Analysis of Genetically Diverse Macrophages Reveals Local and Domain-wide Mechanisms that Control Transcription Factor Binding and Function

Link, Verena M.; Duttke, Sascha H.; Chun, Hyun Bae; Holtman, Inge R.; Westin, Emma; Hoeksema, Marten A.; Abe, Yohei; Skola, Dylan; Romanoski, Casey E.; Tao, Jenhan; Fonseca, Gregory J.; Troutman, Ty Dale; Spann, Nathanael J.; Strid, Tobias; Sakai, Mashito; Yu, Miao; Hu, Rong; Fang, Rongxin; Metzler, Dirk; Ren, Bing; Glass, Christopher K.

doi:10.1016/j.cell.2018.04.018

Cited by 169 publications

(227 citation statements)

References 61 publications

Supporting

Mentioning

210

Contrasting

Order By: Relevance

“…The basic assumption for MAGGIE is that the allele specificity of epigenomic features is derived from the genetic variation between positive and negative sequences that mutate certain TF binding motifs. This assumption is supported by the findings that motif mutations due to local genetic variation is the major explanation for the gain or loss of TF binding sites (Link et al, 2018a;Kundaje et al, 2015). Considering the importance of transcription factors for other epigenomic features like promoter and enhancer function (Reiter et al, 2017;Spitz and Furlong, 2012), we hypothesized that our framework could help identify motifs mediating both TF binding and other epigenomic features affected by TF binding.…”

Section: Overview Of Maggiementioning

confidence: 71%

“…The difference of representative motif score is a good indicator of the binding activity change of the corresponding TF (Martin et al, 2019;Spivakov et al, 2012). For example, by comparing PU.1 binding in macrophages of C57BL/6J (C57) and BALB/cJ (BALB) mice (Link et al, 2018a), we observed a strongly positive correlation between the score difference of PU.1 (or SPI1) motif and the log fold change of PU.1 binding activity quantified by ChIP-seq reads among 1635 PU.1 binding sites that have SPI1 motif mutations between the two mouse strains (Fig. 1C).…”

Section: Computation Of Motif Score and Motif Score Differencementioning

confidence: 99%

“…MAGGIE was then used to identify collaborative TFs. We downloaded the ChIP-seq data of PU.1 and C/EBPβ for C57 and BALB mice from the GEO database with accession number GSE109965 (Link et al, 2018a), and the ChIP-seq data of ATF3 for the same mouse strains with the accession number GSE46494 (Fonseca et al, 2019). The data for C57 were mapped to the mm10 genome using Bowtie2 v2.3.5.1 (Langmead and Salzberg, 2012), while the data for BALB were first mapped to the BALB genome and then shifted to the mm10 genome using the MMARGE v1.0 "shift" function (Link et al, 2018b).…”

Section: Transcription Factor Binding Sitesmentioning

confidence: 99%

See 2 more Smart Citations

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function

Shen

Hoeksema

Ouyang

et al. 2020

Preprint

View full text Add to dashboard Cite

Genetic variation in regulatory elements can alter transcription factor (TF) binding by mutating a TF binding motif, which in turn may affect the activity of the regulatory elements. However, it is unclear which TFs are prone to be affected by a given variant. Current motif analysis tools either prioritize TFs based on motif enrichment without linking to a function or are limited in their applications due to the assumption of linearity between motifs and their functional effects. Here, we present MAGGIE, a novel method for identifying motifs mediating TF binding and function. By leveraging measurements from diverse genotypes, MAGGIE uses a statistical approach to link mutation of a motif to changes of an epigenomic feature without assuming a linear relationship. We benchmark MAGGIE across various applications using both simulated and biological datasets and demonstrate its improvement in sensitivity and specificity compared to the state-of-the-art motif analysis approaches. We use MAGGIE to reveal insights into the divergent functions of distinct NF-κB factors in the pro-inflammatory macrophages, showing its promise in discovering novel functions of TFs. The Python package for MAGGIE is freely available at https://github.com/zeyang-shen/maggie.

show abstract

Section: Overview Of Maggiementioning

confidence: 71%

Section: Computation Of Motif Score and Motif Score Differencementioning

confidence: 99%

Section: Transcription Factor Binding Sitesmentioning

confidence: 99%

See 1 more Smart Citation

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function

Shen

Hoeksema

Ouyang

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…However, we identified three transcription factor genes, Cebpb, Mafb and Klf10, that were apparently excluded from all of the cDC. The role of Cebpb in macrophage differentiation is well-recognised [116][117][118] and one of the datasets includes progenitors from Cebpb -/mice [118]. There is evidence of a negative feedback relationship with Irf8 in monocyte-derived DC [119].…”

Section: Dendritic Cell Co-expression Clustersmentioning

confidence: 99%

Transcriptional network analysis of transcriptomic diversity in resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system

