The cis-Regulatory Atlas of the Mouse Immune System

Yoshida, Hideyuki; Lareau, Caleb A.; Ramirez, Ricardo N.; Rose, Samuel A.; Maier, Bárbara; Wroblewska, Aleksandra; Desland, Fiona; Chudnovskiy, Aleksey; Mortha, Arthur; Dominguez, Claudia X.; Tellier, Julie; Kim, Edy Y.; Dwyer, Dan; Shinton, Susan A.; Nabekura, Tsukasa; Qi, Yilin; Yu, Bingfei; Robinette, Michelle L.; Kim, Ki Wook; Wagers, Amy J.; Rhoads, Andrew; Nutt, Stephen L.; Brown, Brian D.; Mostafavi, Sara; Buenrostro, Jason D.; Benoist, Christophe

doi:10.1016/j.cell.2018.12.036

Cited by 350 publications

(483 citation statements)

References 65 publications

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“…To dissect broadly-permissive and dynamic elements, we applied Gini index as a quantitative approach for objectively defining changes in OCRs. The Gini index was developed for economic dispersion analysis, but was recently applied to quantify chromatin dynamics in the hematopoietic system (Yoshida et al, 2019). Our data affirm Gini index as a valuable approach for quantifying chromatin dynamics in studies involving 3 or more groups, where pairwise analyses may be insufficient.…”

Section: Discussionsupporting

confidence: 52%

“…A limitation of peak overlap based methods for assessing chromatin dynamics in a multipopulation experiment is how to classify peaks that are found in 2 or more populations, but not in all populations. In order to more quantitatively assess chromatin dynamics, we analyzed OCR variability across Sox9 EGFP populations using Gini index, a statistical measure of inequality that assigns higher values to more variable distributions (Yoshida et al, 2019). We visualized Gini index by Uniform Manifold Approximation and Projection (UMAP), where each point on the plot represents a single OCR and the Gini index indicates the extent of variable accessibility across all four Sox9 EGFP populations ( Fig.…”

Section: Open Chromatin Regions Are Dynamic Across Sox9 Populationsmentioning

confidence: 99%

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Quantitative classification of chromatin dynamics reveals regulators of intestinal stem cell differentiation

Raab

Tulasi

Wager

et al. 2019

Preprint

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Intestinal stem cell (ISC) plasticity is thought to be regulated by broadly-permissive chromatin shared between ISCs and their progeny. Here, we utilize a Sox9 EGFP reporter to examine chromatin across ISC differentiation. We find that open chromatin regions (OCRs) can be defined as broadly-permissive or dynamic in a locus-specific manner, with dynamic OCRs found primarily in loci consistent with distal enhancers. By integrating gene expression with chromatin accessibility at transcription factor (TF) motifs in context of Sox9 EGFP populations, we classify broadly-permissive and dynamic chromatin relative to TF usage. These analyses identify known and potential regulators of ISC differentiation via their association with dynamic changes in chromatin. We observe ISC expansion in Id3-null mice, consistent with computational predictions. Finally, we examine the relationship between gene expression and 5hydroxymethylcytosine (5hmC) in Sox9 EGFP populations, which reveals 5hmC enrichment in absorptive lineage specific genes. Our data demonstrate that intestinal chromatin dynamics can be quantitatively defined in a locus-specific manner, identify novel potential regulators of ISC differentiation, and provide a chromatin roadmap for further dissecting the role of cis regulation of cell fate in the intestine. Tetteh et al., 2016). The genetic mechanisms that allow IECs to adopt metastable differentiated fates while exhibiting facultative ISC function are not fully understood.Chromatin landscapes consist of histone post-translational modifications, DNA modifications, and higher-order structural organization, and are known to exert regulatory control on cell fate.

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Section: Discussionsupporting

confidence: 52%

Section: Open Chromatin Regions Are Dynamic Across Sox9 Populationsmentioning

confidence: 99%

Quantitative classification of chromatin dynamics reveals regulators of intestinal stem cell differentiation

Raab

Tulasi

Wager

et al. 2019

Preprint

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“…We have also not considered the application of deep learning models to TFBS, CAGE and ATAC-seq data 16,42 , which is a promising future research direction. Fourth, we focused here on deep learning models trained using human data, but models trained using data from other species may also be informative for human disease 43,42 . Fifth, the forward stepwise elimination procedure that we use to identify jointly significant annotations 19 is a heuristic procedure whose choice of prioritized annotations may be close to arbitrary in the case of highly correlated annotations; nonetheless, our framework does impose rigorous criteria for conditional informativeness.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the informativeness of deep learning annotations for human complex diseases

