Clostridium perfringens is the third most potent toxin after the botulinum and tetanus toxins. Etx is the main agent of enterotoxemia in ruminants and is produced by Clostridium perfringens toxinotypes B and D, causing great economic losses. Etx selectively binds to target cells, oligomerizes and inserts into the plasma membrane, and forms pores. A series of mutants have been previously generated to understand the cellular and molecular mechanisms of the toxin and to obtain valid molecular tools for effective vaccination protocols. Here, two new non-toxic Etx mutants were generated by selective deletions in the binding (Etx-ΔS188-F196) or insertion (Etx-ΔV108-F135) domains of the toxin. As expected, our results showed that Etx-ΔS188-F196 did not exhibit the usual Etx binding pattern but surprisingly recognized specifically an O-glycoprotein present in the proximal tubules of the kidneys in a wide range of animals, including ruminants. Although diminished, Etx-ΔV108-F135 maintained the capacity for binding and even oligomerization, indicating that the mutation particularly affected the pore-forming ability of the toxin.Epsilon toxin (Etx) from  Clostridium perfringens types B and D and causes severe and rapidly fatal enterotoxemia in ruminants, causing high mortality and economic losses [C. perfringens [Epsilon toxin (Etx) is the third most potent clostridial toxin after the botulinum and tetanus toxins. It is produced by c losses ,2,3. Etxfringens ,5. Etx afringens , enters fringens , leadingfringens . Intravefringens . Etx alsfringens ,11, whicfringens . Moreovefringens . In addifringens , causingfringens ,16. The fringens ,18,19,20fringens .d) of around 4 nM [A few cell lines are sensitive to Etx, the most commonly studied being the Madin-Darby canine kidney (MDCK) cell line with a renal origin . In MDCKund 4 nM . More reund 4 nM , the humund 4 nM , and theund 4 nM . In all und 4 nM ,22,24,26Aeromonas hydrophila and other β-pore-forming bacterial toxins [In accordance with the three-step mechanism of action, the crystal structure of Etx has three domains that share a similar structural conformation to those of other bacterial pore-forming toxins (PFTs) of the aerolysin-like family, such as aerolysin from l toxins . In factl toxins . Domain l toxins ,27. Indel toxins . Domain l toxins . Two doml toxins . Moreovel toxins  has one l toxins . Lastly,l toxins .Several Etx mutants have been generated to understand the cellular and molecular mechanisms of the toxin and to obtain non-toxic molecules as candidates for efficient vaccinations ,31,32. TTo develop new non-toxic mutants of Etx, the sequence that encodes the loop in domain 1 and another that encodes the amphipathic loop in domain 2 were deleted, and two new mutants were generated: Etx-ΔS188-F196 and Etx-ΔV108-F135, respectively .We produced the mutants as a fusion protein with the GFP. The inactive and active form of the toxin was named pEtx and Etx, respectively, as shown in Previous studies using fluorescence microscopy to analyze GFP-Etx-injected mice have demonstrated specific binding of Etx to blood vessels in the brain and the distal tubules of the kidneys, resulting in the death of these epithelial cells ,34. MoreHistological analysis of the kidneys from GFP-Etx-injected mice showed pyknotic nuclei (condensed chromatin) in the epithelial cells of the distal kidney tubules  to the MDCK plasma membrane J–L.Western blot analysis showed the formation of an oligomer complex with a high molecular weight in MDCK cells treated with the positive control GFP-Etx lane 2, .However, a thinner band was observed in MDCK cells treated with the non-toxic GFP-Etx-H106P lane 3, , and a fGFP-pEtx binds in vitro to the distal and collecting tubules of the kidneys in mice and ruminants ,34. To sCuriously, although the distal tubules were not stained by GFP-pEtx-ΔS188-F196, this mutant showed selective binding to the proximal tubules in all the species tested  at 2% and at room temperature (RT) were performed with the kidney slides. There was no decrease in the GFP-pEtx-ΔS188-F196 binding in any of the cases, even there was also a slight increase in the binding to the proximal tubules compared to the control arrows, .To check the implications of the glycans in the binding of pEtx-ΔS188-F196 to the epithelial cells of the proximal tubules, several treatments with N-glycosidase F (to hydrolyze the N-glycan chains from glycoconjugates) and β-elimination (to hydrolyze the O-glycan chains from glycoconjugates) were performed. After N-glycosidase F treatment, the binding of GFP-pEtx-ΔS188-F196 and PNA did not change compared to the control compare H,I, but β-elimination treatment for 1 h abolished the binding of GFP-pEtx-ΔS188-F196 to the proximal tubules compare D,J. ConsC. perfringens type D show dark and jelly-like kidneys that are attributed to the effect of Etx [Etx binds to and damages the epithelial cells from the distal and collecting tubules of mouse kidneys ,33,34. It of Etx . Previout of Etx ,13,34. Here, we present two new Etx mutants: Etx-ΔV108-F135, in which the loop responsible for the insertion into the plasma membrane has been deleted, and Etx-ΔS188-F196, in which the loop in domain 1 involved in receptor binding has been deleted. Moreover, we also produced their respective fluorescent forms linked to GFP. In vivo experiments with these new mutants injected into mice revealed that they were non-lethal, did not cross the BBB, and were not neurotoxic. The distal and collecting tubules of the kidneys were preserved after the injection of Etx mutants compared to the pyknotic nuclei present in the distal and collecting tubules of mice injected with wild-type Etx. In addition, Etx-ΔV108-F135 could bind to the endothelium of the brain microvasculature, but could not cross into the brain parenchyma like wild-type Etx could. Etx-ΔS188-F196 did not bind to the endothelium of the brain microvasculature, which was attributed to the deletion of the loop present in domain 1 that is directly involved in the binding of Etx to its receptor.In vitro experiments using MDCK cells revealed that the mutants did not have a cytotoxic effect and did not trigger the release of ATP from the cells, indicating that they were not inserted into the plasma membrane or did not form the pore complex. However, Etx-ΔV108-F135 could bind to the plasma membrane. Nonetheless, this binding was highly reduced compared to that of wild-type Etx, suggesting that the integrity of the structure is required for adequate high-affinity binding. Etx-ΔS188-F196 did not bind to the plasma membrane, which was attributed to the deleted loop in domain 1 that is mainly involved in the binding of Etx to its receptor. Accordingly, if domain 1 is not conserved, the binding to the receptor on target cells is disrupted, and if domain 1 is conserved, the binding to the receptor can be modulated by domain 2. Western blots showed that the mutant GFP-Etx-H106P was able to oligomerize although the oligomer was not inserted into the plasma membrane and did not produce pores or cause cell death. Furthermore, in the Western blot, GFP-Etx-ΔV108-F135 showed a very faint band corresponding to less oligomerization of this Etx mutant, which was in accordance with the reduced binding and absence of pore formation observed in the MDCK cells. GFP-Etx-ΔS188-F196 did not oligomerize in the plasma membrane, which was consistent with its inability to bind to a specific receptor, a step that precedes oligomerization. Incubations of kidney and bladder sections from different species, including the natural targets of Etx, with the Etx mutants produced results similar to those observed in the MDCK cells, as GFP-pEtx-ΔV108-F135 was able to bind to the distal and collecting tubules in the kidneys and to the urothelium in the bladder, although the binding was highly reduced compared to that of wild-type Etx. Curiously, GFP-pEtx-ΔS188-F196 did not bind to the distal and collecting tubules of the kidneys or to the bladder urothelium. However, it bound to the proximal tubules of the kidneys in all the species tested. This was supported by the colocalization of GFP-pEtx-ΔS188-F196 with the lectin DSA, which recognizes the proximal tubules. In fact, co-incubations with pEtx-ΔS188-F196-DyLight550 and pEtx-H106P-DyLight488 also revealed distinct binding to the proximal and distal tubules, respectively. Furthermore, this binding was specific as co-incubation of GFP-pEtx-ΔS188-F196 with an excess of the unlabeled pEtx-ΔS188-F196 markedly reduced the binding of GFP-pEtx-ΔS188-F196 to the proximal tubules. We speculate that the mutation in domain 1 in Etx-ΔS188-F196 could also produce a change in the conformation of domain 2 and/or 3, which would not only impair binding to the receptor (and moved to another binding site) but also toxicity, as the in vivo experiments showed. This mutant was completely non-toxic although it recognized the kidney proximal tubules without producing any toxic effects in them, as shown in the histopathological analysis of the kidneys from mice injected with GFP-Etx-ΔS188-F196. In any case, more experiments should be carried out, including in vitro assays in epithelial cell lines with a proximal tubule origin and from different species to further characterize the involvement of this Etx mutant in the recognition of a new binding site in the kidneys.To characterize the binding of GFP-pEtx-ΔS188-F196 to the kidney proximal tubules, we performed different treatments in kidney tissue sections incubated with GFP-pEtx-ΔS188-F196. Treatments with pronase E reduced the binding of GFP-pEtx-ΔS188-F196 to the proximal tubules, suggesting that the receptor has a protein moiety. On the contrary, treatments with different detergents were not able to reduce the binding of GFP-pEtx-ΔS188-F196 to the proximal tubules. Moreover, the binding of GFP-pEtx-ΔS188-F196 after these treatments was increased in all cases. This effect of the detergents increasing receptor availability has been shown in several models ,40. BasiMoreover, we speculate that these new mutants, Etx-ΔV108-F135 and Etx-ΔS188-F196, could be a useful tool to generate new vaccines against Etx