Tensor decomposition of stimulated monocyte and macrophage gene expression profiles identifies neurodegenerative disease-specific trans-eQTLs

Ramdhani, Satesh; Navarro, Elisa; Udine, Evan; Efthymiou, Anastasia G.; Schilder, Brian M; Parks, Madison; Goate, Alison M.; Raj, Towfique

doi:10.1371/journal.pgen.1008549

Cited by 23 publications

(17 citation statements)

References 57 publications

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“…Extensive follow-up with gene set and transcription factor motif enrichment analyses allowed us to gain additional insight into the functional impact of trans-eQTLs and prioritise loci for further analyses. In addition to replicating two known monocyte-specific trans-eQTLs at the IFNB1 [11,[17][18][19] and LYZ loci [10,20,21], we found that the trans-eQTL at the ARHGEF3 locus detected in multiple whole blood datasets [8][9][10]22] was highly specific to platelets in our analysis. Finally, we also detected a novel association at the SLC39A8 locus that controlled a group of genes encoding zinc-binding proteins in LPS-stimulated monocytes.…”

Section: Introductionsupporting

confidence: 79%

“…To reduce the number of tested phenotypes, co-expression analysis methods are sometimes used to aggregate individual genes to co-expressed modules capturing signalling pathways and cellular processes [5]. Such approaches have been successful in identifying trans-eQTLs in yeast [6] as well as various human tissues [7][8][9] and purified immune cells [10,11].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, both matrix factorisation and co-expression clustering methods have been further extended to incorporate prior information about biological pathways and gene sets, resulting in pathway-level information extractor (PLIER) [9] and funcExplorer [15], respectively. Out of these methods, ICA, WGCNA, SDA and PLIER have previously been used to find trans-eQTLs for modules of co-expressed genes [7][8][9][10][11], but only a single method at a time. However, since different methods solve distinct optimisation problems, they can detect complementary sets of co-expression modules [5].…”

Section: Introductionmentioning

confidence: 99%

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Co-expression analysis reveals interpretable gene modules controlled bytrans-acting genetic variants

Kolberg

Kerimov

Peterson

et al. 2020

Preprint

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BackgroundDeveloping novel therapies for complex disease requires better understanding of the causal processes that contribute to disease onset and progression. Although trans-acting gene expression quantitative trait loci (trans-eQTLs) can be a powerful approach to directly reveal cellular processes modulated by disease variants, detecting trans-eQTLs remains challenging due to their small effect sizes and large number of genes tested. However, if a single trans-eQTL controls a group of co-regulated genes, then multiple testing burden can be greatly reduced by summarising gene expression at the level of co-expression modules prior to trans-eQTL analysis. ResultsWe analysed gene expression and genotype data from six blood cell types from 226 to 710 individuals. We inferred gene co-expression modules with five methods on the full dataset, as

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Section: Introductionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Co-expression analysis reveals interpretable gene modules controlled bytrans-acting genetic variants

Kolberg

Kerimov

Peterson

et al. 2020

Preprint

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“…Besides their role in immune defense, both type I and type II IFNs contribute to physiological brain functions such as regulation of neurogenesis and synaptic function 9,48 . However, increasing evidence indicate that dysregulation of IFN signaling plays a role in ageing and neurodegenerative disease processes 2,3,4 . With respect to PD, genetic and functional studies have identified a link between IFN-γ and disease 5,6,11,13,14,15 .…”

Section: Discussionmentioning

confidence: 99%

“…Emerging data suggest that immune dysfunction contributes, not only to the progression, but also to the onset of neurodegenerative diseases including Parkinson's disease (PD) 1 . In this respect, human genetics and functional genomics studies indicate that interferon-mediated signaling pathways, both type I and type II, contribute to brain ageing and human neurodegenerative diseases 2,3,4 . With regard to PD, there is evidence for a link between expression network signatures of disease loci and IFN-γ signaling 5 .…”

Section: Introductionmentioning

confidence: 99%

Interferon-γ signaling synergizes with LRRK2 in human neurons and microglia

Cicco

Ivanyuk

Haq

et al. 2020

Preprint

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Increasing evidence suggests a role for interferons (IFNs) in neurodegeneration.Parkinson's disease (PD) associated kinase LRRK2 has been implicated in IFN type II (IFN) response in infections and nigral neuronal loss. However, whether and how LRRK2 synergizes with IFN-γ still remains unclear. Here, we employed dopaminergic (DA) neurons and microglia differentiated from patient induced pluripotent stem cells to unravel the role of IFN-γ in LRRK2-PD. We show that IFN-γ induces LRRK2 expression in both DA neurons and microglial cells. LRRK2-G2019S, the most common PD-associated mutation, sensitizes DA neurons to IFN-γ by decreasing AKT phosphorylation. IFN-γ suppresses NFAT activity in both neurons and microglia and synergistically enhances LRRK2-induced defects of NFAT activation.Furthermore, LRRK2-G2019S negatively regulates NFAT via calcium and microtubule dynamics. Importantly, we uncover functional consequences of the reduction of NFAT activity in both cell types, namely defects of neurite elongation and alteration of microglial activation profile and motility. We propose that synergistic IFN-γ/LRRK2 activation serves as a direct link between inflammation and neurodegeneration in PD.

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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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Tensor decomposition of stimulated monocyte and macrophage gene expression profiles identifies neurodegenerative disease-specific trans-eQTLs

Cited by 23 publications

References 57 publications

Co-expression analysis reveals interpretable gene modules controlled bytrans-acting genetic variants

Co-expression analysis reveals interpretable gene modules controlled bytrans-acting genetic variants

Interferon-γ signaling synergizes with LRRK2 in human neurons and microglia

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

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