Castor is a commercially important oilseed crop that provides raw materials for several industries. Currently, the availability of genomic resources for castor is very limited. In this study, genome-wide SNPs were discovered in castor via whole-genome sequencing of 14 diverse lines to an average of 34X coverage. A total of 2,179,759 putative SNPs were detected, and a genotyping array was designed with 6,000 high-quality SNPs representing 2,492 scaffolds of the draft castor genome (87.5% genome coverage). The array was validated by genotyping a panel of 314 inbred castor lines, which resulted in 5,025 scorable SNPs with a high call rate (98%) and reproducibility (100%). Using this array, a consensus linkage map consisting of 1,978 SNP loci was constructed with an average inter-marker distance of 0.55 cM. The genome-wide SNP data, the genotyping array and the dense linkage map are valuable genomic tools for promoting high-throughput genomic research and molecular breeding in castor. Ricinus communis L.) is the only species within the genus, Ricinus in the Euphorbiaceae family, that is both an autogamous and allogamous species with a diploid number of chromosomes (2n = 2x = 20) and a genome of approximately 320 Mb in size1. It is an industrially important oilseed crop and the only source of ricinoleic acid (12-hydroxy-octadeca-9-enoic acid) in plants. Castor oil and its derivatives are used in the manufacturing lubricants, textiles, paper, plastics, paints, inks, cosmetics and pharmaceuticals. The demand for castor is also boosted by its potential for biofuel production owing to its wide adaptability, high productivity and high oil yield2. However, to increase castor production, elite high-yielding cultivars that display resistance to biotic and abiotic stresses need to be developed. Such cultivar development can be accelerated if molecular markers are integrated into current castor breeding programmes, as proven in other crops.Castor  hold promise in genetic studies and breeding applications and can assist in linkage map construction, marker-trait association, and marker-assisted selection because of their abundance, high level of polymorphism, high throughput capability, and cost-effectiveness. SNP markers could be the preferred marker system for crop species, especially oilseeds, in which general microsatellite diversity appears to be low14. Concurrently, several high-throughput SNP array-based genotyping platforms have been developed15. Recently, Genotyping by Sequencing (GBS) has become a popular high-throughput marker detection platform16. GBS is an NGS protocol employed to simultaneously discover and score segregating markers in populations of interest by sequencing highly multiplexed reduced representation libraries of samples17. However, at present, this technique has certain limitations for crop genotyping. A major complication is the high-level DNA sequence duplication in angiosperms. The critical level of sequencing read depth for a given experimental design can be affected by factors such as genome size, genome complexity, ploidy, expected heterozygosity, and others. Poor coverage of specific loci lead to an excessive number of missing values, compromising the accuracy of genetic studies18. Moreover, the experimental operation of GBS and data analysis are beyond the capabilities of an average breeding programme; in contrast, ‘genotyping arrays’ can be used to genotype many samples within a short period, and the data analysis is relatively easy19.With the advent of next-generation sequencing (NGS) technologies, generating genome-wide markers is no longer a limitation for any species. NGS technologies have drastically increased the speed at which a genome sequence can be generated, while greatly reducing costs. Thus, the discovery of genome-wide SNPs has become very simple and inexpensiveIn this context, this study was undertaken with the following objectives: (i) to discover genome-wide SNPs in castor through whole-genome sequencing of a set of diverse genotypes, (ii) to develop and validate an SNP genotyping array, and (iii) to construct a dense linkage map for high-throughput genomic applications in castor.A set of 14 diverse castor genotypes, including cultivars and germplasm accessions, was chosen for whole-genome sequencing Table . The hugThe high-quality filtered reads were aligned with the reference genome. The mapping percentage ranged from 87.4 to 98.1. On an average, 95.8 per cent of the reads could be mapped onto the reference genome. A good level of genome coverage was observed. The per cent genome coverage (without Ns) ranged from 92.8 to 97.4, with an average of 95.4. The number of SNPs/InDels identified between sample and reference ranged from 294,956 to 929,114 Table . A totalThe putative SNPs identified through sequencing of 14 castor genotypes were further filtered for inclusion in genotyping assay development. On on average, 15 per cent of SNPs remained after filtering out the (i) SNPs with a less than 75 bp flanking region, (ii) SNPs located within 100 bp from the target SNP, (iii) insertion or deletion variants (InDels), and (iv) heterozygous SNPs. After filtration, the total number of common SNPs across 14 samples was 130,666, of which 21,185 were from genic regions. From this set, 10,872 SNPs were initially selected for array design, considering their functional roles  and genomic coverage (maximum possible representations of scaffolds of the reference genome). Out of 10,872 SNPs, we finally selected 6,000 SNPs, that required only one bead type for genotyping and had an assay design score of >0.7 for assay development. The details of the SNPs selected for array design are given in Supplementary Table The technical performance and the practical utility of the SNP array were assessed by genotyping a panel of 314 inbred castor lines. Of the 6,000 targeted SNPs, only 5,238 could be included on the Infinium BeadChips. Out of the 5,238 SNPs present on the Infinium BeadChips, high-quality genotype calls were obtained for 5,038 SNPs, while clear genotype clustering was not obtained for the remaining 200 SNPs.Out of 5,038 scorable SNPs, 927 were monomorphic 19%) and 13 SNPs had missing data points of more than 20 per cent in the panel of 314 lines. Finally, 4,098 SNPs were called successfully with high confidence. The GeneTrain and GeneCallC50 scores of all the SNPs were >0.4. The call rate of SNPs ranged from 80 to 100 per cent, with an average of 98 per cent  had an MAF of >0.2 and could be considered as markers with normal allele frequencies. Approximately 18 per cent of SNPs had an MAF of 0.1 to 0.2. A total of 291 SNPs had an MAF of <0.05. In addition, 489 SNPs (12%) showed almost equal allele frequencies (with MAF close to 0.5) for two alternative alleles. The distribution of MAF is shown in Fig. The polymorphism information content (PIC) values for individual SNPs ranged from 0.003 to 0.375, with the peak distribution at >0.3 Fig. .Figure 3The RIL population of the JC12 × 48-1 cross, along with their parents, were genotyped using the SNP array. A total of 1,090 SNP loci were polymorphic and segregated in the population, of which 246 loci (22%) showed segregation distortion towards either of their parents. Out of 1,090 SNPs, 1,028 showed unique segregation. Linkage analysis was performed using the genotypic data from all 1,090 SNP markers. All markers could be mapped, and a genetic linkage map was constructed with 10 linkage groups, corresponding to the number of haploid chromosomes. The total length of the map was 1,139.8 cM. The number of markers per linkage group ranged from 82 to 207  to 75 (LG-1). The consensus map was composed of 1,978 SNP markers and spanned a total length of 995.8 cM with an average distance of 0.55 cM between adjacent markers. The marker orders in the consensus map were in general consistent with the individual genetic linkage maps Fig. . The mapThe pairwise comparison of the linkage maps indicated no potential chromosomal rearrangements, and none of the markers were found to map to different LGs.viz., genome-wide SNP loci, a genotyping array and a linkage map.Even though castor is an industrially important crop, it has not yet benefited from the genomic revolution. The availability of genomic resources for castor is very limited. This study attempted to generate genomic resources, 20 could map only 30 per cent of the reads when they used single-end reads from restriction site associated DNA sequences. By aligning the sample reads with the reference, a total of 2,179,759 SNPs were identified across samples, with a frequency of one SNP per 160 bp. The SNP frequency observed in castor appears to be higher than that observed in self-pollinated crops such as pea (1 SNP/540 bp)21, soybean (1 SNP/490 bp)22, rice (1 SNP/238 bp)23, and wheat (1 SNP/233 bp)23 but lower than in cross-pollinated perennial species such as Eucalyptus (1 SNP/16 bp)24 and oil palm (1 SNP/74 bp)25. However, it is important to note that the frequencies of SNP occurrence reported in the above-mentioned studies