Differential transcript usage unravels gene expression alterations in Alzheimer’s disease human brains

Coelho, Diego Marques; Carvalho, Lukas Iohan da Cruz; Lambert, Jean‐Charles; Costa, Marcos Romualdo

doi:10.1101/2020.03.19.20038703

Cited by 6 publications

(6 citation statements)

References 68 publications

(185 reference statements)

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“…For AD, major themes included vesicle-mediated transport in the synapse, regulation of catabolism, and chemical synaptic transmission, while other themes included organelle organization and macro-and autophagy cellular processes. These processes have been implicated by various AD snRNA-seq studies of DEGs as well (Mathys et al, 2019;Marques-Coelho et al, 2021;Grubman et al, 2019;Srinivasan et al, 2020). bMIND also identified notable enrichment of CTS-DEGs in astrocytes, highlighting gliogenesis, astrocyte differentiation/development, and glial cell proliferation.…”

Section: Discussionmentioning

confidence: 83%

“…The different numbers of DEGs can be explained by sample size and brain region heterogeneity. At the bulk expression level, an existing study (Marques-Coelho et al, 2021) also found more DEGs in the temporal lobe (Mayo data and MSBB Brodmann areas 22 and 36) than in frontal lobe (ROSMAP data and MSBB Brodmann areas 10 and 44). When we contrasted the ExN-DEGs found from snRNA-seq (Mathys et al, 2019), we observed significant overlap with bMIND ExN-DEGs for both the Mayo and MSBB data (Fisher's exact test p-value = 3.9 × 10 −13 and 1.8 × 10 −5 , respectively).…”

Section: Cts Differential Expression Analysis Of Brain Tissue From Alzheimer's Disease Subjectsmentioning

confidence: 82%

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Bayesian estimation of cell type–specific gene expression with prior derived from single-cell data

2021

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When assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, however, scRNA-seq data are known to be noisy. Moreover, constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues, while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate samplelevel cell type-specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detection of CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we demonstrate that bMIND improves the accuracy of sample-level CTS expression estimates and power to discover CTS-DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer's disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS-DEGs. Our results complement findings for CTS-DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes for those cell types. Finally, we calculate CTS-eQTLs for eleven brain regions by analyzing Genotype-Tissue Expression Project data, creating a new resource for biological insights.

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Section: Discussionmentioning

confidence: 83%

Section: Cts Differential Expression Analysis Of Brain Tissue From Alzheimer's Disease Subjectsmentioning

confidence: 82%

Bayesian estimation of cell type–specific gene expression with prior derived from single-cell data

2021

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“…Assignment of newly identified AD risk genes to specific cell classes of the adult brain was performed as previously described 52 . Briefly, middle temporal gyrus (MTG) single-nucleus transcriptomes (15,928 total nuclei derived from 8 human tissue donors ranging in age from 24-66 year), were used to annotate and select 6 main cell classes using S eurat 3 .…”

Section: Methodsmentioning

confidence: 99%

“…Cell type expression. Assignment of newly identified AD risk genes to specific cell classes of the adult brain was performed as previously described 52 Table 23. PRS were calculated as previously described 34 .…”

Section: Definition Of Associated Loci a Region Of +/-500kb Was Defimentioning

confidence: 99%

New insights on the genetic etiology of Alzheimer’s and related dementia

Bellenguez¹,

Küçükali²,

Jansen³

et al. 2020

Preprint

Self Cite

127

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Deciphering the genetic landscape of Alzheimer disease (AD) is essential to define the pathophysiological pathways involved and to successfully translate genomics to potential tailored medical care. To generate the most complete knowledge of the AD genetics, we developed through the European Alzheimer Disease BioBank (EADB) consortium a discovery meta-analysis of genome-wide association studies (GWAS) based on a new large case-control study and previous GWAS (in total 39,106 clinically diagnosed cases, 46,828 proxy-AD cases and 401,577 controls) with the most promising signals followed-up in independent samples (18,063 cases and 23,207 controls). In addition to 34 known AD loci, we report here the genome-wide significant association of 31 new loci with the risk of AD. Pathway-enrichment analyses strongly indicated the involvement of gene sets related to amyloid and Tau, but also highlighted microglia, in which increased gene expression corresponds to more significant AD risk. In addition, we successfully prioritized candidate genes in the majority of our new loci, with nine being primarily expressed in microglia. Finally, we observed that a polygenic risk score generated from this new genetic landscape was strongly associated with the risk of progression from mild cognitive impairment (MCI) to dementia (4,609 MCI cases of whom 1,532 converted to dementia), independently of age and the APOE e4 allele.

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“…The different numbers of DEGs can be explained by sample size and brain region heterogeneity. At the bulk expression level, an existing study (Coelho et al, 2020) also found more DEGs in the temporal lobe (Mayo data and MSBB Brodmann areas 22 and 36) than in frontal lobe (ROSMAP data and MSBB Brodmann areas 10 and 44). When we contrasted the ExN-DEGs found from snRNA-seq (Mathys et al, 2019), we observed significant overlap with bMIND ExN-DEGs for both the Mayo and MSBB data (Fisher's exact test p-value = 3.9 × 10 −13 and 1.8 × 10 −5 , respectively).…”

Section: Cts Differential Expression Analysis Of Brain Tissue From Almentioning

confidence: 82%

Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell data

Wang

Roeder

Devlin

2020

Preprint

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When assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, however, scRNA-seq data are known to be noisy. Moreover, constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell-type-specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detecting CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we demonstrate that bMIND improves the accuracy of sample-level CTS expression estimates and power to discover CTS-DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer’s disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS-DEGs. Our results complement findings for CTS-DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes in those cell types. Finally, we calculate CTS-eQTLs for eleven brain regions by analyzing GTEx V8 data, creating a new resource for biological insights.

show abstract

Differential transcript usage unravels gene expression alterations in Alzheimer’s disease human brains

Cited by 6 publications

References 68 publications

Bayesian estimation of cell type–specific gene expression with prior derived from single-cell data

Bayesian estimation of cell type–specific gene expression with prior derived from single-cell data

New insights on the genetic etiology of Alzheimer’s and related dementia

Bayesian estimation of cell-type-specific gene expression per bulk sample with prior derived from single-cell data

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