Daily Archives: February 18, 2024

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The complete plastome sequences of invasive weed Parthenium hysterophorus: genome organization, evolutionary ... - Nature.com

Identification of a set of gut microbes associated with CRC

Most colorectal cancers arise from adenoma to carcinoma as verified by diet, inflammatory processes, gut microbiota, and genetic alterations. Nonetheless, the mechanism by which the microbiota interacts with these etiologic factors to promote CRC is not clear. Therefore, we collected stool samples, tumor and matched normal tissues from 41 CRC individuals, and carried out multi-omics sequencing analyses to evaluate the interplay between cancer cells and gut microbiome (Fig.1 and Additional file 1: Table S1). As shown in Additional file 1: Fig. S1a, the stool samples were subjected to metagenomic sequencing, achieving an average of 7Gb clean data. Additionally, we conducted whole exome sequencing, ensuring a minimum of more than 100X coverage and 20Gb data, respectively (Additional file 1: Table S2).

Metagenomics sequencing of the stool sample and exome and transcriptome sequencing of mucosa tissue in colorectal cancer. We collected stool specimens and matched tumor and normal mucosa tissue from 41 colorectal cancer patients. The former samples were metagenomically shotgun sequenced to yield taxonomic and functional profiles; the latter were processed using exome and transcriptome sequencing technology respectively. Features of the microbiome were correlated with clinic elements somatic mutations, and differentially expressed genes, respectively

We first examined the microbiome dysbiosis by integrating our metagenomic sequencing data with a public Chinese colorectal cancer cohort3 (CRC cohort2 and CON) (Fig.2A). Compared with healthy controls, the CRC patients in our cohort exhibited a significantly decreased alpha diversity (Additional file 1: Fig. S1b), but no obvious difference in the beta diversities (Additional file 1: Fig. S1c). To investigate the alterations in microbiota structure, we conducted the linear discriminant analysis effect size (LEfSe) analysis to compare healthy controls and combined tumor samples. Totally, there were 2 taxa (Viruses_noname and Fusobacteria) at the phylum level and 10 at the genus level significantly altered respectively (Fig.2B and Additional file 1: Table S3). Notably, we figured out 22 species associated with disease status, of which 14 were elevated in CRC group (Fig.2C). Of them, Bacteroides fragilis (LDA=3.897), Parabacteroides spp. (LDA=3.499) and Prevotella intermedia (LDA=3.452) exhibited the highest abundances in CRC patients. In contrast, eight species were enriched in healthy controls, including Faecalibacterium prausnitzii (LDA=4.299), Eubacterium rectale (LDA score=4.255), Eubacterium eligens (LDA=4.002), and so on.

A Microbiome alteration between healthy and CRC subjects. PCoA plot showed the two cohorts used in our project. B Taxonomic profile difference detected with LEfSe. C Differentially abundant species between healthy controls and CRC patients. D Differentially abundant KEGG pathways between healthy controls and CRC patients. E Unsupervised clustering uncovered associations between differentially abundant species and clinic covariates

To further investigate the functions of 22 tumor-associated bacteria, we used HUManN2 to estimate the relative abundance of KEGG ontology (KO) categories. Disease associated KEGG pathway changes were further identified using the method described in Feng Q. et al.4 We observed that bacteria related metabolic pathways were enriched in CRC groups. Especially, one carbon pool by folate metabolic pathway of microbiota was significantly (Reporter score=3.471) higher in CRC patients (Fig.2D). The one carbon pool by folate is a universal cell metabolic process supporting tumorigenesis, obtaining folate (vitamin B9) and cobalamin (vitamin B12) from diet. Furthermore, the cancer enriched species showed positive correlations with the metabolic pathways such as carbon metabolism and oxidative phosphorylation, whereas some well-known beneficial bacteria (including Faecalibacterium prausnitzii), displayed negative correlations (Additional file 1: Fig. S2).

