexin complex: determinants for folding and cell adhesion

    Neuron 56:979–991.

    https://doi.org/10.1016/j.neuron.2007.11.013

    • PubMed
    • Google Scholar
32. 1. Fan S
    2. Gangwar SP
    3. Machius M
    4. Rudenko G

    (2021) Interplay between hevin, SPARC, and MDGAs: Modulators of neurexin-neuroligin transsynaptic bridges

    Structure (London, England) 29:664–678.

    https://doi.org/10.1016/j.str.2021.01.003

    • PubMed
    • Google Scholar
33. 1. Fuzik J
    2. Zeisel A
    3. Máté Z
    4. Calvigioni D
    5. Yanagawa Y
    6. Szabó G
    7. Linnarsson S
    8. Harkany T

    (2016) Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes

    Nature Biotechnology 34:175–183.

    https://doi.org/10.1038/nbt.3443

    • PubMed
    • Google Scholar
34. 1. Gangwar SP
    2. Zhong X
    3. Seshadrinathan S
    4. Chen H
    5. Machius M
    6. Rudenko G

    (2017) Molecular Mechanism of MDGA1: Regulation of Neuroligin 2:Neurexin Trans-synaptic Bridges

    Neuron 94:1132–1141.

    https://doi.org/10.1016/j.neuron.2017.06.009

    • PubMed
    • Google Scholar
35. 1. Gerrow K
    2. Romorini S
    3. Nabi SM
    4. Colicos MA
    5. Sala C
    6. El-Husseini A

    (2006) A preformed complex of postsynaptic proteins is involved in excitatory synapse development

    Neuron 49:547–562.

    https://doi.org/10.1016/j.neuron.2006.01.015

    • PubMed
    • Google Scholar
36. 1. Giannone G
    2. Mondin M
    3. Grillo-Bosch D
    4. Tessier B
    5. Saint-Michel E
    6. Czöndör K
    7. Sainlos M
    8. Choquet D
    9. Thoumine O

    (2013) Neurexin-1β binding to neuroligin-1 triggers the preferential recruitment of PSD-95 versus gephyrin through tyrosine phosphorylation of neuroligin-1

    Cell Reports 3:1996–2007.

    https://doi.org/10.1016/j.celrep.2013.05.013

    • PubMed
    • Google Scholar
37. 1. Haas KT
    2. Compans B
    3. Letellier M
    4. Bartol TM
    5. Grillo-Bosch D
    6. Sejnowski TJ
    7. Sainlos M
    8. Choquet D
    9. Thoumine O
    10. Hosy E

    (2018) Pre-post synaptic alignment through neuroligin-1 tunes synaptic transmission efficiency

    eLife 7:e5.

    https://doi.org/10.7554/eLife.31755

    • PubMed
    • Google Scholar
38. 1. Heilemann M
    2. van de Linde S
    3. Schüttpelz M
    4. Kasper R
    5. Seefeldt B
    6. Mukherjee A
    7. Tinnefeld P
    8. Sauer M

    (2008) Subdiffraction-resolution fluorescence imaging with conventional fluorescent probes

    Angewandte Chemie (International Ed. in English) 47:6172–6176.

    https://doi.org/10.1002/anie.200802376

    • PubMed
    • Google Scholar
39. 1. Howarth M
    2. Takao K
    3. Hayashi Y
    4. Ting AY

    (2005) Targeting quantum dots to surface proteins in living cells with biotin ligase

    PNAS 102:7583–7588.

    https://doi.org/10.1073/pnas.0503125102

    • PubMed
    • Google Scholar
40. 1. Hruska M
    2. Henderson N
    3. Le Marchand SJ
    4. Jafri H
    5. Dalva MB

    (2018) Synaptic nanomodules underlie the organization and plasticity of spine synapses

    Nature Neuroscience 21:671–682.

    https://doi.org/10.1038/s41593-018-0138-9

    • PubMed
    • Google Scholar
41. 1. Irie M
    2. Hata Y
    3. Takeuchi M
    4. Ichtchenko K
    5. Toyoda A
    6. Hirao K
    7. Takai Y
    8. Rosahl TW
    9. Südhof TC

