Salmonella Gallinarum biovar Pullorum, is fatal for young chickens and is a heavy burden on poultry industry. The pathogen is rare in most developed countries but still extremely difficult to eliminate in China. Efficient epidemiological surveillance necessitates clarifying the origin of the isolates from different regions and their phylogenic relationships. Genomic epidemiological analysis of 97 S. Pullorum strains was carried out to reconstruct the phylogeny and transmission history of S. Pullorum. Further analysis demonstrated that functional gene loss and acquisition occurred simultaneously throughout the evolution of S. Pullorum, both of which reflected adaptation to the changing environment. The result of our study will be helpful in surveillance and prevention of pullorum disease.Pullorum disease, an acute poultry septicemia caused by  Salmonella enterica subspecies enterica serovar Gallinarum biovar Pullorum (S. Pullorum) is the etiological agent of pullorum disease, causing white diarrhea with high mortality in chickens. There are many unsolved issues surrounding the epidemiology of S. Pullorum, including its origin and transmission history as well as the discordance between its phenotypic heterogeneity and genetic monomorphism. In this paper, we report the results of whole-genome sequencing of a panel of 97 S. Pullorum strains isolated between 1962 and 2014 from four countries across three continents. We utilized 6,795 core genome single nucleotide polymorphisms (SNPs) to reconstruct a phylogenetic tree within a spatiotemporal Bayesian framework, estimating that the most recent common ancestor of S. Pullorum emerged in ∼914 CE . The extant S. Pullorum strains can be divided into four distinct lineages, each of which is significantly associated with geographical distribution. The intercontinental transmissions of lineages III and IV can be traced to the mid-19th century and are probably related to the “Hen Fever” prevalent at that time. Further genomic analysis indicated that the loss or pseudogenization of functional genes involved in metabolism and virulence in S. Pullorum has been ongoing since before and after divergence from the ancestor. In contrast, multiple prophages and plasmids have been acquired by S. Pullorum, and these have endowed it with new characteristics, especially the multidrug resistance conferred by two large plasmids in lineage I. The results of this study provide insight into the evolution of S. Pullorum and prove the efficiency of whole-genome sequencing in epidemiological surveillance of pullorum disease.IMPORTANCE Pullorum disease, an acute poultry septicemia caused by Salmonella Gallinarum biovar Pullorum, is fatal for young chickens and is a heavy burden on poultry industry. The pathogen is rare in most developed countries but still extremely difficult to eliminate in China. Efficient epidemiological surveillance necessitates clarifying the origin of the isolates from different regions and their phylogenic relationships. Genomic epidemiological analysis of 97 S. Pullorum strains was carried out to reconstruct the phylogeny and transmission history of S. Pullorum. Further analysis demonstrated that functional gene loss and acquisition occurred simultaneously throughout the evolution of S. Pullorum, both of which reflected adaptation to the changing environment. The result of our study will be helpful in surveillance and prevention of pullorum disease. Salmonella enterica subspecies enterica serovar Gallinarum biovar Pullorum (S. Pullorum) can produce pullorum disease, an acute systemic disease specific to poultry which mainly infects young birds and causes high mortality. Pullorum disease once wrought havoc with the poultry industry worldwide and led to substantial economic losses in the early 20th century. Owing to the introduction of pullorum-typhoid programs based on detection and elimination of affected birds, pullorum disease has been eradicated from commercial poultry in most developed countries, but outbreaks still occur frequently in other regions  share an ancestor which was a direct descendant of S. Enteritidis. In epidemiological investigations, it is necessary not only to distinguish S. Pullorum from its relatives but also to subtype it at an isolate-specific level. As with other highly clonal organisms, S. Pullorum isolates exhibit limited genetic diversity, which frustrated traditional typing methods, such as multilocus enzyme electrophoresis (MLEE), ribotyping, and pulsed-field gel electrophoresis (PFGE)  –8, due t (PFGE) –, but the (PFGE) –.S. Pullorum strains exhibit phenotypic heterogeneity. Notably, S. Pullorum strains could be subdivided into aerogenic and anaerogenic groups using multiple biochemical markers  were called and used to infer the phylogeny in a spatiotemporal Bayesian framework within which the loss of functional genes and the acquisition of mobile genetic elements (MGEs) were further analyzed. In this context, we explored the evolutionary and transmission history of S. Pullorum, as well as the genetic mechanisms of biochemical traits and drug resistance in this organism.With the development of next-generation DNA sequencing (NGS) technology, the cost of sequencing has plummeted . At presS. Pullorum RKS5078. The regions of different forms overlapped with each other. The recombination events recognized by Gubbins (S. Pullorum since the emergence of its most recent common ancestor (MRCA). A total of 6,795 SNPs in the core genome (termed core genome SNPs cgSNPs]) were identified, representing slightly more than the number seen in other genetically monomorphic Salmonella serotypes, such as the 4,887 SNPs of S. Enteritidis  and assigned identifiers of ST3717, ST3720-ST3723, and ST3727. According to the phylogeny, the diverse STs do not provide effective discrimination, since the strains of lineage I, II, and IV all belong to ST92 and its single-locus variants (SLVs). The only apparent correspondence is that the strains of lineage III all belong to either ST2151 or its SLVs. It is evident that the typing method based on analysis of seven housekeeping genes (The maximum likelihood (ML) and maximum clade credibility phylogenetic trees, constructed on the basis of the cgSNPs, exhibit the same topology see . Referenng genes  exaggera10.1128/mSphere.00627-18.1FIG S1S. Pullorum was added to the ML tree and is indicated in blue. Download FIG S1, TIF file, 0.8 MB.Maximum likelihood (ML) tree based on 6,795 cgSNPs. Reference strain RKS5078 of Copyright © 2019 Hu et al.2019Hu et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. Pullorum appeared in 914 CE , approximately 1,100 years ago. The mean mutation rate of the entire population was estimated to be 1.92 × 10−7 substitutions/site/year or 0.80 SNPs/genome/year, comparable with the rates determined previously for S. Enteritidis (2.2 × 10−7), S. Paratyphi A (1.94 × 10−7), and S. Typhimurium DT104 (2.79 × 10−7)  determined that the best Bayesian model in this case consists of the combination of constant population size with a relaxed lognormal clock, and that approach resulted in an estimation that the most recent common ancestor of  × 10−7) , 17, 20. × 10−7) . It is o × 10−7) . Lineage × 10−7)  and was  × 10−7) . Lineage × 10−7) . HoweverNe), which is not applicable to all local clades. By performing an independent model comparison analysis of lineage I, the predominant lineage in China, it was found that the Bayesian SkyGrid model outperformed the others. As shown in Ne of lineage I increased about 100-fold. The Ne declined dramatically between the 1850s and the 1950s, during which period China suffered a “century of humiliation,” and the poultry industry was likely to have been affected seriously by the wars. The reason for the decline of the Ne is likely to have been the absence of hosts in which it could survive, considering the host specificity of S. Pullorum. Subsequently, the Ne rebounded briefly and dropped again in the 1970s, possibly due to the introduction of the use of antibiotics in poultry farming. The supporting evidence suggests that all of the multidrug-resistant clades are distributed in lineage I  and 151 accumulated dependently by the MRCA of S. Pullorum (see astA), putrescine biosynthesis (speC), glycogen biosynthesis (glgB) and degradation (glgX), and tetrathionate respiration  , c-type cytochrome (torC) and their two-component regulation system (torR/torT). TMAO, an osmolyte, is abundant in the tissues of marine animals, protecting against the protein-destabilizing effects of pressure  and OsmU (osmV encoding transporter ATP-binding protein)  and incB, ttrB) , 23. Triglycerol . S. Pullpressure . The inaprotein) . The onlS. Pullorum is ongoing. Lineages I, II, III, and IV have 42, 41, 53, and 60 genes, respectively, that have degraded since diverging from the MRCA. Some of these inactive genes are involved in the “chain reaction” of pathway degradation, i.e., the further degradation of related genes of a primarily inactive pathway. For instance, as mentioned above, the in-frame deletion in the pivotal gene of arginine N-succinyltransferase (AST) (astA) leads to the degradation of l-arginine via the AST pathway in the MRCA of S. Pullorum. In lineage I and lineage II, astC and astB, the genes involved in the subsequent steps of this pathway, are frameshifted. It is almost impossible for the related genes to remain intact without the selective constraints of the functional pathways. Inactivation of pathways also occurs in different lineages. There are three inactivated pathways related to carbohydrate degradation in lineage III, namely, glycerol degradation, l-rhamnose degradation, and d-galactose degradation, resulting from the inactivation of glpK/glpX, rhaA, and yihR. The impact of the pseudogene formation (nonsense mutation of Trp83*) of rhaA was identified by the biochemical assay , and inactivation of the latter is achieved by truncating the γ-aminobutyraldehyde dehydrogenase gene (patD). Lack of putrescine degradation pathways makes bacteria grow defectively under various stress conditions, including oxidative stress, high temperature, and sublethal antibiotic concentrations  of the five genes are disrupted in all strains of S. Pullorum, which indicates that they are unnecessary in establishing chicken infection, despite evidence suggesting that pegA was established to be important in the colonization of avian intestines for S. Enteritidis  and shdA and sinH are intact in all S. Pullorum strains, while all of the three are inactivated in S. Gallinarum.In addition to metabolism-related gene losses, virulence gene losses are common in lineages . Salmonenization , 30. As eritidis . In lineeritidis . The faeshedding . Among tS. Pullorum and S. Gallinarum are nonmotile and nonflagellate. This status is thought to have arisen before their divergence and to have resulted from the presence of the pseudogenes of flhB and flgK  of flagellar genes remain intact in almost all strains, which suggests that they are under selection by functions