The genetic correlation was estimated using 2 (one per color) 2-trait models (each country a trait), including all available data , by maximum profile likelihood while fixing other variance components to within-population estimates. Results showed a closer genetic relationship of Blond strains than of Black strains . Genetic correlation estimates for milk yield were 0.70 in both cases. Based on Fst distances, we expected a lower correlation for Black strains than for Blond ones if dominance or epistasis are important. Thus, we attribute the value of this correlation not being close to 1 mostly to genotype-by-environment interaction, including on-farm management and trait modeling. Regardless, the correlation of 0.7 across populations is encouraging for future joint work of Latxa and Manech breeders, including joint genetic evaluations.Spanish Latxa and French Manech are dairy sheep breeds that split into Blond  and Black  strains. Exchange of genetic material  is becoming more and more frequent across these breeds, within color, to boost both genomic precision using a larger reference population and genetic progress using a larger selection base. This exchange leads to some rams having descendance across both countries. However, additional gains can only be achieved if the selected traits are genetically similar across countries. The objective of this work was to estimate the genetic correlation across breeds for milk yield. We combine across-country, within-color records, pedigree, and marker information. The number of animals with records oscillates from 65,000 (LCN) to 544,000 (MTR), whereas the number of connecting artificial insemination rams (with more than 10 daughters in the other country) is 381 MTR rams in LCR and 58 MTN rams in LCN. Blond strains had a stronger and more extended-in-time connection. The number of genotyped rams goes from 328 (LCN) to 4,901 (MTR). The relatedness of populations was assessed by principal component analysis and F Across population (or countries) genetic and genomic evaluations and later selection can lead to several benefits. Among these benefits, 3 stand out: (1) a greater genetic progress across the 2 populations , (2) a pMTR; and Latxa Cara Rubia, LCR) strains are genetically very similar, and Black strains  are more distinct (The dairy sheep breeds Manech (in France) and Latxa (in Spain) have a common origin in the Western Pyrenees mountains and they are structured in different populations (strains or ecotypes), each with its own selection scheme . Previoudistinct . There idistinct . So, somMY), the most important trait in the breeding objective, separately for the Blond and Black strains of Latxa and Manech. If the correlation is high, exchange is useful and implementing joint evaluations will be useful too.A key parameter for an across-population genetic evaluation is the correlation across breeding objectives , where aData for this study came from an existing database of on-farm performance recording, and ethical approval for the use of animals was thus deemed unnecessary.The data available for each population are described in Given the importance of genealogical connections within color for correct estimation of genetic correlations, we also assessed pedigree completeness. Pedigree was quite complete, with 16% to 30% missing sires for the Blond strain and 25% to 32% missing sires for the Black strain. To consider missing sires, metafounders were defined every 3 yr, separately for Manech and Latxa, resulting in 10 for each within color. Pedigrees were joined within color. It was possible to do so because official identifications of Manech rams were kept when using them in Latxa.The genealogical connection across countries is essential for accurate estimation of the correlation. Thanks to the recent exchanges, there are 381 MTR AI rams with more than 10 daughters in LCR , and 58 MTN rams with more than 10 daughters in LCN . Most of these rams are genotyped. These AI exchanges occurred in the vast majority (88%) after 2000 for MTR and LCR and mainly (62%) after 2005 for MTN and LCN. The exchanges only occurred in one direction: from France (Manech) to Spain (Latxa). In our files, there is no trace of a trade of live animals, which, if it occurs, is very rare.Finally, we also used genotypes at SNP markers. Manech animals were genotyped with the 50k Illumina chip OvineSNP50, whereas Latxa animals had been genotyped with 2 chips: 50k Illumina chip OvineSNP50 and Affymetrix Axiom Ovine Genotyping Array, in proportions close to half and half . Edition and quality control of the genomic information were done in national routine evaluations. We combined the 2 genotype files for a set of common markers across all 4 populations and this resulted in 22,827 SNP loci being used for the across-country estimation for both strains. The total number of animals per breed is shown in PCA) and by means of the Fst differentiation coefficients. Then, we obtained an a priori  estimate of the genetic correlation, using equation (8) in We empirically assessed the proximity of the different populations using genomic information both visually  we ran regular REML (with pedigrees) and single-step genomic REML (with pedigrees + markers). Results were essentially the same in both cases, so for the sake of conciseness only single-step genomic REML results are shown.st coefficients across MTN and LCN were 0.053, and across MTR and LCR were 0.014. This is as expected as the MTR originated from LCR in the 20th century, which is not the case for MTN and LCN, which evolved in parallel (at least for the 20th century) and also because of the larger and older use of MTR in LCR than of MTN in LCN. The other Fst coefficients were 0.052 (LCN − LCR), 0.061 (LCN − MTR), 0.065 (MTN − LCR), and 0.072 (MTN − MTR). The PCA in The FWithin-breed estimated heritabilities for MY were 0.28 (MTN and LCN) and 0.27 (MTR and LCR). Interestingly, repeatabilities differ, with ≈0.60 for both Manech and ≈0.40 for both Latxa. This is probably due to different treatment of first and later lactations, first due to filtering in computation of MY, and later in the respective linear models.st coefficient. The a priori estimates using Fst coefficients yield estimates for the correlation of 0.98 (for Blond strains) and 0.93 (for Black strains). Again, these values do not fully explain the estimate of 0.7. Thus, we speculate that most of the genetic correlation is due to genotype-environment interaction, including models used. Differences in models among countries reside in the manner of combining the elementary effects flock, age, and period in the model, and also in the effect number of lambs born, which is considered as an effect in Latxa but not in Manech. In addition, there could be some differences in farm management and climate.In addition, in presence of nonadditive genetic correlation, we expected a higher value of genetic correlation for Blond than for Black strains, because Blond populations are closer genetically as shown by the PCA in Overall, the correlation of 0.7 across populations is encouraging for future joint work of Latxa and Manech breeders. New studies need to address the individual accuracy of across-population prediction, in particular for candidates to selection, and practicalities such as the optimal time frame for joint predictions .


Sustained activation of signal transducer and activator of transcription 3 (STAT3) is a critical contributor in tumorigenesis and chemoresistance, thus making it an attractive cancer therapeutic target. Here, SH2 domain‐containing adapter protein F (SHF) is identified as a tumor suppressor in glioblastoma Multiforme (GBM) and its negative regulation of STAT3 activity is characterized. Mechanically, SHF selectively binds and inhibits acetylated STAT3 dimerization without affecting STAT3 phosphorylation or acetylation. Additionally, by blocking STAT3‐DNMT1 (DNA Methyltransferase 1) interaction, SHF relieves methylation of tumor suppressor genes. The SH2 domain is documented to be essential for SHF's actions on STAT3, and almost entirely replaces the functions of SHF on STAT3 independently. Moreover, the peptide C16 a peptide derived from the STAT3‐binding sites of SHF inhibits STAT3 dimerization and STAT3/DNMT1 interaction, and achieves remarkable growth inhibition in GBM cells in vitro and in vivo. These findings strongly identify targeting of the SHF/STAT3 interaction as a promising strategy for developing an optimal STAT3 inhibitor and provide early evidence of the potential clinical efficacy of STAT3 inhibitors such as C16 in GBM. SH2 domain‐containing adapter protein F (SHF) is identified as tumor suppressor in glioblastoma and its negative regulation of STAT3 activity is characterized by disrupting acetylated STAT3 dimerization and STAT3‐DNMT1 interaction, thereby relieving methylation of tumor suppressor genes, and a peptide targeting STAT3‐binding sites of SHF is demonstrated as a suppressive strategy. Signal transducer and activator of transcription 3 (STAT3) is a critical mediator of the occurrence, development, and prognosis of GBM. Sustained activation of STAT3 frequently occurs in GBM and has been recognized as a potential treatment target.Glioblastoma Multiforme (GBM) is the most common malignant primary brain tumor in adults, with a rapid growth rate and high local recurrence.1 The  Upon phosphorylation, a STAT3 dimer is formed and translocated to the nucleus, binding to specific DNA‐response elements in the promoter of target genes and inducing or suppressing gene expression. A critical step of STAT3 activation is the dimerization of two STAT3 monomers. Thus, the inhibition of STAT3 dimerization presents an attractive target for the abolishment of STAT3 biological functions. STAT3‐STAT3 dimerization relies on the reciprocal interaction of its SH2 domains. Although disrupting the reciprocal interaction of the SH2 domains has demonstrated efficacy for the inhibition of STAT3 activity, it remains a challenging target for drug discovery, which restricts clinical development.Typically, STAT3 is activated through phosphorylation induced by cytokine receptor associated kinases such as Janus kinases (JAKs) and receptor tyrosine kinases following ligand binding.3 Upon Proteins containing the SH2 domain can dock with