Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease
Open accessAbstract
The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer's disease (LOAD), we constructed gene-regulatory networks in 1,647 postmortem brain tissues from LOAD patients and nondemented subjects, and we demonstrate that LOAD reconfigures specific portions of the molecular interaction structure. Through an integrative network-based approach, we rank-ordered these network structures for relevance to LOAD pathology, highlighting an immune- and microglia-specific module that is dominated by genes involved in pathogen phagocytosis, contains TYROBP as a key regulator, and is upregulated in LOAD. Mouse microglia cells overexpressing intact or truncated TYROBP revealed expression changes that significantly overlapped the human brain TYROBP network. Thus the causal network structure is a useful predictor of response to gene perturbations and presents a framework to test models of disease mechanisms underlying LOAD.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The findings strengthen the growing evidence linking AD to a network of genes expressed in microglia.
“These results reinforce the emerging genetic and epigenetic association between AD and a network of microglial expressed genes2,5,17–21”
From A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease (2017) · cites this paper as [18]
The Results section, “Highlighting the Microglia Pathway with TYROBP as Causal Regulator” (page unavailable), links an immune/microglia gene network to late-onset AD and describes convergent genetic evidence. The citing sentence also attributes an epigenetic association to the citation group, but the passages in our copy do not establish that component. (LLM, conf=0.83)
Experimental — accuracy not yet measuredRNA-Seq offers an unbiased view of known and novel transcripts and has identified candidate hub genes within networks linked to lipid metabolism and innate immunity.
“RNA-Seq offers an unbiased view of known and novel transcripts and has identified candidate “hub” genes within networks linked to lipid metabolism and innate immunity122,130,159,160.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [130]
The Results section, “Highlighting the Microglia Pathway with TYROBP as Causal Regulator” (page unavailable), identifies TYROBP and other key regulators in an immune/microglia network. But “Structure of Causal Networks Guides Differential Expression” describes RNA-Seq as testing expression changes against a network inferred from human brain data, not as identifying those regulators. Our copy does not establish the claimed unbiased view of known and novel transcripts or identify hub genes in a lipid-metabolism network. (LLM, conf=0.85)
Experimental — accuracy not yet measuredA robust linear regression model was applied for blood contamination correction.
“Thus we applied a robust linear regression model for blood contamination correction [49].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [49]
The Results section, “Leveraging a Systems Approach to LOAD” (page unavailable), says the authors used robust linear regression for covariate corrections in gene-expression analyses. It does not identify blood contamination as a covariate or describe correcting for it. (LLM, conf=0.85)
Experimental — accuracy not yet measuredCtss, Fcer1g, Tyrobp, C1qa, and Cyba are primarily expressed in microglia and were identified in the immune/microglia module in LOAD patient brains.
“Importantly, these genes are primarily expressed in microglia and identified in the immune/microglia module in LOAD patient brains (41).”
From The effect of Abi3 locus deletion on the progression of Alzheimer’s disease-related pathologies (2023) · cites this paper as [41]
In Results, “Highlighting the Microglia Pathway with TYROBP as Causal Regulator” (page unavailable), the paper identifies TYROBP and FCER1G in immune/microglia modules and says TYROBP is expressed in innate immune cells, including brain microglia. It names CTSC, not CTSS, among the shared regulators; the passages do not establish module membership or primarily microglial expression for CTSS, C1QA, and CYBA. (LLM, conf=0.86)
Experimental — accuracy not yet measuredTYROBP is the downstream signaling mediator of several microglial receptors implicated in AD, such as TREM2 and CD33.
“Interestingly, it is also the downstream signaling mediator of several microglial receptors that are implicated in AD, such as TREM2 and CD33 (41, 48).”
From The effect of Abi3 locus deletion on the progression of Alzheimer’s disease-related pathologies (2023) · cites this paper as [41]
In Results, under “Highlighting the Microglia Pathway with TYROBP as Causal Regulator” (page not provided), the paper says TREM2 associates with and signals via TYROBP. It places CD33 in the same Alzheimer’s-associated gene network but does not say that CD33 signals through TYROBP. (LLM, conf=0.88)
Experimental — accuracy not yet measured
The significantly upregulated genes (Tyrobp, Ctss, Fcer1g, C1q, Cyba) were previously identified in the immune/microglia module in LOAD patient brains through an integrative network-based approach.
“Importantly, the significantly upregulated genes (Tyrobp, Ctss, Fcer1g, C1q, Cyba) were previously identified in the immune/microglia module in LOAD patient brains through an integrative network-based approach (41).”
From The effect of Abi3 locus deletion on the progression of Alzheimer’s disease-related pathologies (2023) · cites this paper as [41]
The Results section, “Highlighting the Microglia Pathway with TYROBP as Causal Regulator” (page unavailable), identifies TYROBP and FCER1G in the immune/microglia module and reports significant upregulation of TYROBP in LOAD brains. Our copy does not establish that CTSS, C1q, or Cyba were members of that module or significantly upregulated; the listed regulator is CTSC, not CTSS. (LLM, conf=0.88)
Experimental — accuracy not yet measuredRecent studies suggested TYROBP-mediated signaling is involved in multiple important functions as aggregating Aβ activates microglia, including enhanced phagocytosis of damaged neurons and suppression of inflammatory responses, as well as neuronal pruning activity.
“Recent studies suggested TYROBP-mediated signaling is involved in multiple important functions as aggregating Aβ activates microglia, including enhanced phagocytosis of damaged neurons [56, 90] and suppression of inflammatory responses [94], as well as neuronal pruning activity [56].”
From Sex-specific DNA methylation differences in Alzheimer’s disease pathology (2021) · cites this paper as [56]
In Results, under “Highlighting the Microglia Pathway with TYROBP as Causal Regulator” (page unavailable), the paper suggests that TYROBP may be associated with neuronal pruning activity linked to amyloid-β and Tau aggregates. It identifies pathogen-phagocytosis pathways in the microglia network, but does not report enhanced phagocytosis of damaged neurons. The suppression-of-inflammatory-responses clause is cited to [94], not this source. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTYROBP is a key regulator of the complement pathway in the immune/microglia network, which is activated as Aβ accumulates in LOAD brains.
“TYROBP (TYRO protein tyrosine kinase-binding protein) is a key regulator of the complement pathway in the immune/microglia network, which is activated as Aβ accumulates in LOAD brains [56, 90].”
From Sex-specific DNA methylation differences in Alzheimer’s disease pathology (2021) · cites this paper as [56]
The Results section, “Highlighting the Microglia Pathway with TYROBP as Causal Regulator” (page not provided), places TYROBP in the complement pathway and calls it a key regulator of an immune/microglia network activated in LOAD. But it only proposes that complement-related activity may be reawakened via amyloid-β and tau aggregates; it does not establish that the network activates as Aβ accumulates. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe plum3 module is enriched for immune function, with cd37 hub gene related to a Tyrobp-regulated microglial module in AD and Lat2 identified as a core transcriptional signature of AD microglia.
“This module is especially enriched for immune function, with the cd37 hub gene related to a Tyrobp-regulated microglial module in AD [41] and Lat2 identified as a core transcriptional signature of AD microglia [42].”
From Deletion of Abi3/Gngt2 influences age-progressive amyloid β and tau pathologies in distinctive ways (2022) · cites this paper as [41]
The Results section, under “Highlighting the Microglia Pathway with TYROBP as Causal Regulator” (page unavailable), identifies TYROBP as a key regulator of an immune/microglia network in late-onset Alzheimer’s disease. Our copy does not identify CD37 or establish its relationship to that network (searched: CD37). The Lat2 statement has a separate citation and is not assessed against this source. (LLM, conf=0.82)
Experimental — accuracy not yet measuredModule-trait correlations can be relatively low (correlation < 0.5) even when statistically significant.
“and module-trait correlations can be relatively low (correlation < 0.5) even when statistically significant [128].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [128]
Integration of genomic data and gene expression information has proven powerful for determining potential causative pathways in AD.
“Integration of genomic data and gene expression information has proven powerful for determining potential causative pathways in Alzheimer’s disease (Zhang et al., 2013).”
From Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression (2020) · cites this paper as Zhang et al., 2013
Genome-wide association studies have identified polymorphisms in inflammation-associated genes as risk factors for the development of Alzheimer's disease.
“This is supported by genome-wide association studies that have identified polymorphisms in inflammation-associated genes as risk factors for the development of AD.9, 10, 11”
From Markers of microglia in post-mortem brain samples from patients with Alzheimer’s disease: a systematic review (2018) · cites this paper as [11]
Many human brain gene expression changes associated with AD, including neuronal- and microglial-enriched modules, may represent common transcriptional programs activated by the aging process itself.
“This result suggests that many human brain gene expression changes associated with AD, including neuronal- and microglial-enriched modules reported in other studies (Conway et al., 2018; Mostafavi et al., 2018; Zhang et al., 2013), may represent common transcriptional programs activated by the aging process itself.”
From Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models (2020) · cites this paper as Zhang et al., 2013
Recent in-depth and large-scale omics studies of brain tissue have dramatically extended discovery of molecular pathogenesis in Alzheimer's disease.
“Recent breakthroughs, especially in-depth and large-scale omics studies of brain tissue, have dramatically extended our discovery of molecular pathogenesis in AD [18, 49].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [49]
TYROBP is a key regulator of this gene-network module, driving the expression of other genes.
