scholarly journals Identification of CDH11 as an ASD Risk Gene by Matched-gene Co-expression Analysis and Mouse Behavioral Studies

2020 ◽  
Author(s):  
Nan Wu ◽  
Yue Wang ◽  
Jing-Yan Jia ◽  
Yi-Hsuan Pan ◽  
Xiao-Bing Yuan
Keyword(s):  
Expression Analysis ◽  
Gene Knockout ◽  
Expression Profiles ◽  
Cell Types ◽  
Developmental Expression ◽  
Molecular Pathways ◽  
Behavioral Tests ◽  
Risk Genes ◽  
Risk Gene ◽  
Behavioral Studies

Abstract Background: Gene co-expression analysis (GCA) has emerged as an important tool to identify convergent molecular pathways of ASD risk genes. The aim of this study is to identify ASD-relevant genes at the whole-genome level using GCA with consideration of the effect of confounding factors on GCA, including the size, expression level, and guanine-cytosine content of genes. Methods: Pearson’s correlation coefficient was computed to indicate the co-expression of a gene pair based on the BrainSpan human brain transcriptome dataset. Whether a gene is significantly co-expressed with a group of high-confidence ASD risk genes (hcASDs) was determined by statistically comparing the co-expression of this gene with the hcASD gene set to that of this gene with permuted gene sets of matched gene features. This method is referred to as "matched-gene co-expression analysis" (MGCA). Gene ontology (GO) analysis and construction of integrated GO enrichment networks were performed to reveal convergent pathways of co-expressed genes. Behavioral tests were carried out in gene knockout mice. Results: Gene size, mRNA length, mRNA abundance, and guanine-cytosine content were found to affect co-expression profiles of ASD genes. Using the MGCA method, we confirmed the convergence in the developmental expression profiles of hcASDs. MGCA also effectively revealed convergent molecular pathways of ASD risk genes and determined that CDH11, but not CDH9, is associated with ASD. Mouse behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autistic-like behavioral alterations.Limitations: The use of tissue-derived transcriptomes instead of single-cell transcriptomes may have detected coincident expression of some functionally irrelevant genes in different cell types. Some ASD risk genes may have been missed due to the highly stringent statistical standard of MGCA. Another limitation is the relatively small number of animals analyzed in behavioral tests. Conclusions: Results of this study revealed the importance of considering matched gene features in GCA. CDH11 was confirmed to be an important ASD risk gene and Cdh11-null mice were found to be a very useful animal model for investigation of ASD.

scholarly journals Identification of CDH11 as an ASD risk gene by matched-gene co-expression analysis and mouse behavioral studies

2020 ◽  
Author(s):  
Nan Wu ◽  
Yue Wang ◽  
Yi-Hsuan Pan ◽  
Xiao-Bing Yuan
Keyword(s):  
Expression Analysis ◽  
Expression Profiles ◽  
Autism Spectrum ◽  
Molecular Pathways ◽  
Behavioral Alterations ◽  
High Confidence ◽  
Risk Genes ◽  
Risk Gene ◽  
Behavioral Studies ◽  
Mrna Length

AbstractIn the study of autism spectrum disorder (ASD) by gene co-expression analysis (GCA), we found that four gene features, including gene size, mRNA length, mRNA abundance, and guanine-cytosine content, profoundly affect gene co-expression profiles. To circumvent the potential interference of these confounding factors on GCA, we developed the “matched-gene co-expression analysis” (MGCA) to investigate gene co-expression relationships. This method demonstrated the convergent expression profile of high confidence ASD risk genes and effectively revealed convergent molecular pathways of ASD risk genes. Application of MGCA to two ASD candidate genes CDH11 and CDH9 showed association of CDH11, but not CDH9, with ASD. Mouse behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autistic-like behavioral alterations. This study confirmed that CDH11 is an important ASD risk gene and demonstrated the importance of considering matched gene features in the analysis of gene co-expression.

scholarly journals Association of CDH11 with Autism Spectrum Disorder Revealed by Matched-gene Co-expression Analysis and Mouse Behavioral Studies

2021 ◽  
Author(s):  
Nan Wu ◽  
Yue Wang ◽  
Jing-Yan Jia ◽  
Yi-Hsuan Pan ◽  
Xiao-Bing Yuan
Keyword(s):  
Autism Spectrum Disorder ◽  
Expression Analysis ◽  
Gene Prediction ◽  
Expression Profiles ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Developmental Expression ◽  
Behavioral Alterations ◽  
Risk Genes ◽  
Behavioral Studies