in order to study the immunogenicity and the production of neutralizing antibodies although further studies should be performed.Clostridium perfringens type B and D infections. The binding of the new mutant seemed to be mediated by an O-glycoprotein, and lipids were not involved. In addition, mutations in domain 2 altered the binding of Etx to its receptor. These results deserve further studies and could be used to further characterize the toxin-receptor interactions.We can conclude that the loop present in domain 1 is directly involved in the binding of Etx to target cells, while the loop present in domain 2 is required for the binding in addition to oligomerization and pore formation, since Etx binding and subsequent oligomerization are greatly reduced by the deletion of the loop from domain 2. The Etx mutant lacking the loop from domain 1 showed changes in its binding pattern compared to that of wild-type Etx. This mutant specifically recognized the proximal tubules of the kidneys in a wide range of species, including the natural hosts for In all cases, wild-type and mutant DNA was cloned into the pGEX-4T-1 vector  with or without the EGFP coding sequence to produce the corresponding recombinant fusion protein, as previously described ,42.pEtx was produced starting at amino acid A47, therefore lacking the N-terminal peptide but not the C-terminal peptide. Mutations were introduced using the QuikChange Multi Site-Directed Mutagenesis kit . pEtx-H106P, with or without GFP, was generated as previously described ,31. pEtxg for 20 min at 4 °C. The resulting supernatant was incubated with Glutathione SepharoseTM 4B beads  for 1 h at 4 °C. Finally, the recombinant protein was eluted by thrombin cleavage in PB containing 2.5 mM CaCl2.All plasmids were transformed into the RosettaTM (DE3) pLysS Escherichia coli strain for optimal protein expression. Briefly, protein expression was induced overnight in the presence of 1 mM isopropyl beta-D-thiogalactopyranoside (IPTG) at RT in 250 mL of LB medium containing 50 µg/mL of ampicillin. Cells were pelleted and resuspended in 20 mM ice-cold phosphate buffer (PB), pH 7.5, with 250 mM NaCl, before being sonicated and centrifuged at 12,000× ® TGX TM; #456-9033, Bio-Rad, Gaithersburg, USA). The gel was then transferred to a nitrocellulose membrane  and analyzed by Western blot using the anti-GFP rabbit polyclonal antibody  or the dilution ), followpEtx-H106P-DyLight488 and pEtx-ΔS188-F196-DyLight550 were labeled using the DyLightTM 488  and 550  NHS Ester Kit, respectively, according to the manufacturer’s instructions.2) to form stable dye–protein conjugates. Labeled protein was aliquoted at 2 mg/mL at −80 °C until use.Briefly, the NHS ester-activated dye labels proteins and other molecules at primary amines (-NHThe Madin–Darby canine kidney (MDCK) cell line was purchased from ATCC (CCL-34) and maintained in DMEM-F12 medium supplemented with L-glutamine, 15 mM HEPES, 10% fetal bovine serum , and 50 U/mL of penicillin/streptomycin.2.Cells were grown at 37 °C in a humidified atmosphere of 5% COMale OF1 Swiss mice weighing 20 g were housed in standard conditions in temperature-controlled, pathogen-free rooms with free access to standard pelleted food and tap water. The experiments were performed according to European Union guidelines for animal experimentation, and protocols were approved by the Animal Experimentation Ethics Committee of the University of Barcelona (Ref. number: 384/17). To perform in vivo studies, the experiments were carried out in the Animal Research Facility of the University of Barcelona (Bellvitge Campus) in animal preparation rooms equipped with the apparatus necessary for isoflurane anesthesia. Mice were i.v. injected with GFP-Etx or the GFP-Etx-H106P, GFP-Etx-ΔV108-F135, or GFP-Etx-ΔS188-F196 mutants at a final concentration of 2.5 μg/g in PBS-1% BSA. Prior to i.v. injections, the mice were anesthetized with isoflurane until the end of the experiment.Mice were sacrificed by decapitation 10 min after the injection and were routinely processed for histopathological analysis. To study the effects over a longer period of time, some mice were sacrificed 2 h after the injection. Each experimental procedure was performed three times in duplicate.® Plus microscope slides . Slides were stained with hematoxylin and eosin (HE) and were examined in a Leica DMD108 digital microimaging system.In all cases, tissues were fixed by immersion in paraformaldehyde (PFA) for 12 h, embedded in paraffin, cut into 5 µm sections, and mounted on SuperFrost® Plus microscope slides, and stored at −20 °C until use. Sheep, bovine, and caprine kidney and bladder samples were obtained from the Mercabarna slaughterhouse in Barcelona. Tissues were fixed by immersion in PFA, embedded in OCT, and cut into sections as described above until use.To perform in vitro studies, a mouse was anesthetized with an intraperitoneal administration of ketamine (100 mg/kg) and xylazine (10 mg/kg) and then perfused by a gravity-fed system with 4% PFA via the vascular system for 20 min. Tissue was fixed by immersion in PFA for 12 h and embedded in OCT, cut into 10 µm sections with a cryostat, mounted on SuperFrostCells were grown to confluence on coverslips and fixed with 4% PFA for 12 min at RT. After three washes with PBS, the cells were blocked with PBS containing 20% normal goat serum (NGS) and 0.2% gelatin for 1 h at RT. Then, the cells were incubated for 30 min with 200 nM GFP, GFP-pEtx, or GFP-pEtx mutants in PBS containing 1% NGS and 0.2% gelatin for 1 h at RT. After three washes with PBS, nuclei were stained with TO-PRO-3  for 7 min, washed again, and mounted with the aqueous mounting medium Fluoromount . To study the binding of GFP-Etx to different animal tissues, slides were blocked, incubated with GFP-pEtx, GFP-pEtx mutants, or GFP, and mounted as described previously. To verify that the binding of GFP-pEtx-ΔS188-F196 to the proximal tubules was specific, co-incubations with the unlabeled pEtx-ΔS188-F196 at a molar ratio of 20:1 with respect to GFP-pEtx-ΔS188-F196 were performed in slices of mouse kidney for 1 h at RT. After the co-incubations, slices were washed with PBS and mounted as described above.Datura stramonium agglutinin at 10 µg/mL  to detect the proximal tubules or with biotinylated Arachis hypogaea agglutinin at 50 µg/mL  to detect the distal and collecting tubules [To determine that the binding of GFP-pEtx-ΔS188-F196 was restricted to the proximal tubules, kidney sections were pre-incubated for 30 min at RT with biotinylated  tubules ,44. AfteCoverslips and slides were examined under a Leica TCS-SL spectral confocal microscope .MDCK cells were grown in 96-well plates, and the cytotoxic activity of Etx and the mutants (with or without GFP) was determined using the MTS Assay Kit . Briefly, the assay determines the capacity of living cells to reduce the yellow tetrazolium salt into a colored formazan dye that is soluble in cell culture media. The formazan dye is quantified by measuring the absorbance at 490 nm. Cells were incubated with pEtx, Etx, or the Etx mutants (with or without GFP) at 50 nM for 1 h at 37 °C. Triplicates of each condition were performed. MDCK cells were plated in black 96-well plates with a clear flat bottom and grown to confluence in 100 µL of media. ATP released from the cells after GFP-Etx exposure was measured using the luciferin-luciferase method ,25. OnceTo analyze the formation of GFP-Etx and GFP-Etx mutant protein complexes, MDCK cells were grown to confluence in 10 cm tissue culture dishes and incubated with GFP-pEtx, GFP-Etx, or the respective mutants at 50 nM for 30 min at 37 °C.g for 10 min, and the pellet was discarded. Supernatants were centrifuged at 100,000× g for 30 min, and the pellets were resuspended in 100 µL of RIPA buffer and quantified using the PierceTM BCA Protein Assay kit (Thermo Scientific). Forty µg of each sample were electrophoresed in a precast polyacrylamide gel , transferred, and analyzed by Western blot using the anti-GFP antibody as described previously. After incubation, the cells were washed thrice with PBS and then scraped off with 100 µL of ice-cold RIPA buffer  supplemented with a protease inhibitor cocktail . Harvested cells were sonicated on ice using a UP50H ultrasonic processor  at an amplitude of 80% for 10 cycles of 0.5 s each. The cells were centrifuged at 20,000× For the loading control, membranes were incubated with an anti-α-tubulin clone DM 1A antibody  followed by rabbit anti-mouse immunoglobulins/HRP . Membranes were developed with the LuminataTM Crescendo Western HRP substrate (Millipore), and signals were detected using an Amersham Imager 600 .v/v) and 2% H2O2 (v/v) in PBS for 30 min. The slices were pre-incubated for 1 h at RT with PBS containing 20% NGS , 0.2% Triton, and 0.2% gelatin . Then, the sections were incubated overnight at 4 °C with the rabbit polyclonal anti-pEtx antibody [2O2 in PBS for 5 min, before being stopped with PBS. As a control, sections were treated identically but without incubation with the primary antibody. The sections were counterstained with hematoxylin, and the slides were then dehydrated and mounted with a DPX mounting medium. The slides were examined under a Leica DMD108 digital microimaging system.Endogenous peroxidase activity in the slices was first blocked with 10% methanol  in 0.1 M PBS at 37 °C for 30 min. As a control, sections were treated only with the buffer in the same conditions . Datura stramonium agglutinin  and Arachis hypogaea agglutinin  were used as a positive control in the treatments for N-glycan and O-glycan elimination, respectively [To verify the possible participation of N-glycosidic or O-glycosidic residues in the binding of the mutant, N-glycosidase F (Roche) was used to hydrolyze N-glycan chains from glycoproteins , while bectively ,46,47. To assess the sensitivity of the mutant to detergents, kidney sections were incubated with 2% Triton X-100 (Sigma), 2% sodium deoxycholate (Sigma), or 2% sodium cholate (Sigma) at RT for 1 h . After the treatments, the sections were washed three times with PBS at RT and incubated with GFP-Etx-ΔS188-F196 for 1 h at RT. The sections were washed and mounted as described before. In the case of NaOH treatment, the reaction was stopped by adding 0.055 N sulfuric acid for 30 s before the PBS washes. Depending on the treatment, controls were performed by omitting pronase E, N-glycosidase F, NaOH, or detergents from the incubation buffer. The autofluorescence produced by the effect of the treatments on the tissue was digitally eliminated using the Adobe Photoshop program.Results from the cytotoxicity assays were assessed with nonlinear regression analysis using one-way ANOVA with the Dunnett multiple comparison test. Data from the luciferin-luciferase ATP detection assays were assessed using the luminescence values from the area under the curve for GFP-pEtx, GFP-Etx, or the GFP-Etx mutants in relation to the luminescence values from the area under the curve for Triton X-100 in each case. All statistical analyses were performed using GraphPad Prism version 7.00 for Windows .Molecular graphics and analyses were performed with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics of the University of California, San Francisco, with support from NIH P41-GM103311.