    (1997) Binding of neuroligins to PSD-95

    Science 277:1511–1515.

    https://doi.org/10.1126/science.277.5331.1511

    • PubMed
    • Google Scholar
42. 1. Ishikawa T
    2. Gotoh N
    3. Murayama C
    4. Abe T
    5. Iwashita M
    6. Matsuzaki F
    7. Suzuki T
    8. Yamamoto T

    (2011) IgSF molecule MDGA1 is involved in radial migration and positioning of a subset of cortical upper-layer neurons

    Developmental Dynamics 240:96–107.

    https://doi.org/10.1002/dvdy.22496

    • PubMed
    • Google Scholar
43. 1. Izeddin I
    2. Boulanger J
    3. Racine V
    4. Specht CG
    5. Kechkar A
    6. Nair D
    7. Triller A
    8. Choquet D
    9. Dahan M
    10. Sibarita JB

    (2012) Wavelet analysis for single molecule localization microscopy

    Optics Express 20:2081–2095.

    https://doi.org/10.1364/OE.20.002081

    • PubMed
    • Google Scholar
44. 1. Kaech S
    2. Banker G

    (2006) Culturing hippocampal neurons

    Nature Protocols 1:2406–2415.

    https://doi.org/10.1038/nprot.2006.356

    • PubMed
    • Google Scholar
45. 1. Kechkar A
    2. Nair D
    3. Heilemann M
    4. Choquet D
    5. Sibarita JB

    (2013) Real-time analysis and visualization for single-molecule based super-resolution microscopy

    PLOS ONE 8:e62918.

    https://doi.org/10.1371/journal.pone.0062918

    • Google Scholar
46. 1. Kerchner GA
    2. Nicoll RA

    (2008) Silent synapses and the emergence of a postsynaptic mechanism for LTP

    Nature Reviews. Neuroscience 9:813–825.

    https://doi.org/10.1038/nrn2501

    • PubMed
    • Google Scholar
47. 1. Kim JA
    2. Kim D
    3. Won SY
    4. Han KA
    5. Park D
    6. Cho E
    7. Yun N
    8. An HJ
    9. Um JW
    10. Kim E
    11. Lee JO
    12. Ko J
    13. Kim HM

    (2017) Structural Insights into Modulation of Neurexin-Neuroligin Trans-synaptic Adhesion by MDGA1/Neuroligin-2 Complex

    Neuron 94:1121–1131.

    https://doi.org/10.1016/j.neuron.2017.05.034

    • PubMed
    • Google Scholar
48. 1. Klatt O
    2. Repetto D
    3. Brockhaus J
    4. Reissner C
    5. El Khallouqi A
    6. Rohlmann A
    7. Heine M
    8. Missler M

    (2021) Endogenous β-neurexins on axons and within synapses show regulated dynamic behavior

    Cell Reports 35:109266.

    https://doi.org/10.1016/j.celrep.2021.109266

    • PubMed
    • Google Scholar
49. 1. Kwon HB
    2. Kozorovitskiy Y
    3. Oh WJ
    4. Peixoto RT
    5. Akhtar N
    6. Saulnier JL
    7. Gu C
    8. Sabatini BL

    (2012) Neuroligin-1-dependent competition regulates cortical synaptogenesis and synapse number

    Nature Neuroscience 15:1667–1674.

    https://doi.org/10.1038/nn.3256

    • PubMed
    • Google Scholar
50. 1. Labun K
    2. Montague TG
    3. Krause M
    4. Torres Cleuren YN
    5. Tjeldnes H
    6. Valen E

    (2019) CHOPCHOP v3: expanding the CRISPR web toolbox beyond genome editing

    Nucleic Acids Research 47:W171–W174.

    https://doi.org/10.1093/nar/gkz365

    • PubMed
    • Google Scholar
51. 1. Lagardère M
    2. Chamma I
    3. Bouilhol E
    4. Nikolski M
    5. Thoumine O

    (2020) FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins

    Scientific Reports 10:19954.

