1. Developmental Biology
  2. Neuroscience

Complementary vertebrate Wac models exhibit phenotypes relevant to DeSanto-Shinawi Syndrome

  1. Kang-Han Lee
  2. April M Stafford
  3. Maria Pacheco-Vergara
  4. Karol Cichewicz
  5. Cesar P Canales
  6. Nicolas Seban
  7. Melissa Corea
  8. Darlene Rahbarian
  9. Kelly E Bonekamp
  10. Grant R Gillie
  11. Dariangelly Pacheco-Cruz
  12. Alyssa M Gill
  13. Hye-Eun Hwang
  14. Yeong-Eun Kim
  15. Katie L Uhl
  16. Tara E Jager
  17. Marwan Shinawi
  18. Xiaopeng Li
  19. Andre Obenaus
  20. Shane R Crandall
  21. Juhee Jeong
  22. Alex S Nord
  23. Cheol-Hee Kim  Is a corresponding author
  24. Daniel Vogt  Is a corresponding author
  1. Department of Biology, Chungnam National University, Republic of Korea
  2. Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, United States
  3. Department of Molecular Pathology, New York University College of Dentistry, United States
  4. Department of Psychiatry and Behavioral Sciences, University of California Davis, United States
  5. Department of Neurobiology, Physiology and Behavior, University of California Davis, United States
  6. Department of Physiology, Michigan State University, United States
  7. Neuroscience Program, Michigan State University, United States
  8. Corewell Health, United States
  9. Division of Genetics and Genomic Medicine, Department of Pediatrics, Washington University School of Medicine, United States
  10. Director, Preclinical and Translational Imaging Center, School of Medicine, University of California Irvine, United States
https://doi.org/10.7554/eLife.109104.3
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Cite this article
  1. Kang-Han Lee
  2. April M Stafford
  3. Maria Pacheco-Vergara
  4. Karol Cichewicz
  5. Cesar P Canales
  6. Nicolas Seban
  7. Melissa Corea
  8. Darlene Rahbarian
  9. Kelly E Bonekamp
  10. Grant R Gillie
  11. Dariangelly Pacheco-Cruz
  12. Alyssa M Gill
  13. Hye-Eun Hwang
  14. Yeong-Eun Kim
  15. Katie L Uhl
  16. Tara E Jager
  17. Marwan Shinawi
  18. Xiaopeng Li
  19. Andre Obenaus
  20. Shane R Crandall
  21. Juhee Jeong
  22. Alex S Nord
  23. Cheol-Hee Kim
  24. Daniel Vogt
(2026)
Complementary vertebrate Wac models exhibit phenotypes relevant to DeSanto-Shinawi Syndrome
eLife 14:RP109104.
https://doi.org/10.7554/eLife.109104.3
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eLife Assessment

This important study establishes the first vertebrate models of DeSanto-Shinawi Syndrome, revealing conserved craniofacial and social and behavioral phenotypes across mouse and zebrafish that mirror key clinical features. The convincing evidence is supported by behavioral, anatomical, and molecular analyses of Wac animal mutants. This study sets a baseline for future mechanistic studies and reports a platform to test approaches to reverse phenotypes.

https://doi.org/10.7554/eLife.109104.3.sa0

Abstract

Monogenic syndromes are associated with neurodevelopmental changes that result in cognitive impairments and neurobehavioral phenotypes, including autism and seizures. Limited studies and resources are available to make meaningful headway into the underlying molecular mechanisms that result in these symptoms. One such example is DeSanto-Shinawi Syndrome (DESSH), a rare disorder caused by pathogenic variants in the WAC gene. Individuals with DESSH syndrome exhibit a recognizable craniofacial gestalt, developmental delay/intellectual disability, neurobehavioral symptoms that include autism, ADHD, behavioral difficulties, and seizures. However, no thorough studies from a vertebrate model exist to understand how these changes occur. To overcome this, we developed both murine and zebrafish Wac/wac deletion mutants and studied whether their phenotypes recapitulate those described in individuals with DESSH syndrome. We first show that the two Wac models exhibit craniofacial and behavioral changes, reminiscent of abnormalities found in DESSH syndrome. In addition, each model revealed impacts on GABAergic neurons and further studies showed that the mouse mutants are susceptible to seizures, changes in brain volumes that are different between sexes and relevant behaviors. Finally, we uncovered transcriptional impacts of Wac loss-of-function in mice that will pave the way for future molecular studies into DESSH. These studies present two new vertebrate models that begin to uncover biological underpinnings of DESSH syndrome and elucidate the biology of Wac.

Introduction

Pathogenic variants in the WW domain-containing adaptor with coiled coil, Wac, gene cause a neurodevelopmental disorder called DeSanto-Shinawi (DESSH) syndrome (aka WAC-related disorder) (DeSanto et al., 2015). DESSH syndrome is characterized by a constellation of developmental delay/intellectual disability, craniofacial dysmorphism, hypotonia, seizure, gastrointestinal problems (e.g. constipation and gastroesophageal reflux), and ophthalmological abnormalities (e.g. refractive errors and strabismus) (DeSanto et al., 2015). In addition, individuals with DESSH syndrome exhibit arrays of neurobehavioral phenotypes, including aggression, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and anxiety (Alawadhi et al., 2021; DeSanto et al., 2015; Leonardi et al., 2020; Morales et al., 2022; Vanegas et al., 2018). Wac is a known autism risk gene due to the enrichment of pathogenic genetic variants in ASD cohorts (Iossifov et al., 2014). However, little is known about the function of WAC in the brain and whether suitable vertebrate models could explore the underlying biology of DESSH syndrome phenotypes.

The Wac gene encodes a protein with WW and coiled-coil domains, common protein/protein interaction regions. A recent study assessed the role of evolutionarily conserved protein domains in the human WAC protein that uncovered an amino-terminal nuclear localization signal as well as a highly conserved disorganized region that contains several putative phosphorylation motifs that are mutated in humans with known WAC variants (Rudolph et al., 2023). While the functional role of these domains and of human WAC itself are still poorly understood in vertebrates, a multitude of roles for the WAC protein have been proposed, including positive regulation of mammalian target of rapamycin (mTOR) signaling, mitosis, transcription, and autophagy (David-Morrison et al., 2016; McKnight et al., 2012; Qi et al., 2018; Zhang and Yu, 2011), which were worked out in drosophila and immortalized cell lines. However, whether these roles are conserved in vertebrates and their relevance to human DESSH symptoms is still unknown.

To better understand Wac’s role in vertebrates and in particular brain-relevant changes, we generated two species-specific vertebrate deletion models to assess whether Wac depletion could recapitulate symptoms associated with DESSH syndrome. The rationale for choosing mouse and zebrafish models were: (1) Both are vertebrates; (2) Unique genetic approaches in each model; (3) Species-specific phenotypic screening advantages; (4) Finally, utilizing two distinct vertebrate models to study key brain phenotypes will be a powerful means to uncover the fundamental biology of DESSH syndrome in a manner that is likely to reveal novel neurological underpinnings.

Overall, our models revealed some similarities in core DESSH symptoms, including craniofacial dysmorphism and relevant behaviors implicating decreased learning and social interaction. Notably, while each model displayed common decreased GABAergic markers, there were slight differences between species in targets. Additionally, mice had increased seizure susceptibility, while zebrafish did not; mice had only minor changes to sociability while zebrafish showed a strong change. We further took advantage of the mouse model, which more closely resembles humans, to probe into potential unexplored phenotypes and molecular changes. Despite some differences, both models revealed that males were more susceptible in multiple phenotypes and that male mice exhibited novel molecular changes that could be further investigated. The first vertebrate disease models for DESSH syndrome are presented here, and the multiple shared and unique phenotypes that could begin to inform the underlying biological mechanisms of DESSH.

Results

Validation of murine Wac and zebrafish wac mutants

We obtained sperm from the International Mouse Phenotyping Consortium (IMPC); the Wac murine locus on chromosome 18 was genetically modified to include flanking loxP (Flox) sites of exon 5 (Figure 1A). Sanger sequencing of the genotyped PCR validated the locus and an introduced frameshift that resulted in a premature stop codon (Figure 1B). Wac flox founders were bred and crossed to beta-actin-Cre mice, which express Cre in germ cells (Lewandoski et al., 1997). We generated wild-type (WT) and constitutive heterozygous (Het) progeny of similar size (Figure 1C) but not any live homozygous knockouts. Findings were consistent with data from the IMPC; constitutive Wac KOs are embryonic lethal. Results of genotyping using PCR are shown in Figure 1D. WAC protein was also reduced by 64% in Het brains, (shown later in Figure 5).

Figure 1 with 1 supplement see all
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Generation and validation of murine Wac and zebrafish waca mutants.

(A) Floxed mouse Wac genetic locus and genotyping primers. Genotyping primers (magenta lines) reside external to the loxP sites (orange triangles) flanking exon 5 and nearby introns. (B) The recombined DNA band was Sanger-sequenced and results show the start codon (blue) and the novel stop codon introduced by the frameshift mutation resulting from Cre-mediated deletion of the locus used to generate the locus. (C) Example images of adult wild-type (WT) and Wac Het mice. (D) DNA gel of genotyping for WT and recombined (Het) Wac alleles; upper band is ~1.5 kilobases and the lower band is ~500 basepairs. (E) waca KO zebrafish were generated by CRISPR/Cas9. KO target sites for sgRNAs are indicated by red arrowheads and predicted protein structures for KO mutations are indicated below the panel. (F) WT and waca KO zebrafish have grossly normal sizes but waca KO exhibited a shortened jaw (black triangle) at 5 dpf. (G) Example waca genotyping results showing a 116 bp for WT (upper band) and a 111 (lower band) bp for KO. Abbreviations: (aa) amino acid; (bp) base pair; (Chr) chromosome; (dpf) days post fertilization; (WT) wild-type. Scale bars (C) = 1 cm and (F) = 200 µm.

