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Atik, Merve

Mayo Clinic

Mayo Clinic in Florida

Citations 4

Identification of Novel Genetic Risk Factors for Cerebral Amyloid Angiopathy
Atik, Merve, Reddy, Joseph S. , Nguyen, Thuy T., Sotelo, Katie D., Tutor‐New, Frederick Q, Carrasquillo, Minerva M., Graff‐Radford, Jonathan, Graff‐Radford, Neill R., Kantarci, Kejal, DeTure, Michael , Allen, Mariet , Ertekin‐Taner, Nilüfer, Atık, Merve, Nguyen, Thuy T. , Sotelo, Katie D., Tutor‐New, Frederick Q, Carrasquillo, Minerva M. , Graff‐Radford, Jonathan , Graff‐Radford, Neill R. , Kantarci, Kejal , Dickson, Dennis W. , Ertekin‐Taner, Nilüfer

Alzheimer's & Dementia | December 24, 2025 | Volume: 21 | Issue: S1 | Pages: No page available

10.1002/alz70855_104520 Get It

Abstract Background Cerebral amyloid angiopathy (CAA), characterized by the accumulation of amyloid‐beta in the cerebrovasculature, affects blood vessel integrity leading to brain hemorrhages and an accelerated cognitive decline in Alzheimer's Disease (AD) patients. Our previous genome‐wide association study (GWAS) identified a LINC‐PINT splice variant associated with lower CAA levels in individuals without the APOE e4 allele and higher levels of LINC‐PINT expression in the brain of AD donors. In this study, we expand our GWAS to include additional donors with AD and those lacking significant AD neuropathology (non‐AD), with available CAA scores. Method In our prior study we assessed 853 AD donors. We expanded this by addition of genetic data from 550 AD and 502 non‐AD donors from the Mayo Clinic Brain Bank, scored for CAA. We performed QC and imputation (TOPMED) of all datasets. We conducted GWAS in AD only ( N = 1,363), non‐AD only, and all donors ( N = 1,865) by testing imputed variant dosages for association with square root transformed CAA using linear regression, adjusting for relevant covariates. To assess associations in the context of major CAA risk factors, we performed interaction analysis with APOEe4 presence and sex; and pursued stratified analyses. Result Variants at the APOE locus were identified as the most significant in our study. In addition, several other variants approached genome‐wide significance after adjusting for AD neuropathology (Braak stage and Thal phase). The LINC‐PINT splice variant remained associated with lower CAA scores in AD donors without the APOEe4 risk allele . To enhance the robustness of our findings, we are pursuing further expansion of our study cohort to include other available datasets. To explore putative functional consequences of key variants we are collecting peripheral gene expression measures in participants from Mayo Clinic with neuroimaging measures including microhemorrhages. Conclusion We expect this study will provide further insights into the genetic architecture underlying risk for CAA, both in the context of significant AD pathology, and without. Characterization of genetic variants and their functional outcomes may lead to new avenues of research aimed at identifying biomarkers and therapies to treat CAA, a common co‐pathology in those with and without AD.
Microglia‐dependent protective and risk signatures driven by Alzheimer's disease genetic factors
İş, Özkan , Tan, Jianna, Bergman, Jeremiah, Wang, Xue, Tutor‐New, Frederick Q, Min, Yuhao , Quicksall, Zachary , Atik, Merve, Reddy, Joseph S. , Gao, Junli, Tsai, Wei, Sotelo, Katie D., Nguyen, Thuy T. , Kanekiyo, Takahisa , Allen, Mariet , Ertekin‐Taner, Nilüfer, Tutor‐New, Frederick Q, Atık, Merve, Sotelo, Katie D., Dickson, Dennis W. , Ertekin‐Taner, Nilüfer

Alzheimer's & Dementia | December 23, 2025 | Volume: 21 | Issue: S1 | Pages: No page available