“TYROBP is a key regulator of this gene-network module, driving the expression of other genes.”
From The effect of Abi3 locus deletion on the progression of Alzheimer’s disease-related pathologies (2023) · cites this paper as [41]
The TYROBP causal network was previously inferred from a large-scale network analysis of human late-onset AD brains.
“The TYROBP causal network was previously inferred from a large-scale network analysis of human late-onset AD brains [56]”
From Sex-specific DNA methylation differences in Alzheimer’s disease pathology (2021) · cites this paper as [56]
In patients with LOAD, TYROBP was observed to be up-regulated in the brains in multiple cohorts.
“In patients with LOAD, TYROBP was observed to be up-regulated in the brains in multiple cohorts [56].”
From Sex-specific DNA methylation differences in Alzheimer’s disease pathology (2021) · cites this paper as [56]
Among the most upregulated genes in XO4+ microglia were Trem2 and Apoe, as well as Tyrobp, Lpl, Ldlr, and Lrpap1.
“Among the most upregulated genes in XO4+, microglia were the two most highly penetrant LOAD risk factor genes, the receptor Trem2 and its ligand Apoe28, as well as genes encoding their interacting partners, Tyrobp10 and Lpl, Ldlr, Lrpap1”
From Transcriptional signature in microglia associated with Aβ plaque phagocytosis (2021) · cites this paper as [10]
Analysis of regulatory networks of genes differentially expressed in AD patients indicates that immune- and microglia-specific gene modules are key contributors to AD pathology.
“Analysis of regulatory networks of genes differentially expressed in AD patients indicates that immune- and microglia-specific gene modules are key contributors to AD pathology (Zhang et al., 2013).”
From The Major Risk Factors for Alzheimer’s Disease: Age, Sex, and Genes Modulate the Microglia Response to Aβ Plaques (2019) · cites this paper as Zhang et al., 2013
Genes expressed in myeloid cells display abnormal patterns of chromatin modification in AD mouse models and human samples.
“At the epigenetic level, genes expressed in myeloid cells display abnormal patterns of chromatin modification in AD mouse models and human samples18–20.”
From A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease (2017) · cites this paper as [18]
The Results section reports altered gene expression in human Alzheimer’s brain tissue and in mouse microglia after TYROBP perturbation, including down-regulation of genes involved in histone assembly. It does not report abnormal chromatin modifications in myeloid-cell genes in AD mouse models or human samples. Searches for chromatin modification, histone methylation, and acetylation found no passage establishing that claim; page numbers were unavailable in our copy. (LLM, conf=0.75)
Experimental — accuracy not yet measuredThere is a clear separation of molecular signatures between the five AD subtypes.
“As shown by Fig. 1D, there is a clear separation of molecular signatures between the five AD subtypes that can be visually appreciated from the whole-transcriptome gene expression heatmap after identifying gene modules using weighted interaction network analysis (WINA) (24, 27).”
From Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets (2021) · cites this paper as [27]
The Results section, “Remodeling of the Molecular Interaction Structure in LOAD Brains” (page unavailable), compares gene networks in LOAD and non-demented brains, not five AD subtypes. Searches for five subtypes, molecular-signature separation, and WINA found no passage in our copy reporting the claimed separation. (LLM, conf=0.77)
Experimental — accuracy not yet measuredPrevious analyses have prioritized ECHDC3, as it is upregulated in AD post-mortem brains and an eQTL for ECHDC3 was seen in whole blood, though it did not colocalize with the GWAS SNP.
“Previous analyses have prioritized ECHDC3, as it is upregulated in AD post-mortem brains53,54, and an eQTL for ECHDC3 was seen in whole blood55, though it did not colocalize with the GWAS SNP23.”
From Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies (2022) · cites this paper as [53]
The citation to this paper is attached to the claim that the ECHDC3 eQTL did not colocalize with the GWAS SNP. The Results section discusses brain cis eSNPs, but our copy does not report that ECHDC3-specific colocalization result (searched for ECHDC3, colocalization, and the whole-blood eQTL). (LLM, conf=0.75)
Experimental — accuracy not yet measured62 genes were correlated with pathogenic stage (BRAAK) in at least one brain tissue (44 genes in prefrontal cortex, 36 in cerebellum, 1 in visual cortex).
“62 genes were correlated with pathogenic stage (BRAAK) in at least one brain tissue30 (44 genes in prefrontal cortex, the most relevant LOAD tissue; 36 in cerebellum and 1 in visual cortex).”
From Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing (2019) · cites this paper as [30]
In addition to downregulation of genes involved in synaptic transmission and synapse organization, there is prominent upregulation of genes involved in cell stress, immune response activation and cell death in PFC of AD patients.
“In addition to the downregulation of genes involved in synaptic transmission and synapse organization, there is also prominent upregulation of genes involved in cell stress, immune response activation and cell death in PFC of AD patients 257–259.”
From Mechanisms of synaptic transmission dysregulation in the prefrontal cortex: pathophysiological implications (2022) · cites this paper as [258]
Networks can be modeled as graphs consisting of elements that exhibit specific interactions with other elements.
“One way to model such networks is as graphs consisting of elements that exhibit specific interactions with other elements [60–64].”
From Multi-omics approaches to disease (2017) · cites this paper as [64]
Zhang and coworkers examined transcript levels from brains of late onset Alzheimer's disease individuals and used co-expression and Bayesian causal modeling to identify modules associated with disease and key driver genes.
“In another example, Zhang and coworkers [64] examined transcript levels from brains of individuals with late onset AD and analyzed co-expression and Bayesian causal modeling to identify modules associated with disease and key driver genes important in disease regulatory pathways.”
From Multi-omics approaches to disease (2017) · cites this paper as [64]
A network-based integrative analysis found that an immune/microglial gene network had the strongest association with Alzheimer's disease neuropathology.
“a network-based integrative analysis of AD-related genes revealed that an immune/microglial gene network had the strongest association with AD neuropathology (56).”
From Microglia in Alzheimer's disease (2017) · cites this paper as [56]
The PIGs-associated 'Yellow' module is identified as the strongest gain-of-connectivity module in Zhang's study.
“the PIGs-associated 'Yellow' module is identified as the strongest gain-of-connectivity module”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Zhang et al., 2013
m116 is associated with AD diagnosis in another human cohort (Zhang et al., 2013).
“m116 is associated with AD diagnosis in another human cohort (Zhang et al., 2013).”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Zhang et al., 2013
Zhang et al. constructed gene-regulatory networks from 1647 postmortem brain tissues from LOAD patients and controls, and correlated networks with 26 neuropathological traits such as Braak stage and brain atrophy.
“Zhang et al. constructed gene-regulatory networks from 1647 postmortem brain tissues from LOAD patients and controls, and correlated networks with 26 neuropathological traits such as Braak stage and brain atrophy.”
From Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease (2020) · cites this paper as Zhang et al., 2013
eQTL analysis followed by module-level genetic signal enrichment identified several modules enriched for genetic association signals.
“An eQTL analysis followed by module-level genetic signal enrichment identified several modules in which genetic association signals were enriched.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [170]
Bayesian network analysis of an Alzheimer disease-related microglial module implicated TYROBP as a regulatory hub.
“The researchers then applied Bayesian network analysis to evaluate causal relationships in an Alzheimer disease-related microglial module, implicating TYROBP (TYRO protein tyrosine kinase-binding protein) as a regulatory hub.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [170]
A recent study shows that using genetic variation as a causal anchor to define genetically driven network-level changes in Alzheimer disease and provide experimental validation is possible in the CNS.
“A recent study shows that this goal is possible in the CNS by using genetic variation as a causal anchor to define genetically driven network-level changes in Alzheimer disease and to provide experimental validation for network predictions170.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [170]
Zhang and colleagues applied WGCNA to hundreds of post-mortem brain samples from Alzheimer disease, other neurodegenerative diseases, and controls.
“Zhang and colleagues170 applied WGCNA to hundreds of post-mortem brain samples from individuals with Alzheimer disease, other neurodegenerative diseases and controls.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [170]
The role of Tyrobp was experimentally validated in mice, showing that network structure is predictive, as previously demonstrated with co-expression networks.
“The role of Tyrobp was experimentally validated in mice170, showing that network structure is predictive, as had previously been demonstrated with co-expression networks32.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [170]
Multiple transcriptional modules were remodelled in Alzheimer disease: immune and neurogenesis pathways gained connectivity, while GABA signalling and myelination pathways lost connectivity.
“They showed that multiple transcriptional modules were remodelled in Alzheimer disease: gain of connectivity was observed in immune and neurogenesis pathways, and loss of connectivity was predominant in pathways related to GABA signalling and myelination.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [170]
Most molecular profiling studies, especially those using post-mortem tissue, cannot show causality without follow-up controlled experiments or genetic evidence.
“As far as causality is concerned, most molecular profiling studies, especially those using post-mortem tissue, cannot show causality without follow-up controlled experiments or genetic evidence169–171.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [170]
45 of 47 modules showed significant preservation in an independent DLPFC microarray dataset of 229 persons with pathologic and clinical Alzheimer's disease.
“we observed significant evidence for preservation in 45 of the 47 modules in the independent microarray dataset”
From A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease (2018) · cites this paper as [12]
Module-pathologic Alzheimer's disease associations defined in ROSMAP showed strong concordance in strength and direction in the Zhang study.