Figure 1—source data 1

All genotyping.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig1-data1-v1.zip
Figure 1—source data 2

Mouse Wac genotyping.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig1-data2-v1.zip
Figure 1—source data 3

Zebrafish genotype.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig1-data3-v1.zip

Zebrafish harbor two wac genes on chromosome 12, waca, and 2, wacb. A CRISPR/CAS9 approach was used to target these loci to create waca and wacb KOs and then interbreed to generate waca; wacb double KOs. Notably, waca expression was more widespread and higher in the brain of zebrafish compared to wacb (Figure 1—figure supplement 1), indicating that waca might be more important for DESSH. The waca mutant had a deletion in coding exon five (Figure 1E) with waca progeny grossly normal in size by 5 days post fertilization (dpf) but with a shortened lower jaw (Figure 1F); genotyping results are shown in Figure 1G. The wacb KO and waca;wacb double KO zebrafish are shown in Figure 1—figure supplement 1. Since the wacb KOs did not show phenotypes like the waca mutants, we did not report further data. Moreover, since double KOs exhibited early lethality, we chose to solely focus on waca KOs for this report. Some possible causes of lethality are denoted in Figure 1—figure supplement 1, with double KOs showing evidence of heart edema and loss of the swim bladder.

Craniofacial changes in murine and zebrafish mutants

Craniofacial dysmorphism is a cardinal feature in patients with DESSH syndrome (Alsahlawi et al., 2020; DeSanto et al., 2015; Hamdan et al., 2014; Ho et al., 2022; Leonardi et al., 2020; Lugtenberg et al., 2016; Morales et al., 2022; Uehara et al., 2018; Vanegas et al., 2018), with larger frontal facial features being common, including larger foreheads. We first wondered whether a reduction in Wac dosage in mice could recapitulate this finding. Postnatal day (P)0 and P30 mouse skulls were examined. During neonatal ages, a widening of the fontanels and sutures along the midline of the calvaria was noted (Figure 2A and B). While both the anterior and posterior regions of the calvaria were affected, the anterior region had the greater change (Figure 2A, B, E and F with pseudo-colored areas in 2 A’, B’; anterior p=0.0001, posterior p=0.01). By P30, there remained a noticeable gap in the interfrontal suture of the Wac Het. Concomitantly, the width of the skull across the frontal bones was significantly increased in Wac Hets, while the width across the parietal bones was normal (Figure 2C, D, G and H, p=0.002, frontal width). A feature that was notable was a shortened lower jaw in the waca KO zebrafish compared to WTs at 10 dpf (Figure 2I–K, p=0.006), suggesting similar abnormalities in frontal craniofacial structures. In addition, the waca mutants also exhibited changes in other frontal craniofacial features, including an increased width spanning Meckel’s cartilage at 13 dpf (Figure 2L–N, p=0.02). The waca KO zebrafish survived into adulthood and continued to show morphological craniofacial defects of shortened lower jaw, sunken head, and broad nasal tip at adult stage (data not shown). Thus, both models exhibit craniofacial changes relevant to DESSH syndrome.

Figure 2
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Craniofacial changes in mutants.

(A, B) Dorsal views of the P0 calvaria stained with Alizarin red for bone. (A’, B’) The suture and fontanel areas in (A) and (B) are pseudo-colored green (anterior) and orange (posterior). (C, D) Dorsal views of P30 wild-type (WT) and Wac Het skull bones; arrow (D) points to a gap in the interfrontal suture. (F) Frontal bone and (P) Parietal bone; white dashed lines are widths measured in (G, H). (E, F) Quantification of P0 fontanel suture areas pseudo colored in A’ and B’; WT n=4, Het n=5. Quantification of the skull width across the frontal (G) and parietal (H) bones at P30 in WTs and Wac Hets; WT n=4, Het n=4. (I, J) The waca KO zebrafish show a shortened jaw structure, compared to WT. Lines denote lower jaw length. (K) Quantification of lower jaw length; n=9 WT and 4 waca KO. (L, M) Cartilage staining of zebrafish using alcian blue, ventral view. (N) Measurements of the width of Meckel’s cartilage in waca KO zebrafish at 13 dpf (red line); WT n=10 and KO n=8. Data are expressed as the mean ± SEM. *p<0.05, **p<0.01, and ***p<0.001. Scale bars (B’) = 1 mm, (D) = 4 mm, (J, M) = 200 µm.

Behavioral changes in Wac/wac mutants

Loss of Wac may impact social/cognitive behavior, as seen among individuals diagnosed with DESSH syndrome and as previous models exhibit intellectual challenges (Alsahlawi et al., 2020; Leonardi et al., 2020; Lugtenberg et al., 2016; Morales et al., 2022; Uehara et al., 2018). We noticed that Het mutant mice had normal ambulation, total distance traveled, and time spent in different areas of the open field (Figure 3—figure supplement 1). In a 3-chamber social test, while some Wac Hets trended towards less social discrimination using a previously described test (Phillips et al., 2019), they did not significantly differ from WTs (Figure 3A). However, Wac Hets did show significant impairments in the Y-maze that tests working memory (Kraeuter et al., 2019a; Lalonde, 2002; Figure 3B, p=0.047). Anxiety (elevated plus maze) and spatial memory (radial arm water maze) were not grossly changed but similar to the social discrimination test, cohorts of Wac Hets trended towards impairment in each test (Figure 3—figure supplement 1).

Figure 3 with 1 supplement see all
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Behavioral changes in mutants.

(A) Six to eight week WT and Wac Het mice were tested in the 3-chamber social/novel object test, wild-type (WT) n=20 and Het n=20, and the Y-maze (B), n=13 for both groups, with Wac Hets showing deficits in the Y-maze. (C, D) Example heat maps during the 17–19 min timeframe of the social cohesion test in zebrafish; waca KO males and females were more dispersed (E, F), see methods for test details. (G, H) Distance moved was greater in male waca KOs in the novel tank assay, n=11, both groups, compared to females, n=12 (WT) and 11 (KOs). Data are expressed as the mean ± SEM; *p<0.05, **p<0.01, and ***p<0.001.

We next tested zebrafish in a social cohesion assay; WT fish stayed in close contact while waca KOs were dispersed during the trial, see heatmap distribution of zebrafish in Figure 3C and D. While both male and female zebrafish waca KOs showed deficits in this assay, we report the sexes independently since WT males and females had significant differences in baseline social cohesion. Moreover, male zebrafish waca KOs had a greater change in social cohesion than females (Figure 3E and F, p=0.0007 males, p=0.01 females). In a novel tank assay to test hyperactivity, male waca KO fish moved greater distances throughout the test compared to WTs (Figure 3G, p=0.005), while there were no significant difference in females (Figure 3H). While differences exist in zebrafish locomotion/hyperactivity, both models share deficits in some behaviors.

Elevated seizure susceptibility and depleted GABAergic markers in Wac heterozygous mice

The previous behavioral changes suggested that core neuron cell ratios might be altered, potentially through an imbalance in GABAergic/glutamatergic neurons that could impact this and other disorders (Rubenstein and Merzenich, 2003; Sohal and Rubenstein, 2019). Epilepsy occurs frequently in individuals with DESSH syndrome (Alawadhi et al., 2021; Alsahlawi et al., 2020; DeSanto et al., 2015; Leonardi et al., 2020; Lugtenberg et al., 2016), thus, we first tested if loss of Wac expression was sufficient to increase seizures in our mouse model. We assessed whether the GABAA receptor antagonist, pentylenetetrazol (PTZ), could induce seizures in mice with Wac loss of function. The drug was administered intraperitoneal at a subthreshold dose (50 mg PTZ per kilogram of mouse body weight) that rarely elicited a tonic-clonic seizure in WTs. We hypothesized that if loss of Wac promoted a shift favoring brain excitation/inhibition, then Wac hets would exhibit more severe seizure behaviors by P30.

PTZ was administered and mice were observed for 20 min (Figure 4A); most mice fully recovered by 15 min and all WT mice did not exhibit any behavioral changes by 20 min post-injection. Behaviors included freezing, motor twitches, loss of balance, seizures and hyperactivity, which are detailed in the methods. While PTZ led to subtle behavioral changes in WT mice but rarely elicited a seizure. However, Wac Hets exhibited elevated seizure behaviors, including hunched body posture, arching tail, and forelimb clonus, as well as succumbing to seizures (Figure 4B, p<0.0001). Thus, loss of Wac leads to increased seizure susceptibility in mice, consistent with one of the comorbidities observed in ~40% of individuals with DESSH syndrome. In contrast, when zebrafish were tested for seizure susceptibility, the waca KOs did not show any gross changes although both WTs and mutants responded to PTZ (data not shown), suggesting that this specific symptom was not conserved between the 2 vertebrate models.

Figure 4 with 2 supplements see all
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Murine Wac depletion leads to elevated seizure susceptibility and loss of GABAergic markers.

(A) Schema depicting the seizure induction by pentylenetetrazol (PTZ); mouse image was made by BioRender software. Briefly, P30 mice were administered PTZ intraperitoneally and then assessed for the highest seizure severity score over the course of 20 min. The maximum seizure severity score over 20 min was quantified (B); n=16 (wild-types, WTs) and 22 (Hets). (C) Table of cortical interneuron cell counts in the somatosensory cortex at P30. (D–F) Immunofluorescent images of parvalbumin (PV) and cell density quantification in mice somatosensory cortex, n=3, both groups. (G–I) Immunofluorescent images of LHX6 and cell density quantification in mice somatosensory cortex, n=3, both groups. Data are expressed as the mean ± SEM, *p<0.05 and ****p<0.0001. Scale bar = 100 µm (H).

