ETS2 repressor factor (ERF) is involved in T lymphocyte maturation acting as regulator of thymocyte lineage commitment

Tsiomita, Spyridoula; Liveri, Effrosyni Maria; Vardaka, Panagiota; Vogiatzi, Angeliki; Skiadaresis, Argyris; Saridis, George N.; Tsigkas, Ioannis; Michaelidis, Theologos M.; Mavrothalassitis, George; Thyphronitis, George

doi:10.1002/jlb.1a0720-439r

Cited by 3 publications

(2 citation statements)

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“…For instance, in mice it has been shown that the TFs ELK1 and ELK4 function redundantly to restrict the generation of innate-like CD8+ T-cells [36]. Recently, Tsiomita et al showed that ERF is a potential regulator during T-lymphocyte maturation [46]. Further, for innate lymphoid cells (ILC), which are a population of lymphocytes, it can be shown that ELK3 is regulated by the circRNA circTmem241 and that the knockdown of ELK3 significantly decreased the number of ILCs [31].…”

Section: Identification Of Tfs That Are Altered By Genetic Variants M...mentioning

confidence: 99%

A statistical approach to identify regulatory DNA variations

Baumgarten

Rumpf

Kessler

et al. 2023

Preprint

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Non-coding variations located within regulatory elements may alter gene expression by modifying Transcription Factor (TF) binding sites and thereby lead to functional consequences like various traits or diseases. To understand these molecular mechanisms, different TF models are being used to assess the effect of DNA sequence variations, such as Single Nucleotide Polymorphisms (SNPs). However, few statistical approaches exist to compute statistical significance of results but they often are slow for large sets of SNPs, such as data. obtained from a genome-wide association study (GWAS) or allele-specific analysis of chromatin data. Results: We investigate the distribution of maximal differential TF binding scores for general computational models that assess TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo data sets showed that our new approach improves on an existing method in terms of performance and speed. In applications on large sets of eQTL and GWAS SNPs we could illustrate the usefulness of the novel statistic to highlight cell type specific regulators and TF target genes. Conclusions: Our approach allows the evaluation of DNA changes that induce differential TF binding in a fast and accurate manner, permitting computations on large mutation data sets. An implementation of the novel approach is freely available at https://github.com/SchulzLab/SNEEP.

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Section: Identification Of Tfs That Are Altered By Genetic Variants M...mentioning

confidence: 99%

A statistical approach to identify regulatory DNA variations

Baumgarten

Rumpf

Kessler

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For instance, in mice it has been shown that the TFs ELK1 and ELK4 function redundantly to restrict the generation of innate-like CD8+ Tcells (Maurice et al, 2018). Recently, Tsiomita et al showed that ERF is a potential regulator during T-lymphocyte maturation (Tsiomita et al, 2022). Further, for innate lymphoid cells (ILC), which are a population of lymphocytes, it can be shown that ELK3 is regulated by the circRNA circTmem241.…”

Section: Automatic Generation Of a Summary Reportmentioning

confidence: 99%

Prediction of transcription factor binding using epigenomics and genome variation data

Baumgarten

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A central concern in genetics is to identify mechanisms of transcriptional regulation. The aim is to unravel the mapping between the DNA sequence and gene expression. However, it turned out that this is extremely complex. Gene regulation is highly cell type-specific and even moderate changes in gene ex- pression can have functional consequences. Important contributors to gene regulation are transcription factors (TFs), that are able to directly interact with the DNA. Often, a first step in understanding the effect of a TF on the gene’s regulation is to identify the genomic regions a TF binds to. Therefore, one needs to be aware of the TF’s binding preferences, which are commonly summarized in TF binding motifs. Although for many TFs the binding motif is experimentally validated, there is still a large number of TFs where no binding motif is known. There exist many tools that link TF binding motifs to TFs. We developed the method Massif that improves the performance of such tools by incorporating a domain score that uses the DNA binding domain of the studied TF as additional information. TF binding sites are often enriched in regulatory elements (REMs) such as promoters or enhancers, where the latter can be located megabases away from its target gene. However, to understand the regulation of a gene it is crucial to know where the REMs of a gene are located. We introduced the EpiRegio webserver that holds REMs associated to target genes predicted across many cell types and tissues using STITCHIT, a previously established method. Our publicly available webserver enables to query for REMs associated to genes (gene query) and REMs overlapping genomic regions (region query). We illus- trated the usefulness of EpiRegio by pointing to a TF that occurs enriched in the REMs of differential expressed genes in circPLOD2 depleted pericytes. Further, we highlighted genes, which are affected by CRISPR-Cas induced mutations in non-coding genomic regions using EpiRegio’s region query. Non-coding genetic variants within REMs may alter gene expression by modifying TF binding sites, which can lead to various kinds of traits or diseases. To understand the underlying molecular mechanisms, one aims to evaluate the effect of such genetic variations on TF binding sites. We developed an accurate and fast statistical approach, that can assess whether a single nucleotide polymorphism (SNP) is regulatory. Further, we combined this approach with epigenetic data and additional analyses in our Sneep workflow. For instance, it enables to identify TFs whose binding preferences are affected by the analyzed SNPs, which is illustrated on eQTL datasets for different cell types. Additionally, we used our Sneep workflow to highlight cardiovascular disease genes using regulatory SNPs and REM-gene interactions. Overall, the described results allow a better understanding of REM-gene interactions and their interplay with TFs on gene regulation.

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