phosphorylated tyrosine residues on other proteins. Among the SH2 containing proteins, the SH2 domain‐containing adapter proteins comprise a subfamily which plays multifunctional roles across a range of cellular activity. These proteins operate by linking activated tyrosine kinase receptors to downstream signaling components. A highly conserved and common feature of these proteins is that the SH2 domain is located at the C‐terminal. SH2 domain‐containing adapter protein F (SHF) has a unique protein structure compared to other family members, namely in that its N‐terminal lacks a proline‐rich region and phosphotyrosine‐binding (PTB) domain. This makes SHF the only family member negative regulatory action on tyrosine kinase receptor‐oriented signal pathways. Although there are only a few studies on SHF, there are clues that suggest the negative association of SHF with STAT3.The SH2 domain is a structurally conserved protein domain contained in Src and many other intracellular signal‐transducing proteins.13 ProIn the present study, we observed that SHF is the most significant downregulated gene among SH2 domain‐containing adapter proteins in GBM. Its downregulation predicts poor patient survival and is implicated in high carcinogenicity. Functional experiments identified SHF as a tumor suppressor in GBM. Mechanical investigation further disclosed that SHF impairs GBM tumorigenesis by binding to and inhibiting STAT3 dimerization through its SH2 domain. Additionally, by blocking STAT3‐DNMT1 interaction, SHF relieves methylation of multiple tumor suppressor genes. Using protein‐protein docking, a peptide was derived from the STAT3‐binding sites of SHF and demonstrated to be efficacy as a GBM suppressive strategy.22.1http://ualcan.path.uab.edu/). SHF was found to be the most significantly downregulated gene   Figure . Surviva) Figure . Genes n) Figure . To dete) Figure . For sur) Figure , regardl) Figure . Next, w) Figure . As show) Figure .2.2Figure Next, the functions of SHF were investigated in GBM cell lines. Since SHF is expressed relatively lower in GBM cell lines  and the genes are listed in Supplementary Data  SHF binding of STAT3 allows the possibility that SHF binds with either STAT1 or STAT5. As shown in Figure  these results indicate that the tumor suppression action of SHF is mechanically targeted toward STAT3 dimerization.To explore whether STAT3 is mechanically involved in GBM suppression of SHF, the overall STAT3 transcriptional activity was evaluated in GBM cells by a SIE reporter system used previously.19 As 2.4 SHF overexpression failed to induce detectable alterations of STAT3 phosphorylation  in which four lysine residues  were converted to Arg (R). As shown in Figure Phosphorylation and acetylation are critical for STAT3 stable dimer formation upon cytokine or growth factor activation. Although SH2 domain‐containing adaptor proteins regulate several intracellular signal transduction cascades,13 SHFn Figure  in GBM cn Figure , indicatn Figure . To addr2.5 Given that acetylation at K685 in the SH2 domain of SHF is crucial for binding of STAT3 to DNMT1, we explored the possibility that SHF could disrupt the interaction of STAT3 and DNMT1. A reciprocal IP analysis was performed to examine the effects of SHF on the interaction between STAT3 and DNMT1. 293T cells were co‐transfected with Myc‐DNMT1, Flag‐STAT3 and HA‐SHF or GFP plasmids. Results demonstrated that STAT3 and DNMT1 formed a complex, while SHF introduction impaired the binding between STAT3 and DNMT1 . After cross referencing genes positively correlated with SHF and those possessing overall survival significance . Among the four genes evaluated, TCF12, MUM1, and DSCAM mRNA expression was increased in cells expressing ectopic SHF ‐methyltransferase 1 (DNMT1).25 Giv1 Figure . Next, w1 Figure . To furt1 Figure , a databe Figure  using twF Figure . Due to F Figure , suggest2.6Figure Since SHF is a protein containing a SH2 domain, we sought to determine the involvement of the SH2 domain in mediating STAT3 dimerization inhibition and tumor suppression. To this end, we performed IP experiments using a SH2 construct and SH2 deletion mutant based on the known SHF protein structure Figure 6A. Compa2.7https://swissmodel.expasy.org/). To simulate the interaction of SHF and STAT3, Discovery studio 2018 was employed to complete protein‐protein docking using ZDOCK as an initial stage. A rigid body molecular docking algorithm utilizing a fast Fourier transform (FFT) algorithm was used to further improve performance for searching in translational space. All the available structures from PDB were used to calculate the docking poses and the structures obtained were subjected to energy minimization filtration using the smart minimize algorithm . The highest resulting ZDock score was used as the appropriate conformational pose. Regarding ZDOCK models, the suggested interface for SHF included two sites spanning the amino acids 388–402 and 413–421  and STAT3 (aa130‐715). both of which spanned their respective SH2 domain. were simulated by Swiss‐Model server  also exerts transcriptional regulation on certain genes which are not targets of phosphorylated STAT3. Moreover, STAT3 can be acetylated at both the N‐terminal (K49 and K87) and the SH2 domain (K685 and K631), two modifications considered to be required for proper nuclear localization, transcription, and DNA methylation. The most well studied STAT3 acetylation occurs on the K685 residue, a modification critical for the stable dimerization of STAT3. Acetylation on K685 plays vital roles for U‐STAT3 target gene regulation, indicating acetylation regulated STAT3 activity is independent of phosphorylation. To some extent, acetylation it is more important for STAT3 function than phosphorylation. However, using the K4R mutant spanning K685R, the current study found that unacetylated STAT3 can form a dimer. Although acetylation modification at other sites cannot be excluded, at least, this discovery reveals the complexity and diversity of STAT3 dimerization regulation. To date, the roles of U‐STAT3 and non‐acetylated STAT3 in regulating the actions of STAT3 are far from clear. This may also be one of the reasons why STAT3 has dual functions in cancer promotion and inhibition. Thus, it is not difficult to predict the successful clinical application of small‐molecule inhibitors targeting STAT3 dimerization.STAT3 has been extensively studied as a therapeutic target in cancers, and STAT3 inhibitors or drugs have been greatly progressed.28 Two which may rule out the possibility that SHF alters the structure of acetylated STAT3. To date, there is no report documenting the difference between acetylated and non‐acetylated STAT3 in the context of dimerization. Due to the finding that STAT3‐binding of SHF is acetylation dependent, it's reasonable to believe that acetylation is enough to determine the protein binding status of STAT3, and that SHF may occupy the binding site of acetylated STAT3 with other proteins. A limitation of the current study is that only four lysine sites located in the SH2 domain of STAT3 were used to examine the effect of SHF on STAT3 dimerization. K685 is the most studied acetylation modification of STAT3, acting as a required modulator of STAT3 dimer formation and subsequent transcriptional regulation. However, besides K685 and the other three sites, two lysine residues (K49 and K87) in the NH2‐terminus region and two lysine residues (K707 and K709) in the C‐terminus are also acetylated sites in STAT3. The function of these modifications is currently debated, while the importance of acetylation to STAT3 is consistent. The provided evidence that K685R can dimerize indicates that K685 is not the only acetylation site that determines STAT3 dimerization. SHF has no effect on overall STAT3 acetylation, excluding the possibility that SHF affects STAT3 dimerization and activity by modulating acetylation of other sites. It supposed that the effect of SHF on STAT3 is mainly aimed at the dimerization mediated by acetylation in SHF‐binding region of STAT3. The significance of acetylation in this region to STAT3 function needs to be clarified.One novel finding of the current study was that SHF binds STAT3 and selectively blocks acetylated STAT3 dimerization. Although the binding of STAT3 covers K685, SHF failing to inhibit STAT3 acetylation raises two interesting concerns of how SHF acts on acetylated STAT3 dimerization and why SHF has no effect on non‐acetylated STAT3 dimerization. A recent study reported that there is no structural difference between acetylated and non‐acetylated STAT3,23 whi STATs share structural similarities and conserved functional regions, among which the SH2 domain mediates homo‐ and heterodimerization of STAT monomers during activation. For STAT3, the common heterodimer binding proteins are STAT1 and STAT5. STAT3 and STAT5 are currently considered oncogenes, while STAT1 appears to play opposite roles in tumorigenesis. One previous study reported that STAT3 does not suppress STAT1 tyrosine phosphorylation or nuclear translocation but instead sequesters STAT1 and suppresses the formation of DNA‐binding STAT1 homodimers, suggesting STAT3 and STAT1 mutually inhibit each other through heterodimer formation. However, the current data reveals that no heterodimer formation of STAT3 and STAT1 occurs in an environment without inflammatory factors in GBM cells. Although binding STAT1, SHF fails to alter the interaction between STAT3 and STAT1 without external stimulation. It suggests that tumor inhibition of SHF probably underlies the regulation of STAT1 independent of STAT3, a question worthy of further exploration. Moreover, the current finding that SHF inhibits STAT3/DNMT1 interaction provides a clue that SHF also acts on other STAT3 interacting proteins, especially those binding via the STAT3 SH2 domain such as gp130, leukemia inhibitory factor receptor (LIFR), epidermal growth factor receptor (EGFR), interleukin 10 receptor (IL‐10R), etc. The interaction between EGFR and STAT3 occurs in the nucleus upon ligand‐dependent EGFR activation. Given that SH2 containing proteins can dock with phosphorylated tyrosine residues on EGFR, SHF is expected to be a impedance of EGFR/STAT3 complex formation in the nucleus. As an SH2 adaptor protein of putative