Category Archives: Genome

High-quality genome assembly of representative morphotypes

To construct a pan-genome that encompasses the full range of genetic diversity in B. oleracea, we analyzed the resequencing data of 704 globally distributed B. oleracea accessions covering all different morphotypes and their wild relatives (Supplementary Tables 1 and 2). We identified 3,792,290 SNPs and 528,850 InDels in these accessions using cabbage JZS as reference genome22. A phylogenetic tree was then constructed using SNPs, which classified the 704 accessions into the following three main groups: wild B. oleracea and kales, arrested inflorescence lineage (AIL) and leafy head lineage (LHL; Fig. 1a and Supplementary Note 2). The phylogenetic relationship revealed in our study was generally consistent with those reported previously4,5,24,25. Based on the phylogeny and morphotype diversity, we selected 22 representative accessions for de novo genome assembly (Table 1).

a, Phylogenetic tree of 704 B. oleracea accessions. Different colors of branches indicate accessions from different morphotype groups. The images of the 27 representative accessions were placed next to their branches. The light blue, yellow and green backgrounds denote the following three main clusters: the wild/ancestral group, the arrested inflorescence lineage and the leafy head lineage. The red stars denote the 22 newly assembled genomes and the red rectangles denote five previously reported genomes. b, Phylogenetic tree of the 27 representative B. oleracea accessions, with the genome of B. rapa as the outgroup. c, The estimated insertion time (y axis) of all the full-length LTRs in the 27 B. oleracea genomes along the nine chromosomes (x axis) of B. oleracea. The lengths of nine chromosomes were normalized to 0100, proportional to their physical lengths. Each dot represents one LTR insertion event. The heatmap denotes the density of the full-length LTRs. Purple bars below each chromosome denote centromeric regions detected by centromere-specific repetitive sequences. d, Distribution of insertion time of full-length Copia and Gypsy LTRs in the 27 individual genomes. Each line represents a genome in the left graph. The two circles show the Copia and Gypsy LTRs that can be clustered into groups with sequence similarity of 90%. e, The heatmap shows the TAD prediction on chromosome eight of T10 (as an example), in which the region colored in dark red denotes a TAD structure. The line charts below the heatmap show the density of Copia and Gypsy LTRs, respectively, highlighting the enrichment of Copia LTRs in the centromere region, which is surrounded by high density of Gypsy LTRs.

We assembled genome sequences of the 22 accessions by integrating long-reads (PacBio or Nanopore sequencing), optical mapping molecules (BioNano) or high-throughput chromosome conformation capture data (Hi-C) and Illumina short-reads (Methods; Supplementary Note 2 and Supplementary Tables 37). The total genome size of these assemblies ranged from 539.87 to 584.16Mb with an average contig N50 of 19.18Mb (Table 1). An average of 98% contig sequences were anchored to the nine pseudochromosomes of B. oleracea. The completeness of these genome assemblies was assessed using benchmarking universal single-copy orthologs (BUSCO), with an average of 98.70% complete score in these genomes (Supplementary Table 8).

To minimize artifacts that could arise from different gene prediction approaches, we predicted gene models of both the 22 newly assembled genomes and the five reported high-quality genomes5,21,22,23 using the same annotation pipeline (Methods). Using an integrated strategy combining ab initio, homology-based and transcriptome-assisted prediction, we obtained a range of 50,346 to 55,003 protein-coding genes with a mean BUSCO value of 97.9% in these genomes (Table 1). After gene prediction, a phylogenetic tree constructed based on single-copy orthologous genes clustered the 27 genomes into three groups, similar to the results observed in the population (Fig. 1a and b).

A total range of 53.558.5% sequences in these B. oleracea genomes were TEs, with long terminal repeat retrotransposons (LTR-RTs) being the most abundant type (Supplementary Note 2). We further identified 4,703 to 6,253 full-length LTR-RTs (fl-LTRs) in these genomes (Supplementary Table 9), with recently inserted fl-LTRs enriched in centromeric regions (Fig. 1c). We revealed continuous expansion of Copia and Gypsy in all the genomes since four MYA (Fig. 1d). In addition, Copia TEs were clustered into more and larger groups than Gypsy based on sequence similarity (Fig. 1d), suggesting that Copia was under stronger expansion than Gypsy. More than 80% of the centromeric sequences were annotated as Copia in B. oleracea (Fig. 1e and Supplementary Fig. 1). Interestingly, these enriched Copia islands in centromeres were surrounded by high densities (>50%) of Gypsy in all the nine chromosomes of B. oleracea. Moreover, the topologically associating domain (TAD) structures overlapped with the Copia islands in all nine centromeric regions (Supplementary Fig. 1). This pattern was also found in six of ten chromosomes in B. rapa (Supplementary Fig. 2). These results suggest that Copia has an important role in the organization or function of centromeres through maintaining TAD structures.

We constructed an orthologous pan-genome comprising the 27 B. oleracea genomes. In total, we identified 57,137 orthologous gene families using OrthoFinder26 (Supplementary Note 3 and Supplementary Fig. 3). To investigate the retention variation of homoeologous genes among these mesohexaploid B. oleracea genomes, we further performed syntenic orthologous gene analysis (hereafter referred to as syntenic pan-genome). In the orthologous pan-genome, homoeologs were assigned to one orthologous family, whereas syntenic pan-genome separates them into different syntenic gene families. We detected a total of 87,444 syntenic gene families based on genomic synteny among these genomes of which 32,721, 24,902 and 22,423 families were located at LF, MF1 and MF2 subgenomes, respectively. The number of syntenic gene families increased when adding additional genomes and approached a plateau when n=25 (Fig. 2a), consistent with that of the orthologous pan-genome. We further separated all these syntenic gene families into 20,306 (23.2%), 10,086 (11.5%), 55,205 (63.1%) and 1,847 (2.1%) syntenic core, softcore, dispensable and private gene families, respectively, with a mean of 21,680 (41.5%), 10,724 (20.5%), 17,236 (32.9%) and 2,675 (5.1%) per genome (Fig. 2bd). We found significantly more TE insertions in syntenic dispensable and private genes than in syntenic core and softcore genes (Fig. 2e), suggesting that TEs contribute to genetic variations in these genes. The expression levels of syntenic core and softcore genes were significantly higher than those of syntenic dispensable and private genes (Fig. 2f). Moreover, the Ka/Ks values of the syntenic core genes were significantly lower than that of the orthologous core genes (Supplementary Fig. 4b), supporting more conservation of the syntenic core genes. We found that 44.6% of syntenic private and 38.2% of syntenic dispensable genes belong to orthologous core and softcore genes (Supplementary Fig. 4a), respectively. This illustrates the extensive differential gene loss of homoeologs during the evolution and diversification of B. oleracea.