“we observed a strong concordance in both the strength and direction of the module-pathologic AD associations that we defined in ROSMAP subjects (Figure 2D).”
From A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease (2018) · cites this paper as [12]
CMC eQTLs captured most eQTLs found in other independent brain samples, with replication π1 values of 0.98 for GTEx v6 and 0.70-0.93 for several microarray datasets.
“Considering microarray-based eQTLs from the Harvard Brain Bank 15, BrainCloud16, NIH17, and the UK Brain Expression Consortium (UKBEC)18, we estimated π1 to be 0.75, 0.70, 0.79, and 0.93, respectively, indicating that our results captured most eQTLs found in other independent samples.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [15]
Gene co-expression was organized into modules of distinct cellular and functional categories, consistent with prior brain transcriptome studies.
“Consistent with prior studies of the brain transcriptome15,47–50, we find gene co-expression to be organized into modules of distinct cellular and functional categories (Supplementary data file 7).”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [15]
CommonMind Consortium eQTLs replicate most eQTLs found in other independent samples, with π1 values of 0.98 for GTEx v6, 0.75 for Harvard Brain Bank, 0.70 for BrainCloud, 0.79 for NIH, 0.93 for UKBEC, and 0.77 for HBCC.
“GTEx v6 is the largest public dataset of eQTLs from DLPFC tissue (n = 92) assayed by RNA-seq; its replication in CMC is π1 = 0.98.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [15]
Gene co-expression is organized into modules of distinct cellular and functional categories.
“Consistent with prior studies of the brain transcriptome15,47–50, we find gene co-expression to be organized into modules of distinct cellular and functional categories (Supplementary data file 7).”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [15]
Transcriptome networks in AD brain have been examined, but network changes in the AD brain proteome, including early asymptomatic stages, have not been explored.
“While transcriptome networks in AD brain have been examined (Miller et al., 2008; Miller et al., 2013; Zhang et al., 2013), network changes in the AD brain proteome, including those associated with early asymptomatic stages of disease, have not been explored.”
From A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer’s Disease (2017) · cites this paper as Zhang et al., 2013
Recent large transcriptomic and AD GWAS network studies show involvement of microglial gene products and GWAS candidates in AD.
“These results echo recent large transcriptomic and AD GWAS network studies that show the involvement of microglial gene products and GWAS candidates in AD (Jones et al., 2010; Zhang et al., 2013).”
From A Multi-network Approach Identifies Protein-Specific Co-expression in Asymptomatic and Symptomatic Alzheimer’s Disease (2017) · cites this paper as Zhang et al., 2013
Systems-level analyses of post-mortem LOAD brain identified the immune/microglial molecular network as most highly associated with LOAD pathophysiology.
“Systems-level analyses of gene regulatory networks in post-mortem human LOAD brain specimens have identified the immune/microglial molecular network as most highly associated with pathophysiology in LOAD (Zhang et al., 2013).”
From Alzheimer Disease: An Update on Pathobiology and Treatment Strategies (2019) · cites this paper as Zhang et al., 2013
Genetic studies have identified several immune gene polymorphisms as risk factors for Alzheimer's disease, confirming the importance of microglia-mediated neuroinflammation in neurodegeneration.
“The importance of microglia-mediated neuroinflammation in neurodegeneration has been confirmed by genetic studies that identified several immune gene polymorphisms as risk factors for AD [5–8].”
From Identification and therapeutic modulation of a pro-inflammatory subset of disease-associated-microglia in Alzheimer’s disease (2018) · cites this paper as [8]
The network identified is broadly similar to a human network of innate immune genes containing TYROBP, TREM2, MS4A family genes, C1Q members and CD33, identified from human post-mortem tissue.
“The network we identified is also broadly similar to a human network of innate immune genes containing TYROBP, TREM2, MS4A family genes, C1Q members and CD33, identified from human post-mortem tissue bearing in mind the caveats discussed above for human tissue (Forabosco et al., 2013; Zhang et al., 2013).”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Zhang et al., 2013
Zhang et al. identified an Alzheimer's disease-relevant network centred on TYROBP and TREM2 that contained ITGAM/CD11b and LAPTM5.
“Zhang et al. identified an Alzheimer’s disease-relevant network centred on TYROBP and TREM2, which contained ITGAM/CD11b and LAPTM5 (Zhang et al., 2013)”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Zhang et al., 2013
ITGAM/CD11b was highlighted in recent genetic and functional analyses as likely being important for the progression of Alzheimer's disease, with expression driven by SPI1/PU.1 and related to amyloid deposition in mice and humans.
“ITGAM/CD11b was highlighted in recent genetic and functional analyses as likely being important for the progression of Alzheimer’s disease, whose expression was driven by SPI1/PU.1, and related to amyloid deposition in mice and humans (Zhang et al., 2013; Hong et al., 2016; Kamphuis et al., 2016; Olmos-Alonso et al., 2016; Huang et al., 2017; Nam et al., 2018).”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Zhang et al., 2013
The transcriptional network associated with amyloid identified in this analysis, containing the four predicted risk genes and the seven existing GWAS loci, is broadly similar to microglial networks previously identified in human brain analyses.
“Aspects of the transcriptional network associated with amyloid that we identified in our analysis, containing the four predicted risk genes with the existing seven GWAS loci, are broadly similar to microglial networks we and others have previously identified in human brain analyses.”
From Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk (2019) · cites this paper as Zhang et al., 2013
TYROBP is increased in late-onset Alzheimer's disease and TREM2 is upregulated in plaque-associated microglia.
“TYROBP is increased in LOAD and TREM2 is upregulated in plaque-associated microglia [26, 30]”
From Microglial dysfunction in brain aging and Alzheimer's disease (2014) · cites this paper as [26]
An immune- and microglia-specific module is the molecular system most strongly associated with the pathophysiology of late-onset Alzheimer's disease.
“highlighted an immune- and microglia-specific module as the molecular system most strongly associated with the pathophysiology of the disease [26]”
From Microglial dysfunction in brain aging and Alzheimer's disease (2014) · cites this paper as [26]
TYROBP is a key causal regulator in the immune- and microglia-specific module associated with Alzheimer's disease.
“a key causal regulator in this module included TYROBP”
From Microglial dysfunction in brain aging and Alzheimer's disease (2014) · cites this paper as [26]
The network includes genes linked to late-onset AD implicated in microglia activation (Abi3, Sall1, Spi1, Trem2), immune response (Bcl3, Gal3st4, Hla-Dqa1, Inpp5d, Lrrc25, Pycard, Sbno2), and complement pathway (Clu, Ms4a6a).
“Significantly, the network includes a group of genes linked to late-onset AD implicated in microglia activation: (Abi3, Sall1, Spi1, Trem2), immune response (Bcl3, Gal3st4, Hla-Dqa1, Inpp5d, Lrrc25, Pycard, Sbno2), and the complement pathway (Clu, Ms4a6a) (Efthymiou and Goate, 2017; Sims et al., 2017; Zhang et al., 2013).”
From Complement C3aR Inactivation Attenuates Tau Pathology and Reverses an Immune Network Deregulated in Tauopathy Models and Alzheimer’s Disease (2018) · cites this paper as Zhang et al., 2013
Previous network analyses in Alzheimer's disease were limited to larger brain regions and thus could not address cell type-specific vulnerability.
“Previous work examining whole brain lysates from AD patients and non-demented individuals (Miller et al., 2008; Mostafavi et al., 2018; Zhang et al., 2013) demonstrated the promise of network analyses in AD, but these studies were limited to larger brain regions and thus could not address cell type-specific vulnerability.”
From Selective Neuronal Vulnerability in Alzheimer’s Disease: A Network-Based Analysis (2020) · cites this paper as Zhang et al., 2013
Our findings for human AD are consistent with prior studies using independent datasets and modes of analysis which similarly implicated microglial and myeloid expression modules in AD risk and pathobiology.
“Our findings for human AD are consistent with prior studies using independent datasets and modes of analysis which similarly implicated microglial and myeloid expression modules in AD risk and pathobiology (89, 90).”
From The Transcriptional Landscape of Microglial Genes in Aging and Neurodegenerative Disease (2019) · cites this paper as [90]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · see citing works
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033 · see citing works
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,525 · see citing works
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123 · cited 3× · see citing works
“Given these observations and the fact that PFC is more commonly affected in LOAD than CB and VC (Braak and Braak, 1991), a particular attention was paid to this region in our strategy to rank-order modules for relevance to LOAD.”
BackgroundTopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L. S., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,477 · see citing works
Corder, E. H., Saunders, A. M., Strittmatter, W. J., Schmechel, D. E., Gaskell, P. C., Small, G. W., Roses, A. D., Haines, J. L., & Pericak-Vance, M. A. (1993) · Science · doi:10.1126/science.8346443 · Cited by 9,429 · see citing works
MACDONALD, M. (1993) · Cell · doi:10.1016/0092-8674(93)90585-e · Cited by 8,366 · see citing works
Self-citationA General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684 · see citing works
Self-citationHierarchical Organization of Modularity in Metabolic Networks
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224 · see citing works
Abe, S. (2001) · Springer eBooks · doi:10.1007/978-1-4471-0285-4 · Cited by 3,740 · see citing works
Forecasting the global burden of Alzheimer's disease
Brookmeyer, R., Johnson, E., Ziegler‐Graham, K., & Arrighi, H. M. (2007) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2007.04.381 · Cited by 3,370 · see citing works
Shah, M. A., Datson, N. A., Srinidhi, L., Stanton, V. P., MacDonald, M. E., Allard, M. W., Youngman, S., Am, F., Mott, R., Draths, K., et al. (1993) · Cell · Cited by 3,055 · see citing works
Self-citation<i>TREM2</i> Variants in Alzheimer's Disease
Guerreiro, R., Wojtas, A., Bras, J., Carrasquillo, M., Rogaeva, E., Majounie, E., Cruchaga, C., Sassi, C., Kauwe, J. S. K., Younkin, S., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1211851 · Cited by 3,020 · see citing works
Neuropathological Classification of Huntingtonʼs Disease
Vonsattel, J., Myers, R. H., STEVENS, T. J., Ferrante, R. J., Bird, E. D., & Richardson, E. P. (1985) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/00005072-198511000-00003 · Cited by 2,598 · see citing works
Variant of <i>TREM2</i> Associated with the Risk of Alzheimer's Disease
Jonsson, T., Stefansson, H., Steinberg, S., Jonsdottir, I., Jonsson, P. V., Snaedal, J., Bjornsson, S., Huttenlocher, J., Levey, A. I., Lah, J. J., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1211103 · Cited by 2,558 · see citing works
Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R
Langfelder, P., Zhang, B., & Horvath, S. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btm563 · Cited by 1,892 · see citing works
Self-citationAlzheimer Mechanisms and Therapeutic Strategies
Huang, Y., & Mucke, L. (2012) · Cell · doi:10.1016/j.cell.2012.02.040 · Cited by 1,758 · see citing works
A mutation in APP protects against Alzheimer’s disease and age-related cognitive decline
Jonsson, T., Atwal, J. K., Steinberg, S., Snaedal, J., Jonsson, P. V., Bjornsson, S., Stefansson, H., Sulem, P., Gudbjartsson, D., Maloney, J., et al. (2012) · Nature · doi:10.1038/nature11283 · Cited by 1,738 · see citing works
Microglia in neurodegenerative disease
Perry, V. H., Nicoll, J. A. R., & Holmes, C. (2010) · Nature Reviews Neurology · doi:10.1038/nrneurol.2010.17 · Cited by 1,543 · see citing works
Neuropathology of older persons without cognitive impairment from two community-based studies
Bennett, D. A., Schneider, J. A., Arvanitakis, Z., Kelly, J. F., Aggarwal, N. T., Shah, R. C., & Wilson, R. S. (2006) · Neurology · doi:10.1212/01.wnl.0000219668.47116.e6 · Cited by 1,336 · see citing works
Self-citationGenetics of gene expression and its effect on disease
Emilsson, V., Thorleifsson, G., Zhang, B., Leonardson, A. S., Zink, F., Zhu, J., Carlson, S., Helgason, A., Walters, G. B., Gunnarsdottir, S., et al. (2008) · Nature · doi:10.1038/nature06758 · Cited by 1,296 · see citing works
Self-citationBayesian Graphical Models for Discrete Data
Madigan, D., York, J., & Allard, D. (1995) · International Statistical Review / Revue Internationale de Statistique · doi:10.2307/1403615 · Cited by 1,090 · see citing works
Rapid appearance and local toxicity of amyloid-β plaques in a mouse model of Alzheimer’s disease
Meyer-Luehmann, M., Spires-Jones, T. L., Prada, C., Garcia-Alloza, M., de Calignon, A., Rozkalne, A., Koenigsknecht-Talboo, J., Holtzman, D. M., Bacskai, B. J., & Hyman, B. T. (2008) · Nature · doi:10.1038/nature06616 · Cited by 987 · see citing works
Mapping the Genetic Architecture of Gene Expression in Human Liver
Schadt, E. E., Molony, C. M., Chudin, E., Hao, K., Yang, X., Lum, P. Y., Kasarskis, A., Zhang, B., Wang, S. S.-M., Suver, C., et al. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060107 · Cited by 909 · see citing works
Self-citationVariations in DNA elucidate molecular networks that cause disease
Chen, Y., Zhu, J., Lum, P. Y., Yang, X., Pinto, S., MacNeil, D. J., Zhang, C., Lamb, J., Edwards, S., Sieberts, S. K., et al. (2008) · Nature · doi:10.1038/nature06757 · Cited by 887 · see citing works
Self-citationThe Genetics of Alzheimer Disease: Back to the Future
Bertram, L., Lill, C. M., & Tanzi, R. E. (2010) · Neuron · doi:10.1016/j.neuron.2010.10.013 · Cited by 834 · see citing works
Boyles, J. K., Pitas, R. E., Wilson, E., Mahley, R. W., & Taylor, J. M. (1985) · Journal of Clinical Investigation · doi:10.1172/jci112130 · Cited by 829 · see citing works
Molecular networks as sensors and drivers of common human diseases
Schadt, E. E. (2009) · Nature · doi:10.1038/nature08454 · Cited by 806 · see citing works
Self-citationFunctional organization of the transcriptome in human brain
Oldham, M. C., Konopka, G., Iwamoto, K., Langfelder, P., Kato, T., Horvath, S., & Geschwind, D. H. (2008) · Nature Neuroscience · doi:10.1038/nn.2207 · Cited by 752 · cited 2× · see citing works
“…gene signatures, from the Allen Brain Atlas (http://www.brain-map.org/), were enriched in distinct network modules as previously observed (Oldham et al., 2008): neurons in the synaptic transmission modules (11-fold, p = 3.7 3 10 24), astrocytes in the GABA biosynthesis module (22 fold, p…”
BackgroundMethodInfluentialOxidative stress in the progression of Alzheimer disease in the frontal cortex
Ansari, M. A., & Scheff, S. W. (2010) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0b013e3181cb5af4 · Cited by 605 · see citing works
Coskun, P. E., Beal, M. F., & Wallace, D. C. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0403649101 · Cited by 556 · see citing works
Zhu, J., Zhang, B., Smith, E. N., Drees, B., Brem, R. B., Kruglyak, L., Bumgarner, R. E., & Schadt, E. E. (2008) · Nature Genetics · doi:10.1038/ng.167 · Cited by 549 · see citing works
Self-citationEtminan, M., Gill, S., & Samii, A. (2003) · BMJ · doi:10.1136/bmj.327.7407.128 · Cited by 511 · see citing works
Characterization of the proteome, diseases and evolution of the human postsynaptic density
Bayés, À., van de Lagemaat, L. N., Collins, M. O., Croning, M. D. R., Whittle, I. R., Choudhary, J. S., & Grant, S. G. N. (2011) · Nature Neuroscience · doi:10.1038/nn.2719 · Cited by 499 · see citing works
A survey of genetic human cortical gene expression
Myers, A. J., Gibbs, J. R., Webster, J. A., Rohrer, K., Zhao, A., Marlowe, L., Kaleem, M., Leung, D., Bryden, L., Nath, P., et al. (2007) · Nature Genetics · doi:10.1038/ng.2007.16 · Cited by 479 · see citing works
Self-citationInflammation, microglia, and alzheimer's disease
Cameron, B., & Landreth, G. E. (2009) · Neurobiology of Disease · doi:10.1016/j.nbd.2009.10.006 · Cited by 440 · see citing works
Bennett, D. A., Schneider, J. A., Aggarwal, N. T., Arvanitakis, Z., Shah, R. C., Kelly, J. F., Fox, J. H., Cochran, E. J., Arends, D., Treinkman, A., et al. (2006) · Neuroepidemiology · doi:10.1159/000096129 · Cited by 407 · see citing works
Self-citationThe Rush Memory and Aging Project: Study Design and Baseline Characteristics of the Study Cohort
Bennett, D. A., Schneider, J. A., Buchman, A. S., de Leon, C. F. M., Bienias, J. L., & Wilson, R. S. (2005) · Neuroepidemiology · doi:10.1159/000087446 · Cited by 405 · see citing works
Self-citationGenetic insights in Alzheimer's disease
Bettens, K., Sleegers, K., & Broeckhoven, C. V. (2012) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70259-4 · Cited by 326 · see citing works
Genetic Control of Human Brain Transcript Expression in Alzheimer Disease
Webster, J. A., Gibbs, J. R., Clarke, J., Ray, M., Zhang, W., Holmans, P., Rohrer, K., Zhao, A., Marlowe, L., Kaleem, M., et al. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.03.011 · Cited by 304 · see citing works
Self-citationLi, G., Bien-Ly, N., Andrews-Zwilling, Y., Xu, Q., Bernardo, A., Ring, K., Halabisky, B., Deng, C., Mahley, R. W., & Huang, Y. (2009) · Cell Stem Cell · doi:10.1016/j.stem.2009.10.015 · Cited by 288 · see citing works
A network view of disease and compound screening
Schadt, E. E., Friend, S. H., & Shaywitz, D. A. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2826 · Cited by 286 · see citing works
Self-citationSystematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liverPreprint
Yang, X., Zhang, B., Molony, C. M., Chudin, E., Hao, K., Zhu, J., Gaedigk, A., Suver, C., Zhong, H., Leeder, J. S., et al. (2010) · Genome Research · doi:10.1101/gr.103341.109 · Cited by 271 · see citing works
Self-citationStatistical Methods for Identifying Differentially Expressed Genes in DNA Microarrays
Storey, J. D., & Tibshirani, R. J. (2003) · Humana Press eBooks · doi:10.1385/1-59259-364-x:149 · Cited by 253 · see citing works
Warner, J. P., Barron, L. H., & Brock, D. J. H. (1993) · Molecular and Cellular Probes · doi:10.1006/mcpr.1993.1034 · Cited by 244 · see citing works
An integrative genomics approach to the reconstruction of gene networks in segregating populations
Zhu, J., Lum, P. Y., Lamb, J., GuhaThakurta, D., Edwards, S. W., Thieringer, R., Berger, J. P., Wu, M. S., Thompson, J., Sachs, A. B., et al. (2004) · Cytogenetic and Genome Research · doi:10.1159/000078209 · Cited by 221 · see citing works
Self-citationZhu, J., Wiener, M. C., Zhang, C., Fridman, A., Minch, E., Lum, P. Y., Sachs, J. R., & Schadt, E. E. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030069 · Cited by 209 · see citing works
Self-citationMani, K. M., Lefèbvre, C., Wang, K., Lim, W. K., Basso, K., Dalla‐Favera, R., & Califano, A. (2008) · Molecular Systems Biology · doi:10.1038/msb.2008.2 · Cited by 205 · see citing works
Walker, D. G., Link, J., Lue, L., Dalsing-Hernandez, J. E., & Boyes, B. E. (2005) · Journal of Leukocyte Biology · doi:10.1189/jlb.0705377 · Cited by 180 · see citing works
Adiposity, hyperinsulinemia, diabetes and Alzheimer's disease
Luchsinger, J. A. (2008) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2008.02.048 · Cited by 171 · cited 6× · see citing works
“Complex diseases such as late-onset Alzheimer’s disease (LOAD) arise from the downstream interplay of DNA-sequence variants and nongenetic factors that act through molecular networks to confer disease risk (Schadt, 2009).”
