Dataset Information

Genome-Wide Analysis of Differential Gene Expression and Splicing in Excitatory Neurons and Interneuron Subtypes.

ABSTRACT: Cortical circuit activity is shaped by the parvalbumin (PV) and somatostatin (SST) interneurons that inhibit principal excitatory (EXC) neurons and the vasoactive intestinal peptide (VIP) interneurons that suppress activation of other interneurons. To understand the molecular-genetic basis of functional specialization and identify potential drug targets specific to each neuron subtype, we performed a genome wide assessment of both gene expression and splicing across EXC, PV, SST and VIP neurons from male and female mouse brains. These results reveal numerous examples where neuron subtype-specific gene expression, as well as splice-isoform usage, can explain functional differences between neuron subtypes, including in presynaptic plasticity, postsynaptic receptor function, and synaptic connectivity specification. We provide a searchable web resource for exploring differential mRNA expression and splice form usage between excitatory, PV, SST, and VIP neurons (http://research-pub.gene.com/NeuronSubtypeTranscriptomes). This resource, combining a unique new dataset and novel application of analysis methods to multiple relevant datasets, identifies numerous potential drug targets for manipulating circuit function, reveals neuron subtype-specific roles for disease-linked genes, and is useful for understanding gene expression changes observed in human patient brains.SIGNIFICANCE STATEMENT Understanding the basis of functional specialization of neuron subtypes and identifying drug targets for manipulating circuit function requires comprehensive information on cell-type-specific transcriptional profiles. We sorted excitatory neurons and key inhibitory neuron subtypes from mouse brains and assessed differential mRNA expression. We used a genome-wide analysis which not only examined differential gene expression levels but could also detect differences in splice isoform usage. This analysis reveals numerous examples of neuron subtype-specific isoform usage with functional importance, identifies potential drug targets, and provides insight into the neuron subtypes involved in psychiatric disease. We also apply our analysis to two other relevant datasets for comparison, and provide a searchable website for convenient access to the resource.

SUBMITTER: Huntley MA

PROVIDER: S-EPMC6988999 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Genome-Wide Analysis of Differential Gene Expression and Splicing in Excitatory Neurons and Interneuron Subtypes.

Huntley Melanie A MA Srinivasan Karpagam K Friedman Brad A BA Wang Tzu-Ming TM Yee Ada X AX Wang Yuanyuan Y Kaminker Josh S JS Sheng Morgan M Hansen David V DV Hanson Jesse E JE

The Journal of neuroscience : the official journal of the Society for Neuroscience 20191212 5

Cortical circuit activity is shaped by the parvalbumin (PV) and somatostatin (SST) interneurons that inhibit principal excitatory (EXC) neurons and the vasoactive intestinal peptide (VIP) interneurons that suppress activation of other interneurons. To understand the molecular-genetic basis of functional specialization and identify potential drug targets specific to each neuron subtype, we performed a genome wide assessment of both gene expression and splicing across EXC, PV, SST and VIP neurons ...[more]

PMID: 31831521

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Project description:A key feature of excitatory synapses is the existence of subsynaptic protein nanoclusters whose precise alignment across the cleft in a trans-synaptic nanocolumn influences the strength of synaptic transmission. However, whether nanocolumn properties vary between excitatory synapses functioning in different cellular contexts is unknown. We used a combination of confocal and DNA-PAINT super-resolution microscopy to directly compare the organization of shared scaffold proteins at two important excitatory synapses - those forming onto excitatory principal neurons (Ex→Ex synapses) and those forming onto parvalbumin-expressing interneurons (Ex→PV synapses). As in Ex→Ex synapses, we find that in Ex→PV synapses presynaptic Munc13-1 and postsynaptic PSD-95 both form nanoclusters that demonstrate alignment, underscoring synaptic nanostructure and the trans-synaptic nanocolumn as conserved organizational principles of excitatory synapses. Despite the general conservation of these features, we observed specific differences in the characteristics of pre- and postsynaptic Ex→PV nanostructure. Ex→PV synapses contained larger PSDs with fewer PSD-95 NCs when accounting for size than Ex→Ex synapses. Furthermore, the PSD-95 NCs were larger and denser. The identity of the postsynaptic cell also had a retrograde impact on Munc13-1 organization, as Ex→PV synapses hosted larger Munc13-1 puncta that contained less dense but larger and more numerous Munc13-1 NCs. Moreover, we measured the spatial variability of transsynaptic alignment in these synapse types, revealing protein alignment in Ex→PV synapses over a distinct range of distances compared to Ex→Ex synapses. We conclude that while general principles of nanostructure and alignment are shared, cell-specific elements of nanodomain organization likely contribute to functional diversity of excitatory synapses. Understanding the rules of synapse nanodomain assembly, which themselves are cell-type specific, will be essential for illuminating brain network dynamics.

