Cistrome motif investing

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cistrome motif investing

All fetal pancreatic epithelial cells expressed PDX1 at this stage (Fig. S1A). HOMER de novo motif analysis showed that the consensus PDX Over-representation of SRF, MYOD, and STAT binding motifs in PGR occupying sites further suggests interactions be- tween PGR and major muscle. We here present the p65 colon cistrome of these two CRC cell lines. We identify that RELA and AP1 motifs are predominant in both cell lines. BTC 3000 LEVEL

These types of data could enhance the use of TR ChIP-seq data as well as imputed TF binding data, which may not accurately represent TF binding sites in different cell contexts. As differential gene expression experiments are not always carried out in parallel with chromatin profiling experiments, Lisa does not require the corresponding user-generated chromatin profiles but instead uses the DNase-seq and H3K27ac ChIP-seq data that is available in the Cistrome DB to help identify cis-regulatory elements controlling a differential expression gene set.

Changes in H3K27ac ChIP-seq and DNase-seq associated with cell state perturbations are often a matter of degree rather than switch-like; therefore, we base the chrom-RP on reads rather than peaks. The chrom-RP is pre-calculated for each gene Fig. These chrom-RPs quantify the cis-regulatory activities that influence each gene under cell-type-specific conditions. Using L1-regularized logistic regression, Lisa assigns a weight to each selected sample so that the weighted sum of the chrom-RPs on the genes best separates the query and the background gene sets Fig.

Next, by a process of in silico deletion ISD , Lisa evaluates the effect deleting each TR cistrome has on the chromatin landscape model Fig. ISD of a TR cistrome involves setting DNase-seq or H3K27ac ChIP-seq chromatin signal to 0 in the 1-kb intervals containing the peaks in that cistrome and evaluating the effect on the predictions made by the chromatin landscape models. The difference of the model scores before ISD and after ISD quantifies the impact that the deleted TR cistrome is predicted to have on the query and background gene sets.

Whereas the chrom-RP integrates data over kb intervals, the scale of individual cis-regulatory elements is of the order of 1 kb. The ISD approach mitigates the difficulties in transferring information contained in the chrom-RP model from the chrom-RP kb scale to the cis-regulatory element 1 kb scale. Finally, to prioritize the candidate TRs, Lisa compares the predicted effects on the query and background gene sets using the one-sided Wilcoxon rank-sum test Fig. In silico deletion Fig.

CTCF does not influence the chromatin landscape of the downregulated genes and is not likely to regulate the query gene set. We then applied Lisa to differentially expressed gene sets defined by perturbations of individual TFs and examined the TR cistromes predicted to be the key regulators of these gene sets. All samples in this cluster were derived from prostate cancer cell lines.

The identification of GATA6 cistromes in colon cancer cell lines, in addition to gastric cancer cell lines, shows that cistromes derived from cell types that are of related lineages can be used to inform the identification of the relevant regulators, even if the cell types are not the same. In the third example involving glucocorticoid receptor GR activation in the lung cancer cell line A, Lisa correctly identified GR in A as a likely regulator and also identified GR in a different cell type HeLa Fig.

AR, a member of the same nuclear receptor family as GR, is also implicated by Lisa even though the AR cistrome samples do not cluster with GR cistrome samples and have less statistical significance. The three bars to the left of the heatmap display Lisa significance scores for differentially expressed genes sets derived from GR activation in the A cell line upregulated , GATA6 knockdown in gastric cancer downregulated , and AR activation in the LNCaP cell line upregulated.

We repeated the same analysis removing similar data, on the basis of tissue breast and lung instead of on the basis of cell line MCF7 and A Together, these observations indicate that although TRs often bind in cell-type-specific ways, ChIP-seq-derived TR cistromes can be informative about the gene sets that TRs regulate in some other cell types.

Thus, in many cases, the known interactors are highly ranked along with the target activator or repressor. This suggests that even though the available TF ChIP-seq data in different cell types are sparse Additional file 1 : Figure S1d , Lisa can provide insights on possible regulatory TFs since transcriptional machinery tends to be organized in modules of interacting factors [ 60 ] Additional file 1: Figure S4d.

The scatter plots show negative log10 Lisa p values of unique transcriptional regulators for up- and downregulated gene sets. Colors indicate log2 fold changes of the TF gene expression between treatment and control conditions in the gene expression experiments. Dots outlined with a circle denote transcriptional regulators that physically interact with the TF perturbed in the experiment, which is marked with a cross Full size image Systematic evaluation of regulator prediction To systematically evaluate Lisa, we compiled a benchmark panel of differentially expressed gene sets from 61 studies involving the knockdown, knockout, activation, or overexpression of 27 unique human target TFs.