Next, we investigated associations between overall microbiome configuration with CRC clinical covariates. Clinically, of the cohorts 41 individuals (63% male; ages 4679), 26 subjects (63%) belong to COAD and 15 subjects (37%) had carcinomas at rectum. Additionally, 10 subjects were diagnosed at early stage and 31 subjects (76%) at later stage. Among all 41 individuals, we observed that several paraprevotella.ssp were elevated in patients with age<65 (for example, paraprevotella clara, LDA score=3.051; paraprevotella xylaniphila, LDA score=2.478) (Additional file 1: Fig. S3a). Furthermore, Clostridium clostridioforme was predominated found in females (Additional file 1: Fig. S3b, LDA score=3.182). As to Bacteroides genus, the abundance of Bacteroides eggerthii was significantly increased in COAD (LDA score=3.625) whereas Bacteroides massiliensis was enriched in READ (LDA score=4.985) (Additional file 1: Fig. S3c). Bifidobacterium, one of the major probiotics, exhibited a significant increase in the early stage and individuals with age<65 (Bifidobacterium longum, LDA score=3.698; Bifidobacterium dentium, LDA score=2.102) (Additional file 1: Fig. S3a and d).

We also assessed the connections between clinical characteristics and 22 cancer associated bacteria in our subjects through unsupervised clustering (Fig.2E and Additional file 1: Table S4). Of note, we observed significant gender differences (p=0.01) among the C3 community type (Additional file 1: Fig. S4a). Tumor locations (colon or rectum; p=0.01) were linked to the C4 community type, which primarily consisting of the beneficial species (Additional file 1: Fig. S4b).

Previous studies indicated that gut microbes may induce DNA damage, thereby accelerating cancer development [29]. Consequently, we detected somatic mutations using exome sequencing technology from 41 CRC tumors and idntified 4 significantly mutated genes with MutSigCV, including TP53 (Q value=0), APC (Q value=1.26E-11), KRAS (Q value=1.11E-10) and SMAD4 (Q value=7.37E-04) (Fig.3A and Additional file 1: Table S6).

An overview of the associations between cancer genome and microbiome genomes. A Bar plots illustrate the frequently mutated genes in 41 tumor tissues. B The interaction between gut microbial taxa and somatic altered genes

To explore their associations with microbiota composition, we conducted the LEfSe analysis to compare tumors with or without mutated genes (Fig.3B). TP53 is the most prevalent somatic altered genes in our cohort. In TP53 mutated subjects, an enrichment of several disease-associated species, including Alistipes putredinis (LDA score=4.402), Porphyromonas asaccharolytica (LDA score=3.816), and Prevotella intermedia (LDA score=3.795) (Fig.4A). Previous observations uncovered that butyrate treatment could activate the TP53 pathway [30]. Consistently, the abundance of butyrate-producing bacteria, Butyricicoccus pullicaecorum, exhibited a significant reduction in TP53 mutation carriers (LDA score=2.395). Interestingly, Roseburia inulinivorans (LDA score=3.96) and Ruminococcus gnavus (LDA score=3.426), two other butyrate producers, were also significantly depleted in APC mutation carriers (Fig.4B). Besides, the relative abundance of Enterococcus genus was enriched in subjects with KRAS and SMAD4 mutations (Enterococcus faecalis, LDA=2.217; Enterococcus avium, LDA score=3.075) (Fig.4C, D). We also performed similar analysis between gut microbiota and other frequently mutated genes (Additional file 1: Fig. S5). In stool samples, probiotics, including Ruminococcus lactaris(LDA score=3.405), Bifidobacterium bifidum (LDA score=2.425), were dramatically elevated in MUC5B or MUC16 mutated individuals (Additional file 1: Fig. S5e, f). Barnesiella intestinihominis, acting as an enhancer for anticancer therapy, was proven enriched in TNN mutation carriers (LDA score=3.156) (Additional file 1: Fig. S5m).