other than motility.Both and flgK , 34, as Salmonella pathogenicity islands (SPIs) contribute to virulence and host interactions. In S. Pullorum, the SPIs, specifically, SPI-1 to SPI-5, are mostly conserved across all the lineages and appear to have been acquired in the early stage of S. enterica evolution  SEN_RS13585, which encodes a type I secretion system permease, while SPI-17 has an inactivated gtrC, which is a bacteriophage-borne O-antigen acetyltransferase gene. Remarkably, the pseudogene reversion happened again in gtrC of N38, and in two other strains, by reinsertion of one of the eight consecutive adenines at positions 1560 to 1567.volution , and SPIchickens , 37. LarS. Pullorum and S. Gallinarum have lost several effectors due to the absence of prophages, including sseI and gtgE in φSE10 as well as sseK2 in φSE20. Moreover, the slrP, steC, sifB, sopA, and sspH2 genes have been found to be inactivated in all the strains of S. Pullorum, except for the reverted sifB gene in strain RKS2243. Lineage-specific degradation occurred only in lineage IV, where sopE is absent via the partial deletion of φSE12 and sseL, which are disrupted by a frameshift. Two additional effectors, sptP and sopD, are inactivated in N69, a clade of lineage I. Effectors have been proved to be associated with modulation of gut inflammation (S. Pullorum.Multiple type three secretion system (T3SS) effectors have been introduced by prophages. Both ammation , 39 and ammation . Losses Salmonella, the presence of prophages diversifies the genome and potentially changes bacterial phenotypes through lysogenic conversion (S. Enteritidis) (P22virus and the other three to genus P2virus. The main characteristics of these prophages are summarized in In nversion . Compararitidis) , named φattB) of φSPU1 is located within threonyl tRNA gene thrW, consistent with S. Typhimurium bacteriophage P22 . We also identified a potential virulence gene, designated pipB3, the putative product of which shows sequence similarity to the T3SS effector proteins PipB and PipB2. PipB3 is 244 amino acids (aa) in length, with a predicted molecular mass of 26.7 kDa. Like PipB and PipB2, PipB3 contains tandem pentapeptide repeats, a motif which is required for efficient recruitment of kinesin-1 (n = 39) followed by 21 (n = 30).φSPU1 is 41,526 bp in length, with an average G+C content of 47.48%, which is slightly lower than that of the entire genome (52.2%) . The atthage P22 . The genthe host , which sinesin-1 , and the10.1128/mSphere.00627-18.3FIG S3FIG S3, TIF file, 0.3 MB.Alignment of pentapeptide repeats in three PipB-like proteins. Download Copyright © 2019 Hu et al.2019Hu et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. Typhimurium definitive phage type 104 (DT104) , which made us interested in its origin. In order to explore their phylogenetic relationship, we constructed a maximum likelihood tree based on the core genome of φSPU1 and ST104 (DDBJ accession number AB102868)  . φSPU1 pby SISTR  revealedB102868) . From thB102868) . InteresB102868) , much laB102868) .S. Pullorum, including three small plasmids of <5 kb (designated pSPUS1 to pSPUS3) and five large plasmids involved in antimicrobial resistance (AMR) (designated pSPUR1 to pSPUR5)  . The thraneously . Notablyllinarum .S. Pullorum , stability (stb), antirestriction (ard and ccg), and mutagenesis enhancement (muc). pSPUR1 contains a large class 1 integron carrying the intI1 integrase gene and five sequential resistance cassettes: dihydrofolate reductase gene dfrA17, conferring trimethoprim resistance; aminoglycoside-(3′′)-adenylyltransferase gene aadA5, conferring streptomycin and spectinomycin resistance; dihydropteroate synthase gene sul1, conferring sulfonamide resistance; multidrug efflux pump gene qacE, conferring resistance to quaternary ammonium compounds, and a putative GNAT family N-acetyltransferase gene. Notably, the presence of aadA5 is not sufficient to induce high resistance to streptomycin in MIC assays  associated with tetracycline resistance, designated ISspu1, inserts within the fipA gene and disrupts it. This cryptic IS carries four genes, including a putative relaxase gene; tetracycline efflux pump gene tetA and corresponding repressor gene tetR, which are possibly derived from Tn1721 (>99.7% protein identity); and putative inner membrane transporter gene yedA. There is a pair of almost perfect (only one base mismatched) 247-bp inverted repeats (IRs) on the two ends of ISspu1, and those IRs are flanked by a pair of 8-bp direct repeats (DRs) (5′-CCAGCAAG-3′). However, the transposition mechanism of ISspu1 is unknown because it encodes no transposases.pSPUR2 is a 73,222-bp IncX1 plasmid with average G+C content of 47.6%. Compared to the representative IncX1 plasmid pOAL52 . A putative composite transposon flanked by IS4 sequences contains the first of the two arsenic resistance clusters arsB-arsC-arsH-arsR and sulfonamide resistance gene sul2 (inducing no phenotypic resistance). Another arsenic resistance cluster containing six genes, arsP-arsR-arsH-sigL-arsD-arsA, is located downstream. This cluster is close to the ars2 operon in IncI1 plasmid R64 (96.9% nucleotide identity) . In some strains, an ISspu1-like insertion element is present near TnAs1, containing at the other end a duplicated identical 1,752-bp sequence from the transposase gene of TnAs1.The five AMR plasmids belong to three incompatibility groups, IncN, IncX1, and IncQ1, among which pSPUR1 and pSPUR2 are most important, due to their broad prevalence and genetic complexity . pSPUR1 C assays . The geneakpoint . The aadd pOAL52 , pSPUR2 osporins . Within Escherichia coli J53. The conjugants can be recovered for both plasmids, especially for IncN plasmid pSPUR1, which has high conjugation efficiency  and IncX1 plasmid pSPUR2 (right). The azide-resistant mutant of Copyright © 2019 Hu et al.2019Hu et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. enterica serovars and has proven to be an “ultimate” approach for its maximum discriminatory power and reliable epidemiological concordance  to classify S. Pullorum into seven electrophoretic types (ETs), among which Pu1, Pu3, and Pu4 harbor a missense variant in fliC, leading to the conversion of codon 431 from alanine to threonine  in modern history could be traced back to the mid-19th century and might involve a forgotten historical event dubbed Hen Fever, an economic bubble created by chicken fascination which occurred from approximately the 1840s to 1850s. The story began in 1842 when Queen Victoria received seven exotic Cochin fowls shipped from China and was deeply attracted to them. Soon, the hobby of keeping and breeding Asian chickens spread from royalty to the general population and from the United Kingdom to Europe and, finally, to the United States, culminating in the pomp of the Boston Poultry Show of 1849, in which more than 1,400 ornamental birds imported from Asia were exhibited  but maintains its paralog sifA. Second, there are rare but vital spontaneous pseudogene reversions in S. Pullorum  of the GyrA protein, all of the antibiotic resistance phenotypes are mediated by the plasmids in jugation . Chickenenterica –67. In cresponse . Also, icificity . Therefoains see , which iS. Pullorum can be classified into four lineages and that their MRCA emerged around 1100 years ago. In the mid-19th century, two intercontinental transmission events occurred, probably related to the prevalent Hen Fever at that time. Genome degradation, mainly caused by pseudogenization, has been ongoing throughout the evolution of S. Pullorum. In contrast, gene acquisition mediated by bacteriophage lysogeny and plasmid transfer is also common in the S. Pullorum genome and has given the pathogen new properties, especially the multidrug resistance conferred by the large plasmids. The gene loss and gain in the genome of S. Pullorum reflect its adaptation to the changing environment. These data will be useful in further surveillance and prevention of pullorum disease.In conclusion, the results of this study show that the S. Pullorum strains were selected from all the isolates (n = 655) obtained by our laboratory between 1962 and 2014 (n = 81), with 75 isolates from the eastern and central regions  and 5 isolates from the western region . The foreign strains (n = 16), including the isolates from Brazil (n = 5), the United Kingdom (n = 4), and Denmark (n = 2), were kindly donated by P. Barrow. The detailed metadata are listed in S. Pullorum. Genomic DNA was prepared from overnight cultures by using DNeasy blood & tissue kits  according to the manufacturer’s instructions. The DNA was then fragmented with an insertion size of ∼500 bp to prepare the library and was sequenced by the use of a HiSeq 2500 system . More than 3 million paired-end (PE) reads with a length of 100 bp or 150 bp were produced for each genome. Reads with <90% Q30 bases were considered of low quality and discarded. In order to save computing resources for subsequent analyses, the filtered data were downsampled to 1.6 million reads for 150 PE or 2.4 million reads for 100 PE, with a theoretical coverage of ∼100×.A total of 97 10.1128/mSphere.00627-18.6TABLE S1Table S1, XLSX file, 0.02 MB.Metadata of strains used in this study. Download Copyright © 2019 Hu et al.2019Hu et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphere.00627-18.7TABLE S2Table S2, XLSX file, 0.02 MB.Antibiogram achieved from both genotype and phenotype. Download Copyright © 2019 Hu et al.2019Hu et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphere.00627-18.8TABLE S3Table S3, XLSX file, 0.01 MB.Estimation of emergence dates and clock rates. Download Copyright © 2019 Hu et al.2019Hu et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphere.00627-18.9TABLE S4Table S4, XLSX file, 0.1 MB.Pseudogenes and gene deletions. Download Copyright © 2019 Hu et al.2019Hu et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. Pullorum RKS5078 (GenBank accession no. CP003047) (dsvfinder.py) was used to recognize deletion structural variations. Briefly, after de novo assembling from reads by using SPAdes (breseq (The genome of P003047)  was usedP003047) . Four crP003047)  to alignP003047) ; (iii) vP003047) ; and (ivP003047) . Deletiog SPAdes , the drag SPAdes  to fetch  and S. Gallinarum 287/91 (accession no. NC_011274). All the CDS and pseudo-CDS entries were extracted, and the redundant ones were excluded by retrieving the protein IDs and curating manually. We also included the CDSs from the virulence plasmid of S. Pullorum ATCC 9120 (accession no. CP012348) for it was conserved in all the S. Pullorum strains that we examined.A reference CDS set was established by parsing the NCBI GenBank records of orthoanno.py) to identify the pseudogenes. In brief, the script aligned the protein sequences translated from the reference CDSs to each draft genome by tblastn , which are potentially related to mobile genetic elements  and point mutations, respectively. The argfinder.py script used a two-step Blast strategy to locate the ARGs and to find the best hit matches in the Resfinder database  involved in fluoroquinolone resistance to the draft genomes, and then the matched nucleotide sequences were translated and the codons were compared pairwise to find the point mutations. The impacts of gene acquisitions and mutations were interpreted according to the comprehensive antibiotic resistance database  or 20-56 (pSPUR2) and (azide-resistant) recipient strain The biochemical assays were executed using API 20E microbial identification kits , and the results were interpreted according to the manufacturer’s instructions. The MIC was determined by using a miniaturized broth dilution test described previously  and intePRJEB27755. The accession numbers for each strain are listed in LR536428 and LR536427, respectively. The custom scripts are available at GitHub  for public download.The clean reads of sequencing strains used in this study have been deposited in the European Nucleotide Archive (ENA) under project accession no. 10.1128/mSphere.00627-18.10TABLE S5Table S5, XLSX file, 0.01 MB.Degraded metabolic pathways and transport systems affected by gene inactivation. Download Copyright © 2019 Hu et al.2019Hu et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