AbstractA large number of putative risk genes for autism spectrum disorder (ASD) have been reported. The functions of most of these susceptibility genes in developing brains remain unknown, and causal relationships between their variation and autism traits have not been established. The aim of this study was to predict putative risk genes at the whole-genome level based on the analysis of gene co-expression with a group of high-confidence ASD risk genes (hcASDs). The results showed that three gene features – gene size, mRNA abundance, and guanine-cytosine content – affect the genome-wide co-expression profiles of hcASDs. To circumvent the interference of these features in gene co-expression analysis, we developed a method to determine whether a gene is significantly co-expressed with hcASDs by statistically comparing the co-expression profile of this gene with hcASDs to that of this gene with permuted gene sets of feature-matched genes. This method is referred to as "matched-gene co-expression analysis" (MGCA). With MGCA, we demonstrated the convergence in developmental expression profiles of hcASDs and improved the efficacy of risk gene prediction. The results of analysis of two recently-reported ASD candidate genes, CDH11 and CDH9, suggested the involvement of CDH11, but not CDH9, in ASD. Consistent with this prediction, behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autism-like behavioral alterations. This study highlights the power of MGCA in revealing ASD-associated genes and the potential role of CDH11 in ASD.

scholarly journals Identification of the Risk Genes Associated With Vulnerability to Addiction: Major Findings From Transgenic Animals

2022 ◽  
Vol 15 ◽  
Author(s):  
Chloe J. Jordan ◽  
Zheng-Xiong Xi
Keyword(s):  
Drug Use ◽  
Candidate Genes ◽  
Acetylcholine Receptors ◽  
Gene Knockout ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Gene Variant ◽  
Self Administration ◽  
Risk Genes ◽  
Risk Gene

Understanding risk factors for substance use disorders (SUD) can facilitate medication development for SUD treatment. While a rich literature exists discussing environmental factors that influence SUD, fewer articles have focused on genetic factors that convey vulnerability to drug use. Methods to identify SUD risk genes include Genome-Wide Association Studies (GWAS) and transgenic approaches. GWAS have identified hundreds of gene variants or single nucleotide polymorphisms (SNPs). However, few genes identified by GWAS have been verified by clinical or preclinical studies. In contrast, significant progress has been made in transgenic approaches to identify risk genes for SUD. In this article, we review recent progress in identifying candidate genes contributing to drug use and addiction using transgenic approaches. A central hypothesis is if a particular gene variant (e.g., resulting in reduction or deletion of a protein) is associated with increases in drug self-administration or relapse to drug seeking, this gene variant may be considered a risk factor for drug use and addiction. Accordingly, we identified several candidate genes such as those that encode dopamine D2 and D3 receptors, mGluR2, M4 muscarinic acetylcholine receptors, and α5 nicotinic acetylcholine receptors, which appear to meet the risk-gene criteria when their expression is decreased. Here, we describe the role of these receptors in drug reward and addiction, and then summarize major findings from the gene-knockout mice or rats in animal models of addiction. Lastly, we briefly discuss future research directions in identifying addiction-related risk genes and in risk gene-based medication development for the treatment of addiction.

scholarly journals Control of impulsivity by Gi-protein signalling in layer-5 pyramidal neurons of the anterior cingulate cortex

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Bastiaan van der Veen ◽  
Sampath K. T. Kapanaiah ◽  
Kasyoka Kilonzo ◽  
Peter Steele-Perkins ◽  
Martin M. Jendryka ◽  
...  
Keyword(s):  
Gene Expression ◽  
Anterior Cingulate Cortex ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Pyramidal Neurons ◽  
Treatment Options ◽  
Pyramidal Cells ◽  
Cingulate Cortex ◽  
Cell Types ◽  
Anterior Cingulate

AbstractPathological impulsivity is a debilitating symptom of multiple psychiatric diseases with few effective treatment options. To identify druggable receptors with anti-impulsive action we developed a systematic target discovery approach combining behavioural chemogenetics and gene expression analysis. Spatially restricted inhibition of three subdivisions of the prefrontal cortex of mice revealed that the anterior cingulate cortex (ACC) regulates premature responding, a form of motor impulsivity. Probing three G-protein cascades with designer receptors, we found that the activation of Gi-signalling in layer-5 pyramidal cells (L5-PCs) of the ACC strongly, reproducibly, and selectively decreased challenge-induced impulsivity. Differential gene expression analysis across murine ACC cell-types and 402 GPCRs revealed that - among Gi-coupled receptor-encoding genes - Grm2 is the most selectively expressed in L5-PCs while alternative targets were scarce. Validating our approach, we confirmed that mGluR2 activation reduced premature responding. These results suggest Gi-coupled receptors in ACC L5-PCs as therapeutic targets for impulse control disorders.

scholarly journals In-depth transcriptomic analysis of human retina reveals molecular mechanisms underlying diabetic retinopathy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kolja Becker ◽  
Holger Klein ◽  
Eric Simon ◽  
Coralie Viollet ◽  
Christian Haslinger ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Vision Loss ◽  
Ganglion Cells ◽  
Expression Profiles ◽  
Cell Types ◽  
Sequencing Data ◽  
Disease Stages ◽  
Post Transcriptional Regulation