a, The number of syntenic pan and core gene families in the 27 genomes. b, Composition of the syntenic pan-genome. The histogram shows the frequency distribution of syntenic gene families shared by different numbers of genomes. The pie chart shows the proportion of different groups of syntenic gene families. c, Percentage of different groups of syntenic gene families in each of the 27 genomes. d, Presence and absence information of all syntenic gene families in the 27 genomes. e,f, The average number of TE insertions in genes and the expression level of genes in different groups of syntenic gene families (two-sided Students t test; centerline, median; box limits, first and third quartiles; whiskers, 1.5 IQR). Different lowercase letters above the box plots represent significant differences (P<0.05). g, Functional analysis (gene ontology) of lost genes in the syntenic softcore or dispensable gene families, in different B. oleracea morphotypes, highlighting strong function enrichment associated with specific metabolites. The number of lost genes in different morphotypes is provided in the tree diagram. h, Syntenic gene families were separated into three groups corresponding to the numbers of homoeologs (single-, two- or three-copy) retained from the Brassica mesohexaploidization event. The percentage of gene families in different pan-genome classes for these groups is shown in each of the 27 B. oleracea genomes. IQR, interquartile range.

We dived into genes that were prone to being lost in different lineages/morphotypes of B. oleracea. A total of 20,924 syntenic gene families were lost in one to 14 genomes, while they were retained in 15 to 27 genomes. Among these, 2,786 and 5,139 gene families were lost exclusively in LHL and AIL, respectively (Fig. 2g). Intriguingly, in AIL, 556 syntenic gene families with gene loss specifically in broccoli were enriched in functions of sulfate transport, thioester hydrolase activity and riboflavin biosynthesis. In comparison, 1,134 syntenic gene families with gene loss specifically in cauliflower were enriched in nicotinamine biosynthesis and thiamine metabolism. Similarly, syntenic gene families with gene loss only in specific LHL morphotypes were found to be enriched in functions related to specific metabolites (Fig. 2g). The observations that genes specifically lost in different morphotypes were enriched in functions of biosynthesis or metabolism of various nutrient contents, pointing to unique nutritional composition or flavor of specific B. oleracea crops. In addition, our analysis of homoeologous copy-number variation (CNV) among B. oleracea morphotypes revealed morphotype-specific loss of homoeologous genes, which may contribute to the evolution of these morphotypes through variation in gene copy dosage that is associated with expression dosage (Fig. 2h, Supplementary Note 3 and Supplementary Tables 10 and 11).

The 27 high-quality B. oleracea genomes provide essential resources for the accurate identification of large-scale SVs. We aligned the sequences of 26 B. oleracea genomes to the T10 reference genome using Nucmer27. A total of 502,701 SVs were identified using SyRI28, including 452,148 PAVs (50bp to 3.34Mb), 13,090 CNVs (50bp to 243.14kb), 2,263 inversions (1,022bp to 12.18Mb) and 35,200 translocations (9,002 intrachromosomal and 26,198 interchromosomal translocations; 505bp to 5.59Mb; Fig. 3a and Supplementary Fig. 5a). We randomly selected 30 large SVs (>8kb) and 30 short SVs (<8kb) for validation. Approximately 93% of the selected large SVs were validated by Hi-C paired-end reads; the remaining 7% could not be validated (Supplementary Fig. 6). For the selected short SVs, 97% were validated by long-reads; the remaining 3% were found to be false calls (Supplementary Fig. 7 and Supplementary Table 12).

a, The distribution of GC content (3341%), gene numbers (0200Mb1) and TE density (20100%) in the T10 reference genome, the nonredundant SVs (presence, 2100kb/Mb; absence, 20400kb/Mb and all SVs, 10400kbMb1) among 27 genomes, as well as the SNPs (1040kb1) and InDels (1030kbMb1) identified in the 704 B. oleracea accessions. b, The number of different types of SVs from the nonredundant set of SVs in individual B. oleracea genomes. c, The number of SVs present in different numbers of query genomes. The bottom lines colored in light blue, light orange and light green mark these accessions from the wild/ancestral group, the AIL and the LHL, respectively. The sample IDs colored in light orange and light green denote accessions from broccoli/cauliflower and cabbage, respectively. The red rectangle marks the accessions of broccoli/cauliflower, highlighting the lower number of SVs in broccoli/cauliflower compared to the other accessions. d, The number of private SVs in wild B. oleracea, broccoli/cauliflower and cabbage genomes, showing significantly more private SVs in wild B. oleracea than in others (n=7 versus 5 versus 7; two-sided Wilcoxon rank-sum test; centerline, median; box limits, first and third quartiles; whiskers, 1.5 IQR). e, The frequency distribution of SVs in the following five different genomic regions: upstream (within 3kb), exon, intron, downstream (within +3kb) and intergenic regions. The SV ratios in the five regions were calculated for each of the 27 genomes, and these values were then sorted and plotted from small to large for each of the five regions. f, The density of SV sequences per 100bp in gene bodies and 5kb flanking regions in the 27 B. oleracea genomes. The area plots mark the maximum and minimum values across the 27 B. oleracea genomes, and the lines denote average values.