BackgroundInfluentialA 4-Mb High-Density Single Nucleotide Polymorphism-Based Map around Human APOE
Lai, E., Riley, J., Purvis, I. J., & Roses, A. D. (1998) · Genomics · doi:10.1006/geno.1998.5581 · Cited by 161 · see citing works
Multi-tissue coexpression networks reveal unexpected subnetworks associated with disease
Dobrin, R., Zhu, J., Molony, C. M., Argman, C., Parrish, M. L., Carlson, S. L., Allan, M. F., Pomp, D., & Schadt, E. E. (2009) · Genome biology · doi:10.1186/gb-2009-10-5-r55 · Cited by 159 · see citing works
Self-citationMorawski, M., Brückner, G., Jäger, C., Seeger, G., Matthews, R. T., & Arendt, T. (2012) · Brain Pathology · doi:10.1111/j.1750-3639.2011.00557.x · Cited by 157 · see citing works
Huntington's disease: seeing the pathogenic process through a genetic lens
Gusella, J. F., & MacDonald, M. E. (2006) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2006.06.009 · Cited by 140 · see citing works
Self-citationSystems analysis of eleven rodent disease models reveals an inflammatome signature and key drivers
Wang, I., Zhang, B., Yang, X., Zhu, J., Stepaniants, S., Zhang, C., Meng, Q., Peters, M. A., He, Y. D., Ni, C., et al. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.24 · Cited by 130 · see citing works
Self-citationVascular risk factors and Alzheimer’s disease
Cechetto, D. F., Hachinski, V., & Whitehead, S. N. (2008) · Expert Review of Neurotherapeutics · doi:10.1586/14737175.8.5.743 · Cited by 129 · see citing works
Qiu, C., Xu, W., & Fratiglioni, L. (2010) · Journal of Alzheimer's Disease · doi:10.3233/jad-2010-091663 · Cited by 107 · see citing works
Schiffman, S. S. (2002) · Neurobiology of Aging · doi:10.1016/s0197-4580(01)00337-2 · Cited by 105 · see citing works
The cell cycle in Alzheimer disease: A unique target for neuropharmacology
Webber, K. M., Raina, A. K., Marlatt, M. W., Zhu, X., Prat, M. I., Morelli, L., Casadesús, G., Perry, G. L., & Smith, M. A. (2005) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2005.03.024 · Cited by 104 · see citing works
Vascular and metabolic dysfunction in Alzheimer’s disease: a review
Murray, I. V. J., Proza, J. F., Sohrabji, F., & Lawler, J. M. (2011) · Experimental Biology and Medicine · doi:10.1258/ebm.2011.010355 · Cited by 104 · see citing works
Immunotherapy for Alzheimer's disease
Dodel, R. C., Hampel, H. J., & Du, Y. (2003) · The Lancet Neurology · doi:10.1016/s1474-4422(03)00349-1 · Cited by 102 · see citing works
He, Y. D., Dai, H. A., Schadt, E. E., Cavet, G., Edwards, S. W., Stepaniants, S. B., Duenwald, S., Kleinhanz, R. R., Jones, A. R., Shoemaker, D. D., et al. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg126 · Cited by 90 · see citing works
Self-citationGeneration of microglial cells from mouse embryonic stem cells
Beutner, C., Roy, K., Linnartz, B., Napoli, I., & Neumann, H. (2010) · Nature Protocols · doi:10.1038/nprot.2010.90 · Cited by 85 · see citing works
Self-citationNasu–Hakola disease and primary microglial dysfunction
Bianchin, M. M., Martin, K. C., de Souza, A. C., de Oliveira, M. A., & de Mello Rieder, C. R. (2010) · Nature Reviews Neurology · doi:10.1038/nrneurol.2010.17-c1 · Cited by 69 · see citing works
Comparison of the severity of neuropathologic changes in familial and sporadic Alzheimer's disease.
Nochlin, D., van Belle, G., Bird, T. D., & Sumi, S. M. (1993) · PubMed · Cited by 66 · see citing works
Failure is an option: learning from unsuccessful proof-of-concept trials
Schäfer, S., & Kolkhof, P. (2008) · Drug Discovery Today · doi:10.1016/j.drudis.2008.03.026 · Cited by 31 · see citing works
SCHLEINITZ, N., Chiche, L., Guia, S., Bouvier, G., Vernier, J., Morice, A., Houssaint, E., Harle, J. ‐., Kaplanski, G., Montero‐Julian, F. A., et al. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006264 · Cited by 18 · see citing works
Immunotherapy for Alzheimer＇s disease
Weihua, Wang, Liang-Feng, et al. (2012) · 生物化学与生物物理学报：英文版 · Cited by 5 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Neuroinflammation in Alzheimer's disease
Heneka, M. T., Carson, M. J., Khoury, J. E., Landreth, G. E., Brosseron, F., Feinstein, D. L., Jacobs, A. H., Wyss-Coray, T., Vitorica, J., Ransohoff, R. M., et al. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(15)70016-5 · Cited by 6,226 · see citing works
The amyloid hypothesis of Alzheimer's disease at 25 years
Selkoe, D. J., & Hardy, J. (2016) · EMBO Molecular Medicine · doi:10.15252/emmm.201606210 · Cited by 6,164 · see citing works
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062 · see citing works
Self-citationAlzheimer Disease: An Update on Pathobiology and Treatment Strategies
Long, J. M., & Holtzman, D. M. (2019) · Cell · doi:10.1016/j.cell.2019.09.001 · Cited by 3,204 · see citing works
Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
Self-citationScheltens, P., Blennow, K., Breteler, M. M. B., de Strooper, B., Frisoni, G. B., Salloway, S., & Van der Flier, W. M. (2016) · The Lancet · doi:10.1016/s0140-6736(15)01124-1 · Cited by 3,125 · see citing works
Multi-omics approaches to disease
Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953 · see citing works
Single-cell transcriptomic analysis of Alzheimer’s disease
Mathys, H., Davila-Velderrain, J., Peng, Z., Gao, F., Mohammadi, S., Young, J. Z., Menon, M., He, L., Abdurrob, F., Jiang, X., et al. (2019) · Nature · doi:10.1038/s41586-019-1195-2 · Cited by 2,568 · see citing works
Self-citationImmune attack: the role of inflammation in Alzheimer disease
Heppner, F. L., Ransohoff, R. M., & Becher, B. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3880 · Cited by 2,134 · see citing works
The Cellular Phase of Alzheimer’s Disease
De Strooper, B., & Karran, E. (2016) · Cell · doi:10.1016/j.cell.2015.12.056 · Cited by 1,990 · see citing works
Microglia emerge as central players in brain disease
Salter, M. W., & Stevens, B. (2017) · Nature Medicine · doi:10.1038/nm.4397 · Cited by 1,767 · see citing works
Microglia in physiology and disease
Wolf, S. A., Boddeke, H. W. G. M., & Kettenmann, H. (2017) · Annual Review of Physiology · doi:10.1146/annurev-physiol-022516-034406 · Cited by 1,426 · see citing works
Innate immune activation in neurodegenerative disease
Heneka, M. T., Kummer, M. P., & Latz, E. (2014) · Nature Reviews Immunology · doi:10.1038/nri3705 · Cited by 1,437 · see citing works
Alzheimer’s Disease Risk Genes and Mechanisms of Disease Pathogenesis
Karch, C. M., & Goate, A. M. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.05.006 · Cited by 1,357 · see citing works
Gene expression elucidates functional impact of polygenic risk for schizophrenia
Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091 · see citing works
Self-citationAn environment-dependent transcriptional network specifies human microglia identity
Gosselin, D., Skola, D., Coufal, N. G., Holtman, I. R., Schlachetzki, J. C. M., Sajti, E., Jaeger, B. N., O’Connor, C., Fitzpatrick, C., Pasillas, M. P., et al. (2017) · Science · doi:10.1126/science.aal3222 · Cited by 1,220 · see citing works
“(C) Enrichment of the 881 microglia-enriched genes upregulated in HIV-associated neurocognitive disorder (HAND) in centrum semiovale (CS) (48), upregulated in schizophrenia (SCZ) in the anterior prefrontal cortex (PFC) (49), positively correlated with Braak stage of AD in PFC (50), upregulated or downregulated in PD i…”
BackgroundAgeing, neurodegeneration and brain rejuvenation
Wyss-Coray, T. (2016) · Nature · doi:10.1038/nature20411 · Cited by 1,147 · see citing works
Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap
Gandal, M. J., Haney, J. R., Parikshak, N. N., Leppa, V., Ramaswami, G., Hartl, C., Schork, A. J., Appadurai, V., Buil, A., Werge, T. M., et al. (2018) · Science · doi:10.1126/science.aad6469 · Cited by 1,095 · see citing works
Self-citationGene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works
Grubman, A., Chew, G., Ouyang, J. F., Sun, G., Choo, X. Y., McLean, C., Simmons, R. K., Buckberry, S., Vargas-Landin, D. B., Poppe, D., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0539-4 · Cited by 956 · see citing works
Simpson, D. S. A., & Oliver, P. L. (2020) · Antioxidants · doi:10.3390/antiox9080743 · Cited by 1,044 · see citing works
“Characterisation of gene ontology networks in post-mortem AD brains has identified dysregulation of microglia-specific networks as the most strongly correlated to neuropathology [15] and many microglial AD risk-factor genes have been identified [16,17].”