AbstractDiabetic Retinopathy (DR) is among the major global causes for vision loss. With the rise in diabetes prevalence, an increase in DR incidence is expected. Current understanding of both the molecular etiology and pathways involved in the initiation and progression of DR is limited. Via RNA-Sequencing, we analyzed mRNA and miRNA expression profiles of 80 human post-mortem retinal samples from 43 patients diagnosed with various stages of DR. We found differentially expressed transcripts to be predominantly associated with late stage DR and pathways such as hippo and gap junction signaling. A multivariate regression model identified transcripts with progressive changes throughout disease stages, which in turn displayed significant overlap with sphingolipid and cGMP–PKG signaling. Combined analysis of miRNA and mRNA expression further uncovered disease-relevant miRNA/mRNA associations as potential mechanisms of post-transcriptional regulation. Finally, integrating human retinal single cell RNA-Sequencing data revealed a continuous loss of retinal ganglion cells, and Müller cell mediated changes in histidine and β-alanine signaling. While previously considered primarily a vascular disease, attention in DR has shifted to additional mechanisms and cell-types. Our findings offer an unprecedented and unbiased insight into molecular pathways and cell-specific changes in the development of DR, and provide potential avenues for future therapeutic intervention.

scholarly journals sepal: identifying transcript profiles with spatial patterns by diffusion-based modeling

2021 ◽  
Author(s):  
Alma Andersson ◽  
Joakim Lundeberg
Keyword(s):  
Spatial Patterns ◽  
Expression Profiles ◽  
Synthetic Data ◽  
Real Data ◽  
Cell Types ◽  
Statistical Hypothesis ◽  
Supplementary Information ◽  
Statistical Hypothesis Testing ◽  
Transcriptomics Data ◽  
Transcript Profiles

Abstract Motivation Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy. Results We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes’ expression levels and showed better time performance when run with multiple cores. Availabilityand implementation Open-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under an MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Analysis of the transcriptome of bovine endometrial cells isolated by laser micro-dissection (1): specific signatures of stromal, glandular and luminal epithelial cells

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Wiruntita Chankeaw ◽  
Sandra Lignier ◽  
Christophe Richard ◽  
Theodoros Ntallaris ◽  
Mariam Raliou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Localization ◽  
Expression Profiles ◽  
Cell Types ◽  
Molecular Signature ◽  
Receptor Protein ◽  
Specific Gene ◽  
Specific Cell ◽  
Biological Processes ◽  
Endometrial Cells

Abstract Background A number of studies have examined mRNA expression profiles of bovine endometrium at estrus and around the peri-implantation period of pregnancy. However, to date, these studies have been performed on the whole endometrium which is a complex tissue. Consequently, the knowledge of cell-specific gene expression, when analysis performed with whole endometrium, is still weak and obviously limits the relevance of the results of gene expression studies. Thus, the aim of this study was to characterize specific transcriptome of the three main cell-types of the bovine endometrium at day-15 of the estrus cycle. Results In the RNA-Seq analysis, the number of expressed genes detected over 10 transcripts per million was 6622, 7814 and 8242 for LE, GE and ST respectively. ST expressed exclusively 1236 genes while only 551 transcripts were specific to the GE and 330 specific to LE. For ST, over-represented biological processes included many regulation processes and response to stimulus, cell communication and cell adhesion, extracellular matrix organization as well as developmental process. For GE, cilium organization, cilium movement, protein localization to cilium and microtubule-based process were the only four main biological processes enriched. For LE, over-represented biological processes were enzyme linked receptor protein signaling pathway, cell-substrate adhesion and circulatory system process. Conclusion The data show that each endometrial cell-type has a distinct molecular signature and provide a significantly improved overview on the biological process supported by specific cell-types. The most interesting result is that stromal cells express more genes than the two epithelial types and are associated with a greater number of pathways and ontology terms.

scholarly journals Single-cell RNA sequencing reveals the mesangial identity and species diversity of glomerular cell transcriptomes

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bing He ◽  
Ping Chen ◽  
Sonia Zambrano ◽  
Dina Dabaghie ◽  
Yizhou Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Human Kidney ◽  
Cell Types ◽  
Model Organisms ◽  
Mural Cell ◽  
Glomerular Cell ◽  
Individual Gene ◽  
The Individual

AbstractMolecular characterization of the individual cell types in human kidney as well as model organisms are critical in defining organ function and understanding translational aspects of biomedical research. Previous studies have uncovered gene expression profiles of several kidney glomerular cell types, however, important cells, including mesangial (MCs) and glomerular parietal epithelial cells (PECs), are missing or incompletely described, and a systematic comparison between mouse and human kidney is lacking. To this end, we use Smart-seq2 to profile 4332 individual glomerulus-associated cells isolated from human living donor renal biopsies and mouse kidney. The analysis reveals genetic programs for all four glomerular cell types (podocytes, glomerular endothelial cells, MCs and PECs) as well as rare glomerulus-associated macula densa cells. Importantly, we detect heterogeneity in glomerulus-associated Pdgfrb-expressing cells, including bona fide intraglomerular MCs with the functionally active phagocytic molecular machinery, as well as a unique mural cell type located in the central stalk region of the glomerulus tuft. Furthermore, we observe remarkable species differences in the individual gene expression profiles of defined glomerular cell types that highlight translational challenges in the field and provide a guide to design translational studies.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

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