Chromatin Architectures Are Associated with Response to Dark Treatment in the Oil Crop Sesamum indicum, Based on a High-Quality Genome Assembly

Chromatin Architectures Are Associated with Response to Dark Treatment in the Oil Crop Sesamum... Abstract Eukaryotic chromatin is tightly packed into hierarchical structures, allowing appropriate gene transcription in response to environmental and developmental cues. Here, we provide a chromosome-scale de novo genome assembly of sesame with a total length of 292.3 Mb and a scaffold N50 of 20.5 Mb, containing estimated 28,406 coding genes using Pacific Biosciences long reads combined with a genome-wide chromosome conformation capture (Hi-C) approach. Based on this high-quality reference genome, we detected changes in chromatin architectures between normal growth and dark-treated sesame seedlings. Gene expression level was significantly higher in ‘A’ compartment and topologically associated domain (TAD) boundary regions than in ‘B’ compartment and TAD interior regions, which is coincident with the enrichment of H4K3me3 modification in these regions. Moreover, differentially expressed genes (DEGs) induced by dark treated were enriched in the changed TAD-related regions and genomic differential contact regions. Gene Ontology (GO) enrichment analysis of DEGs showed that genes related to ‘response to stress’ and ‘photosynthesis’ functional categories were enriched, which corresponds to dark treatment. These results suggested that chromatin organization is associated with gene transcription in response to dark treatment in sesame. Our results will facilitate the understanding of regulatory mechanisms in response to environmental cues in plants. Accession number: All genome, transcriptome and Hi-C high-throughput sequencing reads and the assembly presented in the article have been submitted to the CNGB Nucleotide Sequence Archive (CNSA) under the accession number CNP0000352. H3K4me3 ChIP-Seq sequencing reads were deposited in NCBI under the accession number SRP225600. Introduction Eukaryotic chromatin is highly enclosed to form a hierarchical structure, allowing appropriate gene expression in different cell types and developmental phases (Gibcus and Dekker 2013, Sexton and Cavalli 2015). The three-dimensional (3D) chromatin architectures proposed to play critical roles in genome integrity, DNA replication and gene expression (Dixon et al. 2012, Sexton et al. 2012, Jin et al. 2013). Based on the genome-wide interaction status, mammalian interphase chromatin is partitioned into active and inactive regions (‘A’ and ‘B’ compartments) that are associated with DNA methylation, open chromatin, transcription, repeats and replication timing (Lieberman-Aiden et al. 2009, Ryba et al. 2010). The ‘A’ and ‘B’ compartments are further partitioned into condensed structures, dubbed topologically associated domains (TADs) (Dixon et al. 2012, Nora et al. 2012). TADs with high intra-interaction frequency are predominant features of the mammalian genomes and spatially confine the interactions between promoter and distal regulatory elements regulating gene activation (Jin et al. 2013, Rao et al. 2014). However, the Arabidopsis genome lacks TAD structures similar to those in mammalian chromosomes, instead of containing relatively small interacting regions scattered throughout the genome, in which interaction patterns are correlated with changes in the epigenome (Feng et al. 2014). In another model plant, rice, thousands of TADs have been identified, the boundaries of which are associated with euchromatic epigenetic marks and active genes (Liu et al. 2017). The packing pattern of rice chromatins resembles that of Arabidopsis but has clear differences at specific structural levels (Dong et al. 2018). In rice, maize, tomato, sorghum and foxtail millet, chromosomes can be partitioned into local ‘A’ and ‘B’ compartments that are associated with euchromatin and heterochromatin; the polycomb proteins and their associated chromatins can be organized into TAD structures, which are not conserved across species (Dong et al. 2017). The substantial variation in chromatin structures across species suggests that plants have complex and unique chromatin architectures (Dong et al. 2017, Liu et al. 2017). Dynamic alteration in chromatin organization and concomitant transcription have vital roles in responses to environmental stimuli (Rosa and Shaw 2013, Probst and Mittelsten Scheid 2015). In Drosophila, heat shock causes rearrangement of chromatin architecture by inducing the relocalization of architecture proteins from TAD boundary to TAD interior regions, which may repress the transcription of most active genes (Li et al. 2015). In rice, cold treatment increases interactions between ‘A’ and ‘B’ compartments and inhibits long-range interactions on the same chromosome, implying that the entire rice genome becomes decondensed (Liu et al. 2017). However, a recent study reported that compartments and TAD structures are unchanged underlying heat shock in human and Drosophila cells; chromatin conformation necessary for a robust heat shock response is inherent and enhancer–promoter interactions are preestablished prior to heat treatment (Ray et al., 2019). Therefore, more studies are required to elucidate the biological functions of chromatin architecture in more species. Single-molecule real-time (SMRT) sequencing on the Pacific Biosciences (PacBio) platform can generate an average read length of 10–15 kb, which is suitable for de novo genome assembly and genome finishing (Eid et al. 2009, Bickhart et al. 2017, Du et al. 2017, Jiao et al. 2017). Genome-wide chromosome conformation capture (Hi-C) technology can detect nuclear interactions throughout a genome and probe the 3D architecture of the whole genome based on the quantitative estimation of proximity-ligation events for millions of loci in the genome, providing a source of long-range information for assigning, ordering and orienting genomic sequences to chromosomes (Dekker et al. 2002, Burton et al. 2013, Dudchenko et al. 2017). High-quality draft genomes with chromosome-length scaffolds have been produced by utilizing SMRT sequencing and Hi-C data (Burton et al. 2013, Xie et al. 2015, Schmitt et al. 2016, Dudchenko et al. 2017, Jiao et al. 2017, Phillippy 2017). Sesame (Sesamum indicum L., 2n = 26), which belongs to the family Pedaliaceae, is grown widely in tropical and subtropical regions (Wang et al. 2014). Sesame seed contains approximately 50% oil and 25% protein and is one of the world’s most important oil crops, but the genetic basis of its oil production and quality is unclear (Johnson et al. 1979, Wang et al. 2014, Wang et al. 2015). The genome of ‘Yuzhi 11’ sesame has been sequenced, generating a draft assembly of 293.7 Mb with a contig N50 value of 19.0 kb and a scaffold N50 value of 22.6 kb (Zhang et al. 2013). Another sesame genome, ‘Zhongzhi 13’, was also assembled, generating a draft genome of 272.7 Mb with a scaffold N50 length of 20.2 Mb (Wang et al. 2014, Wang et al. 2016, Yu et al. 2019). Here, we report a new sesame genome assembly with chromosome-length scaffolds produced using a combination of SMRT sequencing and Hi-C-based chromatin interaction maps. The new assembly is a total of 292.3 Mb with contig and scaffold N50s of 1.1 and 20.5 Mb, respectively. Based on this improved reference genome, we detected 3D genome architecture and alteration in chromatin organization in response to environmental cues in sesame. Our results facilitate the elucidation of biological processes in sesame and provide resources for unveiling the biological functions of 3D chromatin architectures in higher plants. Results Genome assembly and annotation Long-read sequencing data of sesame (Zhongzhi 13) were generated on the PacBio Sequel sequencing platform. Filtered subreads with an average length of 10.2 kb were used for de novo assembly (Supplementary Fig. S1). Based on the genome size of 337 Mb estimated with flow cytometry (Wang et al. 2014), the sequence coverage was approximately 37× for the species. The FALCON package was used for the first round of genome assembly (Chin et al. 2016), and then, the assembly was corrected based on the alignment of Illumina short reads from public data (Wang et al. 2014) using the Pilon package (Walker et al. 2014). The assembled sequences contained 1,001 contigs with a total length of 292.1 Mb and an N50 value of 1.1 Mb (Table 1). Then, Hi-C data were used to integrate the contig assembly into a candidate chromosome-length assembly using the 3D de novo assembly (3D DNA) pipeline and the Juicebox Assembly Tools (Durand et al. 2016a, Dudchenko et al. 2017, 2018). The final sesame assembly (hereafter referred to as ‘newzhongzhi 13’) had a total length of 292.3 Mb, with 93.6% of the assembly contained in the longest 13 scaffolds (each scaffold >16.3 Mb), and an N50 value of 20.5 Mb (Table 1), implying that the assembly is a chromosome-level genome. The annotation of the ‘newzhongzhi 13’ assembly was performed based on transcript and protein alignments using the MAKER annotation pipeline (Cantarel et al. 2008, Campbell et al. 2014). After the masking of repetitive sequences with the RepeatModeler package (Smit et al. 2013–2015), 28,406 protein-coding genes were predicted (Table 1). Of these genes, 22,692 (79.9%) were annotated with the Ensembl Plants datasets (http://plants.ensembl.org) and 5,714 (20.1%) genes had no hits representing novel genes. The annotation quality of the ‘newzhongzhi 13’ was assessed by annotation edit distance (AED) metric (Eilbeck et al. 2009), and the high AED scores suggested a high-quality genome annotation (Supplementary Fig. S2). Table 1 Statistics of the ‘newzhongzhi 13’ genome assembly Assembly . Number . N50 (bp) . N75 (bp) . L50 (n) . L75 (n) . Total length (bp) . Contigs 1,001 1,064,304 390,220 73 182 292,119,914 Scaffolds 597 20,523,558 18,035,395 6 10 292,289,538 Chromosomes 13 273,390,958 Coding genes 28,406 50,211,955 Assembly . Number . N50 (bp) . N75 (bp) . L50 (n) . L75 (n) . Total length (bp) . Contigs 1,001 1,064,304 390,220 73 182 292,119,914 Scaffolds 597 20,523,558 18,035,395 6 10 292,289,538 Chromosomes 13 273,390,958 Coding genes 28,406 50,211,955 Open in new tab Table 1 Statistics of the ‘newzhongzhi 13’ genome assembly Assembly . Number . N50 (bp) . N75 (bp) . L50 (n) . L75 (n) . Total length (bp) . Contigs 1,001 1,064,304 390,220 73 182 292,119,914 Scaffolds 597 20,523,558 18,035,395 6 10 292,289,538 Chromosomes 13 273,390,958 Coding genes 28,406 50,211,955 Assembly . Number . N50 (bp) . N75 (bp) . L50 (n) . L75 (n) . Total length (bp) . Contigs 1,001 1,064,304 390,220 73 182 292,119,914 Scaffolds 597 20,523,558 18,035,395 6 10 292,289,538 Chromosomes 13 273,390,958 Coding genes 28,406 50,211,955 Open in new tab Evaluation of the ‘newzhongzhi 13’ assembly quality We assessed the completeness and contiguity of the ‘newzhongzhi 13’ assembly using the QUAST-LG, BUSCO and MUMmer packages (Kurtz et al. 2004, Simão et al. 2015, Mikheenko et al. 2018, Waterhouse et al. 2018). The BUSCO annotation showed that the completeness of the ‘newzhongzhi 13’ assembly was better than the previous ‘Yuzhi 11’ and ‘Zhongzhi 13’ sesame assemblies (Fig. 1A) (Zhang et al. 2013, Wang et al. 2016). The collinearity analysis demonstrated that the ‘newzhongzhi 13’, ‘Yuzhi 11’ and ‘Zhongzhi 13’ assemblies have highly similar genome