Developmental and stem cell biology's bright future.

The next 50 years of developmental biology will illuminate exciting new discoveries but are also poised to provide solutions to important problems society faces. Ten scientists whose work intersects with developmental biology in various capacities tell us about their vision for the future.

Ex utero monkey embryogenesis from blastocyst to early organogenesis.

The third and fourth weeks of gestation in primates are marked by several developmental milestones, including gastrulation and the formation of organ primordia. However, our understanding of this period is limited due to restricted access to in vivo embryos. To address this gap, we developed an embedded 3D culture system that allows for the extended ex utero culture of cynomolgus monkey embryos for up to 25 days post-fertilization. Morphological, histological, and single-cell RNA-sequencing analyses demonstrate that ex utero cultured monkey embryos largely recapitulated key events of in vivo development. With this platform, we were able to delineate lineage trajectories and genetic programs involved in neural induction, lateral plate mesoderm differentiation, yolk sac hematopoiesis, primitive gut, and primordial germ-cell-like cell development in monkeys. Our embedded 3D culture system provides a robust and reproducible platform for growing monkey embryos from blastocysts to early organogenesis and studying primate embryogenesis ex utero.

Dissecting embryonic and extraembryonic lineage crosstalk with stem cell co-culture.

Embryogenesis necessitates harmonious coordination between embryonic and extraembryonic tissues. Although stem cells of both embryonic and extraembryonic origins have been generated, they are grown in different culture conditions. In this study, utilizing a unified culture condition that activates the FGF, TGF-ß, and WNT pathways, we have successfully derived embryonic stem cells (FTW-ESCs), extraembryonic endoderm stem cells (FTW-XENs), and trophoblast stem cells (FTW-TSCs) from the three foundational tissues of mouse and cynomolgus monkey (Macaca fascicularis) blastocysts. This approach facilitates the co-culture of embryonic and extraembryonic stem cells, revealing a growth inhibition effect exerted by extraembryonic endoderm cells on pluripotent cells, partially through extracellular matrix signaling. Additionally, our cross-species analysis identified both shared and unique transcription factors and pathways regulating FTW-XENs. The embryonic and extraembryonic stem cell co-culture strategy offers promising avenues for developing more faithful embryo models and devising more developmentally pertinent differentiation protocols.

Chimeric contribution of human extended pluripotent stem cells to monkey embryos ex vivo.

Interspecies chimera formation with human pluripotent stem cells (hPSCs) represents a necessary alternative to evaluate hPSC pluripotency in vivo and might constitute a promising strategy for various regenerative medicine applications, including the generation of organs and tissues for transplantation. Studies using mouse and pig embryos suggest that hPSCs do not robustly contribute to chimera formation in species evolutionarily distant to humans. We studied the chimeric competency of human extended pluripotent stem cells (hEPSCs) in cynomolgus monkey (Macaca fascicularis) embryos cultured ex vivo. We demonstrate that hEPSCs survived, proliferated, and generated several peri- and early post-implantation cell lineages inside monkey embryos. We also uncovered signaling events underlying interspecific crosstalk that may help shape the unique developmental trajectories of human and monkey cells within chimeric embryos. These results may help to better understand early human development and primate evolution and develop strategies to improve human chimerism in evolutionarily distant species.

Generation of gene-modified cynomolgus monkey via Cas9/RNA-mediated gene targeting in one-cell embryos.

Monkeys serve as important model species for studying human diseases and developing therapeutic strategies, yet the application of monkeys in biomedical researches has been significantly hindered by the difficulties in producing animals genetically modified at the desired target sites. Here, we first applied the CRISPR/Cas9 system, a versatile tool for editing the genes of different organisms, to target monkey genomes. By coinjection of Cas9 mRNA and sgRNAs into one-cell-stage embryos, we successfully achieve precise gene targeting in cynomolgus monkeys. We also show that this system enables simultaneous disruption of two target genes (Ppar-γ and Rag1) in one step, and no off-target mutagenesis was detected by comprehensive analysis. Thus, coinjection of one-cell-stage embryos with Cas9 mRNA and sgRNAs is an efficient and reliable approach for gene-modified cynomolgus monkey generation.

Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method.

