RESUMEN

Since their initial discovery in maize, transposable elements (TEs) have emerged as being integral to the evolution of maize, accounting for 80% of its genome. However, the repetitive nature of TEs has hindered our understanding of their regulatory potential. Here, we demonstrate that long-read chromatin fiber sequencing (Fiber-seq) permits the comprehensive annotation of the regulatory potential of maize TEs. We uncover that only 94 LTR retrotransposons contain the functional epigenetic architecture required for mobilization within maize leaves. This epigenetic architecture degenerates with evolutionary age, resulting in solo TE enhancers being preferentially marked by simultaneous hyper-CpG methylation and chromatin accessibility, an architecture markedly divergent from canonical enhancers. We find that TEs shape maize gene regulation by creating novel promoters within the TE itself as well as through TE-mediated gene amplification. Lastly, we uncover a pervasive epigenetic code directing TEs to specific loci, including that locus that sparked McClintock's discovery of TEs.

RESUMEN

Accurately quantifying the functional consequences of non-coding mosaic variants requires the pairing of DNA sequence with both accessible and closed chromatin architectures along individual DNA molecules-a pairing that cannot be achieved using traditional fragmentation-based chromatin assays. We demonstrate that targeted single-molecule chromatin fiber sequencing (Fiber-seq) achieves this, permitting single-molecule, long-read genomic and epigenomic profiling across targeted >100 kilobase loci with ~10-fold enrichment over untargeted sequencing. Targeted Fiber-seq reveals that pathogenic expansions of the DMPK CTG repeat that underlie Myotonic Dystrophy 1 are characterized by somatic instability and disruption of multiple nearby regulatory elements, both of which are repeat length-dependent. Furthermore, we reveal that therapeutic adenine base editing of the segmentally duplicated γ-globin (HBG1/HBG2) promoters in primary human hematopoietic cells induced towards an erythroblast lineage increases the accessibility of the HBG1 promoter as well as neighboring regulatory elements. Overall, we find that these non-protein coding mosaic variants can have complex impacts on chromatin architectures, including extending beyond the regulatory element harboring the variant.

RESUMEN

Long-read DNA sequencing has recently emerged as a powerful tool for studying both genetic and epigenetic architectures at single-molecule and single-nucleotide resolution. Long-read epigenetic studies encompass both the direct identification of native cytosine methylation as well as the identification of exogenously placed DNA N6-methyladenine (DNA-m6A). However, detecting DNA-m6A modifications using single-molecule sequencing, as well as coprocessing single-molecule genetic and epigenetic architectures, is limited by computational demands and a lack of supporting tools. Here, we introduce fibertools, a state-of-the-art toolkit that features a semisupervised convolutional neural network for fast and accurate identification of m6A-marked bases using PacBio single-molecule long-read sequencing, as well as the coprocessing of long-read genetic and epigenetic data produced using either PacBio or Oxford Nanopore sequencing platforms. We demonstrate accurate DNA-m6A identification (>90% precision and recall) along >20 kilobase long DNA molecules with a ~1,000-fold improvement in speed. In addition, we demonstrate that fibertools can readily integrate genetic and epigenetic data at single-molecule resolution, including the seamless conversion between molecular and reference coordinate systems, allowing for accurate genetic and epigenetic analyses of long-read data within structurally and somatically variable genomic regions.

RESUMEN

Long-read DNA sequencing has recently emerged as a powerful tool for studying both genetic and epigenetic architectures at single-molecule and single-nucleotide resolution. Long-read epigenetic studies encompass both the direct identification of native cytosine methylation as well as the identification of exogenously placed DNA N6-methyladenine (DNA-m6A). However, detecting DNA-m6A modifications using single-molecule sequencing, as well as co-processing single-molecule genetic and epigenetic architectures, is limited by computational demands and a lack of supporting tools. Here, we introduce fibertools, a state-of-the-art toolkit that features a semi-supervised convolutional neural network for fast and accurate identification of m6A-marked bases using PacBio single-molecule long-read sequencing, as well as the co-processing of long-read genetic and epigenetic data produced using either PacBio or Oxford Nanopore sequencing platforms. We demonstrate accurate DNA-m6A identification (>90% precision and recall) along >20 kilobase long DNA molecules with a ~1,000-fold improvement in speed. In addition, we demonstrate that fibertools can readily integrate genetic and epigenetic data at single-molecule resolution, including the seamless conversion between molecular and reference coordinate systems, allowing for accurate genetic and epigenetic analyses of long-read data within structurally and somatically variable genomic regions.

RESUMEN

The scarcity of accessible sites that are dynamic or cell type-specific in plants may be due in part to tissue heterogeneity in bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to Arabidopsis thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. We find that the entirety of a cell's regulatory landscape and its transcriptome independently capture cell type identity. We leverage this shared information on cell identity to integrate accessibility and transcriptome data to characterize developmental progression, endoreduplication and cell division. We further use the combined data to characterize cell type-specific motif enrichments of transcription factor families and link the expression of family members to changing accessibility at specific loci, resolving direct and indirect effects that shape expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development.

Asunto(s)

Proteínas de Arabidopsis/genética , Proteínas de Arabidopsis/metabolismo , Arabidopsis/genética , Raíces de Plantas/genética , Raíces de Plantas/metabolismo , Biotecnología , Cromatina , Regulación de la Expresión Génica de las Plantas , Redes Reguladoras de Genes , Factores de Transcripción , Transcriptoma

RESUMEN

Thousands of sequenced genomes are now publicly available capturing a significant amount of natural variation within plant species; yet, much of these data remain inaccessible to researchers without significant bioinformatics experience. Here, we present a webtool called ViVa (Visualizing Variation) which aims to empower any researcher to take advantage of the amazing genetic resource collected in the Arabidopsis thaliana 1001 Genomes Project (http://1001genomes.org). ViVa facilitates data mining on the gene, gene family, or gene network level. To test the utility and accessibility of ViVa, we assembled a team with a range of expertise within biology and bioinformatics to analyze the natural variation within the well-studied nuclear auxin signaling pathway. Our analysis has provided further confirmation of existing knowledge and has also helped generate new hypotheses regarding this well-studied pathway. These results highlight how natural variation could be used to generate and test hypotheses about less-studied gene families and networks, especially when paired with biochemical and genetic characterization. ViVa is also readily extensible to databases of interspecific genetic variation in plants as well as other organisms, such as the 3,000 Rice Genomes Project ( http://snp-seek.irri.org/) and human genetic variation ( https://www.ncbi.nlm.nih.gov/clinvar/).