We merged the 502,701 SVs into 56,697 nonredundant SVs. The number of these SVs ranged from 7,449 to 9,848 per genome (Fig. 3b). A total of 50,153 nonredundant PAVs were used in our subsequent analysis. Similar to that of orthologous and syntenic gene families, the number of SVs increased when adding additional genomes; this increase diminished when n=25 (Supplementary Fig. 5b). Modeling this increase29 predicts a total SV number of 58,4101,452. The number of shared SVs sharply declined for the first three genomes and slowly decreased thereafter. We identified 27 SVs present in all 26 query genomes, 168 SVs present in 2425 query genomes, 26,641 SVs present in 223 query genomes and 18,226 SVs present in only one query genome, opposite to the trend of gene family counts (Fig. 3c). The number of private SVs in wild B. oleracea is significantly higher than in broccoli/cauliflower and cabbage, indicating extensive loss of genetic diversity during domestication of B. oleracea (Fig. 3c and d).

SVs distributed preferably in upstream and downstream regions of genes compared to gene bodies (Fig. 3e). Corroborating with this, SV density was the lowest in gene bodies and increased with distance in flanking regions (Fig. 3f), suggesting that SVs affecting regulatory sequences are likely to be under less stringent selection pressure than those disrupting encoding sequences. Besides, we found that 75% of all SVs overlapped with TEs (Supplementary Fig. 5c). We further identified SV gene, being the closest gene to the given SV within a 10-kb radius. In total, we determined 11,377 SV genes based on the syntenic pan-genome, including 9,442 expressed genes. These expressed SV genes were then separated into six groups based on the distance between SVs and corresponding genes (Fig. 4a). The 27 B. oleracea genomes were separated into two groups (presence and absence) based on the SV genotype of each SV gene. To be independent of the reference genome used for SV calling, we defined the genotype with more sequence as presence and the genotype with less sequence as absence. Comparison of SV gene expression between absence and presence groups revealed high percentages of SVs that have an effect on gene expression, decreasing with distance from 83% when located in the CDS region to 66% when located in 510kb upstream of SV genes (Fig. 4a and Supplementary Table 13). In total, for 69% (6,526) of the 9,442 SV genes, the SV was associated with gene expression changes. Of these 6,526 SV genes, SV presence was associated with significantly (P=1.481011, binomial test) more SV genes with suppressed expression (3,536 SV genes) than promoted expression (2,990 SV genes; Fig. 4b).

a, Different types of SV genes, based on the location of the SV relative to the gene, with data on expression, show a high proportion of SV genes with gene expression changes. b, The expression of SV genes from 6,526 syntenic gene families, with separated expression values for the absence and presence genotype groups (of corresponding SV). The x axis shows two groups, of which 3,536 and 2,990 syntenic gene families associated with suppression and promotion SVs, respectively. The y axis shows the normalized (z score) expression values. The green/yellow lines link the average expression values from each syntenic gene family for their presence and absence of genotype groups. c, Comparison of CpG island density and the ratio of highly methylated CpG islands between different types of SVs in 1.5kb (n=484 versus 369 versus 2,794; permutation test for 10,000 times; centerline, median; triangle, mean; box limits, first and third quartiles; whiskers, 1.5 IQR) or 3kb (n=153 versus 148 versus 1,391; permutation test for 10,000 times; centerline, median; triangle, mean; box limits, first and third quartiles; whiskers, 1.5 IQR). d, The expression fold changes of SV genes between the presence and absence of genotype groups. The black stars below the term Suppress denote DNA methylation modifications. The x axis shows the distance between SV and SV genes.

We also found that methylation was strongly associated with the suppressed expression of SV genes (Supplementary Note 4 and Supplementary Fig. 8a). We examined the sequence signature of the SV presence genotype for the 3,536 suppression SVs and found that their CpG site density was significantly higher than that of the 2,990 promotion SVs (Fig. 4c). The methylation levels of these suppression SVs were also significantly higher than that of the promotion SVs (Supplementary Fig. 8b). Both the increased density of CpG sites and their increased methylation levels resulted in a strong increase of highly methylated CpG islands in suppression SVs compared to promotion SVs (Fig. 4c). Besides suppression SVs, promotion SVs were identified that were associated with increased expression of SV genes. We investigated the sequence composition of promotion SVs and found significant (P<0.001, permutation test) enrichment of transcription factor (TF)-binding sites, including TCP, MYB, NAC, ERF and GRAS (Supplementary Table 14). These specific domains, together with low sequence methylation levels and few CpG islands in promotion SVs, may cause increased transcription of corresponding SV genes.

To further assess the strength of the effect of SVs on gene expression in B. oleracea genomes, we calculated the mean expression of corresponding SV genes for each of the two genotype groups (Fig. 4b). SVs affected gene expression ranging from over tenfold reductions to over tenfold increases, with most expression changes falling between one-third and three times (Fig. 4b,d). Furthermore, SVs that affect gene expression were enriched within 3kb flanking regions of genes. These results indicate the important role of SVs in fine-tuning gene expression levels.

We then used the nonredundant 50,153 SVs to construct an integrated graph-based genome with the T10 genome as a standard linear base reference. By mapping reads of 704 B. oleracea accessions to this graph-based genome, we revealed a total of 40,028 SVs in the population (Supplementary Note 4). We randomly selected 62 SVs, of which 59 were validated by PCR amplification (Supplementary Fig. 9 and Supplementary Table 15). Besides SVs, we identified 4,901,625 SNPs and 573,033 InDels in the population. Linkage disequilibrium (LD) analysis between these SVs and SNPs showed that 54.78% of SVs had weak LD (r2<0.5) with SNPs (Supplementary Fig. 10), indicating that SVs cannot be fully represented by SNPs in this genomic study. Of the 7,685 SV genes found in the B. oleracea population, 4,366 SV genes were expressed and 3,216 SV genes were used for downstream analysis (Methods). The percentage of SVs significantly (P<0.05) associated with the expression of SV genes ranged from 68% in the gene body to 59% 510kb away from the genes. In total, 61% of these SVs were substantially associated with expression changes of their SV genes, slightly less than 69% among the 27-genome assemblies. The SV presence was substantially associated with suppressed expression of 1,071 (55%) genes or promoted expression of 888 (45%) genes, similar to that of the 27-genome analysis (54% suppression, 46% promotion).

