Linkage disequilibrium (LD) is a key parameter to study the history of populations and to identify and fine map quantitative trait loci (QTL) and it has been studied for many years in animal populations. The advent of new genotyping technologies has allowed whole-genome LD studies in most cattle populations. However, to date, long-range LD (LRLD) between distant variants on the genome has not been investigated in detail in cattle. Here, we present the first comprehensive study of LRLD in French beef cattle by analysing data on 672 Charolais (CHA), 462 Limousine (LIM) and 326 Blonde d’Aquitaine (BLA) individuals that were genotyped on the Illumina BovineHD Beadchip. Furthermore, whole-genome LD and haplotype block structure were analysed in these three breeds.r2) values for 5.9, 5.6 and 6.0 billion pairs of SNPs on the 29 autosomes of CHA, LIM and BLA, respectively. Mean r2 values drop to less than 0.1 for distances between SNPs greater than 120 kb. However, for the first time, we detected the existence of LRLD in the three main French beef breeds. In total, 598, 266, and 795 LRLD events (r2 ≥ 0.6) were detected in CHA, LIM and BLA, respectively. Each breed had predominantly population-specific LRLD interactions, although shared LRLD events occurred in a number of regions (55 LRLD events were shared between two breeds and nine between the three breeds). Examples of possible functional gene interactions and QTL co-location were observed with some of these LRLD events, which suggests epistatic selection.We computed linkage disequilibrium (We identified long-range linkage disequilibrium for the first time in French beef cattle populations. Epistatic selection may be the main source of the observed LRLD events, but other forces may also be involved. LRLD information should be accounted for in genome-wide association studies.The online version contains supplementary material available at 10.1186/s12711-021-00657-8. Drosophila )We checked, in the cattle QTL database, the presence of QTL associated with the same phenotype in each block of LRLD events. We found five and nine LRLD events showing QTL associated with the same phenotype in CHA and BLA, respectively. Only traits that are selected by breeders in beef cattle were checked, which could explain the small number of overlaps observed between QTL and LRLD events. Indeed, phenotypes related to dairy cattle have been much more widely studied and are thus over-represented in the database. Nevertheless, LRLD events may be an indicator of epistasis between several regions and thus may impact GWAS. Epistasis is considered as one potential explanation of the “missing heritability” , 54. HowAnother possible explanation for some of the detected LRLD events is the presence of errors in the genome assembly. The new bovine genome assembly (ARS-UCD1.2) used in this study has an increased assembly accuracy . However. [. [. [. [In this study, we identified LRLD events in beef cattle and also confirmed the inversely proportional relationship between LD and the physical distance between markers. These results are consistent with the literature. A study conducted by Hozé et al.  on 16 da. [.  estimate. [. [.  in a stu[. [. [.  reported. [PLAG1 gene, which is associated with stature and carcass yield [In the CHA breed, BTA14 showed a lower level of LD decay with physical distance than the other chromosomes. Several QTL associated with growth, carcass, meat quality and eating quality traits have been identified on this chromosome in cattle (e.g. ). Furthe.  identifi.  such as ss yield . This geWe conducted a linkage disequilibrium study on three populations of French beef cattle breeds genotyped on HD chips and report, for the first time, long-range linkage disequilibrium in these three breeds. Several hypotheses can explain this observed long-range linkage disequilibrium, such as bottlenecks, population admixture and epistatic selection. Our results should be taken into account for future genome-wide association studies.Additional file 1: Tables S1–S3. Summary distribution and density of SNPs over the genome in the Charolaise (Table S1), Limousine (Table S2) and Blonde d’Aquitaine (Table S3) breeds. In each table are provided the number of SNPs per chromosome, the chromosome size (kb), average inter-distance (± SD) between markers (kb), and median and maximum distance between markers.Additional file 2: Figures S1–S3. SNP density over autosomes. Each chromosome was divided in windows of 500 kb and SNP density