The full Lisa model was separately applied to the upregulated and downregulated gene sets in each experiment. The putative regulatory cistromes were defined using either ChIP-seq peaks or from TF motif occurrence in the inferred chromatin models. We measured the performance based on their ranking of the perturbed target TF Fig. The upper left red triangles represent the rank of the target TFs based on the analysis of the upregulated gene sets; the lower right blue triangles represent the analysis of downregulated gene sets.

The heatmap includes non-redundant human experiments for the same TF. See Additional file 1 : Figure S5 for the complete list of human and mouse experiments. In overexpression studies, the prediction performance of all methods tended to be better for the upregulated gene sets than for the downregulated ones. The reverse is evident in the knockout and knockdown studies for which the prediction performances are better for the downregulated gene sets Fig.

This suggests that most of the TFs included in the study have a predominant activating role in the regulation of their target genes, under the conditions of the gene expression experiments, allowing these TFs to be more readily identified with the corresponding direction of primary gene expression response. Similar performance patterns were observed in the mouse benchmark datasets Additional file 1 : Figure S5.

To determine whether differences between the up- and downregulated gene sets could be explained by direct or indirect modes of TR recruitment, we studied two experiments involving ER and GR activation in greater detail. Comparing direct and indirect binding sites in the respective ER and GR activation experiments Additional file 1 : Figure S6 , we found that the upregulated gene sets were more significantly associated with the direct binding sites ER p value 1. The downregulated gene sets were more significantly associated with the indirect binding sites ER p value 1.

In some cases, the perturbation of a TR may trigger stress, immune, or cell cycle checkpoint responses that are not directly related to the initial perturbation. In this case, Lisa might be correctly detecting a secondary response to the primary TR perturbation.

We also included a baseline method that ranks TRs by comparing query and background gene sets based on the TR binding site number within 5 kb centered on the TSS. Lisa uses a model based on chromatin data to give more weight to the loci that are more likely to influence the expression of the query gene set. In this way, Lisa improves the performance of TR inference with noisy cistrome profiles such as those imputed from DNA sequence motifs. In addition to being more accurate than other methods in terms of TR prediction, the Lisa web server lisa.

Users can sort results by p value and inspect metadata and quality control statistics for each of the ChIP-seq samples to understand whether the predictive samples may be derived from particular cell types or experimental conditions.

Lisa provides quality control metrics, metadata, publication, and read data repository links for the ChIP-seq data of putative regulatory TRs. Although the motif imputation-based analysis tends to be less accurate than the ChIP-seq based analysis, motifs can indicate roles for regulatory TRs for which ChIP-seq data is not widely available. Robust methods combined with visualization and data exploration features make Lisa a valuable tool for analyzing gene regulation in humans and mice.

Conclusion In this study, we describe an approach for using publicly available ChIP-seq and DNase-seq data to identify the regulators of differentially expressed gene sets in humans and mice. B Heatmap representing p65 binding sites in the two cell lines. D Genomic distribution of p65 binding sites in relation to gene locations. JUND , cell adhesion and migration e.

We also identified a novel pathway, not previously associated with p65, including circadian rhythm in both cell lines Figure 1E. In conclusion, we note highly concordant binding to cis-regulatory chromatin in proximity of genes within expected functions and further identify potential mechanism for p65 regulation of the colon circadian rhythm. In conclusion, this data clearly shows a strong transcriptional activity by p65 in both CRC cell lines, with pbound and regulated genes involved in critical CRC pathways, including apoptosis and cell migration.

C Enrichment signal of p65 binding sites present in both cell lines, illustrated using UCSC genome browser. We selected a data set that also used double crosslinking The enriched biological functions for p65 sites specific for MCF7 also included apoptosis, transcription regulation, cell cycle, and circadian clock Figure 4D. Thus, our study shows a cell specificity of p65 binding, where it binds different motifs and regulates different genes in different tissues or cell lines, but the biological functions of the regulated genes appear to have similar roles in cancer cell lines.

Pathways enriched among the gene ontology functions assigned to genes located nearest to MCF7-specific p65 binding sites. HOMER was used to identify genomic distribution and motifs of p65 binding sites across the genome.

A positive Z score indicates the values above the mean and negative if it is below the mean. These genes were mainly involved in functions such as negative regulation of transcription, negative regulation of cell proliferation, and chromatin remodeling.

This enhanced p65 binding was also evident for all replicates in the density plot Figure 6B. Among these, expression was attenuated in most 24 , and enhanced in some We characterize the p65 genome-wide chromatin binding in two different CRC cell lines, and specify similarities and differences.