sequences, but some large structural differences, such as chromosome fragment inversions and transpositions, are present (Fig. 1B, C). Both the ‘newzhongzhi 13’ and ‘Zhongzhi 13’ assemblies had 13 chromosome-level scaffolds and N50 values of >20 Mb, implying that the two assemblies have the good contiguity, but that of the ‘newzhongzhi 13’ assembly is better than that of the ‘Zhongzhi 13’ assembly (Supplementary Table S1). Overall, these results suggested that the ‘newzhongzhi 13’ assembly is highly complete and contiguous. We estimated small local errors (such as single-base substitutions and short insertions and deletions) in the ‘newzhongzhi 13’ assembly with Illumina short-read alignments using the Pilon package (Walker et al. 2014). The estimated error rate was 0.0057% (substitutions 0.0049%, insertions 0.0003 and deletions 0.0005%). In fact, the actual error rate was smaller than the estimated one because the heterozygosity of the ‘newzhongzhi 13’ assembly introduced a large number of false errors. We compared the genomic structure between the ‘newzhongzhi 13’ and ‘Zhongzhi 13’ assemblies with the visual Hi-C-based chromatin interaction map using Juicebox Assembly Tools (Durand et al. 2016a, Dudchenko et al. 2018). The result suggested that our ‘newzhongzhi 13’ assembly had few misassemblies (Fig. 1D), implying high correctness. Fig. 1 Open in new tabDownload slide Comparison of the ‘newzhongzhi 13’, ‘Yuzhi 11’ and ‘Zhongzhi 13’ genome assemblies. (A) BUSCO annotation of the three genome assemblies. (B) Collinearity analysis of genome sequences between the ‘newzhongzhi 13’, ‘Yuzhi 11’ and ‘Zhongzhi 13’ assemblies. se1–13 indicate chromosome codes of the ‘newzhongzhi 13’ assembly; LG1–16 indicate chromosome codes of the published ‘Yuzhi 11’ assembly; and chr1–13 indicate chromosome codes of the published ‘Zhongzhi 13’ assembly. (C) Comparison of corresponding chromosomes between the ‘newzhongzhi 13’ and ‘Zhongzhi 13’ genome assemblies. se1–13 indicate the chromosome codes of the ‘newzhongzhi 13’ assembly; chr1–13 indicate chromosome codes of the published ‘Zhongzhi 13’ assembly. (D) Comparison of Hi-C contact maps of chromosomes between the ‘newzhongzhi 13’ and ‘Zhongzhi 13’ assemblies. se1, se6, se8 and se9 indicate chromosome codes of the ‘newzhongzhi 13’ assembly; chr2, chr8, chr10 and chr12 indicate chromosome codes of the published ‘Zhongzhi 13’ assembly. se1 and chr8, se6 and chr10, se8 and chr2 and se9 and chr12 are the same chromosomes in the two genomes. The blue arrows indicate possible error assemblies. Fig. 1 Open in new tabDownload slide Comparison of the ‘newzhongzhi 13’, ‘Yuzhi 11’ and ‘Zhongzhi 13’ genome assemblies. (A) BUSCO annotation of the three genome assemblies. (B) Collinearity analysis of genome sequences between the ‘newzhongzhi 13’, ‘Yuzhi 11’ and ‘Zhongzhi 13’ assemblies. se1–13 indicate chromosome codes of the ‘newzhongzhi 13’ assembly; LG1–16 indicate chromosome codes of the published ‘Yuzhi 11’ assembly; and chr1–13 indicate chromosome codes of the published ‘Zhongzhi 13’ assembly. (C) Comparison of corresponding chromosomes between the ‘newzhongzhi 13’ and ‘Zhongzhi 13’ genome assemblies. se1–13 indicate the chromosome codes of the ‘newzhongzhi 13’ assembly; chr1–13 indicate chromosome codes of the published ‘Zhongzhi 13’ assembly. (D) Comparison of Hi-C contact maps of chromosomes between the ‘newzhongzhi 13’ and ‘Zhongzhi 13’ assemblies. se1, se6, se8 and se9 indicate chromosome codes of the ‘newzhongzhi 13’ assembly; chr2, chr8, chr10 and chr12 indicate chromosome codes of the published ‘Zhongzhi 13’ assembly. se1 and chr8, se6 and chr10, se8 and chr2 and se9 and chr12 are the same chromosomes in the two genomes. The blue arrows indicate possible error assemblies. 3D architecture in the sesame genome Based on the high-quality ‘newzhongzhi 13’ reference genome, we detected the 3D architecture of sesame chromosomes using the Hi-C method (Lieberman-Aiden et al. 2009). Hi-C libraries from normally grown ‘Zhongzhi 13’ seedlings (referred to as bg), dark-treated ‘Zhongzhi 13’ seedlings (referred to as bh) and normally grown ‘Zhouhei 1’ seedlings (referred to as h) were constructed, with three replicates per library. Overall, 128, 120 and 146 M paired-end reads were obtained from the three libraries and >59.0% of the reads per library were uniquely mapped to the ‘newzhongzhi 13’ reference genome using the Juicer pipeline (Durand et al. 2016b), of which 78.7%, 78.8% and 76.7% contributed to the Hi-C contacts, respectively (Supplementary Table S2). Among them, 56.4% of the reads contributed to intrachromosomal contacts and 11.1% to interchromosomal contacts in the bg sample, 55.7% and 12.0% in the bh sample and 54.5% and 10.5% in the h sample, respectively (Supplementary Table S2). In addition, 26.1%, 24.8% and 26.3% of the intrachromosomal reads were long-range interactions (>20 kb) (Supplementary Table S2). The replicates for each sample displayed a strong positive relationship, with a correlation coefficient of 0.94 (Supplementary Fig. S3), suggesting that our Hi-C data are reliable. Three two-dimensional contact maps representing within-chromosome (cis) and between-chromosome (trans) interactions at a genome-wide scale were generated, and local contacts (represented as diagonal lines) were prominent across three samples (Supplementary Fig. S4). At chromosome level, obvious changes were displayed in the pairs bh vs. bg and h vs. bg (Fig. 2A, B). According to chromatin contact map, megabase (Mb)-sized chromatin regions were divided into alternating positive and negative eigenvectors, representing ‘A’ and ‘B’ compartments using principal component analysis (Supplementary Fig. S5). The ‘A’ and ‘B’ compartments, corresponding to the euchromatic and heterochromatic regions, were the secondary structural units in chromatin organization, occupying distinct subdomains in the chromosome territory, and were widespread in both animals and plants (Lieberman-Aiden et al. 2009, Yu and Ren 2017, Doğan and Liu 2018). More strong intrachromosomal interaction signals were displayed between ‘A’ compartments or between ‘B’ compartments than between ‘A’ and ‘B’ compartments (Fig. 2C, D and Supplementary Fig. S6). This result was also observed in maize and rice chromatin organization (Dong et al. 2019), suggesting that the feature of chromatin architecture may be widely present in plants. Fig. 2 Open in new tabDownload slide ‘A’ and ‘B’ compartment structural units across the bg, bh and h samples. (A) Comparison of chromosome architectures in the pairs bh vs. bg and h vs. bg at 500-kb resolution. Color legend indicates interaction strength [log2(observed/expected)]. (B) Comparison of ‘A’ and ‘B’ compartments in chromosomes 4 and 5 across the bg, bh and h samples. The black boxes highlight regions with different eigenvector values in the bg, bh and h samples; the shaded green area indicates ‘B’ compartment region. (C) Hi-C contact maps of chromatin interactions between ‘A’ and ‘B’ compartments in chromosome 3 across the bg, bh and h samples. The pink box indicates intrachromosomal interaction region of ‘A’ and ‘A’ compartments; the green box indicates intrachromosomal interaction region of ‘B’ and ‘B’ compartments; the black box indicates intrachromosomal interaction region of ‘A’ and ‘B’ compartments; and legends indicate interaction strength [log2(observed/expected)]. (D) Comparison of the interaction strength between A and B compartments. AA indicates the intrachromosomal interaction between ‘A’ compartments; AB indicates the intrachromosomal interaction between ‘A’ and ‘B’ compartments; and BB indicates the intrachromosomal interaction between ‘B’ compartments. The statistical test is performed with Wilcox function in R software. The bg indicates the normally grown ‘Zhongzhi 13’ seedlings; the bh indicates the dark-treated ‘Zhongzhi 13’ seedlings and the h indicates the normally grown ‘Zhouhei 1’ seedlings. Fig. 2 Open in new tabDownload slide ‘A’ and ‘B’ compartment structural units across the bg, bh and h samples. (A) Comparison of chromosome architectures in the pairs bh vs. bg and h vs. bg at 500-kb resolution. Color legend indicates interaction strength [log2(observed/expected)]. (B) Comparison of ‘A’ and ‘B’ compartments in chromosomes 4 and 5 across the bg, bh and h samples. The black boxes highlight regions with different eigenvector values in the bg, bh and h samples; the shaded green area indicates ‘B’ compartment region. (C) Hi-C contact maps of chromatin interactions between ‘A’ and ‘B’ compartments in chromosome 3 across the bg, bh and h samples. The pink box indicates intrachromosomal interaction region of ‘A’ and ‘A’ compartments; the green box indicates intrachromosomal interaction region of ‘B’ and ‘B’ compartments; the black box indicates intrachromosomal interaction region of ‘A’ and ‘B’ compartments; and legends indicate interaction strength [log2(observed/expected)]. (D) Comparison of the interaction strength between A and B compartments. AA indicates the intrachromosomal interaction between ‘A’ compartments; AB indicates the intrachromosomal interaction between ‘A’ and ‘B’ compartments; and BB indicates the intrachromosomal interaction between ‘B’ compartments. The statistical test is performed with Wilcox function in R software. The bg indicates the normally grown ‘Zhongzhi 13’ seedlings; the bh indicates the dark-treated ‘Zhongzhi 13’ seedlings and the h indicates the normally grown ‘Zhouhei 1’ seedlings. TAD was another major structural unit in chromosome territories, and chromatin interactions in TAD interior regions were stronger than ones in TAD boundary regions (Dixon et al. 2012, Nora et al. 2012, Sexton et al. 2012). TADs were proposed to a principal chromosomal structure, reflecting a tendency to partition chromatin into distinct and autonomously regulated regions (Sexton and Cavalli 2015). In the sesame genome, we detected the TAD-like structures using the arrowhead algorithm in the Juicer pipeline (Durand et al. 2016b), as in rice and maize genomes (Dong et al., 2019). In total, 705, 720 and 734 TADs with the median length of 150 kb were identified at 10-kb resolution across the bg, bh and h samples, respectively (Fig. 3G and Supplementary Fig. S7), covering approximately 49–58% of the sesame genome. Gene density in TAD boundary regions was significantly higher than one in TAD interior regions (Fig. 3A, B). Gene expression level was significantly higher in TAD boundary and ‘A’ compartment regions than that in TAD interior and ‘B’ compartment regions (Fig. 3C, D), concomitant with the enrichment of histone H3 trimethylated at lysine 4 (H3K4me3) in TAD boundary and ‘A’ compartment regions (Fig. 3E, F, Supplementary Fig. S8 and Table S3). In eukaryotes, H3K4me3 was associated with active chromatin and gene expression (Schneider et al. 2004), suggesting that gene transcription preferentially occurs in TAD boundary and ‘A’ compartment regions in sesame genome. Moreover, approximately 21.8% and 22.9% of TAD regions had changed in the pairs bh vs. bg and h vs. bg, respectively (Fig. 3G, H), suggesting that chromatin architecture is changed in response to dark treatment or different sesame cultivars. Fig. 3 Open in new tabDownload slide Gene transcription associated with chromatin structural units. (A) Gene distribution of TAD-related regions across the bg, bh and h samples. The gray line represents gene