Biology subjects

Yuan, X.

Publications and source records attributed to Yuan, X..

At least 127 records · Page 7Linked to original sources

bioRxiv2021

An automated method for precise axon reconstruction from recordings of high-density micro-electrode arrays

Neurons communicate with each other by sending action potentials through their axons. The velocity of axonal signal propagation describes how fast electrical action potentials can travel, and can be affected in a human brain by several pathologies, including multiple sclerosis, traumatic brain injury and channelopathies. High-density microelectrode arrays (HD-MEAs) provide unprecedented spatio-temporal resolution to extracellularly record neural electrical activity. The high density of the recording electrodes enables to image the activity of individual neurons down to subcellular resolution, which includes the propagation of axonal signals. However, axon recon-struction, to date, mainly relies on a manual approach to select the electrodes and channels that seemingly record the signals along a specific axon, while an automated approach to track multiple axonal branches in extracellular action-potential recordings is still missing. In this article, we propose a fully automated approach to reconstruct axons from extracellular electrical-potential landscapes, so-called "electrical footprints" of neurons. After an initial electrode and channel selection, the proposed method first constructs a graph, based on the voltage signal amplitudes and latencies. Then, the graph is interrogated to extract possible axonal branches. Finally, the axonal branches are pruned and axonal action-potential propagation velocities are computed. We first validate our method using simulated data from detailed reconstructions of neurons, showing that our approach is capable of accurately reconstructing axonal branches. We then apply the reconstruction algorithm to experimental recordings of HD-MEAs and show that it can be used to determine axonal morphologies and signal-propagation velocities at high throughput. We introduce a fully automated method to reconstruct axonal branches and estimate axonal action-potential propagation velocities using HD-MEA recordings. Our method yields highly reliable and reproducible velocity estimations, which constitute an important electrophysiological feature of neuronal preparations.

neuroscience↗

bioRxiv2021

Heme oxygenase-2 (HO-2) binds and buffers labile heme, which is largely oxidized, in human embryonic kidney cells

Heme oxygenases (HO) detoxify heme by oxidatively degrading it into carbon monoxide, iron, and biliverdin, which is reduced to bilirubin and excreted. Humans express two isoforms: inducible HO-1, which is up-regulated in response to various stressors, including excess heme, and constitutive HO-2. While much is known about the regulation and physiological function of HO-1, comparatively little is known about the role of HO-2 in regulating heme homeostasis. The biochemical necessity for expressing constitutive HO-2 is largely dependent on whether heme is sufficiently abundant and accessible as a substrate under conditions in which HO-1 is not induced. By measuring labile heme, total heme, and bilirubin in human embryonic kidney HEK293 cells with silenced or over-expressed HO-2, and various HO-2 mutant alleles, we found that endogenous heme is too limiting to support HO-2 catalyzed heme degradation. Rather, we discovered that a novel role for HO-2 is to bind and buffer labile heme. Taken together, in the absence of excess heme, we propose that HO-2 regulates heme homeostasis by acting as a heme buffering factor in control of heme bioavailability. When heme is in excess, HO-1 is induced and both HO-2 and HO-1 can provide protection from heme toxicity by enzymatically degrading it. Our results explain why catalytically inactive mutants of HO-2 are cytoprotective against oxidative stress. Moreover, the change in bioavailable heme due to HO-2 overexpression, which selectively binds ferric over ferrous heme, is consistent with the labile heme pool being oxidized, thereby providing new insights into heme trafficking and signaling.

cell biology↗

bioRxiv2021

MLC Seq: De novo sequencing of full-length tRNA isoforms by mass ladder complementation

tRNAs can exist in distinct isoforms because of different chemical modifications, which confounds attempts to accurately sequence individual tRNA species using next generation sequencing approaches or to quantify different RNA modifications at specific sites on a tRNA strand. Herein, we develop a mass spectrometric (MS) ladder complementation sequencing (MLC-Seq), allowing for direct and simultaneous sequencing of full-length tRNA molecules, including those with low abundance. MLC-seq is achieved by improved instrumentation, and advanced algorithms that identify each tRNA species and related isoforms in an RNA mixture, and assemble full MS ladders from partial ladders with missing ladder components. Using MLC-Seq, we successfully obtained the sequence of tRNA-Phe from yeast and tRNA-Glu from mouse hepatocytes, and simultaneously revealed new tRNA isoforms derived from nucleotide modifications. Importantly, MLC-Seq pinpointed the location and stoichiometry changes of RNA modifications in tRNA-Glu upon the treatment of dealkylated enzyme AlkB, which confirmed its known enzymatic activity and suggested previously unidentified effects in RNA editing.