A previous study has shown that dysregulation of circadian rhythm increases the risk for colorectal cancer The cell lines are indeed different in several respects. While both are derived from primary colon adenocarcinomas, the HT29 cell line is derived from a likely pre-menopausal year-old woman, whereas SW originates from a year-old man 57 , We have also reported sex differences in the non-tumor and tumor transcriptome of CRC patients, which impacted biomarker discovery However, further studies are needed to clarify this.

Aberrant methylation of the CpG islands has been shown to impact chromatin binding and accessibility to transcription factors 60 , These proteins are important transcriptional regulators that can also influence the binding of transcription factors 64 , These factors may all modulate the p65 cistrome. While the p65 binding pattern was similar between the two CRC cell lines, the p65 cistrome of breast cancer cell line MCF7 was more distinctly different.

One of the well-known interaction-partner of p65 is the p53 protein Hence it is possible that the p53 status impacts p65 cistrome in these cell lines, and further studies are needed to explore this hypothesis. These factors may all influence the kinetics of the mechanisms described here, but are not expected to have a major influence on the mechanism per se.

The strength of this study includes the genome-wide approach to decrypt these interactions, which together with the validated high-quality ChIP-Seq data generates unbiased and reliable data. The significance of these findings is reinforced by the use of two different CRC cell lines and the comparison with transcriptional impact, as well as comparisons between our results and published data generated from cells of other origin.

Further, the difference of antibodies used between our study and the breast cancer tissue study 41 , may contribute to the differences found. Author Contributions CW contributed to conceptualization. Validation done by RI and LH. Formal analysis was done by RI and LH.

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Based on the input gene sets, Lisa first uses histone mark ChIP-seq and chromatin accessibility profiles to construct a chromatin model related to the regulation of these genes.

Cistrome motif investing PLoS Comput Biol. For example, in small cell lung cancer SCLCa neuroendocrine lung cancer variant that can emerge de novo or from EGFR-mutant lung adenocarcinoma after targeted kinase inhibition, FOXA1 is highly expressed and encompassed by a super-enhancer We first clustered all cistromes according to their similarity in ChIP-seq signal across our catalog of cis-regulatory elements from luminal breast tumors and identified 7 distinct clusters Supplementary Fig. Glycine final concentration 0. We find the regulators of the upregulated genes and the downregulated ones are often different from each other; therefore, in any analysis of differential gene expression, up- and downregulated gene cistrome motif investing ought to be distinguished.
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DAP-seq is also more suited for TFs that act as homodimers rather than heteromultimeric complexes. Consequently, DAP-seq generally is supplemented with additional empirical evidence from chromatin profiling assays, to support identification of putative TF-bound sites. These ACRs are associated with transcriptional regulation and share several features including reduced nucleosome occupancies, DNA hypo-methylation, and enrichment of TF-binding sites [ 13 , 14 ].

Unlike ChIP-seq, most chromatin profiling methods do not require antibodies, are more scalable, and identify ACRs within nuclear chromatin. Among these, ATAC-seq further allows direct in vitro transposition of sequencing adaptors into chromatin, simplifying library construction and making it widely used to characterize the DNA regulatory landscapes [ 15 ]. Despite these advances, ACRs are usually less defined in replicates and average several hundred base pairs [ 16 ].

In contrast, TF-bound sequences and cis-regulatory sites are generally much smaller, on the order of 10—20 bp. The relatively large, less defined size of ACRs makes it challenging to identify individual TF-bound sites with certainty and to use them for de novo motif discovery [ 8 , 18 ]. One potential strategy to reduce the size of cross-linked DNA-bound fragments is to utilize the exonuclease activities of MNase or other nucleases during sample treatment prior to sequencing [ 12 , 19 — 25 ].

Here we define a cistrome of the developing maize ear, including hundreds of thousands of putative protein-occupied loci along with hundreds of underlying TF motif families. Results A high-throughput approach to identify high-resolution TF footprints genome-wide To define specific candidate TF-binding sites within accessible chromatin regions, we developed MOA-seq to capture the putative footprints of native DNA-protein interactions.

The assay was streamlined to be scalable for high-throughput application and includes a computational pipeline to improve the discovery of putative TF-binding sites as summarized in Fig 1. The protocol S1 File starts with the preservation of DNA-protein interactions by formaldehyde-crosslinking prior to tissue homogenization and nuclei extraction. To recover these small interaction regions, we took advantage of the endo- and the exo-nuclease activities of MNase, both of which are inhibited at sites of protein-bound DNA.