Some GABAergic deficits in mouse and zebrafish Wac/wac mutants

While we only found a susceptibility to seizures in mice, an imbalance in excitatory and inhibitory cell populations may still exist in each model but with differential impacts on seizures and/or behaviors. One possibility is a decrease in GABAergic cortical interneuron (CIN) function, a diverse group of inhibitory neurons whose dysfunction has been implicated in other syndromes with epilepsy and/or autism (Angara et al., 2020; Kim et al., 2018; Malik et al., 2019; Vogt et al., 2018; Vogt et al., 2015; Vogt et al., 2014). To assess whether Wac may impact CIN populations, we assessed three broad groups of CINs; parvalbumin (PV) + and somatostatin (SST)+, which label ~70% of GABAergic interneurons (Wonders and Anderson, 2006), and prospero homeobox 1 (PROX1) expressing, which labels the majority of caudal-derived interneurons (Rubin and Kessaris, 2013). The cell density of SST and PROX1 CINs was normal, but there was ~18% decrease in PV + CINs in the Het (Figure 4C–F, p=0.02); example images of SST and PROX1 cells are shown (Figure 5—figure supplement 1). To probe whether PV expression is due to loss of cells or simply the expression of PV, we assessed for the CIN marker, LHX6, which is expressed in all PV + CINs in the somatosensory cortex (Angara et al., 2020). We found no decrease in LHX6 cell numbers (Figure 4C and G–I), suggesting this phenotype is likely due to impacts upon PV expression rather than loss of this group of interneurons.

We also studied GABAergic markers in waca KO zebrafish. We first probed via in situ for the various parvalbumin transcripts in zebrafish but found no differences, including pvalb6, which is the only one expressed in the brain (Figure 4—figure supplement 1). We next looked at the pan-GABAergic gene, gad1b, in the forebrain and compared gad1b expression in WTs and waca KOs. Notably, we uncovered a significant reduction in forebrain area that expressed the transcript (Figure 4—figure supplement 2, p<0.0001); WT and waca KO zebrafish brains were not altered in general size, suggesting this measurement was not biased due to a smaller or larger brain due to genotype. These and previous data suggest that GABAergic neuron populations are impacted by mouse and zebrafish Wac/waca loss-of-function and potentially contribute to DESSH syndrome.

Normal numbers of major brain cell types and proteins in Wac mutant mice brains

Since many antibodies are not available for zebrafish, and mice are closer to humans in gene conservation, we chose to focus on our mouse model to next assess if any other major brain cell populations, signaling events (including post-translational modifications), or gross synaptic markers were altered by loss of Wac. Thus, we first performed cell density counts for different classes of neuronal cells in our mouse model that included neurons, oligodendrocytes, astrocytes, and microglia in P30 somatosensory cortices (Figure 5A). The total number of neurons was assessed by labeling for NeuN, while oligodendrocytes, astrocytes and microglia were labeled with OLIG2, S100beta and IBA1, respectively; representative images of these markers are shown in Figure 5—figure supplement 1. No differences in these major cell types were observed between the genotypes in mice.

Figure 5 with 2 supplements see all
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Cell counts and biochemical assessment of key markers in P30 Wac wild-type (WT) and Het mice.

(A) WT and Wac Het somatosensory cortices were counted for major cell classes, including markers of neurons and glia, n=3 each group, parentheses indicate standard error of the mean. (B) P30 somatosensory cortex tissue was probed via western blotting for WAC and proteins known to be altered in other models or of brain markers. (C) Quantification of protein bands were normalized to GAPDH or the total protein if a phosphorylated protein. Data are expressed as the mean ± SEM, n=4 all groups. *p<0.05 and **p<0.01. Abbreviations: (WB) Western blot and (kDa) kiloDaltons.

Figure 5—source data 1

Compiled western blots.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig5-data1-v1.pdf
Figure 5—source data 2

Wac western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig5-data2-v1.zip
Figure 5—source data 3

beta-catenin western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig5-data3-v1.zip
Figure 5—source data 4

ERK western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig5-data4-v1.zip
Figure 5—source data 5

GAD65/67 western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig5-data5-v1.zip
Figure 5—source data 6

GAPDH western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig5-data6-v1.zip
Figure 5—source data 7

Gephyrin western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig5-data7-v1.zip
Figure 5—source data 8

mono-ubiquitin H2B western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig5-data8-v1.zip
Figure 5—source data 9

phospho-ERK western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig5-data9-v1.zip
Figure 5—source data 10

phospho-S6 western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig5-data10-v1.zip
Figure 5—source data 11

PSD95 western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig5-data11-v1.zip
Figure 5—source data 12

S6 western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig5-data12-v1.zip

Next, we assessed mouse P30 somatosensory cortices for proteins changed in other Wac models or markers of brain synapses. As mentioned earlier, WAC protein was reduced 64% in the Hets (Figure 5B and C, p=0.002). Despite previous reports in other models (David-Morrison et al., 2016; Zhang and Yu, 2011), we did not observe differences in mTOR activity or ubiquitination of histone 2B. Key pathways, including Wnt/beta-catenin and synaptic markers, PSD95 (excitatory synapses) and Gephyrin/Gad65/67 (inhibitory synapses), were not altered (Figure 5B and C). For the MAPK signaling pathway, there was a decrease in active ERK1, 38% and ERK2, 33%, in the Wac Hets (Figure 5B and C, pERK1 p=0.01, pERK2 p=0.02), suggesting that while major cell types and proteins involved in brain function are not altered, some signaling events may be candidates for future studies to understand WAC function.

Subtle pyramidal neuron lamination but normal glutamatergic electrophysiological properties in mouse Wac Hets

Our data thus far revealed altered GABAergic neuron marker expression but no gross changes in other generic neuron numbers, glia numbers, or synaptic protein abundance. However, these observations could still be true while subtypes of excitatory neurons in distinct cortical lamina or their physiological/synaptic properties are yet to be altered. Thus, we also assessed glutamatergic neuron laminar markers in the somatosensory cortices of P30 mice. SATB2, CTIP2 and TBR1 were labeled and the proportion of expressing neurons assessed in layers 2/3/4, 5, and 6 at P30. While there were no significant laminar changes in the Het for SATB2 and CTIP2, there was a proportional increase of TBR1 + cells in layer 6 (Figure 5—figure supplement 2, p=0.047). We also examined the intrinsic membrane properties of layer 2/3 neurons in the primary somatosensory cortex of adult WT and Het mice. Overall, there were no gross differences in passive or active membrane properties between Wac Het and WT cells (Figure 5—figure supplement 2 and Table 1), suggesting excitatory cortical neurons in the mutants have normal physiological properties. We also assessed synaptic function by measuring spontaneous excitatory postsynaptic currents (EPSCs) in layer 2/3 neurons. We found no differences between WT and Wac Het excitatory neurons (Figure 5—figure supplement 2), suggesting that local excitatory neurons in the neocortex are not measurably impacted by Wac depletion in the Het mice.

Table 1
Electrophysiological properties of L2/3 excitatory cells in the somatosensory cortex of wild-type (WT) and Wac Het mice.
L2/3 excitatory neuronsPTest
WTWac Het
Mean±s.e.m.Mean±s.e.m.
RMP (mV)–86.8±0.7–87.1±0.60.747t-test
Rm (MΩ)66.8±3.659.2±3.70.151t-test
τm (ms)12.9±0.512.3±0.40.209t-test
Cm (pF)197.4±8.8218.3±13.70.720MW U-test
Rheobase (pA)170.0±11.0167.2±12.60.868t-test
Spike threshold (mV)–49.9±0.6–50.1±0.50.788t-test
Spike amplitude (mV)85.0±1.588.1±1.40.142t-test
Spike half-width (mV)0.63±0.020.61±0.020.323t-test
AHP amplitude (mV)–18.3±0.6–18.5±0.50.794t-test
Max rate of rise (mV*ms)526.0±28.8586.3±22.40.109t-test
Max rate of decay (mV*ms)–112.8±4.2–120.0±4.50.243t-test
n=16 cells, 3 micen=16 cells, 3 mice

  1. *See methods for an explanation of how the electrophysiological parameters were defined/measured. All membrane potentials were corrected for a 14 mV liquid junction potential. All statistical tests were two-tailed.

Increased brain volume in Wac Het mice with a male bias

While our zebrafish model is similar to the mouse in many core symptoms that present in humans, the Wac Het mice recapitulate almost all DESSH syndromes yet tested. Thus, we wanted to take advantage of this model to understand if there may be more changes to the mouse Het brain that could underlie core and other symptoms relevant to humans. Since craniofacial symptoms exist in nearly all DESSH patients and our models have these same alterations, we hypothesized that alterations to the skull shape may also be correlated with altered brain volume. To determine what regions of the mutant mouse brains may be anatomically different, we performed magnetic resonance imaging (MRI) on P30 fixed WT and Wac Het brains of both sexes. We first assessed total brain volume of the WT and Hets and found that male Wac mutants exhibit a larger whole brain volume than WT males; there were no significant differences in female brain volumes (Figure 6A, p=0.04). Next, we examined specific regional volumetric changes, starting with cortical domains. Several alterations were observed in both males and some in females, with most cortical regions showing an increase in volume compared to WTs. Notably, only males had increased retrosplenial cortices, while females showed increases in parietal cortices (Figure 6B, retrosplenial cortex male Het vs male WT p=0.002 and female WT vs. female Het p=0.01).

Figure 6 with 2 supplements see all
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Magnetic resonance imaging (MRI) reveals increased brain volume in mouse Wac Het males.

(A) There was a significant increase in whole brain volumes in HET compared to wild-type (WT) male mice. Female HET mice did not exhibit significant changes in brain volume relative to WT. Data are expressed as the mean ± SEM; males and female WTs n=7 and female Hets n=6, *p<0.05. (B) %change between cortical regions were larger in most HET mice areas, with distinct regions different between sexes. (Bolded bars indicate significant differences p<0.05, t-test).

We also examined limbic regions and white matter tract volumes in WT and Wac Het mice. As before, male mice had significant increases in both limbic and white matter volumes compared to females. Specifically, hippocampal ventral CA3 and dorsal CA1 regions were increased in males but not females (Figure 6—figure supplement 1, ventral CA3 male WT vs. male Het p=0.0003 and dorsal CA1 male WT vs. male Het p=0.04). Male-specific increased volume was also observed in white matter dorsal fornix and corpus callosum tracts (Figure 6—figure supplement 2, dorsal fornix male WT vs. male Het p=0.006 and corpus callosum male WT vs. male Het p=0.01). As of this report, no DESSH syndrome individuals have been MRI assessed. However, these observations may be potential predictors of phenotypes that may arise as those with DESSH syndrome age into adulthood and whether there could be sex differences in humans.

