Integrated analysis of motif activity and gene expression changes of transcription factors

Madsen, Jesper Grud Skat; Rauch, Alexander; Hauwaert, Elvira Laila Van; Schmidt, Søren Fisker; Winnefeld, Marc; Mandrup, Susanne

doi:10.1101/gr.227231.117

Cited by 51 publications

(54 citation statements)

References 75 publications

(110 reference statements)

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“…Genomic sequence variation, epigenetic factor, and gene expression are interdependent and jointly contribute to the normal functioning or dysfunction of tissue (Delahaye et al, 2018). For example, the sequencing variations can alter the TF binding strength to regulate gene expression directly (Karczewski et al, 2011;Madsen et al, 2018;Johnston et al, 2019). We are interested in the role of the genome variations in differentiation from human ESCs to trophoblasts.…”

Section: Introductionmentioning

confidence: 99%

Effect for Human Genomic Variation During the BMP4-Induced Conversion From Pluripotent Stem Cells to Trophoblast

Liu

et al. 2020

Front. Genet.

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The role of genomic variation in differentiation is currently not well understood. Here, the genomic variations were determined with the whole-genome sequencing for three pairs of pluripotent stem cell lines and their corresponding BMP4-induced trophoblast cell lines. We identified ∼3,500 single nucleotide variations and ∼4,500 indels by comparing the genome sequenced data between the stem cell lines and the matched BMP4induced trophoblast cell lines and annotated them by integrating the epigenomic and transcriptomic datasets. Relatively, introns enrich more variations. We found ∼45% (42 genes) of the differentially expressed genes in trophoblasts that associate genomic variations. Six variations, located at transcription factor binding sites where H3K4me3 and H3K27ac are enriched in both H1 and H1_BMP4, were identified. The epigenetic status around the genomic variations in H1 was similar to that in H1_BMP4. This means that the variation-associated gene's expression change can not be attributed to epigenetic alteration. The genes associated with the six variations were upregulated in differentiation. We inferred that during the differentiation, an increased in the expression level of the MEF2C gene is due to a genomic variation in chromosomes 5: 88179358 A > G, which is at a binding site of TFs KLF16, NR2C2, and ZNF740 to MEF2C. Allele G shows a higher affinity to the TFs in the induced cells. The increased expression of MEF2C leads to an increased expression of TF MEF2C's target genes, subsequently affecting the differentiation. Although genomic variation should not be a dominant factor in differentiation, we believe that genomic variation could indeed play a role in the differentiation from stem cells into trophoblast.

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Section: Introductionmentioning

confidence: 99%

Effect for Human Genomic Variation During the BMP4-Induced Conversion From Pluripotent Stem Cells to Trophoblast

Liu

et al. 2020

Front. Genet.

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“…For each individual regulon across the integrated network, we obtained the TF binding motif from JASPAR (53). We used a published database containing Pearson correlations between TFs position weight matrix (54,55) and subset, visualised the TFs from integrated regulon network.…”

Section: Comparison Of Regulon Motifsmentioning

confidence: 99%

Predicting gene regulatory networks from cell atlases

Møller

Natarajan

2020

Life Sci. Alliance

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Recent single-cell RNA-sequencing atlases have surveyed and identified major cell types across different mouse tissues. Here, we computationally reconstruct gene regulatory networks from three major mouse cell atlases to capture functional regulators critical for cell identity, while accounting for a variety of technical differences, including sampled tissues, sequencing depth, and author assigned cell type labels. Extracting the regulatory crosstalk from mouse atlases, we identify and distinguish global regulons active in multiple cell types from specialised cell type–specific regulons. We demonstrate that regulon activities accurately distinguish individual cell types, despite differences between individual atlases. We generate an integrated network that further uncovers regulon modules with coordinated activities critical for cell types, and validate modules using available experimental data. Inferring regulatory networks during myeloid differentiation from wild-type and Irf8 KO cells, we uncover functional contribution of Irf8 regulon activity and composition towards monocyte lineage. Our analysis provides an avenue to further extract and integrate the regulatory crosstalk from single-cell expression data.

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“…Although ChIP-seq studies suggest that many TF binding events appear to be not functional, the presence of a sequence motif is still predictive of gene expression [1]. With a linear regression model, in which the sequence information is used to model gene expression, one can learn the TFs that play a major role in gene regulation [5][6][7][8][9]. Typical approaches either use linear regression with L 2 -regularization (Ridge Regression) or a combination of L 1 -and L 2 -regularization (ElasticNet).…”

Section: Introductionmentioning

confidence: 99%

Investigating the effect of dependence between conditions with Bayesian Linear Mixed Models for motif activity analysis

Lederer¹,

Heskes²,

Heeringen³

et al. 2020

PLoS ONE

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Motivation Cellular identity and behavior is controlled by complex gene regulatory networks. Transcription factors (TFs) bind to specific DNA sequences to regulate the transcription of their target genes. On the basis of these TF motifs in cis-regulatory elements we can model the influence of TFs on gene expression. In such models of TF motif activity the data is usually modeled assuming a linear relationship between the motif activity and the gene expression level. A commonly used method to model motif influence is based on Ridge Regression. One important assumption of linear regression is the independence between samples. However, if samples are generated from the same cell line, tissue, or other biological source, this assumption may be invalid. This same assumption of independence is also applied to different yet similar experimental conditions, which may also be inappropriate. In theory, the independence assumption between samples could lead to loss in signal detection. Here we investigate whether a Bayesian model that allows for correlations results in more accurate inference of motif activities. Results We extend the Ridge Regression to a Bayesian Linear Mixed Model, which allows us to model dependence between different samples. In a simulation study, we investigate the differences between the two model assumptions. We show that our Bayesian Linear Mixed Model implementation outperforms Ridge Regression in a simulation scenario where the noise, which is the signal that can not be explained by TF motifs, is uncorrelated. However, we demonstrate that there is no such gain in performance if the noise has a similar covariance structure over samples as the signal that can be explained by motifs. We give a mathematical explanation to why this is the case. Using four representative real datasets we show that at most *â €‹40% of the signal is explained by motifs using the linear model. With these data there is no advantage to using the Bayesian Linear Mixed Model, due to the similarity of the covariance structure.

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Integrated analysis of motif activity and gene expression changes of transcription factors

Cited by 51 publications

References 75 publications

Effect for Human Genomic Variation During the BMP4-Induced Conversion From Pluripotent Stem Cells to Trophoblast

Effect for Human Genomic Variation During the BMP4-Induced Conversion From Pluripotent Stem Cells to Trophoblast

Predicting gene regulatory networks from cell atlases

Investigating the effect of dependence between conditions with Bayesian Linear Mixed Models for motif activity analysis

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