Genetic variability in response to Aβ deposition influences Alzheimer’s risk

Salih, Dervis A.; Sevinç, Bayram; Ms, Guelfi; Reynolds, Regina Romano; Shoai, Maryam; Ryten, Mina; Brenton, Jonathan; Zhang, D; Matarı́n, Mar; Botía, Juan A.; Shah, Raj C.; Brookes, Keeley J.; Guetta-Baranés, Tamar; Morgan, Kevin; Bellou, Eftychia; Cummings, Damian M.; Hardy, John; Edwards, FA; Escott-Price,

doi:10.1101/437657

Cited by 16 publications

(20 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, this module is highly enriched for GO categories involving immune response, cytokine production, and inflammation (Fig D and and ). It furthermore shows a highly significant overlap with a recently published Aβ‐response network (Salih et al , ; LOR: 2.0, P = 2.2e‐16) as well as with the microglia–immune module derived from the brains of late‐onset AD patients (Zhang et al , ; LOR: 2.14, P = 8.0e‐73), demonstrating that this transcriptional response to increasing Aβ is similar in this Aβ mouse model and AD patients.…”

Section: Resultssupporting

confidence: 61%

“…Earlier studies have relied heavily on functional analysis and module connectivity for putative gene selection involved in AD pathogenesis, but whether these genes were also differentially expressed was not considered (Zhang et al , ; Matarin et al , ; Salih et al , ). We used two independent functional criteria: Genes must have a genetic association with AD at P < 0.001 and show a statistically significant change in expression when exposed to increasing levels of Aβ over time, indicating that these genes are part of the cellular response.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Novel Alzheimer risk genes determine the microglia response to amyloid‐β but not to TAU pathology

et al. 2020

View full text Add to dashboard Cite

Polygenic risk scores have identified that genetic variants without genome‐wide significance still add to the genetic risk of developing Alzheimer's disease (AD). Whether and how subthreshold risk loci translate into relevant disease pathways is unknown. We investigate here the involvement of AD risk variants in the transcriptional responses of two mouse models: APPswe/PS1L166P and Thy‐TAU22. A unique gene expression module, highly enriched for AD risk genes, is specifically responsive to Aβ but not TAU pathology. We identify in this module 7 established AD risk genes (APOE, CLU, INPP5D, CD33, PLCG2, SPI1, and FCER1G) and 11 AD GWAS genes below the genome‐wide significance threshold (GPC2, TREML2, SYK, GRN, SLC2A5, SAMSN1, PYDC1, HEXB, RRBP1, LYN, and BLNK), that become significantly upregulated when exposed to Aβ. Single microglia sequencing confirms that Aβ, not TAU, pathology induces marked transcriptional changes in microglia, including increased proportions of activated microglia. We conclude that genetic risk of AD functionally translates into different microglia pathway responses to Aβ pathology, placing AD genetic risk downstream of the amyloid pathway but upstream of TAU pathology.

show abstract

Section: Resultssupporting

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Novel Alzheimer risk genes determine the microglia response to amyloid‐β but not to TAU pathology

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Earlier studies have relied heavily on functional analysis and module connectivity for putative gene selection involved in AD pathogenesis, but whether these genes were also differentially expressed was not considered. 12,13,22 The 5 recently reported novel AD risk genes OAS1, ITGAM, CXCL10, LAPTM5 and LILRB4 are all found within the APPtg-Blue module, but only Itgam and Laptm5 are significantly differentially expressed with increasing Aβ load. 12 We used two independent functional criteria: genes must have a genetic association with AD at p<0.001 and show a statistically significant change in expression when exposed to increasing levels of Aβ over time, indicating that these genes are part of the cellular response.…”

Section: Discussionmentioning

confidence: 99%

“…12,13,22 The 5 recently reported novel AD risk genes OAS1, ITGAM, CXCL10, LAPTM5 and LILRB4 are all found within the APPtg-Blue module, but only Itgam and Laptm5 are significantly differentially expressed with increasing Aβ load. 12 We used two independent functional criteria: genes must have a genetic association with AD at p<0.001 and show a statistically significant change in expression when exposed to increasing levels of Aβ over time, indicating that these genes are part of the cellular response. Therefore, we can predict with high confidence (BY-padj=9.0e-11) that they are indeed involved in an AD relevant disease pathway responding to Aβ pathology.…”