RNA sequencing of mouse forebrains reveals DESSH syndrome candidate transcripts

In a similar logic to our above tests, we chose to focus on the mouse model to assess potential RNA transcripts that are changed due to Wac loss-of-function since the mouse model may be more relevant to humans with DESSH. To discover potential molecular changes that could inform future mechanistic studies, we compared WT and Wac Het bulk RNA-seq at postnatal day 2 (P2), an age when mice are undergoing critical developmental milestones, including neuronal migration, cell death, axon and dendrite outgrowth, as well as beginning synaptogenesis. We sequenced 14 WT and 10 Wac Het sex-balanced forebrain samples to a median sequencing depth of 50 million paired-end reads per sample (Figure 7—figure supplement 1; Supplementary file 1). As expected, we found significant correlations between sample sex and sequencing depth, and principal component analysis (PCA) dimensions; no outliers were detected in the PCA space (Figure 7—figure supplement 2; Supplementary file 1).

Differential expression (DE) analysis was performed using edgeR and included surrogate variable analysis (SVA) batch correction (Leek et al., 2012), which accounted for sex- and batch-biased transcriptomic variability. We found 7 and 16 genes were significantly upregulated and downregulated at FDR <0.1, respectively; and 385 and 564 upregulated and downregulated genes were identified at a more inclusive p<0.05 threshold (Figure 7A). DE was skewed towards downregulation, which is consistent with Wac function in promoting transcription (Zhang and Yu, 2011). While Wac gene expression was modestly upregulated in the mutants (log2FC = 0.09, p=0.01, FDR = 0.5), coverage of floxed Wac exon 5 was reduced by 50% in HET samples (Figure 7—figure supplement 3), consistent with compensatory upregulation. As noted earlier, western blot found decreased WAC protein expression (Figure 5) and no evidence of mutant isoforms, suggesting that mutant Wac transcript is unlikely to be stably translated. Sex-stratified analysis identified three genes passing FDR <0.1 (one upregulated and two downregulated) in females, and 0 in males. At p<0.05, the number of DE genes was higher in males, with 303 upregulated and 275 downregulated, in contrast to 193 upregulated and 149 downregulated genes in females (Figure 7A, Supplementary files 1–5); a volcano plot showing example up and down regulated genes is shown in Figure 7B.

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Transcriptomic characterization by bulk RNA-seq identified shared and divergent transcriptomic dysregulation in males and females.

(A) Number of differential expression (DE) genes identified in the surrogate variable analysis (SVA)-corrected DE analysis and sex-stratified DE analysis. Results are separated by effect direction and significance (p-value <0.05 or FDR <0.1). (B) Volcano plot representing DE of the SVA-corrected DE model in (A). (C) Selected GO analysis of significantly (p<0.05) up and down-regulated genes in the SVA-corrected DE analysis. Results are colored by significance, the size indicated the number of DE genes belonging to that term and are ordered by the ratio of enrichment. (D) Examples of significant DE genes identified in the SVA-corrected model Fggy (FDR = 0.014) and Pcdhga11 (FDR = 0.026), stratified by sex. (E) Curated GO terms from stratified GSEA analysis. Colors indicate significance, size the number of genes in the enriched term, and the enrichment score (NES). (F, G) Volcano plots showing the differential expression of autism spectrum disorder (ASD) genes in males and females. (H) Examples of DE genes which show increased effect size and significance in males, Prr12 (females, p=0.99; males, p=0.0006) and Chd2 (females, p=0.39; males, p=0.0008). (I) Western blots showing PCDH-alpha abundance in wild-types (WT)s and Hets. (J) WB quantification comparing PCDH-alpha normalized to alpha-tubulin expression in P30 somatosensory cortices; n=4 for both genotypes and **p<0.01. Data are expressed as the mean ± SEM.

Figure 7—source data 1

Compiled western blots.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig7-data1-v1.pdf
Figure 7—source data 2

PCDH alpha western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig7-data2-v1.zip
Figure 7—source data 3

alpha tubulin western blot.

https://cdn.elifesciences.org/articles/109104/elife-109104-fig7-data3-v1.zip

We performed gene ontology (GO) enrichment analysis of the DE genes passing a p<0.05 threshold in the sex-combined model (Figure 7C). Upregulated DE genes were enriched for biological processes involved in RNA processing and splicing, whereas downregulated DEGs were enriched for metabolic processes clustered around purine and fatty acid metabolism. Gene set enrichment analysis (GSEA), which uses a rank-based approach to determine enriched pathways amongst up- and down-regulated genes, also identified RNA processing-related terms to be upregulated and metabolic terms downregulated in Wac mutants (Figure 7—figure supplement 4). Fggy is shown as an example of a metabolic gene with strong upregulation (Figure 7D). We also found a number of protocadherin genes (Pcdhga6, Pcdhga8, Pcdhga9, and Pcdhga11) were significantly upregulated (Figure 7B); Pcdhga11 is shown which has consistent DE across sexes and samples (Figure 7D). We next tested for enrichment of disease associated genes using custom gene panels, finding no significant enrichment for high-confidence ASD genes based on rare mutations (Fu et al., 2022) (n=180) or the larger set of SFARI-associated ASD genes (Abrahams et al., 2013) (n=1137), nor was there enrichment for genes associated with microcephaly (n=1031), macrocephaly (n=347), and epilepsy (n=1318) (Piñero et al., 2020; Figure 7—figure supplement 4).

As DE analysis identified a potential sex bias with stronger effects in male Wac mutants (Figure 7A) and as the brain volume phenotype was larger in males (Figure 6A), we further investigated the sex-stratified DE results. Sex-stratified transcriptomic signatures were largely directionally correlated with each other, indicating most DEGs having a similar shift in RNA expression across sexes, with a large fraction of genes that had a stronger effect in males. (Figure 7—figure supplement 4). We used GSEA to identify functional terms that were activated (upregulated) or suppressed (downregulated) in mutants (Figure 7E). Terms identified in the GO analysis of the full dataset showed concordant enrichment in both sexes (RNA binding, RNA processing, perineuronal net, lipid import into cells), but with higher significance in males. Notably, the metabolic-related terms (purine-containing compound metabolic process, and carboxylic acid metabolic process) showed opposite directions with significant suppression in males. There was also concordant activation of dopaminergic neuron differentiation, integrin binding, developmental growth, chromatin organization, and regulation of cell cycle processes, with a stronger effect in males. Among the discordant signatures were suppression of neuron spine, chemokine binding, and regulation of cell migration in females and suppression of oxidative phosphorylation in males. Using a permutation test to assess altered ASD genes, male DEGs (p<0.05) showed a trend of enrichment for high-confidence ASD genes (p=0.07) where females did not (p=0.98) (data not shown). Examples of altered ASD transcripts are shown in Figure 7F and G, where males showed a unique alteration to several ASD genes compared to females. We highlight two ASD-associated genes with stronger effect in males versus females, Prr12 (females, p=0.99; males, p=0.0006) and Chd2 (females, p=0.39; males, p=0.0008) (Figure 7H). Finally, we wanted to validate if one of the more general changes between sexes in gene expression, that of the protocadherin alpha genes, was upregulated at the protein level. We thus probed P30 somatosensory cortices for pan protocadherin alpha via western blot (Figure 7I) as previously performed (Ing-Esteves et al., 2018); protein levels of protocadherin alpha were upregulated in the somatosensory cortices of Wac Hets (Figure 7J, p=0.003).

RNA processing and splicing were identified as perturbed biological processes in the enrichment analysis and the RNF20/40/WAC complex has been reported to play a role in regulating chromatin accessibility and transcription through H2B ubiquitination (Zhang and Yu, 2011), as well as mediating transcription elongation, and indirectly, splicing (Xu and Arnaout, 2002; Zhang and Yu, 2011). We performed event-based differential splicing (DS) analysis using the MAJIQ2.5 pipeline (Jinek et al., 2012). We found 13 DS junctions passing thresholds of 10% delta percent spliced in (dPSI) between WT and Wac Het, at 80% confidence level. As expected, we found two significant events representing floxed Wac exon skipping, and one intron retention in the Cre transgene locus Gt(ROSA)26Sor. We also detected skipped exons in Snhg14, alternative splice donors in Dnmbp and Trmt61b, alternative splicing acceptors in Ssrp1 and Trmt13, retained introns in Gm21992 and Pigf, and reduced intron retention in Zc3h7a (Figure 7—figure supplement 5, Supplementary file 6). None of these genes were identified as significantly differentially expressed. Overall, results of DE analysis capture modest transcriptional differences with evidence for stronger DE effects in males, as well as some evidence for differences in splicing events in the Wac Het mutants. Overall, our data shed new light on the transcriptomic mechanisms and phenotypes of Wac haploinsufficiency and provide further context for the existing studies of DESSH syndrome.

Discussion

DESSH syndrome is a monogenic disorder that results in variable degrees of developmental delay/intellectual disability, craniofacial dysmorphic features, ophthalmological and gastrointestinal problems, hypotonia, and other neurological changes that may underlie the elevated co-diagnoses of ASD, ADHD, and seizures. Previous work identified distinct molecular and cellular changes, including alteration to the mTOR signaling pathway and nuclear regulation of transcription that may also overlap with our studies. A major limitation is that these previous studies were restricted to invertebrates and cell lines but not vertebrates. Thus, we sought to test if genetic depletion models of the Wac gene in vertebrates could recapitulate some of the reported symptoms in humans and further use our mouse model to probe novel anatomical and molecular changes for future studies. While the mouse and zebrafish models mostly recapitulated most core DESSH symptoms tested herein, they were not completely aligned. Importantly, mouse Hets recapitulated many DESSH symptoms while zebrafish waca KOs were needed to realize similar phenotypes. Since zebrafish have both waca and wacb genes, it is possible that some compensation occurs and only a complete deletion of one of the genes, here waca, could reveal symptoms that reflect DESSH patients.