Hume

2020

Preprint

View full text Add to dashboard Cite

The mononuclear phagocyte system (MPS) is a family of cells including progenitors, circulating blood monocytes, resident tissue macrophages and dendritic cells (DC) present in every tissue in the body. To test the relationships between markers and transcriptomic diversity in the MPS, we collected from NCBI-GEO >500 quality RNA-seq datasets generated from mouse MPS cells isolated from multiple tissues. The primary data were randomly down-sized to a depth of 10 million reads and requantified. The resulting dataset was clustered using the network analysis tool Graphia. A sample-to-sample matrix revealed that MPS populations could be separated based upon tissue of origin. Cells identified as classical DC subsets, cDC1 and cDC2, and lacking Fcgr1 (CD64), were centrally-located within the MPS cluster and no more distinct than other MPS cell types. A gene-to-gene correlation matrix identified large generic co-expression clusters associated with MPS maturation and innate immune function. Smaller co-expression clusters including the transcription factors that drive them showed higher expression within defined isolated cells, including macrophages and DC from specific tissues. They include a cluster containing Lyve1 that implies a function in endothelial cell homeostasis, a cluster of transcripts enriched in intestinal macrophages and a generic cDC cluster associated with Ccr7. However, transcripts encoding many other putative MPS subset markers including Adgre1, Itgax, Itgam, Clec9a, Cd163, Mertk, Retnla and H2-A/E (class II MHC) clustered idiosyncratically and were not correlated with underlying functions. The data provide no support for the concept of markers of M2 polarization or the specific adaptation of DC to present antigen to T cells. Co-expression of immediate early genes (e.g. Egr1, Fos, Dusp1) and inflammatory cytokines and chemokines (Tnf, Il1b, Ccl3/4) indicated that all tissue disaggregation protocols activate MPS cells. Tissue-specific expression clusters indicated that all cell isolation procedures also co-purify other unrelated cell types that may interact with MPS cells in vivo. Comparative analysis of public RNA-seq and single cell RNA-seq data from the same lung cell populations showed that the extensive heterogeneity implied by the global cluster analysis may be even greater at a single cell level with few markers strongly correlated with each other. This analysis highlights the power of large datasets to identify the diversity of MPS cellular phenotypes, and the limited predictive value of surface markers to define lineages, functions or subpopulations.

show abstract

“…PLAC-seq libraries were prepared for ex vivo microglia, NEUN nuclei and OLIG2 nuclei as previously described with minor modifications (56,88). In brief, purified populations were cross-linked for 15 minutes at room temperature with 1% formaldehyde and quenched for 5 mins at room temperature with 0.2 M glycine (Thermo Fisher Scientific).…”

Section: Proximity Ligation Chip-sequencing (Plac-seq)mentioning

confidence: 99%

Cell type-specific enhancer-promoter connectivity maps in the human brain and disease risk association

Nott¹,

Holtman²,

Coufal

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Identification of cell type-specific regulatory elements in the human brain enables interpretation of non-coding GWAS risk variants. AbstractUnique cell type-specific patterns of activated enhancers can be leveraged to interpret non-coding genetic variation associated with complex traits and diseases such as neurological and psychiatric disorders. Here, we have defined active promoters and enhancers for major cell types of the human brain. Whereas psychiatric disorders were primarily associated with regulatory regions in neurons, idiopathic Alzheimer's disease (AD) variants were largely confined to microglia enhancers. Interactome maps connecting GWAS variants in cell type-specific enhancers to gene promoters revealed an extended microglia gene network in AD. Deletion of a microglia-specific enhancer harboring ADrisk variants ablated BIN1 expression in microglia but not in neurons or astrocytes. These findings revise and expand the genes likely to be influenced by non-coding variants in AD and suggest the probable brain cell types in which they function. of neurons, microglia, astrocytes, oligodendrocytes and other cell types that reside within the brain (1-4). In contrast, transcriptional mechanisms that control their developmental and functional properties in health and disease remain less well understood. Studies in animal models have provided deep insights into conserved processes, but significant differences between species often limit the translatability of findings in model systems to humans (5).Genome-wide association studies (GWASs) provide a genetic approach to identifying molecular pathways involved in complex traits and diseases by defining associations between genetic variants and phenotypes of interest (6, 7). Large-scale GWASs have discovered hundreds of single nucleotide polymorphisms (SNPs) that are associated with the risk of neurological and psychiatric disorders. The vast majority of disease-risk genetic variants are located in non-coding regions of the genome (7) and the specific cell type (s) in which the disease-risk variants are active is often unclear. Furthermore, linkagedisequilibrium between disease-risk variants at GWAS significant loci complicates the identification of causal variants. Collectively, these limitations have hindered the assignment and interpretation of disease-risk genes.GWAS-identified risk variants that are located in non-coding regions of the genome can exert phenotypic effects through several mechanisms, including perturbation of transcriptional enhancers and promoters (6). While promoters provide the essential sites of transcriptional initiation of mRNAs, they are frequently not sufficient to direct appropriate developmental and signal-dependent levels of gene expression (8,9). This additional information is provided by enhancers, short regions of DNA that, when bound by transcription factors, enhance mRNA expression from target promoters. Enhancers can reside hundreds of thousands of base pairs away from their target gene and their function is generally consid...

show abstract

Analysis of Genetically Diverse Macrophages Reveals Local and Domain-wide Mechanisms that Control Transcription Factor Binding and Function

Cited by 169 publications

References 61 publications

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function

Transcriptional network analysis of transcriptomic diversity in resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system

Cell type-specific enhancer-promoter connectivity maps in the human brain and disease risk association

Contact Info

Product

Resources

About