We also performed SV-based eGWAS analysis using 17,696 expressed genes and 40,028 SVs as traits and markers, respectively (Methods). The expression of 8,180 genes was significantly associated (P<1.001010) with at least one SV. In total, 50,076 SV signals were identified, among which 23% (11,536) and 77% (38,540) were intrachromosomal and interchromosomal signals, respectively (Supplementary Table 16). Of the 11,536 intrachromosomal SV signals, 1,335 were cis-regulatory SVs, with 49% and 51% of them suppressing and promoting gene expression, respectively. The remaining 48,741 SV signals were trans-regulatory SVs, with 47% and 53% suppressing and promoting gene expression, respectively. These results further indicate the important and complex regulatory role of SVs in gene expression.

We adopted the casecontrol GWAS strategy30,31 to identify SVs associated with different morphotypes of B. oleracea (Methods). Using the cauliflower/broccoli accessions characterized by large arrested inflorescences as the case group, we obtained 1,655 SV signals with P<8.161045, representing the top 5% signals (Fig. 5a). These SVs were assigned to 492 SV genes (SV in gene bodies or 3kb flanking regions), of which 378 were expressed, harboring 122 suppression and 109 promotion SVs. One suppression SV (P=1.5410108; 112bp) was located 643bp upstream of the translation start site of the gene BoPNY (PENNYWISE; Fig. 5b), which functions in maintaining inflorescence meristem identity and floral whorl morphogenesis32. This SV was under strong negative selection in the arrested inflorescence morphotype, being present in 2% (4 of 195) of cauliflower/broccoli accessions, contrasting to a presence of 89% (386 of 434) of control group accessions (Fig. 5c). More importantly, BoPNY was significantly higher expressed (P=3.00103) in the absence genotypes (the major allele in cauliflower/broccoli) than in the presence genotypes (Fig. 5d). The methylation levels of both the presence SV and its flanking sequences were significantly (P=8.55106) higher than that of the absence genotype, which was negatively associated with the transcription level of BoPNY (Fig. 5e). We also identified two promotion SVs located closest to gene BoCKX3. Cytokinin oxidase (CKX) catalyzes the degradation of cytokinin and thus negatively regulates cell proliferation of plants33. Mutants of ckx3 and its ortholog ckx5 form more cells and organs become larger34. One SV (SV1; P=5.8110162) involved a 316-bp Helitron-type TE insertion located 86bp downstream of the translation stop site of BoCKX3 (Fig. 5f). SV1 was present in 97% (208 of 214) of the cauliflower/broccoli accessions, contrasting to only 0.2% (1 of 431) of accessions in the control group (Fig. 5g). The other SV (SV2, 257bp) was located in last exon of BoCKX3, resulting in a frame-shift mutation. SV2 was present in only 0.5% (1 of 213) of cauliflower/broccoli accessions, compared to 29% (126 of 434) of accessions in the control group (Fig. 5f,g). These two SVs form four potential haplotypes of BoCKX3; however, the haplotype containing two SVs does not exist in our B. oleracea population (Fig. 5h). The expression of BoCKX3 in haplotype 3 was significantly higher than in haplotypes 1 and 2 (Fig. 5i), supporting the expression-promoting effect of this downstream SV1. BoCKX3 was highly expressed in leaves but not in other organs such as the curd during curd development in cauliflower/broccoli (Fig. 5j). One hypothesis is that BoCKX3 negatively regulates leaf growth, thus saving energy for fast proliferating of curds. These examples demonstrate the bidirectional impacts of SVs on gene expression, specifically associated with morphotypes of cauliflower/broccoli.

a, Manhattan plot showing the SV signals associated with cauliflower/broccoli (significance was calculated by two-tailed Fishers exact test. A Bonferroni-corrected P<0.05 was interpreted as significant). The light red dots show the top 5% P values and deep red dots show the top 1% P values. b, One SV is associated with BoPNY. c, The number of accessions with presence or absence SV (associated with BoPNY) genotype for broccoli/cauliflower accessions and all the other accessions (statistical test: two-tailed Fishers exact test). d, Expression comparison of BoPNY between SV presence and absence accessions (two-sided Students t test; centerline, median; box limits, first and third quartiles; whiskers, 1.5 IQR). e, Sequence methylation level around BoPNY between absence and presence genotype groups, which is negatively associated with the expression level of the gene. f, Two SVs associated with BoCKX3. g, The number of accessions with presence or absence SV (associated with BoCKX3) genotypes for broccoli/cauliflower accessions and all other accessions (statistical test: two-tailed Fishers exact test). h, The four possible haplotype groups are formed by two SVs. Haplotype 4 was not detected in our population. i, Expression comparison of BoCKX3 between the three haplotype groups (two-sided Students t test; centerline, median; box limits, first and third quartiles; whiskers, 1.5 IQR). j, Expression of BoCKX3 in different tissues of cauliflower and cabbage, highlighting high expression of this gene in leaf 2 of cauliflower. Leaf 1 denotes fresh leaf before curd initiation; leaf 2 denotes fresh leaf during curd development; curd 1 denotes developing curd; curd 2 denotes mature curd. N indicates a missing value as cabbage makes no curds.

GWAS analysis was also performed using cabbage accessions as the case group, characterized by the leafy heads (Supplementary Note 5 and Supplementary Figs. 11 and 12). We revealed two promotion SVs (SV1 and SV2) located closest to BoKAN1, which regulates leaf adaxial/abaxial polarity35,36,37. SV1 was introduced by a 970-bp TE (PIF/Harbinger) insertion, which was under strong negative selection in cabbage accessions (Supplementary Fig. 11b and c), and SV2 was introduced by a 157-bp TE (Helitron) insertion, which was also under negative selection in cabbage accessions. Among the four haplotypes formed by the two SVs (Supplementary Fig. 11d), BoKAN1 was significantly (P=3.60107) lower expressed in haplotypes 1 and 2 that lacked SV1 than in haplotypes 3 and 4 that harbored SV1 (Supplementary Fig. 11e). We also revealed one promotion SV (P=3.691091) located closest to BoACS4 (Supplementary Fig. 12a), which encodes the key regulatory enzyme involved in the biosynthesis of the plant hormone ethylene38,39. This insertion was under strong negative selection in cabbage (Supplementary Fig. 12b). Expression of BoACS4 in cabbage accessions lacking this insertion was significantly lower (P=1.901014) than in control group accessions harboring the insertion (Supplementary Fig. 12c).