Cynoglossus semilaevis is a representative and commercially-important flatfish species. Here we combined genome-wide association study and Fst and nucleotide diversity filtration to identify loci important for the disease resistance. Based on 1,016,774 single-nucleotide polymorphisms (SNPs) identified from 650 Gb genome resequencing data of 505 individuals, we detected 33 SNPs significantly associated with disease resistance and 79 candidate regions after filtration steps. Both the allele frequencies and genotype frequencies of the associated loci were significantly different between the resistant and susceptible fish, suggesting a role in the genetic basis of disease resistance. The SNP with strongest association with disease resistance was located in Chr 17, at 145 bp upstream of fblx19 gene, and overlapped with the major quantitative trait locus previously identified. Several genes, such as plekha7, nucb2, and fgfr2, were also identified to potentially play roles in the disease resistance. Furthermore, the expression of some associating genes were likely under epigenetic regulations between the bacterial resistant and susceptible families. These results provide insights into the mechanism that enable variation of disease resistance to bacterial pathogen infection. The identified polymorphisms and genes are valuable targets and molecular resources for disease resistance and other traits, and for advanced breeding practice for superior germplasm in fish aquaculture.The sustainable development of aquaculture has been impeded by infectious diseases worldwide. However, the genomic architecture and the genetic basis underlying the disease resistance remain poorly understood, which severely hampers both the understanding of the evolution of fish disease resistance traits and the prevention of these diseases in the aquaculture community.  Aquaculture has been playing an important role in food security and rural economic development in both developing and developed countries. Recently, the development of aquaculture industry has been heavily arrested by some prevalent problems worldwide, such as degeneration of breeding germplasm and occurrences of diseases. Breeding of superior varieties is a highly efficient and environment-friendly solution to improve the quality and sustainability of the aquatic farming. The detection of genome-wide genetic diversity and the identification of genes contributing to phenotypic improvement play essential roles in achieving this goal. In aquaculture species, genomic resources have been explored for economically important traits like growth, sex determination, and disease resistance   , rainbowo salar) , Japanesmilaevis . The appmilaevis , Atlantimilaevis , sea bren turbot . A majorn turbot . Furthern turbot . In addi species . As a quC. semilaevis, a representative marine flatfish species in China, has a high commercial value in aquaculture. Recently, the farming industry of C. semilaevis is undergoing a dramatical decline due to devastating diseases. From 2005, we have conducted continuous selective breeding for C. semilaevis by family construction and pathogen challenges using the bacterium Vibrio harveyi, which caused prevalent and high mortality across farms. We found that even in strictly controlled and highly consistent cultivating conditions, fishes from different families exhibited distinct phenotypic difference in the disease resistance/susceptibility , with body weights of 10.76 ± 3.69 g and body lengths of 12.35 ± 1.63 cm, were randomly sampled from each of the 106 families. Thus, more than 3,000 fish were collected for infection experiment. We conducted V. harveyi challenge tests by intraperitoneal injection with a medial lethal dose (LD50) of 5 × 105 colony-forming units/gram fish, which was determined using the method similar to that reported by Fish were collected from two farming factories located in Laizhou (LZ) and Haiyang (HY), China, respectively. We established the initial brood stocks in 2005 using wild individuals and performed breeding programs by crossing the selected Briefly, we extracted the genomic DNA from the fin tissue of each fish using DNeasy Blood & Tissue Kit (Qiagen). Pair-ended libraries were constructed following the standard protocol  with an insert distance of 300 bp. Paired reads with a read length of 2 × 100 bp were generated using the Illumina HiSeq2000 platform. We filtered the raw reads using QC-Chain , and theC. semilaevis (NCBI Accession No. GCA_000523025.1) using Burrows–Wheeler aligner with default settings (http://vcftools.sourceforge.net/) with -hwe option. The SNPs departed from HWE with a significance p-value < 0.05 were excluded. Then, we used ANNOVAR  to construct a neighbor-joining tree using the filtered SNP set with a bootstrap value of 1,000. The kinship relatedness between all the individuals were calculated. The software Treeview was used for visualizing the phylogenetic tree (http://taxonomy.zoology.gla.ac.uk/rod/treeview.html)http://cnsgenomics.com/software/gcta/) and the first two dimensional coordinates were plotted. Linkage disequilibrium (LD) level was measured with the correlation coefficient values (r2) between two loci using Haploview (v4.2) software  (software . The parV. harveyi infection: we recorded the trait as 0 if the fish died within 72 hpi (DIE) and 1 if the fish survived (SUR) until the end of challenging experiments. We performed single SNP-GWAS using PLINK (v1.07)  to group in the same haplotype using Haploview (v4.2). The proportion of phenotypic variance in disease resistance explained by the variants was estimated using TASSEL (The phenotypes of disease resistance were defined according to the fish responses to the  (v1.07) , with thg TASSEL .Fst) and the nucleotide diversity (θπ) throughout the whole genome (requiring at least 95% accessibility) between the SUR and DIE groups, with the program VCFtools (v0.1.14). We used a 40 kb nonoverlapping window, with a step size of 20 kb, to screen the whole genome and retained the windows containing more than 20 SNPs for further analyses. The θπ ratio of θπDIE/θπSUR was calculated to represent the difference of nucleotide diversity between the SUR and DIE groups. The windows with the top 5% highest values of Fst and log2(θπ ratio) were considered as candidate regions. Adjacent candidate regions within 200 kb were merged into a single candidate region. In addition, we simulated changes in allele frequency across multiple generations to evaluate the influence of genetic drift. We selected a population Ne of 200, a similar value to our initial breeding population of C. semilaevis, and carried out simulation analyses using Allele Simulator . Changes in allele frequency were calculated over 25 generations in 20 times simulations. T-tests were used to test the significant difference in the allele frequencies in the first and the other generations.To further filter the GWAS identified candidate SNPs, we estimated the fixation index (V. harveyi-susceptible (VS)  and two resistant (VR) families  that were previously constructed by our lab . We collected three immune tissues  and performed quantitative real-time PCR (qPCR) using the 7500 Real-Time PCR System (Applied Biosystems USA), with β-actin gene as the internal control . A t-test p < 0.05 and p < 0.01 was deemed to indicate a significant and extremely significant difference, respectively. In addition, the genotypes of selected genes of the 12 individuals were obtained using PCR and Sanger sequencing , and performed the bisulfite conversion and bisulfite sequencing (BS-Seq). The bisulfite conversion of sample DNA was carried out using a modified NHprotocol . The paiprotocol .C. semilaevis. We validated 62 SNPs using Sanger sequencing and the accuracy of SNP calling was >92%. More than 94,562 SNPs were located in exonic regions of 18,535 genes, including 32,117 nonsynonymous, 62,247 synonymous, 182 stop gain (causing premature stop codons), 16 stop loss (causing elongated transcripts), and 207 splicing SNPs (within 2 bp of a splicing junction). In addition, 396,712 SNPs were located in intronic regions, 2,181 were located in upstream or downstream regions, and the remaining 494,168 SNPs were located in intergenic regions  to 0.08 (73 kb)  yielded four associated loci  (p > 0.05) (p < 10−16), while the reference homozygous genotype frequency was significantly less in the DIE group than in the SUR Group (p < 10−16) (p > 0.05) between the first and other generations. These results indicated that very few genetic divergence were driven by genetic drift alone.To investigate the genetic variations underlying the disease resistance, we performed GWA mapping to identify the associated loci throughout the genome. Using the Bonferroni-corrected significance cutoffs  (θπ ratio [where log2(θπ ratio (θπDIE/θπSUR)) = 0.143] (Fst and log2(θπ ratio) values of the candidate regions were significantly higher compared to those of the genomic background (p < 10−16)  (fblx19) (fblx19 exhibited significantly higher expressions in the spleen and gill in the VR families than in the VS families (p < 0.05)  expressions and potential regulations by DNA methylation. The strongest GWAS signal CsSNP31 −log10p = 8.95 (fblx19)   . Using q10p > 6 (pkca) gene, which also resides within the disease-resistant QTL  (tant QTL . We comp10p = 8.63) occurred in an intron of the gene encoding Pleckstrin homology domain-containing family A member 7 (PLEKHA7). This SNP could explain 2.85% of the phenotypic variance. PLEKHA7 is a cytoplasmic member of the adheres junction proteins, which may induce apoptosis through the lysosomal–mitochondrial pathway. Interestingly, it plays an important role in controlling susceptibility to Staphylococcus aureus α-toxin and was