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To enable computational analysis of regulatory networks within the cancer cell in its natural tumor microenvironment, we develop a two-stage histoepigenetic analysis method. The first stage involves iterative computational deconvolution to estimate sample-specific cancer-cell intrinsic expression of a gene of interest. The second stage places the gene within a network module. We validate the method in simulation experiments, show improved performance relative to differential expression analysis from bulk samples, and apply it to illuminate the role of the mesothelin (MSLN) network in pancreatic ductal adenocarcinoma (PDAC). The network analysis and subsequent experimental validation in a panel of PDAC cell lines suggests AKT activation by MSLN through two known activators, retinoic acid receptor gamma (RARG) and tyrosine kinase non receptor 2 (TNK2). Taken together, these results demonstrate the potential of histoepigenetic analysis to reveal cancer-cell specific molecular interactions directly from patient tumor profiles.

Bacterial communities associated with roots impact the health and nutrition of the host plant. The dynamics of these microbial assemblies over the plant life cycle are, however, not well understood. Here, we use dense temporal sampling of 1,510 samples from root spatial compartments to characterize the bacterial and archaeal components of the root-associated microbiota of field grown rice (Oryza sativa) over the course of 3 consecutive growing seasons, as well as 2 sites in diverse geographic regions. The root microbiota was found to be highly dynamic during the vegetative phase of plant growth and then stabilized compositionally for the remainder of the life cycle. Bacterial and archaeal taxa conserved between field sites were defined as predictive features of rice plant age by modeling using a random forest approach. The age-prediction models revealed that drought-stressed plants have developmentally immature microbiota compared to unstressed plants. Further, by using genotypes with varying developmental rates, we show that shifts in the microbiome are correlated with rates of developmental transitions rather than age alone, such that different microbiota compositions reflect juvenile and adult life stages. These results suggest a model for successional dynamics of the root-associated microbiota over the plant life cycle., Author summary Plant roots are colonized by complex communities of bacterial and archaeal microbiota from the soil, with the potential to affect plant nutrition and fitness. Although root-associated microbes are known to have the potential to be utilized to promote crop productivity, their exploitation has been hindered by a lack of understanding of the compositional dynamics of these communities. Here we investigate temporal changes in the root-associated bacterial and archaeal communities throughout the plant life cycle in field-grown rice over multiple seasons and locations. Our results indicate that root microbiota composition varies with both chronological age and the developmental stage of the plants. We find that a major compositional shift correlates with the transition to reproductive growth, suggestive of distinct root microbiota associations for the juvenile and adult plant phases. The results from this study highlight dynamic relationships between plant growth and associated microbiota that should be considered in strategies for the successful manipulation of microbial communities to enhance crop performance.

INTRODUCTION A majority of imbalances in DNA methylation between homologous chromosomes in humans are sequence-dependent; the DNA sequence differences between the two chromosomes cause differences in the methylation state of neighboring cytosines on the same chromosome. The analyses of this sequence-dependent allele-specific methylation (SD-ASM) traditionally involved measurement of average methylation levels across many cells. Detailed understanding of SD-ASM at the single-cell and single-chromosome levels is lacking. This gap in understanding may hide the connection between SD-ASM, ubiquitous stochastic cell-to-cell and chromosome-to-chromosome variation in DNA methylation, and the puzzling and evolutionarily conserved patterns of intermediate methylation at gene regulatory loci. RATIONALE Whole-genome bisulfite sequencing (WGBS) provides the ultimate single-chromosome level of resolution and comprehensive whole-genome coverage required to explore SD-ASM. However, the exploration of the link between SD-ASM, stochastic variation in DNA methylation, and gene regulation requires deep coverage by WGBS across tissues and individuals and the context of other epigenomic marks and gene transcription. RESULTS We constructed maps of allelic imbalances in DNA methylation, histone marks, and gene transcription in 71 epigenomes from 36 distinct cell and tissue types from 13 donors. Deep (1691-fold) combined WGBS read coverage across 49 methylomes revealed CpG methylation imbalances exceeding 30% differences at 5% of the loci, which is more conservative than previous estimates in the 8 to 10% range; a similar value (8%) is observed in our dataset when we lowered our threshold for detecting allelic imbalance to 20% methylation difference between the two alleles. Extensive sequence-dependent CpG methylation imbalances were observed at thousands of heterozygous regulatory loci. Stochastic switching, defined as random transitions between fully methylated and unmethylated states of DNA, occurred at thousands of regulatory loci bound by transcription factors (TFs). Our results explain the conservation of intermediate methylation states at regulatory loci by showing that the intermediate methylation reflects the relative frequencies of fully methylated and fully unmethylated epialleles. SD-ASM is explainable by different relative frequencies of methylated and unmethylated epialleles for the two alleles. The differences in epiallele frequency spectra of the alleles at thousands of TF-bound regulatory loci correlated with the differences in alleles’ affinities for TF binding, which suggests a mechanistic explanation for SD-ASM. We observed an excess of rare variants among those showing SD-ASM, which suggests that an average human genome harbors at least ~200 detrimental rare variants that also show SD-ASM. The methylome’s sensitivity to genetic variation is unevenly distributed across the genome, which is consistent with buffering of housekeeping genes against the effects of random mutations. By contrast, less essential genes with tissue-specific expression patterns show sensitivity, thus providing opportunity for evolutionary innovation through changes in gene regulation. CONCLUSION Analysis of allelic epigenome maps provides a unifying model that links sequence-dependent allelic imbalances of the epigenome, stochastic switching at gene regulatory loci, selective buffering of the regulatory circuitry against the effects of random mutations, and disease-associated genetic variation.