might be over/under estimated because those studies were based on only a few genes or transcriptomes, not whole genomes. The frequency of SNP occurrence varies widely among crops depending upon their domestication history, their reproductive habits , the diversity of the populations under assessment, and the status (coding or non-coding) of the analysed regions21. From the large catalogue of SNPs, a genotyping array with 6,000 carefully chosen SNPs was designed. The set of SNPs in the array represented 2,492 scaffolds of the draft castor genome with genome coverage of 87.5 per cent. Out of 6,000 SNPs in the final array, 3,619 were from genic regions. SNPs in genic regions could potentially be more informative in genome-wide association studies.To identify genome-wide SNPs, whole-genome sequence data for 14 inbred castor lines were generated with very high coverage (average 34X) and mapped to the reference genome with genome coverage (without Ns) of 92.8 to 97.4 per cent. The high mapping percentage achieved in this study might be due to the use of paired-end sequencing, which helps to precisely map the short reads onto the reference. In sorghum, Nelson and his collegues29. Out of 5,238 SNPs present on the Infinium BeadChips, high-quality genotype calls were obtained for 5,038 SNPs. The success rate observed in this study (96.2%) was higher than the typical ≥90% rate obtained earlier with other crops. The high success rate might be due to the high sequencing depth and stringent filtration criteria applied while selecting SNPs for assay development. We carefully removed additional polymorphisms in the flanking sequences of target SNPs. The lower success rates observed in other studies could be due to their lack of specific selection for conserved SNP flanking sequences during genotype assay development. For example, the success rate in conifers was 69 to 77 per cent30, and in Pinus pinaster was 61 to 73 per cent31, when no specific selection for more conserved SNP flanking sequences was carried out.To assess the utility of the SNP array, a set of 314 castor lines representing the whole spectrum of the diversity available in the species was genotyped using the SNP array. The genotype panel comprised accessions of core germplasm, trait-specific germplasm lines, elite breeding lines, commercial varieties, and parents of commercial hybrids. Of the 6,000 targeted SNPs, only 5,238 could be included on the Infinium BeadChips. The manufacturing loss (12.7%) was within the expected limits and comparable to that in other studiesAround 19 per cent of the SNPs were scored as monomorphic. Such a high level of false positives may have resulted from the use of draft assembly for SNP prediction. It is expected that reads from regions missing in a draft reference may map incorrectly to other regions of the assembly and lead to false SNP predictions. These would then show as monomorphic in the Illumina assay. Nevertheless, the technical performance of the array was superior, as evidenced by the high average call rate (98%) and reproducibility (100%).32. The expected heterozygosity (gene diversity) ranged from 0.003 to 0.500, with an average of 0.351. The low level of heterozygosity could be due to preferential selection of SNPs in genic regions, which usually show reduced diversity. Previous studies have also reported low to moderate gene diversity estimates irrespective of the marker system. Allan and his collegues11 surveyed 41 germplasm accessions from five continents and 35 countries using AFLPs (3 primer combinations) and SSRs (9 primer pairs) and found that the average gene diversity was 0.126 for AFLPs and 0.239 for SSRs. Qiu and his collegues32 reported a relatively higher level of gene diversity (He = 0.41) by genotyping 24 accessions representing the main germplasms of castor from 14 countries using 118 polymorphic EST-SSR markers. Comparable levels of gene diversity (He = 0.38) were observed in our earlier study, in which 144 inbred lines derived from the core germplasm collection of castor were genotyped using 45 SSR markers12. The gene diversity estimate is generally influenced by the number of marker loci and the number/representation of genotypes used in the study. As the present study involved a large number of genotypes  and markers , we can confidently infer that only a moderate level of molecular diversity prevails in castor, despite its being a predominantly outcrossing species. The low level of diversity in castor might be due to its monotypic species status.The diversity parameters derived from the genotypic data for 314 inbred castor lines representing the whole spectrum of the diversity available in the species indicated only a moderate level of genetic diversity in castor. The average observed heterozygosity was 0.11, which is within the expected range of residual heterozygosity reported earlier34. To date, only two linkage maps largely based on SSR markers have been constructed for castor7. In this study, two RIL populations were used to construct a linkage map. The SNP array was used to genotype the population. The total number of polymorphic markers was 1,090 in the JC12 × 48-1 cross and 1,978 in the DCS9 × RG1139 cross. The proportion of skewed markers was more or less similar in both populations (22% in JC12 × 48-1 and 28% in DCS9 × RG1139); however, only 40 markers were shared across populations, indicating that the segregation distortion is specific neither to the cross nor the genomic region. The maps are fairly dense. The average inter-marker distance is 1.12 cM in the JC12 × 48-1 map and 0.81 cM in the DCS9 × RG1139 map.The genetic linkage map is a critical tool for molecular genetic studies and plant breeding applications. It is an important tool for physical mapping of genomes. High-density linkage maps have direct applications in marker-assisted selection through tight linkage of markers with the gene of interest. High-density genetic linkage maps are also useful in orienting and anchoring scaffolds arising from the genome sequence data35. In general, the maps of inter-specific crosses are significantly shorter than those of intra-specific crosses36. This was not the case in the present study, although RG1139 is a wild collection, whereas the other parents are improved breeding lines. Even though the individual populations differed in the extent of recombination, the marker order between individual maps was largely conserved  was smaller than the JC12 × 48-1 map , which may be attributed to reduced recombination in DCS9 × RG1139.8 cM was37 and provide a valuable tool for fine-mapping and map-based cloning.A consensus map was constructed using 392 markers that were shared between the two maps. The consensus map covered 995.8 cM and included 1,978 SNP markers, with only one gap of >10 cM and nine other gaps of >5 cM. The length of the consensus map was in between those of the individual genetic maps. A total of 722 scaffolds of the draft castor genome, covering 185.13 MB were anchored onto the consensus map. These anchored genome sequences could be used as a source for the development of additional markersIn summary, the SNP array developed in this study provides a valuable tool for high-throughput and cost-effective genotyping and mapping applications in castor. The genotyping array will provide the required resolution for discovery of marker-trait association through linkage and association analysis, evaluation of genetic variations, unravelling the genetic architecture of key quantitative traits, and exercising genomic selection in castor. The high-density linkage map can act as a reference for genetic and molecular studies in castor. The map can be readily used for (i) improving the genomic assembly of castor, (ii) QTL identification for several important traits, (iii) reference in constructing framework maps for individual populations, (iv) comparative genetic and genomic studies with related crops like cassava, and (v) identification of novel genes/alleles from related species.A set of 14 inbred castor lines was chosen for whole genome-sequencing based on their genetic distinctness and utility in genetic studies and breeding programmes. The genotype panel represented wild (germplasm accessions) and cultivated  genepools. The details of the selected lines are given in Table The SNP array designed in this study was validated by genotyping a set of 314 inbred castor lines. The composition of the genotype panel included (i) a castor core germplasm set derived from a global collection of over 3,000 accessions, (ii) known sources for important agronomic traits, (iii) elite inbred lines developed from major castor breeding centres in India, and (iv) varieties and parental lines of hybrids released for cultivation; thus, the panel fairly represented the diversity available in castor.Botryotinia ricini, vascular wilt caused by Fusarium oxysporum f.sp. ricini, and reniform nematode (Rotylenchulus reniformis). One true F1 plant of both the crosses was advanced to F2 by selfing. The individual F2 plants were further advanced