Phaseolus vulgaris, an essential food and source of protein, is cultivated across the world. This study was carried out to investigate the diversity and population structure of 34 P. vulgaris landrace accessions collected from the Azad Jammu and Kashmir (AJ&K) regions of Pakistan. The samples were analyzed both morphologically and using genetic variation identified through RNA sequencing. Our results indicated that most genetic variation occurs among local accessions, with little genetic variation occurring between geographical regions. In addition, the accessions fell into two major genetic groups. Morphological analysis revealed that these two genetic groups differ in a number of quantitative traits, including seed length, seed width, and seed weight. One accession, DUD-11, appears to be a mixture of the two major groups genetically as well as morphologically. Among the other accessions, DUD-8, RWK-2, and NGD-1 depicted particularly high seed weight along with higher seed length, seed width, and seed yield per plant. We suggest focusing on these accessions in future breeding programs. More generally, our results provide baseline data that will be useful for crop improvement and effective cultivation practices in Pakistan. Phaseolus species that are cultivated worldwide for the commercial production of beans. Common bean, Phaseolus vulgaris L. (2n = 2x = 22), is the most economically important bean species, having an 85% share in worldwide bean production, followed by P. lunatus, P. coccineus, P. acutifolius and P. dumosus [P. vulgaris is considered a functional food because it is a rich source of protein, fiber, vitamins and essential minerals [The legume family, Leguminosae, is the second most important family among crop plants after Poaceae  and cont dumosus . Furtherminerals . BecauseP. coccineus). For example, Rosales-Serna et al. [P. coccineus cultivars are clearly distinct from P. vulgaris. Likewise, Guerra-Garcia et al. [P. coccineus domestication history and high level of genetic diversity among wild and cultivated populations of Mexico and Spain. In addition, evidence based on morphological [P. vulgaris because of geographical isolation over the last approximately 100,000 years [th century when they travelled through Indian subcontinent. However, there is very little known about the dissemination of common beans in the Himalayan region where a rich diversity of beans occurs [Previous studies have assessed morpho-agronomic and genetic diversity in common bean and its close relative runner bean  regions, where the average annual yield is 0.5t/ha and can be as high as 2.5-5t/ha, under the best cultivation practices. Currently, production is unable to meet the national demand [l demand , 21. Mucl demand . The devl demand –25. It il demand , 27. ThuP. vulgaris centers of origin in the Americas? These analyses should prove helpful in guiding breeding programs for common bean improvement in Pakistan.In a broad sense, the current research aims to assess landrace genotypes that could perform better in low input agriculture systems, to conserve the selected germplasm from possible extinction, and to provide baseline data of selected germplasm for further research purposes. The specific objective of this study is to characterize morphological and genetic variation among Pakistani cultivars and regions of the country. To do this, we address the following questions: (1) How variable are important morphoagronomic traits in Pakistan? (2) What is the heritability of those traits? (3) How much genetic variation is present in Pakistani cultivars? (4) How is the morphological and genetic variation distributed geographically? (5) How does the amount of genetic variation in Pakistan compare to variation present in the P. vulgaris and one accession of P. coccineus were collected from AJ&K. The P. vulgaris accessions were collected from six districts with 1–4 sampling sites in each district and a single P. coccineus accession was collected from the Nagdar site in the Neelum district . The site has an average annual rainfall of 1300 mm and a mean annual temperature of 16 ˚C. The soil in fields in Muzaffarabad is basic in nature with sufficient organic matter [Quantitative morphological traits were measured in a field experiment involving 34 c matter . The fiePhaseolus L. Descriptor list [Thirteen quantitative morphological traits were measured on each of 10 randomly chosen plants per accession at various plant growth stages from seed germination to seed formation following methods recommended by the International Board for Plant Genetic Resources tor list  (Table 2P. vulgaris. RNAseq is commonly used to identify SNPS for characterizing genetic diversity and constructing phylogenies [We use RNA-sequencing (RNAseq) of leaf tissue to determine the sequences of genes expressed in leaf tissue and identify single-nucleotide polymorphisms (SNPs) that are subsequently used to characterize patterns of genetic diversity in logenies –37. Molelogenies . Very lologenies . Specifilogenies  with theTo detect SNPs and indels among the accession, the filtered reads of each accession were aligned to the reference transcripts  and SNPs and indels were called using Samtools mpilup . We filtWe also generated a more stringently filtered set of variants. In this case, biallelic variants were filtered for a minimum minor allele frequency of 1% and complete coverage across individuals , and thinned to a single variant per 1kb. This resulted in a total of 15,149 variants.P) and genetic (VG) variance components were calculated using:Descriptive statistics were calculated for both the morphological and genetic datasets. To determine whether accessions differed for the morphological traits, we performed a standard analysis of variance (ANOVA) using Statistix 8.1 . PhenotyE = Error mean square [                                Where Vn square Broad-sense heritability was calculated as H2=VGVP  with theH2=VGVP . Second,H2=VGVP  implemenH2=VGVP  with 10 H2=VGVP  and the H2=VGVP  to deterS, equal to 0 if the SNP genotype was the same for the two samples, 1 if the two SNPs were homozygous for different alleles, and 0.5 otherwise. Scores were summed to produce ST. To correct bias caused by missing values, a corrected sum was calculated as SC= ST (1+ F), where F is the fraction of SNPs that were missing in at least one sample. π was then calculated as π = SC/N, where N is the total number of nucleotides in the transcriptome that passed filtering criteria . The program also calculates average π and its standard error, and performs a bootstrap analysis with 1,000 replicates to estimate confidence intervals.To further characterize genetic diversity, we calculated nucleotide diversity (π) both within the 34 samples, as well as within and between two clusters of samples identified by STRUCTURE and PCA. Pairwise π, the proportion of nucleotides by which two sequences from different individuals differ, was calculated using an APL program written by one of the authors (MDR). This program first assigns SNP, To determine the extent of geographical trait differentiation, we performed a multiple analysis of variance (MANOVA) in which the factors were Among Districts (random effect), Among Sites Within Districts  and Within-Sites using PROC GLM in the SAS software package, Version 9.4, for Windows. Variance components were estimated using PROC VARCOMP with the REML analysis. Variance components were converted to the proportion of variation explained by each effect. Following the MANOVA, univariate ANOVA analyses were performed.The relationship between trait variation and genetic variation was examined by determining whether traits differed for the two major clusters identified by the analyses described above. Specifically, we performed both MANOVA and univariate ANOVA on trait values in which the main effect was cluster. We excluded the accession DUD_11 from this analysis because it fell outside the two main clusters and because our STRUCTURE analysis suggests that it is a hybrid between the genotypes represented by the two clusters.Analysis of Variance indicates that all morphological traits differed among accessions . The second model included only the district effect, which was also not significant .Univariate analyses of individual traits yielded a similar conclusion. Variance components for both district and site effects were generally small and non-significant . The onlP. vulgaris and P. coccineus accessions revealed that the first two principal components accounted for 75% of the genetic variation. Principal component one separates P. coccineus from P. vulgaris and principal component two separates P. vulgaris into two major groups  was approximately four times greater between the red and blue clusters than within either cluster . AlthougWe examined whether the two genetic clusters identified in the previous section are differentiated with respect to the morphological traits by performing a MANOVA and univariate ANOVAs in which the main effect