was plotted. Summary distribution and density of SNPs over the genome in the Charolaise , Limousine  and Blonde d’Aquitaine  breeds. Each figure shows SNP density across the autosomes. Each chromosome is divided into windows of 500 kb and SNP density was plotted.Additional file 3: Figures S4, S5. Family structure analysis in the Charolaise, Limousine and Blonde d’Aquitaine breeds using SNPRelate R package. Figure S4. Principal component analysis (PCA). Figure S5. Clustering analysis based on the matrix of genome-wide identity by state (IBS) pairwise distances.Additional file 4: Tables S4–S6. Mean linkage disequilibrium (r2) according to physical distance ranges in 500-kb windows in Charolaise (Table S4), Limousine (Table S5) and Blonde d’Aquitaine (Table S6).Additional file 5: Tables S7–S9. Mean linkage disequilibrium (r2) on each autosome (1–29) in 500-kb windows. Number of SNP pairs per chromosome, mean r2, standard deviation (sd), percent of SNP pairs with LD superior to 0.2 (% r2 > 0.2), and percent of SNP pairs with high LD (% r2 > 0.8) in Charolaise (Table S7), Limousine (Table S8) and Blonde d’Aquitaine (Table S9).Additional file 6: Figures S6–S8. LD decay within physical distance on each autosome (1–29) in 500-kb windows in the Charolaise , Limousine  and Blonde d’Aquitaine breeds . Doted horizontal lines are the LD background (computed on a subset of non-syntenic SNPs).Additional file 7: Tables S10–S12. Summary of haplotype blocks for the Charolais (Table S10), Limousine (Table S11) and Blonde d’Aquitaine (Table S12) breeds. Each table provides the number of SNPs (Nb SNPs) per chromosome, chromosome size, number of haplotype blocks per autosome (1–29), block coverage length (Mb), chromosome block coverage (in percent), number of SNPs in blocks (Nb SNPs in Blocks) and percent of SNPs in blocks.Additional file 8: Figure S9. Distribution of haplotype block sizes on each autosome (1–29) for the Charolaise breed. The red dots show haplotype blocks with size ≥ 100 kb. Blue circles show regions with larger haplotypes.Additional file 9: Figure S10. Distribution of haplotype block sizes on each autosome (1–29) for the Limousine breed. The red dots show haplotype blocks with size ≥ 100 kb. Blue circles show regions with larger haplotypes.Additional file 10: Figure S11. Distribution of haplotype block sizes on each autosome (1–29) for the Blonde d’Aquitaine breed. The red dots show haplotype blocks with size ≥ 100 kb. Blue circles show regions with larger haplotypes.Additional file 11: Tables S13–S15. List of large haplotype blocks in the Charolaise (Table S13), Limousine (Table S14) and Blonde d’Aquitaine (Table S15) breeds.Additional file 12: Tables S16–S18. List of LRLD events identified in the Limousine (Table S16), Charolaise (Table S17) and Blonde d’Aquitaine (Table S18) breeds. Each table show the r2 threshold, the pair of blocks number (LRLD number), breed, chromosome (chr), start position of block 1 (start_block1), end position of block 1 (stop_block1), start position of block 2 (start_block2), end position of block 2 (stop_block2) and distance between blocks.Additional file 13: Table S19. List of common LRLD events between two or all the three breeds.Additional file 14: Figure S12. Chromosome-wide LRLD on the Charolaise (CHA), Limousine (LIM) and Blonde d’Aquitaine (BLA) autosomes. Green for CHA, Red for LIM and Blue for BLA. Plots were done using Circos software.Additional file 15: Tables S20–S22. List of LRLD events with functional interactions in the Charolaise (Table S20), Limousine (Table S21) and Blonde d’Aquitaine (Table S22) breeds. STRINGdb shows the interacting proteins in each block in LRLD from the STRING database. Cattle QTL db shows the LRLD blocks with the same QTL name identified in the cattle QTL database.


Salmonella (NTS) serovars and associated subtypes, a rational and scalable approach to identify Salmonella subtypes with differential ability to cause human diseases is not available. Here, we used NTS serovar Saintpaul (S. Saintpaul) as a model to determine if metadata and associated whole-genome sequence (WGS) data in the NCBI Pathogen Detection (PD) database can be used to identify (i) subtypes with differential likelihoods of causing human diseases and (ii) genes and single nucleotide polymorphisms (SNPs) potentially responsible for such differences. S. Saintpaul SNP clusters (n = 211) were assigned different epidemiology types (epi-types) based on statistically significant over- or underrepresentation