up to F6 generation through the single-seed descent method. Finally, a total of 185 F6-RILs of JC12 × 48-1 and 188 F6-RILs of DCS9 × RG1139 were used for linkage map construction.Two recombinant inbred line (RIL) populations derived from the crosses JC12 × 48-1 and DCS9 × RG1139 were used to construct of linkage maps. The parents differed on range agronomic traits, including resistance to gray mold disease caused by Genomic DNA from 14 candidate genotypes was isolated using the CTAB method. The quality of the DNA was checked through 0.8% agarose gel electrophoresis and quantified using a Qubit® Fluorometer. Paired-end (PE) whole-genome sequencing (WGS) libraries were generated using the sample preparation kit from Illumina  per the manufacturer’s protocol. Fragmentation of genomic DNA (1 µg) was carried out using the Covaris S2 System  followed by adapter ligation. Enrichment of adapter-modified DNA fragments by PCR was performed, and the enriched libraries were analyzed for size and quality using a high-sensitivity DNA kit on the Bioanalyzer . A paired-end cluster generation kit  was used for cluster generation on paired-end flow cells. Cluster generation was carried out by hybridization of WGS libraries with a concentration ranging from 9 to 55 nmol/L, which was further diluted to make a stock solution of 4 nmol of molecules on the oligonucleotide-coated surface of the flow cell. Isothermal amplification of the libraries was carried out to generate clonal DNA clusters. Eleven libraries were multiplexed for sequencing on five different lanes of HiSeq2000  with an average read length of 2 × 100 bp. Three libraries were individually sequenced on MiSeq  with an average read length of 2 × 250 bp.38 using the following criteria: (i) Slidingwindow: Perform a sliding window trimming of 25 bp, cutting once the average quality within the window falls below a threshold of 25; (ii) Leading: Cut bases off the start of a read, if below a threshold quality of 25; (iii) Trailing: Cut bases off the end of a read, if below a threshold quality of 25; (iv) Minlength: Drop the read if it is below 50 bp in length; and (v) Sequencing adapter trimming.The raw reads were filtered using Trimmomatic v0.17Ricinus communis L. with a 350 Mb genome size3 was downloaded from http://castorbean.jcvi.org/downloads.php. The filtered sample reads were aligned with the reference genome using BWA software39 with default parameters. SNP discovery and filtration were carried out using SAMtools v0.1.19 (mpileup and varFilter)40 with default parameters. The SNPs were filtered based on the following parameters: (i) minimum RMS mapping quality for SNPs: 25; (ii) minimum read depth: 8; (iii) window size for occurrences of one SNP: 100 bp; and (iv) window size for filtering dense SNPs: 1.An available draft genome sequence of A genotyping array was developed on the Illumina Infinium platform  with 6,000 carefully chosen SNPs. From the set of SNPs that passed the technical quality filters, as described in section 2.3, further filtration was carried out by removing (i) the SNPs with minor allele frequency of <0.2, (ii) SNPs located on the terminal region of the scaffolds and with less a than 75 bp flanking region, (iii) SNPs located within a distance of 100 bp to the target SNP, (iv) the insertion or deletion variant (InDel) and heterozygous SNPs, and (v) SNPs in the repetitive region. Among the SNPs amenable to Illumina Infinium assay development, SNPs were further selected based on functional roles  and genomic coverage (maximum possible representation of scaffolds with respect to the reference genome). The custom Infinium genotyping BeadChips assay was designed using Illumina Assay Design tools (ADT) for the selected SNPs. Finally, 6,000 SNPs that required only one bead type for genotyping and had assay design scores of >0.7 were selected for assay development.The technical performance of the array was evaluated by genotyping a panel of 314 inbred castor lines. The genomic DNA was extracted from 100 mg of leaf samples collected from plants of each of the inbred lines using the CTAB method. The DNA samples were quantified using Quibit and a NanoDrop-8000 UV-Vis Spectrophotometer . The samples’ quality was checked through agarose gel electrophoresis. Illumina Infinium genotyping assay was performed per standard procedures, which are briefly described below.Genomic DNA samples from 314 lines in addition to two biological and two technical replicates, were amplified overnight. The amplified products were subsequently fragmented by a controlled enzymatic process. The DNA fragments were alcohol-precipitated and re-suspended. The BeadChips were prepared for hybridization in the capillary flow-through chamber. Samples were applied onto the BeadChips and incubated overnight. The DNA fragments of samples annealed to locus-specific oligomers during the hybridization step, wherein one bead type corresponded to a specific allele of the SNP locus. After hybridization, allelic specificity was conferred by enzymatic base extension. The products were subsequently fluorescently stained. The intensities of the beads’ fluorescence were detected by the iScan system , and genotype calling was performed using GenomeStudio software  with a GenCall score cut-off of 0.15 (as recommended by Illumina for Infinium data).41.The genotyping array;s quality was assessed based on the call rate, reproducibility, and polymorphism detection ability of the SNPs in the array. Various genetic diversity parameters, namely, minor allele frequency (MAF), observed heterozygosity (Ho), gene diversity or expected heterozygosity (He), and polymorphic information content (PIC), were estimated using the software program PowerMarker version 3.2542 at the logarithm of the odds (LOD) threshold of >8. The ordering of loci within the linkage group was done using the software RECORD43. Map distances were calculated using the Haldane mapping function. The consensus map based on data from two populations was constructed using Mapfuser, a Shiny application at https://plantbreeding.shinyapps.io/mapfuser. Mapfuser constructs consensus genetic maps using LPmerge software44.The RILs of JC12 × 48-1 and DCS9 × RG1139, along with their parents, were genotyped using the SNP array developed in this study. Genotyping was done as described in section 2.5. The markers were assigned to linkage groups using Joinmap 3.0 softwareSupplementary Fig 1Supplementary Table S1Supplementary Table S2Supplementary Table S3Supplementary Table S4


Vigna unguiculata [L.] Walp.). Seed coat pattern in cowpea has been a subject of study for over a century. This study makes use of newly available resources, including mapping populations, a reference genome and additional genome assemblies, and a high-density single nucleotide polymorphism genotyping platform, to map various seed coat pattern traits to three loci, concurrent with the Color Factor (C), Watson (W), and Holstein (H) factors identified previously. Several gene models encoding proteins involved in regulating the later stages of the flavonoid biosynthesis pathway have been identified as candidate genes, including a basic helix–loop–helix gene (Vigun07g110700) for the C locus, a WD-repeat gene (Vigun09g139900) for the W locus and an E3 ubiquitin ligase gene (Vigun10g163900) for the H locus. A model of seed coat development, consisting of six distinct stages, is described to explain some of the observed pattern phenotypes.The appearance of the seed is an important aspect of consumer preference for cowpea ( Vigna unguiculata [L.] Walp.) is a diploid (2n = 22) warm season legume which is primarily grown and serves as a major source of protein and calories in sub-Saharan Africa. Further production occurs in the Mediterranean Basin, Southeast Asia, Latin America, and the United States. Just over 7.4 million metric tonnes of dry cowpeas were reported worldwide in 2017 , “Watson” (W), “Holstein-1” (H-1), and “Holstein-2” (H-2). A three-locus system controlling seed coat pattern was established by O” was used in place of “C.”Seed coat traits in cowpea have been studied since the early 20th century, when A genotyping array for 51,128 single nucleotide polymorphisms (SNP) was recently developed for cowpea  which ofSeed Coat PhenotypingSection and examples can be seen in Ten populations were used for mapping: an eight-parent MAGIC population containing 305 lines , four biOne biparental population consisted of 87 RILs developed at the University of California, Riverside (UCR), derived from a cross between California Blackeye 27 (CB27), which has a black Eye 2 pattern, and IT82E-18, also known as “Big Buff” (BB), which has a brown Full Coat pattern . The secTo temporally describe seed coat development four accessions were examined: CB27, MAGIC059, Sanzi, and Sasaque. CB27 is described above. MAGIC059 has the Starry Night pattern in black and purple and is one of the lines included in the MAGIC population. Sanzi has a Speckled pattern in black and purple. Sasaque has the Full Coat pattern in red and purple.DNA was