PURPOSE: This study aims to assess the diagnostic value of ultrasound habitat sub-region radiomics feature parameters using a fully connected neural networks (FCNN) combination method L2,1-norm in relation to breast cancer Ki-67 status. METHODS: Ultrasound images from 528 cases of female breast cancer at the Affiliated Hospital of Xiangnan University and 232 cases of female breast cancer at the Affiliated Rehabilitation Hospital of Xiangnan University were selected for this study. We utilized deep learning methods to automatically outline the gross tumor volume and perform habitat clustering. Subsequently, habitat sub-regions were extracted to identify radiomics features and underwent feature engineering using the L1,2-norm. A prediction model for the Ki-67 status of breast cancer patients was then developed using a FCNN. The model's performance was evaluated using accuracy, area under the curve (AUC), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), Recall, and F1. In addition, calibration curves and clinical decision curves were plotted for the test set to visually assess the predictive accuracy and clinical benefit of the models. RESULT: Based on the feature engineering using the L1,2-norm, a total of 9 core features were identified. The predictive model, constructed by the FCNN model based on these 9 features, achieved the following scores: ACC 0.856, AUC 0.915, Spe 0.843, PPV 0.920, NPV 0.747, Recall 0.974, and F1 0.890. Furthermore, calibration curves and clinical decision curves of the validation set demonstrated a high level of confidence in the model's performance and its clinical benefit. CONCLUSION: Habitat clustering of ultrasound images of breast cancer is effectively supported by the combined implementation of the L1,2-norm and FCNN algorithms, allowing for the accurate classification of the Ki-67 status in breast cancer patients.

Advanced gastrointestinal stromal tumor: reliable classification of imatinib plasma trough concentration via machine learning.

AIM: Patients with advanced gastrointestinal stromal tumors (GISTs) exhibiting an imatinib plasma trough concentration (IM Cmin) under 1100 ng/ml may show a reduced drug response rate, leading to the suggestion of monitoring for IM Cmin. Consequently, the objective of this research was to create a customized IM Cmin classification model for patients with advanced GISTs from China. METHODS: Initial data and laboratory indicators from patients with advanced GISTs were gathered, and the above information was segmented into a training set, validation set, and testing set in a 6:2:2 ratio. Key variables associated with IM Cmin were identified to construct the classification model using the least absolute shrinkage and selection operator (LASSO) regression and forward stepwise binary logistic regression. Within the training and validation sets, nine ML classification models were constructed via the resampling method and underwent comparison through the Brier scores, the areas under the receiver-operating characteristic curve (AUROC), the decision curve, and the precision-recall (AUPR) curve to determine the most suitable model for this dataset. Two methods of internal validation were used to assess the most suitable model's classification performance: tenfold cross-validation and random split-sample validation (test set), and the value of the test set AUROC was used to evaluate the model's classification performance. RESULTS: Six key variables (gender, daily IM dose, metastatic site, red blood cell count, platelet count, and percentage of neutrophils) were ultimately selected to construct the classification model. In the validation set, it is found by comparison that the Extreme Gradient Boosting (XGBoost) model has the largest AUROC, the lowest Brier score, the largest area under the decision curve, and the largest AUPR value. Furthermore, as evaluated via internal verification, it also performed well in the test set (AUROC = 0.725). CONCLUSION: For patients with advanced GISTs who receive IM, initial data and laboratory indicators could be used to accurately estimate whether the IM Cmin is below 1100 ng/ml. The XGBoost model may stand a chance to assist clinicians in directing the administration of IM.

Neuroprotective Effects of CXCR2 Antagonist SB332235 on Traumatic Brain Injury Through Suppressing NLRP3 Inflammasome.