of human clinical isolates, including human associated , non-human associated , and other (n = 159). Comparative genomic analyses identified 384 and 619 genes overrepresented among isolates in 5 HA and 4 NHA SNP clusters most significantly associated with the respective isolation source. These genes included 5 HA-associated virulence genes previously reported to be present on Gifsy-1/Gifsy-2 prophages. Additionally, premature stop codons in 3 and 7 genes were overrepresented among the selected HA and NHA SNP clusters, respectively. Tissue culture experiments with strains representing 4 HA and 3 NHA SNP clusters did not reveal evidence for enhanced invasion or intracellular survival for HA strains. However, the presence of sodCI (encoding a superoxide dismutase), found in 4 HA and 1 NHA SNP clusters, was positively correlated with intracellular survival in macrophage-like cells. Post hoc analyses also suggested a possible difference in intracellular survival among S. Saintpaul lineages.While differences in human virulence have been reported across nontyphoidal IMPORTANCE Not all Salmonella isolates are equally likely to cause human disease, and Salmonella control strategies may unintentionally focus on serovars and subtypes with high prevalence in source populations but are rarely associated with human clinical illness. We describe a framework leveraging WGS data in the NCBI PD database to identify Salmonella subtypes over- and underrepresented among human clinical cases. While we identified genomic signatures associated with HA/NHA SNP clusters, tissue culture experiments failed to identify consistent phenotypic characteristics indicative of enhanced human virulence of HA strains. Our findings illustrate the challenges of defining hypo- and hypervirulent S. Saintpaul and potential limitations of phenotypic assays when evaluating human virulence, for which in vivo experiments are essential. Identification of sodCI, an HA-associated virulence gene associated with enhanced intracellular survival, however, illustrates the potential of the framework and is consistent with prior work identifying specific genomic features responsible for enhanced or reduced virulence of nontyphoidal Salmonella. Salmonella enterica causes the greatest estimated burden of all foodborne diseases worldwide , an appreciable diversity of serological variants, called serovars, make Salmonella one of the most diverse foodborne pathogens, with >2,600 recognized serovars  Typhi, the model for typhoidal salmonellosis or typhoid fever, is associated with an invasive infection and an estimated case mortality rate of 1%  serovars , such as the model S. Typhimurium, is associated with a self-limiting gastroenteritis, with a considerably lower rate of case fatality  (orldwide . While tte of 1% . Infecti 0.003%) , 5. NontSalmonella serovars are equally likely to cause human clinical disease. For example, some serovars such as S. Cerro are commonly isolated from agricultural animals  this serovar is understudied despite the fact that its incidence among reported human clinical salmonellosis cases has increased from 577 reported cases in 2006 to 778 in 2016 : (i) human-associated (HA) SNP clusters, which have significantly higher proportions of human clinical isolates, (ii) non-human-associated (NHA) SNP clusters, which have significantly lower proportions of human clinical isolates, and (iii) other SNP clusters, which did not show significant over- or underrepresentation of human clinical isolates. We then used comparative genomic analyses to identify genes and core SNPs overrepresented among SNP clusters representing HA or NHA epi-types and used a selected set of strains to phenotypically assess whether these epi-types or genomic signatures found to be overrepresented in these epi-types were associated with the ability of S. Saintpaul to invade human intestinal epithelial cells and/or to survive inside human macrophage-like cells.We recently proposed a framework for using whole-genome sequence data to identify genomic signatures that are over- or underrepresented among isolates assigned to NCBI Pathogen Detection (PD) single nucleotide polymorphism (SNP) clusters with high and low proportions of human clinical isolates, combined with phenotypic experiments to (i) identify phenotypic characteristics reflective of the association of the SNP clusters with human clinical cases and (ii) understand contributions of the identified genomic signatures to pathogenesis, as a modernized