tyrosine phosphorylation sites, SHF is potentially involved in signal transduction, and is itself regulated by phosphorylation. Additionally, other protein posttranslational modifications, including sumoylation, acetylation, ubiquitination, and so on may similarly modulate protein interactions, cellular localization, and functions of SHF.In addition to homodimerization, STAT3 also forms heterodimers with other STATs, followed by translocation to the nucleus where it can mediate unique functional events.40 STA indicating that C16 is a specific nuclear STAT3 gene inhibitor. The current evidence derived from STAT3 dimerization and methylation inhibition to in vivo tumor suppression make C16 an attractive strategy for further development and utilization, such as improving solubility, bioavailability, and tumor‐targeted delivery. Another application direction is to degrade acetylated STAT3 by using PROTAC and other technologies. Using C16 as the medium for sequestering acetylated STAT3 using design proteolysis targeting chimera (PROTAC) STAT3 degraders is regarded as one of the directions for future improvement.In the current study, we demonstrate the application of C16 peptide in targeting the SHF/STAT3 interaction in GBM. The core sequence of C16 is only 34 amino acids, which binds a limited surface of STAT3. Among the four acetylation sites of the STAT3 SH2 domain, it only convers K685, suggesting that its inhibition on STAT3 is K685 acetylation dependent. The critical roles of K685 acetylation for STAT3 stable dimerization and DNA binding is largely recognized,3 indiIn conclusion, this study identifies SHF as a new tumor suppressor for GBM treatment. By blocking acetylated STAT3 dimerization and DNMT1 interaction SHF may serve as an attractive therapeutic target for patients with a range of cancers, not only GBM. The derived therapeutic peptide also deserves further exploration.4http://ualcan.path.uab.edu/; Supplementary Data http://gliovis.bioinfo.cnio.es/; Supplementary Data http://www.gsea‐msigdb.org/) GSEA v4.1.0 and the Molecular Signatures Database (MSigDB database v7.4) were used. Overlaps of SHF correlated gene sets (metric for ranking genes) were computed according to the specified categories in MSigDB collections using the default setting. Pearson was selected in the Metric for ranking genes parameter.The expression of genes coding the SH2 domain‐containing adapter protein was assessed between normal and GBM‐affected brains using the TCGA‐GBM database accessible through UALCAN  multiplied by the staining intensity score (0–3). The proportion score represented the percentage of positive cells while the intensity score represented the average intensity of staining . Two blinded pathologists reviewed and scored the slides. The mean IRS score was considered as the final IRS (Supplementary Data 4).SHF protein expression in GBM tissue was analyzed using immunohistochemical (IHC) staining on a tissue array. The tissue microarray chips contained a total of 64 samples  with follow‐up data obtained from the affiliated hospital. All patient information was obtained and used in accordance with approved protocols from the institutional review boards of the participating institutions (No.(2020)353). The clinical information of the cohort is described in Table 19 Bri changing half of the medium every 2 days. For patient‐derived GBM cell culture, fresh GBM tissues derived from surgery were collected immediately after tumor resection, washed, minced, and enzymatically dissociated. Tumor cells were resuspended and cultured in GBO medium to allow tumor sphere formation. The primary spheres were cultured for 10 days to obtain enough cells for passage. The third or fourth passage of cells was used for subsequent experiments, including tumor xenografts and Western blot assay.The human glioma cell lines U87, U118, U251, T98G, A172, H4, and human embryonic kidney cell line, 293T, were obtained from the Cell Bank of Type Culture Collection of the Chinese Academy of Sciences . All cells were cultured in DMEM with 10% fetal bovine serum . These cells were characterized by Genewiz, Inc. (China) using short tandem repeat markers and were confirmed to be mycoplasma‐free. For 3D‐Spheroid culture, 400 cells were seeded in spheroid microplate  according to a 3D‐spheroids formation and growth protocol. The spheroids were cultured with GBO medium with a minor modification (without insulin) according to a previously published report,44 chaCell growth was quantified using the Cell Counting Kit‐8 Assay Kit . Each experiment was repeated at least six times. For the colony formation assay, 800 cells were seeded into each well of a six‐well plate with soft agar  and maintained in a medium containing 10% FBS for 14 days. The colonies were fixed with methanol and stained with 0.1% crystal violet; the clones containing at least 50 cells were counted using an inverted microscope. Briefly, cells were cultured in 24‐well plate for 36 h followed by an incubation of 10 ≤ µm EdU  for 2–3 h and fixation with 4% paraformaldehyde (PFA). The staining procedure was performed according to the manufacturer's instructions for the kFluor488 Click‐iT EdU Kit . After staining, the coverslips were mounted with Gelmount containing Hoechst 33 342 (Sigma) and the cells in 60 mm dish were analyzed by a FACScan flow cytometry .Cell proliferation and DNA replication was analyzed by EdU incorporation method as described previously.45 Bri−1; BD bioscience, Bedford, MA). A total of 1 × 105 cells in 200 µL of the culture medium (supplemented with 0.1% FBS) were added into the upper chamber, and 600 µL of medium supplemented with 10% FBS were added into the lower chamber to serve as a chemotactic agent. After 48 h of incubation, cells remaining in the upper chamber were carefully cleared, and cells invading into the lower chamber were fixed and stained with crystal violet. The stained cells were dissolved by 3% glacial acetic acid; the number of invading cells was quantified by detecting the absorbance of the solution at OD450.Cell invasion assays were performed using transwell chambers  coated with Matrigel . For sphere growth assay, photographs of tumor spheres were taken at the indicated time points and the sphere area was measured. The sphere growth was expressed as the ratio of the area at different time points relative to the area at 1 day.Tumor sphere formation was analyzed as described previously, with some modification.19 Brim) for 48 h. Cells were harvested and rinsed with ice‐cold PBS and then resuspended in 200 µL of binding buffer. 10 mL of AV‐FITC stock solution was added to cell suspensions and incubated for 15 min at RT. Cells were further rinsed with 200 µL ice‐cold PBS and immediately analyzed by a FACScan flow cytometry.Cell apoptosis was detected by flow cytometry according to the manufacturer's instructions of the Annexin V‐FITC Apoptosis Detection Kit . Briefly, cells were incubated with Doxorubicin . The pRL‐TK Renilla luciferase plasmid (Promega) was also transfected as an internal control reporter. 24 h after transfection, cells were treated with or without IL‐6  for 4 h. Cells were collected, lysed, and measured using the Dual‐Glo Luciferase Assay System kit (Promega).STAT3 transcriptional activity was evaluated by a luciferase reporter system according our previously study.19 BriFull‐length cDNA encoding human SHF, STAT3, and DNMT1 were amplified by PCR and verified by DNA sequencing. The mCherry‐SHF lentivirus was constructed by inserting the cDNA sequence into lentivirus vector GV348  with a mCherry‐tag. HA‐SHF was constructed by inserting cDNA into expression vector GV219 with a HA tag. The SH2 domain plasmid and deletion mutant were constructed based on the wild‐type SHF plasmid. STAT3 plasmids were constructed by inserting cDNA into expression vector GV141 with a Flag or Myc tag. The STAT3 non‐phosphorylation mutants , in which Y705 or/and S727 were replaced with alanine (A), were created from the wild‐type STAT3 plasmid. A STAT3 a phospho‐mimetic (YS2D), in which Y705 and S727 were replaced with aspartic acid (D), was also produced. The SH2 domain acetylation mimetic (K4Q) and non‐acetylation mutant (K4R) were constructed based on the STAT3 vector with lysine (K) mutations in the SH2 domain. Specifically, the lysine (K) mutation was altered to either a glutamine (Q) or arginine (R) at sites K601, K615, K631, and K685. A phospho‐mimetic with a SH2 non‐acetylation mutant (YS2D‐K4R) was constructed based on the YS2D vector. SHF‐DsRed and STAT3‐DsRed were constructed by inserting cDNA into expression vector GV147 with a DsRed2 tag (Genechem). STAT3‐EGFP was constructed by inserting cDNA into expression vector GV230 with a EGFP tag (Genechem). STAT3‐nEGFP, STAT3‐cEGFP, and SHF‐cEGFP were constructed by inserting cDNA into expression vector GV712 (Genechem) with a N‐EGFP  or C‐EGFP  tag. Finally, the DNMT1 expression plasmid was constructed by inserting cDNA into the expression vector GV219 with a Myc tag. Antibodies used to determine the indicated protein are shown in Table Double immunofluorescence (IF) staining was performed according to our previously established protocol.19 AntStandard Western blot assays were used to measure protein expression. Antibodies used to determine the indicated proteins are specified in Table Immunoprecipitation (IP) experiments were performed to analyze the protein interactions. In brief, cells were transfected with the indicated plasmids and whole‐cell lysates were precipitated using Protein A/G beads  with the indicated antibodies. Precipitated products were analyzed by Western blot using the specified antibodies. Antibodies used in IP and Western blot experiments are shown in Table Total RNA was extracted using TRIzol reagent (Invitrogen) according to the manufacturer's instructions. cDNA was synthesized with the M‐MLV Reverse Transcriptase Kit . PCR analyses were conducted to quantify mRNA expression using Real SYBR Mixture (Cwbio) on a Lightcycler 480 II instrument (Roche Applied Science). GAPDH severed as an internal control. The specific oligonucleotide primer pairs are listed in Table 7 cells and 5 µg anti‐STAT3 antibody were used for each ChIP. The mouse IgG and rabbit anti‐Histone H3 antibody was applied as negative control and positive