    https://doi.org/10.1038/s41598-020-75814-y

    • PubMed
    • Google Scholar
52. 1. Lee K
    2. Kim Y
    3. Lee SJ
    4. Qiang Y
    5. Lee D
    6. Lee HW
    7. Kim H
    8. Je HS
    9. Südhof TC
    10. Ko J

    (2013) MDGAs interact selectively with neuroligin-2 but not other neuroligins to regulate inhibitory synapse development

    PNAS 110:336–341.

    https://doi.org/10.1073/pnas.1219987110

    • PubMed
    • Google Scholar
53. 1. Letellier M
    2. Elramah S
    3. Mondin M
    4. Soula A
    5. Penn A
    6. Choquet D
    7. Landry M
    8. Thoumine O
    9. Favereaux A

    (2014) miR-92a regulates expression of synaptic GluA1-containing AMPA receptors during homeostatic scaling

    Nature Neuroscience 17:1040–1042.

    https://doi.org/10.1038/nn.3762

    • PubMed
    • Google Scholar
54. 1. Letellier M
    2. Szíber Z
    3. Chamma I
    4. Saphy C
    5. Papasideri I
    6. Tessier B
    7. Sainlos M
    8. Czöndör K
    9. Thoumine O

    (2018) A unique intracellular tyrosine in neuroligin-1 regulates AMPA receptor recruitment during synapse differentiation and potentiation

    Nature Communications 9:3979.

    https://doi.org/10.1038/s41467-018-06220-2

    • PubMed
    • Google Scholar
55. 1. Letellier M
    2. Lagardère M
    3. Tessier B
    4. Janovjak H
    5. Thoumine O

    (2020) Optogenetic control of excitatory post-synaptic differentiation through neuroligin-1 tyrosine phosphorylation

    eLife 9:e52027.

    https://doi.org/10.7554/eLife.52027

    • PubMed
    • Google Scholar
56. 1. Levinson JN
    2. Chéry N
    3. Huang K
    4. Wong TP
    5. Gerrow K
    6. Kang R
    7. Prange O
    8. Wang YT
    9. El-Husseini A

    (2005) Neuroligins mediate excitatory and inhibitory synapse formation: involvement of PSD-95 and neurexin-1beta in neuroligin-induced synaptic specificity

    The Journal of Biological Chemistry 280:17312–17319.

    https://doi.org/10.1074/jbc.M413812200

    • PubMed
    • Google Scholar
57. 1. Livak KJ
    2. Schmittgen TD

    (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method

    Methods (San Diego, Calif.) 25:402–408.

    https://doi.org/10.1006/meth.2001.1262

    • PubMed
    • Google Scholar
58. 1. Loh KH
    2. Stawski PS
    3. Draycott AS
    4. Udeshi ND
    5. Lehrman EK
    6. Wilton DK
    7. Svinkina T
    8. Deerinck TJ
    9. Ellisman MH
    10. Stevens B
    11. Carr SA
    12. Ting AY

    (2016) Proteomic Analysis of Unbounded Cellular Compartments: Synaptic Clefts

    Cell 166:1295–1307.

    https://doi.org/10.1016/j.cell.2016.07.041

    • PubMed
    • Google Scholar
59. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
60. 1. MacGillavry HD
    2. Song Y
    3. Raghavachari S
    4. Blanpied TA

    (2013) Nanoscale scaffolding domains within the postsynaptic density concentrate synaptic AMPA receptors

    Neuron 78:615–622.

    https://doi.org/10.1016/j.neuron.2013.03.009

    • PubMed
    • Google Scholar
61. 1. Martin M

    (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads

    EMBnet.Journal 17:10.

    https://doi.org/10.14806/ej.17.1.200

    • Google Scholar
62. 1. Mondin M
    2. Labrousse V
    3. Hosy E
    4. Heine M
    5. Tessier B
    6. Levet F
    7. Poujol C
    8. Blanchet C
    9. Choquet D
    10. Thoumine O

    (2011) Neurexin-neuroligin adhesions capture surface-diffusing AMPA receptors through PSD-95 scaffolds