The inflammatory process mediated by nucleotide-binding oligomerization domain (NOD)-like receptor family pyrin domain comprising 3 (NLRP3) inflammasome plays a predominant role in the neurological dysfunction following traumatic brain injury (TBI). SB332235, a highly selective antagonist of chemokine receptor 2 (CXCR2), has been demonstrated to exhibit anti-inflammatory properties and improve neurological outcomes in the central nervous system. We aimed to determine the neuroprotective effects of SB332235 in the acute phase after TBI in mice and to elucidate its underlying mechanisms. Male C57BL/6J animals were exposed to a controlled cortical impact, then received 4 doses of SB332235, with the first dose administered at 30 min after TBI, followed by additional doses at 6, 24, and 30 h. Neurological defects were assessed by the modified neurological severity score, while the motor function was evaluated using the beam balance and open field tests. Cognitive performance was evaluated using the novel object recognition test. Brain tissues were collected for pathological, Western blot, and immunohistochemical analyses. The results showed that SB332235 significantly ameliorated TBI-induced deficits, including motor and cognitive impairments. SB332235 administration suppressed expression of both CXCL1 and CXCR2 in TBI. Moreover, SB332235 substantially mitigated the augmented expression levels and activation of the NLRP3 inflammasome within the peri-contusional cortex induced by TBI. This was accompanied by the blocking of subsequent production of pro-inflammatory cytokines. Additionally, SB332235 hindered microglial activity induced by TBI. These findings confirmed the neuroprotective effects of SB332235 against TBI, and the involved mechanisms were in part due to the suppression of NLRP3 inflammasome activity. This study suggests that SB332235 may act as an anti-inflammatory agent to improve functional outcomes in brain injury when applied clinically.

Endoscopic resection of giant esophageal subepithelial lesions: experience from a large tertiary center.

BACKGROUND AND AIMS: Increased reports on endoscopic resection (ER) of esophageal giant subepithelial lesions (g-SELs) have emerged in recent years. The aim of this study was to evaluate the efficacy, technical difficulty, and safety through our single-center experience. METHODS: Seventy-five patients with g-SELs undergoing endoscopic resection were included in the training set. Clinicopathologic features, procedure-related characteristics, postprocedural outcomes, and follow-up data were analyzed. A predictive nomogram model for procedural difficulty was proposed based on the multivariable logistic regression analysis. Internal and external validations were conducted to verify the model performance. RESULTS: The overall en bloc resection rate was 93.3%. Intraoperative and postoperative adverse events occurred in 7 (9.3%) and 13 (17.3%) patients, respectively. No recurrence or metastasis was observed. Thirty-two (42.7%) patients underwent a difficult procedure. Age (adjusted odds ratio [aOR], .915; P = .004), maximal tumor diameter ≥8 cm (aOR, 9.896; P = .009), irregular shape (aOR, 4.081; P = .053), extraluminal growth pattern (aOR, 5.419; P = .011), and submucosal tunneling endoscopic resection (aOR, .109; P = .042) were found to be statistically or clinically significant factors for predicting endoscopic resection difficulty, based on which a nomogram model was developed. Internal and external validations of the nomogram via receiver-operating characteristic curves and calibration curves achieved favorable results. CONCLUSIONS: Endoscopic resection serves as a promising therapeutic option for esophageal g-SELs. A younger patient age, large tumor size, irregular shape, and extraluminal growth may indicate increased endoscopic resection difficulty, whereas a submucosal tunneling endoscopic resection procedure tends to be of lower difficulty. Our nomogram model performs well for predicting endoscopic resection difficulty for esophageal g-SELs.

Effect of artificial intelligence on novice-performed colonoscopy: a multicenter randomized controlled tandem study.

BACKGROUND AND AIMS: The efficacy and safety of colonoscopy performed by artificial intelligence (AI)-assisted novices remain unknown. The aim of this study was to compare the lesion detection capability of novices, AI-assisted novices, and experts. METHODS: This multicenter, randomized, noninferiority tandem study was conducted across 3 hospitals in China from May 1, 2022, to November 11, 2022. Eligible patients were randomized into 1 of 3 groups: the CN group (control novice group, withdrawal performed by a novice independently), the AN group (AI-assisted novice group, withdrawal performed by a novice with AI assistance), or the CE group (control expert group, withdrawal performed by an expert independently). Participants underwent a repeat colonoscopy conducted by an AI-assisted expert to evaluate the lesion miss rate and ensure lesion detection. The primary outcome was the adenoma miss rate (AMR). RESULTS: A total of 685 eligible patients were analyzed: 229 in the CN group, 227 in the AN group, and 229 in the CE group. Both AMR and polyp miss rate were lower in the AN group than in the CN group (18.82% vs 43.69% [P < .001] and 21.23% vs 35.38% [P < .001], respectively). The noninferiority margin was met between the AN and CE groups of both AMR and polyp miss rate (18.82% vs 26.97% [P = .202] and 21.23% vs 24.10% [P < .249]). CONCLUSIONS: AI-assisted colonoscopy lowered the AMR of novices, making them noninferior to experts. The withdrawal technique of new endoscopists can be enhanced by AI-assisted colonoscopy. (Clinical trial registration number: NCT05323279.).