While this study describes two novel vertebrate models for DESSH syndrome, the mechanisms underlying our findings still need to be elucidated. This will be done in future studies using conditional Wac deletion models that are predicted not to result in lethality due to limited deletion of Wac in distinct brain or other tissues. It is possible that some mechanisms might be conserved from previous studies examining Wac in cell lines and drosophila (David-Morrison et al., 2016; Zhang and Yu, 2011). We previously found that the WAC protein still localizes to the nucleus and has a speckled pattern in mammalian neurons (Rudolph et al., 2023), similar to the first study examining Wac in a cell line (Xu and Arnaout, 2002). Thus, a nuclear function for Wac is still highly likely and needs to be further tested. In addition, the WAC protein’s WW, coiled-coil, and disordered conserved domains are likely to mediate distinct adaptor complex properties and potential other regulatory functions that have yet to be uncovered.

Craniofacial dysmorphism and social behavior deficits are highly prevalent in DESSH, with craniofacial dysmorphism existing in almost 100% of those diagnosed with DESSH while autism behaviors are seen in roughly 30% of patients (Marwan Shinawi, personal communication), which were trending in mice and profound in zebrafish. We also found a robust sensitivity to a seizure-inducing drug in mice but not zebrafish. Together, each model revealed unique strengths and better connections to core DESSH symptoms. Despite these differences, each model showed impacts upon GABAergic neurons. These changes in GABAergic neuron markers may or may not underlie the common behavioral social changes in each model while the seizures may have a different underlying mechanism. Regardless, these data present two novel vertebrate DESSH models that could provide new insights into Wac loss-of-function and the impacts that this gene regulates upon brain function over an evolutionary timeline.

We and others have observed a decrease in PV expression but no loss of MGE-lineage interneurons in other syndromic mouse models like Cntnap2 and other models where just PV protein levels alone were assessed (Paterno et al., 2021; Peñagarikano et al., 2011; Vogt et al., 2018), suggesting this may be a common feature in some monogenic syndromes with overlapping phenotypic features. Individuals with DESSH syndrome can have seizures (Alawadhi et al., 2021; DeSanto et al., 2015; Leonardi et al., 2020; Lugtenberg et al., 2016), and we found that subthreshold GABAA receptor inhibition elicited seizures in Wac Het mice. The reaction to PTZ suggests that their brains are shifted towards increased excitability. It is also possible that this more excitable brain may influence the expression of PV, as we and others have found that brain activity can alter PV expression (Malik et al., 2019; Patz et al., 2004). Thus, it is still unclear whether the loss of PV leads to seizures in our model or if the elevated brain excitability could lead to changes in PV expression. No matter the cause, others have noted the importance of PV expression on behaviors relevant to ASD (Wöhr et al., 2015), suggesting that approaches to restore PV expression may be a future therapeutic to alleviate some symptoms of DESSH syndrome. Moreover, syndromes like DESSH may additionally benefit in the future from stem cell-based therapies that supplement brains with inhibitory neurons, which is currently being tested to treat epilepsy and other conditions with an imbalance of inhibition (Bröer and Vogt, 2024; Yokomizo et al., 2026).

Some novel findings beyond phenotyping of our vertebrate models manifested that may be relevant for human symptoms. First, mutant male mice exhibit volumetric increases in several brain regions as adults, suggesting sex differences may arise in DESSH patients as they age and specific brain regions should be further explored. Zebrafish also have craniofacial extensions in the forebrain region. In addition, while only examined in mice thus far, transcripts changed in male Het mice encompassed more ASD risk genes, which may have future implications for understanding Wac as a regulator of other ASD risk genes in a sex-dependent manner.

Finally, our transcriptomic characterization of the Wac mouse model at P2 identified DE genes enriched in RNA processing and metabolism that may be future targets to understand underlying mechanisms. The magnitude of DE was stronger in males and although the signature was largely concordant in effect direction between the sexes, this could be indicative of a sex-based transcriptional response to reduced Wac dosage. Differential splicing signatures were comparatively more subtle, with 13 altered splicing events in 9 genes, including snhg14, an antisense transcript in the Ube3a locus, associated with Angelman and Prader-Willi syndrome. Of interest, considering various sex-specific phenotypes associated with Wac haploinsufficiency and DESSH, male Wac mutants had stronger transcriptional phenotypes and a significant enrichment for ASD-linked genes among DEGs, suggesting possible convergence of neurodevelopmental pathways. Our forebrain analyses raise questions regarding the cell-type-specific and developmental aspects of transcriptomic phenotypes, which could be addressed by single-cell methods and longitudinal studies in the future. Overall, our transcriptomic results support roles for Wac during neurodevelopment and gene expression. We generated new vertebrate models to assess DESSH syndrome and their role during development in divergent vertebrate species. These phenotypes are a first attempt at understanding this complex ultrarare disorder and offer initial tools to probe further biological mechanisms and therapeutics.

Materials and methods

Animals

Mouse model

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We thank the Wellcome Trust Sanger Institute Mouse Genetics Project (Sanger MGP) and its funders for providing the conditional mutant mouse gamete (C57BL/6N-Wac<tm2 c(EUCOMM)Wtsi>/Wtsi). Funding information may be found at https://www.sanger.ac.uk/collaboration/mouse-resource-portal/ , associated primary phenotypic information at https://www.mousephenotype.org/ and data about the development of these technologies (Bradley et al., 2012; Pettitt et al., 2009; Ryder et al., 2013; Skarnes et al., 2011; White et al., 2013). Sperm harboring the Wac conditional allele was used to fertilize C57BL6/N donor eggs; progeny were then genotyped via polymerase chain reaction (PCR). We next bred Beta actin-Cre mice (Lewandoski et al., 1997) with WacFlox mice to generate wild type (WT) and constitutive Het mice. After germline recombination, Wac Het mice were backcrossed and bred with CD-1 mice for at least three generations before being used for experiments. CD-1 outbred mice were used due to Wac’s importance for survival, as these mice are more robust and can generate and care for more progeny; we attempted to collect embryonic constitutive KOs as early as E12.5 but never collected a KO, suggesting early embryonic lethality. All murine experiments were performed under the approval of Michigan State University’s Campus Animal Resources.

Zebrafish model

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Zebrafish (Danio rerio) were maintained at 28.5℃ with a 14 hr light/10 hr dark condition and fed three times a day. Zebrafish embryos were cultured in egg water (pH 7.4) and embryonic stages were determined by standard method (Kimmel et al., 1995). All zebrafish experiments were approved by the Institutional Animal Care and Use Committees (IACUC) of Chungnam National University (CNU-00866) and zebrafish were reared by standard lab protocols. For all assessments, males and females were tested. If no differences were noted, the data were combined for analyses. If differences were noted, then the data were separated and presented independently. For all experiments, both male and female animals were assessed but only reported as separate sexes if it became apparent that there were differences for specific phenotypes. Otherwise, all data were combined.

Zebrafish waca gene sequence was acquired from the NCBI database (waca: NM_199660.1). The primer for in vitro transcription of sgRNA was designed by 5′- TAATACGACTCACTATAGCTACTACAACTGCAGGACAGGTTTTAGAGCTAGAA –3′ for the waca target site. Primer contained 5′ T7 promoter and 15 nucleotides at 3′ complement to the universal primer. Template for in vitro transcription of the sgRNA to knockout waca was produced by PCR with primer 5′ – TAATACGACTCACTATAGGGAGGAAGGACTGGCACACGTTTTAGAGCTAGAA – 3′ (Jinek et al., 2012). In vitro transcription was carried out using PCR products and MaxiScript T7 Kit (Ambion). Cas9 expression vector pT3TS-nCas9n (Addgene), linearized with XbaI (NEB) and purified via DNA extraction kit (ELPIS). Cas9 mRNA was synthesized using mMESSAGE mMACHINE T3 Kit (Ambion) and tailed poly (A) with E. coli Poly (A) Polymerase (NEB). sgRNA of waca was injected with Cas9 mRNAs into 1 cell stage embryos.

Antibodies

Antibodies and information are detailed in Table 2 below.

Table 2
General parameters of the bulk-seq brains analyzed.

Columns indicate sample ID (ID), Genotype (WT = wild-type, HET = Wac mutant), Sex (M = Male, F = Female), RNA concentration, RNA integrity number (RIN) from bioanalyzer, A230/A260 value from spectrometer, number of aligned reads, and full sample metadata identifier (ID + Genotype + Sex).

Antibody (host/dilution)CompanyCategory #CloneLot #
Alpha-tubulin (mouse, 1:3000)Abcamab7291DM1-AGR310199-6
Beta-catenin (rabbit, 1:4000)Abcamab32572E247GR184212-83
CTIP2 (rat, 1:500)AbcamAb18465n/an/a
ERK1/2 (rabbit, 1:4000)Cell Signaling4695137F535
pERK1/2Thr202/Tyr204;Thr185/Tyr187 (rabbit, 1:4000)Cell Signaling9101n/a31
GAD65/67 (rabbit, 1:4000)Sigma-AldrichG5163n/an/a
GAPDH (rabbit, 1:4000)Cell Signaling211814C1014
Gephyrin (rabbit, 1:4000)Cell Signaling43928D4J4R1
IBA1 (goat, 1:500)Cell Signaling17198E404W1
LHX6 (mouse, 1:200)Santa Cruz Bio.sc-271433A-9B2219
NeuN (mouse, 1:400)MilliporeMAB377A603061189
OLIG2 (rabbit, 1:400)MilliporeAB9610n/a3071572
Parvalbumin (rabbit, 1:400)SwantPV27n/a2014
Protocadherin alpha (rabbit, 1:3000)SySy190 003n/a1–8
PROX1 (rabbit, 1:500)Abcamab199359EPR19273GR3216583
PSD95 (rabbit, 1:4000)Cell Signaling3450D27E114
S100beta (rabbit, 1:500)Proteintech15146–1-APn/a00092556
S6 (rabbit, 1:4000)Cell Signaling22175G107
pS6Ser240/244 (rabbit, 1:4000)Cell Signaling5364D68F88
SATB2 (mouse, 1:500)AbcamAb51502n/an/a
Somatostatin (rat, 1:200)MilliporeMAB354YC72219310 A
TBR1 (rabbit, 1:500)AbcamAb31940n/an/a
mUb-H2B (rabbit, 1:4000)Cell Signaling5546D116
WAC (rabbit, 1:4000)Abcamab109486n/aGR175251-1

Behavior (mouse)

Six to eight week aged mice of both sexes were tested; no differences were observed in any assay between sexes and data represent both groups. The persons performing the behaviors and analyses were blinded to the genotypes.