AD Significantly mutated genes related taxonomic difference. Differentially abundant species between tumors with and without TP53 (A), APC (B), KRAS (C), SMAD4 (D) alterations, respectively

We further characterized the differences of microbial pathways between subjects with specific mutations and control group. Interestingly, the most abundant pathways were generally housekeeping processes encoded by microbes, such as one carbon metabolism, aromatic amino acids, branched chain amino acid and so on (Additional file 1: Fig. S6). One-carbon (1C) metabolism, consistently overexpressed in cancer, supports multiple biological processes, including nucleotides synthesis, methionine recycling pathway and redox defense [31]. An increased level of bacterial purine (reporter score=2.909) and pyrimidine (reporter score=3.188) metabolism were found in TP53 mutation carriers (Additional file 1: Fig. S6a). Similarly, bacterial cysteine-methionine metabolism (reporter score=3.246) and folate biosynthesis (reporter score=1.949) exhibited significant alterations in individuals with APC mutations (Additional file 1: Fig. S6b). Bacteria can synthesize different amino acids. Compared to control group, we found APC (Additional file 1: Fig. S6c) and SMAD4 mutation carriers (Additional file 1: Fig. S6d) were significantly associated with high levels of bacterial tryptophan (Trp) metabolism pathway (reporter score=3.045 and 2.732, respectively). Moreover, we observed an elevated abundance of bacterial phenylalanine metabolism correlated with KRAS mutations (reporter score=4.345) (Additional file 1: Fig. S6c).

We also investigated the relationship between the microbiome composition and the gene expression patterns in CRC. We observed that certain bacterial species were significantly correlated with the gene expression pattern (Additional file 1: Fig. S7 and Table S6). The differentially expressed functional genes were clustered according to their correlation with differentially abundant species, following by annotation with DAVID (Fig.5). We observed that Fusobacterium nucleatum, along with some Clostridium spp. exhibited positive associations nitrogen metabolism and bile secretion pathways, but negatively with cytokine-cytokine receptor interaction pathway.

Correlation of differentially abundant species and deregulated genes. Tumor associated deregulated genes were clustered and annotated with DAVID. The X axis illustrated the DAVID functional annotation and Y axis showed differentially abundant species. Red color represents positive association while green color means negative association

Subsequently, the interaction between 22 bacterial species and up-regulated oncogene expression was explored. As shown in Fig.6A, Fusobacterium nucleatum was positively correlated with PKM (p=0.03), SCD (p=0.0186), FASN (p=0.014), which are key enzymes in glycolysis and fatty acid metabolism. Consistent with the findings, we categorized patients into high and low Fusobacterium nucleatum groups, and found that various metabolism related pathways were significantly enriched in the high groups (pentose and glucuronate interconversions, p=0.026; starch and sucrose metabolism, p=0.007; porphyrin and chlorophyll metabolism, p=0.023; oxidative phosphorylation, p<0.00001) (Fig.6B). Taken together, the intestinal microbiota promotes CRC progression by shaping the expression of host gene expression, especially metabolic pathways.

Gene expression signature and metabolic pathways reprogramming associated with microbial shifts. A The association between up regulated oncogene expression and cancer related species. The X axis represents up regulated cancer genes. Significant associations were highlighted below the heatmap. B Pathway difference between high and low Fusobacterium nucleatum groups

The composition of immune and stromal cell types was identified by XCELL, a gene signature-based method that integrates the advantages of gene set enrichment with deconvolution approaches. Compared with adjacent normal tissues, the overall immune score was significantly lower in tumor tissue (Fig.7A). Especially, the abundance of most B cells and CD8+T cells elevated in tumors while regulatory T cells and T helper cells exhibited a decreasing trend (Additional file 1: Fig. S8), indicating the important role of the immune microenvironment in the progression of CRC. The associations between different microbial species and immune cell types in the CRC were shown in Fig.7B. Fusobacterium nucleatum was negatively associated with dendritic cells and CD8 T cells (Fig.7C). While Faecalibatcerium prausnitzii were significantly positively correlated with dendritic cells and Macrophages M1 (Additional file 1: Fig. S9a).