distribution in random TAD regions (background); the left white area indicates TAD boundary regions; and the right shaded green area indicates TAD interior regions. (B) Comparison of gene density (gene number/10 kb) between TAD boundary and TAD interior regions in the bg, bh and h samples. The diamond shape represents mean value. (C) Comparison of gene expression levels in TAD-related regions. (D) Comparison of gene expression levels in ‘A’ and ‘B’ compartment regions. (E) H3K4me3 distribution in TAD-related regions. (F) H3K4me3 distribution in ‘A’ and ‘B’ compartment regions. The statistical test is performed with Wilcox function in R software in (B)–(F). (G) Hi-C contact map and border index comparison of chromosome 8 among the bg, bh and h samples. The border index was calculated at 10-kb resolution. (H) TAD changes in the pairs bh vs. bg and h vs. bg. The bg, bh and h labels indicate the same samples as in Fig. 2. Fig. 3 Open in new tabDownload slide Gene transcription associated with chromatin structural units. (A) Gene distribution of TAD-related regions across the bg, bh and h samples. The gray line represents gene distribution in random TAD regions (background); the left white area indicates TAD boundary regions; and the right shaded green area indicates TAD interior regions. (B) Comparison of gene density (gene number/10 kb) between TAD boundary and TAD interior regions in the bg, bh and h samples. The diamond shape represents mean value. (C) Comparison of gene expression levels in TAD-related regions. (D) Comparison of gene expression levels in ‘A’ and ‘B’ compartment regions. (E) H3K4me3 distribution in TAD-related regions. (F) H3K4me3 distribution in ‘A’ and ‘B’ compartment regions. The statistical test is performed with Wilcox function in R software in (B)–(F). (G) Hi-C contact map and border index comparison of chromosome 8 among the bg, bh and h samples. The border index was calculated at 10-kb resolution. (H) TAD changes in the pairs bh vs. bg and h vs. bg. The bg, bh and h labels indicate the same samples as in Fig. 2. Chromatin loops were the basic chromatin structures, the formation of which allows direct physical contact between distant DNA elements and their transcriptional units, facilitating transcriptional activation (Liu and Weigel 2015). We detected 1,458, 1,513 and 1,742 chromatin loops from the interaction matrices of the bg, bh and h samples, respectively, using the HiCCUPS algorithm in the Juicer pipeline (Durand et al. 2016b) (Supplementary Table S4). Aggregate peak analysis (APA) was performed to measure aggregate enrichment of a set of putative peaks for the identification of chromatin loops (Durand et al. 2016b) (Supplementary Fig. S9). The number of chromatin loops identified in sesame was very low compared to the numbers in Arabidopsis and rice (Liu et al. 2016, Dong et al. 2018), possibly because low-resolution interaction maps did not explore small chromatin loops. However, the results suggested that chromatin loop structures also present in the sesame genome. Chromatin differential contacts in the pairs bh vs. bg and h vs. bg We detected 3,861 and 2,461 chromatin differential interactions in the pairs bh vs. bg and h vs. bg at 5 kb resolution with a false discovery rate (FDR) of ≤0.05 using the HiCcompare package (Stansfield et al. 2018) (Supplementary Table S5). The promoter density of coding genes was higher in the differential contact regions than in the random regions (background) (Fig. 4A), indicating that changes in chromatin architecture occur preferentially in promoter regions in sesame. The result was coincident with that differentially expressed genes (DEGs) between bh and bg samples are enriched in the differential contact regions (Fig. 4E), suggesting that gene transcription induced by dark treatment is associated with alteration in chromatin organization in sesame. Fig. 4 Open in new tabDownload slide DEGs relevant to the changes in chromatin structure in the pair bh vs. bg. (A) Differential chromatin contacts induced by dark treated occur preferentially in promoter regions. (B) DEG number in the common and changed ‘A’ and ‘B’ compartment regions. (C and D) DEG number in the common and changed TAD-related regions. (E) DEG number in the common and differential chromatin contact regions. The hypergeometric distribution test is performed with the phyper function in R software in (B–E). (F) Chromatin contact profiles of SiDIN1, SiSTP1 and SiPHOT2 in the bh and bg samples. The bg, bh and h labels indicate the same samples as in Fig. 2. Fig. 4 Open in new tabDownload slide DEGs relevant to the changes in chromatin structure in the pair bh vs. bg. (A) Differential chromatin contacts induced by dark treated occur preferentially in promoter regions. (B) DEG number in the common and changed ‘A’ and ‘B’ compartment regions. (C and D) DEG number in the common and changed TAD-related regions. (E) DEG number in the common and differential chromatin contact regions. The hypergeometric distribution test is performed with the phyper function in R software in (B–E). (F) Chromatin contact profiles of SiDIN1, SiSTP1 and SiPHOT2 in the bh and bg samples. The bg, bh and h labels indicate the same samples as in Fig. 2. Chromatin structure changes associated with gene transcription in response to dark treatment We identified 4,901 DEGs from the pair dark-treated vs. normally grown seedlings (bh vs. bg) with a fold change of ≥3.0 and an FDR of ≤0.01 (Supplementary Table S6). GO enrichment analysis showed that genes related to ‘response to stress’ and ‘photosynthesis’ functional categories are enriched (Supplementary Fig. S10 and Table S7), which corresponds to dark treatment. DEGs were enriched in the changed TAD interior and border regions but not in the common TAD-related regions and ‘A’ and ‘B’ compartment regions, implying that gene transcription induced by dark treated is relevant to TAD changes (Fig. 4B–D). Moreover, promoter regions of 1,554 DEGs overlapped with the differential contact regions in the pair bh vs. bg (Supplementary Table S8), implying that approximately one-third of DEGs induced by dark-treated is associated with chromatin architecture changes. The result is coincident with that DEGs are enriched in the differential contact regions (Fig. 4E). For example, sesame012486 (SiDIN1), sesame018342 (SiSTP1) and sesame020579 (SiPHOT2) genes related to light response had obviously different chromatin interaction patterns in respective chromosomes between bg and bh samples (Fig. 4F). Discussion The packing of chromatin in the nucleus is an important factor in the regulation of gene transcription in many cellular processes (Doğan and Liu 2018). The 3D structure of chromosomes displays a hierarchical pattern in which individual chromosomes are partitioned into three levels of functional substructures according to domain size: ‘A’ and ‘B’ compartments, TAD domains and chromatin loops (Rao et al. 2014, Yu and Ren 2017). This type of chromatin organization has been reported previously in plants, such as Arabidopsis, rice, maize, tomato, sorghum and foxtail millet (Liu et al. 2017, Doğan and Liu 2018, Dong et al. 2018). Here, chromatin structural units, such as ‘A’ and ‘B’ compartments, TAD-like domains and chromatin loops, were detected in sesame chromosomes using the Hi-C approach and an improved sesame genome. This result indicates that the hierarchical architecture of chromatin is extensive in plants. Life bodies frequently encounter various environmental stimuli during growth and development. Dynamic alteration in chromatin organization has vital roles in responses to environmental stimuli (Rosa and Shaw 2013, Li et al. 2015, Probst and Mittelsten Scheid 2015, Liu et al. 2017). It is reasonable that chromatin organization should participate in response to environmental cues. However, Ray et al. (2019) reported that chromatin interactions between regulatory elements and their target promoters are preestablished before heat shock; ‘A’ and ‘B’ compartments and TAD structures have no significant changes after head shock. The conclusion is inconsistent with that transcription changes after heat shock are accompanied by dramatic alterations in TAD boundary strength (Li et al. 2015). Dong et al. (2019) reported that plant chromatin structural units are stable across tissues; new formation of TAD borders is associated with transcription activation. These results suggested that the function of chromatin organization in response to environmental cues is complicated. In sesame, we revealed that gene transcription is associated with chromatin architecture changes in response to dark treatment (Fig. 4C–E), supporting that the dynamic alteration in chromatin architecture is associated with gene transcription in response to exogenous environmental stimuli (Li et al. 2015, Probst and Mittelsten Scheid 2015, Liu et al. 2017). For example, the sesame012486 (SiDIN1), sesame018342 (SiSTP1) and sesame020579 (SiPHOT2) genes had different chromatin interaction patterns between bh and bg samples (Fig. 4F). The three genes are homologous to Arabidopsis DARK-INDUCIBLE 1 (DIN1), SUGAR TRANSPORTER 1 (STP1) and PHOTOTROPIN 2 (PHOT2). DIN1 is a senescence-associated dark-inducible protein regulated by plant defense signaling and senescence responses (Schenk et al. 2005). STP1 is an H+/monosaccharide cotransporter and regulates hexose transport in plant tissues (Slewinski 2011). PHOT2 regulates both the avoidance and accumulation responses of chloroplasts under high- and low-light intensity, as well as phototropism, leaf flattening and stomatal opening, which could enhance leaf photosynthesis and promote biomass production in Arabidopsis (Christie 2007, Gotoh et al. 2018). After dark treatment, the expression of SiDIN1 was promoted, while SiSTP1 and SiPHOT2 were inhibited (Supplementary Fig. S11 and Table S9), which might be relevant to chromatin architecture changes in sesame. TAD, mainly as a chromatin structure, is associated with gene transcription by confining interactions between genes and their distal DNA regulatory elements (Sexton and Cavalli 2015 ). Recent studies have shown that the TAD-like domains are also present in plants (Liu et al. 2017, Dong et al. 2017, 2018), but TAD arrangement is irregular, which is not same as the checkerboard plaid-like arrangement of TADs in mammals (Lieberman-Aiden et al. 2009). In mammals, CCCTC-binding factor protein has an important role in the establishment of chromatin architecture and is highly enriched in TAD boundary regions (Dixon et al. 2012, Baranello et al. 2014, Tang, et al. 2015) but has not been found in plants, which may lead to the arrangement of irregular TAD in plants. In sesame, TAD arrangement was also irregular and gene density in TAD boundary regions was significantly higher than that in TAD interior regions (Fig. 3A), corresponding to that TAD formation is intimately linked to protein-coding gene density (Liu et al. 2017). Materials and Methods Plant materials Two sesame cultivar seedlings (‘Zhongzhi 13’ and ‘Zhouhei 1’) were grown under controlled environmental conditions (16/8 h and 28/20°C light/dark cycle). The ‘Zhongzhi 13’ cultivar has a white seed coat, and the ‘Zhouhei 1’ cultivar has a black seed coat. Three-week-old ‘Zhongzhi 13’ seedlings were dark-treated for 24 h. Then, the normally grown (bg) and dark-treated (bh) ‘Zhongzhi 13’ seedlings were harvested for