Another interesting SV was present in all 18 ornamental kale accessions, but absent in any other accession. This SV was a 280-bp TE (PIF/Harbinger) insertion, located 289bp upstream of the translation start site of a MYB TF (hereafter referred to as BoMYBtf; Fig. 6a and b). Previously, MYB TFs were found to be associated with purple traits in cultivars of B. oleracea, such as kale, kohlrabi and cabbage40. The expression level of BoMYBtf was significantly higher in ornamental kale than in other morphotypes (Fig. 6c), indicating that this TE insertion was associated with the promoted expression of BoMYBtf. TF-binding sites (that is NAC, TCP and ERF), which were substantially enriched in promotion SVs as aforementioned, were also found in this PIF/Harbinger TE sequence (Fig. 6d). We hypothesize that these TF-binding sites, hitchhiking with the TE insertion, are causal factors promoting the transcriptional activity of BoMYBtf.

a, One SV (PIF/Harbinger-type TE insertion) is associated with BoMYBtf. b, The number of accessions with presence or absence of SV (associated with BoMYBtf) genotypes for ornamental kale accessions and all other accessions (statistical test: two-tailed Fishers exact test). c, Expression comparison of BoMYBtf between SV presence and absence accessions (two-sided Students t test; centerline, median; box limits, first and third quartiles; whiskers, 1.5 IQR). d, TF-binding elements identified in the PIF/Harbinger insertion. e, Schematic diagrams of reporter constructs used for the LUC/REN assay. The upstream sequences of BoMYBtf from ornamental kale T18 (with TE, 1,239bp), wild B. oleracea T10 (without TE, 951bp), cabbage T20 (without TE, 968bp) and the SV sequence (TE itself, 280bp). The empty vector was set as mock control. The activities of these promoter constructs are reflected by the LUC/REN ratio (two-sided Students t test; data are presented as the means.d.). f, Distribution of the PIF/Harbinger insertion in the 27 B. oleracea genomes. g, Boxplot showing normalized (z score) expression of 44 syntenic gene families, with a PIF/Harbinger insertion within a 3kb region from the nearest genes. The light blue and light purple backgrounds denote these syntenic gene families with PIF/Harbinger insertions located within 1.5kb and 3kb to 1.5kb, respectively, of corresponding gene members (red stars); whereas the gray dots denote their syntenic gene members without PIF/Harbinger insertion (centerline, median; box limits, first and third quartiles; whiskers, 1.5 IQR).

The role of this PIF/Harbinger TE in increasing transcription of BoMYBtf in ornamental kale was further validated by the luciferase reporter experiment (Fig. 6e). Briefly, the MYB promoters of ornamental kale T18 (with TE), wild B. oleracea T10 (without TE), cabbage JZS T20 (without TE) and the SV (TE itself) were fused in pMini-LUC as reporters and transfected into tobacco leaves (Methods). The LUC/REN ratio of mini-T18 and mini-SV was significantly higher (P<0.05) than that of other samples, while no significant difference was observed between mock, mini-T10 and mini-JZS, confirming the expression promotion effect of this PIF/Harbinger TE. Moreover, we investigated this PIF/Harbinger TE across all the 27 B. oleracea genomes. We found 60 insertions located within 3kb flanking regions of genes, with 44 associated genes being expressed (Fig. 6f). When comparing their expression among the 27 genomes, 31 genes harboring the insertion showed higher expression levels than their counterparts lacking the insertion, whereas this insertion in the remaining 13 genes did not result in increased expression (Fig. 6g). These results further support the common transcription promotion function of this PIF/Harbinger TE insertion in B. oleracea genomes.

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Near-gapless and haplotype-resolved apple genomes provide insights into the genetic basis of rootstock-induced ... - Nature.com

14 February 2024 News Release

Researchers at CSIRO, Australias national science agency, have sequenced the first genome of the Night Parrot, one of the worlds rarest and most elusive birds.

The development will answer questions about population genetics and biology that could boost conservation hopes for the recently rediscovered species.

The genome will enable us to explore the genetic basis of why the Night Parrot is nocturnal, a very unusual feature in parrots. Well investigate faculties like navigation, smell, beak shape and its less-than-optimal night vision, Dr Leo Joseph, Director of CSIROs Australian National Wildlife Collection said.

Researchers will also be able to run statistical analyses on the genome of this individual to estimate past population sizes of Night Parrot populations in Australia.

Now, we have the capability to compare this annotated genome with other, closely related parrots, shedding light on the reasons behind its scarcity and limited distribution compared to many of its relatives.

CSIRO researchers sequenced the Night Parrot genome its genetic blueprint using tissue obtained from Dr Kenny Travouillon, Acting Curator of Ornithology at the Western Australian Museum, after Traditional Owners in the Pilbara found the deceased specimen and delivered it to the Museum Boola Bardip.

The specimen, which is the best-preserved on display in the world, is now open to public viewing at the WA Museum Boola Bardip.

Dr Gunjan Pandey, who led the Night Parrot genomics project, said access to high-throughput DNA sequencing technology under CSIROs Applied Genomics Initiative is accelerating genomics research in Australia.

We can now generate very high-quality genomes from really tiny tissue samples even as small as an ants head or a single mosquito, Dr Pandey said.

This level of quality and detail just wasnt possible even five years ago.

The genetic data can be used to ensure conservation programs maximise diversity, so the species is resilient and has the best chance of long-term survival.

Once more widespread in arid Australia, the Night Parrot declined due to environmental changes such as predation by cats and foxes.

