scholarly journals Cell Type-Specific Predictive Models Perform Prioritization of Genes and Gene Sets Associated With Autism

2021 ◽  
Vol 11 ◽  
Author(s):  
Jinting Guan ◽  
Yang Wang ◽  
Yiping Lin ◽  
Qingyang Yin ◽  
Yibo Zhuang ◽  
...  
Keyword(s):  
Predictive Models ◽  
Autism Spectrum ◽  
Brain Cell ◽  
Projection Neurons ◽  
Cell Type ◽  
Cortical Projection ◽  
Effective Interventions ◽  
Gene Sets ◽  
Layer 4 ◽  
Cell Type Specific

Bulk transcriptomic analyses of autism spectrum disorder (ASD) have revealed dysregulated pathways, while the brain cell type-specific molecular pathology of ASD still needs to be studied. Machine learning-based studies can be conducted for ASD, prioritizing high-confidence gene candidates and promoting the design of effective interventions. Using human brain nucleus gene expression of ASD and controls, we construct cell type-specific predictive models for ASD based on individual genes and gene sets, respectively, to screen cell type-specific ASD-associated genes and gene sets. These two kinds of predictive models can predict the diagnosis of a nucleus with known cell type. Then, we construct a multi-label predictive model for predicting the cell type and diagnosis of a nucleus at the same time. Our findings suggest that layer 2/3 and layer 4 excitatory neurons, layer 5/6 cortico-cortical projection neurons, parvalbumin interneurons, and protoplasmic astrocytes are preferentially affected in ASD. The functions of genes with predictive power for ASD are different and the top important genes are distinct across different cells, highlighting the cell-type heterogeneity of ASD. The constructed predictive models can promote the diagnosis of ASD, and the prioritized cell type-specific ASD-associated genes and gene sets may be used as potential biomarkers of ASD.

scholarly journals Cell type-specific predictive models for prioritizing genes and gene sets associated with autism spectrum disorder

2020 ◽  
Author(s):  
Jinting Guan ◽  
Yang Wang ◽  
Yiping Lin ◽  
Qingyang Yin ◽  
Yibo Zhuang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Predictive Models ◽  
Autism Spectrum ◽  
Specific Gene ◽  
Cell Type ◽  
Gene Sets ◽  
Cell Type Specific ◽  
Protoplasmic Astrocytes

Abstract Background Autism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity. Although bulk transcriptomic analyses revealed convergence of disease pathology on common pathways, the brain cell type-specific molecular pathology of ASD is still needed to study. Different gene functions may be dysregulated and causal genes may be distinct among different brain cells in ASD. Gene expression profiling-based machine learning studies can be conducted for the diagnosis of ASD, prioritizing high-confidence gene candidates and promoting the design of effective interventions.Methods To characterize the cell type heterogeneity of ASD and to take advantage of the potential of gene expression signature as diagnostic biomarkers for ASD, we construct multiple kinds of classification models for ASD based on the recently available human brain nucleus gene expression data of ASD and controls. Firstly, we construct cell type-specific predictive models based on individual genes to screen cell type-specific genes associated with ASD. Then from the view of gene set, we construct cell type-specific gene set-based predictive models to screen cell type-specific gene sets associated with ASD. These two kinds of predictive models can be applied to predict the diagnosis of a given nucleus with known cell type. Lastly, we further construct a multi-label predictive model for predicting the cell type and diagnosis of a given nucleus at the same time.Results It is found that the functions of genes with predictive power for ASD are not consistent and the top important genes are distinct among different cells, demonstrating the cell type heterogeneity of ASD. Our findings suggest that layer 2/3 and layer 4 excitatory neurons, layer 5/6 cortico-cortical projection neurons, parvalbumin interneurons, and protoplasmic astrocytes are preferentially affected in ASD. Gene BCYRN1 and CCK are prioritized in excitatory neurons, and HSPA1A is of note in protoplasmic astrocytes.Limitations Our study utilized methods of machine learning to identify biomarkers of ASD, while it is more convincing if subsequent experiments could be conducted to validate the results.Conclusions The results show that it may be feasible to use single cell/nucleus gene expression for ASD detection and the constructed predictive models can promote the diagnosis of ASD. Our analytical pipeline prioritizes ASD-associated cell type-specific genes and gene sets, which may be used as potential biomarkers of ASD.