biochemistry↗

bioRxiv2021

A novel type of IRES having variable numbers of eIF4E-binding site and synergistic effect in RNA2 of WYMV isolates

Some viral proteins were translated in cap-independent manner via internal ribosome entry site (IRES), which ever maintained conservative characteristic among different isolates of same species of virus. However, IRES activity presented 7-fold of variance in RNA2 of wheat yellow mosaic virus (WYMV) HC and LYJN isolates. Based on RNA structure probing and mutagenesis assay, the loosened middle stem of H1 and hepta-nucleotide top loop of H2 in LYJN isolate synergistically ensured the higher IRES activity than that in HC isolate. In addition, the conserved top loop of H1 ensured basic IRES activity in HC and LYJN isolates. RNA2 5'-UTR specifically interacted with the wheat eIF4E, which was accomplished by the top loop of H1 in HC isolate or the top loop of H1 and H2 in LYJN isolate. Different IRES activity of WYMV RNA2 was regulated by different numbers of eIF4E-binding site and their synergistic effect, which was accomplished by the proximity of H1 and H2 due to the flexibility of middle stem in H1. It is represented a novel evolution pattern of IRES.

microbiology↗

bioRxiv2021

A deep learning model for fish classification base on DNA barcode

Fish is one of the most extensive distributed organisms in the world, fish taxonomy is an important part of biodiversity and is also the basis of fishery resources management. However, the morphological characters are so subtle to identify and intact specimens are not available sometimes, making the research and application of morphological method laborious and time-consuming. DNA barcoding based on a fragment of the cytochrome c oxidase subunit I (COI) gene is a valuable molecular tool for species identification and biodiversity studies. In this paper, a novel deep learning classification approach that fuses Elastic Net-Stacked Autoencoder (EN-SAE) with Kernel Density Estimation (KDE), named ESK-model, is proposed bases on DNA barcode. In stage one, ESK-model preprocesses the original data from COI fragments. In stage two, EN-SAE is used to learn the deep features and obtain the outgroup score of each fish. In stage three, KDE is used to select the threshold base on the outgroup scores and classify fish from different families. The effectiveness and superiority of ESK-model have been validated by experiment on three dominant fish families and comparisons with state-of-the-art methods. Those findings confirm that the ESK-model can accurately classify fish from different family base on DNA barcode.

molecular biology↗

bioRxiv2020

Landscapes and dynamic diversifications of B-cell receptor repertoires in COVID-19 patients

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the pandemic of coronavirus disease 2019 (COVID-19). Great international efforts have been put into the development of prophylactic vaccines and neutralizing antibodies. However, the knowledge about the B cell immune response induced by the SARS-CoV-2 virus is still limited. Here, we report a comprehensive characterization of the dynamics of immunoglobin heavy chain (IGH) repertoire in COVID-19 patients. By using next-generation sequencing technology, we examined the temporal changes in the landscape of the patients immunological status, and found dramatic changes in the IGH within the patients immune system after the onset of COVID-19 symptoms. Although different patients have distinct immune responses to SARS-CoV-2 infection, by employing clonotype overlap, lineage expansion and clonotype network analyses, we observed a higher clonotype overlap and substantial lineage expansion of B cell clones during 2-3 weeks of illness, which is of great importance to B-cell immune responses. Meanwhile, for preferences of V gene usage during SARS-CoV-2 infection, IGHV3-74 and IGHV4-34 and IGHV4-39 in COVID-19 patients were more abundant than that of healthy controls. Overall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development as well as mechanistic research.

immunology↗

bioRxiv2020

Fusobacterium nucleatum facilitates cetuximab resistance in colorectal cancer via the PI3K/AKT and JAK/STAT3 pathways