control, respectively. After reversal of cross‐links and DNA purification, the samples were used for quantitative real‐time PCR with specific primers targeting gene promoters  according to the manufacturer's instructions. Briefly, 2 × 10s (Table 19https://swissmodel.expasy.org/), using Mus musculus STAT3 (1BG1) and Homo sapiens Tyrosine‐protein kinase ABL1 (6AMW) as templates, respectively. To simulate the interaction of SHF and STAT3, Discovery studio 2018 was employed to complete protein‐protein docking using ZDOCK (http://zdock.umassmed.edu/) as an starting point. All the available structures from Protein Data Bank  were used to calculate the docking poses and the structures obtained were subjected to energy minimization using the smart minimize algorithm . The resulting ZDock scores with the highest values were used as appropriate conformational poses. The resulting interface for SHF included two closely located sites. The sequence spanning these two sites was used as a peptide design master. To obtain a peptide with high internalization efficiency and a strong half‐life, we introduced a palmitic acid at the N‐terminus of the master sequence and amidated the C‐terminus. To facilitate screening and identification, 3× His tags were added to the C‐terminal. The designed peptide was named C16 and synthesized directly . A scrambled peptide (SC) containing the same flanking sequence with a scrambled sequence (PASSNCEIVPMYHIRKLPIKGAEHMSLLYPVAIR) targeting Spot1 of the master sequence was designed and used as a negative control.To achieve an optimal targeting strategy for STAT3 inhibition of SHF, more precise identification of the STAT3, SHF binding site was analyzed. Briefly, the structures of human SHF (aa 265–421) and STAT3 (aa 130–715) spanning the SH2 domain were simulated by Swiss‐Model server  by a microwave synthesizer . Fmoc‐protected amino acids  were used. After the quantified resin was swelled, deprotected, and washed, a mixed solution of Fmoc‐protected amino acid and activator  was added. Then the mixture was bubbled with nitrogen under microwave irradiation and washed with DMF . The procedures of deprotection and coupling were repeated with the relevant Fmoc‐protected amino acids to create the peptide‐resin complex. Then resin was washed successively with DMF three times. The final peptide was cleaved using TFA/TIS/H2O  for 4 h at room temperature and precipitated with cold ethyl ether. Then ethyl ether was removed through centrifugation. All peptides were purified by preparative reversed‐phase high‐performance liquid chromatography  on a C18 column . The purity of C16 was demonstrated above 95%.m, respectively). The mixture was stirred constantly for 12 h until complete adsorption of C16 and scramble from STAT3. The free C16 and scramble peptide concentration in the PBS solution was evaluated using NanoDrop 2000 by measuring absorbance intensity at λmax = 240 nm. The Langmuir isotherm equation was applied to identify the Qmax (nmol g−1) and Kd (nmol mL−1) of C16.Adsorption equilibrium experiments were carried out to explore the adsorption behavior of C16 by reconstituting 0.755 mg (powder) of STAT3 into flasks containing 20 mL of PBS solution mixed with different concentrations of C16 and scramble  ELISA Easy Kit . Levels of 5‐methylcytosine (5‐mC) were calculated according to the manufacturer's instructions. In brief, the purified and quantified genomic DNA were bound to strip‐wells treated for high DNA affinity. The methylated fraction of the DNA was detected using capture and detection antibodies. Reaction was then quantified colorimetrically by reading the absorbance in a microplate spectrophotometer. The percentage of methylated DNA was proportional to the OD intensity measured.Global DNA methyltransferase activity was measured using the EpiQuik DNMT (DNA Methyltransferase) Activity/Inhibition Assay Ultra Kit (Epigentek). In this assay, the unique cytosine‐rich DNA substrate is stably coated on the strip wells. Cell nuclear protein was extracted and incubated with the substrate. The methylated DNA can then be recognized with an anti‐5‐methylcytosine antibody. The ratio or amount of methylated DNA, which is proportional to enzyme activity, was colorimetrically quantified through an ELISA‐like reaction.http://www.urogene.org/methprimer/index.html; Table β‐actin) gene was used as a reference gene. No‐template controls were included in each run as negative controls. An EpiTect Control DNA, a 100% methylated DNA , was used as a positive control for all genes studied. The percentage of methylated reference value (PMR) was calculated by dividing the gene/ACTB ratio in a sample by the gene/ACTB ratio in the EpiTect Control DNA (Qiagen) sample and multiplied by 100. Parallel PCR reactions were carried out for the genes of interest and reference. PMR values were detected using the comparative CT method. The relationship between the percentages of methylated DNA molecules and CT is described as PMR = 2−ΔΔCT × 100%.Genomic DNA from U87 control cells or cells expressing ectopic SHF were isolated using Universal Genomic DNA kit (Cwbio) and quantified using NanoDrop . Bisulfite converted DNA was used to perform the qMSP by Methylamp MS‐qPCR Fast Kit  according the manufacturer instructions. For each reaction, 20 ng bisulfite‐treated DNA was used as a template. Primers for the genes of interest were designed using MethPrimer  in 24 well‐plates. For detecting SHF inhibiting STAT3 dimerization, ectopic SHF‐expressing stable U251 cells were transfected with STAT3‐DsRed and STAT3‐EGFP. For detecting SHF binding STAT3, U251 cells were transfected with SHF‐DsRed and STAT3‐EGFP. Cells were imaged on a Leica TCS SP8 confocal microscope  after 24 h. Cells were excited with 488 nm laser (donor), and the emission of the acceptor DsRed was recorded at 565 nm. The FRET efficiency was calculated on a pixel‐by‐pixel basis with the following equation: FRET efficiency = FRETcorr / (FRETcorr + Donor). “FRETcorr” is the pixel intensity in the corrected FRET image and “Donor” is the intensity of the corresponding pixel in the donor channel image. To increase the reliability of the calculations and prevent noise from distorting the calculated ratio, we excluded pixels below 50 intensity units and saturated pixels from the calculations and set their intensities to zero. These pixels are shown in black in the pseudo‐colored FRET efficiency images. An exact measurement of the concentration of proteins participating in the interaction was beyond the scope of this study.Spectral fluorescence resonance energy transfer (FRET) assay was used to examine the dimerization of STAT3 and interaction between STAT3 and SHF. The procedure was performed as described previously.46 Bri The principle of BiFC analysis of STAT3‐STAT3 and STAT3‐SHF interactions was shown in a schematic view  analysis was performed as described previously.47 The5 cells was stereotactically injected into the brains of individual mice. Tumor formation was monitored by magnetic resonance imaging  at 28 days after implantation. For U87 cell intracranial models, cells expressing the firefly luciferase gene were infected with SHF‐overexpressing lentivirus or control lentivirus. A total of 5 × 105 cells were stereotactically implanted into the brains of individual mice. Tumor growth was monitored via bioluminescent imaging using IVIS Spectrum system and quantified by Living Image Software. To determine the effect of C16 on tumorigenicity, mice were injected i.p. once every 2 days with 10 µg of C16 or scrambled control (SC) from third day after tumor implantation. Tumor growth was monitored via bioluminescent imaging. Additionally, to examine the tumor inhibition of the SH2 construct, a total of 5 × 106 U87 cells expressing ectopic SH2 or empty vector were injected s.c. into the flank of each nude mouse. Tumor growth was assessed by tumor diameters measured every 3 days. To measure tumor cell proliferation, mice were injected i.p. with EdU  24 h before sacrifice. At the end of experiments, mice were sacrificed via anesthetic overdose. Whole brains or tumors were removed, paraffin‐embedded, sectioned, and stained with hematoxylin and eosin (H&E) or EdU staining. Images were captured using a laser scanning confocal microscope. All animal care and handling procedures were performed in accordance with the National Institutes of Health's Guide for the Care and Use of Laboratory Animals. All animal experiments were approved by the Institutional Review Board of Nanjing Medical University (No.(2020)353). All animal experiments were performed by two technicians blinded to the treatment condition of the mice.Four week‐old female nude mice were purchased from the Shanghai Animal Center, Chinese Academy of Sciences, and maintained under specific pathogen‐free conditions. All mice were assigned randomly. For primary GBM tumor cells tumor tumorigenicity, the second or third passage of 3D‐cultured spheres were dissociated and counted. A total of 5 × 10χ2‐test, or Spearman's rank correlation analysis. The F test was used to test variance equality. Differences were considered significant when p < 0.05. SPSS 16.0 package (IBM) and Graphpad prism 8.0 software (GraphPad Software) were used for all statistical analyses and data graphing, respectively.Data were expressed as mean ± SD. The difference between groups was performed using a one‐way analysis of variance (ANOVA) followed by a Newman Keuls’ multiple comparison test, the Kaplan–Meier method with log‐rank test, The authors declare no conflict of interest.The study was approved by the Institutional Ethics Committee of Wuxi People's Hospital of Nanjing Medical University to the commencement of the study.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supplemental Table 1Click here for additional data file.Supplemental Table 2Click here for additional data file.Supplemental Table 3Click here for additional data file.Supplemental Table 4Click here for additional data file.Supplemental Table 5Click here for additional data file.Supplemental Table 6Click here for additional data file.Supplemental Table 7Click here for additional data file.Supplemental Table 8Click here for additional data file.Supplemental Table 9Click here for additional data file.Supplemental Table 10Click here for additional data file.