extracted from young leaf tissue using the Qiagen DNeasy Plant Mini Kit . A total of 51,128 SNPs were assayed in each sample using the Illumina Cowpea iSelect Consortium Array , individuals with the Eye 1 phenotype were scored as “0” instead of as missing data since marking too many lines as missing data caused r/mpMap to fail. For mapping purposes, each observed pattern was scored individually and mapped independently with scores assigned as “1” indicating presence of the trait and a “0” indicating absence. For example, a line expressing the Eye 1 pattern would be scored as “1” for the Eye 1 trait and “0” for all other traits. Pattern phenotypes are mutually exclusive. As the Eye 1 pattern appears to be epistatic towards the Expected segregation ratios reported in C locus, three parents  express the Eye 1 phenotype and are proposed to have a 1C1C genotype, while the other five parents are proposed to have a 2C2C genotype. Based on this, a given line in the population is expected to have a 3/8 probability of having a 1C1C genotype and a 5/8 probability of have a 2C2C genotype. At the W and H loci, one parent (CB27) is proposed to have the 0H0H and 0W0W genotypes, while the other seven parents are proposed to have the 1W1W and 1H1H genotypes. Based on this, a line should have a 1/8 probability of having the 0W0W and a 1/8 probability of having the 0H0Hgenotype. By multiplying the probabilities at each locus, the probability of a given genotype can be determined using the following equation:For the MAGIC population, based on how the population was constructed  it was aC is the probability of a given allele at the C locus, PW is the probability of a given allele at the W locus, PH is the probability of a given allele at the H locus, and Pnet is the probability of a given genotype. For example, the probability of a 2C2H1H1W0W0C genotype, which would have a Holstein phenotype would be 35/512 ([5/8]*[7/8]*[1/8]). The above method results in a predicted 192:5:35:35:245 phenotypic ratio for the Eye 1 (1C1C), Eye 2 (2C2H0H0W0W0C), Holstein (2C2H1H1W0W0C), Watson (2C2H0H0W1W1C), and Full Coat (2C2H1H1W1W1C) patterns, respectively.where Pp = 8.10E−05 [−log10(p) = 4.09]. Due to the high number of markers in the genotype data, imputed markers spaced at 1 cM intervals were used.Trait mapping was achieved with different methods for each type of population. In the MAGIC population, the R package “mpMap”  was usedIn the biparental RIL populations, the R packages “qtl”  and “snoFor the F2 populations, the genotype calls of each bulked DNA pool in the population were filtered to leave only the markers known to be polymorphic between the parents, and these were then sorted based on physical positions in the pseudochromosomes available from Phytozome . Each population’s genotype was then examined visually in Microsoft Excel for areas where the recessive bulk was homozygous, and the dominant bulk was heterozygous. Duplicated populations were examined in conjunction.Once significant regions were established through mapping analysis, the overlapping area shared between the four biparental RIL populations was examined to determine the minimal area where all four biparental populations had overlapping haplotype blocks. SNPs located in the hotspots of pseudochromosomes Vu07, Vu09, and Vu10 were examined visually in Microsoft Excel for regions of identity within phenotypic groups. SNPs located in the hotspots which had been removed during trait mapping due to high levels of missing data were added back as presence/absence variations and segregated similar to nucleotide polymorphisms.1C1C) pattern available from the Legume Information System (legumeinfo.org) were examined for expression in developing seed tissue. Genes encoding proteins known to be involved in regulation of the flavonoid biosynthesis pathway were prioritized. Genes were examined within each minimal haplotype block. Gene expression data , from th2C2H0H0W1W1C), CB27/BB-113 seeds had a Holstein pattern (2C2H1H1W0W0C), and CB27/BB-074 seeds had a Full Coat pattern (2C2H1H1W1W1C). An additional cross was available from the early development of the MAGIC population, where the phenotype of the seed coat on seeds from a maternal 1C2C heterozygote was Full Coat. IT84S-2246  was crossed with IT93K-503-1  to yield this 2C1H1H1W1W1C maternal parent.Dominance relationships were determined by examining the phenotypes of several F1 progeny in addition to segregation ratios in the F2 populations. Crosses were made between CB27 and three lines from the CB27 by BB population . Seeds from these F1 plants were visually examined for seed coat patterns. CB27/BB-090 seeds had a Watson pattern (Vigna radiata (mung bean), as a map of cowpea was unavailable. The cowpea reference sequence is of IT97K-499-35. Among the additional sequenced genomes, CB5-2 has the Eye 2 pattern (2C2C), Suvita-2 has the Full Coat pattern (2C2H1H1W1W1C), Sanzi has a Speckled pattern, and UCR779 has the Full Coat pattern (2C2H1H1W1W1C). See Seed Coat Phenotyping Section for pattern descriptions and The genome sequences of the candidate genes from each of five genome sequences  and about 3 kb of upstream sequence were compared using A plasmid Editor . Transcription factor binding sites were predicted in the upstream regulatory region of each gene using the binding site prediction function available from the Plant Transcription Factor Database , discovered from whole-genome shotgun sequencing of 37 diverse accessions (1C1C) phenotype and “−1” for those without. The genotype was recorded as “+1” for accessions matching the reference genotype, “−1” for the alternate homozygote, and “0” for the heterozygote  were grown in a greenhouse at the University of California, Riverside  at a constant temperature of about 32°C from March through May 2018. Three plants were used for each accession. Upon flowering, each flower was tagged with the date it opened. The flowers were permitted to self-fertilize. For each day after the flower opened, beginning on the second day, on each of the three test plants a pod was collected until no more green pods were observed.Results section). Each photograph was scored using this scale.Seeds from each collected pod were photographed using a Canon EOS Rebel T6i at a 90º angle under consistent lighting conditions. The length of the most advanced seed within the pod was measured using ImageJ (imagej.nih.gov). A developmental scale from 0 to 5 was designed based on the visual observations of the spread of pigmentation  and were concentrated on three chromosomes: Vu07 (C locus), Vu09 (H locus), and Vu10 (W locus). Mapping results can be found in A total of 35 SNP loci were identified using different methods for each population type (see C locus) the minimal haplotype block was between 2_12939 and 2_09638  and contained ten genes. On Vu09 the minimal haplotype block was between 2_33224 and 2_12692  and contained seventeen genes. On Vu10 the minimal haplotype block was between 2_12467 and 2_15325  and contained eleven genes. The list of candidate genes can be found in Following trait mapping, all called SNPs on chromosomes Vu07, Vu09, and Vu10 were examined for minimal haplotype blocks in the overlapping significant regions in the four biparental RIL populations. On Vu07 (Discussion section for details). This led to the determination of a single major candidate gene on each of Vu07, Vu09, and Vu10. Each of the candidate genes belongs to a class which is known to be involved in transcriptional control of the later stages of flavonoid biosynthesis. No Color, Eye 1, and Full Coat mapped to an overlapping area on Vu07, where the gene Vigun07g110700, encoding a basic helix–loop–helix protein, was noted as a strong candidate gene. Eye 2, Holstein, Watson, and Full Coat mapped to a similar area on Vu09, where the gene Vigun09g139900, encoding a WD-repeat gene, was noted as a strong candidate gene. Eye 1, Eye 2, Holstein, Watson, and Full Coat mapped to an overlapping area on Vu10, where the gene Vigun10g163900, encoding an E3 ubiquitin ligase protein with a zinc finger, was noted as a strong candidate gene.A predominant candidate gene was identified at each locus based on high relative expression in the developing seeds , 1W over 0W  revealed SNPs and small insertions or deletions 3. None Vigun07g110700 (C locus candidate gene) showed a C/T SNP variation between the reference genome and the four other genome sequences on Vu07 at 20,544,306 bp. The reference genome has a T at this position while the other four sequences have a C. Transcription factor binding site prediction from the Plant Transcription Factor Database (planttfdb.cbi.pku.edu.cn/) indicated that this variation constitutes either a WRKY binding site in the C allele or an ERF binding site in the T allele. Of the 28 accessions in the SNP selection panel, eleven expressed the Eye 1 (1C) pattern and 17 did not. Twenty accessions had a CC genotype, six had a TT genotype, and two had a TC genotype. The correlation test gave an estimated correlation value of 0.75, with a p-value