Infectious pathogens contribute to about 20% of the total tumor burden. Fusobacterium nucleatum (Fn) has been associated with the initiation, progression, and therapy resistance in colorectal cancer (CRC). The over-abundance of Fn has been observed in patients with right-sided CRC than in those with left-sided CRC. While the KRAS/NRAS/BRAF wild-type status of the CRC conferred better response to cetuximab in patients with left-sided CRC than with right-sided CRC. However, treatment failure remains the leading cause of tumor relapse and poor clinical outcome in patients with CRC. Here, we have studied the association of Fn to cetuximab resistance. Our functional studies indicate that Fn facilitates resistance of CRC to cetuximab in vitro and in vivo. Moreover, Fn was found to target the PI3K/AKT and JAK/STAT3 pathways, which altered the response to cetuximab therapy. Therefore, assessing the levels and targeting Fn and the associated signaling pathways may allow modulating the treatment regimen and improve prognoses of CRC patients.

cancer biology↗

bioRxiv2020

GATA4/5/6 family transcription factors are conserved determinants of cardiac versus pharyngeal mesoderm fate

GATA4/5/6 transcription factors play essential, conserved roles in heart development. How these factors mediate the transition from multipotent mesoderm progenitors to a committed cardiac fate is unclear. To understand how GATA4/5/6 modulate cell fate decisions we labelled, isolated, and performed single-cell gene expression analysis on cells that express gata5 at pre-cardiac time points spanning gastrulation to somitogenesis. We found that most mesendoderm-derived lineages had dynamic gata5/6 expression. In the absence of Gata5/6, the population structure of mesendoderm-derived cells was dramatically altered. In addition to the expected absence of cardiac mesoderm, we observed a concomitant expansion of cranial-pharyngeal mesoderm. Functional genetic analyses in zebrafish and the invertebrate chordate Ciona, which possess a single GATA4/5/6 homolog, revealed an essential and cell-autonomous role for GATA4/5/6 in promoting cardiac and inhibiting pharyngeal mesoderm identity. Overall, the maintenance and repression of GATA4/5/6 activity plays a critical, evolutionarily conserved role in early development.

developmental biology↗

bioRxiv2020

A deep learning framework with hybrid encoding for protein coding regions prediction in biological sequences

Protein coding regions prediction is a very important but overlooked subtask for tasks such as prediction of complete gene structure, coding/noncoding RNA. Many machine learning methods have been proposed for this problem, they first encode a biological sequence into numerical values and then feed them into a classifier for final prediction. However, encoding schemes directly influence the classifiers capability to capture coding features and how to choose a proper encoding scheme remains uncertain. Recently, we proposed a protein coding region prediction method in transcript sequences based on a bidirectional recurrent neural network with non-overlapping 3-mer feature, and achieved considerable improvement over existing methods, but there is still much room to improve the performance. First, 3-mer feature that counts the occurrence frequency of trinucleotides in a biological sequence only reflect local sequence order information between the most contiguous nucleotides, which loses almost all the global sequence order information. Second, kmer features of length k larger than three (e.g., hexamer) may also contain useful information. Based on the two points, we here present a deep learning framework with hybrid encoding for protein coding regions prediction in biological sequences, which effectively exploit global sequence order information, non-overlapping gapped kmer (gkm) features and statistical dependencies among coding labels. 3-fold cross-validation tests on human and mouse biological sequences demonstrate that our proposed method significantly outperforms existing state-of-the-art methods.

bioinformatics↗

bioRxiv2020

Reporting quality of trend analyses in leading medicine and oncology journals

Reporting quality of clinical research is critical for evidence-based medicine and reproducibility of clinical research. Most of the works focused on reporting quality of clinical trials and observational longitudinal studies. However, few focused on that of trend analyses. The reporting of recommended statistic metrics in trend analyses was also largely unclear. Therefore, we examined reporting quality of the trend analyses based on reporting of recommended statistic metrics. We systemically searched the PubMed for the trend-analysis articles published in 10 leading medicine and oncology journals during the 11 years from 2008 to 2018. The studies published after 2019 were not included due to the sudden, significant increase of publication number during and immediately after the COVID-19 pandemic. Only original articles, research letters and meta-analyses/systematic reviews were included. We scored the reporting quality of these articles based on whether they reported p-values/effect-sizes, and beta/co-efficient/slope/annual-percentage-change (APC). There were 297 qualified articles, among which 193 (66.0%) and 216 (72.7%) articles reported P-value and effect-size, respectively. Only 13 (5.8%) analyses reported neither p-value/effect size nor beta/coefficient/slope/APC. In multivariable regression models, author affiliation of epidemiology department was associated with less reporting effect-size, but that of statistics department with more reporting. Interestingly, U.S. senior-authors (versus non-U.S.) more likely reported p-values. No factors were independently linked to reporting APC. The reporting quality of trend analyses in leading medicine and oncology journals appear moderate and should be further improved. We thus call for more research and awareness of reporting-quality in trend analyses in oncology research and beyond.

