Discrimination of DNA Methylation Signal from Background Variation for Clinical Diagnostics

Phenotypic plasticity for improved light harvesting, in tandem with methylome repatterning in reef-building corals

Acclimatization through phenotypic plasticity represents a more rapid response to environmental change than adaptation and is vital to optimize organisms' performance in different conditions. Generally, animals are less phenotypically plastic than plants, but reef-building corals exhibit plant-like properties. They are light dependent with a sessile and modular construction that facilitates rapid morphological changes within their lifetime. We induced phenotypic changes by altering light exposure in a reciprocal transplant experiment and found that coral plasticity is a colony trait emerging from comprehensive morphological and physiological changes within the colony. Plasticity in skeletal features optimized coral light harvesting and utilization and paralleled significant methylome and transcriptome modifications. Network-associated responses resulted in the identification of hub genes and clusters associated to the change in phenotype: inter-partner recognition and phagocytosis, soft tissue growth and biomineralization. Furthermore, we identified hub genes putatively involved in animal photoreception-phototransduction. These findings fundamentally advance our understanding of how reef-building corals repattern the methylome and adjust a phenotype, revealing an important role of light sensing by the coral animal to optimize photosynthetic performance of the symbionts.

Sensory plastid-associated PsbP DOMAIN-CONTAINING PROTEIN 3 triggers plant growth- and defense-related epigenetic responses

Sensory plastids are important in plant responses to environmental changes. Previous studies show that MutS HOMOLOG 1 (MSH1) perturbation in sensory plastids induces heritable epigenetic phenotype adjustment. Previously, the PsbP homolog DOMAIN-CONTAINING PROTEIN 3 (PPD3), a protein of unknown function, was postulated to be an interactor with MSH1. This study investigates the relationship of PPD3 with MSH1 and with plant environmental sensing. The ppd3 mutant displays a whole-plant phenotype variably altered in growth rate, flowering time, reactive oxygen species (ROS) modulation and response to salt, with effects on meristem growth. Present in both chloroplasts and sensory plastids, PPD3 colocalized with MSH1 in root tips but not in leaf tissues. The suppression or overexpression of PPD3 affected the plant growth rate and stress tolerance, and led to a heritable, heterogenous 'memory' state with both dwarfed and vigorous growth phenotypes. Gene expression and DNA methylome data sets from PPD3-OX and derived memory states showed enrichment in growth versus defense networks and meristem effects. Our results support a model of sensory plastid influence on nuclear epigenetic behavior and ppd3 as a second trigger, functioning within meristem plastids to recalibrate growth plasticity.

On the thermodynamics of DNA methylation process

DNA methylation is an epigenetic mechanism that plays important roles in various biological processes including transcriptional and post-transcriptional regulation, genomic imprinting, aging, and stress response to environmental changes and disease. Consistent with thermodynamic principles acting within living systems and the application of maximum entropy principle, we propose a theoretical framework to understand and decode the DNA methylation process. A central tenet of this argument is that the probability density function of DNA methylation information-divergence summarizes the statistical biophysics underlying spontaneous methylation background and implicitly bears on the channel capacity of molecular machines conforming to Shannon's capacity theorem. On this theoretical basis, contributions from the molecular machine (enzyme) logical operations to Gibb entropy (S) and Helmholtz free energy (F) are intrinsic. Application to the estimations of S on datasets from Arabidopsis thaliana suggests that, as a thermodynamic state variable, individual methylome entropy is completely determined by the current state of the system, which in biological terms translates to a correspondence between estimated entropy values and observable phenotypic state. In patients with different types of cancer, results suggest that a significant information loss occurs in the transition from differentiated (healthy) tissues to cancer cells. This type of analysis may have important implications for early-stage diagnostics. The analysis of entropy fluctuations on experimental datasets revealed existence of restrictions on the magnitude of genome-wide methylation changes originating by organismal response to environmental changes. Only dysfunctional stages observed in the Arabidopsis mutant met1 and in cancer cells do not conform to these rules.

Re-analysis of publicly available methylomes using signal detection yields new information

Cytosine methylation is an epigenetic mark that participates in regulation of gene expression and chromatin stability in plants. Advancements in whole genome sequencing technologies have enabled investigation of methylome dynamics under different conditions. However, the computational methods for analyzing bisulfite sequence data have not been unified. Contention remains in the correlation of differentially methylated positions with the investigated treatment and exclusion of noise, inherent to these stochastic datasets. The prevalent approaches apply Fisher's exact test, logistic, or beta regression, followed by an arbitrary cut-off for differences in methylation levels. A different strategy, the MethylIT pipeline, utilizes signal detection to determine cut-off based on a fitted generalized gamma probability distribution of methylation divergence. Re-analysis of publicly available BS-seq data from two epigenetic studies in Arabidopsis and applying MethylIT revealed additional, previously unreported results. Methylome repatterning in response to phosphate starvation was confirmed to be tissue-specific and included phosphate assimilation genes in addition to sulfate metabolism genes not implicated in the original study. During seed germination plants undergo major methylome reprogramming and use of MethylIT allowed us to identify stage-specific gene networks. We surmise from these comparative studies that robust methylome experiments must account for data stochasticity to achieve meaningful functional analyses.

Methylome decoding of RdDM-mediated reprogramming effects in the Arabidopsis MSH1 system

Background

Plants undergo programmed chromatin changes in response to environment, influencing heritable phenotypic plasticity. The RNA-directed DNA methylation (RdDM) pathway is an essential component of this reprogramming process. The relationship of epigenomic changes to gene networks on a genome-wide basis has been elusive, particularly for intragenic DNA methylation repatterning.