Fusobacterium nucleatum promoted CRC by modifying the tumor immune environment and TNFSF9 expression. A Comparison of immune cell scores between tumor and adjacent normal tissues. B The heatmap illustrates the correlations between differential abundant species and immune cells. The stars indicate the level of statistical significance. C Significant association of F. nucleatum and aDC and CD8 T cells. D Pathway alteration between normal and tumor tissue. E Significant association between Fusobacterium nucleatum and TNFSF9 gene expression

Interestingly, Gene set enrichment analysis of revealed that the cytokine-cytokine receptor interaction (p<0.001) was significantly altered in CRC (Fig.7D). Correlation analysis of genes related to cytokine-cytokine receptor interaction pathway related genes and 22 species uncovered several significant associations (Additional file 1: Fig. S9b). Among them, Fusobacterium nucleatum exhibited a positive association with TNFSF9, a member of TNF (tumor necrosis factor) family members (r=0.443, p=0.0037) (Fig.7E). Previous study showed that Fusobacterium nucleatum autoinducer-2 (AI-2) enhanced the mobility and M1 polarization of macrophages, possibly through TNFSF9/TRAF1/p-AKT/IL-1 signaling. Our results further suggested that pathogenic bacteria, like Fusobacterium nucleatum, may interact with CRC cells and modify the tumor immune environment by TNFSF9, finally facilitating the tumor development.

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Multi-omic profiling reveals associations between the gut microbiome, host genome and transcriptome in patients with ... - Journal of Translational...

Researchers at EPFL, led by Didier Trono, have developed a novel method to detect previously undetectable transposable elements (TEs) in the human genome, significantly expanding our knowledge of DNA composition. This discovery has profound implications for understanding genetic diseases and the genomes response to various stresses.

The human genome, a complex mosaic of genetic data essential for life, has proven to be a treasure trove of strange features. Among them are segments of DNA that can jump around and move within the genome, known as transposable elements (TEs).

As they change their position within the genome, TEs can potentially cause mutations and alter the cells genetic profile but also are master orchestrators of our genomes organization and expression. For example, TEs contribute to regulatory elements, transcription factor binding sites, and the creation of chimeric transcripts genetic sequences created when segments from two different genes or parts of the genome join together to form a new, hybrid RNA molecule.

Matching their functional importance, TEs have been recognized to account for half of the human DNA. However, as they move and age, TEs pick up changes that mask their original form. Over time, TEs degenerate and become less recognizable, making it difficult for scientists to identify and track them in our genetic blueprint.

In a new study, researchers in the group of Didier Trono at EPFL have found a way to improve the detection of TEs in the human genome by using reconstructed ancestral genomes from various species, which allowed them to identify previously undetectable degenerate TEs in the human genome. The study is published in Cell Genomics.

The scientists used a database of reconstructed ancestral genomes from different kinds of species, like a genomic time machine. By comparing the human genome with the reconstructed ancestral genomes, they could identify TEs in the latter that, over millions of years, have become degenerate (worn out) in humans.

This comparison allowed them to detect (annotate) TEs that might have been missed in previous studies that used data only from the human genome.

Using this approach, the scientists uncovered a larger number of TEs than previously known, adding significantly to the share of our DNA that is contributed by TEs. Furthermore, they could demonstrate that these newly unearthed TE sequences played all the same regulatory roles as their more recent, already-identified relatives.

The potential applications are vast: Better understanding TEs and their regulators could lead to insights into human diseases, many of which are believed to be influenced by genetic factors, says Didier Trono. First and foremost, cancer, but also auto-immune and metabolic disorders, and more generally our bodys response to environmental stresses and aging.

Reference: Ancestral genome reconstruction enhances transposable element annotation by identifying degenerate integrants by Wayo Matsushima, Evarist Planet and Didier Trono, 30 January 2024, Cell Genomics. DOI: 10.1016/j.xgen.2024.100497

The study was funded by the European Research Council, the Swiss National Science Foundation, the EMBO Postdoctoral Fellowship, and the JSPS Overseas Research Fellowship.