3-chamber social interaction

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During habituation, test mice had 10 min in the center of a rectangular chamber divided into 3-chambers by plexiglass containing small holes to navigate. Side chambers contained empty holders, with small holes allowing snout contact. Following initial habituation, an unfamiliar sex- and age-matched mouse (habituated to round holders/cages previously) was put in one of the chambers randomly, with doors of the lateral chambers closed and a novel object in the other. The test subject explored the apparatus for 10 min. Sniffing time was recorded using an overhead video camera and analyzed using ANY-Maze software. Time spent in each chamber and interaction with mouse/object was recorded.

Elevated plus maze

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Individual mice were placed in the center of a two-open and two-enclosed-arm 50 cm height platform, facing an open arm (Kraeuter et al., 2019b). Mice were allowed to explore for 5 min while being tracked and later analyzed by ANY-maze software. The % open-arm entries [(total number of entries/number of open-arm entries)×100] and % time spent in the open arms were assessed.

Open field

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Individual mice were placed in an open chamber with a center region designated by a 10×10 cm2 area and the remaining area to the wall designated as periphery (Kraeuter et al., 2019c). Mice roamed freely for 10 min while being tracked by an overhead camera. The total distance traveled and the mean speed were analyzed using ANY-maze software, as well as time spent in center versus periphery.

Radial arm water maze

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Individual mice were placed in a corner of a circular swimming pool containing six swim arms radiating from a central area (Alamed et al., 2006); water temperature of 26 °C. A hidden escape platform located at the end of one arm; mice were allowed up to 60 s/trial (15 trials total) on day 1 to find the platform in a room with four distinct spatial cues mounted onto each of the four walls of the room. Each trial was followed by a 15 s rest period on the platform and each trial will alternate between having a visual cue on the platform and being hidden. If a mouse did not find the platform within 60 s, it was guided there and allowed the rest period. After learning trials, each mouse was dried with a small towel and allowed to rest for 30 min in a dry cage with a recirculating water blanket under half of the cage to provide warmth. On the second day, each mouse underwent 15 trials using only the spatial cues (hidden platform) and the number of errors (wrong arm entries) was recorded by an overhead camera and reported.

Y-maze

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Individual mice were placed at the end of one arm of a Y-maze and allowed to explore for 5 min while being filmed by an overhead camera. Entries into all arms were noted (four paws need to be inside the arm for a valid entry) and a spontaneous alternation was counted if an animal enters three different arms consecutively. The percentage of spontaneous alternation were calculated according to the following formula: [(number of alternations) / (total number of arm entries − 2)]×100.

Behavior (zebrafish)

Novel tank assay

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Novel tank assay was performed as previously described (Choi et al., 2018; Kim et al., 2017; Park et al., 2019). 3 month/older WT and waca KO sibling zebrafish were tested in the behavior tank (24×15 ×15 cm). The back and sides were covered with non-transparent white paper. Behavior tests were recorded for 20 min using a video camera (Sony, HDRCX190). All fish were returned to the facility system tanks after completion of the behavioral tests. Recorded video files were analyzed using EthoVision XT7 software (Noldus).

Social cohesion test

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Zebrafish group social cohesion was measured (Choi et al., 2018; Kim et al., 2017). During one session, 5 WT sibling or waca KO sibling zebrafish were placed in the behavior tank. Fish groups were recorded for 20 min using a video camera (Sony, HDRCX190). Videos were analyzed using 12 individual screenshots taken every 10 s for 3–5 min as early phase and 17–19 min as late phase. Distances between individual zebrafish in the group were measured using ImageJ for each screenshot and compiled.

Cranio-facial analyses

Mouse model

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The skulls were stained with Alizarin red for bone as previously published (Yen et al., 2010). Quantification of the suture and fontanel areas and the skull width was performed from photographs using ImageJ.

Zebrafish model

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Zebrafish cartilage was stained with Alcian Blue (Sigma) as previously described (Dalcq et al., 2012; Pruvot et al., 2014). Zebrafish larvae at 13 dpf were fixed with 4% paraformaldehyde (PFA) overnight at 4 °C and treated with bleaching solution (3% H2O2/0.5% KOH in distilled water) for 30 min, rinsed out with Phosphate-buffered saline with 0.1% Tween 20 (PBST). Specimens were transferred to methanol to store at –20°C. Zebrafish cartilage was stained with Alcian blue solution (0.1% Alcian blue with 70% EtOH) for 1 hr and then dehydrated in 70% EtOH. Specimens were rinsed in 100% PBST and then transferred to 90% glycerol with 1% KOH to analyze on a dissecting microscope.

Differential splicing (DS) analysis

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Differential splicing (DS) analysis was performed using MAJIQ v2.5.1 (Vaquero-Garcia et al., 2023). In short, the majiq build module was executed on WT (n=14) and Wac Het (n=10) BAM files using GRCm38.95.gff3 genomic reference. DS quantification was performed using majiq deltapsi module, with two.majiq file groups delineating WT and Het samples. DS splicing was visualized using the voila view command with abs(E(dPSI)) threshold set to 0.1 and confidence threshold set to 0.8. Output files from the MAJIQ pipeline are deposited at Zenodo (https://doi.org/10.5281/zenodo.10999584).

Electrophysiology

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Coronal cortical slices (300 μm thick) were prepared (between postnatal ages 46 and 55 using methods previously described Crandall et al., 2017; Crandall et al., 2015). Briefly, mice were deeply anesthetized with isoflurane before decapitation. Brains were then quickly removed and placed in a cold (~4 °C) oxygenated slicing solution (95% O2, 5% CO2) containing (in mM): 3 KCl, 1.25 NaH2PO4, 10 MgSO4, 0.5 CaCl2, 26 NaHCO3, 10 glucose, and 234 sucrose. Slices were cut using a vibrating tissue microtome (Leica VT1200S) and then transferred into a chamber containing warm (32 °C) oxygenated (95% O2, 5% CO2) artificial cerebrospinal fluid (ACSF) containing (in mM): 126 NaCl, 3 KCl, 1.25 NaH2PO4, 2 MgSO4, 2 CaCl2, 26 NaHCO3, and 10 glucose. Slices were kept at 32 °C for 20 min followed by room temperature for an additional 40 min before recording.

For recordings, individual slices were transferred to a submersion recording chamber and continually perfused (~3 ml/min) with warm (32 °C) oxygenated (95% O2, 5% CO2) ACSF containing (in mM): 126 NaCl, 3 KCl, 1.25 NaH2PO4, 2 MgSO4, 2 CaCl2, 26 NaHCO3, and 10 glucose. Neurons were visualized using infrared differential interference contrast (IR-DIC) and fluorescence imaging using a Zeiss Axio Examiner.A1 microscope mounted with a video camera (Olympus XM10-IR) and a 40 x water-immersion objective. Whole-cell recordings were obtained from pyramidal neurons in layer 2/3 of the primary somatosensory cortex using borosilicate glass pipettes (4–6 MΩ tip resistance) containing a potassium-based internal solution (mM): 130 K-gluconate, 4 KCl, 2 NaCl, 10 HEPES, 0.2 EGTA, 4 ATP-Mg, 0.3 GTP-Tris, and 14 phosphocreatine-K (pH 7.25, 290 mOsm). All whole-cell recordings were corrected for a 14 mV liquid junction potential.

Data were recorded and digitized at 20 or 50 kHz using Molecular Devices hardware and software (MultiClamp 700B amplifier, Digidata 1550B4, and pClamp11.1). Signals were low-pass filtered at 10 kHz before digitizing. During the recordings, the pipette capacitances were neutralized, and series resistances (typically between 10 and 25 MΩ) were compensated online (100% for current-clamp, 70% for voltage-clamp). Series resistances were continually monitored throughout the recordings.

Analysis of electrophysiological data was performed in Molecular Devices and Microsoft Excel as described (Crandall et al., 2017). Resting membrane potentials (RMP, mV) were measured after break-in with no applied current. Input resistance (Rin, MΩ) was measured using Ohm’s law by measuring the voltage response from rest to an injection of a small negative current (15–25 pA). Membrane time constants (τm, ms) were measured from the average response (see Rin above) by fitting a single exponential to the initial falling phase of the response (100–300 ms; omitting the 1st ms). Membrane capacitance (Cin, pF) was calculated by τm/Rin. Rheobase currents (pA) were defined as the minimum positive current (5 pA steps) to elicit an AP from a holding potential of −84 mV. AP properties were measured from the first spike evoked by the rheobase current. AP threshold (mV) was defined as the membrane potential at which its first derivative (dV/dt) exceeded 10 mV/ms. AP amplitudes (mV) were defined as the voltage difference between the threshold and the peak of the AP. AP half-widths (ms) were measured at the half-height between the threshold and the AP peak. The max rate of rise and decay (mV/ms) was defined as the maximal dV/dt during the rising phase and falling phase of the same AP, respectively. Frequency-intensity (F/I) relationships were obtained by holding the soma at −84 mV with intracellular current and injecting suprathreshold positive current (50 pA steps, 500 ms duration). F/I slopes (Hz/pA) were determined using the initial frequency (reciprocal of the first interspike interval) over the entire F-I plot.

Spontaneous excitatory postsynaptic currents (sEPSCs) were recorded at –94 mV. Initially, individual events were manually selected and compiled to create an averaged sEPSC template that was then used to automatically detect similar events. Using this template, individual events were automatically selected by the software, but each event was also checked visually to confirm quality. Fifty sEPSCs were selected for each neuron for analysis of synaptic properties, including current amplitude and frequency. sEPSC amplitude and frequency were measured from events and automatically selected using our template. For each preselected event, the peak inward current amplitude was detected by the waveform template and was compared to a baseline immediately preceding each event.