Following sequencing and read mapping to a reference genome summarized in S1 Table , we plotted the density of aligned fragment midpoints to determine MOA footprints MFs, average We then performed de-novo motif discovery Fig 1 , Step 5 to annotate potential cis-elements and compare them to previously defined TF motifs in plants.

In summary, this MOA-seq protocol was designed to repurpose MNase from mapping nucleosomes to mapping smaller particles within ACRs, resulting in a simple, scaleable, and antibody-free approach to globaly identify putative TF-bound cis-elements at relatively high spatial resolution. This overlap was particularly strong for enhancers defined via multiple epigenomic marks and chromatin accessibility [ 13 ].

However, we can not exclude the possibility that some of these intergenic sites may be non-annotated genes. Consistent with this idea, we observed that some of these candidate enhancers displayed gene-like features such as RNA coverage or were annotated as genes in previous B73 assemblies S7 Fig. Additional analyses and integration with other epigenomic information will be key to advance functional tests needed to ascertain the predictive power and myriad hypotheses generated from knowledge of these motifs.

This approach and the resulting cistrome atlas represents the most comprehensive map of putative TF-binding sites produced for a crop species. This relatively simple and scalable genome-wide native chromatin structure assay is expected to be applicable to attempts to broadly define and analyze gene regulatory networks.

Knowledge of chromatin landscapes should help focus genome editing and accelerate larger applied research efforts such as those guiding precision agriculture and medicine. Methods Plant materials Earshoots from B73 wild-type maize were harvested from field-grown plants during mid-morning. The tissue harvesting for materials used in this paper is the same as that used for nuclease sensitivity profiling, DNS-seq, as previously described [ 7 ].

Multiple earshoots were ground frozen in liquid nitrogen, followed by subsequent aliquoting of the frozen powder for multiple preparation replicates. It includes tissue fixation, nuclei isolation, MNAse digestion, library preparation, and library size selection. The size-selected indexed libraries were subjected to an equimolar pool of 10 libraries summarized in S1 Table.

The 10 libraries correspond to 2 replicates of each size class, A, B, C, and D, and technical replicates of the two B samples. The technical replicates are from the production of two different libraries made from the light digest pools for B biorep1 and B biorep2.

All other parameters were set to their defaults in CutAdapt version 1. All other parameters were set to their defaults in Bowtie 2 version 2. Aligned reads were processed using various programs from the BEDTools suite [ 54 , 55 ], as described below. We set the BC values to: 1. The settings used are minimum window length of 20 bp and maximum window length of bp. When computing the sample statistics, we removed the global zero regions in each MOA-seq to reduce the degree of distortion caused by sparsity.

If needed, adjacent book-ended peaks or those separated by 1 bp were merged to produce the final peaks BED files. To optimize the comparisons across different datasets and genome assemblies B73v3 and B73v5 we used a genomic fraction equivalency approach to select peaks that captured 0.

Unique reads were filtered by mapping quality q13 and PCR duplicates removed using Samtools v. Transcript accumulation was analyzed in R v. Comparative analysis of MOA-seq to other genomic annotations Several published or shared datasets were analyzed. We obtained a recently published dataset of ATAC-seq peaks from nuclei isolated from 1 cm field-grown earshoots [ 31 , 32 ]. For knotted1 [ 34 ] and fasciated ear4 [ 33 ] we obtained published ChIP-seq peaks and used their genomic coordinates as central features to plot the average local MOA-seq coverage.

Some of the peaks were below the minimum size limit for RSAT input 24 bp. For these, we expanded the peaks to 24 bp to retain them in the input data. NsitePL with the PlantProm database [ 38 ], which includes 3, previously identified plant TF-binding sites found in experimentally tested promoter sequences, was further used to identify putative TFs and motifs underlying MOA-seq peaks.

Supporting information S1 Fig. MOA-seq library preparation. For each of the eight light-digest libraries, the 2—3 digestions chosen for pooling are indicated yellow boxes. B Agilent Bioanalyzer electropherograms red line trace plots for the 10 libraries after BluePippin-based size selection. Inset box shows upper purple line and lower green line internal size standards marked in the densitometry plots for all ten final libraries with sample and library ID table.

EPS S2 Fig. EPS S3 Fig. A Browser screenshot from a kb region around the maize tb1 gene showing congruence of MOA-seq profiles with those of open chromatin from DNS-seq [ 7 ]. Browser tracks show "Clean Repeats excluding dust," from plants. EPS S4 Fig. Genome browser views of regions of the genome showing MOA-seq peak segments from this study along with previously published comparable earshoot peaks of open chromatin profiling assays from MNase-based DNS-seq Turpin et al.

Other tracks are displayed as described in S3 Fig.

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