Results

Epigenomic reprogramming is tractable to detailed study and cross-species modeling in the MSH1 system, where perturbation of the plant-specific gene MSH1 triggers at least four distinct nongenetic states to impact plant stress response and growth vigor. Within this system, we have defined RdDM target loci toward decoding phenotype-relevant methylome data. We analyze intragenic methylome repatterning associated with phenotype transitions, identifying state-specific cytosine methylation changes in pivotal growth-versus-stress, chromatin remodeling, and RNA spliceosome gene networks that encompass 871 genes. Over 77% of these genes, and 81% of their central network hubs, are functionally confirmed as RdDM targets based on analysis of mutant datasets and sRNA cluster associations. These dcl2/dcl3/dcl4-sensitive gene methylation sites, many present as singular cytosines, reside within identifiable sequence motifs. These data reflect intragenic methylation repatterning that is targeted and amenable to prediction.

Conclusions

A prevailing assumption that biologically relevant DNA methylation variation occurs predominantly in density-defined differentially methylated regions overlooks behavioral features of intragenic, single-site cytosine methylation variation. RdDM-dependent methylation changes within identifiable sequence motifs reveal gene hubs within networks discriminating stress response and growth vigor epigenetic phenotypes. This study uncovers components of a methylome “code” for de novo intragenic methylation repatterning during plant phenotype transitions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02731-w.

MSH1-induced heritable enhanced growth vigor through grafting is associated with the RdDM pathway in plants

Plants transmit signals long distances, as evidenced in grafting experiments that create distinct rootstock-scion junctions. Noncoding small RNA is a signaling molecule that is graft transmissible, participating in RNA-directed DNA methylation; but the meiotic transmissibility of graft-mediated epigenetic changes remains unclear. Here, we exploit the MSH1 system in Arabidopsis and tomato to introduce rootstock epigenetic variation to grafting experiments. Introducing mutations dcl2, dcl3 and dcl4 to the msh1 rootstock disrupts siRNA production and reveals RdDM targets of methylation repatterning. Progeny from grafting experiments show enhanced growth vigor relative to controls. This heritable enhancement-through-grafting phenotype is RdDM-dependent, involving 1380 differentially methylated genes, many within auxin-related gene pathways. Growth vigor is associated with robust root growth of msh1 graft progeny, a phenotype associated with auxin transport based on inhibitor assays. Large-scale field experiments show msh1 grafting effects on tomato plant performance, heritable over five generations, demonstrating the agricultural potential of epigenetic variation.

The meiotic transmissibility and progeny phenotypic influence of graft-mediated epigenetic changes remain unclear. Here, the authors use the msh1 mutant in the rootstock to trigger heritable enhanced growth vigor in Arabidopsis and tomato, and show it is associated with the RNA-directed DNA methylation pathway.

Segregation of an MSH1 RNAi transgene produces heritable non-genetic memory in association with methylome reprogramming

MSH1 is a plant-specific protein. RNAi suppression of MSH1 results in phenotype variability for developmental and stress response pathways. Segregation of the RNAi transgene produces non-genetic msh1 ‘memory’ with multi-generational inheritance. First-generation memory versus non-memory comparison, and six-generation inheritance studies, identifies gene-associated, heritable methylation repatterning. Genome-wide methylome analysis integrated with RNAseq and network-based enrichment studies identifies altered circadian clock networks, and phytohormone and stress response pathways that intersect with circadian control. A total of 373 differentially methylated loci comprising these networks are sufficient to discriminate memory from nonmemory full sibs. Methylation inhibitor 5-azacytidine diminishes the differences between memory and wild type for growth, gene expression and methylation patterning. The msh1 reprogramming is dependent on functional HISTONE DEACETYLASE 6 and methyltransferase MET1, and transition to memory requires the RNA-directed DNA methylation pathway. This system of phenotypic plasticity may serve as a potent model for defining accelerated plant adaptation during environmental change.

Segregation of an MSH1 RNAi transgene produces non-genetic memory that displays transgenerational inheritance in Arabidopsis. Here, the authors compare memory and non-memory full-sib progenies to show the involvement of DNA methylation reprogramming, involving the RdDM pathway, in transition to a heritable memory state.

Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia

Genome-wide DNA methylation and gene expression are commonly altered in pediatric acute lymphoblastic leukemia (PALL). Integrated network analysis of cytosine methylation and expression datasets has the potential to provide deeper insights into the complex disease states and their causes than individual disconnected analyses. With the purpose of identifying reliable cancer-associated methylation signal in gene regions from leukemia patients, we present an integrative network analysis of differentially methylated (DMGs) and differentially expressed genes (DEGs). The application of a novel signal detection-machine learning approach to methylation analysis of whole genome bisulfite sequencing (WGBS) data permitted a high level of methylation signal resolution in cancer-associated genes and pathways. This integrative network analysis approach revealed that gene expression and methylation consistently targeted the same gene pathways relevant to cancer: Pathways in cancer, Ras signaling pathway, PI3K-Akt signaling pathway, and Rap1 signaling pathway, among others. Detected gene hubs and hub sub-networks were integrated by signature loci associated with cancer that include, for example, NOTCH1, RAC1, PIK3CD, BCL2, and EGFR. Statistical analysis disclosed a stochastic deterministic relationship between methylation and gene expression within the set of genes simultaneously identified as DEGs and DMGs, where larger values of gene expression changes were probabilistically associated with larger values of methylation changes. Concordance analysis of the overlap between enriched pathways in DEG and DMG datasets revealed statistically significant agreement between gene expression and methylation changes. These results support the potential identification of reliable and stable methylation biomarkers at genes for cancer diagnosis and prognosis.