Castanea mollissima Bl.) is one of the earliest domesticated and cultivated fruit trees, and it is widely distributed in China. Because of the high quality of its nuts and its high resistance to abiotic and biotic stresses, Chinese chestnut could be used to improve edible chestnut varieties worldwide. However, the unclear domestication history and highly complex genetic background of Chinese chestnut have prevented the efficiency of breeding efforts. To explore the genetic diversity and structure of Chinese chestnut populations and generate new insights that could aid chestnut breeding, heterozygosity statistics, molecular variance analysis, ADMIXTURE analysis, principal component analysis, and phylogenetic analysis were conducted to analyze single nucleotide polymorphism data from 185 Chinese chestnut landraces from five geographical regions in China via genotyping by sequencing. Results showed that the genetic diversity level of the five populations from different regions was relatively high, with an observed heterozygosity of 0.2796–0.3427. The genetic diversity level of the population in the mid-western regions was the highest, while the population north of the Yellow River was the lowest. Molecular variance analysis showed that the variation among different populations was only 2.07%, while the intra-group variation reached 97.93%. The Chinese chestnut samples could be divided into two groups: a northern and southern population, separated by the Yellow River; however, some samples from the southern population were genetically closer to samples from the northern population. We speculate that this might be related to the migration of humans during the Han dynasty due to the frequent wars that took place during this period, which might have led to the introduction of chestnut to southern regions. Some samples from Shandong Province and Beijing City were outliers that did not cluster with their respective groups, and this might be caused by the special geographical, political, and economic significance of these two regions. The findings of our study showed the complex genetic relationships among Chinese chestnut landraces and the high genetic diversity of these resources.Chinese chestnut ( Castanea mollissima Blume, which is commonly referred to as the Chinese chestnut, is a member of the family Fagaceae. Seven species of Castanea are widely distributed in the temperate zone of the northern hemisphere. Four species are distributed in Asia: C. mollissima Blume, C. seguinii Dode, and C. henryi (Skan) Rehd. et Wils. in China and C. crenata Sieb. et Zucc. (Japanese chestnut) in Japan and the Korean Peninsula. Two species are distributed in North America, C. dentata Borkh. (American chestnut) and C. pumila Mill. (American chinquapin), and one species, C. sativa Mill. (European or sweet chestnut), is distributed in Europe. Chinese chestnut, European chestnut, and Japanese chestnut are the main cultivated species, and they are widely planted because of their high yields of edible chestnuts and economic value. These species also have abundant germplasm resources, can grow under diverse environmental conditions, and bear nutritious fruits; Chinese chestnut is also an important source of genes for the improvement of edible varieties [arieties ,2. Asianarieties . Given tarieties . Chinesearieties .The origin of chestnut species and the center of genetic diversity of chestnuts is in mainland China . The secThe development of next-generation sequencing technology has greatly increased sequencing throughput and reduced sequencing costs . The genThe factors affecting variation in the genome and genetic variation among populations, such as natural selection, mutation, gene drift, and gene flow, can be inferred through the study of polymorphic sites based on high-coverage whole-genome data . PopulatSingle nucleotide polymorphisms (SNPs) are single-base differences in the genome of different individuals of a species, and they are considered the most common type of genetic variation ,15. SNPsExploration of the origin and evolution of plants is important for enhancing the development and utilization of plant resources and genetically improving existing cultivars. The traditional classification of chestnut varieties was based on their morphological characteristics , biological characteristics, and economic traits . HoweverGenotyping by sequencing (GBS) is a particularly promising genomic approach that has proven to be an effective tool for genetic studies of plants, such as barley, switchgrass, yellow mustard, olive, and Norway spruce ,23,24. GHere, GBS was performed on 185 Chinese chestnut landraces from five regions  to obtain genome-wide SNP data used to characterize genetic diversity and population structure of Chinese chestnut. ADMIXTURE analysis, principal component analysis (PCA), and phylogenetic analysis were performed to clarify the genetic relationships among Chinese chestnut landraces from different regions. We also aimed to clarify the role that humans have played in the diffusion of Chinese chestnut populations.C. mollissima) from five regions were collected, including 39 samples from the North of the Yellow River region, 90 samples from the Eastern Coastal region, 22 samples from the Yangtze River Basin region, 24 samples from the South Central region, and 10 samples from the Midwest region. These regions span 14 provinces in China, including Beijing City, Hebei Province, Anhui Province, Jiangsu Province, Shandong Province, Zhejiang Province, Shanghai City, Hubei Province, Hunan Province, Guangxi Province, Guangdong Province, Yunnan Province, Henan Province, and Shaanxi Province (C. mollissima ‘Hongli’ × Wild Chinese chestnut), all other accessions are locally grown C. mollissima cultivars.A total of 185 different landraces of Chinese chestnut  using the Burrows-Wheeler Alignment tool (BWA) [The raw data obtained from sequencing were filtered by quality control using the following criteria: (1) reads containing adapters were filtered; (2) when the N content (undetected base) in single-end sequencing reads exceeded 10% of the length of the read, the paired reads were removed; and (3) when the number of low-quality (≤5) bases contained in single-end sequencing reads exceeded 50% of the length of the read, the paired reads were removed. The products of the GBS digestion were statistically analyzed to evaluate enzymatic digestion efficiency. The number of reads with two ends generated by MseI and the ratio of the number of captured reads to the number of high-quality reads (enzymeCatchRatio) were determined. After obtaining the high-quality sequencing data, they were mapped to the reference genome (ol (BWA) . The parSAMTOOLS was used to detect population data in our study . A BayesPopulation genetic structure refers to the non-random distribution of genetic variation in a species or population. The subpopulations based on their geographical distributions or other criteria are usually geographically isolated individuals or populations. Different individuals within the same subpopulations are closely related to each other, and individuals of different subpopulations are more distantly related. ADMIXTURE is a program for the maximum likelihood estimation of individual ancestries based on large SNP genotype datasets. Here, it was utilized to analyze the population structure of Chinese chestnut. After generating the PLINK input Ped file, ADMIXTURE analysis was performed to characterize population genetic structure and identify population lineages.PCA is a dimensionality reduction method that can transform the initial data set into a set of linearly uncorrelated variables. It depends on the date set and divides into two or three major axes of variation for visualization. PCA can also be used for cluster analysis. Eigenvectors and eigenvalues were calculated using GCTA software, and 2D and 3D PCA graphs were plotted using R. Individuals were clustered into different subgroups based on principal components according to SNP differences in individual genomes. We used this approach to explore the genetic structure of Chinese chestnut subpopulations.http://treesoft.sourceforge.net/treebest.shtml, accessed on 19 October 2007) was used to generate the distance matrix. The neighbor-joining method was used to construct the phylogenetic tree of Chinese chestnut with 1000 bootstrap replicates.TreeBeST software   with a normal GC distribution .No samples were contaminated by adapter sequences, indicating a successful library construction. Then, sequencing data of 185 chestnuts were mapped to the reference genome . The average mapping rate of the population samples was 91–98.13%, and the average sequencing depth of the genome was 6.51–14.75× with a 1× coverage rate (at least one base coverage) for over 5.08% of the samples .Next, we detected a total of 3,962,053 SNP sites using SAMTOOLS, which were filtered to obtain high-quality SNPs using the criteria of dp2, Miss0.2, Maf0.01, and the filtering criteria were performed according to the description of Lipka et al., 2012 . A totalWe found that the five populations have high heterozygosity with obvious differences . ResultsMolecular variance analysis is an analysis of molecular differences. It defines different genetic structures and carries out statistical tests by classifying and dividing the studied populations at different levels, so as to estimate the proportion of differences within populations and among individuals in the total variation among populations. The variance component among the five Chinese chestnut populations was 8.759, and the variation percentage was 2.07%. Moreover, the variance component within the population was 414.740, and the variation percentage was 97.93%, which indicates that the genetic variation of the Chinese chestnut population mainly came from within the five populations . These rADMIXTURE analysis was performed on 299,015 SNPs to explore the population structure of all Chinese chestnuts. The number of populations (K) was set from 1 to 12, and the K optimum was selected based on the cross-validation error compared to other K values . As we wA total of 185 chestnuts were divided into two groups when k = 2 . The firAll chestnut samples were divided into three groups of 98, 63, and 24 samples when k = 3 b. There https://tassel.bitbucket.io/, accessed on 23 February 2021) was used to analyze the results of PCA based on N × SNP matrix [Tassel v5.0 software  and common walnut (Juglans regia) were also greatly affected by human behavior [In the same way, previous studies have shown that iversity . SSR mariversity . The Wesiversity ,37. Thesbehavior ,39.Chinese chestnut, which is considered a woody grain, is well known for its short growth cycle, high yield, strong adaptability, and its ability to grow in mountainous areas. With its deep roots and luxuriant leaves, Chinese chestnut can not only increase the income of farmers, but also regulate the climate, fertilize the topsoil, and provide ecosystems services . This suA lack of knowledge of the genetic relationships among chestnut populations, patterns of genetic diversity, and the domestication history of chestnut impedes future chestnut breeding efforts. SNPs, a third-generation molecular marker, were used in this study to explore the population structure of chestnuts in China. Our study showed that the genetic diversity level of the five Chinese chestnut populations from different regions was relatively high in observed heterozygosity. The population in the mid-western regions showed the highest genetic diversity, while the population north of the Yellow River showed the lowest. Molecular variance analysis showed higher variation within the group, which indicated the genetic variation of chestnut mainly from the intra-populations. ADMIXTURE analysis with k = 2 revealed a north–south division of the samples, with the Yellow River as the geographical boundary. The results of the PCA and phylogenetic analysis were consistent with the results of the ADMIXTURE analysis. However, chestnuts from Shandong Province and Beijing City were outliers that were not clustered with their respective groups. Therefore, we speculate that the historical distribution of Chinese chestnut has been shaped by human activities, including several migration events driven by wars. The chestnuts in Shandong Province and Beijing City occur in an area with samples from both northern and southern regions, which is likely related to their geographical and political importance. Ultimately, we aimed to obtain information that could aid the genetic diversity evaluation and conservation strategies of chestnut genetic resources in China.


