Discordant transcriptional signatures of mitochondrial genes in Parkinson’s disease human myeloid cells

Navarro, Elisa; Udine, Evan; Lopes, Kátia de Paiva; Parks, Madison; Riboldi, Giulietta; Schilder, Brian M; Humphrey, Jack; Snijders, Gijsje; Vialle, Ricardo Assunção; Zhuang, Maojuan; Sikder, Tamjeed; Argyrou, Charalambos; Allan, Amanda; Chao, Michael J.; Farrell, Kurt; Henderson, Brooklyn; Simon, Sarah; Raymond, Deborah; Elango, Sonya; Ortega, Roberto A.; Shanker, Vicki; Swan, Matthew; Zhu, Carolyn W.; Ramdhani, Ritesh A.; Walker, Ruth H.; Tse, Winona; Sano, Mary; Pereira, Ana C.; Ahfeldt, Tim; Goate, Alison; Bressman, Susan; Crary, John F.; Witte, Lotje D. De; Frucht, Steven J.; Saunders‐Pullman, Rachel; Raj, Towfique

doi:10.1101/2020.07.20.212407

Cited by 3 publications

(1 citation statement)

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“…Another challenge is that sequence prediction models have not typically addressed the tissue‐ and cell‐type–specific nature of mutational effects. Recent advances in single‐cell transcriptomics, epigenomics, and proteomics have permitted the accumulation of single‐cell atlases in model organisms of dementia‐related diseases, primary cells from living patients (e.g., blood), 117 post mortem samples (e.g., brain tissue), 118 and patient‐derived induced pluripotent stem cells (iPSCs) 119 . Some dedicated databases have emerged for integrating and hosting AD‐related single‐cell datasets 120 .…”

Section: Goals Of the Studies Implementing Machine Learning Approache...mentioning

confidence: 99%

Artificial intelligence for dementia research methods optimization

Bucholc,

James,

Khleifat

et al. 2023

Alzheimer's & Dementia

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Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high‐dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well‐documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI‐enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co‐produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care.HIGHLIGHTS Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

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Section: Goals Of the Studies Implementing Machine Learning Approache...mentioning

confidence: 99%

Artificial intelligence for dementia research methods optimization

Bucholc,

James,

Khleifat

et al. 2023

Alzheimer's & Dementia

View full text Add to dashboard Cite

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Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies

Gjl

Humphrey

et al. 2021

Alzheimer's & Dementia

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Background Microglia are tissue‐resident macrophages of the central nervous system that are essential for homeostasis, immune response, neurogenesis, and neuronal plasticity. Genetic studies have strongly implicated microglial dysfunction in multiple neurodegenerative diseases. However, investigating genetically‐driven changes in gene expression in microglia has been limited by lack of access to these cells from the number of subjects required to perform well‐powered genomic analysis. Method Here we describe the transcriptome analysis of 255 primary human CD11b+ microglia samples isolated at autopsy from multiple brain regions of 100 human subjects with neurodegenerative and neuropsychiatric disorders. We performed systematic analyses to investigate various aspects of microglial heterogeneities including brain region, age, sex, and disease. By intersecting transcriptomics and genetics, we performed expression and splicing QTL analyses and by combining microglia from four different brain regions using a multivariate meta‐analysis method (mashR). Result We observed widespread transcriptome variation associated with microglia, suggesting that these genes may play important role in diversified responses to pathological stimuli of microglia at different locations. We also observed 1,693 genes (FDR < 0.05) whose expression is associated with age including many genes in Alzheimer’s (AD) (MS4A6A, FCER1G, and CR1) or Parkinson’s disease (PD) (BST1 and FCGR2A) GWAS loci. We identified 3,611 eQTLs (mashR local false sign rate < 0.05), of which 50% (1,791) show region‐specific effects. We identified over 300 eQTLs that colocalize with a known risk locus for a neurodegenerative or neuropsychiatric disease, nearly half of which are not found in prefrontal cortex or in peripheral monocytes. We prioritized 7 and 13 putative causal genes for AD and PD, respectively, many of which are novel genes (ITGAX, USP6NL, TSPOAP1, P2RY12, FCGR2C, and FGF20). Fine‐mapping of these colocalized loci with CNS chromatin accessibility (ATAC‐seq) and histone modification (H3K27ac) data nominates candidate causal variants that are within microglia‐specific enhancers and are likely to modify disease susceptibility by regulating gene expression and/or splicing in microglia. Conclusion In summary, we have built the most comprehensive catalog to date of genetic effects on the microglia transcriptome and propose molecular mechanisms of action of candidate functional variants in several neurological and neuropsychiatric diseases.

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Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies

Lopes

Snijders

Humphrey

et al. 2020

Preprint

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Microglial cells have emerged as potential key players in brain aging and pathology. To capture the heterogeneity of microglia across ages and regions, and to understand how genetic risk for neurological and psychiatric brain disorders is related to microglial function, large transcriptome studies are essential. Here, we describe the transcriptome analysis of 255 primary human microglia samples isolated at autopsy from multiple brain regions of 100 human subjects. We performed systematic analyses to investigate various aspects of microglial heterogeneities, including brain region, age and sex. We mapped expression and splicing quantitative trait loci and showed that many neurological disease susceptibility loci are mediated through gene expression or splicing in microglia. Fine-mapping of these loci nominated candidate causal variants that are within microglia-specific enhancers, including novel associations with microglia expression of USP6NL for Alzheimer′s disease, and P2RY12 for Parkinson′s disease. In summary, we have built the most comprehensive catalog to date of genetic effects on the microglia transcriptome and propose molecular mechanisms of action of candidate functional variants in several neurological and psychiatric diseases.

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Discordant transcriptional signatures of mitochondrial genes in Parkinson’s disease human myeloid cells

Cited by 3 publications

References 100 publications

Artificial intelligence for dementia research methods optimization

Artificial intelligence for dementia research methods optimization

Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies

Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies

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