approach to controlling  disease . To demo in 2016 , and , which were assigned to 211 SNP clusters, were available in the database. Human clinical isolates were significantly (Benjamini-Hochberg [BH]-corrected P < 0.05) overrepresented among isolates assigned to 29 SNP clusters (designated HA SNP clusters) and were significantly underrepresented among isolates assigned to 23 SNP clusters (designated NHA SNP clusters) (S. Saintpaul SNP clusters), indicating that S. Saintpaul isolates assigned to different SNP clusters may vary in their likelihood for causing human illness .We used whole-genome sequencing (WGS)-associated metadata available on the NCBI PD platform to calculate odds ratios and associated adjusted 10.1128/mSphere.00730-21.5DATA SET S1S. Saintpaul SNP clusters and the 100 SNP clusters of Salmonella enterica subsp. enterica serovars that have the greatest number of isolates in the NCBI PD database (accessed 20 February 2020). Tab 1 shows the statistics used to assess the association between each S. Saintpaul SNP cluster and human clinical salmonellosis cases, as well as the final epi-type assigned; tab 2 shows the proportion of human clinical isolates of each of the top 100 SNP clusters, as well as the associated statistics. Download Data Set S1, XLSX file, 0.05 MB.Statistics of Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. Saintpaul, we inferred a maximum likelihood phylogenetic tree from the alignment of core SNPs for representative isolates for the 211 S. Saintpaul SNP clusters, 313 reference isolates representing unique Salmonella enterica subsp. enterica serovars, and 5 additional reference isolates representing the other recognized Salmonella enterica subspecies . The tree topology suggested that S. Saintpaul is a polyphyletic serovar within S. enterica subsp. enterica clade A  and 22 SNP clusters , respectively; S. Saintpaul group II represents a paraphyletic group containing 5 SNP clusters  as well as the representative isolate for S. Heidelberg; S. Saintpaul group I represents a paraphyletic group containing 178 SNP clusters  as well as the representative isolates for S. Haifa, S. Coeln, S. Typhimurium, and S. I 1,4,[5],12:i:-  selected from each of the 5 HA and 5 NHA SNP clusters with the most significant association with their corresponding isolation sources. Download Data Set S2, XLSX file, 0.1 MB.Statistics of Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the P values) with the corresponding isolation sources among all S. Saintpaul SNP clusters, excluding clusters that were likely predominantly represented by isolates from a single outbreak  . Importaoutbreak . The oddoutbreak . TogetheS. Saintpaul group I isolates. The majority of S. Saintpaul SNP clusters within group I can be grouped into 4 monophyletic lineages (lineages IA to ID). Lineage IA consists of 80 SNP clusters , lineage IB consists of 46 SNP clusters , lineage IC consists of 30 SNP clusters , and lineage ID consists of 17 SNP clusters , which are clustered with S. Haifa   belongs to lineage ID, indicating that isolates within this SNP cluster are more closely related to serovar Haifa than to the other selected HA and NHA SNP clusters. Indeed, the average core SNP distance between the representative isolates in HA and NHA SNP clusters belonging to lineages IA to IC was considerably lower  than the average core SNP distance between these lineage IA to IC representative isolates and those from the NHA SNP cluster PDS000004163   . The 5 Hue, 100) , suggesterences) . Due to 10.1128/mSphere.00730-21.3FIG S1n = 10 per SNP cluster) from the 5 HA (marked in red) and 5 NHA (marked in blue) SNP clusters that have the most significant association with the corresponding isolation sources, as well as the reference isolates for S. Coeln and S. Haifa. SNP clusters and the corresponding lineages are shown at the top of the heatmap . Representative isolates of the NHA SNP cluster PDS000004163 (lower left corner) show larger core SNP distances to the other 5 HA and 4 NHA SNP clusters, and therefore this SNP cluster was excluded from the downstream genomic and phenotypic characterizations. Download FIG S1, PDF file, 1.0 MB.Heatmap display of the core SNP distances among the representative isolates  overrepresented among isolates in HA SNP clusters (designated “HA genes”) and NHA SNP clusters (designated “NHA genes”), respectively  Composition of the pangenome. Core genes are present in 100% of the isolates, shell genes are present in 15 to 99% of