scientific communication and education↗

bioRxiv2020

Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data

Most of the biomedical datasets, including those of omics, population studies and surveys, are rectangular in shape and have few missing data. Recently, their sample sizes have grown significantly. Rigorous analyses on these large datasets demand considerably more efficient and more accurate algorithms. Machine learning (ML) algorithms have been used to classify outcomes in biomedical datasets, including random forests (RF), decision tree (DT), artificial neural networks (ANN) and support vector machine (SVM). However, their performance and efficiency in classifying multi-category outcomes in rectangular data are poorly understood. Therefore, we aimed to compare these metrics among the 4 ML algorithms. As an example, we created a large rectangular dataset using the female breast cancers in the Surveillance, Epidemiology, and End Results-18 (SEER-18) database which were diagnosed in 2004 and followed up until December 2016. The outcome was the 6-category cause of death, namely alive, non-breast cancer, breast cancer, cardiovascular disease, infection and other cause. We included 58 dichotomized features from ~53,000 patients. All analyses were performed using MatLab (version 2018a) and the 10-fold cross validation approach. The accuracy in classifying 6-category cause of death with DT, RF, ANN and SVM was 72.68%, 72.66%, 70.01% and 71.85%, respectively. Based on the information entropy and information gain of feature values, we optimized dimension reduction (i.e. reduce the number of features in models). We found 22 or more features were required to maintain the similar accuracy, while the running time decreased from 440s for 58 features to 90s for 22 features in RF, from 70s to 40s in ANN and from 440s to 80s in SVM. In summary, we here show that RF, DT, ANN and SVM had similar accuracy for classifying multi-category outcomes in this large rectangular dataset. Dimension reduction based on information gain will significantly increase models efficiency while maintaining classification accuracy.

bioinformatics↗

bioRxiv2020

The influence of major S protein mutations of SARS-CoV-2 on the potential B cell epitopes

SARS-CoV-2 has rapidly transmitted worldwide and results in the COVID-19 pandemic. Spike glycoprotein on surface is a key factor of viral transmission, and has appeared a lot of variants due to gene mutations, which may influence the viral antigenicity and vaccine efficacy. Here, we used bioinformatic tools to analyze B-cell epitopes of prototype S protein and its 9 common variants. 12 potential linear and 53 discontinuous epitopes of B-cells were predicted from the S protein prototype. Importantly, by comparing the epitope alterations between prototype and variants, we demonstrate that B-cell epitopes and antigenicity of 9 variants appear significantly different alterations. The dominant D614G variant impacts the potential epitope least, only with moderately elevated antigenicity, while the epitopes and antigenicity of some mutants(V483A, V367F, etc.) with small incidence in the population change greatly. These results suggest that the currently developed vaccines should be valid for a majority of SARS-CoV-2 infectors. This study provides a scientific basis for large-scale application of SARS-CoV-2 vaccines and for taking precautions against the probable appearance of antigen escape induced by genetic variation after vaccination. Author SummaryThe global pandemic of SARS-CoV-2 has lasted for more than half a year and has not yet been contained. Until now there is no effective treatment for SARS-CoV-2 caused disease (COVID-19). Successful vaccine development seems to be the only hope. However, this novel coronavirus belongs to the RNA virus, there is a high mutation rate in the genome, and these mutations often locate on the Spike proteins of virus, the gripper of the virus entering the cells. Vaccination induce the generation of antibodies, which block Spike protein. However, the Spike protein variants may change the recognition and binding of antibodies and make the vaccine ineffective. In this study, we predict neutralizing antibody recognition sites (B cell epitopes) of the prototype S protein of SARS-COV2, along with several common variants using bioinformatics tools. We discovered the variability in antigenicity among the mutants, for instance, in the more widespread D614G variant the change of epitope was least affected, only with slight increase of antigenicity. However, the antigenic epitopes of some mutants change greatly. These results could be of potential importance for future vaccine design and application against SARS-CoV2 variants.