scholarly journals Interaction studies of risk proteins in human induced neurons reveal convergent biology and novel mechanisms underlying autism spectrum disorders

2021 ◽  
Author(s):  
Greta Pintacuda ◽  
Yu-Han H Hsu ◽  
Kalliopi Tsafou ◽  
Ka Wan Li ◽  
Jacqueline M Martin ◽  
...  
Keyword(s):  
Autism Spectrum Disorders ◽  
Rare Variant ◽  
Specific Interaction ◽  
Autism Spectrum ◽  
Brain Cell ◽  
Synaptic Proteins ◽  
Cell Type ◽  
Risk Genes ◽  
Cell Type Specific ◽  
Spectrum Disorders

Sequencing studies of autism spectrum disorders (ASDs) have identified numerous risk genes with enriched expression in the human brain, but it is still unclear how these genes converge into cell type-specific networks and how their encoded proteins mechanistically contribute to ASDs. To address this question, we performed brain cell type-specific interaction proteomics to build a protein-protein interaction network for 13 ASD risk genes in human excitatory neurons derived from iPS cells. The network contains many (>90%) reproducible interactions not reported in the literature and is enriched for transcriptionally perturbed genes observed in layer 2/3 cortical neurons of ASD patients, indicating that it can be explored for ASD-relevant biological discovery. We leveraged the network dataset to show that the brain-specific isoform of ANK2 is important for its interactions with synaptic proteins and characterized a PTEN-AKAP8L interaction that influences neuronal growth through the mTOR pathway. The IGF2BP1-3 complex emerges as a point of convergence in the network, and we showed that this complex is involved in a transcriptional circuit concentrating both common and rare variant risk of ASDs. Finally, we found the network itself enriched for ASD rare variant risk, indicating that it can complement genetic datasets for prioritizing additional risk genes. Our findings establish brain cell type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in ASDs and illustrate how both individual and convergent interactions lead to biological insights into the disease.

scholarly journals Cell-type-specific synaptic imbalance and disrupted homeostatic plasticity in cortical circuits of ASD-associated Chd8 haploinsufficient mice

2020 ◽  
Author(s):  
Robert A. Ellingford ◽  
Emilie Rabeshala de Meritens ◽  
Raghav Shaunak ◽  
Liam Naybour ◽  
M. Albert Basson ◽  
...  
Keyword(s):  
Synaptic Transmission ◽  
Deep Layer ◽  
Autism Spectrum ◽  
Homeostatic Plasticity ◽  
Heterozygous Mutation ◽  
Projection Neurons ◽  
Cortical Circuits ◽  
Cell Type ◽  
Inhibitory Synaptic Transmission ◽  
Cell Type Specific

AbstractHeterozygous mutation of chromodomain helicase DNA binding protein 8 (CHD8) is strongly associated with autism spectrum disorder (ASD) and results in dysregulated expression of neurodevelopmental and synaptic genes during brain development. To reveal how these changes affect ASD-associated cortical circuits, we studied synaptic transmission in the prefrontal cortex of a haploinsufficient Chd8 mouse model. We report profound alterations to both excitatory and inhibitory synaptic transmission onto deep layer projection neurons, resulting in a reduced excitatory:inhibitory balance, which were found to vary dynamically across neurodevelopment and result from distinct effects of reduced Chd8 expression within individual neuronal subtypes. These changes were associated with disrupted regulation of homeostatic plasticity mechanisms operating via spontaneous neurotransmission. These findings therefore directly implicate CHD8 mutation in the disruption of ASD-relevant circuits in the cortex.

scholarly journals Cell-type-specific synaptic imbalance and disrupted homeostatic plasticity in cortical circuits of ASD-associated Chd8 haploinsufficient mice