the isolates, and cloud genes are present in <15% of the isolates. (B) Rarefaction curves showing the number of pan (purple) and core (green) genes with the addition of each successive genome . (C) Heatmap showing the presence/absence pattern across the representative isolates for each gene in the pangenome. Download FIG S2, PDF file, 0.5 MB.Assessment plots for the pangenome estimation among all 90 Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphere.00730-21.7DATA SET S3Data Set S3, XLSX file, 5.0 MB.Statistics of gene families identified among the 90 isolates included in the comparative genomic analysis. Tab 1 shows the information for each gene family in the pangenome estimated among the 90 isolates; tab 2 shows additional information for the gene families significantly over- or underrepresented among HA/NHA isolates. Download Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Salmonella to human- and non-human-associated environments (Mobile genetic elements (MGEs) represent important components of the accessory genome . As plasronments , 17, we ronments ; among tronments . Among t10.1128/mSphere.00730-21.2TABLE S2Table S2, DOCX file, 0.02 MB.Range and median number of HA/NHA-associated genes across the representative isolates of each SNP cluster. Download Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphere.00730-21.8DATA SET S4Data Set S4, XLSX file, 0.2 MB.Statistics of plasmid-borne and prophage-borne sequences. Tab 1 shows the information for contigs of each isolate that were classified as plasmid sequences; tab 2 shows the information for prophage regions identified in the genome of each isolate. Download Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the gtgA, sodCI, sseI, gtgE, and sspH1 , multidrug efflux systems , the type I-E CRISPR system , pathogenesis , and an unknown process . On the other hand, HA-associated PMSCs  and sulfoacetate (STM14_0610), as well as a virulence gene (avrA) previously reported to enhance the survival of S. Typhimurium inside infected macrophages  and was intact in all isolates from lineages IA and IC (representing 3 HA and 1 NHA clusters), malX was intact in all isolates from lineage IB and was disrupted in all isolates from lineages IA and IC  cells. The invasion efficiency was quantified as the recovery rate, which was defined as the ratio of the number of salmonellae recovered 1 h postinfection (hpi) to the number of salmonellae used for infection. Among the strains included in the phenotypic experiments, the recovery rate ranged from 0.01  to 0.04  for strains representing HA SNP clusters and from 0.01  to 0.05  for strains representing NHA SNP clusters.As the ability of NTS to invade cells in the intestinal epithelium is central to the proinflammatory response that crobiota , we initS. Saintpaul strains (the INV model) included epi-type, lineage, and the genotype of bglA/bepF as fixed effects (P < 0.001), suggesting that there were differences in invasion efficiency across S. Saintpaul from the 3 different lineages represented among the strains tested. Post hoc pairwise comparisons revealed that the invasion efficiency was significantly higher for lineage IA strains  than for lineage IB  and IC  strains and for lineage IC strains than lineage IB strains (P = 0.002)  for strains in which these genes were disrupted  than for strains in which these genes were intact    . The bgln effect ; the invM, 0.02) ; howeverM, 0.02) , the RRE= 0.053) . OverallsodCI), which encodes a copper/zinc co-factored (Cu-Zn) superoxide dismutase that has been previously shown to contribute to Salmonella intracellular survival in macrophages  epi-type, (ii) lineage, and (iii) relevant genomic signatures (defined as presence/absence of HA virulence genes and HA/NHA-associated core SNPs leading to PMSCs). Intracellular survival during different time periods  was quantified as the differences in the log10-transformed level (CFU/mL) of the S. Saintpaul strains recovered from human macrophage-like cells. During the 0 to 2 hpi time period, all strains decreased in level with respect to the initial level used for infection; the differences in log10 CFU/mL ranged from −1.07  to −0.65  for strains representing HA SNP clusters and from −1.18  to −0.79  for strains representing NHA SNP clusters. During the 2 to 6 hpi time period, an increase in level was observed for all strains, ranging from 0.12  to 0.34  for strains representing HA SNP clusters and from 0.21  to 0.32  for strains representing NHA SNP