was genetic cluster . In the multivariate analysis, the cluster effect was significant .The univariate analyses indicated that this differentiation was nominally significant for several traits: leaflet length, leaflet width, pod length, pod width, seed width, seed weight, and seeds per pod . Each ofP. vulgaris landraces have been persistent in crop systems despite the import of seeds from other countries to meet local bean demand, presumably because of local cultural values and the inherent quality of local common bean genotypes [P. vulgaris accessions were characterized both morphologically and genetically to examine geographical patterns of variation.In Pakistan, the production of common bean has been centered on local landrace accessions cultivated by small-scale farmers mostly under low-input agriculture . The traenotypes . The locenotypes . In the P. vulgaris bean cultivars in AJ&K, Pakistan has revealed two major patterns. The first is that there is little large-scale geographic differentiation, either genetically or in trait characteristics. Instead, most variation occurs within sampling sites. A similar lack of geographic differentiation has been reported in other countries [Our survey of diversity among ountries , 51. Oneountries . In addiountries . Finallyountries .A second pattern is that most of the genetic variation in traits is between two major genetic clusters with similar geographic distributions, as has been found in previous studies in other countries –56. IdenGenetic diversity in the Mesoamerican samples is considerably less than that reported for land races from this region. Our estimate of π for Pakistani Mesoamerican derivatives (0.00137) is less than ½ to 1/7 that of Mesoamerican land races Click here for additional data file.S2 FigEach point represents one accession.(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S4 FigColors of clusters correspond to colors of clusters in (DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableMeans are averages across 2017 and 2018 for 34 Phaseolus vulgaris accessions.(DOCX)Click here for additional data file.S4 TableA. Statistics for Blue cluster. B. Statistics for Red cluster.(DOCX)Click here for additional data file.S5 TableLow: lowest value of trait among accessions within cluster. High: Highest value of trait among accession within clusters.(DOCX)Click here for additional data file.


Chinese traditional culture is a typical “moral culture,” and traditional morality is the core and essence of culture. However, in recent years, the phenomenon of following the trend of rural construction has been particularly serious, and many villages have lost their original features. To solve the above problems, the genetic algorithm can be used to further explore the traditional culture of urbanization construction. A genetic algorithm is a natural evolutionary process that imitates natural selection and genetic operation in nature to obtain optimal solution, in which genetic operation mainly includes the processes of gene replication, crossover, and mutation. This paper studies the traditional culture of urbanization construction based on the genetic algorithm under the concept of environmental protection. Among the accuracy of urban construction land expansion, in 2018, the accuracy of ant colony algorithm, data mining algorithm, and particle swarm optimization algorithm is 58%, 51.8%, and 56.7%, respectively. The accuracy of this genetic algorithm is as high as 58.8%. It can be seen that the genetic algorithm in this paper has the highest accuracy in the expansion of urban construction land. Therefore, in the process of large-scale urbanization based on the genetic algorithm, we should pay attention to not being separated from traditional culture, not letting farmers lose their regional culture, local culture, and grassroots culture, and protecting the cultural-ecological environment on which these cultures depend. The original meaning of “culture” is “rule of culture” and “enlightenment,” that is, to influence and restrict people with ethics and etiquette and music rules and regulations. Chinese traditional culture is a typical “virtue culture,” and traditional morality is the core and essence of culture. However, in recent years, the phenomenon of following the trend of rural construction is particularly serious. Many villages have lost their original characteristics. The history and culture of countryside have also been damaged or even destroyed to varying degrees. This blind demolition and construction and disorderly development and construction pose a serious threat to the rural pattern and the historical style inherited for thousands of years . At the Under the concept of environmental protection, this paper analyzes the protection of traditional culture in urbanization construction and uses the optimization algorithm of the genetic algorithm to solve some problems of traditional culture in urbanization construction. A genetic algorithm is a natural evolutionary process that imitates natural selection and genetic operation in nature to obtain optimal solution. Among them, genetic operation mainly includes the process of gene replication, crossover, and mutation. Therefore, the operation process of the genetic algorithm is also the process of genetic variation of simulated genes and the calculation method of selecting excellent individuals through copying, crossover and mutation operations, and selection operations. As we all know, biological evolution is based on Darwin's biological evolution theory, and the next generation of biological groups is produced by the reproduction of the previous generation of biological groups. Therefore, the main content of the abstract genetic algorithm, which simulates the evolution process of organisms, is to simulate the evolution process of biological groups. In essence, the genetic algorithm is a direct search method that does not depend on specific problems. The genetic algorithm has been applied in pattern recognition, neural network, image processing, machine learning, industrial optimization control, adaptive control, biological science, social science, and so on. The main problem to be solved in the process of genetic algorithm operation is the chromosome coding method, which is the key problem of genetic algorithm implementation, the method of transforming optimal solution from solution space-to-gene space, and the gene expression of optimal solution . The quaThis paper constructs the traditional cultural structure model of urbanization. The solution process of this model is similar to the evolution process of biological natural selection, and it is a process of iterative evolution from generation to generation. The fitness of individuals is calculated by fitness function, and proportion is divided on the wheel according to the fitness. Each individual is like a sector in the wheel, and a pointer is placed on the wheel to randomly select. The greater the fitness, the greater the probability of being selected, and the smaller the fitness, the smaller the probability of being selected.The trend of the urban construction land expansion rate was tested and analyzed. From the perspective of the expansion rate, the growth rate from 2016 to 2021 showed the characteristics of first increasing and then decreasing. From 2016 to 2019, the expansion rate of urban construction land under the genetic algorithm reached 12% at the fastest, followed by 10% under the particle swarm algorithm, while the ant colony algorithm and data mining algorithm were only about 7% and 1%, respectively. It can be seen that the genetic algorithm in this paper is important in the expansion rate of urban construction land.People in different regions and environments have gradually formed their own unique local culture and regional culture in the long-term production practice. The forms of expression include local drama, arts and crafts, folk stories, and handicrafts. Different regions have also shaped the cultural elements, such as the character and beliefs of the local people. Under the concept of environmental protection, the traditional culture of urbanization construction is studied based on the genetic algorithm. In the process of promoting the overall development of urban and rural areas, one of the policies and measures to implement industry-feeding agriculture and cities supporting rural areas is to promote industrialization and urbanization. Industrialization is an important part of economic development, and urbanization is the main carrier and driving force of economic development . ThroughThe overall structure of this paper consists of five parts.The first chapter introduces the background and significance of traditional culture in urbanization. The second chapter mainly describes the research status of traditional culture in urbanization at home and abroad. The third chapter describes the principle and model of the genetic algorithm and the content of traditional culture protection and inheritance in urbanization. In chapter 4, simulation verification and analysis are carried out. The fifth chapter is a summary of the full text.Guo et al. proposed that in the process of traditional urbanization, due to the lack of restraint mechanism for the protection and development conditions of rural cultural resources and the lack of attention to the scientific development of rural culture, rural culture is distorted and distorted, and the value of rural culture is greatly reduced . Cheung In this paper, the traditional culture of urbanization construction is studied based on the genetic algorithm under the concept of environmental protection. Traditional urbanization is not conducive to the protection, inheritance, and innovation of rural culture. In the process of new urbanization construction, the homesickness concept is used to guide the protection, inheritance, and innovation of rural culture, hoping to find a way to realize that the protection and development of rural culture complement each other, and inheritance and innovation keep pace with each other. Compared with rural areas, urban areas are more efficient, so we think that