UsIL-6: An unbalanced learning strategy for identifying IL-6 inducing peptides by undersampling technique.

BACKGROUND AND OBJECTIVE: Interleukin-6 (IL-6) is the critical factor of early warning, monitoring, and prognosis in the inflammatory storm of COVID-19 cases. IL-6 inducing peptides, which can induce cytokine IL-6 production, are very important for the development of diagnosis and immunotherapy. Although the existing methods have some success in predicting IL-6 inducing peptides, there is still room for improvement in the performance of these models in practical application. METHODS: In this study, we proposed UsIL-6, a high-performance bioinformatics tool for identifying IL-6 inducing peptides. First, we extracted five groups of physicochemical properties and sequence structural information from IL-6 inducing peptide sequences, and obtained a 636-dimensional feature vector, we also employed NearMiss3 undersampling method and normalization method StandardScaler to process the data. Then, a 40-dimensional optimal feature vector was obtained by Boruta feature selection method. Finally, we combined this feature vector with extreme randomization tree classifier to build the final model UsIL-6. RESULTS: The AUC value of UsIL-6 on the independent test dataset was 0.87, and the BACC value was 0.808, which indicated that UsIL-6 had better performance than the existing methods in IL-6 inducing peptide recognition. CONCLUSIONS: The performance comparison on independent test dataset confirmed that UsIL-6 could achieve the highest performance, best robustness, and most excellent generalization ability. We hope that UsIL-6 will become a valuable method to identify, annotate and characterize new IL-6 inducing peptides.

Is ALPPS still appropriate for large or locally advanced hepatocellular carcinoma in an era of targeted agents and immunotherapy?

Therapeutic options for large or locally advanced hepatocellular carcinoma (HCC) have limited efficacy. This study investigated the efficacy and safety of drug-eluting beads trans-arterial chemo-embolization (dTACE), portal vein embolization (PVE), tyrosine kinase inhibitor (TKI), and immune checkpoint inhibitors (ICI) compared to Associating Liver Partition and Portal vein ligation for Staged hepatectomy (ALPPS) for large or locally advanced HCC.Data regarding clinicopathological details, safety, and oncological outcomes were reviewed for the quadruple therapy (dTACE-PVE-TKI-ICI) and compared with ALPPS.From 2019 to 2020, 10 patients with large or locally advanced HCC underwent future remnant liver (FRL) modulation (dTACE-PVE-TKI-ICI: 5; ALPPS: 5). All five dTACE-PVE-TKI-ICI cases responded well, with patients #4 and #5 achieving complete tumor necrosis. The overall response rate (ORR) was 5/5. Patients #1-4 underwent hepatectomy, while #5 declined surgery due to complete tumor necrosis. Mean FRL volume increased by 75.3% (range 60.0%-89.4%) in 2-4 months, compared to 104.6% (range 51.3%-160.8%) in 21-37 days for ALPPS (P = 0.032). Major postoperative complications occurred in 1/5 ALPPS patients. Resection rates were 4/4 for quadruple therapy and 5/5 for ALPPS. 2-year progression free survival for dTACE-PVE-TKI-ICI and ALPPS were 5/5 and 3/5, respectively.Quadruple therapy is a feasible, effective strategy for enhancing resectability by downsizing tumors and inducing FRL hypertrophy, with manageable complications and improved long-term prognosis. In addition, it provokes the re-examination of the application of ALPPS in an era of molecular and immune treatments.

Prevalence, patterns, risk factors and outcomes of peritoneal metastases after laparoscopic hepatectomy for hepatocellular carcinoma: a multicenter study from China.