    The Journal of Neuroscience 31:13500–13515.

    https://doi.org/10.1523/JNEUROSCI.6439-10.2011

    • PubMed
    • Google Scholar
63. 1. Nair D
    2. Hosy E
    3. Petersen JD
    4. Constals A
    5. Giannone G
    6. Choquet D
    7. Sibarita JB

    (2013) Super-resolution imaging reveals that AMPA receptors inside synapses are dynamically organized in nanodomains regulated by PSD95

    The Journal of Neuroscience 33:13204–13224.

    https://doi.org/10.1523/JNEUROSCI.2381-12.2013

    • PubMed
    • Google Scholar
64. 1. Neupert C
    2. Schneider R
    3. Klatt O
    4. Reissner C
    5. Repetto D
    6. Biermann B
    7. Niesmann K
    8. Missler M
    9. Heine M

    (2015) Regulated Dynamic Trafficking of Neurexins Inside and Outside of Synaptic Terminals

    The Journal of Neuroscience 35:13629–13647.

    https://doi.org/10.1523/JNEUROSCI.4041-14.2015

    • PubMed
    • Google Scholar
65. 1. Peixoto RT
    2. Kunz PA
    3. Kwon H
    4. Mabb AM
    5. Sabatini BL
    6. Philpot BD
    7. Ehlers MD

    (2012) Transsynaptic signaling by activity-dependent cleavage of neuroligin-1

    Neuron 76:396–409.

    https://doi.org/10.1016/j.neuron.2012.07.006

    • PubMed
    • Google Scholar
66. 1. Pettem KL
    2. Yokomaku D
    3. Takahashi H
    4. Ge Y
    5. Craig AM

    (2013) Interaction between autism-linked MDGAs and neuroligins suppresses inhibitory synapse development

    The Journal of Cell Biology 200:321–336.

    https://doi.org/10.1083/jcb.201206028

    • PubMed
    • Google Scholar
67. 1. Poulopoulos A
    2. Aramuni G
    3. Meyer G
    4. Soykan T
    5. Hoon M
    6. Papadopoulos T
    7. Zhang M
    8. Paarmann I
    9. Fuchs C
    10. Harvey K
    11. Jedlicka P
    12. Schwarzacher SW
    13. Betz H
    14. Harvey RJ
    15. Brose N
    16. Zhang W
    17. Varoqueaux F

    (2009) Neuroligin 2 drives postsynaptic assembly at perisomatic inhibitory synapses through gephyrin and collybistin

    Neuron 63:628–642.

    https://doi.org/10.1016/j.neuron.2009.08.023

    • PubMed
    • Google Scholar
68. 1. Poulopoulos A
    2. Soykan T
    3. Tuffy LP
    4. Hammer M
    5. Varoqueaux F
    6. Brose N

    (2012) Homodimerization and isoform-specific heterodimerization of neuroligins

    The Biochemical Journal 446:321–330.

    https://doi.org/10.1042/BJ20120808

    • PubMed
    • Google Scholar
69. Conference

    1. Racine V
    2. Hertzog A
    3. Jouanneau J
    4. Salamero J
    5. Kervrann C
    6. Sibarita J

    (2006) Multiple-target tracking of 3D fluorescent objects based on simulated annealing

    3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006. pp. 1020–1023.

    https://doi.org/10.1109/ISBI.2006.1625094

    • Google Scholar
70. 1. Ran FA
    2. Hsu PD
    3. Wright J
    4. Agarwala V
    5. Scott DA
    6. Zhang F

    (2013) Genome engineering using the CRISPR-Cas9 system

    Nature Protocols 8:2281–2308.

    https://doi.org/10.1038/nprot.2013.143

    • PubMed
    • Google Scholar
71. 1. Renner M
    2. Choquet D
    3. Triller A

    (2009) Control of the postsynaptic membrane viscosity

    The Journal of Neuroscience 29:2926–2937.