Section: Discussionmentioning

confidence: 99%

“…It furthermore shows a highly significant overlap with a recently published Aβ-response network (LOR: 2.0, p=2.2e-16) as well as with the microglia-immune module derived from the brains of late-onset AD patients (LOR: 2.14, p=8.0e-73), demonstrating that this transcriptional response to increasing Aβ is similar in this Aβ mouse model and AD patients. 12,13 We identified transcription factors potentially regulating this module. Out of 16 APPtg-Blue-associated transcription factors, Spi1 (a.k.a.…”

Section: Ad Risk Genes Are Co-regulated In a Specific Functional Genementioning

confidence: 99%

See 1 more Smart Citation

Novel Alzheimer risk genes determine the microglia response to amyloid-β but not to TAU pathology

Sierksma

Salta

et al. 2018

Preprint

View full text Add to dashboard Cite

Background: Thousands of SNPs associated with risk of Alzheimer's disease (AD) in genome-wide association studies (GWAS) do not reach genome-wide significance. When combined, they contribute however to a highly predictive polygenic risk score. The relevance of these subthreshold risk genes to disease, and how their combined predictive power translates into functionally relevant disease pathways, is unknown. We investigate here at the genome-wide level and in an unbiased way to what extent AD risk genes show altered gene expression in the context of increasing Aβ or Tau pathology in mouse models of AD. Methods:We used an existing GWAS data set to generate lists of candidate AD genes at different levels of significance. We performed transcriptomic analysis on wild-type and transgenic APP/PS1 (APPtg) and Thy-TAU22 (TAUtg) mouse models at early and late stage of disease. We used unbiased weighted gene co-expression network analysis (WGCNA) to identify clusters of co-regulated genes responsive to Aβ or TAU pathology. Gene set enrichment was used to identify clusters that were enriched for AD risk genes.Findings: Consistent and significant enrichment of AD risk genes was found in only one out of 63 coexpression modules. This module is highly responsive to Aβ but not to TAU pathology. We identify in this module 18 AD risk genes (p-value=9.0e-11) including 11 new ones, GPC2, TREML2, SYK, GRN, SLC2A5, SAMSN1, PYDC1, HEXB, RRBP1, LYN and BLNK. All are expressed in microglia, have a binding site for the transcription factor SPI1 (PU.1), and become significantly upregulated when exposed to Aβ.A subset regulates FC-gamma receptor mediated phagocytosis.Interpretation: Genetic risk of AD is functionally translated into a microglia pathway responsive to Aβ pathology. This insight integrates aspects of the amyloid hypothesis with genetic risk associated to sporadic AD.

show abstract

Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease

Chen

Craessaerts

et al. 2020

Cell

619

595

View full text Add to dashboard Cite

While complex inflammatory-like alterations are observed around the amyloid plaques of Alzheimer disease (AD), little is known about the molecular changes and cellular interactions that characterize this response. We investigate here in an AD mouse model the transcriptional changes occurring in tissue domains of 100 µm diameter around the amyloid plaques using spatial transcriptomics. We demonstrate early alterations in a gene co-expression network enriched for myelin and oligodendrocyte genes (OLIG), while a multicellular gene coexpression network of Plaque-Induced Genes (PIGs) involving the complement system, oxidative stress, lysosomes and inflammation is prominent in the later phase of the disease. We confirm the majority of the observed alterations at the cellular level using in situ sequencing on mouse and human brain sections. Genome-wide spatial transcriptomic analysis provides an unprecedented approach to untangle the dysregulated cellular network in the vicinity of pathogenic hallmarks of AD and other brain diseases.

show abstract

Genetic variability in response to Aβ deposition influences Alzheimer’s risk

Abstract: 31

Cited by 16 publications

References 61 publications

Novel Alzheimer risk genes determine the microglia response to amyloid‐β but not to TAU pathology

Novel Alzheimer risk genes determine the microglia response to amyloid‐β but not to TAU pathology

Novel Alzheimer risk genes determine the microglia response to amyloid-β but not to TAU pathology

Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease

Contact Info

Product

Resources

About