Glycine soja) accessions collected from three major agro‐ecological zones in China to parse the genomic basis of local adaptation in wild soybean. The population genomic diversity pattern exhibited clear agro‐ecological zone‐based population structure, and multiple environmental factors were observed to contribute to the genetic divergence. Demographic analysis shows that wild soybeans from the three ecological zones diverged about 1 × 105 years ago, and then the effective population sizes have undergone different degrees of expansions. Genome‐environment association identified multiple genes involved in the local adaptation, such as flowering time and temperature‐related genes. A locus containing two adjacent MADS‐box transcription factors on chromosome 19 was identified for multiple environmental factors, and it experienced positive selection that enables the adaptation to high‐latitude environment. This study provides insights into the genetic mechanism of ecological adaptation in wild soybean that may facilitate climate‐resilient soybean breeding.Global climate change has threatened world crop production and food security. Decoding the adaptive genetic basis of wild relatives provides an invaluable genomic resource for climate‐smart crop breedinG. Here, we performed whole‐genome sequencing of 185 diverse wild soybean ( About 90% of world wild soybean germplasms have been preserved in China , Huanghuai region (HR) and Northeast region (NER), which are corresponding to the major soybean cultivation regions  and expanded into the Northeast region after LGM, and that environmental factors contributed more to population differentiation than geographic factors in G. soja   accessions were collected from three major ecological zones in China, which correspond to the three major soybean production regions including the Northeast region , Huanghuai region  and Southern region   Figure . The thr) Figure  (Xu et a) Figure . For exa2.2Genomic DNA was isolated from the fresh leaves of the 185 accessions using the CTAB method . The sequencing depth and coverage compared with the reference genome were calculated based on the alignments. Single‐nucleotide polymorphisms (SNPs) and short insertion and deletions (Indels) were called using the UnifiedGenotyper function from the Genomic Analysis Toolkit  with default parameters (http://www.broadinstitute.org/gatk/)    was used to visualize the neighbour‐joining tree  with default setting  for visualization     decay was evaluated using 2.6distm function from the geosphere package (Version: 1.5–14) in R 4.1.3. Identity‐by‐state (IBS) between accessions was calculated using the snpgdsIBS function in the SNPRelate package  with 9999 permutations.Geographic distance between accessions was calculated using the 2.7−8, and the one generation per year. R custom code was used to visualize the result.Pairwise sequentially Markovian coalescent (PSMC) model (Version: 0.6.5‐r67) was used to refer to the history of effective population size changes  was used to construct a maximum likelihood tree for the four groups with two migration events  using the raster package (Version: 3.5–21). Pairwise Spearman correlation coefficients between the 19 factors were calculated. Once the correlation between two variables was larger than 0.8, one of them was removed . The standard deviation of 3 was used as a cut‐off to identify the outlier adaptive SNPs.The RDA was performed to demonstrate the associations between genetic variation and environmental factors. We estimated the proportion of genetic variance that is explained by five bioclimatic factors and three geographical factors. The RDA was performed by the 2.9topGO package (Version: 2.46.0) with the ‘weight01’ algorithm and ‘fisher’ statistic, the significant GO terms were determined with p < 0.05. The GO annotation for soybean (Williams 82) genes was downloaded from the Phytozome database  enrichment was performed using the Extended haplotype homozygosity (EHH) decay was analysed using the REHH package (Version: 2.0.2)  were localized in exons Table . There w3.2G. soja. To examine it, we built a phylogenetic tree for the 185 G. soja accessions using the genome‐wide SNPs. The phylogenetic analysis showed that the population was split into two major clades , and suggested that natural selection or local environments is the major driver of the diversity pattern in G. soja. Consistently, the principal component analysis (PCA) and model‐based population structure analysis also supported the phylogenetic result  between geographic distance and genetic distance  using the mantel test with 9999 times permutation to test the significance. The result showed a significant positive correlation  using five environmental factors  and three geographic factors   explained 4.73% of genomic variations, and it represented the variations within each group. We observed that environmental factors, such as temperature and latitude, played critical roles in the divergence of G. soja  Figure . The RDAa Figure . These r3.3G. soja have different genetic diversity. Overall, we did not observe big differences amongst the groups except for group NER.II, which had the lowest level of nucleotide diversity (π = 1.22 × 10−3). Specifically, the HR group had the highest nucleotide diversity (π = 1.40 × 10−3), followed by the SR group (π = 1.37 × 10−3) and the NER.I group (π = 1.36 × 10−3)  region on chromosome 8  to 1.21 (NER.I) amongst the four groups. It varied across genomes and diverged at some genomic regions amongst four groups. For example, NER.II group has much lower Tajima's D in the region from 18 Mb to 34 Mb on chromosome 4, which suggests NER.II experienced positive selection at this genomic region to adapt to the local environment  amongst the groups to assess whether the geography‐based groups of ) Figure . The hig8 Figure . The divt Figure .The LD decay reflects natural selection on the genome of a population. To compare the LD decay amongst the different groups, the LD decay was calculated for each group Figure . In the 3.4G. soja population. We asked whether the different growing habitats affected the demographic history of each G. soja group. To evaluate it, we analysed the demographic history by evaluating the expansion time of effective population size of different groups as inferred by the pairwise sequentially Markovian coalescent (PSMC). The PSMC analysis was performed by merging all variants identified from all the accessions within a group due to the selfing feature of G. soja  was performed using two methods: RDA and latent factor mixed models (LFMM). There were 43,973 outlier SNPs detected by the RDA‐based genome scan method for five RDAs , and adaptation‐related terms, for example, flower development , photoperiodism , and biotic and abiotic stress response terms , and nicotianamine biosynthetic process  , a total of 24,368 SNPs colocalized with 4860 genes were identified for three geographic  and five environmental factors  Figure . To iden) Figure . Exocyto) Figure , and the) Figure . Interes3.6Glycine soja is a short‐day plant, and the adaptation to different photoperiods requires the changes in flowering‐time genes, which was also supported by the GO enrichment for flowering‐related pathways  on chromosome 4 were colocalized with one flowering‐time gene GmELF3 (Glyma.04G050200)    colocalized with the outlier SNP rs18_57649585 on chromosome 18 (RDA score = −0.0396 in RDA2) , a major flowering time regulator in soybean, was colocalized with one SNP , which was identified using LFMM for longitude  was colocalized with the SNP rs15_11428805 (p = 1.33 × 10−5), which was identified using LFMM for altitude  Table . GmELF3 ) Figure  (Cai et ) Figure . Another) Figure . GmFT1a e Figure . Phytoche Figure . PhyB is3.7p = 1.62 × 10−8) on chromosome 10 for isothermality (Bio3) from LFMM was physically close to GmOST1.1 (Glyma.10G108000) (Table GmOST1.2 (Glyma.11G058800)  for altitude on chromosome 6  signalling during cold tolerance     was strongly induced under cold, drought or other abiotic stresses  is induced during cold tolerance and confers cold tolerance in Arabidopsis  Table , and the) Figure . Both ge6 Figure . In addi) Figure  and Glym) Figure . GmDREB1) Figure , which w3.8Glyma.19G034600, which encodes an AGAMOUS‐like 8 (AGL8) gene  were associated with flower development  and Glyma.19G034600 (GmFUL), encode MADS‐box transcription factor family protein with K‐box region. GmSEP1, an ortholog of the Arabidopsis AtAGL4 (AT3G02310), has been validated to play an important role in reproductive development, specifically in petal, and seed coat development in soybean  in Arabidopsis regulates flowering time and reproductive organ size  , whereas no expression was detected under long‐day condition (16‐h light: 8‐h dark)  Figure . Similar44.1Glycine soja is widely distributed from low to high latitude in East Asia, and it possesses the ability to adapt to a wide range of diverse environments. These diverse environments may result in the different selection forces for G. soja in different locations. In this study, the phylogenetic analysis together with model‐based clustering and PCA has demonstrated that the divergence of G. soja was geography oriented, which is in agreement with the ecological zones in China, including Southern, central and Northeast regions  (1 × 105–1 × 104 years ago). However, we did not detect significant bottlenecks in G. soja during LGP, and this finding was supported by previous studies  and