scholarly journals Moderate DNA methylation changes associated with nitrogen remobilization and leaf senescence in Arabidopsis

2021 ◽  
Author(s):  
Emil Vatov ◽  
Ulrike Zentgraf ◽  
Uwe Ludewig
Keyword(s):  
Dna Methylation ◽  
Transcription Factor ◽  
Leaf Senescence ◽  
De Novo ◽  
Transcription Factor Binding ◽  
List Type ◽  
Nitrogen Remobilization ◽  
Factor Binding ◽  
Genetic Components

SummaryThe lifespan of plants and tissues is restricted by environmental and genetic components. Following the transition to reproductive growth, leaf senescence ceases cellular life in monocarpic plants to remobilize nutrients to storage organs.We observed altered leaf to seed ratios, faster senescence progression and enhanced nitrogen remobilization from the leaves in two methylation mutants (ros1 and the triple dmr1/2 cmt3 knockout).DNA methylation in wild type Col-0 leaves initially moderately declined with progressing leaf senescence, predominantly in the CG context, while the ultimate phase of leaf discoloration was associated with moderate de novo methylation of cytosines, primarily in the CHH context.Relatively few differentially methylated regions, including one in the ROS1 promoter linked to the down-regulation of ROS1, were present, but these were unrelated to known senescence-associated genes.Differential methylation patterns were identified in transcription factor binding sites, such as the W-boxes that are targeted by WRKYs, which impaired transcription factor binding when methylated in vitro.Mutants that are defective in DNA methylation showed distinct nitrogen remobilization, which was associated with altered patterns of leaf senescence progression. But moderate methylome changes during leaf senescence were not specifically associated with up-regulated genes during senescence.

scholarly journals DNA methylation changes are a late event in acute promyelocytic leukemia and coincide with loss of transcription factor binding

Blood ◽  
2013 ◽  
Vol 121 (1) ◽  
pp. 178-187 ◽  
Author(s):  
Till Schoofs ◽  
Christian Rohde ◽  
Katja Hebestreit ◽  
Hans-Ulrich Klein ◽  
Stefanie Göllner ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Transcription Factor ◽  
Retinoic Acid ◽  
Acute Promyelocytic Leukemia ◽  
Binding Sites ◽  
Embryonic Stem ◽  
Transcription Factor Binding ◽  
Promyelocytic Leukemia ◽  
Factor Binding ◽  
Aberrant Dna Methylation

Abstract The origin of aberrant DNA methylation in cancer remains largely unknown. In the present study, we elucidated the DNA methylome in primary acute promyelocytic leukemia (APL) and the role of promyelocytic leukemia–retinoic acid receptor α (PML-RARα) in establishing these patterns. Cells from APL patients showed increased genome-wide DNA methylation with higher variability than healthy CD34+ cells, promyelocytes, and remission BM cells. A core set of differentially methylated regions in APL was identified. Age at diagnosis, Sanz score, and Flt3-mutation status characterized methylation subtypes. Transcription factor–binding sites (eg, the c-myc–binding sites) were associated with low methylation. However, SUZ12- and REST-binding sites identified in embryonic stem cells were preferentially DNA hypermethylated in APL cells. Unexpectedly, PML-RARα–binding sites were also protected from aberrant DNA methylation in APL cells. Consistent with this, myeloid cells from preleukemic PML-RARα knock-in mice did not show altered DNA methylation and the expression of PML-RARα in hematopoietic progenitor cells prevented differentiation without affecting DNA methylation. Treatment of APL blasts with all-trans retinoic acid also did not result in immediate DNA methylation changes. The results of the present study suggest that aberrant DNA methylation is associated with leukemia phenotype but is not required for PML-RARα–mediated initiation of leukemogenesis.

scholarly journals The interplay between DNA methylation and sequence divergence in recent human evolution

2015 ◽  
Author(s):  
Irene Hernando-Herraez ◽  
Holger Heyn ◽  
Marcos Fernandez-Callejo ◽  
Enrique Vidal ◽  
Hugo Fernandez-Bellon ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Transcription Factor ◽  
Binding Sites ◽  
Incomplete Lineage Sorting ◽  
Epigenetic Modification ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Methylation Patterns ◽  
Human Specific