Background: We aim to investigate the prevalence, patterns, risk factors, and outcomes of peritoneal metastases (PM) after curative laparoscopic hepatectomy (LH) for hepatocellular carcinoma (HCC). Methods: A multicenter cohort of 2,138 HCC patients who underwent curative LH from August 2010 to December 2016 from seven hospitals in China was retrospectively analyzed. The incidence of PM following LH was evaluated and compared with that in open hepatectomy (OH) after 1:1 propensity score matching (PSM). Results: PM prevalence was 5.1% (15/295) in the early period [2010-2013], 2.6% (47/1,843) in the later period [2014-2016], and 2.9% (62/2,138) in all LH patients, which was similar to 4.0% (59/1,490) in the OH patients. The recurrence patterns, timing, and treatment did not significantly vary between the LH and OH patients (P>0.05). Multivariate logistic regression revealed that tumor diameter >5 cm, non-anatomical resection, presence of microvascular invasion, and lesions <2 cm from major blood vessels were independent risk factors of PM after LH. Of the 62 cases with PM, 26 (41.9%) had PM only, 34 (54.9%) had intrahepatic recurrence (IHR) and PM, and 2 (3.2%) had synchronous extraperitoneal metastases (EPM). Patients with resectable PM had a 5-year overall survival (OS) of 65.0% compared to 9.0% for unresectable PM (P=0.001). Conclusions: The prevalence, patterns and independent risk factors of PM were identified for HCC patients after LH. LH was not associated with increased incidence of PM in HCC patients for experienced surgeons. Surgical re-excision of PM was associated with prolonged survival.

Removal of a partial genomic duplication restores synaptic transmission and behavior in the MyosinVA mutant mouse Flailer.

BACKGROUND: Copy number variations, and particularly duplications of genomic regions, have been strongly associated with various neurodegenerative conditions including autism spectrum disorder (ASD). These genetic variations have been found to have a significant impact on brain development and function, which can lead to the emergence of neurological and behavioral symptoms. Developing strategies to target these genomic duplications has been challenging, as the presence of endogenous copies of the duplicate genes often complicates the editing strategies. RESULTS: Using the ASD and anxiety mouse model Flailer, which contains a partial genomic duplication working as a dominant negative for MyoVa, we demonstrate the use of DN-CRISPRs to remove a 700 bp genomic region in vitro and in vivo. Importantly, DN-CRISPRs have not been used to remove genomic regions using sgRNA with an offset greater than 300 bp. We found that editing the flailer gene in primary cortical neurons reverts synaptic transport and transmission defects. Moreover, long-term depression (LTD), disrupted in Flailer animals, is recovered after gene editing. Delivery of DN-CRISPRs in vivo shows that local delivery to the ventral hippocampus can rescue some of the mutant behaviors, while intracerebroventricular delivery, completely recovers the Flailer animal phenotype associated to anxiety and ASD. CONCLUSIONS: Our results demonstrate the potential of DN-CRISPR to efficiently remove larger genomic duplications, working as a new gene therapy approach for treating neurodegenerative diseases.

Removal of a genomic duplication by double-nicking CRISPR restores synaptic transmission and behavior in the MyosinVA mutant mouse Flailer.

Copy number variations, and particularly duplications of genomic regions, have been strongly associated with various neurodegenerative conditions including autism spectrum disorder (ASD). These genetic variations have been found to have a significant impact on brain development and function, which can lead to the emergence of neurological and behavioral symptoms. Developing strategies to target these genomic duplications has been challenging, as the presence of endogenous copies of the duplicate genes often complicates the editing strategies. Using the ASD and anxiety mouse model Flailer, that contains a duplication working as a dominant negative for MyoVa, we demonstrate the use of DN-CRISPRs to remove a 700bp genomic duplication in vitro and in vivo . Importantly, DN-CRISPRs have not been used to remove more gene regions <100bp successfully and with high efficiency. We found that editing the flailer gene in primary cortical neurons reverts synaptic transport and transmission defects. Moreover, long-term depression (LTD), disrupted in Flailer animals, is recovered after gene edition. Delivery of DN-CRISPRs in vivo shows that local delivery to the ventral hippocampus can rescues some of the mutant behaviors, while intracerebroventricular delivery, completely recovers Flailer animal phenotype associated to anxiety and ASD. Our results demonstrate the potential of DN-CRISPR to efficiently (>60% editing in vivo) remove large genomic duplications, working as a new gene therapy approach for treating neurodegenerative diseases.

Collagen I-DDR1 signaling promotes hepatocellular carcinoma cell stemness via Hippo signaling repression.