A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation.

BACKGROUND: The choice of polypectomy device and surveillance intervals for colorectal polyps are primarily decided by polyp size. We developed a deep learning-based system (ENDOANGEL-CPS) to estimate colorectal polyp size in real time. METHODS: ENDOANGEL-CPS calculates polyp size by estimating the distance from the endoscope lens to the polyp using the parameters of the lens. The depth estimator network was developed on 7297 images from five virtually produced colon videos and tested on 730 images from seven virtual colon videos. The performance of the system was first evaluated in nine videos of a simulated colon with polyps attached, then tested in 157 real-world prospective videos from three hospitals, with the outcomes compared with that of nine endoscopists over 69 videos. Inappropriate surveillance recommendations caused by incorrect estimation of polyp size were also analyzed. RESULTS: The relative error of depth estimation was 11.3% (SD 6.0%) in successive virtual colon images. The concordance correlation coefficients (CCCs) between system estimation and ground truth were 0.89 and 0.93 in images of a simulated colon and multicenter videos of 157 polyps. The mean CCC of ENDOANGEL-CPS surpassed all endoscopists (0.89 vs. 0.41 [SD 0.29]; P<0.001). The relative accuracy of ENDOANGEL-CPS was significantly higher than that of endoscopists (89.9% vs. 54.7%; P<0.001). Regarding inappropriate surveillance recommendations, the system's error rate is also lower than that of endoscopists (1.5% vs. 16.6%; P<0.001). CONCLUSIONS: ENDOANGEL-CPS could potentially improve the accuracy of colorectal polyp size measurements and size-based surveillance intervals.

A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma.

OBJECTIVES: At present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT. METHODS: A total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model. RESULTS: By incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model. CONCLUSIONS: Leveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.

The relationship between Glasgow Prognostic Score and hospital duration in patients with inflammatory bowel diseases.

BACKGROUND AND OBJECTIVES: Both hypoalbuminemia and inflammation were common in patients with inflammatory bowel diseases (IBD), however, the combination of the two parameters on hospital duration re-mained unknown. METHODS AND STUDY DESIGN: This is a retrospective two-centre study performed in two tertiary hospitals in Shanghai, China. Serum levels of C-Reactive Protein (CRP) and albumin (ALB) were measured within 2 days of admission. Glasgow prognostic score (GPS), based on CRP and ALB, was calculated as follows: point "0" as CRP <10 mg/L and ALB ≥35 g/L; point "1" as either CRP ≥10 mg/L or ALB <35 g/L; point "2" as CRP ≥10 mg/L and ALB <35 g/L. Patients with point "0" were classified as low-risk while point "2" as high-risk. Length of hospital stay (LOS) was defined as the interval between admission and discharge. RESULTS: The proportion of low-risk and high-risk was 69.3% and 10.5% respectively among 3,009 patients (65% men). GPS was associated with LOS [ß=6.2 d; 95% CI (confidence interval): 4.0 d, 8.4 d] after adjustment of potential co-variates. Each point of GPS was associated with 2.9 days (95% CI: 1.9 d, 3.9 d; ptrend<0.001) longer in fully adjusted model. The association was stronger in patients with low prealbumin levels, hypocalcaemia, and hypokalaemia relative to their counterparts. CONCLUSIONS: GPS was associated with LOS in IBD patients. Our results highlighted that GPS could serve as a convenient prognostic tool associated with nutritional status and clinical outcome.

LightR-YOLOv5: A compact rotating detector for SARS-CoV-2 antigen-detection rapid diagnostic test results.