immunology↗

bioRxiv2020

rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated tool for Genome-Wide Association Study

Along with the development of high-throughout sequencing technologies, both sample size and number of SNPs are increasing rapidly in Genome-Wide Association Studies (GWAS) and the associated computation is more challenging than ever. Here we present a Memory-efficient, Visualization-enhanced, and Parallel-accelerated R package called "rMVP" to address the need for improved GWAS computation. rMVP can: (1) effectively process large GWAS data; (2) rapidly evaluate population structure; (3) efficiently estimate variance components by EMMAX, FaST-LMM, and HE regression algorithms; (4) implement parallel-accelerated association tests of markers using GLM, MLM, and FarmCPU methods; (5) compute fast with a globally efficient design in the GWAS processes; and (6) generate various visualizations of GWAS related information. Accelerated by block matrix multiplication strategy and multiple threads, the association test methods embedded in rMVP are approximately 5-20 times faster than PLINK, GEMMA, and FarmCPU_pkg. rMVP is freely available at https://github.com/xiaolei-lab/rMVP.

genetics↗

bioRxiv2020

In-Vivo Toxicity Studies and In-Vitro Inactivation of SARS-CoV-2 by Povidone-iodine In-situ Gel Forming Formulations

To curb the spread of SARS-CoV-2, the etiologic agent of the COVID-19 pandemic, we characterize the virucidal activity of long-acting Povidone Iodine (PVP-I) compositions developed using an in-situ gel forming technology. The PVP-I gel forming nasal spray (IVIEW-1503) and PVP-I gel forming ophthalmic eye drop (IVIEW-1201) rapidly inactivated SARS-CoV-2, inhibiting the viral infection of VERO76 cells. No toxicity was observed for the PVP-I formulations. Significant inactivation was noted with preincubation of the virus with these PVP-I formulations at the lowest concentrations tested. It has been demonstrated that both PVP-I formulations can inactivate SARS-CoV-2 virus efficiently in both a dose-dependent and a time-dependent manner. These results suggest IVIEW-1503 and IVIEW-1201 could be potential agents to reduce or prevent the transmission of the virus through the nasal cavity and the eye, respectively. Further studies are needed to clinically evaluate these formulations in early-stage COVID-19 patients.

microbiology↗

bioRxiv2020

RIPK2 Stabilizes c-Myc and is an Actionable Target for Inhibiting Prostate Cancer Metastasis

Despite advances in diagnosis and treatment, metastatic prostate cancer remains incurable and is associated with high mortality rates. Thus, novel actionable drug targets are urgently needed for therapeutic interventions in advanced prostate cancer. Here we report receptor-interacting protein kinase 2 (RIPK2) as an actionable drug target for suppressing prostate cancer metastasis. RIPK2 is frequently amplified in lethal prostate cancers and its overexpression is associated with disease progression and aggressiveness. Genetic and pharmacological inhibition of RIPK2 significantly suppressed prostate cancer progression in vitro and metastasis in vivo. Multi-level proteomic analysis revealed that RIPK2 strongly regulates c-Myc protein stability and activity, largely by activating the MKK7/JNK/c-Myc phosphorylation pathway--a novel, non-canonical RIPK2 signaling pathway. Targeting RIPK2 inhibits this phosphorylation pathway, and thus promotes the degradation of c-Myc--a potent oncoprotein for which no drugs have been approved for clinical use yet. These results support targeting RIPK2 for personalized therapy in prostate cancer patients towards improving survival.

cancer biology↗

bioRxiv2020

Single-cell RNA-sequencing of peripheral neuroblastic tumors reveals an aggressive transitional cell state at the junction of an adrenergic-mesenchymal transdifferentiation trajectory