identified as a potential nonessential host target to reduce S. aureus virulence during epithelial infections (plekha7 was significantly higher in the immune organs of the VR families than in the VS families (10p = 8.53), was mapped to the intergenic region of plekha7 and nucleobindin-2 (nucb2) gene. The proportion of phenotypic variance explained by this SNP was 4.32%. Nucb2 encodes a calcium-binding protein that may have a role in calcium homeostasis and plays an important role in hypothalamic pathways regulating food intake and energy homeostasis (nucb2 was significantly higher in the liver of the VS families than in the VR families (p < 0.05) (nucb2 gene was upmethylated in the VR families compared with the VS families (p < 0.05)  (fections . Quantiteostasis . Increaseostasis . qPCR anst5) gene that encodes a guanine nucleotide exchange factor, was within 5 kb distance to a strong association signal CsSNP97 (−log10p = 7.919)  in an intron of fibroblast growth factor receptor 2 (fgfr2) gene  or SNP chip  database with Bioproject accession PRJNA542202.This study was carried out in accordance with the recommendations of the Care and Use of Laboratory Animals of the Chinese Academy of Fishery Sciences. The protocol was approved by the Animal Care and Use Committee of the Chinese Academy of Fishery Sciences.SC and QZ conceived the study and designed the analytical strategy. YLi, FL, YLiu, LW, XZ and SL performed animal work and prepared biological samples. QZ, ZS, and GL analyzed the data. SW, TG, and MW performed the qPCR experiments. QZ, SC and GL wrote the manuscript. All authors reviewed the manuscript.This work was supported by National Natural Science Foundation of China ; National Key R&D Program of China (grant number 2018YFD0900301); AoShan Talents Cultivation Program Supported by Qingdao National Laboratory for Marine Science and Technology (grant number 2017ASTCP-OS15) and Taishan Scholar Climbing Project of Shandong Province of China.Authors ZS, XZ, and GL was employed by Novogene Bioinformatics Technology Co., Ltd.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Plasmodium falciparum isolates collected in The Gambia in 2008 and 2014 were analysed to identify changes possibly related to the scale-up of antimalarial interventions that occurred during this period. Overall, there were 15 regions across the genomes with signatures of positive selection. Five of these were sweeps around known drug resistance and antigenic loci. Signatures at antigenic loci such as thrombospodin related adhesive protein (Pftrap) were most frequent in eastern Gambia, where parasite prevalence and transmission remain high. There was a strong temporal differentiation at a non-synonymous SNP in a cysteine desulfarase (Pfnfs) involved in iron-sulphur complex biogenesis. During the 7-year period, the frequency of the lysine variant at codon 65 (Pfnfs-Q65K) increased by 22% (10% to 32%) in the Greater Banjul area. Between 2014 and 2015, the frequency of this variant increased by 6% (20% to 26%) in eastern Gambia. IC50 for lumefantrine was significantly higher in Pfnfs-65K isolates. This is probably the first evidence of directional selection on Pfnfs or linked loci by lumefantrine. Given the declining malaria transmission, the consequent loss of population immunity, and sustained drug pressure, it is important to monitor Gambian P. falciparum populations for further signs of adaptation.Genome sequences of 247  However, this enthusiasm is threatened by the emergence of artemisinin resistance (artR) in South East Asia (SEA)3, resulting into coordinated effort to limit its spread beyond SEA and detect any emergence in sub-Saharan Africa (sSA)4. Detailed surveillance for spatial and temporal trends in the frequencies of mutations associated with resistance to artemisinin and partner drugs is essential, particularly for the non-synonymous single nucleotide polymorphisms (SNP) in the Kelch propeller and BTB-POZ domains (KPBD) of the P. falciparum gene  that have been associated with delayed parasite clearance; the artemisinin resistance phenotype5. The prevalence of Pfk13 artR polymorphisms is high in SEA but low in sSA7. However, there are increasing reports that suggest reduced susceptibility of P. falciparum to ACTs beyond SEA, including cases of complete treatment failure in Africa10. These artemisinin-tolerant parasites may have been selected by the increased use of ACTs in the last decade. Such selection would leave genetic markers of adaptation in the P. falciparum population.The success of malaria interventions such as artemisinin-based combination therapies (ACTs), seasonal malaria chemoprevention, long lasting insecticidal nets (LLIN) and indoor residual spraying (IRS) in reducing malaria burden has raised hopes for malaria eliminationP. falciparum genomes between The Gambia and Guinea, two sites within Ghana, and across eight locations spanning ~1000 Km in Mauritania13. These studies assessed the allele frequency spectra and patterns of linkage disequilibrium, allowing for the identification of signatures of differentiation and positive selection, which could be due to local adaptation. Isolates from these sites were mostly collected over a single transmission season and do not provide information on the stability of genomic structure of the populations over a longer period, during which changes may occur, including coverage of control interventions. Temporal analysis has helped in understanding the evolution of drug resistance loci16. In addition, candidate markers for artemisinin resistance in P. falciparum isolates from SEA were identified by examining SNPs with allele frequency shifts similar to the Pfk13-C580Y17 resistance marker. Presently, there is no reference marker for artR such as the Pfk13-C580Y for sSA parasites, so this approach may not be feasible. However, by comparing the genomes of isolates before and after the introduction of ACTs, genomic changes and signatures of sustained antimalarial drug pressure and decline in disease burden can be identified in settings such as The Gambia.Population genetics approaches to identify markers of local adaptation often include comparison of genetic variation between spatially separated populations. In sSA, this has been demonstrated in pairwise comparisons of 19. Malaria transmission in the country has become increasingly heterogeneous, with prevalence of infection between ~5% and >40% in the west and eastern regions respectively20. AL was introduced as first-line treatment in 2008, replacing Sulfadoxine-Pyrimethamine (SP), which remains in use for intermittent preventive treatment during pregnancy and for seasonal malaria chemoprevention (SMC), combined with amodiaquine (since 2014)21. Chloroquine and other antimalarial monotherapy remains available and poorly regulated, in the private and informal sector 22.Between 2005 and 2015, the incidence of malaria in the Gambia dropped by over 75% following the widespread use of artemether-lumefantrine (AL) and high coverage of vector control interventions, namely LLINs and IRSP. falciparum isolates with ex vivo tolerance to lumefantrine in western Gambia23. Resistance to SP and other quinolines remain high23, with whole genome scans of the 2008 population showing strong signatures of selection around resistance markers of these drugs25. Building on this evidence, this study compared these earlier detected signatures to those of current populations. We detected sustained selection in genomic regions and new loci under differential selection that could have been selected for by drug pressure and other interventions.We recently reported an increasing proportion of 2 of 0.05 within 5000 base pairs , we identified 1066SNPs within 11 regions of hard sweeps on chromosomes 1, 4, 6, 7, 8, 9, 11, 13 and 14  on chromosome 7. The only two SNPs that reached the significance threshold for |iHS| in the recent 2014 population from GBJ were on coding regions of conserved proteins located 60kbp from Pfcrt . The sweep on chromosome 11 spanning Pfama1 gene was only significant in the GBJ 2008 population, while that spanning the Pfmsp1 and Pftrap genes on chromosomes 9 and 13, respectively were the strongest in the 2014 Basse population. Other antigenic loci within sweep signatures included the SURFINs  and merozoite surface proteins 7–like gene cluster on chromosome 13. |iHS| values are listed in Supplementary Tables .Regions that have undergone positive selection would show reduced heterozygosity and extended haplotypes27. We identified 15 genomic regions  with differences in the range of extended haplotypes between population pairs  located on chromosome 13 had significant differences in extended haplotype between the temporal population pair (identified only by Rsb), though it is not targeted by antifolate antimalarial regimes29.Genomic loci with differences in recent selective signatures between populations were consistent for both the relative integrated extended haplotype homozygosity test between populations (Rsb) and the cross population extended haplotype homozygosity (XP-EHH) index Fig. 27. We idPfclag8, while that on chromosome 13 was around the Pfmsp7-like cluster and Pftrap. SNPs in the Pftrap region had the highest |iHS| in the Basse population, indicating continuous selection on these antigenic loci. Other antigenic regions of differential positive selection were coding for Pfdblmsp2 and Pflsa on chromosome 10; Pfresa and Pflsap1 on chromosome 12, which were specifically different by Rsb between temporal population pairs , which is linked to artemisinin resistance mechanisms30.The widest genomic region with clusters of SNPs with extended haplotype differences between the pair of temporal populations spanned 220 Kb on chromosome 5. SNPs with the highest Rsb value in this region were within the coding frame of tRNA pseudouridine synthase, putative PF3D7_0516300), downstream of the cation transporter 300, downFST) between our temporal population pair on chromosomes 7, 13 and 14 , which had three ns-SNPs with high FST values. Sliding window analysis of FST values located a 27kbp haplotype around Pfnfs on chromosome 7 with significant differences in frequencies between the temporal population pair  and vacuolar protein sorting-associated protein 53, putative  were also identified as outliers by the extended Lewontin and Krakauer (LK) test, FLK, between temporal populations  differentiating by population fixation index , . High FST at ns-SNPs clustered within the coding frame of a conserved protein of unknown function (PF3D7_0710200), which has homologous domains with reticulocyte binding protein 1 (Pfrh1). This gene had significant FST values for 5 ns-SNPs and 1 s-SNP. The widest window of SNPs with high FST values between the spatial population pair was on chromosome 4, spanning the drug resistance locus Pfdhfr, which also showed sweep differences detected by Rsb and XP-EHH between the spatial population pair. SNPs around Pfdhfr were within 0.01% of top hits for haplotype frequency