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Genomic Time Machine Reveals Secrets of Human DNA - SciTechDaily

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Over two decades after the first human genome was sequenced, a team of researchers has discovered ~1 million new exons in the human genome.

The research group, from the University of Torontos (U of T) Donnelly Centre for Cellular and Biomolecular Research, said that none of the newly discovered exons are consistently found in the genomes of other species. They seem to appear in the human genome mainly due to random mutation and are unlikely to play a significant role in our biology, explained Dr. Timothy Hughes, principal investigator on the study and professor and chair of molecular genetics at U of Ts Temerty Faculty of Medicine. This is evidence that evolution in humans involves a lot of trial and error most likely enabled by the vast size of our genome.

The study is published in Genome Research.

The human genome comprises ~20,000 genes. Genes consist of exons, DNA bases that encode protein, which are separated by introns non-coding DNA sequences. When a gene is transcribed, a process called splicing removes introns, so that only exons are included in the final mRNA product, which is then translated into protein. Exons are regarded as autonomous if they do not require any external help to splice into a mature RNA transcript.

Hughes and colleagues assayed large fragments (100500 base pairs) of the human genome using a method known as exon trapping. They wanted to test the exon definition model, a molecular biology concept that describes how splicing machinery is able to recognize exons during pre-mRNA processing. An assumption of this model is that the accurate removal of introns is achieved because there are clear and consistent indicators of where exons start, and where exons end. Sometimes, though, exon splicing doesnt go as planned and mature RNA transcripts containing nonfunctional components are produced.

Exon trapping is a traditional molecular biology technique that is used to find and isolate exons. A fragment of DNA is inserted into a vector that carries the DNA for introduction into a host cell. The RNA produced by the host cell is then analyzed, and exons that are expressed and trapped in the RNA can be detected using sequencing methods.

We used a classical exon trapping assay to survey the human genome for autonomous exons whereby genomic fragments are assayed outside of their normal contextual setting, for example, flanking exons, promoter, transcription level and distal intronic sequences, the authors described.

We reasoned that this survey would allow us to query whether protein-coding exons are generally autonomous, whether exons exist elsewhere in the genome, what sequence features they possess and whether exons arise at random, which would partly explain the existence of long non-coding RNAs (lncRNAs).

Hughes and colleagues defined any trapped exons as autonomous, of which there were ~1.25 million, including most known mRNAs and annotated lncRNAs.

Almost 1 million of the trapped exons are not annotated, Hughes and colleagues said: These exons are not conserved, suggesting they are nonfunctional and arose from random mutations. They are nonetheless highly enriched with known splicing promoting sequence features that delineate known exons.

The translation of randomly mutated exons could have consequences for human health. lncRNAs are autonomous but lack a known function though they have been associated with the development of cancer.

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This is an interesting study that broadens our knowledge of sequences across the human genome that have the potential to be recognized as exons in transcribed RNA, Dr. Benjamin Blencowe, professor of molecular genetics at U of T, who was not involved in the study, said. While the significance of the majority of the newly detected exons is unclear, some of them may be activated in certain contexts for example, by disease mutations and therefore cataloging them is important. This study will further serve as a valuable resource facilitating ongoing efforts directed at deciphering the splicing code.

The researchers are confident that their exon trapping data will also be helpful when fed into programs such as SpliceAI, a tool that is used widely to determine splice sites. SpliceAI often doesnt provide details on the characteristics of exons and has a poor ability to predict splicing in exons that arent already cataloged, said Hughes. Our exon trapping data contains biologically meaningful information that can be fed into SpliceAI and other splicing predictors to open up new paths for exploring the dark genome.

Reference: Stepankiw N, Yang AWH, Hughes TR. The human genome contains over a million autonomous exons.Genome Res. 2023. doi: 10.1101/gr.277792.123

This article is a rework of a press release issued by [name of institute]. Material has been edited for length and content.