Hi-C and chromatin immunoprecipitation (ChIP) library construction and RNA sequencing. The normally grown ‘Zhouhei 1’ seedlings (h) were fixed for Hi-C library construction. Three biological replicates per sample were generated for Hi-C library construction, and two biological replicates per sample were generated for RNA sequencing. PacBio sequencing and assembly PacBio sequencing was performed on a PacBio Sequel sequencer by Novogene Bioinformatics Technology (Beijing, China). The polymerase reads were filtered with the parameters—minLength = 50 and minReadScore = 0.8 using PacBio SMRT Link software (version 5.0). Filtered subreads were used for de novo assembly with the FALCON package (version 0.3.0) with the parameters length cutoff = 1,000, seed coverage = 25 and length cutoff preassembly = 11,000 (Chin et al. 2016). Assembled contigs were corrected with Illumina short reads from National Center for Biotechnology Information (NCBI) Sequence Read Archive data (SRA122008) using the Pilon package (version 1.23) (Walker et al. 2014). The contigs were then assembled into a candidate chromosome-length assembly with Hi-C data using a 3D de novo assembly (3D DNA) pipeline (Dudchenko et al. 2017). The candidate assembly was further improved by quality control and interactive correction in Juicebox Assembly Tools (Durand et al. 2016a, Dudchenko et al. 2018). Genome annotation and analysis The annotation of the genome assembly was performed using the MAKER genome annotation pipeline (version 2.31.10) (Cantarel et al. 2008, Campbell et al. 2014), after masking repetitive sequences based on a custom repeat library with the RepeatModeler package (Smit et al. 2013–2015). Transcript and protein sequences were used as evidence for ab initio gene prediction. De novo transcriptome assembly was performed with Illumina short reads from our transcriptome sequencing data, NCBI Sequence Read Archive data (SRA122023) and RefSeq sesame transcript data using the Trinity package (version 2.2.0) with default parameters (Grabherr et al. 2011, Haas et al. 2013). The protein data were from the Ensembl Plants Database (http://plants.ensembl.org). The training of the gene prediction model was performed using the Semi-HMM-based Nucleic Acid Parser and AUGUSTUS programs within MAKER. A detailed description of this process, including ancillary scripts and example command calls for the MAKER pipeline, is provided in the MAKER Wiki (http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/Main_Page). The annotation quality of the assembly was assessed by the AED algorithm within MAKER. The completeness and contiguity of the ‘newzhongzhi 13’ assembly were assessed using the QUAST-LG, BUSCO (version 3.0), MUMmer (version 4.0) and MCScanX packages (Kurtz et al. 2004, Wang et al. 2012, Simão et al. 2015, Mikheenko et al. 2018). Small local errors in the ‘newzhongzhi 13’ assembly, such as single-base substitutions and short insertions and deletions, were estimated with Illumina short-read alignments using the Pilon package (version 1.22) (Walker et al. 2014). Large structural errors, i.e. misassemblies, were assessed by the visual Hi-C-based chromatin interaction map method using Juicebox Assembly Tools (Durand et al. 2016a, Dudchenko et al. 2018). Hi-C library preparation Hi-C library preparation was performed as previously described (Wang et al. 2015). Plant materials were cross-linked with a 2% formaldehyde solution at room temperature for 30 min in vacuum. Glycine (2.5 M) was added to quench the cross-linking reaction. Approximately 0.5 g of fixed tissue was ground with liquid nitrogen for DNA isolation. The extracted nuclei were resuspended with 0.5% Sodium dodecyl sulfate (SDS) followed by incubation at 62°C for 10 min. After quenching SDS with 10% Triton X-100 and incubation at 37°C for 15 min, DNA was digested with the four-cutter restriction enzyme DpnII and incubated at 37°C overnight. Then, the DpnII enzyme was inactivated at 62°C for 20 min. The cohesive ends were filled with Klenow and incubated at 37°C for 30 min. The proximal chromatin DNA was religated with T4 DNA ligation enzyme at room temperature for 4 h. After centrifugation at 1,500 × g for 3 min, the reaction mixture was resuspended in SDS buffer (50 mM Tris-HCl, 1% SDS, 10 mM ethylene diamine tetraacetic acid (EDTA), pH 8.0), proteinase K was added and the mixture was incubated at 55°C for 30 min. The formaldehyde cross-linking of the nuclear complexes was reversed by the addition of 30 μl of 5 M NaCl and incubation at 65°C overnight. Subsequent chromatin DNA manipulations were performed as previously described (Wang et al. 2015). The final libraries were sequenced on an Illumina HiSeq-X Ten platform (PE 150 bp). Hi-C data analysis pipeline Hi-C sequencing data were processed using the Juicer pipeline (Durand et al. 2016b). Each read end was mapped to the ‘newzhongzhi 13’ or ‘Zhongzhi 13’ reference genome using the BWA package (version 0.7.17) with the default parameters (Li and Durbin 2010). Duplicate and near-duplicate reads mapping to the same restriction fragment reads were removed. The remaining reads were filtered based on the mapping quality score. Normalized contact matrices were generated at different resolutions. Hi-C contact maps were generated and visualized using the Juicebox package (Durand et al. 2016a). Annotation of chromatin structural features and statistical analysis Contact matrices were annotated with a suite of algorithms, as described in the Juicer pipeline (Durand et al. 2016b). The eigenvectors, which are the first principal components of Pearson’s matrix, were calculated using the eigenvector algorithm. The signs of the eigenvectors indicated chromosome ‘A’ or ‘B’ compartments. TAD domains with different normalization methods were identified at different resolutions using the Arrowhead algorithm, which relies on the application of the Arrowhead matrix transformation to a normalized contact matrix (Rao et al. 2014). Chromatin loops were identified using the HiCCUPS algorithm (Rao et al. 2014), which is a local peak caller that searches for each pixel in a contact matrix for which the contact frequency is enriched relative to the local background regions surrounding the pixel. Differential chromatin contacts between the two Hi-C contact matrices were identified using the HiCcompare R package and filtering out interactions of <10 (Stansfield et al. 2018). Statistical test analysis was performed using the Welch two-sample t-test in the R software (https://cran.r-project.org). Aggregate peak analysis The aggregate enrichment of a set of putative peaks in a contact matrix was tested with the APA algorithm (Rao et al. 2014), which is very useful for assessing low-resolution contact matrices. The resulting APA plot displayed the total number of contacts in the entire putative peak set at the center of the contact matrix. For an APA plot, focal enrichment across the peak set in aggregate shows a larger value at the center of the plot. RNA isolation, sequencing and analysis Total RNA was isolated from seedlings using the plant total RNA extraction reagent pBIOZOL (Bioflux, Germany). Libraries were prepared with a NEBNext Ultra RNA Library Prep Kit for Illumina (NEB, USA) according to the manufacturer’s instructions. The libraries were sequenced on an Illumina HiSeq 2500 platform, and 150-base paired-end reads were produced. Two biological replicates were generated for control and dark-treated seedlings. RNA sequencing reads were aligned against the ‘newzhongzhi 13’ reference genome using Subread (version 1.6.2) with the default parameters (Liao et al. 2013, 2014). DEGs were identified with the edgeR R package (Robinson et al. 2010). Genes with an FDR of ≤0.01 and the expression fold change of ≥3.0 were considered as DEGs. GO annotation of DEGs was performed using DAVID (version 6.8) (Huang et al. 2009a, Huang et al. 2009b). Quantitative real-time PCR (qPCR) for gene expression The cDNA was synthesized using a PrimeScript RT Reagent Kit (Takara, Otsu, Japan). qPCR was performed on a LightCycler 480 II (Roche, Penzberg, Germany) using the SYBR Green I Kit (Roche). SiUBQ gene was used as internal reference and two or three biological replicates for each sample were used for qPCR analysis. Primers for qPCR are listed in Supplementary Table S8. The relative expression values of genes were calculated by the 2−ΔΔCT method. The statistical test is performed with t.test function in R software (https://cran.r-project.org). ChIP for histone modification The ChIP assay was performed as previously reported (Saleh et al. 2008). First, 5.0 g of fresh 21-day-old seedlings were cross-linked in the cross-linking buffer [0.4 M sucrose, 10 mM Tris-HCl (pH 8.0), 1 mM Phenylmethanesulfonyl fluoride, 1 mM EDTA, 1% formaldehyde] for 3 × 5 min using vacuum infiltration and the reaction was terminated in 2.5 M glycine. Chromatin was sheared to an average size of 150 bp with an asonicator (Bioruptor Pico, Diagenode). Then, the sonicated samples were immunoprecipitated with 3.0 μg anti-H3K4me3 (Active Motif 39159) antibodies. After incubation at 4°C for about 6 h, the antibodies were recovered with 30 μl of Protein A/G Magnetic Beads (Millipore 16-663). After reverse cross-link, ChIP-ed DNA was extracted by MinElute Reaction Cleanup Kit (Qiagen 28206) and the sequencing library was constructed by VAHTS® Universal DNA Library Prep Kit for Illumina V3 (Vazyme ND607). The library was sequenced using an Illumina HiSeq-X Ten instrument. H3K4me3 peaks (Supplementary Table S9) were identified with MACS2 (version 2.1.1.20160309) with default arguments (Zhang et al. 2008). Spearman correlation analysis between biological replicates was performed with plotCorrelation function in deepTools software (Ramírez et al. 2016). Manipulation of genomic features in the full text was performed using BEDTools software (version 2.29) (Quinlan and Hall 2010). Acknowledgments We gratefully acknowledge technical support from Wenjing Wang and acknowledge the Central Laboratory of the Xishuangbanna Tropical Botanical Garden for providing high-performance computing and other research facilities. Funding National Natural Science Foundation of China (31500534 and 31670612); Natural Science Foundation of Henan Province (162300410346); Key Scientific Research Project in Colleges and Universities of Henan Province (15A180024); Startup Fund for Advanced Talents of Zhoukou Normal University (ZKNU2014108); and School-Based Program of Zhoukou Normal University (ZKUNB115203). Disclosures The authors declared that they have no conflicts of interest to this work. References Baranello L. , Kouzine F., Levens D. ( 2014 ) CTCF and cohesin cooperate to organize the 3D structure of the mammalian genome . Proc. Natl Acad. Sci. USA 111 : 889 – 890 . 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( 2013 ) Genome sequencing of the important oilseed crop Sesamum indicum L . Genome Biol. 14 : 401 . Google Scholar Crossref Search ADS PubMed WorldCat Author notes " Song Feng Li and Patrick J. Allen contributed equally. © The Author(s) 2020. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Plant and Cell Physiology Oxford University Press

Chromatin Architectures Are Associated with Response to Dark Treatment in the Oil Crop Sesamum indicum, Based on a High-Quality Genome Assembly

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Oxford University Press
