Sex dependent glial-specific changes in the chromatin accessibility landscape in late-onset Alzheimer’s disease brains

Barrera, Julio; Song, Lingyun; Safi, Alexias; Yun, Young Won; Garrett, Melanie E.; Gamache, Julia; Premasinghe, Ivana; Sprague, Daniel; Chipman, Danielle; Li, Jeffrey; Fradin, Hélène; Soldano, Karen; Gordân, Raluca; Ashley–Koch, Allison; Crawford, Gregory E.; Chiba‐Falek, Ornit

doi:10.1101/2021.04.07.438835

Cited by 3 publications

(1 citation statement)

References 96 publications

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“…JUND belongs to the JUN protein family, possessing the potential to impact the cell apoptosis (48,49). ELK4 was proved to be associated with glia or neuron functions in AD (51); FOSL2, a member of the FOS protein family, participates in gene regulation through collaboratively forming TF complex AP-1 with JUN protein family (52). We focused on chr11-70646709-70647600, which was bound by FOS and FOLS2, and found that it had decreasing regulatory strength on Mink1 ( Figure 4F ).…”

Section: Resultsmentioning

confidence: 99%

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Wang

et al. 2022

Preprint

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We present an algorithm, STREAM, for enhancer-driven gene regulatory network (eGRN) inference from transcriptome and chromatin accessibility profiled in the same cells. The algorithm improves the prediction accuracy of relations among transcription factors (TFs), enhancers, and genes by two new ideas: (i) a Steiner forest problem model to identify the set of highly confident enhancer-gene relations underlying a context-specific functional gene module; and (ii) a hybrid biclustering pipeline integrated with submodular optimization for inferring eGRNs by identifying the optimal set of hybrid biclusters, each of which represents co-regulated genes by the same TF and co-accessible enhancers bound by the same TF over a cell subpopulation. These two ideas are embedded in an iterative framework by finding patterns in a pair of transcriptome and chromatin accessibility matrices. Benchmarking analysis shows that the performance, assessed by f-scores, precision, or recall, was significantly improved by our program compared to four state-of-the-art tools on nine datasets. Besides, by implementing STREAM on an Alzheimer's disease dataset, we identified TF-enhancer-gene relations associated with pseudotime and investigated the changing of enhancer-gene relations alongside cell lineages. Additionally, by implementing STREAM on a diffuse small lymphocytic lymphoma dataset, we excavated key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

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Section: Resultsmentioning

confidence: 99%

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Wang

et al. 2022

Preprint

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Integrative single-nucleus multi-omics analysis prioritizes candidatecisandtransregulatory networks and their target genes in Alzheimer’s disease brains

Gamache

Gingerich

Shwab

et al. 2023

Preprint

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BackgroundThe genetic underpinnings of late-onset Alzheimer’s disease (LOAD) are yet to be fully elucidated. Although numerous LOAD-associated loci have been discovered, the causal variants and their target genes remain largely unknown. Since the brain is composed of heterogenous cell subtypes, it is imperative to study the brain on a cell subtype specific level to explore the biological processes underlying LOAD.MethodsHere, we present the largestparallelsingle-nucleus (sn) multi-omics study to simultaneously profile gene expression (snRNA-seq) and chromatin accessibility (snATAC-seq) to date, using nuclei from 12 normal and 12 LOAD brains. We identified cell subtype clusters based on gene expression and chromatin accessibility profiles and characterized cell subtype-specific LOAD-associated differentially expressed genes (DEGs), differentially accessible peaks (DAPs) andcisco-accessibility networks (CCANs).ResultsIntegrative analysis defined disease-relevant CCANs in multiple cell subtypes and discovered LOAD-associated cell subtype specific candidatecisregulatory elements (cCREs), their candidate target genes, andtrans-interacting transcription factors (TFs), some of which were LOAD-DEG, for example,ELK1in excitatory neurons (Exc1) andKLF13andJUN, found in multiple cell subtypes. Finally, we focused on a subset of cell subtype-specific CCANs that overlap known LOAD-GWAS regions and catalogued putative functional SNPs changing the affinities of TF motifs within LOAD-cCREs linked to LOAD-DEGs including,APOEandMYO1Ein a specific subtype of microglia andBIN1in a subpopulation of oligodendrocytes.ConclusionsTo our knowledge, this study represents the most comprehensive systematic interrogation to date of regulatory networks and the impact of genetic variants on gene dysregulation in LOAD at a cell subtype resolution. Our findings revealed crosstalk between epigenetic, genomic, and transcriptomic determinates of LOAD pathogenesis and define catalogues of candidate genes, cCREs, and variants involved in LOAD genetic etiology and the cell subtypes in which they act to exert their pathogenic effects. Overall, these results suggest that cell subtype-specificcis-transinteractions between regulatory elements and TFs, and the genes dysregulated by these networks contribute to the development of LOAD.

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Integrative single-nucleus multi-omics analysis prioritizes candidate cis and trans regulatory networks and their target genes in Alzheimer’s disease brains

Gamache,

Gingerich,

Shwab

et al. 2023

Cell Biosci

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Background The genetic underpinnings of late-onset Alzheimer’s disease (LOAD) are yet to be fully elucidated. Although numerous LOAD-associated loci have been discovered, the causal variants and their target genes remain largely unknown. Since the brain is composed of heterogenous cell subtypes, it is imperative to study the brain on a cell subtype specific level to explore the biological processes underlying LOAD. Methods Here, we present the largest parallel single-nucleus (sn) multi-omics study to simultaneously profile gene expression (snRNA-seq) and chromatin accessibility (snATAC-seq) to date, using nuclei from 12 normal and 12 LOAD brains. We identified cell subtype clusters based on gene expression and chromatin accessibility profiles and characterized cell subtype-specific LOAD-associated differentially expressed genes (DEGs), differentially accessible peaks (DAPs) and cis co-accessibility networks (CCANs). Results Integrative analysis defined disease-relevant CCANs in multiple cell subtypes and discovered LOAD-associated cell subtype-specific candidate cis regulatory elements (cCREs), their candidate target genes, and trans-interacting transcription factors (TFs), some of which, including ELK1, JUN, and SMAD4 in excitatory neurons, were also LOAD-DEGs. Finally, we focused on a subset of cell subtype-specific CCANs that overlap known LOAD-GWAS regions and catalogued putative functional SNPs changing the affinities of TF motifs within LOAD-cCREs linked to LOAD-DEGs, including APOE and MYO1E in a specific subtype of microglia and BIN1 in a subpopulation of oligodendrocytes. Conclusions To our knowledge, this study represents the most comprehensive systematic interrogation to date of regulatory networks and the impact of genetic variants on gene dysregulation in LOAD at a cell subtype resolution. Our findings reveal crosstalk between epigenetic, genomic, and transcriptomic determinants of LOAD pathogenesis and define catalogues of candidate genes, cCREs, and variants involved in LOAD genetic etiology and the cell subtypes in which they act to exert their pathogenic effects. Overall, these results suggest that cell subtype-specific cis–trans interactions between regulatory elements and TFs, and the genes dysregulated by these networks contribute to the development of LOAD.

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Sex dependent glial-specific changes in the chromatin accessibility landscape in late-onset Alzheimer’s disease brains

Cited by 3 publications

References 96 publications

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Integrative single-nucleus multi-omics analysis prioritizes candidatecisandtransregulatory networks and their target genes in Alzheimer’s disease brains

Integrative single-nucleus multi-omics analysis prioritizes candidate cis and trans regulatory networks and their target genes in Alzheimer’s disease brains

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