DNA methylation is a key regulatory mechanism in mammalian genomes. Despite the increasing knowledge about this epigenetic modification, the understanding of human epigenome evolution is in its infancy. We used whole genome bisulfite sequencing to study DNA methylation and nucleotide divergence between human and great apes. We identified 360 and 210 differentially hypo- and hypermethylated regions (DMRs) in humans compared to non-human primates and estimated that 20% and 36% of these regions, respectively, were detectable throughout several human tissues. Human DMRs were enriched for specific histone modifications and contrary to expectations, the majority were located distal to transcription start sites, highlighting the importance of regions outside the direct regulatory context. We also found a significant excess of endogenous retrovirus elements in human-specific hypomethylated regions suggesting their association with local epigenetic changes. We also reported for the first time a close interplay between inter-species genetic and epigenetic variation in regions of incomplete lineage sorting, transcription factor binding sites and human differentially hypermethylated regions. Specifically, we observed an excess of human-specific substitutions in transcription factor binding sites located within human DMRs, suggesting that alteration of regulatory motifs underlies some human-specific methylation patterns. We also found that the acquisition of DNA hypermethylation in the human lineage is frequently coupled with a rapid evolution at nucleotide level in the neighborhood of these CpG sites. Taken together, our results reveal new insights into the mechanistic basis of human-specific DNA methylation patterns and the interpretation of inter-species non-coding variation.

scholarly journals DNA methylation alone does not cause most cell-type selective transcription factor binding

2013 ◽  
Vol 6 (S1) ◽  
Author(s):  
Matthew T Maurano ◽  
Hao Wang ◽  
Anthony Shafer ◽  
Sam John ◽  
John A Stamatoyannopoulos
Keyword(s):  
Dna Methylation ◽  
Transcription Factor ◽  
Transcription Factor Binding ◽  
Cell Type ◽  
Factor Binding

Methylation quantitative trait locus analysis of chronic postsurgical pain uncovers epigenetic mediators of genetic risk

Epigenomics ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 613-630
Author(s):  
Vidya Chidambaran ◽  
Xue Zhang ◽  
Valentina Pilipenko ◽  
Xiaoting Chen ◽  
Benjamin Wronowski ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Transcription Factor ◽  
Quantitative Trait ◽  
Genetic Risk ◽  
Transcription Factor Binding ◽  
Clinical Trial Registration ◽  
Single Nucleotide ◽  
Postsurgical Pain ◽  
Factor Binding ◽  
Chronic Postsurgical Pain

Background: Overlap of pathways enriched by single nucleotide polymorphisms and DNA-methylation underlying chronic postsurgical pain (CPSP), prompted pilot study of CPSP-associated methylation quantitative trait loci (meQTL). Materials & methods: Children undergoing spine-fusion were recruited prospectively. Logistic-regression for genome- and epigenome-wide CPSP association and DNA-methylation-single nucleotide polymorphism association/mediation analyses to identify meQTLs were followed by functional genomics analyses. Results: CPSP (n = 20/58) and non-CPSP groups differed in pain-measures. Of 2753 meQTLs, DNA-methylation at 127 cytosine–guanine dinucleotides mediated association of 470 meQTLs with CPSP (p < 0.05). At PARK16 locus, CPSP risk meQTLs were associated with decreased DNA-methylation at RAB7L1 and increased DNA-methylation at PM20D1. Corresponding RAB7L1/PM20D1 blood eQTLs (GTEx) and cytosine–guanine dinucleotide-loci enrichment for histone marks, transcription factor binding sites and ATAC-seq peaks suggest altered transcription factor-binding. Conclusion: CPSP-associated meQTLs indicate epigenetic mechanisms mediate genetic risk. Clinical trial registration: NCT01839461 , NCT01731873  (ClinicalTrials.gov).

scholarly journals Base-pair resolution detection of transcription factor binding site by deep deconvolutional network

2018 ◽  
Author(s):  
Sirajul Salekin ◽  
Jianqiu (Michelle) Zhang ◽  
Yufei Huang
Keyword(s):  
Transcription Factor ◽  
Base Pair ◽  
De Novo ◽  
Learning Algorithm ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Deep Learning Algorithm ◽  
Single Base Pair ◽  
Regulatory Analysis ◽  
Nucleotide Resolution