of 3.51E-06, indicating significant correlation between the genotype and phenotype values such that this SNP is a reliable marker for distinguishing between the 1C (Eye 1) and the 2C (Eye 2) alleles. Two of the 28 lines had the No Color (0C) phenotype, but had the CC genotype, indicating that this SNP is not a good marker for the 0C allele (for a possible explanation see Discussion section). The regulatory region of Vigun09g139900 (W locus candidate gene) showed a C/T variation between the reference genome and CB5-2 against the other three genome sequences on Vu09 at 30,207,722 bp. This SNP was not included in the list from the SNP selection panel and so could not be examined like the previous SNP. Transcription factor binding site prediction did not indicate that the site was a target for any transcription factor in either form. The upstream regulatory region of Vigun10g163900 (H locus candidate gene) did not have any distinguishing variation.The regulatory region of A model of seed coat development has been formulated consisting of six stages based on the spread of pigmentation. In Stage 0, there is no color on the seed coat. In Stage 1, color appears at the base of the hilum. In Stage 2, color appears around the hilum. In Stage 3, color begins to spread along the outside edges of the seed. In Stage 4, color begins to fill in on the edges of the testa. In Stage 5, the color has completely developed to the mature level. After Stage 5 the pod and seeds begin to desiccate. Of the observed varieties, only Sasaque and Sanzi completed all six stages. MAGIC059 reached Stage 4, while CB27 only reached Stage 2. No seeds in Stage 0 were observed for Sasaque. Images of each tested variety at various stages can be seen in C (Color Factor), W (Watson), and one of the H (Holstein) factors identified by C locus encodes a “constriction” factor while the W and H loci encode distinct “expansion” factors. The C locus is the primary locus controlling seed coat pattern. Pigmentation may be not visible , constrained to an eye , or distributed throughout the seed coat . The extent of distribution is modified by the H and W loci, whose contribution is visible only with an unconstrained allele (2C) at the C locus. In the presence of Holstein (1H) and absence of Watson (0W), a Holstein pattern is expressed. Conversely, in the presence of Watson (1W) and absence of Holstein (0H), a Watson pattern is expressed. In combination, the Watson (1W) and Holstein (1H) factors result in the Full Coat phenotype. Segregation ratios and dominance data Figure 20C0C genotype will express the No Color pattern, regardless of the genotypes at the W and H loci, and an individual with the 1C1C genotype will express the Eye 1 pattern, regardless of the genotypes at the W and H loci. However, when not constricted by a 0C or 1Callele  the “expansion” factors can be observed. An individual with the 2––W0W0H1––C genotype expresses the Holstein pattern, while and individual with the 2––W1––H0H0C genotype expresses the Watson pattern. An individual with the 2––W1––H1––C genotype, with both “expansion” factors, expresses the Full Coat pattern. An individual with the2––W0W0H0H0C genotype expresses the Eye 2 pattern. In this latter case the eye pattern is observed despite the unconstricted 2C allele due to the absence of the “expansion” factors. Based on this model, the CB27 by BB and CB27 by 556 populations segregate at the W and H loci  phenotype but is not always present when the phenotype is Eye 1 (1C). The Blue-grey Ring phenotype may represent another (fourth) allele at the C locus, or it may result from a combination of the 1C (Eye 1) allele and other pigmentation genes. However, from other unpublished work on seed coat color there does not appear to be a strict correlation between seed coat color and presence of the Blue-grey Ring. Further research is required to clarify the basis of the Blue-grey Ring phenotype.An additional pattern phenotype of Blue-grey Ring was noted in some of the tested populations. Blue-grey Ring consists of a pale ring of bluish-grey surrounding the eye Figure 1Color Factor (C), Watson (W), Holstein-1 (H-1), and Holstein-2 (H-2). The present data suggest the presence of only one Holstein locus or that the two loci are very closely linked in the tested populations. To avoid possible confusion, the Holstein locus discussed here is simply termed “H.”Several regions of the genome are hotspots for seed coat pattern traits , 1920, wC is at that position with allelism at the locus. Mapping results from the Tvu-15426 by MAGIC014 F2 populations indicate that the H locus is on Vu10. Additional evidence for the H locus being located on Vu10 comes from Anasazi locus  on chromosome 10 of common bean, which is homologous to Vu10 , where color development proceeds only to Stage 2. It is likely that other varieties which have distinct eye sizes proceed to varied stages of development. For example, varieties with the No Color (0C) phenotype would not proceed past Stage 0. However, the three gene model presented here does not explain every seed coat pattern. An example is the pattern observed in mature Sanzi seed, which exhibits a Speckled black and purple seed coat (see Seed Coat Phenotyping Section for a description and 2C2W1W1H1H1C), was used for mapping the black seed coat color; there was a perfect correlation between black seed coat color and the Speckled pattern  pattern, and at Stage 4 in MAGIC059, which expresses the Starry Night pattern  and Vu09 (W locus) encode a bHLH and WD40 protein, respectively. A candidate gene on Vu10 (H locus) encodes an E3UL protein. This information lends itself to a model in which Vigun07g110700 (bHLH) serves as a “master switch” controlling the extent of pigmentation constriction while Vigun09g139900 (WD40) and Vigun10g163900 (E3UL) act as “modulating switches” controlling the type of expanded pattern, altering the effect of the pathway to result in the observed Holstein and Watson patterns (Vigun07g110700 (bHLH) was identified as a candidate gene controlling flower color in cowpea by Lo et al. (2018), indicating a possible dual function of the gene. Indeed, Vigun07g110700 have been identified in other legumes as Mendel’s A gene controlling flower color in Pisum sativum , and a WD-repeat protein WD40; . E3 Ubiqpatterns Figure 4 sativum  and as tvulgaris .MYB genes (Vigun10g165300 and Vigun10g165400) are located only 110 kb downstream of Vigun10g163900 (H locus candidate gene). However, these fall outside of the haplotype blocks identified in the CB27 by BB and CB27 by 556 populations, indicating that they are not the source of the observed phenotypic variation. However, there may be interaction between one or both of these MYBs and the E3UL responsible for the Holstein pattern; this hypothesis could be investigated through additional research.Two R2R3 Vigun07g110700 (bHLH) at 20,544,306 bp constitutes a difference between a WRKY binding site in the 2C (Eye 2) allele versus an ERF binding site in the 1C (Eye 1) allele. WRKY proteins are positive regulators of seed coat pigment biosynthesis in Arabidopsis  allele may be caused by other variants, such as a small deletion interrupting gene function, which has been shown in P. vulgaris  and 1W (Watson) alleles, despite not necessarily being the cause of the observed phenotypic variation. No single variation was identified for Vigun10g163900 alleles. However, haplotype blocks determined from the biparental RIL populations can be used for future breeding efforts. Two SNPs which fall within the genome sequence of Vigun10g163900 segregate with the phenotype in the biparental RIL populations. At 2_24359, the lines with the 0H (not Holstein) allele have an A genotype and the lines with the 1H (Holstein) allele have a G genotype. At 2_24360, the lines with the 0H (not Holstein) allele have an A and the lines with the 1H (Holstein) allele have a C. Future research is needed to develop more perfect markers for the three loci.The observed C/T SNP variation in the regulatory sequence of bidopsis  while ERbidopsis . This SNvulgaris . Such a All datasets [SNPs] for this study are included in the manuscript/IH performed all trait mapping, statistical analysis, and interpretation. RL performed analysis of the seed coat development. MM-A assisted in trait mapping and provided SNP data. SaL assisted in trait mapping. Y-NG extracted DNA for genotyping. B-LH provided the MAGIC population and its genotypic information. ML performed crosses used for allelic series analysis. ZJ assisted in statistical analysis. PR and TC provided guidance and access to population and genetic resources. StL assisted with the SNP selection panel data. TC assisted IH with the writing.This study was supported by the Feed the Future Innovation Lab for Climate Resilient Cowpea (USAID Cooperative Agreement AID-OAA-A-13-00070), the National Science Foundation BREAD project “Advancing the Cowpea Genome for Food Security” (NSF IOS-1543963), and Hatch Project CA-R-BPS-5306-H.The 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.