began to expand and diverged due to the heterogeneity of the environment  climatic changes affected the demography and distribution of plants Hewitt, , 2004. I4.3Glycine soja has a broad distribution, spanning 24°N and 53°N latitude in East Asia. Environments vary extensively across their range, with altitude ranging from 0 to 2670 metres, yearly precipitation ranging from 300 mm to 2300 mm, and mean annual temperature ranging from −3.1°C to 18.2°C (Anderson et al., G. soja to these diverse environments requires selection on various fitness‐related traits, such as flowering time, seed amount, growth patterns, biotic and abiotic stress tolerance (Kofsky et al., GmELF3, GmFT1a, GmFT5a and several cold‐tolerance‐related loci in soybean. These associated pathways and genes indicated multiple pathways were involved in the local adaptation of G. soja, which implies the polygenic adaptation. Some of these genes have been proved to play an important role in the adaptation of G. max. For example, the loss‐of‐function alleles of GmELF3 were highly enriched in the low‐latitude adaptation of soybean (Lu et al., G. soja remains for further functional verifications.4.4The two approaches were widely used for identifying the genome‐environment associations. As the adaptive loci largely vary under the different selection pressures or mechanisms, different methods may help to identify the selection loci caused by various forces or selection mechanisms (Forester et al., 4.5G. soja accessions from three major agro‐ecological zones in China and analysed genomic diversity to investigate the genetic basis of local adaptation using the whole‐genome sequencing data. We revealed that G. soja exhibited clear geographic population structure and multiple environmental factors contribute to the genetic differentiation. The demographic history analysis showed G. soja from the three ecological zones diverged about 1 × 105 years ago, and then its effective population sizes have undergone different degrees of expansions. Genome‐wide environment associations identified multiple genes involved in the local adaptation of G. soja, especially the flowering time and temperature‐related genes. Lastly, the present study elaborates on the genetic basis of the local adaptation of G. soja and provides new insights into the G. soja divergence, which is helpful for breeding climate‐resilient soybean varieties in wider regions beyond the current major soybean cultivation areas.In summary, we collected 185 The author declares that there is no conflict of interest.Figures S1‐S7Click here for additional data file.Tables S1‐S6Click here for additional data file.


The increasing global demand for food, due to the continuous growth of human population, requires improvements in the efficiency and sustainability of animal production systems. In addition, several challenges facing farming of aquatic and terrestrial organisms need to be overcome to ensure food security in the upcoming decades, e.g. adaptation to climate change, reduced availability of conventional animal feed ingredients, emerging infectious and parasitic diseases, among others. Genomic technologies such as massive parallel sequencing, high‐throughput genotyping, genome selection and gene editing, combined with highly efficient computational methods can accelerate the rate of genetic progress in animal breeding. Thus, such technologies can help us meet the needs for protein sources for human consumption in the upcoming years. This Special Issue aims at presenting current advancements in the field of genomic tools applied to aquatic and terrestrial farmed animal populations. In addition, several challenges facing farming of aquatic and terrestrial organisms need to be overcome to ensure food security in the upcoming decades, e.g., adaptation to climate change, reduced availability of conventional animal feed ingredients, emerging infectious and parasitic diseases, among others. Genomic technologies such as massive parallel sequencing, high‐throughput genotyping, genome selection and gene editing, combined with highly efficient computational methods can accelerate the rate of genetic progress in animal breeding. Thus, such technologies can help us meet the needs for protein sources for human consumption in the upcoming years. This Special Issue aims at presenting current advancements in the field of genomic tools applied to aquatic and terrestrial farmed animal populations. From a general stand point, this Special Issue provides a better understanding on the utilization of genetic variability to improve aquaculture and livestock populations through the use of genomics. The contributions presented here demonstrate the utility of genomics in studying a variety of aspects of quantitative traits and selection applied to aquatic and terrestrial farmed species, including: (i) assessing genetic diversity of populations by using high‐resolution genomic information, (ii) identifying the genetic architecture of important quantitative traits, (iii) leveraging genome‐wide information for genetic evaluations, (iv) understanding genomic variation shaped by domestication and selection, (v) integration of different sources of information (e.g. non‐coding DNA sequences) to uncover mechanisms underlying complex traits, as well as, (vi) development and application of novel statistical methods for the analysis of genetic information.2Sus scrofa domestica), chicken  and cattle , coho salmon (Oncorhynchus kisutch), rainbow trout  genotyping panels have been crucial to both improve basic knowledge on the genomic control of complex traits and increase the rate of genetic progress in livestock and aquaculture species. In fact, genomic technologies are currently being routinely used in selective breeding to support livestock production, including species such as pigs, chicken and cattle  female‐mediated adaptive introgression in domestic cattle during the dispersal of the species in Europe . By using whole‐genome sequences of animals from seven breeds it has been revealed that the genetic diversity and inbreeding levels of domestic pigeons are consistent with human‐driven selection, indicating that genomic regions under selection in commercial populations for production harbor genes associated to body size, reproduction, and feather color; while in ornamental populations genes are mostly related to skeletal‐muscle features, potentially affecting appearance traits  . A genomic characterization of the genetic diversity and evaluation of economically important traits has been recently performed in Nordic farmed populations of Arctic charr by using a genotyping‐by‐sequencing approach , which is considered a promising emerging for New Zealand aquaculture , a valued species for marine aquaculture in Asia, has been recently reported (Gong et al., Over the past decade, genomic information has been introduced in selective breeding programs for the most important terrestrial livestock species, doubling the rate of genetic progress in some cases (Georges et al., To leverage the advantages offered by the incorporation of genomic information into breeding programs, generally genotyping several thousands of animals on each generation is required. The extra cost of generating genomic data can be not affordable for small‐scale livestock and aquaculture producers. Therefore, the cost of genotyping is considered a strong limitation for the routine implementation of genomic selection in low‐input smallholder animal production systems (Calus et al., The use of ultra‐dense genomic information available from re‐sequencing data has been extensively evaluated in livestock to increase resolution of GWAS and improve accuracy of genomic selection (Ni et al., 6Now that genomic selection has been increasingly and extensively adopted in aquaculture and livestock, the understanding of the current limitations is better acknowledged and there is renewed interest in the next steps for further innovation by the industry and scientific community. For instance, the identification, validation, and incorporation of causative variants into genomic selection models is one of the topics attracting great attention in animal breeders, because this source of information is expected to increase accuracy of selection and the rate of genetic progress (Pérez‐Enciso et al., Today, we also know that the molecular control of the expression of quantitative traits might be highly complex. A better understanding of the mechanisms involved in the gene regulation of complex traits comes from transcriptomic studies, which evaluate not only gene expression but also the role of non‐coding transcripts in trait variation. For example, it has been recently shown that differentially expressed long non‐coding RNA might be affecting meat quality traits in cattle, suggesting that specific transcripts have a role in tenderness and marbling (Muniz et al., 7The livestock and aquaculture industries have been visionary and innovative in the application of genomic technologies to improve production through selective breeding. The articles available in this special issue represent excellent examples of the current status in the application of genomics in animal breeding and also potential developments for further improvements. The utilization of genomic approaches to both evaluate and exploit heritable variation for key traits through genomic‐assisted breeding programs is paramount to the continued development and stability of worldwide animal production.