AbstractMotivationTranscription factor (TF) binds to the promoter region of a gene to control gene expression. Identifying precise transcription factor binding sites (TFBS) is essential for understanding the detailed mechanisms of TF mediated gene regulation. However, there is a shortage of computational approach that can deliver single base pair (bp) resolution prediction of TFBS.ResultsIn this paper, we propose DeepSNR, a Deep Learning algorithm for predicting transcription factor binding location at Single Nucleotide Resolution de novo from DNA sequence. DeepSNR adopts a novel deconvolutional network (deconvNet) model and is inspired by the similarity to image segmentation by deconvNet. The proposed deconvNet architecture is constructed on top of ‘Deep-Bind’ and we trained the entire model using TF specific data from ChIP-exonuclease (ChIP-exo) experiments. DeepSNR has been shown to outperform motif search based methods for several evaluation metrics. We have also demonstrated the usefulness of DeepSNR in the regulatory analysis of TFBS as well as in improving the TFBS prediction specificity using ChIP-seq data.AvailabilityDeepSNR is available open source in the GitHub repository (https://github.com/sirajulsalekin/DeepSNR)[email protected]

scholarly journals Widespread effects of DNA methylation and intra-motif dependencies revealed by novel transcription factor binding models

2020 ◽  
Author(s):  
Jan Grau ◽  
Florian Schmidt ◽  
Marcel H. Schulz
Keyword(s):  
Dna Methylation ◽  
Transcription Factor ◽  
Large Scale ◽  
Transcription Factor Binding ◽  
Cpg Methylation ◽  
Motif Analysis ◽  
Factor Binding ◽  
Large Scale Study ◽  
Binding Models ◽  
The Impact

AbstractSeveral studies suggested that transcription factor (TF) binding to DNA may be impaired or enhanced by DNA methylation. We present MeDeMo, a toolbox for TF motif analysis that combines information about DNA methylation with models capturing intra-motif dependencies. In a large-scale study using ChIP-seq data for 335 TFs, we identify novel TFs that are affected by DNA methylation. Overall, we find that CpG methylation decreases the likelihood of binding for the majority of TFs. For a considerable subset of TFs, we show that intra-motif dependencies are pivotal for accurately modelling the impact of DNA methylation on TF binding.

scholarly journals Application of alternative de novo motif recognition models for analysis of structural heterogeneity of transcription factor binding sites: a case study of FOXA2 binding sites

2021 ◽  
Vol 25 (1) ◽  
pp. 7-17
Author(s):  
A. V. Tsukanov ◽  
V. G. Levitsky ◽  
T. I. Merkulova
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
De Novo ◽  
Transcription Factor Binding Sites ◽  
Structural Heterogeneity ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Model Training ◽  
Motif Recognition ◽  
Positional Weight

The most popular model for the search of ChIP-seq data for transcription factor binding sites (TFBS) is the positional weight matrix (PWM). However, this model does not take into account dependencies between nucleotide occurrences in different site positions. Currently, two recently proposed models, BaMM and InMoDe, can do as much. However, application of these models was usually limited only to comparing their recognition accuracies with that of PWMs, while none of the analyses of the co-prediction and relative positioning of hits of different models in peaks has yet been performed. To close this gap, we propose the pipeline called MultiDeNA. This pipeline includes stages of model training, assessing their recognition accuracy, scanning ChIP-seq peaks and their classif ication based on scan results. We applied our pipeline to 22 ChIP-seq datasets of TF FOXA2 and considered PWM, dinucleotide PWM (diPWM), BaMM and InMoDe models. The combination of these four models allowed a signif icant increase in the fraction of recognized peaks compared to that for the sole PWM model: the increase was 26.3 %. The BaMM model provided the main contribution to the recognition of sites. Although the major fraction of predicted peaks contained TFBS of different models with coincided positions, the medians of the fraction of peaks containing the predictions of sole models were 1.08, 0.49, 4.15 and 1.73 % for PWM, diPWM, BaMM and InMoDe, respectively. Thus, FOXA2 BSs were not fully described by only a sole model, which indicates theirs heterogeneity. We assume that the BaMM model is the most successful in describing the structure of the FOXA2 BS in ChIP-seq datasets under study.

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