Dey

Geijn

Kim

et al. 2019

Preprint

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Deep learning models have shown great promise in predicting genome-wide regulatory effects from DNA sequence, but their informativeness for human complex diseases and traits is not fully understood. Here, we evaluate the disease informativeness of two types of deep learning annotations: (1) variant-level annotations (based on the reference allele), assessing whether they are more informative for complex disease than the underlying experimental data used to train the predictive models; and (2) allelic-effect annotations (absolute value of the predicted difference between reference and variant alleles), which have been a major focus of recent work. In each case, we primarily consider annotations constructed using two previously trained deep learning models, DeepSEA and Basenji. We apply stratified LD score regression (S-LDSC) to 41 independent diseases and complex traits (average N =320K) to evaluate each annotation's informativeness for disease heritability conditional on a broad set of coding, conserved, regulatory and LDrelated annotations from the baseline-LD model and other sources; as a secondary metric, we also evaluate the accuracy of models that incorporate deep learning annotations in predicting disease-associated or fine-mapped SNPs. We aggregated annotations across all tissues (resp. blood cell types or brain tissues) in metaanalyses across all 41 traits (resp. 11 blood-related traits or 8 brain-related traits). Variant-level annotations, despite being highly enriched for disease heritability, produced no conditionally significant results in meta-analyses across all 41 traits or 11 blood-related traits, but brain-specific DeepSEA-H3K4me3 and Basenji-H3K27ac annotations were conditionally significant in meta-analyses across 8 brain-related traits; a sequence motif analysis suggests that these annotations could be capturing unique information about nucleosome occupancy. Allelic-effect annotations were also highly enriched for disease heritability, and produced conditionally significant results for Basenji-H3K4me3 in meta-analyses across all 41 traits and brain-specific Basenji-H3K4me3 in meta-analyses across 8 brain-related traits. We conclude that deep learning models are informative for disease, but their informativeness cannot be inferred from metrics based on their accuracy in predicting regulatory annotations.

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“…Many regulatory elements that exhibited Bhlhe40 binding showed an increase in H3K27 acetylation in DKO AMs (Figs 5B and F,and EV4A and D). The frequency of regions with increased H3K27 acetylation in DKO AMs was higher for regulatory elements that exhibited Bhlhe40 binding than for those that did not have a Bhlhe40 peak in their close proximity (AM ATAC-seq peaks (Yoshida et al, 2019), see Materials and Methods for details) (Figs 5F and EV4D). In line with the effects of Bhlhe40 binding on gene expression described above, increased H3K27 acetylation in DKO AMs was most pronounced at the regulatory elements associated with the high-ranking Bhlhe40 peaks (Figs 5F and EV4D).…”

Section: Broad Footprint Of Bhlhe40/bhlhe41 Deficiency On the Am Tranmentioning

confidence: 99%

“…The identified peaks were then assigned to target genes as described (Revilla-i-Domingo et al, 2012). Bhlhe40 peaks overlapping with the transcription start site (TSS) were referred to as promoter peaks in Fig 5D. For our H3K27ac ChIP-seq samples and for Immgen AM ATAC-seq sample (GSM2692306) (Yoshida et al, 2019), reads were aligned to the mouse genome assembly version of July 2007 (NCBI37/mm9), using the Bowtie2 program (Langmead & Salzberg, 2012) (usegalaxy.eu; galaxy version 2.3.4.2). Peak calling for ATAC-seq was performed as described above for Bhlhe40 ChIPseq.…”

Section: Analysis Of Chip-seq Datamentioning

confidence: 99%

Bhlhe40 and Bhlhe41 transcription factors regulate alveolar macrophage self‐renewal and identity

et al. 2019

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Tissues in multicellular organisms are populated by resident macrophages, which perform both generic and tissue‐specific functions. The latter are induced by signals from the microenvironment and rely on unique tissue‐specific molecular programs requiring the combinatorial action of tissue‐specific and broadly expressed transcriptional regulators. Here, we identify the transcription factors Bhlhe40 and Bhlhe41 as novel regulators of alveolar macrophages (AMs)—a population that provides the first line of immune defense and executes homeostatic functions in lung alveoli. In the absence of these factors, AMs exhibited decreased proliferation that resulted in a severe disadvantage of knockout AMs in a competitive setting. Gene expression analyses revealed a broad cell‐intrinsic footprint of Bhlhe40/Bhlhe41 deficiency manifested by a downregulation of AM signature genes and induction of signature genes of other macrophage lineages. Genome‐wide characterization of Bhlhe40 DNA binding suggested that these transcription factors directly repress the expression of lineage‐inappropriate genes in AMs. Taken together, these results identify Bhlhe40 and Bhlhe41 as key regulators of AM self‐renewal and guardians of their identity.

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The cis-Regulatory Atlas of the Mouse Immune System

Cited by 350 publications

References 65 publications

Quantitative classification of chromatin dynamics reveals regulators of intestinal stem cell differentiation

Quantitative classification of chromatin dynamics reveals regulators of intestinal stem cell differentiation

Evaluating the informativeness of deep learning annotations for human complex diseases

Bhlhe40 and Bhlhe41 transcription factors regulate alveolar macrophage self‐renewal and identity

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