Nucleic acid testing is currently the golden reference for coronaviruses (SARS-CoV-2) detection, while the SARS-CoV-2 antigen-detection rapid diagnostic tests (RDT) is an important adjunct. RDT can be widely used in the community or regional screening management as self-test tools and may need to be verified by healthcare authorities. However, manual verification of RDT results is a time-consuming task, and existing object detection algorithms usually suffer from high model complexity and computational effort, making them difficult to deploy. We propose LightR-YOLOv5, a compact rotating SARS-CoV-2 antigen-detection RDT results detector. Firstly, we employ an extremely light-weight L-ShuffleNetV2 network as a feature extraction network with a slight reduction in recognition accuracy. Secondly, we combine semantic and texture features in different layers by judiciously combining and employing GSConv, depth-wise convolution, and other modules, and further employ the NAM attention to locate the RDT result detection region. Furthermore, we propose a new data augmentation approach, Single-Copy-Paste, for increasing data samples for the specific task of RDT result detection while achieving a small improvement in model accuracy. Compared with some mainstream rotating object detection networks, the model size of our LightR-YOLOv5 is only 2.03MB, and it is 12.6%, 6.4%, and 7.3% higher in mAP@.5:.95 metrics compared to RetianNet, FCOS, and R3Det, respectively.

MG53 attenuates nitrogen mustard-induced acute lung injury.

Nitrogen mustard (NM) is an alkylating vesicant that causes severe pulmonary injury. Currently, there are no effective means to counteract vesicant-induced lung injury. MG53 is a vital component of cell membrane repair and lung protection. Here, we show that mice with ablation of MG53 are more susceptible to NM-induced lung injury than the wild-type mice. Treatment of wild-type mice with exogenous recombinant human MG53 (rhMG53) protein ameliorates NM-induced lung injury by restoring arterial blood oxygen level, by improving dynamic lung compliance and by reducing airway resistance. Exposure of lung epithelial and endothelial cells to NM leads to intracellular oxidative stress that compromises the intrinsic cell membrane repair function of MG53. Exogenous rhMG53 protein applied to the culture medium protects lung epithelial and endothelial cells from NM-induced membrane injury and oxidative stress, and enhances survival of the cells. Additionally, we show that loss of MG53 leads to increased vulnerability of macrophages to vesicant-induced cell death. Overall, these findings support the therapeutic potential of rhMG53 to counteract vesicant-induced lung injury.

Membrane-delimited signaling and cytosolic action of MG53 preserve hepatocyte integrity during drug-induced liver injury.

BACKGROUND & AIMS: Drug-induced liver injury (DILI) remains challenging to treat and is still a leading cause of acute liver failure. MG53 is a muscle-derived tissue-repair protein that circulates in the bloodstream and whose physiological role in protection against DILI has not been examined. METHODS: Recombinant MG53 protein (rhMG53) was administered exogenously, using mice with deletion of Mg53 or Ripk3. Live-cell imaging, histological, biochemical, and molecular studies were used to investigate the mechanisms that underlie the extracellular and intracellular action of rhMG53 in hepatoprotection. RESULTS: Systemic administration of rhMG53 protein, in mice, can prophylactically and therapeutically treat DILI induced through exposure to acetaminophen, tetracycline, concanavalin A, carbon tetrachloride, or thioacetamide. Circulating MG53 protects hepatocytes from injury through direct interaction with MLKL at the plasma membrane. Extracellular MG53 can enter hepatocytes and act as an E3-ligase to mitigate RIPK3-mediated MLKL phosphorylation and membrane translocation. CONCLUSIONS: Our data show that the membrane-delimited signaling and cytosolic dual action of MG53 effectively preserves hepatocyte integrity during DILI. rhMG53 may be a potential treatment option for patients with DILI. LAY SUMMARY: Interventions to treat drug-induced liver injury and halt its progression into liver failure are of great value to society. The present study reveals that muscle-liver cross talk, with MG53 as a messenger, serves an important role in liver cell protection. Thus, MG53 is a potential treatment option for patients with drug-induced liver injury.

Mapping developmental paths of monkey primordial germ-like cells differentiation from pluripotent stem cells by single cell ribonucleic acid sequencing analysis†.