10.1002/alz70855_098044 Get It

Abstract Background Alzheimer's disease (AD) affects all brain cells and has complex genomic and immunological alterations. Previous research discovered missense AD risk or protective variants in microglial genes ABI3 and PLCG2, respectively. Expression levels of these genes are altered in AD and can influence microglial function. This study aims to uncover protective, and risk microglial molecular signatures associated with these variants to determine their role in microglial subtypes and states in AD by single cell expression and functional studies. Method We generated microglia‐enriched snRNAseq data from donors harboring either AD protective PLCG2 or AD risk ABI3 missense mutations, or neither mutation. After standard snRNAseq QC, we performed differential expressed gene (DEG) analysis of all microglial cells between variant carriers and non‐carriers using MAST. We defined protective signature as genes that are both down in ABI3 and up in PLCG2 mutation‐carriers. In contrast, risk signature genes are up in ABI3 and down in PLCG2 mutation‐carriers. We investigated the conservation of protective and risk signatures across multiple datasets, including scRNAseq data from iPSC‐derived microglia cells carrying PLCG2 protective variant, snRNAseq data from AD‐resilient donors, and data from AD and other diagnostic groups across multiple brain regions sourced from external datasets. Result We obtained snRNAseq profiles of 35,000 microglia from AD variant carriers. Our DEG analysis among all microglia cells provided 227 microglial protective and 293 risk signature genes defined by these variants. Using integrated analysis of multiple internal and external datasets, we further narrowed down these signatures. We determined that these high‐confidence protective signature genes are downregulated in early AD, upregulated in late AD brains and positively‐correlated with protective variant load in in vitro models. In contrast, risk signature genes are upregulated in early AD, downregulated in late AD and resilient donors. Risk signature expression is decreased with protective PLCG2 variant load, and altered with Aβ treatment in in vitro models. Conclusion Our study uncovers microglia specific protective and risk signatures associated with AD using sn/scRNAseq datasets from multiple sources and models. These findings nominate novel immune targets and pathways with implications for microglial function in health and disease, and ultimately therapeutic potential.
Microglia‐dependent protective and risk signatures driven by Alzheimer's disease genetic factors
İş, Özkan , Tan, Jianna, Bergman, Jeremiah, Wang, Xue, Tutor‐New, Frederick Q, Min, Yuhao , Quicksall, Zachary , Atik, Merve, Reddy, Joseph S. , Gao, Junli, Tsai, Wei, Sotelo, Katie D., Nguyen, Thuy T. , Kanekiyo, Takahisa , Allen, Mariet , Ertekin‐Taner, Nilüfer, Tutor‐New, Frederick Q, Atık, Merve, Sotelo, Katie D., Dickson, Dennis W. , Ertekin‐Taner, Nilüfer

Alzheimer's & Dementia | December 23, 2025 | Volume: 21 | Issue: S8 | Pages: No page available