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© The Author(s) 2020. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com
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0032-0781
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1471-9053
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10.1093/pcp/pcaa026
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Abstract

Abstract Eukaryotic chromatin is tightly packed into hierarchical structures, allowing appropriate gene transcription in response to environmental and developmental cues. Here, we provide a chromosome-scale de novo genome assembly of sesame with a total length of 292.3 Mb and a scaffold N50 of 20.5 Mb, containing estimated 28,406 coding genes using Pacific Biosciences long reads combined with a genome-wide chromosome conformation capture (Hi-C) approach. Based on this high-quality reference genome, we detected changes in chromatin architectures between normal growth and dark-treated sesame seedlings. Gene expression level was significantly higher in ‘A’ compartment and topologically associated domain (TAD) boundary regions than in ‘B’ compartment and TAD interior regions, which is coincident with the enrichment of H4K3me3 modification in these regions. Moreover, differentially expressed genes (DEGs) induced by dark treated were enriched in the changed TAD-related regions and genomic differential contact regions. Gene Ontology (GO) enrichment analysis of DEGs showed that genes related to ‘response to stress’ and ‘photosynthesis’ functional categories were enriched, which corresponds to dark treatment. These results suggested that chromatin organization is associated with gene transcription in response to dark treatment in sesame. Our results will facilitate the understanding of regulatory mechanisms in response to environmental cues in plants. Accession number: All genome, transcriptome and Hi-C high-throughput sequencing reads and the assembly presented in the article have been submitted to the CNGB Nucleotide Sequence Archive (CNSA) under the accession number CNP0000352. H3K4me3 ChIP-Seq sequencing reads were deposited in NCBI under the accession number SRP225600. Introduction Eukaryotic chromatin is highly enclosed to form a hierarchical structure, allowing appropriate gene expression in different cell types and developmental phases (Gibcus and Dekker 2013, Sexton and Cavalli 2015). The three-dimensional (3D) chromatin architectures proposed to play critical roles in genome integrity, DNA replication and gene expression (Dixon et al. 2012, Sexton et al. 2012, Jin et al. 2013). Based on the genome-wide interaction status, mammalian interphase chromatin is partitioned into active and inactive regions (‘A’ and ‘B’ compartments) that are associated with DNA methylation, open chromatin, transcription, repeats and replication timing (Lieberman-Aiden et al. 2009, Ryba et al. 2010). The ‘A’ and ‘B’ compartments are further partitioned into condensed structures, dubbed topologically associated domains (TADs) (Dixon et al. 2012, Nora et al. 2012). TADs with high intra-interaction frequency are predominant features of the mammalian genomes and spatially confine the interactions between promoter and distal regulatory elements regulating gene activation (Jin et al. 2013, Rao et al. 2014). However, the Arabidopsis genome lacks TAD structures similar to those in mammalian chromosomes, instead of containing relatively small interacting regions scattered throughout the genome, in which interaction patterns are correlated with changes in the epigenome (Feng et al. 2014). In another model plant, rice, thousands of TADs have been identified, the boundaries of which are associated with euchromatic epigenetic marks and active genes (Liu et al. 2017). The packing pattern of rice chromatins resembles that of Arabidopsis but has clear differences at specific structural levels (Dong et al. 2018). In rice, maize, tomato, sorghum and foxtail millet, chromosomes can be partitioned into local ‘A’ and ‘B’ compartments that are associated with euchromatin and heterochromatin; the polycomb proteins and their associated chromatins can be organized into TAD structures, which are not conserved across species (Dong et al. 2017). The substantial variation in chromatin structures across species suggests that plants have complex and unique chromatin architectures (Dong et al. 2017, Liu et al. 2017). Dynamic alteration in chromatin organization and concomitant transcription have vital roles in responses to environmental stimuli (Rosa and Shaw 2013, Probst and Mittelsten Scheid 2015). In Drosophila, heat shock causes rearrangement of chromatin architecture by inducing the relocalization of architecture proteins from TAD boundary to TAD interior regions, which may repress the transcription of most active genes (Li et al. 2015). In rice, cold treatment increases interactions between ‘A’ and ‘B’ compartments and inhibits long-range interactions on the same chromosome, implying that the entire rice genome becomes decondensed (Liu et al. 2017). However, a recent study reported that compartments and TAD structures are unchanged underlying heat shock in human and Drosophila cells; chromatin conformation necessary for a robust heat shock response is inherent and enhancer–promoter interactions are preestablished prior to heat treatment (Ray et al., 2019). Therefore, more studies are required to elucidate the biological functions of chromatin architecture in more species. Single-molecule real-time (SMRT) sequencing on the Pacific Biosciences (PacBio) platform can generate an average read length of 10–15 kb, which is suitable for de novo genome assembly and genome finishing (Eid et al. 2009, Bickhart et al. 2017, Du et al. 2017, Jiao et al. 2017). Genome-wide chromosome conformation capture (Hi-C) technology can detect nuclear interactions throughout a genome and probe the 3D architecture of the whole genome based on the quantitative estimation of proximity-ligation events for millions of loci in the genome, providing a source of long-range information for assigning, ordering and orienting genomic sequences to chromosomes (Dekker et al. 2002, Burton et al. 2013, Dudchenko et al. 2017). High-quality draft genomes with chromosome-length scaffolds have been produced by utilizing SMRT sequencing and Hi-C data (Burton et al. 2013, Xie et al. 2015, Schmitt et al. 2016, Dudchenko et al. 2017, Jiao et al. 2017, Phillippy 2017). Sesame (Sesamum indicum L., 2n = 26), which belongs to the family Pedaliaceae, is grown widely in tropical and subtropical regions (Wang et al. 2014). Sesame seed contains approximately 50% oil and 25% protein and is one of the world’s most important oil crops, but the genetic basis of its oil production and quality is unclear (Johnson et al. 1979, Wang et al. 2014, Wang et al. 2015). The genome of ‘Yuzhi 11’ sesame has been sequenced, generating a draft assembly of 293.7 Mb with a contig N50 value of 19.0 kb and a scaffold N50 value of 22.6 kb (Zhang et al. 2013). Another sesame genome, ‘Zhongzhi 13’, was also assembled, generating a draft genome of 272.7 Mb with a scaffold N50 length of 20.2 Mb (Wang et al. 2014, Wang et al. 2016, Yu et al. 2019). Here, we report a new sesame genome assembly with chromosome-length scaffolds produced using a combination of SMRT sequencing and Hi-C-based chromatin interaction maps. The new assembly is a total of 292.3 Mb with contig and scaffold N50s of 1.1 and 20.5 Mb, respectively. Based on this improved reference genome, we detected 3D genome architecture and alteration in chromatin organization in response to environmental cues in sesame. Our results facilitate the elucidation of biological processes in sesame and provide resources for unveiling the biological functions of 3D chromatin architectures in higher plants. Results Genome assembly and annotation Long-read sequencing data of sesame (Zhongzhi 13) were generated on the PacBio Sequel sequencing platform. Filtered subreads with an average length of 10.2 kb were used for de novo assembly (Supplementary Fig. S1). Based on the genome size of 337 Mb estimated with flow cytometry (Wang et al. 2014), the sequence coverage was approximately 37× for the species. The FALCON package was used for the first round of genome assembly (Chin et al. 2016), and then, the assembly was corrected based on the alignment of Illumina short reads from public data (Wang et al. 2014) using the Pilon package (Walker et al. 2014). The assembled sequences contained 1,001 contigs with a total length of 292.1 Mb and an N50 value of 1.1 Mb (Table 1). Then, Hi-C data were used to integrate the contig assembly into a candidate chromosome-length assembly using the 3D de novo assembly (3D DNA) pipeline and the Juicebox Assembly Tools (Durand et al. 2016a, Dudchenko et al. 2017, 2018). The final sesame assembly (hereafter referred to as ‘newzhongzhi 13’) had a total length of 292.3 Mb, with 93.6% of the assembly contained in the longest 13 scaffolds (each scaffold >16.3 Mb), and an N50 value of 20.5 Mb (Table 1), implying that the assembly is a chromosome-level genome. The annotation of the ‘newzhongzhi 13’ assembly was performed based on transcript and protein alignments using the MAKER annotation pipeline (Cantarel et al. 2008, Campbell et al. 2014). After the masking of repetitive sequences with the RepeatModeler package (Smit et al. 2013–2015), 28,406 protein-coding genes were predicted (Table 1). Of these genes, 22,692 (79.9%) were annotated with the Ensembl Plants datasets (http://plants.ensembl.org) and 5,714 (20.1%) genes had no hits representing novel genes. The annotation quality of the ‘newzhongzhi 13’ was assessed by annotation edit distance (AED) metric (Eilbeck et al. 2009), and the high AED scores suggested a high-quality genome annotation (Supplementary Fig. S2). Table 1 Statistics of the ‘newzhongzhi 13’ genome assembly Assembly . Number . N50 (bp) . N75 (bp) . L50 (n) . L75 (n) . Total length (bp) . Contigs 1,001 1,064,304 390,220 73 182 292,119,914 Scaffolds 597 20,523,558 18,035,395 6 10 292,289,538 Chromosomes 13 273,390,958 Coding genes 28,406 50,211,955 Assembly . Number . N50 (bp) . N75 (bp) . L50 (n) . L75 (n) . Total length (bp) . Contigs 1,001 1,064,304 390,220 73 182 292,119,914 Scaffolds 597 20,523,558 18,035,395 6 10 292,289,538 Chromosomes 13 273,390,958 Coding genes 28,406 50,211,955 Open in new tab Table 1 Statistics of the ‘newzhongzhi 13’ genome assembly Assembly . Number . N50 (bp) . N75 (bp) . L50 (n) . L75 (n) . Total length (bp) . Contigs 1,001 1,064,304 390,220 73 182 292,119,914 Scaffolds 597 20,523,558 18,035,395 6 10 292,289,538 Chromosomes 13 273,390,958 Coding genes 28,406 50,211,955 Assembly . Number . N50 (bp) . N75 (bp) . L50 (n) . L75 (n) . Total length (bp) . Contigs 1,001 1,064,304 390,220 73 182 292,119,914 Scaffolds 597 20,523,558 18,035,395 6 10 292,289,538 Chromosomes 13 273,390,958 Coding genes 28,406 50,211,955 Open in new tab Evaluation of the ‘newzhongzhi 13’ assembly quality We assessed the completeness and contiguity of the ‘newzhongzhi 13’ assembly using the QUAST-LG, BUSCO and MUMmer packages (Kurtz et al. 2004, Simão et al. 2015, Mikheenko et al. 2018, Waterhouse et al. 2018). The BUSCO annotation showed that the completeness of the ‘newzhongzhi 13’ assembly was better than the previous ‘Yuzhi 11’ and ‘Zhongzhi 13’ sesame assemblies (Fig. 1A) (Zhang et al. 2013, Wang et al. 