differences between spatial populations determined by spatial hapFLK  on chromosome 11 was specifically detected by hapFLK between spatial population pair. Evidence of differentiation on haplotypes common to both temporal and spatial comparisons population pairs were seen on Pfclag2 on chromosome 2, Pfset1 on chromosome 6 and Pfsurfin14.1 on chromosome 14. Indices of differentiation for SNPs are listed in Supplementary Tables file .In line with the higher genome-wide fixation index between the spatial population pair, more SNPs (20) were significantly differentiating between GBJ and Basse isolates collected in 2014 Fig. . Half ofPfnfs) gene in 521 P. falciparum isolates collected yearly between 2008 and 2015. The reference allele at this locus codes for the polar charged amino acid, lysine (K) at codon 65 of Pfnfs. The alternative allele codes for the polar but uncharged amino acid, glutamine (Q). Allelic discrimination analysis of temporally collected isolates spanning this period from GBJ showed a steady increase in the frequency of the reference type allele of the Pfnfs SNP from 2010 , , Fig. . We founPlasmodium falciparum is under intense pressure from malaria control interventions across Africa and this could result in adaptation in some populations31. To identify regions of P. falciparum genome with recent signatures of selection, we compared the genomes of recently collected isolates to those collected in 2008, prior to the introduction of ACTs32. We also compared genomes across a gradient of transmission intensity to detect common selective signatures in the more recent populations as well as local signatures that could suggest an effect of variance in intervention, transmission or host characteristics between sites34. The population pairs analysed showed no significant structure, as expected for African populations. This indicates sustained genetic diversity within The Gambian GBJ population and significant mixing of parasites between GBJ and Basse, despite the 6-years between sampling, intense interventions and spatial separation of 350 km across the country.Pfnfs). Cysteine desulfarases are involved in the delivery of sulphur to multiple metabolic pathways, including Fe-S complex biogenesis35. Fe-S cluster-containing proteins in apicomplexans are modulated during stress conditions, drug resistance and cell development37. They play vital roles in housekeeping, gene expression and other cellular processes and are targeted by antimalarial drugs39. Moreover, iron homeostasis is essential for erythrocytic development and is implicated in drug mechanisms of quinolines41, same drug family like Lumefantrine. We therefore selected the SNP within the Pfnfs for an initial analysis of temporal changes in allele frequency. The frequency of the reference and minor lysine variant in Pfnfs-codon K65Q increased steadily in the GBJ population, two years after the introduction of ACTs in the country. Low transmission intensity and lower recombination in the GBJ population will favour directional selection of this allele if it confers a selective advantage. Coupled to our recent report of temporal increase in the IC50s to Lumefantrine in the GBJ Gambian population23, it is plausible that Pfnfs may be under selection from Lumefantrine. We believe this is imposed by ACTs as the IC50s to Lumefantrine for isolates carrying the lysine allele were higher in the recent GBJ population. Moreover, the increase in the lysine allele frequencies in Basse, further suggest a common driver; such as ACTs, in both populations. Determining if the driver of selection is at linked sites in Pftim50 and Pfvps53, or any of the other 7 genes within the window of differentiation could identify associated markers for Lumefantrine sensitivity monitoring. Tim50 proteins are involved in protein complex formation and trafficking across membranes42, while vps53 is a component of the heterotetrameric tethering factor named Golgi-associated retrograde protein (GARP)43. We are currently exploring these signatures in other regions of Africa with extensive Artemether-Lumefantrine use.Differentiation between the GBJ temporal population pair was most evident at a cysteine desulfarase gene  biobank. Isolates from 2008 had been sequenced and analysed previously for signatures of selection32.Between 2012 and 2015, patients diagnosed with uncomplicated malaria at Brikama and Basse health centres were invited to participate in the study. After obtaining a written informed consent, a 2 ml venous blood sample was collected from each participant. Brikama is a town in the Greater Banjul (GBJ) region on the western end, while Basse is about 350 km away on the eastern end of country. These sites reflect the gradient of transmission across the country, with low transmission in the west  and moderate to high transmission in the east Plasmodium falciparum parasite genomic DNA for sequencing was extracted from whole blood depleted of leukocytes by filtration through CF11 cellulose columns as previously described55. Sequencing was done on Illumina HiSeq platform in collaboration with the MalariaGEN consortium at the Wellcome Trust Sanger Institute, Hinxton, UK56. Paired end DNA sequence-reads (150 bp) were obtained from 44 and 124 isolates collected in 2014 from western and eastern Gambia, respectively. Together with reads from 79 isolates sequenced in 2008, they were aligned to the P. falciparum 3D7 reference genome (version 3) as previously described57. Nucleotide variants were called using custom in-house calling algorithms, filtering and retaining the dominant allele at bi-allelic coding variable sites in a genotype file. Each population was genotyped for 791,385 high quality SNPs supported by a minimum of five short sequence reads as defined by MalariaGEN/Pf3K pipeline. Binary haplotype data was created denoting variable nucleotides as reference (0) or non-reference (1) allele. The short reads are available via the ENA database (www.ena.org).57. Pairwise SNP linkage disequilibrium (LD) expressed as the coefficient of correlation (r2) between alleles at physically separated loci with a minor allele frequency (maf) of at least 2% across all populations was calculated in plink. A LOESS spline was fitted for r2 between loci within 100 kbp to compare the decay in LD with physical distance in each population. To determine if there was structure between the temporal and spatial populations, all SNPs at an LD threshold (r2) < 0.5 were used to generate a pairwise (nxn) matrix of shared alleles between isolates, with n as isolates. The matrix was employed in principal component analysis, with the first and second principal components for each isolate plotted for 2008 against 2014 GBJ populations, and for GBJ against the Basse 2014 populations.For each isolate, infection complexity was determined by Fws as previously described58. For iHS analysis, the reference and non-reference alleles were employed in lieu of the ancestral and derived alleles, respectively. For significance, the REHH package generates a two-sided p-value asTo detect loci under recent positive selection, we calculated the extended haplotype homozygosity (EHH)–derived indices; standardised intra-population integrated haplotype score (|iHS|), and the cross-population ratio of the EHH expressed as Rsb and XP-EHH for each SNP with the R package REHHFST per SNP, calculated with the hierfstat package in R. The variance component of FST between the pair of temporal and spatial populations were also determined in hierfstat. The significance of FST values were determined by sampling from ten million permutations of the FST distribution for each pairwise comparison. We minimised the false positive rate for differentiating loci by computing the extended Lewontin and Krakauer test, FLK, and the linked derivative hapFLK, which focuses instead on the differences of haplotype frequencies between populations60. The FLK index accounts for population size heterogeneity and possible hierarchical structure between populations. It computes a global FST for each SNP, but allele frequencies are first rescaled using a population kinship matrix, estimated from the observed genome wide data. With this rescaling, allele frequency differences obtained with small populations are typically down-weighted. HapFLK is an extension of FLK, integrating a multipoint linkage disequilibrium that regroups individual chromosomes into local haplotype clusters before measuring differentiation between populations59. A software for calculating FLK and HapFLK is free available on from (https://forge-dga.jouy.inra.fr/projects/hapflk). A p-value of 10−5 for FST and FLK was arbitrarily set as threshold for significance.Differentiation at SNP loci between population pairs were analysed using Wier and Cockerham’s FST, Rsb and XP-EHH values per chromosome with the GenWin package in R using genome-wide mean and variance values to normalise across chromosomes. GenWin defines a t-test like statistic, W, generated such that:2 is the sample variance of index across the genome, and n is the number of observations (i.e. markers) in the window61. Windows of 5000 to 25000 bp were assessed and overlapping segments with a W value > =5, with more than 10 SNPs were considered significant. Functional annotations for genes within significant windows were obtained from a combination of tools defining gene ontologies in PlasmoDB, PANTHER, DAVID, LAMP and gene ontology tools .To determine genomic windows with contiguous significant signatures, we fitted a spline on P. falciparum cysteine desulfarase gene (PF3D7_0727200) had the most significant temporal difference in allele frequency. We designed Taqman allelic discrimination assay for this polymorphism using the online assay design tool provided by life technologies (Life Technologies assay ID: AN7DRNA). We also designed an assay for a randomly chosen SNP at position 1,115,521 on chromosome 8 within HECT-like E3 ubiquitin ligase, putative (PF3D7_0826100), (Life Technologies assay ID: ANRWF4A). We genotyped these loci in 521 P. falciparum isolates from GBJ region collected in 2008 (53), 2010 (68), 2012 (57), 2013 (42), 2014 (35) and 2015 (125); and from Basse (eastern region) in 2014 (60) and 2015 (81). Furthermore, we determined the difference in Lumefantrine IC50 values for isolates with the reference or non-reference allele for 61 isolates assayed from the 2015 GBJ population. Mann Whitney t-test was used to determine the significance of the difference in mean IC50s between allele-groups with a significance level set at 0.05.The non-synonymous SNP at physical position 1,158,217 of chromosome 7 within the Short sequence reads analysed are available via European Nucleotide Archive.Supplementary figuresSupplementary Dataset 1