10.1002/alz70862_110127 Get It

Abstract Background Alzheimer's disease (AD) affects all brain cells and has complex genomic and immunological alterations. Previous research discovered missense AD risk or protective variants in microglial genes ABI3 and PLCG2 , respectively. Expression levels of these genes are altered in AD and can influence microglial function. This study aims to uncover protective, and risk microglial molecular signatures associated with these variants to determine their role in microglial subtypes and states in AD by single cell expression and functional studies. Method We generated microglia‐enriched snRNAseq data from donors harboring either AD protective PLCG2 or AD risk ABI3 missense mutations, or neither mutation. After standard snRNAseq QC, we performed differential expressed gene (DEG) analysis of all microglial cells between variant carriers and non‐carriers using MAST. We defined protective signature as genes that are both down in ABI3 and up in PLCG2 mutation‐carriers. In contrast, risk signature genes are up in ABI3 and down in PLCG2 mutation‐carriers. We investigated the conservation of protective and risk signatures across multiple datasets, including scRNAseq data from iPSC‐derived microglia cells carrying PLCG2 protective variant, snRNAseq data from AD‐resilient donors, and data from AD and other diagnostic groups across multiple brain regions sourced from external datasets. Result We obtained snRNAseq profiles of 35,000 microglia from AD variant carriers. Our DEG analysis among all microglia cells provided 227 microglial protective and 293 risk signature genes defined by these variants. Using integrated analysis of multiple internal and external datasets, we further narrowed down these signatures. We determined that these high‐confidence protective signature genes are downregulated in early AD, upregulated in late AD brains and positively‐correlated with protective variant load in in vitro models. In contrast, risk signature genes are upregulated in early AD, downregulated in late AD and resilient donors. Risk signature expression is decreased with protective PLCG2 variant load, and altered with Aβ treatment in in vitro models. Conclusion Our study uncovers microglia specific protective and risk signatures associated with AD using sn/scRNAseq datasets from multiple sources and models. These findings nominate novel immune targets and pathways with implications for microglial function in health and disease, and ultimately therapeutic potential.
Identification of Novel Genetic Risk Factors for Cerebral Amyloid Angiopathy
Atik, Merve, Reddy, Joseph S. , Nguyen, Thuy T. , Sotelo, Katie D., Tutor‐New, Frederick Q, Carrasquillo, Minerva M., Graff‐Radford, Jonathan, Graff‐Radford, Neill R., Kantarci, Kejal, DeTure, Michael , Allen, Mariet , Ertekin‐Taner, Nilüfer, Atık, Merve, Sotelo, Katie D., Tutor‐New, Frederick Q, Carrasquillo, Minerva M. , Graff‐Radford, Jonathan , Graff‐Radford, Neill R. , Kantarci, Kejal , Dickson, Dennis W. , Ertekin‐Taner, Nilüfer

Alzheimer's & Dementia | December 23, 2025 | Volume: 21 | Issue: S8 | Pages: No page available

10.1002/alz70862_110058 Get It

Abstract Background Cerebral amyloid angiopathy (CAA), characterized by the accumulation of amyloid‐beta in the cerebrovasculature, affects blood vessel integrity leading to brain hemorrhages and an accelerated cognitive decline in Alzheimer’s Disease (AD) patients. Our previous genome‐wide association study (GWAS) identified a LINC‐PINT splice variant associated with lower CAA levels in individuals without the APOE e4 allele and higher levels of LINC‐PINT expression in the brain of AD donors. In this study, we expand our GWAS to include additional donors with AD and those lacking significant AD neuropathology (non‐AD), with available CAA scores. Method In our prior study we assessed 853 AD donors. We expanded this by addition of genetic data from 550 AD and 502 non‐AD donors from the Mayo Clinic Brain Bank, scored for CAA. We performed QC and imputation (TOPMED) of all datasets. We conducted GWAS in AD only ( N = 1,363), non‐AD only, and all donors ( N = 1,865) by testing imputed variant dosages for association with square root transformed CAA using linear regression, adjusting for relevant covariates. To assess associations in the context of major CAA risk factors, we performed interaction analysis with APOEe4 presence and sex; and pursued stratified analyses. Result Variants at the APOE locus were identified as the most significant in our study. In addition, several other variants approached genome‐wide significance after adjusting for AD neuropathology (Braak stage and Thal phase). T he LINC‐PINT splice variant remained associated with lower CAA scores in AD donors without the APOEe4 risk allele. To enhance the robustness of our findings, we are pursuing further expansion of our study cohort to include other available datasets. To explore putative functional consequences of key variants we are collecting peripheral gene expression measures in participants from Mayo Clinic with neuroimaging measures including microhemorrhages. Conclusion We expect this study will provide further insights into the genetic architecture underlying risk for CAA, both in the context of significant AD pathology, and without. Characterization of genetic variants and their functional outcomes may lead to new avenues of research aimed at identifying biomarkers and therapies to treat CAA, a common co‐pathology in those with and without AD.