BackgroundMicroglia-derived ASC specks cross-seed amyloid-β in Alzheimer’s disease
Venegas, C., Kumar, S., Franklin, B. S., Dierkes, T., Brinkschulte, R., Tejera, D., Vieira-Saecker, A., Schwartz, S., Santarelli, F., Kummer, M. P., et al. (2017) · Nature · doi:10.1038/nature25158 · Cited by 983 · see citing works
Sala Frigerio, C., Wolfs, L., Fattorelli, N., Thrupp, N., Voytyuk, I., Schmidt, I., Mancuso, R., Chen, W.-T., Woodbury, M. E., Srivastava, G., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.03.099 · Cited by 926 · see citing works
Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease
Chen, W.-T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Corthout, N., Qian, X., Laláková, J., Kühnemund, M., Voytyuk, I., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.038 · Cited by 949 · see citing works
Johnson, E. C. B., Dammer, E. B., Duong, D. M., Ping, L., Zhou, M., Yin, L., Higginbotham, L. A., Guajardo, A., White, B., Troncoso, J. C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0815-6 · Cited by 1,001 · see citing works
Self-citationNecroptosis and RIPK1-mediated neuroinflammation in CNS diseases
Yuan, J., Amin, P., & Ofengeim, D. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-018-0093-1 · Cited by 967 · see citing works
Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain
Lake, B. B., Chen, S., Sos, B. C., Fan, J., Kaeser, G. E., Yung, Y. C., Duong, T. E., Gao, D., Chun, J., Kharchenko, P. V., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4038 · Cited by 983 · see citing works
DNA methylation in newborns and maternal smoking in pregnancy: genome-wide consortium meta-analysis
Joubert, B. R., Felix, J. F., Yousefi, P., Bakulski, K. M., Just, A. C., Breton, C., Reese, S. E., Markunas, C. A., Richmond, R. C., Xu, C.-J., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.02.019 · Cited by 886 · see citing works
Microglia in Alzheimer's disease
Sarlus, H., & Heneka, M. T. (2017) · Journal of Clinical Investigation · doi:10.1172/jci90606 · Cited by 911 · see citing works
Microglial Phagocytosis of Live Neurons
Brown, G. C., & Neher, J. J. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3710 · Cited by 853 · see citing works
Sex-specific transcriptional signatures in human depression
Labonté, B., Engmann, O., Purushothaman, I., Menard, C., Wang, J., Tan, C., Scarpa, J. R., Moy, G., Loh, Y.-H. E., Cahill, M., et al. (2017) · Nature Medicine · doi:10.1038/nm.4386 · Cited by 748 · see citing works
Self-citationTemporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution
Mathys, H., Adaikkan, C., Gao, F., Young, J. Z., Manet, E., Hemberg, M., De Jager, P. L., Ransohoff, R. M., Regev, A., & Tsai, L.-H. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.09.039 · Cited by 740 · see citing works
Readhead, B., Haure-Mirande, J.-V., Funk, C. C., Richards, M. A., Shannon, P., Haroutunian, V., Sano, M., Liang, W. S., Beckmann, N. D., Price, N. D., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.05.023 · Cited by 708 · see citing works
Self-citationSingle-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease
Morabito, S., Miyoshi, E., Michael, N., Shahin, S., Martini, A. C., Head, E., Silva, J., Leavy, K., Perez-Rosendahl, M., & Swarup, V. (2021) · Nature Genetics · doi:10.1038/s41588-021-00894-z · Cited by 643 · see citing works
Friedman, B. A., Srinivasan, K., Ayalon, G., Meilandt, W. J., Lin, H., Huntley, M. A., Cao, Y., Lee, S.-H., Haddick, P. C. G., Ngu, H., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2017.12.066 · Cited by 662 · see citing works
Jay, T. R., Miller, C. M., Cheng, P. J., Graham, L. C., Bemiller, S., Broihier, M. L., Xu, G., Margevicius, D., Karlo, J. C., Sousa, G. L., et al. (2015) · Journal of Experimental Medicine · doi:10.1084/jem.20142322 · Cited by 682 · see citing works
Systems genetics approaches to understand complex traits
Civelek, M., & Lusis, A. J. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3575 · Cited by 621 · see citing works
Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer's disease
Gjoneska, E., Pfenning, A. R., Mathys, H., Quon, G., Kundaje, A., Tsai, L.-H., & Kellis, M. (2015) · Nature · doi:10.1038/nature14252 · Cited by 576 · see citing works
Microglial dysfunction in brain aging and Alzheimer's disease
Mosher, K. I., & Wyss-Coray, T. (2014) · Biochemical Pharmacology · doi:10.1016/j.bcp.2014.01.008 · Cited by 618 · see citing works
Deep Multilayer Brain Proteomics Identifies Molecular Networks in Alzheimer’s Disease Progression
Bai, B., Wang, X., Li, Y., Chen, P.-C., Yu, K., Dey, K. K., Yarbro, J. M., Han, X., Lutz, B. M., Rao, S., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.015 · Cited by 632 · see citing works
“Integration of genomic data and gene expression information has proven powerful for determining potential causative pathways in Alzheimer’s disease (Zhang et al., 2013).”