    https://doi.org/10.1523/JNEUROSCI.4445-08.2009

    • PubMed
    • Google Scholar
72. 1. Rimbault C
    2. Maruthi K
    3. Breillat C
    4. Genuer C
    5. Crespillo S
    6. Puente-Muñoz V
    7. Chamma I
    8. Gauthereau I
    9. Antoine S
    10. Thibaut C
    11. Tai FWJ
    12. Dartigues B
    13. Grillo-Bosch D
    14. Claverol S
    15. Poujol C
    16. Choquet D
    17. Mackereth CD
    18. Sainlos M

    (2019) Engineering selective competitors for the discrimination of highly conserved protein-protein interaction modules

    Nature Communications 10:4521.

    https://doi.org/10.1038/s41467-019-12528-4

    • PubMed
    • Google Scholar
73. 1. Shipman SL
    2. Schnell E
    3. Hirai T
    4. Chen B-S
    5. Roche KW
    6. Nicoll RA

    (2011) Functional dependence of neuroligin on a new non-PDZ intracellular domain

    Nature Neuroscience 14:718–726.

    https://doi.org/10.1038/nn.2825

    • PubMed
    • Google Scholar
74. 1. Silva MM
    2. Rodrigues B
    3. Fernandes J
    4. Santos SD
    5. Carreto L
    6. Santos MAS
    7. Pinheiro P
    8. Carvalho AL

    (2019) MicroRNA-186-5p controls GluA2 surface expression and synaptic scaling in hippocampal neurons

    PNAS 116:5727–5736.

    https://doi.org/10.1073/pnas.1900338116

    • PubMed
    • Google Scholar
75. 1. Stogsdill JA
    2. Ramirez J
    3. Liu D
    4. Kim YH
    5. Baldwin KT
    6. Enustun E
    7. Ejikeme T
    8. Ji RR
    9. Eroglu C

    (2017) Astrocytic neuroligins control astrocyte morphogenesis and synaptogenesis

    Nature 551:192–197.

    https://doi.org/10.1038/nature24638

    • PubMed
    • Google Scholar
76. 1. Stoppini L
    2. Buchs PA
    3. Muller D

    (1991) A simple method for organotypic cultures of nervous tissue

    Journal of Neuroscience Methods 37:173–182.

    https://doi.org/10.1016/0165-0270(91)90128-m

    • PubMed
    • Google Scholar
77. 1. Südhof TC

    (2017) Synaptic Neurexin Complexes: A Molecular Code for the Logic of Neural Circuits

    Cell 171:745–769.

    https://doi.org/10.1016/j.cell.2017.10.024

    • PubMed
    • Google Scholar
78. 1. Suzuki K
    2. Hayashi Y
    3. Nakahara S
    4. Kumazaki H
    5. Prox J
    6. Horiuchi K
    7. Zeng M
    8. Tanimura S
    9. Nishiyama Y
    10. Osawa S
    11. Sehara-Fujisawa A
    12. Saftig P
    13. Yokoshima S
    14. Fukuyama T
    15. Matsuki N
    16. Koyama R
    17. Tomita T
    18. Iwatsubo T

    (2012) Activity-dependent proteolytic cleavage of neuroligin-1

    Neuron 76:410–422.

    https://doi.org/10.1016/j.neuron.2012.10.003

    • PubMed
    • Google Scholar
79. 1. Thoumine O
    2. Marchot P

    (2017) A Triad of Crystals Sheds Light on MDGA Interference with Neuroligation

    Neuron 95:729–732.

    https://doi.org/10.1016/j.neuron.2017.08.001

    • PubMed
    • Google Scholar
80. 1. Trotter JH
    2. Hao J
    3. Maxeiner S
    4. Tsetsenis T
    5. Liu Z
    6. Zhuang X
    7. Südhof TC

    (2019) Synaptic neurexin-1 assembles into dynamically regulated active zone nanoclusters

    The Journal of Cell Biology 218:2677–2698.

    https://doi.org/10.1083/jcb.201812076

    • PubMed
    • Google Scholar
81. 1. Varoqueaux F
    2. Jamain S
    3. Brose N

    (2004) Neuroligin 2 is exclusively localized to inhibitory synapses

    European Journal of Cell Biology 83:449–456.