The induction of primordial germ-like cells (PGCLCs) from pluripotent stem cells (PSCs) provides a powerful system to study the cellular and molecular mechanisms underlying germline specification, which are difficult to study in vivo. The studies reveal the existence of a species-specific mechanism underlying PGCLCs between humans and mice, highlighting the necessity to study regulatory networks in more species, especially in primates. Harnessing the power of single-cell RNA sequencing (scRNA-seq) analysis, the detailed trajectory of human PGCLCs specification in vitro has been achieved. However, the study of nonhuman primates is still needed. Here, we applied an embryoid body (EB) differentiation system to induce PGCLCs specification from cynomolgus monkey male and female PSCs, and then performed high throughput scRNA-seq analysis of approximately 40 000 PSCs and cells within EBs. We found that EBs provided a niche for PGCLCs differentiation by secreting growth factors critical for PGCLC specification, such as bone morphogenetic protein 2 (BMP2), BMP4, and Wnt Family Member 3. Moreover, the developmental trajectory of PGCLCs was reconstituted, and gene expression dynamics were revealed. Our study outlines the roadmap of PGCLC specification from PSCs and provides insights that will improve the differentiation efficiency of PGCLCs from PSCs.

Histone Deacetylase Inhibition Restores Behavioral and Synaptic Function in a Mouse Model of 16p11.2 Deletion.

BACKGROUND: Microdeletion of the human 16p11.2 gene locus confers risk for autism spectrum disorders and intellectual disability. How 16p11.2 deletion is linked to these neurodevelopmental disorders and whether there are treatment avenues for the manifested phenotypes remain to be elucidated. Emerging evidence suggests that epigenetic aberrations are strongly implicated in autism. METHODS: We performed behavioral and electrophysiological experiments to examine the therapeutic effects of epigenetic drugs in transgenic mice carrying 16p11.2 deletion (16p11del/+). RESULTS: We found that 16p11del/+ mice exhibited a significantly reduced level of histone acetylation in the prefrontal cortex (PFC). A short (3-day) treatment with class I histone deacetylase (HDAC) inhibitor MS-275 or Romidepsin led to the prolonged (3-4 weeks) rescue of social and cognitive deficits in 16p11del/+ mice. Concomitantly, MS-275 treatment reversed the hypoactivity of PFC pyramidal neurons and the hyperactivity of PFC fast-spiking interneurons. Moreover, the diminished N-methyl-D-aspartate (NMDA) receptor-mediated synaptic currents and the elevated GABAA receptor-mediated synaptic currents in PFC pyramidal neurons of 16p11del/+ mice were restored to control levels by MS-275 treatment. CONCLUSIONS: Our results suggest that HDAC inhibition provides a highly effective therapeutic strategy for behavioral deficits and excitation/inhibition imbalance in 16p11del/+ mice, likely via normalization of synaptic function in the PFC.

Reversal of synaptic and behavioral deficits in a 16p11.2 duplication mouse model via restoration of the GABA synapse regulator Npas4.

The human 16p11.2 gene locus is a hot spot for copy number variations, which predispose carriers to a range of neuropsychiatric phenotypes. Microduplications of 16p11.2 are associated with autism spectrum disorder (ASD), intellectual disability (ID), and schizophrenia (SZ). Despite the debilitating nature of 16p11.2 duplications, the underlying molecular mechanisms remain poorly understood. Here we performed a comprehensive behavioral characterization of 16p11.2 duplication mice (16p11.2dp/+) and identified social and cognitive deficits reminiscent of ASD and ID phenotypes. 16p11.2dp/+ mice did not exhibit the SZ-related sensorimotor gating deficits, psychostimulant-induced hypersensitivity, or motor impairment. Electrophysiological recordings of 16p11.2dp/+ mice found deficient GABAergic synaptic transmission and elevated neuronal excitability in the prefrontal cortex (PFC), a brain region critical for social and cognitive functions. RNA-sequencing identified genome-wide transcriptional aberrance in the PFC of 16p11.2dp/+ mice, including downregulation of the GABA synapse regulator Npas4. Restoring Npas4 expression in PFC of 16p11.2dp/+ mice ameliorated the social and cognitive deficits and reversed GABAergic synaptic impairment and neuronal hyperexcitability. These findings suggest that prefrontal cortical GABAergic synaptic circuitry and Npas4 are strongly implicated in 16p11.2 duplication pathology, and may represent potential targets for therapeutic intervention in ASD.