BackgroundMethodSelf-citationLate onset Alzheimer’s disease genetics implicates microglial pathways in disease risk
Efthymiou, A. G., & Goate, A. M. (2017) · Molecular Neurodegeneration · doi:10.1186/s13024-017-0184-x · Cited by 562 · see citing works
Holtman, I. R., Raj, D. D., Miller, J. A., Schaafsma, W., Yin, Z., Brouwer, N., Wes, P. D., Möller, T., Orre, M., Kamphuis, W., et al. (2015) · Acta Neuropathologica Communications · doi:10.1186/s40478-015-0203-5 · Cited by 619 · see citing works
New insights on the role of microglia in synaptic pruning in health and disease
Hong, S., Dissing-Olesen, L., & Stevens, B. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.12.004 · Cited by 557 · see citing works
Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche
Study, A. O. C., Perry, J. R. B., The GENICA Network, kConFab, Study, T. L. C., The InterAct Consortium, Early Growth Genetics (EGG) Consortium, Day, F., Elks, C. E., Sulem, P., et al. (2014) · Nature · doi:10.1038/nature13545 · Cited by 604 · see citing works
Self-citationMostafavi, S., Gaiteri, C., Sullivan, S. E., White, C. C., Tasaki, S., Xu, J., Taga, M., Klein, H.-U., Patrick, E., Komashko, V., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0154-9 · Cited by 509 · see citing works
Self-citationHopperton, K. E., Mohammad, D., Trépanier, M. O., Giuliano, V, & Bazinet, R. P. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.246 · Cited by 567 · see citing works
Fibrinogen in neurological diseases: mechanisms, imaging and therapeutics
Petersen, M. A., Ryu, J. K., & Akassoglou, K. (2018) · Nature reviews. Neuroscience · doi:10.1038/nrn.2018.13 · Cited by 527 · see citing works
Seyfried, N. T., Dammer, E. B., Swarup, V., Nandakumar, D., Duong, D. M., Yin, L., Deng, Q., Nguyen, T., Hales, C. M., Wingo, T., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2016.11.006 · Cited by 477 · see citing works
Schwartzentruber, J., Cooper, S., Liu, J. Z., Barrio-Hernandez, I., Bello, E., Kumasaka, N., Young, A. M. H., Franklin, R. J. M., Johnson, T., Estrada, K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00776-w · Cited by 532 · see citing works
Olmos-Alonso, A., Schetters, S. T. T., Sri, S., Askew, K., Mancuso, R., Vargas-Caballero, M., Holscher, C., Perry, V. H., & Gomez-Nicola, D. (2016) · Brain · doi:10.1093/brain/awv379 · Cited by 532 · see citing works
TREM2 sustains microglial expansion during aging and response to demyelination
Poliani, P. L., Wang, Y., Fontana, E., Robinette, M. L., Yamanishi, Y., Gilfillan, S., & Colonna, M. (2015) · Journal of Clinical Investigation · doi:10.1172/jci77983 · Cited by 525 · see citing works
Litvinchuk, A., Wan, Y.-W., Swartzlander, D. B., Chen, F., Cole, A., Propson, N. E., Wang, Q., Zhang, B., Liu, Z., & Zheng, H. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.031 · Cited by 523 · see citing works
Self-citationBrain Cell Type Specific Gene Expression and Co-expression Network Architectures
McKenzie, A. T., Wang, M., Hauberg, M. E., Fullard, J. F., Kozlenkov, A., Keenan, A., Hurd, Y. L., Dracheva, S., Casaccia, P., Roussos, P., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-27293-5 · Cited by 481 · see citing works
Self-citationSystems biology and gene networks in neurodevelopmental and neurodegenerative disorders
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430 · see citing works
A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease
The International Genomics of Alzheimer's Project, Huang, K.-L., Initiative, T. A. D. N., Marcora, E., Pimenova, A. A., Di Narzo, A. F., Kapoor, M., Jin, S. C., Harari, O., Bertelsen, S., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4587 · Cited by 423 · see citing works
Self-citationRangaraju, S., Dammer, E. B., Raza, S. A., Rathakrishnan, P., Xiao, H., Gao, T., Duong, D. M., Pennington, M. W., Lah, J. J., Seyfried, N. T., et al. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0254-8 · Cited by 430 · see citing works
Alzheimer’s Disease Genetics: From the Bench to the Clinic
Karch, C. M., Cruchaga, C., & Goate, A. M. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.041 · Cited by 473 · see citing works
Lee, C. Y. D., Daggett, A., Gu, X., Jiang, L.-L., Langfelder, P., Li, X., Wang, N., Zhao, Y., Park, C. S., Cooper, Y., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.02.002 · Cited by 396 · see citing works
TREM2 variants: new keys to decipher Alzheimer disease pathogenesis
Colonna, M., & Wang, Y. (2016) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.7 · Cited by 418 · see citing works
Hui, K. Y., Fernandez-Hernandez, H., Hu, J., Schaffner, A., Pankratz, N., Hsu, N.-Y., Chuang, L.-S., Carmi, S., Villaverde, N., Li, X., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aai7795 · Cited by 384 · see citing works
“This type of probabilistic causal network structure has previously been demonstrated to represent biologically functional pathways across a broad range of diseases including obesity and diabetes (20, 50–52), asthma and chronic obstructive pulmonary disease (53, 54), and Alzheimer’s disease (55).”
BackgroundRayaprolu, S., Mullen, B., Baker, M., Lynch, T., Finger, E., Seeley, W. W., Hatanpaa, K. J., Lomen-Hoerth, C., Kertesz, A., Bigio, E. H., et al. (2013) · Molecular Neurodegeneration · doi:10.1186/1750-1326-8-19 · Cited by 404 · see citing works
The age factor in Alzheimer’s disease
Guerreiro, R., & Bras, J. (2015) · Genome Medicine · doi:10.1186/s13073-015-0232-5 · Cited by 415 · see citing works
Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice
Langfelder, P., Cantle, J. P., Chatzopoulou, D., Wang, N., Gao, F., Al-Ramahi, I., Lu, X.-H., Ramos, E. M., El-Zein, K., Zhao, Y., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4256 · Cited by 385 · see citing works
Isolation of glia from Alzheimer’s mice reveals inflammation and dysfunction
Orre, M., Kamphuis, W., Osborn, L. M., Jansen, A. H. P., Kooijman, L., Bossers, K., & Hol, E. M. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.06.004 · Cited by 394 · see citing works
Do glia drive synaptic and cognitive impairment in disease?
Chung, W.-S., Welsh, C. A., Barres, B. A., & Stevens, B. (2015) · Nature Neuroscience · doi:10.1038/nn.4142 · Cited by 385 · see citing works
The innate immunity protein IFITM3 modulates γ-secretase in Alzheimer’s disease
Hur, J.-Y., Frost, G. R., Wu, X., Crump, C., Pan, S. J., Wong, E., Barros, M., Li, T., Nie, P., Zhai, Y., et al. (2020) · Nature · doi:10.1038/s41586-020-2681-2 · Cited by 382 · see citing works
Self-citationDube, U., Del-Aguila, J. L., Li, Z., Budde, J. P., Jiang, S., Hsu, S., Ibanez, L., Fernandez, M. V., Farias, F., Norton, J., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0501-5 · Cited by 345 · see citing works
Il10 Deficiency Rebalances Innate Immunity to Mitigate Alzheimer-Like Pathology
Guillot-Sestier, M.-V., Doty, K. R., Gate, D., Rodriguez, J., Leung, B. P., Rezai-Zadeh, K., & Town, T. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.068 · Cited by 363 · see citing works
Microglia: Agents of the CNS Pro-Inflammatory Response
Rodríguez-Gómez, J. A., Kavanagh, E., Engskog-Vlachos, P., Engskog, M. K. R., Herrera, A. J., Espinosa-Oliva, A. M., Joseph, B., Hajji, N., Venero, J. L., & Burguillos, M. A. (2020) · Cells · doi:10.3390/cells9071717 · Cited by 365 · see citing works
A Path Toward Precision Medicine for Neuroinflammatory Mechanisms in Alzheimer's Disease
Hampel, H., Caraci, F., Cuello, A. C., Caruso, G., Nisticò, R., Corbo, M., Baldacci, F., Toschi, N., Garaci, F., Chiesa, P. A., et al. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.00456 · Cited by 360 · see citing works
“In addition, the study of regulatory networks of genes showing differential expression in AD brains indicates that immune- and microglia-specific gene modules primarily contribute to AD pathophysiology (98).”
BackgroundTranscriptional signature in microglia associated with Aβ plaque phagocytosis
Grubman, A., Choo, X. Y., Chew, G., Ouyang, J. F., Sun, G., Croft, N. P., Rossello, F. J., Simmons, R., Buckberry, S., Landin, D. V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23111-1 · Cited by 357 · see citing works
Sankowski, R., Mader, S., & Valdés-Ferrer, S. I. (2015) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2015.00028 · Cited by 351 · see citing works
Elucidating the Role of TREM2 in Alzheimer’s Disease
Ulrich, J. D., Ulland, T. K., Colonna, M., & Holtzman, D. M. (2017) · Neuron · doi:10.1016/j.neuron.2017.02.042 · Cited by 325 · see citing works
Ajami, B., Samusik, N., Wieghofer, P., Ho, P. P., Crotti, A., Bjornson, Z., Prinz, M., Fantl, W. J., Nolan, G. P., & Steinman, L. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0100-x · Cited by 338 · see citing works
Homeostatic and injury‐induced microglia behavior in the aging brain
Hefendehl, J. K., Neher, J. J., Sühs, R. B., Kohsaka, S., Skodras, A., & Jucker, M. (2014) · Aging Cell · doi:10.1111/acel.12149 · Cited by 319 · see citing works
Innate Immunity and Neurodegeneration
Labzin, L. I., Heneka, M. T., & Latz, E. (2018) · Annual Review of Medicine · doi:10.1146/annurev-med-050715-104343 · Cited by 319 · see citing works
Bagot, R. C., Cates, H. M., Purushothaman, I., Lorsch, Z. S., Walker, D. M., Wang, J., Huang, X., Schlüter, O. M., Maze, I., Peña, C. J., et al. (2016) · Neuron · doi:10.1016/j.neuron.2016.04.015 · Cited by 333 · see citing works
Self-citationPSEN1 Mutant iPSC-Derived Model Reveals Severe Astrocyte Pathology in Alzheimer's Disease
Oksanen, M., Petersen, A. J., Naumenko, N., Puttonen, K., Lehtonen, Š., Gubert Olivé, M., Shakirzyanova, A., Leskelä, S., Sarajärvi, T., Viitanen, M., et al. (2017) · Stem Cell Reports · doi:10.1016/j.stemcr.2017.10.016 · Cited by 332 · see citing works
Huang, Y. J., Marsland, B. J., Bunyavanich, S., O'Mahony, L., Leung, D. Y. M., Muraro, A., & Fleisher, T. A. (2017) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2017.02.007 · Cited by 330 · see citing works
ApoE attenuates unresolvable inflammation by complex formation with activated C1q
Yin, C., Ackermann, S., Ma, Z., Mohanta, S. K., Zhang, C., Li, Y., Nietzsche, S., Westermann, M., Peng, L., Hu, D., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0336-8 · Cited by 305 · see citing works
Self-citationDream: powerful differential expression analysis for repeated measures designs
Hoffman, G. E., & Roussos, P. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa687 · Cited by 319 · see citing works
Yan, Z., & Rein, B. (2022) · Molecular Psychiatry · doi:10.1038/s41380-021-01092-3 · Cited by 300 · see citing works
Kaushal, V, Dye, R., Pakavathkumar, P., Foveau, B., Flores, J., Hyman, B., Ghetti, B., Koller, B. H., & LeBlanc, A. C. (2015) · Cell Death & Differentiation · doi:10.1038/cdd.2015.16 · Cited by 297 · see citing works
Wang, M., Roussos, P., McKenzie, A., Zhou, X., Kajiwara, Y., Brennand, K. J., De Luca, G. C., Crary, J. F., Casaccia, P., Buxbaum, J. D., et al. (2016) · Genome Medicine · doi:10.1186/s13073-016-0355-3 · Cited by 314 · see citing works
Self-citationThe identity and function of microglia in neurodegeneration
Song, W. M., & Colonna, M. (2018) · Nature Immunology · doi:10.1038/s41590-018-0212-1 · Cited by 308 · see citing works
Neff, R. A., Wang, M., Vatansever, S., Guo, L., Ming, C., Wang, Q., Wang, E., Horgusluoglu-Moloch, E., Song, W.-M., Li, A., et al. (2021) · Science Advances · doi:10.1126/sciadv.abb5398 · Cited by 306 · see citing works
“1D, there is a clear separation of molecular signatures between the five AD subtypes that can be visually appreciated from the whole-transcriptome gene expression heatmap after identifying gene modules using weighted interaction network analysis (WINA) (24, 27).”