2021 ◽  
Author(s):  
Robert A. Ellingford ◽  
Martyna J. Panasiuk ◽  
Emilie Rabesahala de Meritens ◽  
Raghav Shaunak ◽  
Liam Naybour ◽  
...  
Keyword(s):  
Synaptic Transmission ◽  
Deep Layer ◽  
Autism Spectrum ◽  
Homeostatic Plasticity ◽  
Heterozygous Mutation ◽  
Projection Neurons ◽  
Cortical Circuits ◽  
Cell Type ◽  
Inhibitory Synaptic Transmission ◽  
Cell Type Specific

AbstractHeterozygous mutation of chromodomain helicase DNA binding protein 8 (CHD8) is strongly associated with autism spectrum disorder (ASD) and results in dysregulated expression of neurodevelopmental and synaptic genes during brain development. To reveal how these changes affect ASD-associated cortical circuits, we studied synaptic transmission in the prefrontal cortex of a haploinsufficient Chd8 mouse model. We report profound alterations to both excitatory and inhibitory synaptic transmission onto deep layer projection neurons, resulting in a reduced excitatory:inhibitory balance, which were found to vary dynamically across neurodevelopment and result from distinct effects of reduced Chd8 expression within individual neuronal subtypes. These changes were associated with disrupted regulation of homeostatic plasticity mechanisms operating via spontaneous neurotransmission. These findings therefore directly implicate CHD8 mutation in the disruption of ASD-relevant circuits in the cortex.

scholarly journals Connectivity characterization of the mouse basolateral amygdalar complex

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Houri Hintiryan ◽  
Ian Bowman ◽  
David L. Johnson ◽  
Laura Korobkova ◽  
Muye Zhu ◽  
...  
Keyword(s):  
Granular Cell ◽  
Cell Types ◽  
Projection Neurons ◽  
Cell Type ◽  
Connectivity Map ◽  
Analysis Techniques ◽  
Domain Specific ◽  
Cell Type Specific ◽  
Unique Domain

AbstractThe basolateral amygdalar complex (BLA) is implicated in behaviors ranging from fear acquisition to addiction. Optogenetic methods have enabled the association of circuit-specific functions to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. Here, we apply machine-learning based computational and informatics analysis techniques to the results of circuit-tracing experiments to create a foundational, comprehensive BLA connectivity map. The analyses identify three distinct domains within the anterior BLA (BLAa) that house target-specific projection neurons with distinguishable morphological features. We identify brain-wide targets of projection neurons in the three BLAa domains, as well as in the posterior BLA, ventral BLA, posterior basomedial, and lateral amygdalar nuclei. Inputs to each nucleus also are identified via retrograde tracing. The data suggests that connectionally unique, domain-specific BLAa neurons are associated with distinct behavior networks.

scholarly journals An analytical method for the identification of cell type-specific disease gene modules

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jinting Guan ◽  
Yiping Lin ◽  
Yang Wang ◽  
Junchao Gao ◽  
Guoli Ji
Keyword(s):  
Disease Gene ◽  
Gene Interaction ◽  
Cell Types ◽  
Autism Spectrum ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Disease ◽  
Cell Type Specific ◽  
Gene Modules ◽  
Disease Associated Genes

Abstract Background Genome-wide association studies have identified genetic variants associated with the risk of brain-related diseases, such as neurological and psychiatric disorders, while the causal variants and the specific vulnerable cell types are often needed to be studied. Many disease-associated genes are expressed in multiple cell types of human brains, while the pathologic variants affect primarily specific cell types. We hypothesize a model in which what determines the manifestation of a disease in a cell type is the presence of disease module comprised of disease-associated genes, instead of individual genes. Therefore, it is essential to identify the presence/absence of disease gene modules in cells. Methods To characterize the cell type-specificity of brain-related diseases, we construct human brain cell type-specific gene interaction networks integrating human brain nucleus gene expression data with a referenced tissue-specific gene interaction network. Then from the cell type-specific gene interaction networks, we identify significant cell type-specific disease gene modules by performing statistical tests. Results Between neurons and glia cells, the constructed cell type-specific gene networks and their gene functions are distinct. Then we identify cell type-specific disease gene modules associated with autism spectrum disorder and find that different gene modules are formed and distinct gene functions may be dysregulated in different cells. We also study the similarity and dissimilarity in cell type-specific disease gene modules among autism spectrum disorder, schizophrenia and bipolar disorder. The functions of neurons-specific disease gene modules are associated with synapse for all three diseases, while those in glia cells are different. To facilitate the use of our method, we develop an R package, CtsDGM, for the identification of cell type-specific disease gene modules. Conclusions The results support our hypothesis that a disease manifests itself in a cell type through forming a statistically significant disease gene module. The identification of cell type-specific disease gene modules can promote the development of more targeted biomarkers and treatments for the disease. Our method can be applied for depicting the cell type heterogeneity of a given disease, and also for studying the similarity and dissimilarity between different disorders, providing new insights into the molecular mechanisms underlying the pathogenesis and progression of diseases.