urbanization should be efficient in all aspects, including land conservation, but our urbanization is constantly devouring our land. The ultimate goal of the overall planning of urban and rural areas through the genetic algorithm is the integration of urban and rural areas, which is the advanced stage of modernization and urbanization, and the process of mutual absorption of advanced and healthy factors, rejection of backward and morbid conditions, mutual integration, and two-way evolution between urban and rural areas. On the basis of digging and protecting local history and culture in the genetic algorithm, rural development develops regional characteristic environmental protection industry, which drives rural development. However, completely copying or letting it develop is not conducive to the further advancement of urbanization in China. Therefore, it is necessary to take functional cultivation as foundation, cultural characteristics as the fulcrum, industrialization as the driving force, regional urban cluster construction, coordinated development of the large, medium, and small cities and small towns as the basic mode to make a good plan, and take the urbanization road of sustainable development in accordance with the principles of gradual progress, land conservation, intensive development, and rational layout. For the protection and inheritance of rural history and culture, a feasible planning method is put forward in the genetic algorithm under the concept of environmental protection, so as to protect the development space of environmental protection concept of Chinese history and culture, enhance its vitality, and have very important practical significance for carrying forward local culture and protecting the integrity of the Chinese nation's historical context.Genetic algorithm is an adaptive probabilistic search method, and the search process is based on global search. Its algorithm idea is similar to natural selection evolution. It is a simulation of biological genetic evolution in nature. It has penetrated into various fields of research and engineering with its strong problem-solving ability and wide adaptability, and achieved good results . GeneticWhen the population size is set, if the value is small, the diversity of population will be lost, resulting in premature convergence and failure to reach optimal solution. However, if the value is too large, the operation time of the genetic algorithm program will be prolonged, which will affect the operational efficiency.When the crossover probability is chosen, if the crossover probability is too large, it will affect the dominant gene inheritance of population itself, which is not conducive to the optimization of the whole population. However, if the crossover probability is too small, the generation speed of new individuals will slow down, and when the number of iterations reaches, optimal solution can still not be reached.When mutation probability is selected, if mutation probability is too large, it will lead to too many gene mutations, which will make the difference between the new individual and the parent individual too large, thus losing the adaptive advantage of the genetic algorithm, resulting in the search process of optimal solution having no direction, and becoming the random search for optimal solution. However, if the mutation probability is too small, it will be difficult to generate new individuals, resulting in the generation of local optimal solutions .When the impact on the result is positive, the data standardization formula is as follows:A is the standardized value of the index, Aij is the actual value of the j index in the i year, Ajmin is the minimum value of index j, and Ajmin is the maximum value of j index.When the impact on the result is negative, the data standardization formula is as follows:ω corresponding to the maximum eigenvalue λmax of the judgment matrix can be further obtained. The formula is as follows:According to the judgment matrix, the eigenvector ω=T.Then, the feature vector is normalized. The result of normalization is the ranking of the importance of corresponding index factors, that is, weight, The transformation potential of the renewal project is evaluated, and the formula expression isIn the genetic algorithm, it is realized through the selection operator. The probability of individuals with high fitness to be inherited to the next generation population is relatively large, and the probability of individuals with low fitness to be inherited to the next generation population is relatively small. Finally, they continue to approach optimal solution. At the beginning of the development of rural tangible and intangible cultural resources by the genetic algorithm, it is necessary to clarify the constraints on the protection and development of rural cultural resources, that is, to find the constraints affecting the protection and development of rural cultural resources . The devPi is the potential value of the i plot to be evaluated, aj is the index weight of the j index, and the standardized index value of the j index of the i plot.The transformation potential of the urban land parcel can comprehensively and objectively measure the importance of urban renewal projects. The greater the transformation potential, the less difficult the transformation of the urban land parcel and the greater the benefits. The potential of urban reconstruction plays an extremely important role in judging the site selection of priority reconstruction projects. The formula for calculating the potential of urban land parcel projects to be evaluated is as follows:ui is the urgency of the i plot project to be evaluated, bj is the index weight of the j index, and the standardized index value of the j index of the i plot project.A multi-factor comprehensive evaluation method is used to analyze and evaluate the urgency of renewal projects. The calculation formula of the urgency of the plot project to be evaluated is as follows:ci is the area of each plot and S is the priority transformation mode.Due to limited renovation funds and other reasons, it is impossible to renovate all updated plots in a short period of time, so it is necessary to limit the total area of the plots that have priority for renovation, namely,S should meet the following conditions in quantity.That is, In the genetic algorithm, it is very important to determine fitness function. Selection, also known as replication, is the process of selecting excellent individuals in population to produce new population. The main body of biological evolution is population, and the calculation object of genetic calculation is also a set composed of individuals, that is, population. The operation and solution process of the genetic algorithm is very similar to the evolution process of biological natural selection. It is an iterative evolution process from generation to generation. We calculate the fitness of individuals through fitness function, and divide proportion on the roulette according to the fitness. Each individual is like a sector part of the disc. We put a pointer on the roulette for random selection. The greater the individual fitness, the greater the probability of being selected, and the smaller the fitness, the smaller the probability of being selected. Generally speaking, there are three common methods to solve optimal solution: enumeration method, heuristic method, and search method. Because the types of problems are different and the scale of problems is also different, it is unrealistic and difficult to find a general method to solve all optimization problems. However, the genetic algorithm can provide us with a practical and effective way and a relatively general and universal model and create a global heuristic optimization search algorithm.China has a cultural awareness of advocating nature since ancient times. Influenced by Chinese traditional culture, it believes that the Earth is an organic natural body, of which human beings are only a part. Therefore, in rural site selection and the formation of rural texture under the concept of environmental protection, people are mostly integrated into nature and live with nature according to natural conditions. The coordinated development of new urbanization also requires the integration of nature and humanities, and the combination of modern elements and traditional culture. In new urbanization, we advocate the concept of environmental protection, respect for nature, and conform to nature, which embodies the concept of harmony between man and nature. In terms of landscape, relying on the unique natural scenery in rural areas at present, let new towns integrate into nature. Using the natural environment conditions, it can be roughly summarized into three types of site selection: along the river, by the mountain, and by the lake, so that the layout of the countryside is well integrated with the natural landscape, which has been passed down to this day by the long river of time. In the protection and inheritance of excellent traditional culture in rural areas, professionals should emancipate their minds and engage in this work in a way that keeps pace with the times. The reason why culture can still be preserved in the changes of the millennium depends on the spread of the spirit and core of the environmental protection concept, and some forms of changes will not destroy its intrinsic value. In order to realize the great development and prosperity of rural cultural undertakings and the sustainable development of rural cultural industries, it is necessary to develop various technologies and equipment dedicated to rural culture and art with various environmental protection concepts, so as to provide a technical and benefit platform for rural environmental protection cultural industries.Excellent traditional culture is the embodiment of regional soft power and the basis of national identity, national cohesion, innovation, and development. We should pay attention to its influence and constantly explore the path of inheritance and development of traditional culture. The relevance between tradition and modernity makes the renewal process