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1 Million Unannotated Exons Discovered in the Human Genome - Technology Networks

The mysterious night parrot has long perplexed ecologists and birders from its presumed extinction in the 20th century, to the triumphant discovery of live birds in Queensland and Western Australia during the 2010s.

Its still one of the worlds most rarely seen birds, with only a handful of photographs and specimens surfacing over the last 20 years.

But now, Australian scientists have another feather in their nocturnal cap: theyve sequenced and annotated the night parrots genome.

This library of genetic information can now be used to learn more about, and conserve, the night parrot.

We never thought wed say those three words together in one sentence: night parrot genome, Dr Leo Joseph, director of the Australian National Wildlife Collection at the CSIRO, tells Cosmos.

It says a lot about hope for how we can learn more about our biodiversity, including really interesting, quirky species like this.

The opportunity to sequence the birds full genome arose last year, when Traditional Owners in the Pilbara found an injured night parrot caught on a fence.

The bird died from its injuries, so the Traditional Owners delivered it to the West Australian Museum, where the specimen was preserved and put on display last week.

Curator Dr Kenny Travouillon gave a small tissue sample from the bird to the CSIRO, so that researchers could run it through ANUs genetic sequencing technology under their Applied Genomics Initiative.

Dr Gunjan Pandey, a research scientist who led the sequencing project for the CSIRO, tells Cosmos that they were able to sequence the whole genome in 3-4 months, and take another month to annotate it a fast turnaround.

We have optimised workflows and pipelines to do high throughput genome assemblies, says Pandey.

In the last couple of years, we have done over 100 genomes.

The researchers finished annotating the genome yesterday, and have released it publicly on the Genbank database.

The idea here is to make the genome available to everybody so all of us can look at it together, rather than keeping it as our property, says Pandey.

The Australian community is paying for a lot of this work, and its only fair then that publicly supported science be publicly available, says Joseph.

But the researchers have their own plans to study the genome too.

We are going to compare it with genomes from other parrots and nocturnal birds and see what is happening, says Pandey.

The team is also interested in using the genome to learn about the night parrots camouflage, beak morphology, genetic diversity and population structure.

There are many dimensions to understanding a bird like this. One is to understand its habitat. One is to understand its vocalisations, says Joseph.

But if we start to think about genetics, and how genetics can contribute its own dimension to conservation, we can start to think about understanding the longer-term evolutionary history of the night parrot.

Joseph likens sequencing a creatures full genome to making a roadmap.

If you imagine a roadmap of Australia, with no place names, thats a bit like just saying we sequenced a genome.

[] But annotating the genome means you can put all the place names on the map, you can put all the genes on it.

So we start to get that genetic blueprint for an organism. We start to have a way of understanding what it is that makes up a night parrot. We can look into genes that we know from other birds are related to nocturnality, and we can understand its biology down to that level.

And we can use it in conjunction with other pieces of genetic data to understand the genetic structure, of the night parrot today, across its range which genes might be varying and which genes might not varying.

The researchers can also now compare the parrots DNA to DNA from other night parrot samples, like from feathers some of which are a century old.

With DNA from feathers, you dont get very good quality. But whatever fragmented DNA we get, now, we can use that information to get into the genetic diversity and the population structure, says Pandey.

Ecologists can also get a better sense of where night parrots have been without observing them in person through environmental DNA, or eDNA.

A bird watcher colleague of mine once said to me: the night parrot was the only bird in the world that no person living had shown to another person, which was a really good way to sum up the mystery, says Joseph.

The researchers are hoping that both they, and other scientists, will use the genetic information to help save the critically endangered species.

I think another level of interest in the night parrot is what it holds symbolically, says Joseph.

It says a lot about environmental change in Australia. It says a lot about how weve nearly lost bits of our biodiversity heritage, that we have lost bits.

And it says a lot about hope.

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Hope for the night parrot: bird's full genome has been sequenced - Cosmos

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Multi-omics resources for the Australian southern stuttering frog (Mixophyes australis) reveal assorted antimicrobial ... - Nature.com

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