Multi-omics mapping of chromatin interaction resolves the fine hierarchy of 3D genome in allotetraploid cotton.
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ABSTRACT:
SUBMITTER: Huang X
PROVIDER: S-EPMC9398308 | biostudies-literature | 2022 Sep
REPOSITORIES: biostudies-literature
Publications
Multi-omics mapping of chromatin interaction resolves the fine hierarchy of 3D genome in allotetraploid cotton.
Huang Xianhui X Tian Xuehan X Pei Liuling L Luo Xuanxuan X Zhang Yuqi Y Zhang Xingtan X Zhang Xianlong X Zhu Longfu L Wang Maojun M
Plant biotechnology journal 20220716 9
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Project description:A narrow germplasm base and a complex allotetraploid genome have made the discovery of single nucleotide polymorphism (SNP) markers difficult in cotton (Gossypium hirsutum). To generate sequence for SNP discovery, we conducted a genome reduction experiment (EcoRI, BafI double digest, followed by adapter ligation, biotin-streptavidin purification, and agarose gel separation) on two accessions of G. hirsutum and two accessions of G. barbadense. From the genome reduction experiment, a total of 2.04 million genomic sequence reads were assembled into contigs with an N(50) of 508 bp and analyzed for SNPs. A previously generated assembly of expressed sequence tags (ESTs) provided an additional source for SNP discovery. Using highly conservative parameters (minimum coverage of 8× at each SNP and 20% minor allele frequency), a total of 11,834 and 1,679 non-genic SNPs were identified between accessions of G. hirsutum and G. barbadense in genome reduction assemblies, respectively. An additional 4,327 genic SNPs were also identified between accessions of G. hirsutum in the EST assembly. KBioscience KASPar assays were designed for a portion of the intra-specific G. hirsutum SNPs. From 704 non-genic and 348 genic markers developed, a total of 367 (267 non-genic, 100 genic) mapped in a segregating F(2) population (Acala Maxxa × TX2094) using the Fluidigm EP1 system. A G. hirsutum genetic linkage map of 1,688 cM was constructed based entirely on these new SNP markers. Of the genic-based SNPs, we were able to identify within which genome ('A' or 'D') each SNP resided using diploid species sequence data. Genetic maps generated by these newly identified markers are being used to locate quantitative, economically important regions within the cotton genome.
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Project description:Although epigenetic modification has long been recognized as a vital force influencing gene regulation in plants, the dynamics of chromatin structure implicated in the intertwined transcriptional regulation of duplicated genes in polyploids have yet to be understood. Here, we document the dynamic organization of chromatin structure in two subgenomes of allotetraploid cotton (Gossypium hirsutum) by generating 3D genomic, epigenomic and transcriptomic datasets from 12 major tissues/developmental stages covering the life cycle. We systematically identify a subset of genes that are closely associated with specific tissue functions. Interestingly, these genes exhibit not only higher tissue specificity but also a more pronounced homoeologous bias. We comprehensively elucidate the intricate process of subgenomic collaboration and divergence across various tissues. A comparison among subgenomes in the 12 tissues reveals widespread differences in the reorganization of 3D genome structures, with the Dt subgenome exhibiting a higher extent of dynamic chromatin status than the At subgenome. Moreover, we construct a comprehensive atlas of putative functional genome elements and discover that 37 cis-regulatory elements (CREs) have selection signals acquired during domestication and improvement. These data and analyses are publicly available to the research community through a web portal. In summary, this study provides abundant resources and depicts the regulatory architecture of the genome, which thereby facilitates the understanding of biological processes and guides cotton breeding.
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Project description:Upland cotton (Gossypium hirsutum L., 2n = 52, AADD) is an allotetraploid, therefore the discovery of single nucleotide polymorphism (SNP) markers is difficult. The recent emergence of genome complexity reduction technologies based on the next-generation sequencing (NGS) platform has greatly expedited SNP discovery in crops with highly repetitive and complex genomes. Here we applied restriction-site associated DNA (RAD) sequencing technology for de novo SNP discovery in allotetraploid cotton. We identified 21,109 SNPs between the two parents and used these for genotyping of 161 recombinant inbred lines (RILs). Finally, a high dense linkage map comprising 4,153 loci over 3500-cM was developed based on the previous result. Using this map quantitative trait locus (QTLs) conferring fiber strength and Verticillium Wilt (VW) resistance were mapped to a more accurate region in comparison to the 1576-cM interval determined using the simple sequence repeat (SSR) genetic map. This suggests that the newly constructed map has more power and resolution than the previous SSR map. It will pave the way for the rapid identification of the marker-assisted selection in cotton breeding and cloning of QTL of interest traits.