more natural and coherent, so as to better solve the inheritance of local history and culture and integration with modern society. In the process of renewal and development, the main context and constituent elements of rural texture should be organically renewed on the basis of respecting the original texture and structure, so as to maintain the original rural characteristics. It is worth noting that we should pay attention to three principles in the inheritance of traditional culture under the concept of environmental protection. The protection and restoration of existing ancient relics and ancient buildings should pay attention to the authenticity, repair the old as the old, and try not to destroy the traditional cultural value contained therein. Integrity is to consider the overall environment as much as possible in the protection of historical buildings and traditional villages. Integration is the integration of nature and humanities, modernity and tradition in the protection, and inheritance of the overall traditional culture. The protection of important ancient buildings in rural areas, especially historical and cultural heritage, should be consistent with the local traditional context. For some important residential buildings and historical relics, specific environmental protection plans should be made to ensure reasonable implementation. On this basis, we should deeply explore the historical knowledge behind it, so as to create a local cultural card. Therefore, some innovative cultural protection behaviors should be supported as appropriate. For example, Kaifeng rural areas can also promote some tourism peripheral products through e-commerce platforms, and use group purchase, discount, etc. to promote the increase of tourist flow in the scenic spot. In the process of the era of the environmental protection concept, it is also necessary to pass on some ancient civilizations by “following the fashion”.Generally speaking, urbanization has been developing at a high speed since the reform and opening up. According to the official data released by a province from 2016 to 2021, this paper conducted an experiment on the urbanization rate and its growth rate. The experimental results are shown in As can be seen from In 2021, the main economic indicators of a province were analyzed experimentally, as shown in It can be seen from A genetic algorithm is used to calculate the landscape pattern index of urban construction land in a municipal area from 2018 to 2021, so as to analyze the landscape pattern characteristics of urban construction land expansion in a municipal area. Among them, LPI represents the proportion of the largest patch area of urban construction land in the total area, Contag represents the spatial aggregation degree of urban construction land, and Shei represents the balance of urban construction land distribution. The landscape pattern index of urban construction land is shown in As can be seen from Three regions for experimental analysis were selected, namely Shandong, Guangdong, and Hebei, and carried out experiments on their urbanization rate from 2016 to 2021. The experimental results are shown in It can be seen from The simulation benchmark data selected in this study is the land use data of a municipal area in 2021. Taking the simulation from 2019 to 2021 as the benchmark, the initial weight is calculated to simulate the expansion of urban construction land in a municipal area from 2019 to 2021. The area statistics of various land use types in a municipal district in 2019, 2020, and 2021 are shown in As can be seen from From 2016 to 2021, this experiment used ant colony algorithm, data mining algorithm, particle swarm optimization algorithm, and genetic algorithm to study the expansion rate of urban construction land. The experimental results are shown in As can be seen from From 2016 to 2021, the accuracy of urban construction land expansion was studied by ant colony algorithm, data mining algorithm, particle swarm optimization algorithm, and genetic algorithm. The experimental results are shown in As can be seen from This experiment is still in 2016–2021. Ant colony algorithm, data mining algorithm, particle swarm optimization algorithm, and genetic algorithm are used to study the growth rate of urban construction land expansion. The experimental results are shown in As can be seen from To sum up, this paper studies the traditional culture of urbanization construction based on the genetic algorithm under the concept of environmental protection. Among the accuracy of urban construction land expansion, in 2018, the accuracy of the ant colony algorithm for urban construction land expansion was 58%, that of the data mining algorithm was 51.8%, that of the particle swarm optimization algorithm was 56.7%, and that of the genetic algorithm in this paper was as high as 58.8%. It can be seen that the genetic algorithm in this paper has the highest accuracy in the expansion of urban construction land. On the practical level, urbanization construction through the genetic algorithm should pay special attention to the concept of environmental protection, the inheritance and embodiment of cultural traditions, and advocate the establishment of a constructive development concept characterized by showing uniqueness with an open concept, so as to build a colorful world where constructive development and culture coexist harmoniously. In China, villages are earlier than cities, and their emergence and development have certain rules to follow, which endows them with profound historical accumulation. If we want to protect history and culture under the concept of environmental protection, we must clearly show it and let it continue to develop healthily, so as to achieve the purpose of protection. In the process of development, it should be noted that urban and rural areas are different spatial areas, which must have different characteristics and respective advantages. In order to realize the great development and prosperity of rural cultural undertakings and the sustainable development of rural cultural industries, it is necessary to develop various technologies and equipment dedicated to rural culture and art with various environmental protection concepts, so as to provide a technical and benefit platform for rural environmental protection cultural industries.


Long-term breeding schemes using genomic selection (GS) can boost the response to selection per year. Although several studies have shown that GS delivers a higher response to selection, only a few analyze which stage GS produces better results and how to update the training population to maintain prediction accuracy. We used stochastic simulation to compare five GS breeding schemes in a self-pollinated long-term breeding program. Also, we evaluated four strategies, using distinct methods and sizes, to update the training set. Finally, regarding breeding schemes, we proposed a new approach using GS to select the best individuals in each F2 progeny, based on genomic estimated breeding values and genetic divergence, to cross them and generate a new recombination event. Our results showed that the best scenario was using GS in F2, followed by the phenotypic selection of new parents in F4. For TS updating, adding new data every cycle (over 768) to update the TS maintains the prediction accuracy at satisfactory levels for more breeding cycles. However, only the last three generations can be kept in the TS, optimizing the genetic relationship between TS and the targeted population and reducing the computing demand and risks. Hence, we believe that our results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes. Genomic selection (GS) has been successfully employed in plant and animal breeding . In thisSeveral studies have examined the optimal employment of GS in self-pollinated crop breeding programs . The selLong-term breeding schemes should be analyzed in terms of genetic gain, response to selection, genetic variance, and GS accuracy. Even though several studies involve GS in long-term recurrent selection schemes , only a Another point to highlight in long-term breeding schemes employing GS is that prediction accuracy (PA) declines over cycles . This phOryza sativa L.) as a self-pollinated model crop and stochastic simulations performed by the AlphaSimR package  . For thaa) were sampled of a gamma distribution with scale and shape parameters equal to 1 and randomly assigned for each QTN. Similarly, dominance effects (d) for each QTN were computed by multiplying the absolute value of its additive effect (ia) by locus-specific dominance degree (iδ). Dominance degrees were sampled of a Gaussian distribution with δμ is the average dominance degree equal to 0.22 and In order to simulate a quantitative trait as yield, we used the genetic parameters obtained by H2 = 0.53) and narrow (h2 = 0.50) heritabilities.Phenotypic values were obtained by adding a random error sampled of a Gaussian distribution with mean equal to 0 and variance , with nearly 52 plants per cross, from the base population after the burn-in stage. Markers effects were predicted using the ridge-regression best linear unbiased prediction (RRBLUP)  accordiny is the vector of individual phenotypic values from the TS; μ is the mean (intercept); u is the vector of marker effects, where ϵ is the vector of random residuals. 