Splicing accuracy varies across human introns, tissues and age

García-Ruiz, Sonia; Zhang, David; Rocamora-Perez, Guillermo; Grant‐Peters, Melissa; Fairbrother-Browne, Aine; Brenton, Jonathan; Gil-Martínez, Ana Luisa; Chen, Zhongbo; Rio, Donald C.; Botía, Juan A.; Guelfi, Sebastian; Collado‐Torres, Leonardo; Ryten, Mina

doi:10.1101/2023.03.29.534370

Cited by 3 publications

(2 citation statements)

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“…Altered chromatin accessibility or RNA methylation, could, for instance, explain why certain RBP binding sites are not used in neurons anymore. Furthermore, neuronal genes - by definition more expressed in neurons - are more susceptible to missplicing 84 . While we did not focus on missplicing, this indicates that splicing mechanisms might be different in neurons.…”

Section: Discussionmentioning

confidence: 99%

Predicting cell-type-specific exon inclusion in the human brain reveals more complex splicing mechanisms in neurons than glia

Michielsen,

Hsu,

Joglekar

et al. 2024

Preprint

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Alternative splicing contributes to molecular diversity across brain cell types. RNA-binding proteins (RBPs) regulate splicing, but the genome-wide mechanisms remain poorly understood. Here, we used RBP binding sites and/or the genomic sequence to predict exon inclusion in neurons and glia as measured by long-read single-cell data in human hippocampus and frontal cortex. We found that alternative splicing is harder to predict in neurons compared to glia in both brain regions. Comparing neurons and glia, the position of RBP binding sites in alternatively spliced exons in neurons differ more from non-variable exons indicating distinct splicing mechanisms. Model interpretation pinpointed RBPs, including QKI, potentially regulating alternative splicing between neurons and glia. Finally, using our models, we accurately predict and prioritize the effect of splicing QTLs. Taken together, our models provide new insights into the mechanisms regulating cell-type-specific alternative splicing and can accurately predict the effect of genetic variants on splicing.

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Section: Discussionmentioning

confidence: 99%

Predicting cell-type-specific exon inclusion in the human brain reveals more complex splicing mechanisms in neurons than glia

Michielsen,

Hsu,

Joglekar

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Cell type enrichment of the cross-disease network was performed similarly to previous reports ( 62, 63 ). We used two separate datasets for estimation of cell type enrichment: the Human Multiple Cortical Areas SMART-seq dataset (freely available through the Allen Brain Atlas data portal, https://portal.brain-map.org/atlases-and-data/rnaseq)and a dorsolateral prefrontal cortex snRNAseq dataset ( 64 ) (now publicly available), as references.…”

Section: Methodsmentioning

confidence: 99%

Network nature of ligand-receptor interactions underlies disease comorbidity in the brain

Grant-Peters,

Fairbrother-Browne,

Hicks

et al. 2024

Preprint

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Neurodegenerative disorders have overlapping symptoms and have high comorbidity rates, but this is not reflected in overlaps of risk genes. We have investigated whether ligand-receptor interactions (LRIs) are a mechanism by which distinct genes associated with disease risk can impact overlapping outcomes. We found that LRIs are likely disrupted in neurological disease and that the ligand-receptor networks associated with neurological diseases have substantial overlaps. Specifically, 96.8% of LRIs associated with disease risk are interconnected in a single LR network. These ligands and receptors are enriched for roles in inflammatory pathways and highlight the role of glia in cross-disease risk. Disruption to this LR network due to disease-associated processes (e.g. differential transcript use, protein misfolding) is likely to contribute to disease progression and risk of comorbidity. Our findings have implications for drug development, as they highlight the potential benefits and risks of pursuing cross-disease drug targets.

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Long-read transcriptomic identification of synaptic adaptation to amyloid pathology in theApp^NL-G-Fknock-in mouse model of the earliest phase of Alzheimer’s disease

Yaman,

Banks,

Gustavsson

et al. 2024

Preprint

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Genome-wide association studies (GWAS) have identified a transcriptional network of Alzheimer’s disease (AD) risk genes that are primarily expressed in microglia and are associated with AD pathology. However, traditional short-read sequencers have limited our ability to fully characterize how GWAS variants exert their effects on gene expression regulation or alternative splicing in response to the pathology, particularly resulting in inaccurate detection of splicing. To address this gap, we utilized long-read RNA-sequencing (RNA-seq) in theAppNL-G-Fknock-in mouse model to identify changes in splicing and novel transcript isoforms in response to amyloid-β. We show that long-read RNA-seq can recapitulate the expected induction of microglial expressed risk genes such asTrem2in response to amyloid-β at 9 months of age associated with ageing-dependent deficiencies in spatial short-term memory in theAppNL-G-Fknock-in mice. Our results not only identified novel splicing events and transcript isoform abundance in genes associated with AD, but also revealed the complex regulation of gene expression through splicing in response to amyloid plaques. Surprisingly, the regulation of alternative splicing in response to amyloid was seen in genes previously not identified as AD risk genes, expressed in microglia, neurons and oligodendrocytes, and included genes such asSyngr1that modulate synaptic physiology. We saw alternative splicing in genes such asCtsa,Clta,Dennd2a,Irf9andSmad4in mice in response to amyloid, and the orthologues of these genes also showed transcript usage changes in human AD brains. Our data suggests a model whereby induction of AD risk gene expression associated with microglial proliferation and activation is concomitant with alternative splicing in a different class of genes expressed by microglia and neurons, which act to adapt or preserve synaptic activity in response to amyloid-β during early stages of the disease. Our study provides new insights into the mechanisms and effects of the regulation of genes associated with amyloid pathology, which may ultimately enable better disease diagnosis, and improved tracking of disease progression. Additionally, our findings identify new therapeutic avenues for treatment of AD.

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Splicing accuracy varies across human introns, tissues and age

Cited by 3 publications

References 100 publications

Predicting cell-type-specific exon inclusion in the human brain reveals more complex splicing mechanisms in neurons than glia

Predicting cell-type-specific exon inclusion in the human brain reveals more complex splicing mechanisms in neurons than glia

Network nature of ligand-receptor interactions underlies disease comorbidity in the brain

Long-read transcriptomic identification of synaptic adaptation to amyloid pathology in theApp^NL-G-Fknock-in mouse model of the earliest phase of Alzheimer’s disease

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Splicing accuracy varies across human introns, tissues and age

Cited by 3 publications

References 100 publications

Predicting cell-type-specific exon inclusion in the human brain reveals more complex splicing mechanisms in neurons than glia

Predicting cell-type-specific exon inclusion in the human brain reveals more complex splicing mechanisms in neurons than glia

Network nature of ligand-receptor interactions underlies disease comorbidity in the brain

Long-read transcriptomic identification of synaptic adaptation to amyloid pathology in theAppNL-G-Fknock-in mouse model of the earliest phase of Alzheimer’s disease

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Product

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Long-read transcriptomic identification of synaptic adaptation to amyloid pathology in theApp^NL-G-Fknock-in mouse model of the earliest phase of Alzheimer’s disease