Epigenomic regulation of OTU5 in Arabidopsis thaliana

Estimating genome-wide DNA methylation heterogeneity with methylation patterns

BACKGROUND

In a heterogeneous population of cells, individual cells can behave differently and respond variably to the environment. This cellular diversity can be assessed by measuring DNA methylation patterns. The loci with variable methylation patterns are informative of cellular heterogeneity and may serve as biomarkers of diseases and developmental progression. Cell-to-cell methylation heterogeneity can be evaluated through single-cell methylomes or computational techniques for pooled cells. However, the feasibility and performance of these approaches to precisely estimate methylation heterogeneity require further assessment.

RESULTS

Here, we proposed model-based methods adopted from a mathematical framework originally from biodiversity, to estimate genome-wide DNA methylation heterogeneity. We evaluated the performance of our models and the existing methods with feature comparison, and tested on both synthetic datasets and real data. Overall, our methods have demonstrated advantages over others because of their better correlation with the actual heterogeneity. We also demonstrated that methylation heterogeneity offers an additional layer of biological information distinct from the conventional methylation level. In the case studies, we showed that distinct profiles of methylation heterogeneity in CG and non-CG methylation can predict the regulatory roles between genomic elements in Arabidopsis. This opens up a new direction for plant epigenomics. Finally, we demonstrated that our score might be able to identify loci in human cancer samples as putative biomarkers for early cancer detection.

CONCLUSIONS

We adopted the mathematical framework from biodiversity into three model-based methods for analyzing genome-wide DNA methylation heterogeneity to monitor cellular heterogeneity. Our methods, namely MeH, have been implemented, evaluated with existing methods, and are open to the research community.

The Arabidopsis Deubiquitylase OTU5 Suppresses Flowering by Histone Modification-Mediated Activation of the Major Flowering Repressors FLC, MAF4, and MAF5

Distinct phylogeny and substrate specificities suggest that 12 Arabidopsis Ovarian Tumor domain-containing (OTU) deubiquitinases participate in conserved or plant-specific functions. The otu5-1 null mutant displayed a pleiotropic phenotype, including early flowering, mimicking that of mutants harboring defects in subunits (e.g., ARP6) of the SWR1 complex (SWR1c) involved in histone H2A.Z deposition. Transcriptome and RT-qPCR analyses suggest that downregulated FLC and MAF4-5 are responsible for the early flowering of otu5-1. qChIP analyses revealed a reduction and increase in activating and repressive histone marks, respectively, on FLC and MAF4-5 in otu5-1. Subcellular fractionation, GFP-fusion expression, and MNase treatment of chromatin showed that OTU5 is nucleus-enriched and chromatin-associated. Moreover, OTU5 was found to be associated with FLC and MAF4-5. The OTU5-associated protein complex(es) appears to be distinct from SWR1c, as the molecular weights of OTU5 complex(es) were unaltered in arp6-1 plants. Furthermore, the otu5-1 arp6-1 double mutant exhibited synergistic phenotypes, and H2A.Z levels on FLC/MAF4-5 were reduced in arp6-1 but not otu5-1. Our results support the proposition that Arabidopsis OTU5, acting independently of SWR1c, suppresses flowering by activating FLC and MAF4-5 through histone modification. Double-mutant analyses also indicate that OTU5 acts independently of the HUB1-mediated pathway, but it is partially required for FLC-mediated flowering suppression in autonomous pathway mutants and FRIGIDA-Col.

Plant deubiquitinases: from structure and activity to biological functions

This article attempts to provide comprehensive review of plant deubiquitinases, paying special attention to recent advances in their biochemical activities and biological functions. Proteins in eukaryotes are subjected to post-translational modifications, in which ubiquitination is regarded as a reversible process. Cellular deubiquitinases (DUBs) are a key component of the ubiquitin (Ub)-proteasome system responsible for cellular protein homeostasis. DUBs recycle Ub by hydrolyzing poly-Ub chains on target proteins, and maintain a balance of the cellular Ub pool. In addition, some DUBs prefer to cleave poly-Ub chains not linked through the conventional K48 residue, which often alter the substrate activity instead of its stability. In plants, all seven known DUB subfamilies have been identified, namely Ub-binding protease/Ub-specific protease (UBP/USP), Ub C-terminal hydrolase (UCH), Machado-Joseph domain-containing protease (MJD), ovarian-tumor domain-containing protease (OTU), zinc finger with UFM1-specific peptidase domain protease (ZUFSP), motif interacting with Ub-containing novel DUB family (MINDY), and JAB1/MPN/MOV34 protease (JAMM). This review focuses on recent advances in the structure, activity, and biological functions of plant DUBs, particularly in the model plant Arabidopsis.

Methylome Imputation by Methylation Patterns

DNA methylation is studied extensively for its relations with several biological processes such as transcriptional regulation. While methylation levels are usually estimated per cytosine or genomic region, additional information on methylation heterogeneity can be obtained when considering stretches of successive cytosines on the same reads; however, the majority of methylomes suffer from low coverage of genomic regions with sequencing depths enough for accurate estimation of methylation heterogeneity using existing methods. Here we describe a probabilistic-based imputation method that makes use of methylation information from neighboring sites to recover partially observed methylation patterns. Our method and software are proven to be faster and more accurate among all evaluated. Ultimately, our method allows for a more streamlined monitoring of epigenetic changes within cellular populations and their putative role in disease.

BSImp: Imputing Partially Observed Methylation Patterns for Evaluating Methylation Heterogeneity

DNA methylation is one of the most studied epigenetic modifications that has applications ranging from transcriptional regulation to aging, and can be assessed by bisulfite sequencing (BS-seq) or enzymatic methyl sequencing (EM-seq) at single base-pair resolution. The permutations of methylation statuses given by aligned reads reflect the methylation patterns of individual cells. These patterns at specific genomic locations are sought to be indicative of cellular heterogeneity within a cellular population, which are predictive of developments and diseases; therefore, methylation heterogeneity has potentials in early detection of these changes. Computational methods have been developed to assess methylation heterogeneity using methylation patterns formed by four consecutive CpGs, but the nature of shotgun sequencing often give partially observed patterns, which makes very limited data available for downstream analysis. While many programs are developed to impute genome-wide methylation levels, currently there is only one method developed for recovering partially observed methylation patterns; however, the program needs lots of data to train and cannot be used directly; therefore, we developed a probabilistic-based imputation method that uses information from neighbouring sites to recover partially observed methylation patterns speedily. It is demonstrated to allow for the evaluation of methylation heterogeneity at 15% more regions genome-wide with high accuracy for data with moderate depth. To make it more user-friendly we also provide a computational pipeline for genome-screening, which can be used in both evaluating methylation levels and profiling methylation patterns genomewide for all cytosine contexts, which is the first of its kind. Our method allows for accurate estimation of methylation levels and makes evaluating methylation heterogeneity available for much more data with reasonable coverage, which has important implications in using methylation heterogeneity for monitoring changes within the cellular populations that were impossible to detect for the assessment of development and diseases.