It is now known only from localised parts of southwest Queensland and Western Australia.

A couple of dozen scientific specimens were collected during the nineteenth century and one in 1912. Then a specimen was found in 1990 in southwest Queensland, Dr Joseph said.

Live birds were reported from the same area in 2013, and a live parrot was finally caught and tagged in 2015.

While the Night Parrot genome is an exciting scientific resource to understand more about this bird, protecting the species from cats, foxes, fire and habitat loss is also crucial for their conservation.

The Night Parrot genome will open up numerous opportunities for further research to help conserve this species, Dr Pandey said.

This will empower scientists to develop a plan for saving the Night Parrot, which is the ultimate goal of sequencing the genome and making it publicly available.

Note to editors

The National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM) annotated the genome sequence of the Night Parrot (Pezoporus occidentalis). The locations of individual genes were found using NCBIs Eukaryotic Annotation Pipeline (EGAP). The annotated genome is now available online as part of the NCBI Reference Sequence (RefSeq) Database through NCBI Datasets.

CSIRO together with the Threatened Species Initiative, supported by Bioplatforms Australia, will continue genetic studies to understand more about the Night Parrot and other closely related birds such as the Eastern Ground Parrot.

CSIROs Applied Genomics Initiative (AGI) uses high throughput sequencing technology to deliver reference genomes and large-scale diversity datasets for new insights and applied outcomes.

The genome was sequenced using Oxford Nanopore technology at the Biomolecular Resource Facility (BRF) at the Australian National University (ANU). BRF is a service node of Bioplatforms Australia which is made possible through investment funding provided by the Commonwealth Government National Collaborative Research Infrastructure Strategy (NCRIS). The AGI has successfully assembled over 100 genomes across diverse life forms in recent years, and many of these annotated genomes are accessible to the publicvia GenBank .

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Secrets of Night Parrot unlocked after first genome sequenced - CSIRO

In the realm of scientific innovation, the past decade has seen the CRISPR/Cas systems emerge as a groundbreaking tool in genome editing, boasting applications that span from enhancing crop yields to pioneering gene therapy.

The recent advent of CRISPR-COPIES by the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) marks a significant leap forward, refining CRISPRs flexibility and user-friendliness.

CRISPR-COPIES represents a cutting-edge solution designed to swiftly pinpoint ideal chromosomal sites for genetic modification across any species.

It will accelerate our work in the metabolic engineering of non-model yeasts for cost-effective production of chemicals and biofuels, explains Huimin Zhao, a prominent figure at CABBI and the University of Illinois.

The essence of gene editing lies in its ability to precisely alter genetic codes, enabling the introduction of novel traits such as pest resistance or enhanced biochemical production.

While CRISPR/Cas systems have facilitated targeted genetic modifications, the challenge of identifying optimal genomic integration sites persisted as a significant bottleneck, often involving cumbersome manual screening and testing processes.

Enter CRISPR-COPIES, the Computational Pipeline for the Identification of CRISPR/Cas-facilitated Integration Sites.

This innovation transforms genome-wide neutral integration site identification into a rapid, efficient process, taking mere minutes to accomplish what once was a daunting task.

Finding the integration site in the genome manually is like searching for a needle in a haystack, said Aashutosh Boob, a ChBE Ph.D. student at the University of Illinois and primary author of the study.

However, with CRISPR-COPIES, we transform the haystack into a searchable space, empowering researchers to efficiently locate all the needles that align with their specific criteria.

The versatility and efficiency of CRISPR-COPIES were showcased in a study published in Nucleic Acids Research, demonstrating its application across various species to enhance the production of valuable biochemicals.

Moreover, the creation of a user-friendly web interface makes this tool accessible to researchers with limited bioinformatics background, democratizing the advanced capabilities of CRISPR/Cas systems.

A primary goal of CABBI is to harness non-model yeasts for the sustainable production of chemicals and fuels from plant biomass.

Traditional genome-editing techniques, hindered by their labor-intensive nature and the scarcity of genetic tools, posed significant challenges to this endeavor.

CRISPR-COPIES addresses these issues by offering a streamlined approach for the rapid identification of stable integration sites, thereby facilitating the engineering of strains for enhanced biochemical yields and crop traits.

This innovative software is poised to significantly accelerate the strain construction process, offering a boon to researchers worldwide in both academic and industrial settings.

By simplifying genetic engineering tasks, CRISPR-COPIES not only saves time and resources but also opens new avenues for the development of transgenic crops and the efficient conversion of biomass to valuable chemicals.

In summary, CRISPR-COPIES stands as a monumental advancement in the field of genetic engineering, offering researchers a powerful and accessible tool for precision genome editing.

By streamlining the identification of optimal genetic integration sites, it accelerates the pace of scientific discovery and innovation while advancing new possibilities to address some of the most pressing challenges in agriculture, biofuel production, and gene therapy.

As this technology continues to evolve and become more integrated into various fields of research, CRISPR-COPIES promises to drive forward the boundaries of whats possible.

This new technology gives the world with a significant leap towards a future where genetic engineering can be conducted more efficiently, accurately, and with greater impact than ever before.

The full study was published in the journal Nucleic Acids Research.

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CRISPR gene editing tool gets a revolutionary high-tech upgrade - Earth.com

image:

A myelinating oligodendrocyte(green)

Credit: Peggy Assinck, Altos Labs-Cambridge Institute of Science

Researchers report February 15 in the journal Cell that ancient viruses may be to thank for myelinand, by extension, our large, complex brains. The team found that a retrovirus-derived genetic element or retrotransposon is essential for myelin production in mammals, amphibians, and fish. The gene sequence, which they dubbed RetroMyelin, is likely a result of ancient viral infection, and comparisons of RetroMyelin in mammals, amphibians, and fish suggest that retroviral infection and genome-invasion events occurred separately in each of these groups.

Retroviruses were required for vertebrate evolution to take off, says senior author and neuroscientist Robin Franklin of Altos Labs-Cambridge Institute of Science. If we didnt have retroviruses sticking their sequences into the vertebrate genome, then myelination wouldnt have happened, and without myelination, the whole diversity of vertebrates as we know it would never have happened.