2016). The collinearity analysis demonstrated that the ‘newzhongzhi 13’, ‘Yuzhi 11’ and ‘Zhongzhi 13’ assemblies have highly similar genome sequences, but some large structural differences, such as chromosome fragment inversions and transpositions, are present (Fig. 1B, C). Both the ‘newzhongzhi 13’ and ‘Zhongzhi 13’ assemblies had 13 chromosome-level scaffolds and N50 values of >20 Mb, implying that the two assemblies have the good contiguity, but that of the ‘newzhongzhi 13’ assembly is better than that of the ‘Zhongzhi 13’ assembly (Supplementary Table S1). Overall, these results suggested that the ‘newzhongzhi 13’ assembly is highly complete and contiguous. We estimated small local errors (such as single-base substitutions and short insertions and deletions) in the ‘newzhongzhi 13’ assembly with Illumina short-read alignments using the Pilon package (Walker et al. 2014). The estimated error rate was 0.0057% (substitutions 0.0049%, insertions 0.0003 and deletions 0.0005%). In fact, the actual error rate was smaller than the estimated one because the heterozygosity of the ‘newzhongzhi 13’ assembly introduced a large number of false errors. We compared the genomic structure between the ‘newzhongzhi 13’ and ‘Zhongzhi 13’ assemblies with the visual Hi-C-based chromatin interaction map using Juicebox Assembly Tools (Durand et al. 2016a, Dudchenko et al. 2018). The result suggested that our ‘newzhongzhi 13’ assembly had few misassemblies (Fig. 1D), implying high correctness. Fig. 1 Open in new tabDownload slide Comparison of the ‘newzhongzhi 13’, ‘Yuzhi 11’ and ‘Zhongzhi 13’ genome assemblies. (A) BUSCO annotation of the three genome assemblies. (B) Collinearity analysis of genome sequences between the ‘newzhongzhi 13’, ‘Yuzhi 11’ and ‘Zhongzhi 13’ assemblies. se1–13 indicate chromosome codes of the ‘newzhongzhi 13’ assembly; LG1–16 indicate chromosome codes of the published ‘Yuzhi 11’ assembly; and chr1–13 indicate chromosome codes of the published ‘Zhongzhi 13’ assembly. (C) Comparison of corresponding chromosomes between the ‘newzhongzhi 13’ and ‘Zhongzhi 13’ genome assemblies. se1–13 indicate the chromosome codes of the ‘newzhongzhi 13’ assembly; chr1–13 indicate chromosome codes of the published ‘Zhongzhi 13’ assembly. (D) Comparison of Hi-C contact maps of chromosomes between the ‘newzhongzhi 13’ and ‘Zhongzhi 13’ assemblies. se1, se6, se8 and se9 indicate chromosome codes of the ‘newzhongzhi 13’ assembly; chr2, chr8, chr10 and chr12 indicate chromosome codes of the published ‘Zhongzhi 13’ assembly. se1 and chr8, se6 and chr10, se8 and chr2 and se9 and chr12 are the same chromosomes in the two genomes. The blue arrows indicate possible error assemblies. Fig. 1 Open in new tabDownload slide Comparison of the ‘newzhongzhi 13’, ‘Yuzhi 11’ and ‘Zhongzhi 13’ genome assemblies. (A) BUSCO annotation of the three genome assemblies. (B) Collinearity analysis of genome sequences between the ‘newzhongzhi 13’, ‘Yuzhi 11’ and ‘Zhongzhi 13’ assemblies. se1–13 indicate chromosome codes of the ‘newzhongzhi 13’ assembly; LG1–16 indicate chromosome codes of the published ‘Yuzhi 11’ assembly; and chr1–13 indicate chromosome codes of the published ‘Zhongzhi 13’ assembly. (C) Comparison of corresponding chromosomes between the ‘newzhongzhi 13’ and ‘Zhongzhi 13’ genome assemblies. se1–13 indicate the chromosome codes of the ‘newzhongzhi 13’ assembly; chr1–13 indicate chromosome codes of the published ‘Zhongzhi 13’ assembly. (D) Comparison of Hi-C contact maps of chromosomes between the ‘newzhongzhi 13’ and ‘Zhongzhi 13’ assemblies. se1, se6, se8 and se9 indicate chromosome codes of the ‘newzhongzhi 13’ assembly; chr2, chr8, chr10 and chr12 indicate chromosome codes of the published ‘Zhongzhi 13’ assembly. se1 and chr8, se6 and chr10, se8 and chr2 and se9 and chr12 are the same chromosomes in the two genomes. The blue arrows indicate possible error assemblies. 3D architecture in the sesame genome Based on the high-quality ‘newzhongzhi 13’ reference genome, we detected the 3D architecture of sesame chromosomes using the Hi-C method (Lieberman-Aiden et al. 2009). Hi-C libraries from normally grown ‘Zhongzhi 13’ seedlings (referred to as bg), dark-treated ‘Zhongzhi 13’ seedlings (referred to as bh) and normally grown ‘Zhouhei 1’ seedlings (referred to as h) were constructed, with three replicates per library. Overall, 128, 120 and 146 M paired-end reads were obtained from the three libraries and >59.0% of the reads per library were uniquely mapped to the ‘newzhongzhi 13’ reference genome using the Juicer pipeline (Durand et al. 2016b), of which 78.7%, 78.8% and 76.7% contributed to the Hi-C contacts, respectively (Supplementary Table S2). Among them, 56.4% of the reads contributed to intrachromosomal contacts and 11.1% to interchromosomal contacts in the bg sample, 55.7% and 12.0% in the bh sample and 54.5% and 10.5% in the h sample, respectively (Supplementary Table S2). In addition, 26.1%, 24.8% and 26.3% of the intrachromosomal reads were long-range interactions (>20 kb) (Supplementary Table S2). The replicates for each sample displayed a strong positive relationship, with a correlation coefficient of 0.94 (Supplementary Fig. S3), suggesting that our Hi-C data are reliable. Three two-dimensional contact maps representing within-chromosome (cis) and between-chromosome (trans) interactions at a genome-wide scale were generated, and local contacts (represented as diagonal lines) were prominent across three samples (Supplementary Fig. S4). At chromosome level, obvious changes were displayed in the pairs bh vs. bg and h vs. bg (Fig. 2A, B). According to chromatin contact map, megabase (Mb)-sized chromatin regions were divided into alternating positive and negative eigenvectors, representing ‘A’ and ‘B’ compartments using principal component analysis (Supplementary Fig. S5). The ‘A’ and ‘B’ compartments, corresponding to the euchromatic and heterochromatic regions, were the secondary structural units in chromatin organization, occupying distinct subdomains in the chromosome territory, and were widespread in both animals and plants (Lieberman-Aiden et al. 2009, Yu and Ren 2017, Doğan and Liu 2018). More strong intrachromosomal interaction signals were displayed between ‘A’ compartments or between ‘B’ compartments than between ‘A’ and ‘B’ compartments (Fig. 2C, D and Supplementary Fig. S6). This result was also observed in maize and rice chromatin organization (Dong et al. 2019), suggesting that the feature of chromatin architecture may be widely present in plants. Fig. 2 Open in new tabDownload slide ‘A’ and ‘B’ compartment structural units across the bg, bh and h samples. (A) Comparison of chromosome architectures in the pairs bh vs. bg and h vs. bg at 500-kb resolution. Color legend indicates interaction strength [log2(observed/expected)]. (B) Comparison of ‘A’ and ‘B’ compartments in chromosomes 4 and 5 across the bg, bh and h samples. The black boxes highlight regions with different eigenvector values in the bg, bh and h samples; the shaded green area indicates ‘B’ compartment region. (C) Hi-C contact maps of chromatin interactions between ‘A’ and ‘B’ compartments in chromosome 3 across the bg, bh and h samples. The pink box indicates intrachromosomal interaction region of ‘A’ and ‘A’ compartments; the green box indicates intrachromosomal interaction region of ‘B’ and ‘B’ compartments; the black box indicates intrachromosomal interaction region of ‘A’ and ‘B’ compartments; and legends indicate interaction strength [log2(observed/expected)]. (D) Comparison of the interaction strength between A and B compartments. AA indicates the intrachromosomal interaction between ‘A’ compartments; AB indicates the intrachromosomal interaction between ‘A’ and ‘B’ compartments; and BB indicates the intrachromosomal interaction between ‘B’ compartments. The statistical test is performed with Wilcox function in R software. The bg indicates the normally grown ‘Zhongzhi 13’ seedlings; the bh indicates the dark-treated ‘Zhongzhi 13’ seedlings and the h indicates the normally grown ‘Zhouhei 1’ seedlings. Fig. 2 Open in new tabDownload slide ‘A’ and ‘B’ compartment structural units across the bg, bh and h samples. (A) Comparison of chromosome architectures in the pairs bh vs. bg and h vs. bg at 500-kb resolution. Color legend indicates interaction strength [log2(observed/expected)]. (B) Comparison of ‘A’ and ‘B’ compartments in chromosomes 4 and 5 across the bg, bh and h samples. The black boxes highlight regions with different eigenvector values in the bg, bh and h samples; the shaded green area indicates ‘B’ compartment region. (C) Hi-C contact maps of chromatin interactions between ‘A’ and ‘B’ compartments in chromosome 3 across the bg, bh and h samples. The pink box indicates intrachromosomal interaction region of ‘A’ and ‘A’ compartments; the green box indicates intrachromosomal interaction region of ‘B’ and ‘B’ compartments; the black box indicates intrachromosomal interaction region of ‘A’ and ‘B’ compartments; and legends indicate interaction strength [log2(observed/expected)]. (D) Comparison of the interaction strength between A and B compartments. AA indicates the intrachromosomal interaction between ‘A’ compartments; AB indicates the intrachromosomal interaction between ‘A’ and ‘B’ compartments; and BB indicates the intrachromosomal interaction between ‘B’ compartments. The statistical test is performed with Wilcox function in R software. The bg indicates the normally grown ‘Zhongzhi 13’ seedlings; the bh indicates the dark-treated ‘Zhongzhi 13’ seedlings and the h indicates the normally grown ‘Zhouhei 1’ seedlings. TAD was another major structural unit in chromosome territories, and chromatin interactions in TAD interior regions were stronger than ones in TAD boundary regions (Dixon et al. 2012, Nora et al. 2012, Sexton et al. 2012). TADs were proposed to a principal chromosomal structure, reflecting a tendency to partition chromatin into distinct and autonomously regulated regions (Sexton and Cavalli 2015). In the sesame genome, we detected the TAD-like structures using the arrowhead algorithm in the Juicer pipeline (Durand et al. 2016b), as in rice and maize genomes (Dong et al., 2019). In total, 705, 720 and 734 TADs with the median length of 150 kb were identified at 10-kb resolution across the bg, bh and h samples, respectively (Fig. 3G and Supplementary Fig. S7), covering approximately 49–58% of the sesame genome. Gene density in TAD boundary regions was significantly higher than one in TAD interior regions (Fig. 3A, B). Gene expression level was significantly higher in TAD boundary and ‘A’ compartment regions than that in TAD interior and ‘B’ compartment regions (Fig. 3C, D), concomitant with the enrichment of histone H3 trimethylated at lysine 4 (H3K4me3) in TAD boundary and ‘A’ compartment regions (Fig. 3E, F, Supplementary Fig. S8 and Table S3). In eukaryotes, H3K4me3 was associated with active chromatin and gene expression (Schneider et al. 2004), suggesting that gene transcription preferentially occurs in TAD boundary and ‘A’ compartment regions in sesame genome. Moreover, approximately 21.8% and 22.9% of TAD regions had changed in the pairs bh vs. bg and h vs. bg, respectively (Fig. 3G, H), suggesting that chromatin architecture is changed in response to dark treatment or different sesame cultivars. Fig. 3 Open in new tabDownload slide Gene transcription