1 is the vector of ones and uZ is the incidence matrix of TS genotypes for m markers. uZ is coded as 1 for homozygous A1A1, -1 for homozygous A2A2, and 0 for heterozygous A1A2.where GEBV = Mu, where M is the incidence matrix of selection candidate genotypes, and u is the vector of predicted marker effects.To perform the GS, the genomic estimated breeding value (GEBV) was estimated using the following equation: Simulations were based on the rice breeding program structure from the International Rice Research Institute (IRRI) . For all2 generation. Thirty crosses were randomly made from the parental lines. Then, F1 plants were selfed to produce an F2 segregating generation with 230 F2 plants per cross , we used the same base population aforementioned. In the first cycle, all breeding schemes were the same from crossing to the Fer cross . From thstimates Figure 1Traditional (Trad) and drift (Drift) schemes were conducted by the SSD method from F2 to F6 generation. Each generation lasted about 180 days, totaling four years for each cycle. These two schemes only differed on parental group selection: the best phenotypic values for Trad and a random sample for Drift. The line fixation phase for breeding schemes using GS was conducted by the rapid generation advanced (RGA) method. The RGA method is an SSD method carried out in greenhouses or screenhouses to accelerate and shorten the growth plant cycle (~ 90-100 days) . All breF2: GS was performed in the F2 stage, and all plants were genotyped. Individuals with the highest GEBV in F2 were selected (24 or 48), regardless of progenies, to compound the next cycle (one year per cycle).GSF2SF4: GS was performed in the F2 stage, and genotypes with the highest GEBV within each progeny were selected for the next generation (F3). So, 230 F3 plants from the selected F2 plants were conducted until F4. Then, in F4, individuals with the highest phenotypic values were selected, regardless of progenies, to compound the next cycle (2 years per cycle).GSF4: GS was performed in the F4 stage, and all plants were genotyped. Individuals with the highest GEBV in F4 were selected (24 or 48), regardless of progenies, to compound the next cycle (2 years per cycle).GS2 stage, and the two individuals with the highest GEBV within each progeny were selected and crossed to generate 230 F1seg segregating plants. These plants were conducted until F4, and individuals with the highest phenotypic values were selected, regardless of progenies, to compound the next cycle (2.33 years per cycle).GS_FSb: GS was performed in the F2 stage, and the highest individuals (10%) based on GEBV were selected to calculate the Euclidean genetic distance between them. The most divergent sibs were crossed within each progeny to generate 230 F1seg segregating plants. These plants were conducted until F4, and individuals with the highest phenotypic values were selected, regardless of progenies, to compound the next cycle (2.33 years per cycle).GS_FSd: GS was performed in the Fh2), whereas for the Drift scheme as considered zero due to random selection. Furthermore, the response to selection after ten years of breeding was calculated following the equation:Theveragee genetic value, the genetic value of the best genotype, additive genetic variance, and PA were calculated for each breeding cycle. The PA was calculated as the Pearson correlation between true genetic values and GEBV. For the Trad scheme, PA was computed as the square root of heritability , increasing TS size by 384 each cycle . Marker effects were estimated in each cycle and used to predict genetic values and select genotypes in the next cycle.TSGPO: TS was composed of three sets of 384 individuals, one set per generation . Every cycle, the newest set  was included, and the oldest one was removed. Markers effects were estimated in each cycle and used to predict genetic values and select genotypes in the next cycle.TSGPO scenario, three different TS sizes  were simulated to add into the TS every breeding cycle, aiming to recalibrate the marker effects and keep the GS accuracy at satisfactory levels for more generations. Each TS updating strategy was simulated over 20 breeding cycles and replicated 100 times. Similar to the breeding schemes, PA was calculated for each cycle and replicate.Finally, only for the TSF2SF4 showed the best performance for GS-based methods for both parental sizes. Furthermore, GS_FSb and GSF2SF4 revealed higher population mean and the best genotype performance in the earliest breeding cycles , the best genotype performance, PA, and additive genetic variance  produced similar responses in terms of PA over cycles and outperformed the TSC0 (no updates). Also, TSC0 presented a steep decline in the earliest breeding cycles  or small sample sizes, such as 384  or more every cycle, keeps the GS accuracy at satisfactory levels for more cycles (up 8 or 10), outperforming the TSh as 384 Figure 52 progeny are identified and crossed to generate a new recombination event. These progenies are then fixed using the RGA method until F4 to select new parents for the next recurrent cycle.GS can speed up breeding schemes and increase genetic gain . HoweverOur results showed that the number of parental lines to compose the crossing block influences all genetic parameters over breeding cycles. This influence was expected due to the relationship between the number of parental lines, the effective population size (Ne), and the selection intensity. In the scenarios examined here, the number of crosses (crosses = 30) was the same in both scenarios . However, fewer parents increase the number of crosses with the same parent, which reduces the final Ne and, consequently, the genetic variance. Also, a smaller Ne increases the effect caused by the drift , reducinF2) and 608% (GS_FSd) higher than phenotypic selection (Trad) in the first breeding cycle for response to selection per year  and the most divergent between best GEBVs (GS_FSd). Among the GS-based schemes, the GS_FSb reached the highest population mean over the breeding cycles , which the TS size effect can explain  every cycle into the training set allows re-estimation of the marker’s effects. In other words, updating the LD between markers and underlying QTL guarantees the highest genetic gain over recurrent selection cycles. Finally, only the last three generations should be kept in the TS, optimizing the genetic relationship between TS and the targeted population in a closed system.Implementation of genomic selection on long-term breeding schemes may accelerate genetic gains. However, it is crucial to determine at and when to implement the genomic selection since it can produce lower genetic gain than phenotypic selection, depending on the strategy. For example, applying genomic selection in an FThe original contributions presented in the study are included in the article/FS wrote the manuscript, discussion, contributing to ideas and graphs. JD and JP contributed to the writing, mainly discussion. RF-N elaborated on the hypothesis, conducted the analyses, interpreted the results, and contributed to the writing. All authors contributed to the article and approved the submitted version.AGGRi Alliance (Accelerated Genetic Gain in Rice in South Asia and Africa - OPP1194889) - Bill and Melinda Gates Foundation (BMGF).To Dr. Gary Atlin (Bill and Melinda Gates Foundation), IRRI Breeders, Excellence in Breeding - CGIAR platform, and Allogamous Breeding lab-USP members for the comments and suggestions.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Seed aging is the key factor leading to the loss of genetic integrity. In this study, the seeds of Dongxiang wild rice, Xianggu, 9194 and Nipponbare were kept in a plant incubator with constant temperature and humidity for artificial aging treatment. The genetic integrity of germplasm resources with different germination gradients were analyzed using 44 SSR markers. The results suggested that different accessions could be ranked in order of aging resistance from highest to lowest as common wild rice > Xianggu > 9194 > Nipponbare. In order to maintain the genetic diversity of rice, the population size for reproduction and regeneration should be between 60 and 140. After aging, the number of polymorphic alleles, the number of specific single plant, the ratio of polymorphic bands, the number of alleles, the number of effective alleles, gene diversity index and Shannon index of different accessions all decreased with the decrease of germination rate. The germination rate of 60% was the critical value to maintain genetic integrity. Besides, the genetic integrity of eighteen SSR markers was rapidly lost or significantly increased. The regions of these markers were closely related to seed viability or genetic integrity. This study provides a theoretical basis for determining the population size for reproduction and regeneration and the critical value of germination rate of rice resources. Changes in the genetic integrity of germplasm refer to the gradual loss of genetic integrity with the decrease of seed germination rate and viability during storage, including the differential differentiation between the progeny populations and the parent populations of germplasm resources after reproduction and regeneration3. The genetic integrity of germplasm is affected by many factors, such as seed aging, population size for reproduction and regeneration, and pollination methods4. To maintain the genetic integrity of germplasm is to maximize genetic similarity in the process of reproduction and regeneration, and to minimize genetic variation during the long-term conservation5. The aging of germplasm resources in storage will decrease seed germination rate, cause natural selection and genetic drift of the genetic genotype, and finally result in the loss of genetic integrity6. In short, it is of great scientific to study the effects of seed aging on the genetic integrity of germplasm resources.Genetic integrity refers to the original genetic composition of germplasm resources. The protection of genetic integrity refers to the maintenance of the complete genetic structure of germplasm populations in the process of reproduction, regeneration or long-term conservation7. Although these two methods can effectively extend seed longevity, they cannot prevent decline in seed viability or death of the seeds8. China has established more than forty medium- and long-term gene banks of different types and preserved more than 1 million accessions of germplasm resources9. Seeds stored in gene banks will gradually lose their germination ability, which is called natural aging. When the germination rate of stored seeds decreases to a certain lower limit, the germplasm resources must be reproduced and regenerated10. The critical value of germination rate standard for the reproduction and regeneration of crop germplasm resources is from 60 to 85% in China, but from 50 to 75% in countries such as