MethodResultInfluentialSelf-citationAging and Alzheimer's disease: comparison and associations from molecular to system level
Xia, X., Jiang, Q., McDermott, J., & Han, J. J. (2018) · Aging Cell · doi:10.1111/acel.12802 · Cited by 301 · see citing works
Microglia actions in Alzheimer's disease
Prokop, S., Miller, K. R., & Heppner, F. L. (2013) · Acta Neuropathologica · doi:10.1007/s00401-013-1182-x · Cited by 291 · see citing works
Zhang, J., Yu, C., Zhang, X., Chen, H., Dong, J., Lu, W., Song, Z., & Zhou, W. (2018) · Journal of Neuroinflammation · doi:10.1186/s12974-017-1052-x · Cited by 297 · see citing works
Wan, Y.-W., Al-Ouran, R., Mangleburg, C. G., Perumal, T. M., Lee, T. V., Allison, K., Swarup, V., Funk, C. C., Gaiteri, C., Allen, M., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.107908 · Cited by 295 · see citing works
“This result suggests that many human brain gene expression changes associated with AD, including neuronal- and microglial-enriched modules reported in other studies (Conway et al., 2018; Mostafavi et al., 2018; Zhang et al., 2013), may represent common transcriptional programs activated by the aging process itself.”
ResultSelf-citationNovel Alzheimer risk genes determine the microglia response to amyloid-β but not to TAU pathology
Sierksma, A., Lu, A., Mancuso, R., Fattorelli, N., Thrupp, N., Salta, E., Zoco, J., Blum, D., Buée, L., De Strooper, B., et al. (2020) · EMBO Molecular Medicine · doi:10.15252/emmm.201910606 · Cited by 291 · see citing works
Gaiteri, C., Ding, Y., French, B., Tseng, G. C., & Sibille, E. (2014) · Genes, Brain and Behavior · doi:10.1111/gbb.12106 · Cited by 289 · see citing works
Self-citationTREM2-Ligand Interactions in Health and Disease
Kober, D. L., & Brett, T. J. (2017) · Journal of Molecular Biology · doi:10.1016/j.jmb.2017.04.004 · Cited by 284 · see citing works
“A uthor M anuscript A uthor M anuscript A uthor M anuscript A uthor M anuscript CL cardiolipin COPD chronic obstructive pulmonary disease CS Chondroitin sulfate CSF-1R Colony stimulating factor 1 receptor DAG diacylglycerol DAMPs Damage-associated molecular patters DAP12 DNAX-activation protein 12 DC Dendritic cell EL…”
BackgroundInfluentialFernandez, C. G., Hamby, M. E., McReynolds, M. L., & Ray, W. J. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00014 · Cited by 277 · see citing works
A Role for Noncoding Variation in Schizophrenia
Roussos, P., Mitchell, A. C., Voloudakis, G., Fullard, J. F., Pothula, V. M., Tsang, J., Stahl, E. A., Georgakopoulos, A., Ruderfer, D. M., Charney, A., et al. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.10.015 · Cited by 251 · see citing works
Self-citationLopes, K. D. P., Snijders, G. J. L., Humphrey, J., Allan, A., Sneeboer, M. A. M., Navarro, E., Schilder, B. M., Vialle, R. A., Parks, M., Missall, R., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00976-y · Cited by 273 · see citing works
Hallmarks of Alzheimer’s Disease in Stem-Cell-Derived Human Neurons Transplanted into Mouse Brain
Espuny-Camacho, I., Arranz, A. M., Fiers, M., Snellinx, A., Ando, K., Munck, S., Bonnefont, J., Lambot, L., Corthout, N., Omodho, L., et al. (2017) · Neuron · doi:10.1016/j.neuron.2017.02.001 · Cited by 244 · see citing works
Neurodegeneration by Activation of the Microglial Complement-Phagosome Pathway
Bodea, L.-G., Wang, Y., Linnartz-Gerlach, B., Kopatz, J., Sinkkonen, L., Musgrove, R., Kaoma, T., Muller, A., Vallar, L., Di Monte, D. A., et al. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.5002-13.2014 · Cited by 234 · see citing works
Self-citationImmune hyperreactivity of Aβ plaque-associated microglia in Alzheimer's disease
Yin, Z., Raj, D., Saiepour, N., Van Dam, D., Brouwer, N., Holtman, I. R., Eggen, B. J. L., Möller, T., Tamm, J. A., Abdourahman, A., et al. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.03.021 · Cited by 238 · see citing works
Neuroprotective Natural Products for Alzheimer’s Disease
Chen, X., Drew, J., Berney, W., & Lei, W. (2021) · Cells · doi:10.3390/cells10061309 · Cited by 236 · see citing works
- Authors
- Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., Fluder, E., Clurman, B., Melquist, S., Narayanan, M., Suver, C., Shah, H., Mahajan, M., Gillis, T., Mysore, J., MacDonald, M. E., Lamb, J. R., Bennett, D. A., Molony, C., Stone, D. J., Gudnason, V., Myers, A. J., Schadt, E. E., Neumann, H., Zhu, J., & Emilsson, V.
- Affiliations
- Sage Bionetworks (US)
- University of Bonn (DE)
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- Fred Hutch Cancer Center (US)
- Icahn School of Medicine at Mount Sinai (US)
- Massachusetts General Hospital (US)
- Genomics Institute of the Novartis Research Foundation (US)
- Rush University Medical Center (US)
- University of Iceland (IS)
- University of Miami (US)
- Corresponding author
- Zhang, Bin
- Emilsson, Valur
- ORCID
- Zhang, Bin
- Gaiteri, Chris
- Bodea, Liviu-Gabriel
- McElwee, Joshua
- Zhang, Chunsheng
- Xie, Tao
- Tran, Linh
- Dobrin, Radu
- Clurman, Bruce
- Melquist, Stacey
- Narayanan, Manikandan
- Suver, Christine
- Shah, Hardik
- Gillis, Tammy
- Lamb, John R.
- Bennett, David A.
- Molony, Cliona
- Gudnason, Vilmundur
- Myers, Amanda J.
- Schadt, Eric E.
- Neumann, Harald
- Zhu, Jun
- Emilsson, Valur
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2013-04
- Volume
- 153
- Issue
- 3
- Pages
- 707-720
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC3677161
- OpenAlex
- W2033105784
- Cited by
- 1,657
- References
- 68
- Citations by year
- 2026: 56 · 2025: 91 · 2024: 142 · 2023: 127 · 2022: 154 · 2021: 160 · 2020: 166 · 2019: 146 · 2018: 144 · 2017: 129 · 2016: 106 · 2015: 132 · 2014: 80 · 2013: 24
- FWCI
- 79.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (NS39764)
- National Institutes of Health (R01 AG11101, NS032765, P30 AG10161, R01 AG17917, R01 AG15819, R01 AG030146, R01 AG034504, K08 AG034290, 211002, P50-AG08671, 30 AG19610)
- Leonard M. Miller School of Medicine
- Medical Research Council
- Alzheimer’s Research UK
- Fondazione ART per la Ricerca sui Trapianti
- Deutsche Forschungsgemeinschaft (KFO177, FOR1336, SFB704)
- Higher Education Funding Council for England
- Illinois Department of Public Health
- BRACE
- University of Miami
- National Institute on Aging (05128)
- National Institute of Mental Health (MH60451)
- Arizona Biomedical Research Commission (1001, 0011, 05-901, 4001)
- Internationale Stichting Alzheimer Onderzoek
- Newcastle University
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- late-onset Alzheimer's disease, gene regulatory networks, molecular networks, postmortem brain, microglia, TYROBP, disease mechanisms
- MeSH terms
- Alzheimer Disease, Animals, Bayes Theorem, Brain, Humans, Membrane Proteins, Microglia, Adaptor Proteins, Signal Transducing, Mice, Gene Regulatory Networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
