Genome-Wide Discriminatory Information Patterns of Cytosine DNA Methylation

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.

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.

Approaches to Whole-Genome Methylome Analysis in Plants

Cytosine methylation as a reversible chromatin mark has been investigated extensively for its influence on gene silencing and the regulation of its dynamic association-disassociation at specific sites within a eukaryotic genome. With the remarkable reductions in cost and time associated with whole-genome DNA sequence analysis, coupled with the high fidelity of bisulfite-treated DNA sequencing, single nucleotide resolution of cytosine methylation repatterning within even very large genomes is increasingly achievable. What remains a challenge is the analysis of genome-wide methylome datasets and, consequently, a clear understanding of the overall influence of methylation repatterning on gene expression or vice versa. Reported data have sometimes been subject to stringent data filtering methods that can serve to skew downstream biological interpretation. These complications derive from methylome analysis procedures that vary widely in method and parameter setting. DNA methylation as a chromatin feature that influences DNA stability can be dynamic and rapidly responsive to environmental change. Consequently, methods to discriminate background "noise" of the system from biological signal in response to specific perturbation is essential in some types of experiments. We describe numerous aspects of whole-genome bisulfite sequence data that must be contemplated as well as the various steps of methylome data analysis which impact the biological interpretation of the final output.

Machine learning for epigenetics and future medical applications

Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review.

Stress-responsive pathways and small RNA changes distinguish variable developmental phenotypes caused by MSH1 loss

BACKGROUND

Proper regulation of nuclear-encoded, organelle-targeted genes is crucial for plastid and mitochondrial function. Among these genes, MutS Homolog 1 (MSH1) is notable for generating an assortment of mutant phenotypes with varying degrees of penetrance and pleiotropy. Stronger phenotypes have been connected to stress tolerance and epigenetic changes, and in Arabidopsis T-DNA mutants, two generations of homozygosity with the msh1 insertion are required before severe phenotypes begin to emerge. These observations prompted us to examine how msh1 mutants contrast according to generation and phenotype by profiling their respective transcriptomes and small RNA populations.

RESULTS

Using RNA-seq, we analyze pathways that are associated with MSH1 loss, including abiotic stresses such as cold response, pathogen defense and immune response, salicylic acid, MAPK signaling, and circadian rhythm. Subtle redox and environment-responsive changes also begin in the first generation, in the absence of strong phenotypes. Using small RNA-seq we further identify miRNA changes, and uncover siRNA trends that indicate modifications at the chromatin organization level. In all cases, the magnitude of changes among protein-coding genes, transposable elements, and small RNAs increases according to generation and phenotypic severity.

CONCLUSION

Loss of MSH1 is sufficient to cause large-scale regulatory changes in pathways that have been individually linked to one another, but rarely described all together within a single mutant background. This study enforces the recognition of organelles as critical integrators of both internal and external cues, and highlights the relationship between organelle and nuclear regulation in fundamental aspects of plant development and stress signaling. Our findings also encourage further investigation into potential connections between organelle state and genome regulation vis-á-vis small RNA feedback.

Data Analysis in Chemistry and Bio-Medical Sciences

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