scholarly journals Periaqueductal efferents to dopamine and GABA neurons of the VTA

2017 ◽  
Author(s):  
Niels R. Ntamati ◽  
Meaghan Creed ◽  
Christian Lüscher
Keyword(s):  
Functional Analysis ◽  
Ventral Tegmental Area ◽  
Periaqueductal Gray ◽  
Retrograde Tracing ◽  
The Other ◽  
Projection Neurons ◽  
Cell Type ◽  
Gaba Neurons ◽  
Other Hand ◽  
Cell Type Specific

AbstractNeurons in the periaqueductal gray (PAG) modulate threat responses and nociception. Activity in the ventral tegmental area (VTA) on the other hand can cause reinforcement and aversion. While in many situations these behaviors are related, the anatomical substrate of a crosstalk between the PAG and VTA remains poorly understood. Here we describe the anatomical and electrophysiological organization of the VTA-projecting PAG neurons. Using rabies-based, cell type-specific retrograde tracing, we observed that PAG to VTA projection neurons are evenly distributed along the rostro-caudal axis of the PAG, but concentrated in its posterior and ventrolateral segments. Optogenetic projection targeting demonstrated that the PAG-to-VTA pathway is predominantly excitatory and targets similar proportions of Ih-expressing VTA DA and GABA neurons. Taken together, these results set the framework for functional analysis of the interplay between PAG and VTA in the regulation of reward and aversion.

scholarly journals Target-specific M1 inputs to infragranular S1 pyramidal neurons

2016 ◽  
Vol 116 (3) ◽  
pp. 1261-1274 ◽  
Author(s):  
Amanda K. Kinnischtzke ◽  
Erika E. Fanselow ◽  
Daniel J. Simons
Keyword(s):  
Primary Motor Cortex ◽  
Pyramidal Neurons ◽  
Projection Neurons ◽  
Cell Type ◽  
Intrinsic Properties ◽  
Subcortical Structures ◽  
Medial Nucleus ◽  
Cell Type Specific ◽  
Posterior Medial Nucleus

The functional role of input from the primary motor cortex (M1) to primary somatosensory cortex (S1) is unclear; one key to understanding this pathway may lie in elucidating the cell-type specific microcircuits that connect S1 and M1. Recently, we discovered that a subset of pyramidal neurons in the infragranular layers of S1 receive especially strong input from M1 (Kinnischtzke AK, Simons DJ, Fanselow EE. Cereb Cortex 24: 2237–2248, 2014), suggesting that M1 may affect specific classes of pyramidal neurons differently. Here, using combined optogenetic and retrograde labeling approaches in the mouse, we examined the strengths of M1 inputs to five classes of infragranular S1 neurons categorized by their projections to particular cortical and subcortical targets. We found that the magnitude of M1 synaptic input to S1 pyramidal neurons varies greatly depending on the projection target of the postsynaptic neuron. Of the populations examined, M1-projecting corticocortical neurons in L6 received the strongest M1 inputs, whereas ventral posterior medial nucleus-projecting corticothalamic neurons, also located in L6, received the weakest. Each population also possessed distinct intrinsic properties. The results suggest that M1 differentially engages specific classes of S1 projection neurons, thereby regulating the motor-related influence S1 exerts over subcortical structures.

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