Mango is a commercially important tropical fruit. During its processing, peel and seed kernel are discarded as waste but they could be recovered as an excellent and cost-effective source of health-promoting ingredients. This study aimed to characterize some of them, including carotenoids like the provitamin A β-carotene and lutein, with an interest beyond its role in eye health. Other health-promoting compounds like tocopherols and polyphenols were also evaluated, as well as the in vitro antioxidant capacity of mango by-products. Regarding isoprenoids, α-tocopherol was mainly found in the peels and carotenoids concentration was higher in the pulps. β-carotene was the most abundant carotene in pulp and seed kernel, whereas peel was the only source of lutein, with violaxanthin the most abundant xanthophyll in the different mango organs tested. With regard to polyphenols, peels exhibited greater variability in its phenolic composition, being the total content up to 85 and 10 times higher than the pulp and seed kernels, respectively. On the other hand, peels also stood out for being a very rich source of mangiferin. Seed kernels and peels showed higher antioxidant capacity values than the pulps. These results contribute to the valorization of mango by-products as new natural ingredients for the pharma and food industries. Mangifera indica) is considered one of the most consumed fresh fruits in the world, with extensive marketing and production taking place in 115 countries - as the main fragment ions in the peaks 12, 13 and 14 revealed the presence of three different quercetin pentosides and recognized as quercetin xyloside (12), arabinopyranoside (13) and arabinofuranoside (14), respectively. Finally, the occurrence of an ion (M–H)− at m/z 447 in the compound 15 indicated the existence of a quercetin rhamnoside. These flavonoids were observed in mango peels but not in pulps and seed kernels. This agrees with previously reported data by Berardini et al. [Peak 8 was identified as kaempferol derivative according to their UV spectra and MS fragmentation leading to the kaempferol aglycone at ive mode . With reive mode   and epiafzelechin-epicatechin (600 μg/g D.W.) dimers, constituted the most abundant phenolic compounds after mangiferin . These cin vitro . As natuin vitro .On the other hand, flavonol glycosides obtained in mango peel were identified as quercetin glycosides, being quercetin galactoside and quercetin glucoside the most abundant , accordiThe total phenolic content (TPC), evaluated as the sum of the content of individual phenolic, showed significant differences between the different fractions of mango. The order observed was peels > seed kernels > pulps . This isThe health-promoting effects of both bioactive compounds characterized, polyphenols and isoprenoids, have been strongly associated with their antioxidant capacity. Thus, in this present study has been also evaluated the antioxidant capacity of the mango by-products. As shown On the other hand, the order observed in Folin-Ciocalteu assay by mango organs, seed kernels  > peels  > pulps (563.01 mg/100 g GAE), coincided with that described in the antioxidant activity. This result makes sense, because Folin-Ciocalteu assay determines the content of polyphenols and other reducing bioactive compounds  that have antioxidant properties.Finally, the correlation phytochemical composition  versus antioxidant capacity (DPPH and ORAC) was positive and direct .In summary, the results obtained demonstrate that the non-edible parts of mango, the peels and seed kernels commonly managed as a waste in the industry, are a good source of bioactive compounds like isoprenoids, especially carotenoids and α-tocopherol and polyphenols with high nutritional value and health benefit effects. In particular, mango peels constituted the great part of the fruit in terms of bioactive compounds contents, following by the seed kernels and both compared to the pulp, being some of these components essential nutrients in the human diet, such as lutein and α-tocopherol. In addition, mango by-products, especially seed kernels, exhibited higher antioxidant activity than pulps. Therefore, mango by-products could be exploited as a natural preservative by the pharmaceutical and agro-food industry, as well as being used as a natural ingredient contributing to different health benefits to consumers. Besides, the amount of waste generated during the processing of the mango could be reduced. Finally, future studies will be carried out deeply to elucidate the bioavailability and safety of bioactive compounds of mango by-products.


Brassica napus  is an important allopolyploid crop derived from interspecific crosses between Brassica rapa  and Brassica oleracea . However, no truly wild B. napus populations are known; its origin and improvement processes remain unclear. Here, we resequence 588 B. napus accessions. We uncover that the A subgenome may evolve from the ancestor of European turnip and the C subgenome may evolve from the common ancestor of kohlrabi, cauliflower, broccoli, and Chinese kale. Additionally, winter oilseed may be the original form of B. napus. Subgenome-specific selection of defense-response genes has contributed to environmental adaptation after formation of the species, whereas asymmetrical subgenomic selection has led to ecotype change. By integrating genome-wide association studies, selection signals, and transcriptome analyses, we identify genes associated with improved stress tolerance, oil content, seed quality, and ecotype improvement. They are candidates for further functional characterization and genetic improvement of B. napus. Brassica napus is a globally important oil crop, but the origin of the allotetraploid genome and its improvement process are largely unknown. Here, the authors take a population genetic approach to resolve its origin and evolutionary history, and identify candidate genes related to important agricultural traits. During B. napus breeding, the undesirable oil components, erucic acid and aliphatic glucosinolate, in the seeds have undergone a dramatic reduction, whereas oil content, seed yield, and disease resistance have been significantly improved2. As described by the Triangle of U3, three allopolyploid species, Brassica napus , Brassica juncea , and Brassica carinata  each originated from hybridization between two of three ancestral diploid species, Brassica rapa , Brassica oleracea , and Brassica nigra . The three Brassica diploid progenitors are themselves ancient polyploids that experienced a lineage-specific whole-genome triplication, making the allopolyploid descendant B. napus an excellent model for investigating processes of polyploid speciation, evolution, and selection.BB. napus was formed by hybridization of B. rapa and B. oleracea1. The estimated formation time of B. napus were ~67004 and ~7500 years ago5 and 38,000−51,000 years ago6, based on two synonymous substitution (Ks) estimations and a Bayesian Markov chain Monte Carlo (MCMC) simulation, respectively. The literatures recorded that winter B. napus was first cultivated in Europe7. Around the year 1700, spring B. napus was developed and spread to England in the late 18th century8. The semi-winter ecotype was mainly cultivated in China, which was introduced from Europe in the 1930–1940s9. However, no truly wild B. napus populations are known7, leading to the precise identities of the two progenitors that hybridized to form B. napus remain elusive, as B. rapa and B. oleracea have morphologically diverse subspecies and have commonly been cultivated throughout Europe for hundreds of years. The natural hybridization among these species occurred occasionally under appropriate conditions. Although a recent study suggested that the B. napus A subgenome might be derived from the ancestor of European turnip (B. rapa ssp. rapa)6, more evidence to support the conclusion need to be provided, due to only 5 B. napus and 27 B. rapa accessions were included in their analysis. Previous studies based on nuclear and chloroplast markers also suggested that B. napus may have developed from an interspecific cross between turnip and broccoli, or resulted from several independent hybridization events11. To further understand the evolution of B. napus, it is necessary to reveal whether wild species or domesticated donors were parental progenitors, which B. rapa and B. oleracea subspecies involved in the formation of B. napus.As one of the earliest allopolyploid crops, Oryza sativa)12, tomato (Solanum lycopersicum)13, and soybean (Glycine max)14. Reference genomes of the three Brassica progenitors and the allopolyploid species B. napus and B. juncea have been released17. Resequencing of 199 B. rapa and 119 B. oleracea accessions18 will facilitate investigations of selection acting in each B. napus subgenome and will clarify the progenitors of the species.Genome resequencing has been used to identify genes that have contributed to domestication and improvement in crop plants, such as rice  and 1,307,151 indels  subgenome SNP set . Similarly, the C (B. oleracea and B. napus) subgenome SNP set (denoted as BolC), including 675,457 SNPs, was obtained  and other subspecies   near the European turnip  represent the optimal model , broccoli , cauliflower (B. oleracea var. botrytis), and Chinese kale –suggesting that the B. napus C subgenome might have evolved from the common ancestor of these lineages . In the fastsimcoal2 results, model a, which suggests that the B. napus C subgenome originated from the common ancestor of kohlrabi, cauliflower, and broccoli, was supported with a much higher log-likelihood  than model b, suggesting that the B. napus C subgenome originated during divergence of ancestors of four B. oleracea subspecies.In the BolC ML tree, the ges Fig. . To valiB. napus C subgenome. Our data support model c , indicating that the ancestor of B. napus split from the common ancestor of four B. oleracea subspecies, with recent gene flow into B. napus ~108–898 years ago , five models (models c–g) were compared to infer the evolutionary history of the n = 19) was the closest subspecies to B. napus landraces, followed by Chinese kale, broccoli, and cauliflower  decay of different groups, based on progenitors of the A and C subgenomes (33 accessions of European turnip and 66 accessions of four B. oleracea subspecies) and B. napus accessions in the three SNP sets  and B. oleracea (27.90 kb) was stronger than those in the A (19.30 kb) and C (1365.30 kb) subgenomes of B. napus, respectively  and divergence times for different ecotypes and usages of B. napus using SMC++22. The Ne values for different B. napus ecotypes showed similar dynamics  was the process from original B. napus to landrace, while the second stage of improvement (SSI) represented the improvement from landraces to improved cultivars. As no wild B. napus populations are known7, we pooled two groups of progenitors, European turnip (n = 33) and four B. oleracea subspecies (n = 66), to represent the pseudo-wild ancestral A (denoted AA) and C (denoted CA) subgenomes of B. napus, respectively. To avoid the influence of introgression derived from non-ancestors of progenitors, we identified FSI-selection signals from the shared variations between B. napus