the United States, Britain, and India12. Li et al.13 analyzed the purity of 2794 soybean cultivars or lines by using fifty-nine SSR markers. They suggest that the population size should be kept larger during the propagation for those lower-purity accessions compared with high-purity accessions; otherwise the low frequent genotypes are easy to be lost. Sun et al.14 studied the effect of regenerate population on the genetic integrity of sesame germplasm and showed that the genetic integrity could be maintained when the population size of released cultivar reached 35 to 40. Due to the obvious differences in seed structure and inclusions among different types of crops, the difficulty of aging as well as reproduction and regeneration varies significantly. Therefore, it is of great significance to determine the optimal population size and critical value of germination rate for reproduction to maintain the genetic integrity of germplasm resources.As seed longevity is short in the natural conditions, low-temperature conservation and room-temperature drying are mainly used to prolong seed longevity15. Methods for identifying the genetic integrity of germplasm resources mainly include morphological markers, cellular markers, biochemical markers and molecular markers. Among them, morphological markers and biochemical markers have obvious limitations as they are easily affected by environment, time-consuming and complicated to operate16. With the development of molecular biotechnology, molecular marker technologies such as RFLP, AFLP and SSR provide efficient and simple methods for exploring the genetic integrity of germplasm resources at the molecular level. Molecular markers can directly detect differences and changes in DNA without being restricted by environmental conditions17. In recent years, molecular marker detection technology has become a powerful supplement to morphological, cellular and biochemical markers in the identification of genetic integrity and is widely used in testing the genetic integrity of wheat, corn, cotton, rye and other crops18,19. Since there are few studies on the identification of the genetic integrity of different rice accessions, the method of using molecular technology for detecting genetic integrity is still unclear. In this study, common wild rice, rice landraces, indica cultivars and japonica cultivars were used as experimental materials. Germplasm populations with different germination rates were obtained through artificial aging. SSR markers were used to identify the genetic integrity of rice resources with different germination rates. And the critical value of germination rate and population size for reproduction and regeneration were determined in order to maintain the genetic integrity of rice resources. Thus, this study aims to provide a theoretical basis for the maintenance of genetic integrity and safe conservation of germplasm resources.Maintaining the genetic integrity of stored crop germplasm resources is the core issue for the safe conservation and regeneration of germplasm resources. In particular, the identification of the purity or authenticity of varieties at the molecular level is critical to the maintenance of the genetic integrity of germplasmThe aim of the aging test was to determine the aging resistance of different accessions and to obtain the germplasm populations with different germination rates. The actual germination parameters of all samples are presented in Table Xianggu is a typical landrace with rich genetic diversity. The phenotypes of most of the individual plants in Xianggu were medium maturity, hemp husks and short awn, while that of some individual plants were late maturity, hemp husks and long awn or early maturity, yellow husks and no awn Fig. . SignifiIn order to determine the change of phenotypic characters as the germination rate decreases after aging test, Xianggu, with obvious differences in phenotypic traits, was selected to study the population size for reproduction and regeneration. The germination rate and seed viability declined as the aging level increased. The population size for reproduction had a significant effect on the frequency of individual plants with late maturity, hemp husks and long awn or early maturity, yellow husks and no awn Table . When thA total of 650 pairs of SSR primers were selected, which distribute evenly over the whole rice genome. These primers were used to detect the polymorphism of 200 individual plants in the control varieties  with the germination rate of 90%. According to PAGE detection, only 44 pairs of primers were polymorphic among the control varieties. Finally, these polymorphic primers were selected for the detection of genetic integrity. The number of the polymorphic primers in Dongxiang wild rice, Xianggu, 9194 and Nipponbare was twenty-five, sixteen, twelve and eight, respectively , high throughput sequencing, and omics technology, more than twenty-three key genes related to seed vigor have been identified31. Dong et al.32 identified the QTLs for seed storability using two sets of recombinant inbred lines (RILs). Ten QTLs were detected on chromosomes 1, 2, 3, 4, 6, 8, and 12 in SL-RILs, and a total of 12 QTLs were identified on chromosomes 2, 3, 4, 6, 9, and 10 in SH-RILs.At present, lots of germplasm resources with strong viability and storability have been discovered through research on different types of germplasm resources and genetic groups. Seed viability and storability are complex quantitative traits controlled by multiple genes, and the related markers or quantitative trait locus (QTLs) are located on twelve chromosomes of riceIn this study, a total of forty-four pairs of SSR markers were selected for the detection of genetic integrity. These markers were unevenly distributed on chromosomes one to nine and eleven. Among them, the number of SSR markers on chromosome two was the highest while that of chromosomes ten and twelve were the lowest. The result showed that that there were significant differences in the richness of chromosome genetic integrity of different rice resources. In addition, the integrity of RM154 significantly decreased in Xianggu but significantly increased in 9194, which implied that location with significant changes in genetic integrity could be related to seed storability or viability. Through comparative analysis, this study also found that the genetic integrity of eighteen markers increased or decreased significantly after aging test. Furthermore, three primers RM282, RM296 and RM552 appeared in Dongxiang wild rice, Xianggu and 9194 at the same time, which were located on chromosomes three, nine and eleven, respectively. These implied that these markers were associated with the QTLs related to seed viability and storability, which would be the key locations for maintaining the genetic integrity and seed viability. Thus, in order to provide some excellent genetic resources for the maintenance of genetic integrity of germplasm resources, we still need to use relevant materials to construct genetic populations and explore genes for seed viability and storability.Oryza rufipogon Griff.), Xianggu , 9194  and Nipponbare  were used as experimental materials. In 2015, these materials were planted in the experimental field of Rice Research Institute of Jiangxi Academy of Agricultural Sciences. 4 kg seeds of each accession with high seed viability (that is water content lower than 14% and germination rate higher than 90%) were harvested. In order to break dormancy, the seeds were treated at 45 °C for two days and then stored under constant temperature and low humidity for later use.In this study, Dongxiang wild rice (Aging test was run with four accessions. 100 g seeds of each variety were maintained in a plant incubator (43 °C and 85% of relative humidity). From the second day, the germination rate of these seeds was measured until it decreased to 0. The aging time for different accessions of the germination rates of 80–85%, 60–70%, 50% and 30% was recorded  were kept in the germinator at 25 °C. Then, all of the seedlings were transplanted to the experimental field. The main phenotypic traits such as the number of effective panicles, heading stage, initial heading stage, full heading stage and mature stage of each plant were measured and the fresh leaves were collected to extract DNA.33. A total of 650 pairs of SSR primers were selected from the rice gene database (http:// www.gramene.org/). These primers distributed evenly over the whole rice genome and synthesized by Beijing Dingguo Biological Co., Ltd. The PCR reaction was performed after purification. Each 10 μL of the amplification reaction consisted of 1 μL of 10 × PCR buffer, 0.3 μL of the dNTP mixture, 1 μL of primers, 0.1 μL of Taq polymerase, 1 μL of genomic DNA, and sterile molecular biology-grade water for making up the volume. All amplifications were performed under the following conditions: 5 min at 94 °C, followed by 30 s at 94 °C, 30 s at 55 °C and 30 s at 72 °C for 35 cycles and 10 min at 72 °C for a final extension. The PCR products were inspected on 6% polyacrylamide gel electrophoresis (PAGE).The genomic DNA of rice was extracted by the hexadecyl trimethylammonium bromide (CTAB) method34. Genetic diversity was estimated by the number of alleles (Na), effective number of alleles (Ne), Nei’s genetic diversity index (He) and Shannon’s Information index (I) using POPGENE 32. The significance of differences of Na, Ne, He and I between the same name populations collected in different periods was calculated using SPSS software4.The electrophoresis results were compared and revised manually. The present bands were labeled as ‘1’, while absent bands were marked as ‘0’ to establish the data matrix in Excelp < 0.0535.All the data were analyzed using SPSS software to illustrate the influence of ageing on studied parameters. Studied parameters were subjected to one-way ANOVA to test for differences within the species. Values presented are mean ± SD of three replicates. Relationships between various parameters were tested via Pearson’s correlation coefficient test. Significant differences in the mean values were determined at