Hypothesis testing for two discrete populations based on the Hellinger distance

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.

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.

Discrimination of DNA Methylation Signal from Background Variation for Clinical Diagnostics

Advances in the study of human DNA methylation variation offer a new avenue for the translation of epigenetic research results to clinical applications. Although current approaches to methylome analysis have been helpful in revealing an epigenetic influence in major human diseases, this type of analysis has proven inadequate for the translation of these advances to clinical diagnostics. As in any clinical test, the use of a methylation signal for diagnostic purposes requires the estimation of an optimal cutoff value for the signal, which is necessary to discriminate a signal induced by a disease state from natural background variation. To address this issue, we propose the application of a fundamental signal detection theory and machine learning approaches. Simulation studies and tests of two available methylome datasets from autism and leukemia patients demonstrate the feasibility of this approach in clinical diagnostics, providing high discriminatory power for the methylation signal induced by disease, as well as high classification performance. Specifically, the analysis of whole biomarker genomic regions could suffice for a diagnostic, markedly decreasing its cost.

Responses to Social Vocalizations in the Dorsal Cochlear Nucleus of Mice

Identifying sounds is critical for an animal to make appropriate behavioral responses to environmental stimuli, including vocalizations from conspecifics. Identification of vocalizations may be supported by neuronal selectivity in the auditory pathway. The first place in the ascending auditory pathway where neuronal selectivity to vocalizations has been found is in the inferior colliculus (IC), but very few brainstem nuclei have been evaluated. Here, we tested whether selectivity to vocalizations is present in the dorsal cochlear nucleus (DCN). We recorded extracellular neural responses in the DCN of mice and found that fusiform cells responded in a heterogeneous and selective manner to mouse ultrasonic vocalizations. Most fusiform cells responded to vocalizations that contained spectral energy at much higher frequencies than the characteristic frequencies of the cells. To understand this mismatch of stimulus properties and frequency tuning of the cells, we developed a dynamic, nonlinear model of the cochlea that simulates cochlear distortion products on the basilar membrane. We preprocessed the vocalization stimuli through this model and compared responses to these distorted vocalizations with responses to the original vocalizations. We found that fusiform cells in the DCN respond in a heterogeneous manner to vocalizations, and that these neurons can use distortion products as a mechanism for encoding ultrasonic vocalizations. In addition, the selective neuronal responses were dependent on the presence of inhibitory sidebands that modulated the response depending on the temporal structure of the distortion product. These findings suggest that important processing of complex sounds occurs at a very early stage of central auditory processing and is not strictly a function of the cortex.