The prediction of possibility and risk classification of collapse is an important issue in the process of highway construction in mountain area. Based on the principle of rough set and support vector machine, a landslide hazard prediction model was established. First of all, according to field investigation, an evaluation index system and a sample set of evaluation index data were established, the rough set decision table was constructed by preprocessing the original data based on the function classification of standard evaluation index, and then, the influence indexes of the collapse activity were reduced by rough set theory, and the main 9 indexes affecting the collapse activity as the key discriminant factors of support vector machine model, namely slope shape of slope, aspect of slope, slope of slope, height of slope, exposed structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate and weathering degree of rock were extracted. Then, taking the data of 13 post earthquake collapses in Yingxiu-Wolong highway of Hanchuan County measured by the authors in the field as training samples, the optimal model parameters were analyzed and calculated. When the penalty parameter  With the deepening of the reform and opening-up and the implementation of the western development strategy, highway, railway and other projects continue to extend to the mountain area, high and steep slopes are more and more, so slope collapse and rockfall disasters are also increasing. In particular, the high and steep slopes after an earthquake, which are often unfavourable for earthquake resistance, are prone to collapse and rockfall disasters. For example, the 5.12 earthquake (2008 Wenchuan earthquake) triggered a large number of collapses, seriously damaging the transportation infrastructure such as highways etc., has greatly affected the disaster area people’s production and life and earthquake relief work. Therefore, it will provide strong support for regional geological disaster assessment and sustainable development of geological environment to carry out the evaluation and prediction of highway collapse geological disaster by information, quantification and science.Collapse is a geological phenomenon in which the rock and soil mass on a steep slope suddenly separates from the parent body under the action of gravity and other external forces to fall, roll and accumulates in a valley (or slope foot)5 applied the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation method to evaluate the risk of collapse disaster, Zhang et al.6 used the factor weighted summation model of the improved analytic hierarchy process to evaluate the sensitivity of landslides induced by the earthquake of Beichuan County, Xue et al.7 proposed the risk evaluation model of collapse disaster based on extension theory and fuzzy theory, Gao et al.8 constructed a landslide collapse risk assessment model based on GIS and information quantity model, He et al.10 established a comprehensive evaluation model of collapse hazard based on uncertainty measure and an information entropy and distance discriminant analysis model for predicting the grade of collapse hazard, Liu11 used Newmark displacement calculation model and probability method etc. to evaluated the risk of landslide induced by volcanic eruption of Changbai mountain pool in the sky. Broeckx et al.12, Greco and Sorriso-Valvo13, Mandal and Mondal14, Yang et al.15 used logistic regression model to evaluate and predict the susceptibility of landslides in the study area. Feng16 used the weight of evidence model, information model and logistic regression model to evaluate the susceptibility of landslides in Shilou-Jixian section of the middle and lower reaches of the Yellow River based on ArcGIS platform etc.Because of the numerous and uncertainty factors that affect the collapse activity, many methods for predicting and evaluating the collapse geological hazards have emerged. For example, LiuHowever, with the deepening of the research, it is found that the indexes affecting the activity of highway collapse are quite complex, which include the internal characteristics of the collapse body itself, such as elevation, slope direction, slope, stratum lithology, exposed structural face, soil type, etc., there are also external factors such as groundwater, precipitation, rock weathering, earthquake and various human activities that induce collapse disasters. Some of these indexes are redundant and have nothing to do with the evaluation results. When the above-mentioned mathematical theory method was used for evaluation, the attribute importance degree of several indexes was not analyzed in order to optimize the evaluation indexes, especially, as a non-linear, multi-level, fuzzy and complex system, and the conditions of highway collapse are different in different areas, it is difficult to obtain the complete collapse index data. As a scientific research of intelligent computing, the rough set (RS) theory can be used to analyze the attribute dependence degree of many indexes that affect the highway collapse, delete the relatively unimportant attributes, and achieve the goal of index optimization, and the weight distribution of the reduced indexes is carried out. As a pattern recognition method of minimize structural risk based on statistical theory, the support vector machine (SVM) can deal with the objective and practical problems such as small sample or finite sample, non-linearity and so on.22.However, the SVM can’t determine which knowledge in the data is useful and which is redundant when processing the training samples, so the dimension of the information space can’t be simplified. Thus, when the dimension of input information space is large, the training needs a long time, which will reduce the real-time performance of the prediction system. The RS theory can find the relationship between the data without any prior knowledge. It can’t only remove the redundant input information, but also simplify the spatial dimension of input information. However, the RS theory is sensitive to noise when dealing with practical problems, so the result of training samples without noise is not good in noisy environment. The SVM has a better ability to suppress noiseTherefore, in this paper, the RS theory and SVM technology are combined to construct a mathematical model for comprehensive assessment and prediction of highway collapse risk. Based on the data analysis of the collapse of Yingxiu-Wolong highway in Hanchuan County of Sichuan Province after the 5.12 earthquake in Wenchuan County, the spatial dimension of the input information, which is the initial index, is reduced and optimized, find out the key index system that affects the evaluation and forecast of highway collapse risk, and remove the irrelevant index. On this basis, more purposeful and targeted research on this section of the highway has been carried out again to obtain the survey data, and apply the support vector machine model to carry out a comprehensive evaluation of the risk of collapse in this section of the highway. The good results are obtained, it is significant to evaluate and forecast the risk of highway collapse.27 as a scientific study of intelligent computation was put forward by the Polish mathematician Pawlak in 1982, which is a set of mathematical theory methods for expression, learning, induction, etc. of incomplete data, imprecise knowledge. The essence of this theory is attribute reduction. It is well known that when the data in the knowledge expression system (information system) is collected at random, there is general redundancy. Decision table is a knowledge expression system with conditional attribute and decision attribute. A knowledge representation system can be expressed as Rough set theoryReduction is usually not unique, and all or minimum reduction in finding attributes has been proved to be an NP -hard problem. At present, the rough set attribute reduction algorithms mainly include Johnson greedy algorithm, exhaustive algorithm, attribute importance heuristic algorithm, genetic algorithm, dynamic reduction, concept lattice and so on. The exhaustion algorithm is to simplify the resolution function derived from the resolution matrix by the absorption law, and make it a minimum disjunctive normal form to obtain the reduction of the data attribute set. Considering that the exhaustion algorithm is only suitable for small data sets, and all reduction can be obtained despite the complexity of the algorithm, so the paper adopts the exhaustion algorithm to simplify the evaluation index of highway collapse risk grade.37, in which supervised learning models for classification and regression analysis are related to related learning algorithms, they can analyze data and identify patterns. SVM allows for the optimal classification of linear and non-linear separable data.In 1995, Corinna Cortes and Vapnik first proposed the support vector machine (SVM)The basic idea of SVM classification is to map mentclass2pt{minimmentclass2pt{minimThe dual form of Eq.  is2\docuFirstly, the factors affecting the risk of highway collapse are comprehensively analyzed, the key evaluation indexes are selected, the evaluation index system is constructed. The data sample set of evaluation indexes is constructed by data collection and field investigation. According to the grading standards of the evaluation indexes, the RS decision table is constructed by preprocessing the original data, and the RS attribute reduction is used to eliminate the redundant and unimportant attributes, so as to extract the features of highway collapse risk information. Then, training samples and test samples are selected from the data preprocessed by reduction, and the suitable kernel functions are selected and parameters are optimized. Support vector classification is trained with training samples, RS-SVM highway collapse risk assessment model is established to evaluate the risk level of the test evaluation samples. The specific flow of highway collapse risk assessment based on RS-SVM is shown in Fig. The S303 Yingxiu-Wolong highway in Hanchuan County, Sichuan Province was paved before the May 12, 2008 earthquake. The highway’s total length is 45.5 km and it is an important trunk route connecting Yingxiu and Wolong, As the closest highway to the epicenter of the Wenchuan earthquake, the highway is basically routed along the Longmenshan tectonic belt, starts from the Beichuan-Yingxiu fault  and passes through the Maoxian-Hanchuan fault (the Houshan fault) in Longmenshan. It has complicated geological conditions, is the earthquake geological disaster most development, the damage is most serious a highway  are used as test samples (Table 38. The form of kernel function and the determination of its parameters determine the performance and complexity of the classifier, but there is no effective way to choose the best kernel function for a specific problem and to determine the parameters of kernel function. Considering that the number of support vectors in the Gauss radial basis function is less and the number of iterations is the least, we uses Gauss radial basis function In this paper, the SVM model is implemented by the latest open source libSVM package in Python environment, which is developed by Chih-Chung Chang and Chih-Jen LinIn order to verify the correctness and reliability of highway collapse risk discrimination based on the RS-SVM model, the RS-SVM model was used to distinguish training samples BT05 to YBT03. The results are shown in Table In the evaluation and prediction of highway collapse risk, firstly, rough set theory is used to reduce the indexes, and the relatively unimportant indexes are deleted, so as to achieve the goal of index optimization. Then, the 9 key indexes which affect the collapse activity, such as slope shape of slope, aspect of slope, slope of slope, height of slope, exposed structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate and weathering degree of rock, are extracted to be used as the discriminant factors of the support vector machine classification model.The actual survey data of 17 collapse points optimized from Yingxiu to Wolong section of the S303 highway are selected as the training and testing samples of the support vector machine, the best parameters of combinatorial optimization are obtained through the training of the training samples, the RS-SVM model is used to evaluate and predict the test samples.The research results show that the highway collapse discriminant analysis model based on RS-SVM achieves the goal of index optimization, not only can guarantee the accuracy of evaluation, but also reduce the computational load of the model, the learning performance is good, and the prediction accuracy is high, it is an effective method to forecast and evaluate the risk of highway collapse.