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Project description:MotivationChromatin interactions play an important role in genome architecture and gene regulation. The Hi-C assay generates such interactions maps genome-wide, but at relatively low resolutions (e.g. 5-25 kb), which is substantially coarser than the resolution of transcription factor binding sites or open chromatin sites that are potential sources of such interactions.ResultsTo predict the sources of Hi-C-identified interactions at a high resolution (e.g. 100 bp), we developed a computational method that integrates data from DNase-seq and ChIP-seq of TFs and histone marks. Our method, χ-CNN, uses this data to first train a convolutional neural network (CNN) to discriminate between called Hi-C interactions and non-interactions. χ-CNN then predicts the high-resolution source of each Hi-C interaction using a feature attribution method. We show these predictions recover original Hi-C peaks after extending them to be coarser. We also show χ-CNN predictions enrich for evolutionarily conserved bases, eQTLs and CTCF motifs, supporting their biological significance. χ-CNN provides an approach for analyzing important aspects of genome architecture and gene regulation at a higher resolution than previously possible.Availability and implementationχ-CNN software is available on GitHub (https://github.com/ernstlab/X-CNN).Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Transcription factor (TF) binding to DNA is crucial for transcriptional regulation. There are multiple methods for mapping such binding. These methods balance between input requirements, spatial resolution, and compatibility with high-throughput automation. Here, we describe SLIM-ChIP (short-fragment-enriched, low-input, indexed MNase ChIP), which combines enzymatic fragmentation of chromatin and on-bead indexing to address these desiderata. SLIM-ChIP reproduces a high-resolution binding map of yeast Reb1 comparable with existing methods, yet with less input material and full compatibility with high-throughput procedures. We demonstrate the robustness and flexibility of SLIM-ChIP by probing additional factors in yeast and mouse. Finally, we show that SLIM-ChIP provides information on the chromatin landscape surrounding the bound transcription factor. We identify a class of Reb1 sites where the proximal -1 nucleosome tightly interacts with Reb1 and maintains unidirectional transcription. SLIM-ChIP is an attractive solution for mapping DNA binding proteins and charting the surrounding chromatin occupancy landscape at a single-cell level.
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Project description:Recent studies have identified thousands of regions in the genome associated with chromatin modifications, which may in turn be affecting gene expression. Existing works have used heuristic methods to investigate the relationships between genome, epigenome, and gene expression, but, to our knowledge, none have explicitly modeled the chain of causality whereby genetic variants impact chromatin, which impacts gene expression. In this work we introduce a new hierarchical fine-mapping framework that integrates information across all three levels of data to better identify the causal variant and chromatin mark that are concordantly influencing gene expression. In simulations we show that our method is more accurate than existing approaches at identifying the causal mark influencing expression. We analyze empirical genetic, chromatin, and gene expression data from 65 African-ancestry and 47 European-ancestry individuals and show that many of the paths prioritized by our method are consistent with the proposed causal model and often lie in likely functional regions.
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Project description:BackgroundHi-C and capture Hi-C (CHi-C) are used to map physical contacts between chromatin regions in cell nuclei using high-throughput sequencing. Analysis typically proceeds considering the evidence for contacts between each possible pair of fragments independent from other pairs. This can produce long runs of fragments which appear to all make contact with the same baited fragment of interest.ResultsWe hypothesised that these long runs could result from a smaller subset of direct contacts and propose a new method, based on a Bayesian sparse variable selection approach, which attempts to fine map these direct contacts. Our model is conceptually novel, exploiting the spatial pattern of counts in CHi-C data. Although we use only the CHi-C count data in fitting the model, we show that the fragments prioritised display biological properties that would be expected of true contacts: for bait fragments corresponding to gene promoters, we identify contact fragments with active chromatin and contacts that correspond to edges found in previously defined enhancer-target networks; conversely, for intergenic bait fragments, we identify contact fragments corresponding to promoters for genes expressed in that cell type. We show that long runs of apparently co-contacting fragments can typically be explained using a subset of direct contacts consisting of <10% of the number in the full run, suggesting that greater resolution can be extracted from existing datasets.ConclusionsOur results appear largely complementary to those from a per-fragment analytical approach, suggesting that they provide an additional level of interpretation that may be used to increase resolution for mapping direct contacts in CHi-C experiments.
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