Persea americana Mill.) single nucleotide polymorphisms and characterizing the diversity of germplasm collection is a powerful tool for breeding. However, discovery is a costly process, due to loss of loci that are proven to be non-informative when genotyping the germplasm.Discovering a genome-wide set of avocado  were homozygous in the three pools but for different alleles. The polymorphic loci were validated using 192 randomly selected SNPs by genotyping the accessions within each pool. The sensitivity of polymorphic locus prediction ranged from 0.77 to 0.94. The correlation between the allele frequency estimated from the pooled sequences and actual allele frequency from genotype calling of individual accessions was r = 0.8. A subset of 109 SNPs were then used to evaluate the genetic relationships among avocado accessions and the genetic diversity of the collection. The three races were distinctly clustered by projecting the genetic variation on a PCA plot. As expected, by estimating the kinship coefficient for all the accessions, many of the cultivars from the California breeding program were closely related to each other, especially, the Hass-like ones. The green-skin avocados, e.g., ‘Bacon’, ‘Zutano’, ‘Ettinger’ and ‘Fuerte’ were also closely related to each other.A framework for SNP discovery and genetically characterizing of a breeder‘s accessions was described. Sequencing pools of gDNA is a cost-effective approach to create a genome-wide stock of polymorphic loci for a breeding program. Reassessing the botanical and the genetic knowledge about the germplasm accessions is valuable for future breeding. Kinship analysis may be used as a first step in finding a parental candidates in a parentage analyses.The online version of this article (10.1186/s12864-019-5672-7) contains supplementary material, which is available to authorized users. Persea americana Mill.) is a subtropical tree which belongs to the Lauraceae family, one of the oldest known flowering plant families . PSA was calculated as: of loci .Hierarchical clustering was performed on a pairwise D distance matrix and the Ward’s agglomerative method  was applThe subpopulation structure of the germplasm collection was estimated by running a simulation of STRUCTURE software v2.3.3  with 500ST [ST is the genetic differentiation of a subpopulation due to genetic drift, HS is the weighted average of all subpopulations’ expected heterozygosity, and HT is the expected heterozygosity in the entire population (germplasm collection).The fixation index FST  was calcPrincipal component analysis was performed on the core samples only using SNPRelate R-package  snpgdsPCThe kinship between pairs of avocado accessions was estimated using the robust method of moments called KING, which calculates moments of IBD coefficients . The accAdditional file 1:Table S1. The Agricultural Research Organization (ARO) avocado collection. (DOCX 34 kb)Additional file 2:Table S2. Classification of avocado accessions. (DOCX 26 kb)Additional file 3:Table S3. A list of single nucleotide polymorphisms (SNPs) in avocado transcriptome. Header descriptions: Contig – mRNA contig ID; Position – SNP position; Ref allele – Allele call on the reference contig; Alt allele – Allele call which is different from the reference allele; Guatemalan coverage – Reads’ depth of the Guatemalan pool sequencing; Guatemalan alt prop – proportion of the alternative allele; Mexican coverage – Reads’ depth of the Mexican pool sequencing; Mexican coverage – Reads’ depth of the Mexican pool sequencing; WestIndian coverage – Reads’ depth of the WestIndian pool sequencing; WestIndian alt prop – proportion of the alternative allele; Guatemalan – Guatemalan polymorphism call; Mexican – Mexican polymorphism call; WestIndian – WestIndian polymorphism call; Contig sequence – Sequence of the contig; Polymorphism call description: AA – homozygous to reference allele; BB – homozygous to alternative allele; AB - heterozygous (XLSX 50424 kb)Additional file 4:Figure S1. Distribution of avocado transcriptome coverage. Three pools of genomic DNA representing the three avocado races were sequenced on Illumina Hi-Seq platform. Reads were aligned with the avocado transcriptome after trimming adpters and cliping of low quality base calls. The distributions of read depths for the three pools are illustrated as solid line , dotted line (Mexican pool), and dashed line (West Indian pool). Red vertical lines are the bounds the coverage for SNP discovery. (DOCX 183 kb)Additional file 5Figure S2. Correlation between the allele frequency estimated from the pools and that from genotype calling of the collection’s individuals. The proportion of SNP alleles were calculated for each locus and each pool. The proportions of SNP alleles of the corresponding loci and avocado accessions were calculated from the genotype calls using the Fluidigm platform. (DOCX 112 kb)


The EphA2 receptor tyrosine kinase is capable of activating multiple diverse signaling pathways with roles in processes such as tissue homeostasis and cancer. EphA2 is known to form activated oligomers in the presence of ephrin-A ligands. Here, we characterize the lateral interactions between full-length EphA2 molecules in the plasma membrane in the presence of three types of ligands  as well as in the absence of ligand, using a quantitative FRET technique. The data show that EphA2 forms higher-order oligomers and two different types of dimers that all lead to increased EphA2 tyrosine phosphorylation, which is indicative of increased kinase-dependent signaling. We find that different ligands stabilize conformationally distinct oligomers that are assembled through two different interfaces. Our results suggest that these different oligomeric assemblies could have distinct signaling properties, contributing to the diverse activities of the EphA2 receptor. Deo Singh et al. use Fully Quantified Spectral Imaging-FRET to show that the EphA2 receptor forms dimers or higher order oligomers depending on the type of ligand, and that different ligands stabilize EphA2 dimers through distinct interfaces. These findings may explain how EphA2 activates diverse signaling pathways. EphA2 has the strongest links to cancer of any of the 14 Eph receptors, and thus has been extensively studied6. EphA2 is known to mediate diverse, and even opposite, effects through different signaling mechanisms8. The ligand and kinase activity-dependent form of EphA2 signaling involves receptor tyrosine phosphorylation and is potently induced by cell surface-anchored ephrinA ligands, such as ephrinA1, as well as by soluble forms of these ligands dimerized by fusion to Fc and clustered with anti-Fc antibodies2. Soluble monomeric forms of the ephrinA ligands, which can be released from cells by proteases, as well as engineered short peptide ligands can also promote EphA2 tyrosine phosphorylation and signaling through mechanisms that have remained mysterious14. EphA2 kinase-dependent signaling has been linked to a variety of functional outcomes, such as suppression of the AKT–mTORC1 and RAS–ERK oncogenic pathways and inhibition of cell adhesion and migration/invasion, but also enhancement of cancer cell dispersal and promotion of tumor angiogenesis8. In addition, EphA2 signaling plays a role in inflammation, atherosclerosis, and infection15. EphA2 can also behave as an oncoprotein through another form of signaling that does not require either ligands or kinase activity and involves phosphorylation on S897 in the segment linking the kinase domain with the SAM domain7. EphA2 S897 phosphorylation is due to serine/threonine kinases such as AKT, RSK, and PKA and promotes cell migration/invasion, metastasis, and cancer stem cell-like features17.The Eph receptors are the largest family of receptor tyrosine kinases and play critically important roles in tissue organization and homeostasis as well as in many pathological processesGiven the high diversity of functional outcomes mediated by EphA2 kinase-dependent signaling, we asked whether this receptor may be capable of forming different types of oligomers (dimers or clusters) in the plasma membrane, depending on the nature of the activating ligand. To explore this possibility, we assessed the homo-association of EphA2 receptor molecules in the plasma membrane in the presence of three types of ligands  in comparison with EphA2 in the absence of ligand.19. We characterized the lateral interactions between full-length EphA2 molecules in the plasma membrane in quantitative terms, using a FRET-based spectral imaging methodology that reports the type and abundance of transmembrane protein oligomers21. To probe the interfaces in the different EphA2 dimers and clusters, we used mutagenesis guided by the solved crystal structures of the EphA2 extracellular region19. The distinctive effects we observed for mutations in the dimerization or the clustering interface show that EphA2 is capable of forming several oligomers that are stabilized through distinct interfaces.The architecture of the EphA2 receptor, like that of most other receptor tyrosine kinases, includes a large extracellular region, a single transmembrane helix, and an intracellular region containing a kinase domain. The extracellular region is composed of an N-terminal ligand-binding domain, a cysteine-rich domain, and two fibronectin type III domains. High-resolution structural information on the EphA2 extracellular region is available. In crystals, neighboring extracellular regions have been shown to interact with each other