Genotyping

Mouse model

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Primers to detect the recombined allele of the Wac genetic locus: Forward 5′-AGCTATGCGTGCTGTTGGG-3′ and Reverse 5′-CAAATCCCACAGTCCAATGC-3′. Thermocycling conditions were: 95 °C 3 min, (95 °C 30 s, 58 °C 30 s, and 72 °C 45 s for 35 cycles), 72 °C 3 min. Sanger sequencing to validate the recombined locus was performed by GeneWiz using the same primers on gel-purified PCR products.

Zebrafish model

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Primers to detect the alleles of waca: Forward 5′- ATTTGAACCGGCAGATGATT-3′ and Reverse 5′-TCAGTGGGAGAAACCCAAAG-3′. Thermocycling conditions were: 95 °C 5 min, (95 °C 30 s, 60 °C 40 s, and 72 °C 20 s for 40 cycles), 72 °C 7 min and 15 °C 10 min. Primers to detect the genotypic alleles of wacb: Forward 5′-ATACCAGTCAAAGAGTCACTCAGCGAATGA-3′ and Reverse 5′-CACGTTCACCGGCCCCGTGAGAGAGACCAC-3′.

Immunofluorescent staining

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At P30, mice were transcardially perfused with phosphate-buffered saline (PBS), followed by 4% paraformaldehyde. The brains were then removed and postfixed in PFA for 30 min. Brains were transferred to 30% sucrose for cryoprotection overnight after fixation and then embedded in optimal cutting temperature compound before being coronally sectioned at 25 µm via cryostat. Sections were permeabilized in a wash of PBS with 0.3% Triton-X100, then blocked with the same solution containing 5% bovine serum albumin. Primary antibodies (details can be found in Table 2) were applied overnight at 4 °C, followed by three washes. Secondary antibodies (Alexa-conjugated 488 or 594 anti-rabbit or mouse, 1:300, Thermo Fisher) were applied for 1–2 hr at room temperature, followed by three washes and cover-slipped with Vectashield. Two to three images from the somatosensory cortex were acquired that contained all cortical layers. Cell counts were performed on these images using the cell counter function in Image-J software and images from the same animal were averaged to report a biological ‘n.’. Several hundred cells were counted per brain and 3–4 biological replicates were performed for each marker.

MRI acquisition and analysis

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High-resolution ex vivo MRI was conducted on brains that were transcardially perfused with 4% paraformaldehyde, using a 9.4T Bruker Advance (Bruker Biospin, Billerica, MA, USA) with the following parameters: TR/TE 3397/10.6, 10 evenly spaced echoes 10ms apart, matrix 1.75×1.25 (91 μm isotropic), and 30 slices 0.5 mm thick. T2-weighted imaging (T2WI) and T2 relaxation maps were computed as we previously described (Jullienne et al., 2020). T2WI scans were skull stripped (masking) using the segmentation tool from the ITK-SNAP software (version 3.8.0, RRID:SCR_002010) (Yushkevich et al., 2006). The T2WI mask was then used to generate a 3D reconstruction of the brain. T2WI parametric maps were generated and corrected for bias field inhomogeneities prior to registration. From T2WI data, brain and regional volumes and T2 relaxation times were obtained. The Australian Mouse Brain Mapping Consortium (AMBMC) atlas (Richards et al., 2011; Ullmann et al., 2013) was utilized to extract 40 bilateral regions using non-linear registration to each subject’s T2 images. Regional labels were applied using Advanced Normalization Tools (ANTs) and manually inspected for registration quality. Outliers were identified by calculating the interquartile range (IQR) for each regions, where value1.5IQR above the first quartile or below the third quartile were identified and excluded using Microsoft Excel.

Microscopy

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Mouse fluorescent images were acquired using a Leica DM2000 microscope with a mounted monochrome DFC3000G camera. Fluorescent images were adjusted for brightness/contrast and merged using Image-J software. The skulls were photographed with Nikon SMZ1500 stereomicroscope and Nikon DSRi1 camera. Zebrafish images were acquired using Leica TL 5000 microscope with DFC 7000T camera.

PTZ-induced seizure assessments

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One gram of Pentylenetetrazole (PTZ) powder was dissolved in 250 ml of PBS and injected intraperitoneally at 50 mg of PTZ per gram of mouse body weight. After injection, mice explored a clean cage for 20 min. Seizure severity was scored based on a modified Racine scale (Vogt et al., 2011): (1) Freezing or idle behavior; (2) Muscle twitches and/or rhythmic head nodding; (3) Tail arching over the mouse’s backside accompanied by the mouse assuming a hunched posture; (4) Forelimb clonus; (5) Tonic-clonic seizures with a recovery to normal behavior; (6) Uncontrolled running/jumping/hyperactivity; (7) Full body extension of limbs; (8) Death.

RNA-sequencing and bioinformatics analysis

Twenty-four mouse brains (14 WT and 10 Wac HETs; 7, 7 and 5, 5, males, females, respectively) were collected at P2 following instant decapitation. Samples were snap-frozen on dry ice and stored at –80°C until RNA preparation. On the day of RNA isolation, left and right forebrain hemispheres were separated from the hindbrain and the total RNA was consistently isolated from the right forebrain hemisphere. Total RNA was obtained using Ambion RNAqueous Total RNA Isolation Kit (cat# AM1912) and assayed via Agilent RNA 6000 Nano Bioanalyzer kit/instrument. Sample RIN scores ranged from 6.9 to 9.8, with a mean RIN score of 9.4.

Poly-A-enriched mRNA libraries were prepared at Novogene using Illumina reagents. Libraries were sequenced using the Illumina NovaSeq 6000 S4 system, paired-end 150 (PE150) method. Reads were aligned to mouse genome (GRCm38/mm10) using STAR (version 2.5.4b) (Dobin et al., 2013), and gene counts were produced using featureCounts (Liao et al., 2014). On average, 50 million paired-end reads (PE) aligned per sample, with a range of 32–84 million reads.

Data quality was assessed using FastQC. FastQC: A Quality Control Tool for High Throughput Sequence Data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ and principal component analysis (PCA) was used to determine the presence of sample outliers. All 24 samples were qualified for the analysis. Raw RNA-seq fastq files and a gene count matrix is available on GEO (GSE264597).

Bioinformatic analysis was performed using R programming language version 4.2.1. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ and RStudio integrated development environment version 2023.06.0. RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/. Plots were generated using ggplot2 R package version 3.4.0 (Wickham, 2016). Heatmaps were generated using the pheatmap R package 1.0.12.

Differential expression (DE) analysis

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For DE analysis, we used edgeR R package (Robinson et al., 2010). Genes with a minimum of one counts per million (CPM) in at least six samples were included in the analysis. The first surrogate variable from the SVA-batch-correction method was used as a covariate in the edgeR GLM model (Leek, 2011). For sex-stratified DE, we used a threshold of CPM >1 in at least three samples, and no batch correction. Reads Per Kilobase per Million mapped reads (RPKM) were used for plotting summary heatmaps and expression data of individual genes.

Gene ontology enrichment analysis

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To test for enrichment of GO terms, we used the clusterProfiler R package version 4.14.6 (Bioconductor). Mouse Gene Ontology (GO) data for biological processes (BP), molecular function (MF), and cellular component (CC) were downloaded from Bioconductor (org.Mm.eg.db). For the analysis presented here, DE genes with a p-value <0.05 were used as input, and we required a minimum set of 5 and a maximum of 1000 as well, as at least two significant DE genes in a GO term. We applied a Benjamini-Hochberg (BH) correction on significance, a strategy recommended for gene set analysis that accounts for multiple testing comparisons. We reported terms with p-value <0.05. The set of DE genes was compared against the background set of genes expressed in our study based on minimum read-count cutoffs described above. To test for enrichment of GO terms, we used the fgsea R package version 1.32.4, via clusterProfiler. To control for transcriptional noise, we only used protein-coding transcripts with a logCPM >1. We tested against terms for BP, MF, and CC using a minimal gene set size of 5 and maximum of 1000. We again used a Benjamini-Hochberg (BH) correction on significance and reported terms with p<0.05. For the custom GSEA we pulled genes associated with epilepsy (C0016399), microcephaly (C1837501), and macrocephaly (C1836599) from DisGeNET (Piñero et al., 2020), SFARI genes from Abrahams et al., 2013, and high-confidence ASD genes from Fu et al., 2022 with a FDR <0.05.

Permutation test

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To determine the significance of the overlap between DE genes identified in RNA-seq and disease gene sets, we used a permutation test. We used the DE genes with p<0.05 to compare against the ASD gene set from Fu et al., 2022 FDR <0.05 and the epilepsy gene set from DisGeNET (Piñero et al., 2020). To generate a null distribution, we performed 10,000 permutations. In each iteration, a random set of genes equal in size to the DE gene list was sampled without replacement from the background gene set of those tested in DE analysis. The overlap between the disease gene set and each permuted gene set was recorded. A p-value was calculated as the proportion of permutations in which the overlap was greater than or equal to the observed overlap.

Analysis code is available from GitHub (copy archived at NordNeurogenomicsLab, 2026).

Western blots

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P30 somatosensory cortices were dissected and frozen on dry ice. Next, they were lysed in RIPA buffer containing protease and phosphatase inhibitors and combined with Laemmli buffer containing 2-Mercaptoethanol and incubated at 95 °C for 5 min to denature the proteins. Equal amounts of protein lysates were separated on 10% SDS-PAGE gels and then transferred to nitrocellulose membranes. The membranes were washed in Tris-buffered saline with Tween-20 (TBST) and then blocked for 1 hr in TBST containing 5% non-fat dry milk (blotto, sc-2324 Santa Cruz Biotechnology). Membranes were then incubated with primary antibodies overnight at 4 °C, washed three times with TBST, incubated with secondary antibodies for 1 hr at room temperature and then washed three more times with TBST. Membranes were next incubated in ECL solution (BioRad Clarity substrate 1705061) for 5 min and chemiluminescent images obtained using a BioRad Chemidoc MP imaging system. Antibody details can be found in Table 2, and uncropped western blots are provided as source files associated with each figure.