Cancer stem cells (CSCs) are a minority population of cancer cells with stemness and multiple differentiation potentials, leading to cancer progression and therapeutic resistance. However, the concrete mechanism of CSCs in hepatocellular carcinoma (HCC) remains obscure. We found that in advanced HCC tissues, collagen I was upregulated, which is consistent with the expression of its receptor DDR1. Accordingly, high collagen I levels accompanied by high DDR1 expression are associated with poor prognoses in patients with HCC. Collagen I-induced DDR1 activation enhanced HCC cell stemness in vitro and in vivo. Mechanistically, DDR1 interacts with CD44, which acts as a co-receptor that amplifies collagen I-induced DDR1 signaling, and collagen I-DDR1 signaling antagonized Hippo signaling by facilitating the recruitment of PP2AA to MST1, leading to exaggerated YAP activation. The combined inhibition of DDR1 and YAP synergistically abrogated HCC cell stemness in vitro and tumorigenesis in vivo. A radiomic model based on T2 weighted images can noninvasively predict collagen I expression. These findings reveal the molecular basis of collagen I-DDR1 signaling inhibiting Hippo signaling and highlight the role of CD44/DDR1/YAP axis in promoting cancer cell stemness, suggesting that DDR1 and YAP may serve as novel prognostic biomarkers and therapeutic targets in HCC.

Hederagenin improves Alzheimer's disease through PPARα/TFEB-mediated autophagy.

BACKGROUND: Autophagic flux is coordinated by a network of master regulatory genes, which centered on transcription factor EB (TFEB). The disorders of autophagic flux are closely associated with Alzheimer's disease (AD), and thus restoring autophagic flux to degrade pathogenic proteins has become a hot therapeutic strategy. Hederagenin (HD), a triterpene compound, isolated from a variety food such as Matoa (Pometia pinnata) Fruit, Medicago sativa, Medicago polymorpha L. Previous studies have shown that HD has the neuroprotective effect. However, the effect of HD on AD and underlying mechanisms are unclear. PURPOSE: To determine the effect of HD on AD and whether it promotes autophagy to reduce AD symptoms. STUDY DESIGN: BV2 cells, C. elegans and APP/PS1 transgenic mice were used to explore the alleviative effect of HD on AD and the molecular mechanism in vivo and in vitro. METHODS: The APP/PS1 transgenic mice at 10 months were randomized into 5 groups (n = 10 in each group) and orally administrated with either vehicle (0.5% CMCNa), WY14643 (10 mg/kg/d), low-dose of HD (25 mg/kg/d), high-dose of HD (50 mg/kg/d) or MK-886 (10 mg/kg/d) + HD (50 mg/kg/d) for consecutive 2 months. The behavioral experiments including morris water maze test, object recognition test and Y maze test were performed. The effects of HD on Aß deposition and alleviates Aß pathology in transgenic C. elegans were operated using paralysis assay and fluorescence staining assay. The roles of HD in promoting PPARα/TFEB-dependent autophagy were investigated using the BV2 cells via western blot analysis, real-time quantitative PCR (RT-qPCR), molecular docking, molecular dynamic (MD) simulation, electron microscope assay and immunofluorescence. RESULTS: In this study, we found that HD upregulated mRNA and protein level of TFEB and increased the distribution of TFEB in the nucleus, and the expressions of its target genes. HD also promoted the expressions of LC3BII/LC3BI, LAMP2, etc., and promoted autophagy and the degradation of Aß. HD reduced Aß deposition in the head area of C. elegans and Aß-induced paralysis. HD improved cognitive impairment and pathological changes in APP/PS1 mice by promoting autophagy and activating TFEB. And our results also showed that HD could strongly target PPARα. More importantly, these effects were reversed by treatment of MK-886, a selective PPARα antagonist. CONCLUSION: Our present findings demonstrated that HD attenuated the pathology of AD through inducing autophagy and the underlying mechanism associated with PPARα/TFEB pathway.

scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data.