via two interfaces, often referred to as the “dimerization (or heterodimerization)” and “clustering” interfaces22. This ligand is a chimeric protein composed of ephrinA1 fused to the Fc region of an IgG1 antibody and binds to EphA2 with sub-nanomolar to low nanomolar affinity23. Ephrin Fc fusion proteins are dimeric and for some Eph receptors they have been shown to cause receptor clustering and high activation only when they are oligomerized with anti-Fc antibodies, mimicking the clustering induced by the endogenous plasma membrane-anchored ephrin ligands27.Kinase-dependent signaling by the EphA2 receptor is strongly activated in cells stimulated with ephrinA1-Fc19. We further sought to quantify the fraction of EphA2 molecules that are in oligomers (dimers or clusters) as a function of receptor concentration, and to determine how perturbations in the two predicted interfaces affect the dimeric/clustered population of EphA2 molecules. We performed these investigations using the recently developed fully quantified spectral imaging-forster resonance energy transfer (FSI-FRET) method20. The EphA2 receptor was tagged at its C-terminus with the fluorescent proteins mTurquoise  or eYFP (acceptor) via a (GGS)5 flexible linker. These two fluorescent proteins are a FRET pair that has been previously successfully used in FSI-FRET experiments20, and we have previously shown that the attachment of the fluorescent proteins to the EphA2 C-terminus does not detectablyaffect phosphorylation of the receptor28.To determine whether dimeric ephrinA1-Fc induces EphA2 dimerization or higher order oligomerization, we performed FRET experiments in the presence of 5 μg per ml (~50 nM) ephrinA1-Fc. This concentration greatly exceeds the apparent dissociation constant, so that all EphA2 molecules are ligand-bound. We also sought to determine whether the interfaces between associated EphA2 extracellular domains in the full-length receptor in the plasma membrane of live cells correspond to the two interfaces predicted from the solved crystal structures of the EphA2 extracellular region20. The method yields the FRET efficiency and the concentration of donor-labeled and acceptor-labeled receptors in the plasma membrane20. Determination of these two-dimensional concentrations, which also allows the generation of binding curves, is possible only if the cells have a flat, unwrinkled plasma membrane. This is achieved by subjecting the cells to hypo-osmotic conditions, which do not alter the FRET efficiencies measured for membrane proteins and thus their association20. It should be noted that hypo-osmotic conditions can occur physiologically, do not cause irreversible cell damage, and the changes they induce in cells are fully reversible31.The FSI method requires the acquision of complete FRET and acceptor spectra, and uses an assumption-free, fully resolved system of equations to calculate FRET efficiencies in the plasma membrane with high precision32. Thermodynamic models are built for different types of oligomerization (with oligomer order = n) and fitted to the data to calculate the mean squared error (MSE). Extensive evaluation of this approach has shown that an MSE minimum at n = 2 reliably identifies dimer populations21. An MSE minimum at n > 2 or the same MSE value for different oligomer orders points to the presence of oligomers larger than dimers, although the presence of some dimers cannot be excluded21. While the exact order of the oligomers  cannot be defined from this analysis, the method reliably yields the receptor fraction that exists in an oligomeric state 21.The data generated with the FSI-FRET method are interpreted within the context of thermodynamic models based on the Kinetic Theory of FRET20. The FRET efficiency vs. acceptor concentration  and thus indicating that EphA2 forms oligomers larger than dimers  in the presence of ephrinA1-Fc. The fraction of clustered EphA2 calculated from the data exceeds 70% over the entire receptor concentration range, demonstrating the effectiveness of ephrinA1-Fc in clustering EphA2 even when the receptor is present at low concentrations in the plasma membrane  is stabilized by receptor–ligand and receptor–receptor contacts in the ligand-binding domain, including contacts involving amino acid G131. We therefore used an EphA2 L223R/L254R/V255R triple mutant37 and an EphA2 G131Y mutant to separately destabilize each of the two interfaces. The raw FRET data for EphA2 wild-type and the two mutants in the presence of ephrinA1-Fc are compared in Fig. Clusters of EphA2 extracellular regions in crystals are stabilized via two distinct interfaces Fig. . One of 38. This is a critical contact for ephrin binding because activation of EphA2 by ephrinA1-Fc is severely impaired by the R103E mutation38. FRET experiments show that the EphA2 R103E mutant can form clusters in the presence of ephrinA1-Fc  affects EphA2 lateral association and sought to characterize the nature of the association. Experiments were performed in the presence of 5 μg per ml (~200 nM) m-ephrinA1, a concentration that exceeds the dissociation constant of about 20–30 nM10. Thus, most of the EphA2 receptors are expected to be ligand-bound.The ephrinA1 ligand is a cell surface-anchored protein that can undergo clustering in the plasma membraneThe FRET experiments revealed that EphA2 wild-type, the L223R/L254R/V255R mutant, and the G131Y mutant all form dimers in the plasma membrane when bound to m-ephrinA1 Fig. . As woul30.Consistent with the FRET data, we found that in the presence of m-ephrinA1 the G131Y mutant has lower Y772 phosphorylation and higher S897 phosphorylation compared to EphA2 wild-type, whereas the L223R/L254R/V255R mutant exhibits similar Y772 and S897 phosphorylation as EphA2 wild-type Fig. . These f38. Thus, decreased ligand binding and decreased dimerization are likely responsible for the impaired biological effects reported for the EphA2 R103E mutant38.FRET experiments revealed that the EphA2 R103E mutant in the presence of m-ephrinA1 is also a dimer Fig.  and drasKD of ~200 nM and a 1:1 binding stoichiometry41. Despite being monomeric, this “YSA” peptide is an agonist that promotes EphA2 tyrosine phosphorylation and activation. We performed FRET experiments in the presence of 6 μM YSA peptide in the culture medium, thus ensuring that most or all of the EphA2 molecules are bound to YSA. We have previously shown that the YSA peptide ligand under these conditions stabilizes EphA2 wild-type and L223R/L254R/V255R dimers30. Here we show that the EphA2 G131Y and R103E mutants also form dimers that are stabilized by YSA  binds to the ephrin-binding pocket of EphA2 with a 8. For example, EphA2 has been implicated in both decreased and increased cell migration, and has been shown to both inhibit and promote cancer cell malignancy. Intriguingly, EphA2 kinase-dependent signaling has been linked to opposite effects on the activity of some downstream signaling proteins3, but the molecular mechanisms underpinning these opposite effects are currently unknown. While the cellular context may contribute to differences in EphA2 signaling activities, the biophysical characterization of EphA2 assemblies presented here could also provide a mechanistic explanation for diverse biological responses, as it reveals that different ligands can stabilize conformationally diverse oligomers that could have distinctive signaling properties.EphA2 signaling is complex and can have diverse effects on cell behavior19, the dimerization and the clustering interface, we have gained insight into the architecture of EphA2 oligomeric assemblies in cells. We show that in the absence of ligand-binding EphA2 can form dimers that are stabilized through the clustering, but not the dimerization interface. Binding of the YSA peptide ligand, which appears to be monomeric, further stabilizes the same type of dimer, through interactions within the clustering interface. In contrast, the action of the monomeric ligand m-ephrinA1 is distinctly different, as it leads to the formation of an alternate dimer that is stabilized through the dimerization interface , a substantial portion of the receptors is monomeric at saturating m-ephrinA1 and YSA concentrations. Thus, EphA2 monomers can co-exist with the dimers on the cell surface, and mediate different biological functions.As compared to other receptor tyrosine kinases, EphA2 signaling is much more complex as it involves cluster formation41. This mechanism may cause different changes compared to those induced by the binding of m-ephrinA1. Allosteric effects involving similar receptor regions but with opposite consequences can be also hypothesized for the R103E mutation, which inhibits not only ligand binding but also dimerization in the absence of ligand. Third, we observed EphA2 cluster formation in response to ephrinA1-Fc, demonstrating that the binding of this dimeric ligand to EphA2 is sufficient to induce EphA2 clustering. A long-standing view of some researchers in the field has been that soluble forms of the ephrin ligands have to be at least tetrameric in order to mimic the cell surface-anchored ephrins and activate Eph receptor signal transduction, whereas dimeric or monomeric ligands do not promote signaling and even function as antagonists45. Therefore, it is common to “pre-cluster” the dimeric ephrin-Fc proteins using anti-Fc antibodies in order to obtain multimeric forms of the ligands to induce Eph receptor clustering2. However, our FRET data show that unclustered, dimeric ephrinA1-Fc can induce EphA2 oligomers