landraces and their ancestors by comparing genomic variations with high fixation index (FST) and reduction of diversity (ROD) between the A subgenome of B. napus landraces (denoted AL) and AA, and between the C subgenome of B. napus landraces (denoted CL) and CA, using B. napus landraces as the derived group and progenitors as the control group. SSI-selection signals were detected by comparing B. napus landraces with improved cultivars  and by comparing cultivars with different ecotypes.We divided the π) decreased from 7.23 × 10−4 in AA to 5.40 × 10−4 in AL and from 7.45 × 10−4 in CA to 4.97 × 10−4 in CL (Supplementary Data B. napus C subgenome than in the A subgenome. The FST(AL/AA) was 0.136 (Supplementary Data FST(CL/CA) (0.246) (Supplementary Data FST(AL/AA) and FST(CL/CA) were higher than FST(improved cultivar/landrace) (0.016) , abscisic acid metabolic process (GO:0009687), and regulation of immune response (GO:0050776)), development (axillary shoot meristem initiation (GO:0090506) and meristem initiation (GO:0010014)), and metabolic pathways (unsaturated fatty acid biosynthetic process (GO:0006636), triglyceride biosynthetic process (GO:0019432) and terpenoid metabolic process(GO:0006721))  enrichment analysis revealed that genes in the FST, ROD, XP-CLR, and XP-EHH) , reproductive structure development (GO:0048608) and regulation of secondary shoot formation (GO:2000032)  simultaneously detected by at least three of the four comparisons (HH) Fig. . The 261ss GO:00214, reproFurthermore, candidate genes involved in glucosinolate transport and fatty acid elongation were located within syntenic regions in the A and C subgenomes  between imputed and true genotypes for two biological replicates of 20 accessions was 0.956 with minimum and maximum values ranging from 0.928 to 0.967  = 6.61] and 4.85 × 10−6 .To identify candidate genes responsible for 11 important traits, we conducted multi-locus random mixed linear model analysis for genome-wide association studies (GWAS) using mrMLM v1.326 were also detected here. As traits related to oil content and yield are complex and easily affected by environmental variation, we integrated the significant loci from this study with quantitative trait loci (QTLs) identified previously28. Candidate genes controlling different traits thus were identified based on functional annotation and transcriptome analysis in intersecting regions of selection outliers and QTL, as discussed below.In total, we identified 60 loci significantly associated with 10 target traits, including 5 related to seed yield, 3 to silique length, 4 to oil content, and 48 to seed quality Fig. . In addiB. napus reference genome  , leaf (Le), and silique pericarp at 7 (SP7D), 10 (SP7D), and 14 (SP7D) days after flowering in Zhongshuang11 were significantly over-represented in the glucosinolate biosynthetic process  plays a major role in the regulation of ABA (abscisic acid) biosynthesis in response to water deficit29. Besides, NCED5 (Bra032359) contributes, along with NCED3, to ABA production, thereby affecting plant growth and water stress tolerance30.During the FSI, defense responsive genes may play key roles in B. napus, and laid a solid foundation for its cultivation in diverse environments. Other candidate genes involved in ABA metabolism, are also noteworthy, such as CYP707A3 (Bra021965 and Bra025083), XERICO (Bra013211), and PHYB (Bra001650), all of which are associated with drought stress responses, via regulation of ABA accumulation  is one of the notable genes. As a receptor for the immune signal salicylic acid in plants, NPR3 controls the proteasome-mediated degradation of NPR1, which is involved in negative regulation of defense responses against bacterial and oomycete pathogens31. Similar defense-responsive genes, BAH1(Bra032581), NHL25 (Bra028103), and NDR1 (Bra035766) are also involved in regulating plant innate immunity to microbes  are silenced in the seeds of the double-low cultivar, and the other five are moderately expressed  are located in major QTL regions. Further reduction of anti-nutritional glucosinolates, in B. napus seeds, may be achieved through silencing these five GTR2 genes, especially those located in major QTL regions.Glucosinolates are synthesized in rosette leaves and silique walls and subsequently relocated to the embryo through the phloem by transporters. We identified the glucosinolate-specific transporter, GTR2, as likely responsible for glucosinolate delivery from source tissues into seeds in sed Fig. ; of thesBnaA08g11130D) and C03 (BnaC03g65980D)  genes whose products control erucic acid synthesis from oleoyl-CoA (C18:1-CoA)33. These two genes were significantly highly expressed in the high-erucic acid cultivar, suggesting that FAE1 silencing is an important contributor to reduced seed erucic acid content. Additional GWAS signals associated with erucic acid were on chromosome C02 and C07,  and ATGLL (BnaC07g30920D) and may represent SSI-selection genes whose products reduce seed erucic acid.Four GWAS signals associated with erucic acid were identified during the SSI, including two on chromosomes A08 (0D) Fig.  near the07, Fig. , near AMB. napus is divided into winter (requiring a prolonged cold period), semi-winter (requiring a short cold period), and spring (no cold requirement) ecotypes. The original B. napus was a winter oilseed; spring and semi-winter ecotypes were developed for adaptation to different environments. To identify candidate genes responsible for ecotype improvement, we scanned the selection outliers using the winter ecotype as the control and the spring and semi-winter ecotypes as derived groups , floral organ abscission (GO:0010227), and regulation of floral meristem growth (GO:0010080), suggesting that these SSI-selection signals might be critical for local environmental adaptation of A. thaliana from FLOR-ID (FLOweRing Interactive Database)34 and identified 1225 orthologs in B. napus. Among the 24 flowering-time genes simultaneously located within outlier regions of the two comparisons  encodes a core regulator in B. napus ecotype improvement  is an important repressor of floral transition35. A recent study reported that different FLC paralogs contribute differentially to natural variation in B. napus flowering time, and that a 2.833-kb insertion in BnFLC.A2 and its homeologous exchange (HE) with BnFLC.C2 generated early-flowering B. napus ecotpyes36. Our GWAS detected three loci associated with flowering time at the suggestive threshold  controlling flowering time variation, each explained ~8% of the total phenotypic variance , encoding components of the facilitates chromatin transcription (FACT) complex, and FLC repressor gene VRN2 (BnaC01g21540D), encoding a component of the polycomb repressive complex 2 (PRC2).Genes involved in chromatin modification also experienced extensive selection for ecotype improvement, via regulation of ion Fig. , such asArabidopsis, the histone chaperones SSRP1 in the FACT complex assist the progression of RNA polymerase II and promote FLC expression37. In contrast, VRN2 plays a critical role in maintaining transcriptional repression of FLC, after vernalization, by regulating the deposition of H3K27me3 and H3K9me3 repressive marks at the FLC locus38. These results suggest that epigenetic variations, especially histone modifications involved in regulating FLC expression, are closely associated with flowering time in B. napus. These genes therefore represent promising regulatory targets in future B. napus breeding programs  on chromosome A02  is an ortholog of Arabidopsis FATA ACYL-ACP THIOESTERASE 1 on chromosome A03 , which split from the European-Central Asian B. rapa (European turnip) cluster, ~3715–6190 years ago, is not likely a wild B. rapa46. Since the estimated origin times of B. napus in previous and our studies were all earlier than the divergence time between rapini and the European-Central Asian B. rapa, European turnip might be the only possible A subgenome donor for B. napus formation, as the first domesticated B. rapa. Sampling of more wild B. rapa and B. oleracea relatives would be helpful for better understanding the complex events that occurred during B. napus origin. Noting prior evidence that the maternal ancestor of B. napus was highly unlikely to be B. oleracea1, we posit that B. napus resulted from hybridization between a maternal ancestor of European turnip and a paternal common ancestor of the four aforementioned B. oleracea subspecies.Previous archeological and linguistic lines of evidence suggest that turnip is likely the first domesticated B. napus with regard to adaptation to environmental variation, reflecting the fact that strong survival pressure may often be the first challenge to a newly evolved plant species. Furthermore, the improvement of oil accumulation in B. napus resulted mainly from A subgenome-specific selection on triglyceride biosynthetic genes. This is similar to the history of upland cotton, whose D-subgenome-specific selection led to prolonged fiber elongation in cultivated cottons47, suggesting that asymmetrical subgenomic selection may often occur during the FSI.During the FSI, A subgenome-specific selection on defense-response genes greatly improved B. napus dramatically reduced its genetic diversity and affected SSI-selection signals. Combining this information with GWAS results and previous QTL information, we identified a number of outlier regions and candidate genes associated with seed yield, seed oil, glucosinolate, and erucic acid contents and flowering time. Similarly, improvement of seed quality due to subgenome parallel selection was also observed during the artificial selection of B. napus.Artificial breeding of B. napus and will facilitate the dissection of the genetic bases of important agronomic complex traits. The significant SNPs associated with favorable variants, selection signals, and candidate genes will be a valuable resource for further improving the yield, seed quality, oil content, and adaptability of this recent allopolyploid crop and its relatives.Our study provides a valuable resource for understanding the origin and improvement history of B. napus accessions, including 466 from Asia, 102 from Europe, 13 from North America, and 7 from Australia . Two biological replicates for 20 accessions were selected for whole-genome resequencing to assess the accuracy of sequencing in this study.Prior to sequencing and phenotyping, all B. rapa and 119 B. oleracea accessions published previously were retrieved from the National Center for Biotechnology Information (NCBI) database. Low-quality bases from paired-reads were trimmed using Trimmomatic (version 0.33)48, with the parameter LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:80. To investigate the origin of B. napus, the genome sequences of B. rapa (version 1.5)15 and B. oleracea (version 1.0)16 were merged as a B. napus ancestral pseudo-genome. Filtered B. napus sequencing reads were aligned to the B. napus reference genome (version 4.1)5 