The progress of single-cell RNA sequencing (scRNA-seq) has led to a large number of scRNA-seq data, which are widely used in biomedical research. The noise in the raw data and tens of thousands of genes pose a challenge to capture the real structure and effective information of scRNA-seq data. Most of the existing single-cell analysis methods assume that the low-dimensional embedding of the raw data belongs to a Gaussian distribution or a low-dimensional nonlinear space without any prior information, which limits the flexibility and controllability of the model to a great extent. In addition, many existing methods need high computational cost, which makes them difficult to be used to deal with large-scale datasets. Here, we design and develop a depth generation model named Gaussian mixture adversarial autoencoders (scGMAAE), assuming that the low-dimensional embedding of different types of cells follows different Gaussian distributions, integrating Bayesian variational inference and adversarial training, as to give the interpretable latent representation of complex data and discover the statistical distribution of different types of cells. The scGMAAE is provided with good controllability, interpretability and scalability. Therefore, it can process large-scale datasets in a short time and give competitive results. scGMAAE outperforms existing methods in several ways, including dimensionality reduction visualization, cell clustering, differential expression analysis and batch effect removal. Importantly, compared with most deep learning methods, scGMAAE requires less iterations to generate the best results.

iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework.

Enhancers are small regions of DNA that bind to proteins, which enhance the transcription of genes. The enhancer may be located upstream or downstream of the gene. It is not necessarily close to the gene to be acted on, because the entanglement structure of chromatin allows the positions far apart in the sequence to have the opportunity to contact each other. Therefore, identifying enhancers and their strength is a complex and challenging task. In this article, a new prediction method based on deep learning is proposed to identify enhancers and enhancer strength, called iEnhancer-DCLA. Firstly, we use word2vec to convert k-mers into number vectors to construct an input matrix. Secondly, we use convolutional neural network and bidirectional long short-term memory network to extract sequence features, and finally use the attention mechanism to extract relatively important features. In the task of predicting enhancers and their strengths, this method has improved to a certain extent in most evaluation indexes. In summary, we believe that this method provides new ideas in the analysis of enhancers.

scSSA: A clustering method for single cell RNA-seq data based on semi-supervised autoencoder.

BACKGROUND: Single cell sequencing is a technology for high-throughput sequencing analysis of genome, transcriptome and epigenome at the single cell level. It can improve the shortcomings of traditional methods, reveal the gene structure and gene expression state of a single cell, and reflect the heterogeneity between cells. Among them, the clustering analysis of single-cell RNA data is a very important step, but the clustering of single-cell RNA data is faced with two difficulties, dropout events and dimension curse. At present, many methods are only driven by data, and do not make full use of the existing biological information. RESULTS: In this work, we propose scSSA, a clustering model based on semi-supervised autoencoder, fast independent component analysis (FastICA) and Gaussian mixture clustering. Firstly, the semi-supervised autoencoder imputes and denoises the scRNA-seq data, and then get the low-dimensional latent representation. Secondly, the low-dimensional representation is reduced the dimension and clustered by FastICA and Gaussian mixture model respectively. Finally, scSSA is compared with Seurat, CIDR and other methods on 10 public scRNA-seq datasets. CONCLUSION: The results show that scSSA has superior performance in cell clustering on 10 public datasets. In conclusion, scSSA can accurately identify the cell types and is generally applicable to all kinds of single cell datasets. scSSA has great application potential in the field of scRNA-seq data analysis. Details in the code have been uploaded to the website https://github.com/houtongshuai123/scSSA/.

iPromoter-CLA: Identifying promoters and their strength by deep capsule networks with bidirectional long short-term memory.

BACKGROUND AND OBJECTIVE: The promoter is a fragment of DNA and a specific sequence with transcriptional regulation function in DNA. Promoters are located upstream at the transcription start site, which is used to initiate downstream gene expression. So far, promoter identification is mainly achieved by biological methods, which often require more effort. It has become a more effective classification and prediction method to identify promoter types through computational methods. METHODS: In this study, we proposed a new capsule network and recurrent neural network hybrid model to identify promoters and predict their strength. Firstly, we used one-hot to encode DNA sequence. Secondly, we used three one-dimensional convolutional layers, a one-dimensional convolutional capsule layer and digit capsule layer to learn local features. Thirdly, a bidirectional long short-time memory was utilized to extract global features. Finally, we adopted the self-attention mechanism to improve the contribution of relatively important features, which further enhances the performance of the model. RESULTS: Our model attains a cross-validation accuracy of 86% and 73.46% in prokaryotic promoter recognition and their strength prediction, which showcases a better performance compared with the existing approaches in both the first layer promoter identification and the second layer promoter's strength prediction. CONCLUSIONS: our model not only combines convolutional neural network and capsule layer but also uses a self-attention mechanism to better capture hidden information features from the perspective of sequence. Thus, we hope that our model can be widely applied to other components.