that are larger than dimers. This result is consistent with prior findings that EphA2, unlike some other Eph receptors, can be potently activated by dimeric ephrinA1-Fc22, which may depend on the high clustering propensity of EphA2 interfaces46.Our FRET experiments yield several unexpected findings. First, they suggest that the binding of m-ephrinA1 to EphA2 may promote a conformational switch that favors the alternative dimerization interface, which also includes receptor–ephrin contacts. Such an effect could not be predicted based on the crystal structure of the isolated EphA2 extracellular region solved in the presence of a monomeric ephrinA ligand, since EphA2 extracellular regions form clusters engaging both interfaces under the conditions used for crystallization. It is interesting that in our experiments m-ephrinA1 binding does not appear to simply engage the dimerization interface to cause EphA2 clustering, at the high EphA2 concentrations where the clustering interface is also engaged independently of ligand binding. Second, since the ability of the YSA peptide to antagonize ephrin binding suggests that the YSA peptide interacts with the ephrin-binding pocket of EphA2, it appears that m-ephrinA1 and YSA bind to the same binding pocket in the EphA2 ligand-binding domain, but induce the formation of distinctly different dimers stabilized through alternative interfaces. Because the YSA-binding site is on the opposite site of the clustering interface, the FRET results suggest that YSA likely exerts its dimer stabilizing effects via an allosteric mechanism as previously proposed12, that Eph receptor oligomers of different sizes bind different cellular effectors and trigger different cellular responses, and that the tyrosine phosphorylation pattern of an Eph receptor and its downstream signaling network utilization depend on the nature of the ephrin ligand51. These findings support our hypothesis that different EphA2 assemblies can have distinctive signaling properties. It is further conceivable that different EphA2 assemblies are trafficked through different endosomal routes, and some evidence supports the notion that EphA2 signaling from different cellular compartments can lead to different functional consequences55. For example, phosphosite specific phosphotyrosine phosphatases differentially localized in the various endosomal compartments could differentially affect the pattern of EphA2 phosphorylated residues and, therefore, downstream signaling59. In conclusion, our biophysical characterization of EphA2 oligomerization in live cells suggests that different EphA2 ligands may be linked to selective receptor signaling functions by shaping the assembly of the EphA2 complexes through distinct interfaces.Our quantitative FRET data explain, for the first time to our knowledge, how monomeric ephrins and engineered peptide ligands can act as EphA2 agonists. The mechanism involves promoting EphA2 dimerization, which is sufficient to induce EphA2 kinase activity. They also pose new intriguing questions, since currently it is not known if the signals initiated by different types of EphA2 oligomers are fundamentally different, with differently assembled oligomers engaging different downstream effectors. Alternatively, or in addition, the difference may be quantitative, with the strength of the signals scaling with the size and type of EphA2 oligomers. This can now be investigated, with the help of the mutants characterized in this work, and by direct comparisons of EphA2 signaling pathways activated in response to different ligands. There are already some indications that the response of EphA2 to different ligands may be different. For example, it has been reported that m-ephrinA1 activates EphA2 with slower kinetics than ephrinA1-Fc5, and either mTurquoise or eYFP at the C terminus37. The EphA2 G131Y, R103E and L223R/L254R/V255R mutants were generated using the QuikChange II site-directed mutagenesis kit , following the manufacturer’s recommended protocol. To generate the L223R/L254R/V255R EphA2 mutant, we consecutively engineered the L223R mutation, the L254R mutation, and the V255R mutation as described37. All primers used to generate the plasmids used in this study are shown in Supplementary Table For all constructs, we used the pcDNA3.1 (+) vector (Invitrogen) for expression in mammalian cells. The EphA2 constructs consist of EphA2, a flexible 15 amino acid linker (GGS)2 environment. The cells were co-transfected with mixtures of each EphA2 mutant tagged with mTurq or eYFP using Lipofectamine 3000 (Invitrogen) according to the manufacturer’s recommended protocol. Twelve hours after transfection, the cells were serum starved for at least 12 h. Before imaging, the medium was replaced with hypo-osmotic medium  to induce swelling, as described previously31. The cells were allowed to settle for 10 min and imaged for approximately 2 h.HEK293T cells were purchased from American Type Culture Collection . 24 h before transfection, the cells were cultured on collagen-coated, glass bottom 35 mm Petri dishes  in Dulbecco’s modified Eagle medium supplemented with 10% fetal bovine serum , 3.5 g/L (19.4 mM) D-glucose and 1.5 g/L (17.9 mM) sodium bicarbonate at 37 °C in a 5% CO60. In brief, a Mai Tai laser  that generates femtosecond mode locked pulses at wavelengths between 690 and 1040 nm was used as excitation source. Two images for each cell were acquired: one at 800 nm to primarily excite the donor fluorophore, and another at 960 nm to primarily excite the acceptor. Experiments were performed in HEK293T cells under reversible osmotic stress. The reversible osmotic swelling was necessary because the cell membrane is normally highly “wrinkled”, and the reversible osmotic stress eliminates these wrinkles. Thus, the effective 3D protein concentration, determined using purified fluorescent protein standards of known concentration, can be converted into 2D receptor concentrations in the plasma membrane20. Only areas of plasma membrane not in contact with other cells were imaged to ensure that EphA2 receptors did not interact with ephrins from neighboring cells.Imaging was performed using a two photon microscope equipped with the OptiMis True Line Spectral Imaging system . The details of the microscope have been described previously32:n represents the oligomer order, D] is the concentration of donors. d, according to61:Here R0 is the Förster radius of the FRET pair .In Equation , R0 is tThe FRET efficiency due to specific interactions in the oligomer is written as:K and the total receptor concentration, [T] = [D] + [A], according to equation  and 0.2 ml were added to each insert of the Transwell provided with the kit, which contains a polycarbonate membrane with pore size of 8 μm, coated with collagen I on the lower side. Inserts were incubated for 4 h at 37 °C in the plate containing 0.5 ml medium with 10% FBS. Serum-free medium was aspirated from the inserts and the upper side of the polycarbonate membrane inside the inserts was cleaned with cotton swabs in order to remove the cells that had not migrated across the membrane. The inserts were placed in new clean wells with 0.3 ml 1 × Lysis Buffer/CyQuant® GR dye and incubated for 10 min at room temperature. To measure the fluorescence of the dye solution at 480 nm/520 nm, 0.2 ml of the lysate were placed into the well of a new 96-well plate. The output of this assay is fluorescence intensity, which is directly proportional to the number of cells that have migrated through the polycarbonate membrane.To assess the migratory ability of the cells, the CytoSelect™ Cell Haptotaxis Assay Kit  was used according to the manufacturer’s recommended protocol with some modifications as follows. HEK 293T cells were seeded at a density of 3.5 × 10g for 15 min at 4 °C and stored at −20 °C. BCA assays (Bio-Rad) were used to measure the protein concentrations of the samples. Lysates mixed with LDS sample buffer and reducing buffer were run on 3–8% NuPAGEHNovexHTris-Acetate mini gels  and transferred onto nitrocellulose membranes. The membranes were blocked with 5% non-fat milk in 1 × TBST. EphA2 expression was quantified using anti-EphA2 antibodies . S897 and Y772 phosphorylation levels were quantified using anti-phospho-Ser897 and anti-phospho-Tyr772 antibodies  followed by an anti-rabbit HRP conjugated antibody . Nitrocellulose membranes were incubated for 2 min with Amersham ECL Plus Western Blotting Detection Reagent  and exposed from 1 to 60 s to capture images with the ChemiDoc imaging system . Uncropped images of all Western blots presented in the study are shown in Supplementary Figures Cells were transfected with plasmids using Lipofectamine 3000, grown for about 12 h, serum-starved for ~12 h and treated with FBS for 15 min, in some cases in the presence of 5 μg/ml (200 nM) m-ephrinA1. Cell lysates were collected with lysis buffer ). The lysates were centrifuged at 14,000 × A commercial code supporting OptiMiS spectral acquisition is available from Aurora. The custom code for the OptiMiS spectral acquisition analysis used in this work is available from Dr. Christopher King on reasonable request. There are no restrictions on code availability.https://figshare.com/projects/The_EphA2_receptor_is_activated_through_induction_of_distinct_ligand-dependent_oligomeric_structures/28470. There are no restrictions on data availability.All data generated or analyzed during this study are included in this published article (and its Supplementary Information files), and Supplementary Information