Myelin is a complex, fatty tissue that ensheathes vertebrate nerve axons. It enables rapid impulse conduction without needing to increase axonal diameter, which means nerves can be packed closer together. It also provides metabolic support to nerves, which means nerves can be longer. Myelin first appeared in the tree of life around the same time as jaws, and its importance in vertebrate evolution has long been recognized, but until now, it was unclear what molecular mechanisms triggered its appearance.

The researchers noticed RetroMyelins role in myelin production when they were examining the gene networks utilized by oligodendrocytes, the cells that produce myelin in the central nervous system. Specifically, the team was investigating the role of noncoding regions including retrotransposons in these gene networkssomething that hasnt previously been explored in the context of myelin biology.

Retrotransposons compose about 40% of our genomes, but nothing is known about how they might have helped animals acquire specific characteristics during evolution, says first author Tanay Ghosh, a computational biologist at Altos Labs-Cambridge Institute of Science. Our motivation was to know how these molecules are helping evolutionary processes, specifically in the context of myelination.

In rodents, the researchers found that the RNA transcript of RetroMyelin regulates the expression of myelin basic protein, one of the key components of myelin. When they experimentally inhibited RetroMyelin in oligodendrocytes and oligodendrocyte progenitor cells (the stem cells from which oligodendrocytes are derived), the cells could no longer produce myelin basic protein.

To examine whether RetroMyelin is present in other vertebrate species, the team searched for similar sequences within the genomes of jawed vertebrates, jawless vertebrates, and several invertebrate species. They identified analogous sequences in all other classes of jawed vertebrates (birds, fish, reptiles, and amphibians) but did not find a similar sequence in jawless vertebrates or invertebrates.

Theres been an evolutionary drive to make impulse conduction of our axons quicker because having quicker impulse conduction means you can catch things or flee from things more rapidly, says Franklin.

Next, the researchers wanted to know whether RetroMyelin was incorporated once into the ancestor of all jawed vertebrates or whether there were separate retroviral invasions in the different branches. To answer these questions, they constructed a phylogenetic tree from 22 jawed vertebrate species and compared their RetroMyelin sequences. The analysis revealed that RetroMyelin sequences were more similar within than between species, which suggests that RetroMyelin was acquired multiple times through the process of convergent evolution.

The team also showed that RetroMyelin plays a functional role in myelination in fish and amphibians. When they experimentally disrupted the RetroMyelin gene sequence in the fertilized eggs of zebrafish and frogs, they found that the developing fish and tadpoles produced significantly less myelin than usual.

The study highlights the importance of non-coding regions of the genome for physiology and evolution, the researchers say. Our findings open up a new avenue of research to explore how retroviruses are more generally involved in directing evolution, says Ghosh.

###

This research was supported by the Adelson Medical Research Foundation, the UK Multiple Sclerosis Society, the Wellcome Trust, and the Altos Labs-Cambridge Institute of Science.

Cell, Ghosh et al., A retroviral link to vertebrate myelination through retrotransposon RNA mediated control of myelin gene expression https://cell.com/cell/fulltext/S0092-8674(24)00013-8

Cell (@CellCellPress), the flagship journal of Cell Press, is a bimonthly journal that publishes findings of unusual significance in any area of experimental biology, including but not limited to cell biology; molecular biology; neuroscience; immunology; virology and microbiology; cancer; human genetics; systems biology; signaling; and disease mechanisms and therapeutics. Visit http://www.cell.com/cell. To receive Cell Press media alerts, contact press@cell.com.

Experimental study

Animals

A retroviral link to vertebrate myelination through retrotransposon RNA-mediated control of myelin gene expression

15-Feb-2024

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Ancient retroviruses played a key role in the evolution of vertebrate brains - EurekAlert

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Natural selection and genetic diversity maintenance in a parasitic wasp during continuous biological control application - Nature.com

The Earth formed 4.5 billion years ago, and life less than a billion years after that. Although life as we know it is dependent on four major macromolecules DNA, RNA, proteins and lipids only one is thought to have been present at the beginning of life: RNA.

It is no surprise that RNA likely came first. It is the only one of those major macromolecules that can both replicate itself and catalyze chemical reactions, both of which are essential for life. Like DNA, RNA is made from individual nucleotides linked into chains. Scientists initially understood that genetic information flows in one direction: DNA is transcribed into RNA, and RNA is translated into proteins. That principle is called the central dogma of molecular biology. But there are many deviations.

One major example of an exception to the central dogma is that some RNAs are never translated or coded into proteins. This fascinating diversion from the central dogma is what led me to dedicate my scientific career to understanding how it works. Indeed, research on RNA has lagged behind the other macromolecules. Although there are multiple classes of these so-called noncoding RNAs, researchers like myself have started to focus a great deal of attention on short stretches of genetic material called microRNAs and their potential to treat various diseases, including cancer.

Scientists regard microRNAs as master regulators of the genome due to their ability to bind to and alter the expression of many protein-coding RNAs. Indeed, a single microRNA can regulate anywhere from 10 to 100 protein-coding RNAs. Rather than translating DNA to proteins, they instead can bind to protein-coding RNAs to silence genes.

The reason microRNAs can regulate such a diverse pool of RNAs stems from their ability to bind to target RNAs they dont perfectly match up with. This means a single microRNA can often regulate a pool of targets that are all involved in similar processes in the cell, leading to an enhanced response.

Because a single microRNA can regulate multiple genes, many microRNAs can contribute to disease when they become dysfunctional.

In 2002, researchers first identified the role dysfunctional microRNAs play in disease through patients with a type of blood and bone marrow cancer called chronic lymphocytic leukemia. This cancer results from the loss of two microRNAs normally involved in blocking tumor cell growth. Since then, scientists have identified over 2,000 microRNAs in people, many of which are altered in various diseases.