DeeProPre: A promoter predictor based on deep learning.

The promoter is a DNA sequence recognized, bound and transcribed by RNA polymerase. It is usually located at the upstream or 5'end of the transcription start site (TSS). Studies have shown that the structure of the promoter affects its affinity for RNA polymerase, thus affecting the level of gene expression. Therefore, the correct identification of core promoter and common structural gene is of great significance in the field of biomedicine. At present, many methods have been proposed to improve the accuracy of promoter recognition, but the performances still need to be further improved. In this study, a deep learning algorithm (DeeProPre) based on bidirectional long short-term memory (BiLSTM) and convolutional neural network (CNN) was proposed. Firstly, the supervised embedding layer was applied to map the sequence to a high-dimensional space. Secondly, two 1D convolutional layers, BiLSTM and attentional mechanism layer were used for extracting features. Finally, the full connection layer activated by Sigmoid function was used to obtain the probability of classification into target categories. This model can identify the promoter region of eukaryotes with high accuracy, providing an analytical basis for further understanding of promoter physiological functions and studies of gene transcription mechanisms. The source code of DeeProPre is freely available at https://github.com/zzwwmmm/DeeProPre/tree/master.

1H-NMR-guided isolation of enantiomeric coumarin-monoterpenes with anti-inflammatory activity from Gerbera piloselloides.

1H-NMR guided fractionation led to the isolation of twenty-two coumarin monoterpenes from the whole plant of Gerbera piloselloides, among which fourteen were undescribed. All coumarin monoterpenes were initially found to be racemates without optical activity. Subsequently, eleven pairs of optically pure enantiomers were successfully separated by chiral phase HPLC. Their structures and absolute configurations were unambiguously determined based on their spectroscopic data, calculated/experimental electronic circular dichroism (ECD) data, and X-ray diffraction analysis. Bioassays in LPS-treated RAW 264.7 cells revealed that the four compounds possessed moderate anti-inflammatory activity. In addition, the correlations between the cotton effect (CE) from δ-lactone at approximately 210-220 nm in CD spectra and γ-C or the ring fused at γ-C of the skeleton were reported for the first time.

Comparative Proteome and Cis-Regulatory Element Analysis Reveals Specific Molecular Pathways Conserved in Dog and Human Brains.

Brain development and function are governed by precisely regulated protein expressions in different regions. To date, multiregional brain proteomes have been systematically analyzed only for adult human and mouse brains. To understand the underpinnings of brain development and function, we generated proteomes from six regions of the postnatal brain at three developmental stages of domestic dogs (Canis familiaris), which are special among animals in terms of their remarkable human-like social cognitive abilities. Quantitative analysis of the spatiotemporal proteomes identified region-enriched synapse types at different developmental stages and differential myelination progression in different brain regions. Through integrative analysis of inter-regional expression patterns of orthologous proteins and genome-wide cis-regulatory element frequencies, we found that proteins related with myelination and hippocampus were highly correlated between dog and human but not between mouse and human, although mouse is phylogenetically closer to human. Moreover, the global expression patterns of neurodegenerative disease and autism spectrum disorder-associated proteins in dog brain more resemble human brain than in mouse brain. The high similarity of myelination and hippocampus-related pathways in dog and human at both proteomic and genetic levels may contribute to their shared social cognitive abilities. The inter-regional expression patterns of disease-associated proteins in the brain of different species provide important information to guide mechanistic and translational study using appropriate animal models.

scCNC: a method based on capsule network for clustering scRNA-seq data.