    https://doi.org/10.1078/0171-9335-00410

    • PubMed
    • Google Scholar
82. 1. Varoqueaux F
    2. Aramuni G
    3. Rawson RL
    4. Mohrmann R
    5. Missler M
    6. Gottmann K
    7. Zhang W
    8. Südhof TC
    9. Brose N

    (2006) Neuroligins determine synapse maturation and function

    Neuron 51:741–754.

    https://doi.org/10.1016/j.neuron.2006.09.003

    • PubMed
    • Google Scholar
83. 1. Wu X
    2. Morishita WK
    3. Riley AM
    4. Hale WD
    5. Südhof TC
    6. Malenka RC

    (2019) Neuroligin-1 Signaling Controls LTP and NMDA Receptors by Distinct Molecular Pathways

    Neuron 102:621–635.

    https://doi.org/10.1016/j.neuron.2019.02.013

    • PubMed
    • Google Scholar

Author details

1. Andrea Toledo

   University of Bordeaux, CNRS UMR 5297, Interdisciplinary Institute for Neuroscience, Bordeaux, France

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

2. Mathieu Letellier

   University of Bordeaux, CNRS UMR 5297, Interdisciplinary Institute for Neuroscience, Bordeaux, France

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Contributed equally with

   Giorgia Bimbi

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4008-298X

3. Giorgia Bimbi

   University of Bordeaux, CNRS UMR 5297, Interdisciplinary Institute for Neuroscience, Bordeaux, France

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing – review and editing

   Contributed equally with

   Mathieu Letellier

   Competing interests

   No competing interests declared

4. Béatrice Tessier

   University of Bordeaux, CNRS UMR 5297, Interdisciplinary Institute for Neuroscience, Bordeaux, France

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Sophie Daburon

   University of Bordeaux, CNRS UMR 5297, Interdisciplinary Institute for Neuroscience, Bordeaux, France

   Contribution

   Conceptualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

6. Alexandre Favereaux

   University of Bordeaux, CNRS UMR 5297, Interdisciplinary Institute for Neuroscience, Bordeaux, France

   Contribution

   Conceptualization, Data curation, Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Ingrid Chamma

   University of Bordeaux, CNRS UMR 5297, Interdisciplinary Institute for Neuroscience, Bordeaux, France

   Contribution

   Data curation, Methodology

   Competing interests

   No competing interests declared

8. Kristel Vennekens

   VIB Center for Brain & Disease Research and KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium

   Contribution

   Methodology

   Competing interests

   No competing interests declared

9. Jeroen Vanderlinden

   VIB Center for Brain & Disease Research and KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium

   Contribution

   Data curation, Methodology

   Competing interests

   No competing interests declared

10. Matthieu Sainlos

    University of Bordeaux, CNRS UMR 5297, Interdisciplinary Institute for Neuroscience, Bordeaux, France

    Contribution

    Conceptualization, Methodology, Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5465-5641

11. Joris de Wit

    VIB Center for Brain & Disease Research and KU Leuven, Department of Neurosciences, Leuven Brain Institute, Leuven, Belgium

    Contribution

    Conceptualization, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

12. Daniel Choquet

    1. University of Bordeaux, CNRS UMR 5297, Interdisciplinary Institute for Neuroscience, Bordeaux, France
    2. University of Bordeaux, CNRS UAR 3420, INSERM, Bordeaux Imaging Center, Bordeaux, France

    Contribution

    Conceptualization, Project administration, Resources, Supervision, Writing – review and editing

    Contributed equally with

    Olivier Thoumine

    For correspondence

    daniel.choquet@u-bordeaux.fr

    Competing interests

    No competing interests declared

13. Olivier Thoumine

    University of Bordeaux, CNRS UMR 5297, Interdisciplinary Institute for Neuroscience, Bordeaux, France

    Contribution

    Conceptualization, Methodology, Project administration, Resources, Supervision, Methodology, Writing – review and editing

    Contributed equally with

    Daniel Choquet

    For correspondence

    olivier.thoumine@u-bordeaux.fr

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8041-1349

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