Project description:BACKGROUND:Despite the large increase of transcriptomic studies that look for gene signatures on diseases, there is still a need for integrative approaches that obtain separation of multiple pathological states providing robust selection of gene markers for each disease subtype and information about the possible links or relations between those genes. RESULTS:We present a network-oriented and data-driven bioinformatic approach that searches for association of genes and diseases based on the analysis of genome-wide expression data derived from microarrays or RNA-Seq studies. The approach aims to (i) identify gene sets associated to different pathological states analysed together; (ii) identify a minimum subset within these genes that unequivocally differentiates and classifies the compared disease subtypes; (iii) provide a measurement of the discriminant power of these genes and (iv) identify links between the genes that characterise each of the disease subtypes. This bioinformatic approach is implemented in an R package, named geNetClassifier, available as an open access tool in Bioconductor. To illustrate the performance of the tool, we applied it to two independent datasets: 250 samples from patients with four major leukemia subtypes analysed using expression arrays; another leukemia dataset analysed with RNA-Seq that includes a subtype also present in the previous set. The results show the selection of key deregulated genes recently reported in the literature and assigned to the leukemia subtypes studied. We also show, using these independent datasets, the selection of similar genes in a network built for the same disease subtype. CONCLUSIONS:The construction of gene networks related to specific disease subtypes that include parameters such as gene-to-gene association, gene disease specificity and gene discriminant power can be very useful to draw gene-disease maps and to unravel the molecular features that characterize specific pathological states. The application of the bioinformatic tool here presented shows a neat way to achieve such molecular characterization of the diseases using genome-wide expression data.

Project description:BackgroundAlternative splicing is a mechanism for increasing protein diversity by excluding or including exons during post-transcriptional processing. Alternatively spliced proteins are particularly relevant in oncology since they may contribute to the etiology of cancer, provide selective drug targets, or serve as a marker set for cancer diagnosis. While conventional identification of splice variants generally targets individual genes, we present here a new exon-centric array (GeneChip Human Exon 1.0 ST) that allows genome-wide identification of differential splice variation, and concurrently provides a flexible and inclusive analysis of gene expression.ResultsWe analyzed 20 paired tumor-normal colon cancer samples using a microarray designed to detect over one million putative exons that can be virtually assembled into potential gene-level transcripts according to various levels of prior supporting evidence. Analysis of high confidence (empirically supported) transcripts identified 160 differentially expressed genes, with 42 genes occupying a network impacting cell proliferation and another twenty nine genes with unknown functions. A more speculative analysis, including transcripts based solely on computational prediction, produced another 160 differentially expressed genes, three-fourths of which have no previous annotation. We also present a comparison of gene signal estimations from the Exon 1.0 ST and the U133 Plus 2.0 arrays. Novel splicing events were predicted by experimental algorithms that compare the relative contribution of each exon to the cognate transcript intensity in each tissue. The resulting candidate splice variants were validated with RT-PCR. We found nine genes that were differentially spliced between colon tumors and normal colon tissues, several of which have not been previously implicated in cancer. Top scoring candidates from our analysis were also found to substantially overlap with EST-based bioinformatic predictions of alternative splicing in cancer.ConclusionDifferential expression of high confidence transcripts correlated extremely well with known cancer genes and pathways, suggesting that the more speculative transcripts, largely based solely on computational prediction and mostly with no previous annotation, might be novel targets in colon cancer. Five of the identified splicing events affect mediators of cytoskeletal organization (ACTN1, VCL, CALD1, CTTN, TPM1), two affect extracellular matrix proteins (FN1, COL6A3) and another participates in integrin signaling (SLC3A2). Altogether they form a pattern of colon-cancer specific alterations that may particularly impact cell motility.

Dataset Information

Genome-Wide Analysis of Differential Gene Expression and Splicing in Excitatory Neurons and Interneuron Subtypes.

Publications

Genome-Wide Analysis of Differential Gene Expression and Splicing in Excitatory Neurons and Interneuron Subtypes.

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