Whole-mount in situ hybridization (WISH)

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WISH was performed using DIG-labeled antisense RNA probes for gad1b. RNA probes were synthesized using DIG-RNA labeling kit (Roche) (Thisse and Thisse, 2008). Zebrafish larvae were fixed in 4% PFA in PBS overnight at 4 °C then dehydrated with 100% Methanol. Larvae were rehydrated with 0.1% Diethyl pyrocarbonate with PBST (DEPC-PBST, Sigma Aldrich). Larvae were permeabilized with 10 μg/ml proteinase K (Roche) according to developmental stage. Larvae were re-fixed for 15 min in 4% PFA, washed with DEPC-treated PBST, and pre-hybridized in HYB solution (50% formamide, 5 X saline sodium citrate, 50 μg/ml heparin, 500 µg/ml torula RNA, 46 mM citric acid pH 6.0, 0.1% Tween20) for 1 hr at 70 °C. Antisense DIG-labeled RNA probes were added in HYB solution and incubated overnight at 70 °C. Larvae were washed in a preheated mixture of 50% saline sodium citrate containing 0.1% Tween-20 and 50% hybridization solution at 70 °C. Larvae were blocked and then incubated with 1/4000 concentration of anti-DIG Fab fragment conjugated with alkaline phosphatase (Roche) overnight at 4 °C. Larvae were washed in PBST, then incubated in staining solution, including NBT/BCIP as alkaline phosphatase substrate in the dark until sufficient staining appeared. Larvae were mounted in 90% glycerol in PBST and were visualized via dissecting microscope.

Quantification and statistical analysis

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Statistical analyses were performed using Prism versions 7 and 10, a p-value of <0.05 was considered significant. For parametric measures of two groups, a two-tailed t-test was performed; parametric measures of three or more groups utilized a ONE-Way ANOVA with Tukey post-test. For the male retropleinal cortex, male parietal cortex, and female corpus callosum MRI data, the data were not distributed normally, and we used Mann–Whitney test. If other statistical analyses were used, they are denoted in the results or figure legends.

Data availability

RNA-sequencing data were performed and are embedded in the manuscript files.

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Article and author information

Author details

  1. Kang-Han Lee

    Department of Biology, Chungnam National University, Daejeon, Republic of Korea
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    April M Stafford
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5369-1496

  2. April M Stafford

    Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, United States
    Contribution
    Data curation, Formal analysis, Investigation
    Contributed equally with
    Kang-Han Lee
    Competing interests
    No competing interests declared

  3. Maria Pacheco-Vergara

    Department of Molecular Pathology, New York University College of Dentistry, New York, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared

  4. Karol Cichewicz

    1. Department of Psychiatry and Behavioral Sciences, University of California Davis, Davis, United States
    2. Department of Neurobiology, Physiology and Behavior, University of California Davis, Davis, United States
    Contribution
    Data curation, Formal analysis, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5926-3663

  5. Cesar P Canales

    1. Department of Psychiatry and Behavioral Sciences, University of California Davis, Davis, United States
    2. Department of Neurobiology, Physiology and Behavior, University of California Davis, Davis, United States
    Contribution
    Data curation, Formal analysis, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2505-8367

  6. Nicolas Seban

    1. Department of Psychiatry and Behavioral Sciences, University of California Davis, Davis, United States
    2. Department of Neurobiology, Physiology and Behavior, University of California Davis, Davis, United States
    Contribution
    Data curation, Formal analysis, Investigation
    Competing interests
    No competing interests declared

  7. Melissa Corea

    1. Department of Psychiatry and Behavioral Sciences, University of California Davis, Davis, United States
    2. Department of Neurobiology, Physiology and Behavior, University of California Davis, Davis, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared

  8. Darlene Rahbarian

    1. Department of Psychiatry and Behavioral Sciences, University of California Davis, Davis, United States
    2. Department of Neurobiology, Physiology and Behavior, University of California Davis, Davis, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared

  9. Kelly E Bonekamp

    Department of Physiology, Michigan State University, East Lansing, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared

  10. Grant R Gillie

    Department of Physiology, Michigan State University, East Lansing, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared

  11. Dariangelly Pacheco-Cruz

    1. Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, United States
    2. Neuroscience Program, Michigan State University, East Lansing, United States
    Contribution
    Resources, Data curation, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0009-8010-7513

  12. Alyssa M Gill

    Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, United States
    Contribution
    Formal analysis, Investigation
    Competing interests
    No competing interests declared

  13. Hye-Eun Hwang

    Department of Biology, Chungnam National University, Daejeon, Republic of Korea
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  14. Yeong-Eun Kim

    Department of Biology, Chungnam National University, Daejeon, Republic of Korea
    Contribution
    Data curation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0001-0141-5131

  15. Katie L Uhl

    Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared

  16. Tara E Jager

    Corewell Health, Grand Rapids, United States
    Contribution
    Data curation
    Competing interests
    No competing interests declared

  17. Marwan Shinawi

    Division of Genetics and Genomic Medicine, Department of Pediatrics, Washington University School of Medicine, St. Louis, United States
    Contribution
    Supervision, Investigation
    Competing interests
    No competing interests declared

  18. Xiaopeng Li

    Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, United States
    Contribution
    Resources, Supervision, Investigation
    Competing interests
    No competing interests declared

  19. Andre Obenaus

    Director, Preclinical and Translational Imaging Center, School of Medicine, University of California Irvine, Irvine, United States
    Contribution
    Software, Formal analysis
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0081-6950

  20. Shane R Crandall

    1. Department of Physiology, Michigan State University, East Lansing, United States
    2. Neuroscience Program, Michigan State University, East Lansing, United States
    Contribution
    Software, Supervision, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0611-3981

  21. Juhee Jeong

    Department of Molecular Pathology, New York University College of Dentistry, New York, United States
    Contribution
    Supervision, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared

  22. Alex S Nord

    1. Department of Psychiatry and Behavioral Sciences, University of California Davis, Davis, United States
    2. Department of Neurobiology, Physiology and Behavior, University of California Davis, Davis, United States
    Contribution
    Resources, Software, Supervision, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4259-7514

  23. Cheol-Hee Kim

    Department of Biology, Chungnam National University, Daejeon, Republic of Korea
    Contribution
    Conceptualization, Resources, Visualization, Methodology, Writing – original draft, Writing – review and editing
    For correspondence
    zebrakim@cnu.ac.kr
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3019-0699

  24. Daniel Vogt

    1. Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, United States
    2. Neuroscience Program, Michigan State University, East Lansing, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Writing – original draft, Writing – review and editing
    For correspondence
    vogtdan2@msu.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1876-5936

Funding

National Institutes of Health (5T32GM142521)

  • Dariangelly Pacheco-Cruz

National Institutes of Health (DE026798)

  • Juhee Jeong
  • Maria Pacheco-Vergara

Cystic Fibrosis Foundation (LI19XX0)

  • Xiaopeng Li
  • Katie L Uhl
  • Tara E Jager

National Institutes of Health (HL153165-01A1)

  • Xiaopeng Li

National Institutes of Health (MH120513)

  • Alex S Nord

National Research Foundation of Korea (RS-2024-00443043)

  • Cheol-Hee Kim

National Research Foundation of Korea (RS-2024-00349650)

  • Cheol-Hee Kim

Michigan State University

  • Daniel Vogt
  • April M Stafford
  • Katie L Uhl
  • Dariangelly Pacheco-Cruz
  • Xiaopeng Li
  • Tara E Jager

Korea National Institute of Health (2026-ER0807-00)

  • Cheol-Hee Kim

Korea National Institute of Health (2026-ER0602-00)

  • Cheol-Hee Kim

Korea National Institute of Health (2024-ER0519-00)

  • Cheol-Hee Kim

Korea National Institute of Health (2024-ER0703-00)

  • Cheol-Hee Kim

Corewell Health

  • Daniel Vogt
  • April M Stafford
  • Katie L Uhl
  • Dariangelly Pacheco-Cruz
  • Xiaopeng Li
  • Tara E Jager

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

Acknowledgements

AMS, DV, KU, DP-C, TEJ, and XL were funded by the Michigan State University/Corewell Health Corporation. DP-C was additionally supported by an Integrative Pharmacological Sciences Training Program (IPSTP) grant, 5T32GM142521. We would like to thank DubDub and Harrison Levon-James Allen for critical insights and for providing resources during this study; MP-V and JJ were funded by NIDCR R01 DE026798; KU, TEJ, and XL were also funded by the Cystic Fibrosis Foundation LI19XX0, the Cystic Fibrosis Research Inc, R01 HL153165-01A1. ASN was funded by NIMH R01 MH120513. C-H K was supported by grants from the National Research Foundation of Korea (RS-2024–00443043, RS-2024–00349650) and the Korea National Institute of Health (KNIH) research project (2024-ER0519-00, 2024-ER0703-00, 2026-ER0602-00, 2026-ER0807-00). RNA-seq data were funded by a Michigan State University Neuroscience Program grant. We would also like to thank Tom and Cathy Mall for their generous gift to support our research underlying autism genetics at Michigan State University.

Ethics

All animal work was approved by either Michigan State University's IACUC or the IACUC of Chungnam National University (CNU-00866).

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© 2025, Lee, Stafford et al.

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  1. Kang-Han Lee
  2. April M Stafford
  3. Maria Pacheco-Vergara
  4. Karol Cichewicz
  5. Cesar P Canales
  6. Nicolas Seban
  7. Melissa Corea
  8. Darlene Rahbarian
  9. Kelly E Bonekamp
  10. Grant R Gillie
  11. Dariangelly Pacheco-Cruz
  12. Alyssa M Gill
  13. Hye-Eun Hwang
  14. Yeong-Eun Kim
  15. Katie L Uhl
  16. Tara E Jager
  17. Marwan Shinawi
  18. Xiaopeng Li
  19. Andre Obenaus
  20. Shane R Crandall
  21. Juhee Jeong
  22. Alex S Nord
  23. Cheol-Hee Kim
  24. Daniel Vogt
(2026)
Complementary vertebrate Wac models exhibit phenotypes relevant to DeSanto-Shinawi Syndrome
eLife 14:RP109104.
https://doi.org/10.7554/eLife.109104.3

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