associated with chromatin structural units. (A) Gene distribution of TAD-related regions across the bg, bh and h samples. The gray line represents gene distribution in random TAD regions (background); the left white area indicates TAD boundary regions; and the right shaded green area indicates TAD interior regions. (B) Comparison of gene density (gene number/10 kb) between TAD boundary and TAD interior regions in the bg, bh and h samples. The diamond shape represents mean value. (C) Comparison of gene expression levels in TAD-related regions. (D) Comparison of gene expression levels in ‘A’ and ‘B’ compartment regions. (E) H3K4me3 distribution in TAD-related regions. (F) H3K4me3 distribution in ‘A’ and ‘B’ compartment regions. The statistical test is performed with Wilcox function in R software in (B)–(F). (G) Hi-C contact map and border index comparison of chromosome 8 among the bg, bh and h samples. The border index was calculated at 10-kb resolution. (H) TAD changes in the pairs bh vs. bg and h vs. bg. The bg, bh and h labels indicate the same samples as in Fig. 2. Fig. 3 Open in new tabDownload slide Gene transcription associated with chromatin structural units. (A) Gene distribution of TAD-related regions across the bg, bh and h samples. The gray line represents gene distribution in random TAD regions (background); the left white area indicates TAD boundary regions; and the right shaded green area indicates TAD interior regions. (B) Comparison of gene density (gene number/10 kb) between TAD boundary and TAD interior regions in the bg, bh and h samples. The diamond shape represents mean value. (C) Comparison of gene expression levels in TAD-related regions. (D) Comparison of gene expression levels in ‘A’ and ‘B’ compartment regions. (E) H3K4me3 distribution in TAD-related regions. (F) H3K4me3 distribution in ‘A’ and ‘B’ compartment regions. The statistical test is performed with Wilcox function in R software in (B)–(F). (G) Hi-C contact map and border index comparison of chromosome 8 among the bg, bh and h samples. The border index was calculated at 10-kb resolution. (H) TAD changes in the pairs bh vs. bg and h vs. bg. The bg, bh and h labels indicate the same samples as in Fig. 2. Chromatin loops were the basic chromatin structures, the formation of which allows direct physical contact between distant DNA elements and their transcriptional units, facilitating transcriptional activation (Liu and Weigel 2015). We detected 1,458, 1,513 and 1,742 chromatin loops from the interaction matrices of the bg, bh and h samples, respectively, using the HiCCUPS algorithm in the Juicer pipeline (Durand et al. 2016b) (Supplementary Table S4). Aggregate peak analysis (APA) was performed to measure aggregate enrichment of a set of putative peaks for the identification of chromatin loops (Durand et al. 2016b) (Supplementary Fig. S9). The number of chromatin loops identified in sesame was very low compared to the numbers in Arabidopsis and rice (Liu et al. 2016, Dong et al. 2018), possibly because low-resolution interaction maps did not explore small chromatin loops. However, the results suggested that chromatin loop structures also present in the sesame genome. Chromatin differential contacts in the pairs bh vs. bg and h vs. bg We detected 3,861 and 2,461 chromatin differential interactions in the pairs bh vs. bg and h vs. bg at 5 kb resolution with a false discovery rate (FDR) of ≤0.05 using the HiCcompare package (Stansfield et al. 2018) (Supplementary Table S5). The promoter density of coding genes was higher in the differential contact regions than in the random regions (background) (Fig. 4A), indicating that changes in chromatin architecture occur preferentially in promoter regions in sesame. The result was coincident with that differentially expressed genes (DEGs) between bh and bg samples are enriched in the differential contact regions (Fig. 4E), suggesting that gene transcription induced by dark treatment is associated with alteration in chromatin organization in sesame. Fig. 4 Open in new tabDownload slide DEGs relevant to the changes in chromatin structure in the pair bh vs. bg. (A) Differential chromatin contacts induced by dark treated occur preferentially in promoter regions. (B) DEG number in the common and changed ‘A’ and ‘B’ compartment regions. (C and D) DEG number in the common and changed TAD-related regions. (E) DEG number in the common and differential chromatin contact regions. The hypergeometric distribution test is performed with the phyper function in R software in (B–E). (F) Chromatin contact profiles of SiDIN1, SiSTP1 and SiPHOT2 in the bh and bg samples. The bg, bh and h labels indicate the same samples as in Fig. 2. Fig. 4 Open in new tabDownload slide DEGs relevant to the changes in chromatin structure in the pair bh vs. bg. (A) Differential chromatin contacts induced by dark treated occur preferentially in promoter regions. (B) DEG number in the common and changed ‘A’ and ‘B’ compartment regions. (C and D) DEG number in the common and changed TAD-related regions. (E) DEG number in the common and differential chromatin contact regions. The hypergeometric distribution test is performed with the phyper function in R software in (B–E). (F) Chromatin contact profiles of SiDIN1, SiSTP1 and SiPHOT2 in the bh and bg samples. The bg, bh and h labels indicate the same samples as in Fig. 2. Chromatin structure changes associated with gene transcription in response to dark treatment We identified 4,901 DEGs from the pair dark-treated vs. normally grown seedlings (bh vs. bg) with a fold change of ≥3.0 and an FDR of ≤0.01 (Supplementary Table S6). GO enrichment analysis showed that genes related to ‘response to stress’ and ‘photosynthesis’ functional categories are enriched (Supplementary Fig. S10 and Table S7), which corresponds to dark treatment. DEGs were enriched in the changed TAD interior and border regions but not in the common TAD-related regions and ‘A’ and ‘B’ compartment regions, implying that gene transcription induced by dark treated is relevant to TAD changes (Fig. 4B–D). Moreover, promoter regions of 1,554 DEGs overlapped with the differential contact regions in the pair bh vs. bg (Supplementary Table S8), implying that approximately one-third of DEGs induced by dark-treated is associated with chromatin architecture changes. The result is coincident with that DEGs are enriched in the differential contact regions (Fig. 4E). For example, sesame012486 (SiDIN1), sesame018342 (SiSTP1) and sesame020579 (SiPHOT2) genes related to light response had obviously different chromatin interaction patterns in respective chromosomes between bg and bh samples (Fig. 4F). Discussion The packing of chromatin in the nucleus is an important factor in the regulation of gene transcription in many cellular processes (Doğan and Liu 2018). The 3D structure of chromosomes displays a hierarchical pattern in which individual chromosomes are partitioned into three levels of functional substructures according to domain size: ‘A’ and ‘B’ compartments, TAD domains and chromatin loops (Rao et al. 2014, Yu and Ren 2017). This type of chromatin organization has been reported previously in plants, such as Arabidopsis, rice, maize, tomato, sorghum and foxtail millet (Liu et al. 2017, Doğan and Liu 2018, Dong et al. 2018). Here, chromatin structural units, such as ‘A’ and ‘B’ compartments, TAD-like domains and chromatin loops, were detected in sesame chromosomes using the Hi-C approach and an improved sesame genome. This result indicates that the hierarchical architecture of chromatin is extensive in plants. Life bodies frequently encounter various environmental stimuli during growth and development. Dynamic alteration in chromatin organization has vital roles in responses to environmental stimuli (Rosa and Shaw 2013, Li et al. 2015, Probst and Mittelsten Scheid 2015, Liu et al. 2017). It is reasonable that chromatin organization should participate in response to environmental cues. However, Ray et al. (2019) reported that chromatin interactions between regulatory elements and their target promoters are preestablished before heat shock; ‘A’ and ‘B’ compartments and TAD structures have no significant changes after head shock. The conclusion is inconsistent with that transcription changes after heat shock are accompanied by dramatic alterations in TAD boundary strength (Li et al. 2015). Dong et al. (2019) reported that plant chromatin structural units are stable across tissues; new formation of TAD borders is associated with transcription activation. These results suggested that the function of chromatin organization in response to environmental cues is complicated. In sesame, we revealed that gene transcription is associated with chromatin architecture changes in response to dark treatment (Fig. 4C–E), supporting that the dynamic alteration in chromatin architecture is associated with gene transcription in response to exogenous environmental stimuli (Li et al. 2015, Probst and Mittelsten Scheid 2015, Liu et al. 2017). For example, the sesame012486 (SiDIN1), sesame018342 (SiSTP1) and sesame020579 (SiPHOT2) genes had different chromatin interaction patterns between bh and bg samples (Fig. 4F). The three genes are homologous to Arabidopsis DARK-INDUCIBLE 1 (DIN1), SUGAR TRANSPORTER 1 (STP1) and PHOTOTROPIN 2 (PHOT2). DIN1 is a senescence-associated dark-inducible protein regulated by plant defense signaling and senescence responses (Schenk et al. 2005). STP1 is an H+/monosaccharide cotransporter and regulates hexose transport in plant tissues (Slewinski 2011). PHOT2 regulates both the avoidance and accumulation responses of chloroplasts under high- and low-light intensity, as well as phototropism, leaf flattening and stomatal opening, which could enhance leaf photosynthesis and promote biomass production in Arabidopsis (Christie 2007, Gotoh et al. 2018). After dark treatment, the expression of SiDIN1 was promoted, while SiSTP1 and SiPHOT2 were inhibited (Supplementary Fig. S11 and Table S9), which might be relevant to chromatin architecture changes in sesame. TAD, mainly as a chromatin structure, is associated with gene transcription by confining interactions between genes and their distal DNA regulatory elements (Sexton and Cavalli 2015 ). Recent studies have shown that the TAD-like domains are also present in plants (Liu et al. 2017, Dong et al. 2017, 2018), but TAD arrangement is irregular, which is not same as the checkerboard plaid-like arrangement of TADs in mammals (Lieberman-Aiden et al. 2009). In mammals, CCCTC-binding factor protein has an important role in the establishment of chromatin architecture and is highly enriched in TAD boundary regions (Dixon et al. 2012, Baranello et al. 2014, Tang, et al. 2015) but has not been found in plants, which may lead to the arrangement of irregular TAD in plants. In sesame, TAD arrangement was also irregular and gene density in TAD boundary regions was significantly higher than that in TAD interior regions (Fig. 3A), corresponding to that TAD formation is intimately linked to protein-coding gene density (Liu et al. 2017). Materials and Methods Plant materials Two sesame cultivar seedlings (‘Zhongzhi 13’ and ‘Zhouhei 1’) were grown under controlled environmental conditions (16/8 h and 28/20°C light/dark cycle). The ‘Zhongzhi 13’ cultivar has a white seed coat, and the ‘Zhouhei 1’ cultivar has a black seed coat. Three-week-old ‘Zhongzhi 13’ seedlings were dark-treated for 24 h. Then, the normally grown (bg) and dark-treated (bh) ‘Zhongzhi 13’ seedlings were harvested for Hi-C and chromatin immunoprecipitation (ChIP) library construction and RNA sequencing. The normally grown ‘Zhouhei 1’ seedlings (h) were fixed for Hi-C library construction. Three biological replicates per sample were generated for Hi-C library construction, and two biological replicates per sample were generated for RNA sequencing. PacBio sequencing and assembly PacBio sequencing was performed on a PacBio Sequel sequencer by Novogene Bioinformatics Technology (Beijing, China). The polymerase reads were filtered with the parameters—minLength = 50 and minReadScore = 0.8 using PacBio SMRT Link software (version 5.0). Filtered subreads were used for de novo assembly with the FALCON package (version 0.3.0) with the parameters length cutoff = 1,000, seed coverage = 25 and length cutoff preassembly = 11,000 (Chin et al. 2016). Assembled contigs were corrected with Illumina short reads from National Center for Biotechnology Information (NCBI) Sequence Read Archive data (SRA122008) using the Pilon package (version 1.23) (Walker et al. 2014). The contigs were then assembled into a candidate chromosome-length assembly with Hi-C data using a 3D de novo assembly (3D DNA) pipeline (Dudchenko et al. 2017). The candidate assembly was further improved by quality control and interactive correction in Juicebox Assembly Tools (Durand et al. 2016a, Dudchenko et al. 2018). Genome annotation and analysis The annotation of the genome assembly was performed using the MAKER genome annotation pipeline (version 2.31.10) (Cantarel et al. 2008, Campbell et al. 2014), after masking repetitive sequences based on a custom repeat library with the RepeatModeler package (Smit et al. 2013–2015). Transcript and protein sequences were used as evidence for ab initio gene prediction. De novo transcriptome assembly was performed with Illumina short reads from our transcriptome sequencing data, NCBI Sequence Read Archive data (SRA122023) and RefSeq sesame transcript data using the Trinity package (version 2.2.0) with default parameters (Grabherr et al. 2011, Haas et al. 2013). The protein data were from the Ensembl Plants Database (http://plants.ensembl.org). The training of the gene prediction model was performed using the Semi-HMM-based Nucleic Acid Parser and AUGUSTUS programs within MAKER. A detailed description of this process, including ancillary scripts and example command calls for the MAKER pipeline, is provided in the MAKER Wiki (http://weatherby.genetics.utah.edu/MAKER/wiki/index.php/Main_Page). The annotation quality of the assembly was assessed by the AED algorithm within MAKER. The completeness and contiguity of the ‘newzhongzhi 13’ assembly were assessed using the QUAST-LG, BUSCO (version 3.0), MUMmer (version 4.0) and MCScanX packages (Kurtz et al. 2004, Wang et al. 2012, Simão et al. 2015, Mikheenko et al. 2018). Small local errors in the ‘newzhongzhi 13’ assembly, such as single-base substitutions and short insertions and deletions, were estimated with Illumina short-read alignments using the Pilon package (version 1.22) (Walker et al. 2014). Large structural errors, i.e. misassemblies, were assessed by the visual Hi-C-based chromatin interaction map method using Juicebox Assembly Tools (Durand et al. 2016a, Dudchenko et al. 2018). Hi-C library preparation Hi-C library preparation was performed as previously described (Wang et al. 2015). Plant materials were cross-linked with a 2% formaldehyde solution at room temperature for 30 min in vacuum. Glycine (2.5 M) was added to quench the cross-linking reaction. Approximately 0.5 g of fixed tissue was ground with liquid nitrogen for DNA isolation. The extracted nuclei were resuspended with 0.5% Sodium dodecyl sulfate (SDS) followed by incubation at 62°C for 10 min. After quenching SDS with 10% Triton X-100 and incubation at 37°C for 15 min, DNA was digested with the four-cutter restriction enzyme DpnII and incubated at 37°C overnight. Then, the DpnII enzyme was inactivated at 62°C for 20 min. The cohesive ends were filled with Klenow and incubated at 37°C for 30 min. The proximal chromatin DNA was religated with T4 DNA ligation enzyme at room temperature for 4 h. After centrifugation at 1,500 × g for 3 min, the reaction mixture was resuspended in SDS buffer (50 mM Tris-HCl, 1% SDS, 10 mM ethylene diamine tetraacetic acid (EDTA), pH 8.0), proteinase K was added and the mixture was incubated at 55°C for 30 min. The formaldehyde cross-linking of the nuclear complexes was reversed by the addition of 30 μl of 5 M NaCl and incubation at 65°C overnight. Subsequent chromatin DNA manipulations were performed as previously described (Wang et al. 2015). The final libraries were sequenced on an Illumina HiSeq-X Ten platform (PE 150 bp). Hi-C data analysis pipeline Hi-C sequencing data were processed using the Juicer pipeline (Durand et al. 2016b). Each read end was mapped to the ‘newzhongzhi 13’ or ‘Zhongzhi 13’ reference genome using the BWA package (version 0.7.17) with the default parameters (Li and Durbin 2010). Duplicate and near-duplicate reads mapping to the same restriction fragment reads were removed. The remaining reads were filtered based on the mapping quality score. Normalized contact matrices were generated at different resolutions. Hi-C contact maps were generated and visualized using the Juicebox package (Durand et al. 2016a). Annotation of chromatin structural features and statistical analysis Contact matrices were annotated with a suite of algorithms, as described in the Juicer pipeline (Durand et al. 2016b). The eigenvectors, which are the first principal components of Pearson’s matrix, were calculated using the eigenvector algorithm. The signs of the eigenvectors indicated chromosome ‘A’ or ‘B’ compartments. TAD domains with different normalization methods were identified at different resolutions using the Arrowhead algorithm, which relies on the application of the Arrowhead matrix transformation to a normalized contact matrix (Rao et al. 2014). Chromatin loops were identified using the HiCCUPS algorithm (Rao et al. 2014), which is a local peak caller that searches for each pixel in a contact matrix for which the contact frequency is enriched relative to the local background regions surrounding the pixel. Differential chromatin contacts between the two Hi-C contact matrices were identified using the HiCcompare R package and filtering out interactions of <10 (Stansfield et al. 2018). Statistical test analysis was performed using the Welch two-sample t-test in the R software (https://cran.r-project.org). Aggregate peak analysis The aggregate enrichment of a set of putative peaks in a contact matrix was tested with the APA algorithm (Rao et al. 2014), which is very useful for assessing low-resolution contact matrices. The resulting APA plot displayed the total number of contacts in the entire putative peak set at the center of the contact matrix. For an APA plot, focal enrichment across the peak set in aggregate shows a larger value at the center of the plot. RNA isolation, sequencing and analysis Total RNA was isolated from seedlings using the plant total RNA extraction reagent pBIOZOL (Bioflux, Germany). Libraries were prepared with a NEBNext Ultra RNA Library Prep Kit for Illumina (NEB, USA) according to the manufacturer’s instructions. The libraries were sequenced on an Illumina HiSeq 2500 platform, and 150-base paired-end reads were produced. Two biological replicates were generated for control and dark-treated seedlings. RNA sequencing reads were aligned against the ‘newzhongzhi 13’ reference genome using Subread (version 1.6.2) with the default parameters (Liao et al. 2013, 2014). DEGs were identified with the edgeR R package (Robinson et al. 2010). Genes with an FDR of ≤0.01 and the expression fold change of ≥3.0 were considered as DEGs. GO annotation of DEGs was performed using DAVID (version 6.8) (Huang et al. 2009a, Huang et al. 2009b). Quantitative real-time PCR (qPCR) for gene expression The cDNA was synthesized using a PrimeScript RT Reagent Kit (Takara, Otsu, Japan). qPCR was performed on a LightCycler 480 II (Roche, Penzberg, Germany) using the SYBR Green I Kit (Roche). SiUBQ gene was used as internal reference and two or three biological replicates for each sample were used for qPCR analysis. Primers for qPCR are listed in Supplementary Table S8. The relative expression values of genes were calculated by the 2−ΔΔCT method. The statistical test is performed with t.test function in R software (https://cran.r-project.org). ChIP for histone modification The ChIP assay was performed as previously reported (Saleh et al. 2008). First, 5.0 g of fresh 21-day-old seedlings were cross-linked in the cross-linking buffer [0.4 M sucrose, 10 mM Tris-HCl (pH 8.0), 1 mM Phenylmethanesulfonyl fluoride, 1 mM EDTA, 1% formaldehyde] for 3 × 5 min using vacuum infiltration and the reaction was terminated in 2.5 M glycine. Chromatin was sheared to an average size of 150 bp with an asonicator (Bioruptor Pico, Diagenode). Then, the sonicated samples were immunoprecipitated with 3.0 μg anti-H3K4me3 (Active Motif 39159) antibodies. After incubation at 4°C for about 6 h, the antibodies were recovered with 30 μl of Protein A/G Magnetic Beads (Millipore 16-663). After reverse cross-link, ChIP-ed DNA was extracted by MinElute Reaction Cleanup Kit (Qiagen 28206) and the sequencing library was constructed by VAHTS® Universal DNA Library Prep Kit for Illumina V3 (Vazyme ND607). The library was sequenced using an Illumina HiSeq-X Ten instrument. H3K4me3 peaks (Supplementary Table S9) were identified with MACS2 (version 2.1.1.20160309) with default arguments (Zhang et al. 2008). Spearman correlation analysis between biological replicates was performed with plotCorrelation function in deepTools software (Ramírez et al. 2016). Manipulation of genomic features in the full text was performed using BEDTools software (version 2.29) (Quinlan and Hall 2010). Acknowledgments We gratefully acknowledge technical support from Wenjing Wang and acknowledge the Central Laboratory of the Xishuangbanna Tropical Botanical Garden for providing high-performance computing and other research facilities. Funding National Natural Science Foundation of China (31500534 and 31670612); Natural Science Foundation of Henan Province (162300410346); Key Scientific Research Project in Colleges and Universities of Henan Province (15A180024); Startup Fund for Advanced Talents of Zhoukou Normal University (ZKNU2014108); and School-Based Program of Zhoukou Normal University (ZKUNB115203). Disclosures The authors declared that they have no conflicts of interest to this work. References Baranello L. , Kouzine F., Levens D. ( 2014 ) CTCF and cohesin cooperate to organize the 3D structure of the mammalian genome . Proc. Natl Acad. Sci. USA 111 : 889 – 890 . 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( 2013 ) Genome sequencing of the important oilseed crop Sesamum indicum L . Genome Biol. 14 : 401 . Google Scholar Crossref Search ADS PubMed WorldCat Author notes " Song Feng Li and Patrick J. Allen contributed equally. © The Author(s) 2020. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

Plant and Cell PhysiologyOxford University Press

Published: May 1, 2020

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