The field has also developed a fairly solid understanding of how microRNA dysfunction contributes to disease. Changing one microRNA can change several other genes, resulting in a plethora of alterations that can collectively reshape the cells physiology. For example, over half of all cancers have significantly reduced activity in a microRNA called miR-34a. Because miR-34a regulates many genes involved in preventing the growth and migration of cancer cells, losing miR-34a can increase the risk of developing cancer.

Researchers are looking into using microRNAs as therapeutics for cancer, heart disease, neurodegenerative disease and others. While results in the laboratory have been promising, bringing microRNA treatments into the clinic has met multiple challenges. Many are related to inefficient delivery into target cells and poor stability, which limit their effectiveness.

One reason why delivering microRNA treatments into cells is difficult is because microRNA treatments need to be delivered specifically to diseased cells while avoiding healthy cells. Unlike mRNA COVID-19 vaccines that are taken up by scavenging immune cells whose job is to detect foreign materials, microRNA treatments need to fool the body into thinking they arent foreign in order to avoid immune attack and get to their intended cells.

Scientists are studying various ways to deliver microRNA treatments to their specific target cells. One method garnering a great deal of attention relies on directly linking the microRNA to a ligand, a kind of small molecule that binds to specific proteins on the surface of cells. Compared with healthy cells, diseased cells can have a disproportionate number of some surface proteins, or receptors. So, ligands can help microRNAs home specifically to diseased cells while avoiding healthy cells. The first ligand approved by the U.S. Food and Drug Administration to deliver small RNAs like microRNAs, N-acetylgalactosamine, or GalNAc, preferentially delivers RNAs to liver cells.

Identifying ligands that can deliver small RNAs to other cells requires finding receptors expressed at high enough levels on the surface of target cells. Typically, over one million copies per cell are needed in order to achieve sufficient delivery of the drug.

One ligand that stands out is folate, also referred to as vitamin B9, a small molecule critical during periods of rapid cell growth such as fetal development. Because some tumor cells have over one million folate receptors, this ligand provides sufficient opportunity to deliver enough of a therapeutic RNA to target different types of cancer. For example, my laboratory developed a new molecule called FolamiR-34a folate linked to miR-34a that reduced the size of breast and lung cancer tumors in mice.

One of the other challenges with using small RNAs is their poor stability, which leads to their rapid degradation. As such, RNA-based treatments are generally short-lived in the body and require frequent doses to maintain a therapeutic effect.

To overcome this challenge, researchers are modifying small RNAs in various ways. While each RNA requires a specific modification pattern, successful changes can significantly increase their stability. This reduces the need for frequent dosing, subsequently decreasing treatment burden and cost.

For example, modified GalNAc-siRNAs, another form of small RNAs, reduces dosing from every few days to once every six months in nondividing cells. My team developed folate ligands linked to modified microRNAs for cancer treatment that reduced dosing from once every other day to once a week. For diseases like cancer where cells are rapidly dividing and quickly diluting the delivered microRNA, this increase in activity is a significant advancement in the field. We anticipate this accomplishment will facilitate further development of this folate-linked microRNA as a cancer treatment in the years to come.

While there is still considerable work to be done to overcome the hurdles associated with microRNA treatments, its clear that RNA shows promise as a therapeutic for many diseases.

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MicroRNA is the master regulator of the genome researchers are learning how to treat disease by harnessing the ... - The Conversation

Entire genome sequences for nearly half a million people have been released by the UK Biobank, representing the largest dataset of its kind in the world.

The resource has the potential to offer new insights into the causes of major common diseases and guide the choice of potential therapeutic targets.

It has hailed as a step change in genomics and is available to approved researchers around the world through the UK Biobank Research Analysis Platform.

This is a veritable treasure trove for approved scientists undertaking health research, and I expect it to have transformative results for diagnoses, treatments and cures around the globe, said UK Biobank principal investigator Sir Rory Collins, PhD.

Executive vice president for innovative medicine research and development at industry partner Johnson & Johnson John Reed, PhD, maintained the findings could pave the way for more efficient clinical development and drive progress towards precision medicine.

This landmark dataset will enable us to leverage the power of artificial intelligence and machine learning for rapidly identifying novel disease targets and helping researchers predict how a candidate medicine might impact certain subpopulations of patients, based on their genetics, he said.

The UK Biobank whole genome sequencing (WGS) consortium was formed in 2018 with the goal of sequencing the genomes of all UK biobank participants.

The five-year project cost 200m, involved 11 partners and took 350,000 hours of sequencing time to create 27.5 petabytes of genetic data. At its peak, over 20,000 whole genomes, each with around three billion base pairs of DNA, were being sequenced each month. It resulted in the genomes of 491,554 UK Biobank volunteers being sequenced overall.

Half the funding came from the U.K. government and the Wellcome research organisation. The remaining 100 million was given by the biopharmaceutical and healthcare companies Amgen, AstraZeneca, GlaxoSmithKline, and Johnson & Johnson.

In return for their 25m investment, each of the four companies received a nine-month head start with the data before its public release.

The large-scale biomedical database and research UK Biobank resource follows the health of half a million volunteers recruited in 2006 and has already provided numerous clinical insights.

Data collected on over 10,000 variables, including blood pressure, cognitive function, diet and bone density, have been studied to examine why having the same genetic predisposition for a disease can result in different outcomes, reactions and side-effects to identical treatments.

It has led to thousands of scientific studies being published, and major insights such as the discovery that Type 1 diabetes is as common in adults as children.

Executive vice president of research and development at Amgen David Rees, PhD, said: This ground-breaking dataset allows scientists to explore how genetics affect levels of proteins, metabolites and other physiological factors, more closely than ever before, promising to accelerate our understanding of the genetic underpinnings of disease.

Chief executive of UK Research and Innovation (UKRI) professor Dame Ottoline Leyser, PhD, noted: Researchers can now apply to access de-identified full genome data from half a million participants, alongside a rich combination of medical, biochemical, lifestyle and environmental data from volunteers involved.

Today marks an important milestone in UKRIs commitment to realise the potential of genetics for biomedical research, innovation and translation to the clinic.

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"Ground-Breaking" Release of World's Largest Whole Genome Resource - Inside Precision Medicine

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