Peripheral neuroblastic tumors (PNTs) are the most common extracranial solid tumors in early childhood. They represent a spectrum of neural crest derived tumors including neuroblastoma, ganglioneuroblastoma and ganglioneuroma. PNTs exhibit heterogeneity due to interconverting malignant cell states described as adrenergic/nor-adrenergic or mesenchymal/neural crest cell in origin. The factors determining individual patient levels of tumor heterogeneity, their impact on the malignant phenotype, and the presence of other cell states are unknown. Here, single-cell RNA-sequencing analysis of 4267 cells from 7 PNTs demonstrated extensive transcriptomic heterogeneity. Trajectory modelling showed that malignant neuroblasts move between adrenergic and mesenchymal cell states via a novel state that we termed a "transitional" phenotype. Transitional cells are characterized by gene expression programs linked to a sympathoadrenal development, and aggressive tumor phenotypes such as rapid proliferation and tumor dissemination. Among primary bulk tumor patient cohorts, high expression of the transitional gene signature was highly predictive of poor prognosis when compared to adrenergic and mesenchymal expression patterns. High transitional gene expression in neuroblastoma cell lines identified a similar transitional H3K27-acetylation super-enhancer landscape, supporting the concept that PNTs have phenotypic plasticity and transdifferentiation capacity. Additionally, examination of PNT microenvironments, found that neuroblastomas contained low immune cell infiltration, high levels of non-inflammatory macrophages, and low cytotoxic T lymphocyte levels compared with more benign PNT subtypes. Modeling of cell-cell signaling in the tumor microenvironment predicted specific paracrine effects toward the various subtypes of malignant cells, suggesting further cell-extrinsic influences on malignant cell phenotype. Collectively, our study reveals the presence of a previously unrecognized transitional cell state with high malignant potential and an immune cell architecture which serve both as potential biomarkers and therapeutic targets.

cancer biology↗

bioRxiv2020

Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level

Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Existing labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescence indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended times. We report on a novel dual-mode high-density microelectrode array, which can simultaneously record in i) full-frame mode with 19,584 recording sites and ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we developed reliable analysis tools with drastically increased throughput for extracting axonal morphology and conduction parameters.

neuroscience↗

bioRxiv2020

Altered Expression of Cadherin-8 and Cadherin-11 in Neural Circuit Development: Implications for Autism

Autism spectrum disorder (ASD) is a neurological condition characterized by alterations in social interaction and communication, and restricted and/or repetitive behaviors. The classical type II cadherins cadherin-8 (Cdh8, CDH8) and cadherin-11 (Cdh11, CDH11) have been implicated as autism risk gene candidates. To explore the role of cadherins in the etiology of autism, we investigated their expression patterns during mouse brain development and in autism-specific human tissue. In mice, expression of cadherin-8 and cadherin-11 was developmentally regulated and enriched in the cortex, hippocampus, and thalamus/striatum during the peak of dendrite formation and synaptogenesis. Both cadherins were expressed in synaptic compartments but only cadherin-8 associated with the excitatory synaptic marker neuroligin-1. Induced pluripotent stem cell (iPSC)-derived cortical neural precursor cells (NPCs) and cortical organoids generated from individuals with autism showed upregulated CDH8 expression levels while CDH11 expression levels were downregulated. We used Cdh11 knockout mice of both sexes to analyze the function of cadherin-11, which could help explain phenotypes observed in autism. Cdh11-/- hippocampal neurons exhibited increased dendritic complexity along with altered neuronal and synaptic activity. Similar to the expression profiles in human tissue, levels of cadherin-8 were significantly elevated in Cdh11 knockout brains. Additionally, excitatory synaptic markers neuroligin-1 and PSD-95 were both increased. Together, these results strongly suggest that cadherin-11 is involved in regulating the development of neuronal circuitry and that alterations in the expression levels of cadherin-11 may contribute to the etiology of autism. Significance StatementAutism is a neurodevelopmental condition with high genetic and phenotypic heterogeneity. Multiple genes have been implicated in autism, including the cadherin superfamily of adhesion molecules, cadherin-8 and cadherin-11. This study first characterizes the expression profiles of cadherin-8 and cadherin-11 to understand the potential roles they play in the development of neurons. The study further describes novel contributions of cadherin-11 in neural circuit formation. Loss of cadherin-11 in mice results in altered levels of several synaptic proteins, including PSD-95, neuroligin-1, and cadherin-8, and changes the morphology and activity of excitatory neurons. The levels of cadherin-8 and cadherin-11 in human cells of autistic individuals are both altered, strengthening the hypothesis that these two cadherins may involve in aspects of autism etiology.

neuroscience↗