and the B. napus ancestral pseudo-genome, and B. rapa and B. oleracea sequencing reads were mapped only to their corresponding reference genomes using the Burrows-Wheeler Aligner (version 0.7.10-r789)49. Local realignment, duplicate read marking, and base quality recalibration for the alignment results were further processed sequentially using Picard  and GATK (version 3.2–2-gec30cee)50. SNP and InDel calling for each sample were performed using SAMtools’s mpileup (version 0.1.19–44428cd) and GATK with the parameter–ts_filter_level 90. To remove false variants, only biallelic SNPs simultaneously detected by both methods were retained, using the function SelectVariants in GATK with the following parameters:–filterExpression QD < 2.0 || FS > 60.0 || MQ < 40.0–clusterWindowSize 5–clusterSize 2; SNPs with minor allele frequency (MAF) of lower than 5% in the population were discarded. A total of 103 SNPs were randomly selected for accuracy validation of SNP calling using conventional PCR and Sanger-based sequencing  and C subgenome (BolC) SNP sets were obtained as the following procedure. We mapped the B. napus resequencing data onto the B. napus ancestral pseudo-genome to call SNPs, which were split into two SNP sets based on the two subgenomes. Then, the SNPs retrieved from the B. rapa or B. oleracea resequencing data using corresponding genomes as reference were combined with two SNP sets obtained previously to represent BraA and BolC SNP sets, respectively. To perform selective sweep analysis and GWAS in B. napus, the SNPs called from B. napus resequencing data (Bna) using the B. napus genome as reference were also obtained.To reveal the original subtaxa of 19, according to the best model determined by the Bayesian information criterion (BIC). The reliability of the ML trees was estimated using the ultrafast bootstrap approach (UFboot) with 1000 replicates. An online tool Interactive tree of life (iTOL) v3 (https://itol.embl.de)51 was used to display the consensus ML trees. Principal component analysis of the three SNP sets was performed using EIGENSOFT (version 6.1.4)52.To construct maximum likelihood (ML) trees, we screened 17,000, 19,377, and 19,548 SNPs at fourfold-degenerate sites (MAF > 5%) from the BraA, BolC, and Bna SNP sets, respectively. We generated three ML trees using IQ-TREE v1.6.6B. napus landraces and two progenitors, 199 B. rapa and 119 B. oleracea accessions, using STRUCTURE53, with two subsets of SNPs  evenly distributed throughout the reference genomes. Each K value, as a putative number of populations set from 1 to 10, was obtained with five independent runs. The length of the burn-in period and number of MCMC replications after burn-in were set to 50,000 and 100,000, respectively. The optimum number of subgroups (K) was determined based on the log probability of the data (lnP(K)) and an ad hoc statistic ΔK method54.We inferred the population structure of 50 B. napus C subgenome originated from the common ancestor of the four aforementioned B. oleracea subspecies, the B. napus C subgenome originated from the ancestor of European turnip, and whether and when migration into B. napus occurred, we investigated the demographic history using both diffusion approximations for demographic inference (∂a∂i) version 1.7.020 and fastsimcoal2 v. 2.6.0.321. To mitigate the effect of LD, one SNP per 10 kb was selected, and SNPs located 10 kb away from genes were used to convert into a site frequency spectrum (SFS) using easySFS (https://github.com/isaacovercast/easySFS).To assess whether the B. napus A and C subgenomes were respectively evaluated using fastsimcoal221. To ensure convergence, we ran fastsimcoal2 50 times, and kept the run that resulted in the highest log likelihood for each model. Estimates were obtained from 100,000 coalescent simulations per likelihood estimation  and 40 Expectation/Conditional Maximization cycle (-L40). The best model with the maximum likelihood was retained. We estimated 95% confidence intervals (CI) using parametric bootstrap estimates based on 80 data sets simulated according to Conditional Maximum Likelihood estimates in the best model estimation parameters. The optimal demographic models obtained from fastsimcoal2 were also validated using ∂a∂i20. Since up to three subpopulations can be handled in ∂a∂i at a time, three demographic models for both B. napus two subgenomes were compared. The best fitting model was selected with the highest log-likelihood under the best-fit parameter set.Five and seven models  using PLINK v1.0755, LD decay statistics were calculated for different (sub)populations, and LD decay graphs were plotted using PopLDdecay3.26 (https://github.com/BGI-shenzhen/PopLDdecay), with the parameter -MaxDist 1000 for SNP set BraA, and -MaxDist 5000 for SNP sets BraC and Bna.Linkage disequilibrium (LD) between pairs of SNPs up to 100 kb apart on each chromosome were estimated as the correlation coefficient (26. The flowering time (FT) was defined as the day on which the plants reached Biologische Bundesanstalt, Bundessortenamt and CHemical (BBCH) stage 6156.All accessions evaluated in this study were cultivated in field trials in Beibei, Chongqing, China  during the 2013 and 2014 growing seasons. A randomized complete block design with three replications was employed. Each accession was grown in three rows of 10 plants per row, with a distance of 20 cm between plants and 30 cm between rows. Open-pollinated seeds were harvested from five randomly selected plants for measurements of seed yield per plant (SY) and silique length (SL). Oil content (OC), seed color (SC), and total glucosinolate content (GC) were detected by near-infrared reflectance spectroscopy (NIRS) analysis. The fatty acids palmitic acid, stearic acid, oleic acid, linoleic acid, linolenic acid, eicosenoic acid, and erucic acid were quantified by gas liquid chromatography, according to a previous reportπ), and population fixation statistics (FST) were calculated using vcftools  in a 100-kb sliding window with a step size of 10 kb. π is the expected heterozygosity per site derived from the average number of sequence differences in a group of samples, and FST is the inbreeding coefficient of a subpopulation, used for estimating the degree of pairwise genomic differentiation on candidate genes between pairs of subpopulations. The reduction of diversity (ROD) values were calculated based on the ratio of π for a subpopulation with respect to a control subpopulation. All the output results of ROD and FST were standardized and transformed into z-scores using a 100-kb sliding window with a 10-kb step size. The outlier windows of ROD , and FST , which characterize candidate genes involved in the FSI and SSI of B. napus, were determined by z-tests with a significance level of α = 0.05 corresponding to a z-score of 1.645.To identify candidate regions potentially affected by selections, nucleotide diversity  using XP-CLR v1.0 (–w1 0.005 200 10000–p0 0.95)n = 33) and B. oleracea (n = 66) involved in the original formation of B. napus versus B. napus landraces and the SNP sets BraA and BolC were used to detect FSI events. The improvement signals were detected based on the Bna SNP set and the following three pairwise comparisons: first, B. napus landraces versus improved cultivars to identify SSI events; second, double-low versus double-high B. napus accessions to identify selection signals related to seed quality improvement; third, comparisons between spring and winter ecotype, and between spring and winter ecotype to identify selection signals associated with ecotype improvement.In the FSI-selection analysis, European turnip  between imputed and true genotypes, which were calculated at each locus for 20 accessions of biological replicates , genotyping calling errors are inevitable, particularly at sites of low MAF. To improve the mapping resolution for GWAS, imputation for sporadic missing genotypes in the Bna SNP set was implemented in Beagle v.4.1www.eXtension.org/pages/61006), based on a linear model and corresponding traits with three replicates over 2 years. The resulting values were used as phenotypes for the association analysis. The multi-locus random-SNP-effect mixed linear model was used to test trait-SNP associations in mrMLM v1.324; PCA and K were controlled as fixed and random effects, respectively. The effective number (n) of independent SNPs was calculated using GEC software25. Significant  and suggestive (1/n) and P-value thresholds were set to control the genome-wide type I error rate. To identify reliable significant association signals in our GWAS, only LD blocks containing at least one significant and one suggestive SNPs were regarded as significant loci.The best linear unbiased prediction (BLUP) for each investigated trait of each accession was obtained using an R script (56. Seeds (Se) and silique pericarps (SP) on the main inflorescence were harvested 7, 10, 15, and 45 days after flowering. For each sample, two biological replicates, each replicate obtained from three independent plants, were pooled for transcriptome sequencing.Total RNA was isolated from 11 tissues of two representative accessions with different oil contents and seed qualities . Tissues from roots (Ro), stems (St), mature leaves (Le), and flowers (Fl) were harvested at flowering stage 63–65 as defined in the BBCHB. napus reference genome using STAR 2.4.2a with default parameters60. Gene expression levels were quantified by the cuffquant program in terms of fragments per kilobase of transcript per million mapped reads (FPKM)61. The DEGs were then detected in each tissue between two accessions using the cufdiff program, with the following standards: FDR < 0.01 and absolute fold change >2.Library preparation, sequencing, and read filtering of 44 libraries  were conducted by the Biomarker Technologies Corporation . Filtered reads were then mapped to the B. rapa, B. oleracea, and B. napus proteins were assigned based on BLASTP analysis against the entire A. thaliana proteome, with an e-value cut-off of 1e-5 and max_target_seqs of 1. The R package clusterProfiler was then used to identify significantly enriched GO terms  < 0.05)62. The resulting P-values were corrected for multiple comparisons using the method of Benjamini and Hochberg. To avoid overestimation, tandem-duplicated genes in the outlier regions were filtered before the GO enrichment analysis.Gene ontology (GO) terms for all Further information on experimental design is available in the Supplementary InformationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Supplementary Data 17Supplementary Data 18Supplementary Data 19Supplementary Data 20Supplementary Data 21Supplementary Data 22Supplementary Data 23Supplementary Data 24Supplementary Data 25Supplementary Data 26Supplementary Data 27Supplementary Data 28Supplementary Data 29Supplementary Data 30Supplementary Data 31Supplementary Data 32Supplementary Data 33Supplementary Data 34Supplementary Data 35Supplementary Data 36Supplementary Data 37Source Data