MOTIVATION: A large number of studies have shown that clustering is a crucial step in scRNA-seq analysis. Most existing methods are based on unsupervised learning without the prior exploitation of any domain knowledge, which does not utilize available gold-standard labels. When confronted by the high dimensionality and general dropout events of scRNA-seq data, purely unsupervised clustering methods may not produce biologically interpretable clusters, which complicate cell type assignment. RESULTS: In this article, we propose a semi-supervised clustering method based on a capsule network named scCNC that integrates domain knowledge into the clustering step. Significantly, we also propose a Semi-supervised Greedy Iterative Training method used to train the whole network. Experiments on some real scRNA-seq datasets show that scCNC can significantly improve clustering performance and facilitate downstream analyses. AVAILABILITY AND IMPLEMENTATION: The source code of scCNC is freely available at https://github.com/WHY-17/scCNC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

scCDG: A Method Based on DAE and GCN for scRNA-Seq Data Analysis.

Identifying cell types is one of the main goals of single-cell RNA sequencing (scRNA-seq) analysis, and clustering is a common method for this item. However, the massive amount of data and the excess noise level bring challenge for single cell clustering. To address this challenge, in this paper, we introduced a novel method named single-cell clustering based on denoising autoencoder and graph convolution network (scCDG), which consists of two core models. The first model is a denoising autoencoder (DAE) used to fit the data distribution for data denoising. The second model is a graph autoencoder using graph convolution network (GCN), which projects the data into a low-dimensional space (compressed) preserving topological structure information and feature information in scRNA-seq data simultaneously. Extensive analysis on seven real scRNA-seq datasets demonstrate that scCDG outperforms state-of-the-art methods in some research sub-fields, including single cell clustering, visualization of transcriptome landscape, and trajectory inference.

Identification of driver genes based on gene mutational effects and network centrality.

BACKGROUND: As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments. RESULTS: To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein-protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality. CONCLUSIONS: Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.

SUSCC: Secondary Construction of Feature Space based on UMAP for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data.

Clustering is a common method to identify cell types in single cell analysis, but the increasing size of scRNA-seq datasets brings challenges to single cell clustering. Therefore, it is an urgent need to design a faster and more accurate clustering method for large-scale scRNA-seq data. In this paper, we proposed a new method for single cell clustering. First, a count matrix is constructed through normalization and gene filtration. Second, the raw data of gene expression matrix are projected to feature space constructed by secondary construction of feature space based on UMAP (Uniform Manifold Approximation and Projection). Third, the low-dimensional matrix on the feature space is randomly divided into two sub-matrices according to a certain proportion for clustering and classifying, respectively. Finally, one subset is clustered by k-means algorithm and then the other subset is classified by k-nearest neighbor algorithm based on clustering results. Experimental results show that our method can cluster the scRNA-seq datasets effectively.

Myosin Va Brain-Specific Mutation Alters Mouse Behavior and Disrupts Hippocampal Synapses.

Myosin Va (MyoVa) is a plus-end filamentous-actin motor protein that is highly and broadly expressed in the vertebrate body, including in the nervous system. In excitatory neurons, MyoVa transports cargo toward the tip of the dendritic spine, where the postsynaptic density (PSD) is formed and maintained. MyoVa mutations in humans cause neurologic dysfunction, intellectual disability, hypomelanation, and death in infancy or childhood. Here, we characterize the Flailer (Flr) mutant mouse, which is homozygous for a myo5a mutation that drives high levels of mutant MyoVa (Flr protein) specifically in the CNS. Flr protein functions as a dominant-negative MyoVa, sequestering cargo and blocking its transport to the PSD. Flr mice have early seizures and mild ataxia but mature and breed normally. Flr mice display several abnormal behaviors known to be associated with brain regions that show high expression of Flr protein. Flr mice are defective in the transport of synaptic components to the PSD and in mGluR-dependent long-term depression (LTD) and have a reduced number of mature dendritic spines. The synaptic and behavioral abnormalities of Flr mice result in anxiety and memory deficits similar to that of other mouse mutants with obsessive-compulsive disorder and autism spectrum disorder (ASD). Because of the dominant-negative nature of the Flr protein, the Flr mouse offers a powerful system for the analysis of how the disruption of synaptic transport and lack of LTD can alter synaptic function, development and wiring of the brain and result in symptoms that characterize many neuropsychiatric disorders.