clusters. Finally, during the 6 to 24 hpi time period, the S. Saintpaul strains tended to either maintain or show a decrease in their intracellular levels; the differences in log10 CFU/mL for this time period ranged from −0.35  to −0.01  for strains representing HA SNP clusters and from −0.50  to 0.04  for strains representing NHA SNP clusters.Besides the ability to attach and invade human intestinal epithelial cells, survival and replication in macrophages represent another infection-relevant process associated with systemic infection –25. In a–rophages , 27. TheS. Saintpaul strains in macrophage-like cells within a given time period. Different factors were retained in the models as fixed effects, including (i) lineage (comparison between IC and IA/IB only), the bglA/bepF genotype, and the ygcH/stfC genotype for the ICS-1 model, (ii) lineage for the ICS-2 model, and (iii) the sodCI genotype for the ICS-3 model  than the strains representing lineages IA and IB  . Therefore, further investigations of this gene, among other genomic signatures specific for lineage IC, may provide insights into the survival mechanisms of S. Saintpaul upon entry into macrophage-like cells. In contrast to the results for invasion efficiency, the intracellular growth between 2 and 6 hpi was significantly lower for strains representing lineage IA  than for strains representing lineages IB  and IC . Finally, virulence gene sodCI, which was present in 4 HA and 1 NHA SNP clusters, was significantly associated with differences in intracellular survival between 6 and 24 hpi  showed a significantly higher ability to survive in macrophage-like cells between 6 and 24 hpi than strains without this gene .Separate linear regression models were constructed for each time period  to assess the impact of epi-type, lineage, and relevant genomic signatures on the survival of -3 model . Between, −0.83) . Notablyd 24 hpi . StrainsFor all three time periods , no differences in intracellular survival or growth were identified between strains representing HA and NHA SNP clusters, suggesting that the strains representing NHA SNP clusters did not show a consistent deficiency in their ability to resist macrophage killing or grow in macrophages in comparison to HA SNP clusters.https://www.healthypeople.gov/2020/topics-objectives/topic/food-safety) from a benchmark of 15.0 per 100,000 population for 2006 to 2008; in 2019, the preliminary incidence reported was 17.1 cases per 100,000 people  phenotypes that reflect Salmonella behavior under infection-relevant conditions. While tissue culture experiments failed to identify consistent phenotypic characteristics indicative of enhanced human virulence of HA strains, identification of a candidate gene  overrepresented among HA isolates and associated with enhanced intracellular survival in human macrophage-like cells illustrates the potential of the overall approach used here and is consistent with prior data suggesting specific genomic signatures for enhanced or reduced human virulence of nontyphoidal Salmonella. Identification of multiple virulence factors (including sodCI) that merit further assessments for understanding their role in human clinical salmonellosis also indicates that the approach used here can be valuable for identifying putative Salmonella virulence-associated genomic signatures across a wide range of serovars.The Healthy People 2020 initiative aimed to reduce the incidence of clinically diagnosed nontyphoidal salmonellosis in the United States to 11.4 cases per 100,000 population per year  that carry different PMSCs in the virulence gene inlA . It is important to note that we restricted our analyses to examining the virulence potential in humans and that some of the NHA isolates identified may retain the ability to cause disease in other animals. Regardless, our findings illustrate the challenges for defining hypo- and hypervirulent Salmonella isolates across diverse serovars and potential limitations of phenotypic assays when evaluating human virulence.Overall, we identified 29 and 23 SNP clusters that showed significant over- and underrepresentation of human clinical isolates, respectively. While it is likely that some if not many of these SNP clusters represent clonal groups with an enhanced or reduced ability to cause human diseases, a subset of these SNP clusters may show an over- or underrepresentation of human clinical isolates due to biases in isolate collection and deposition into the NCBI PD database. However, we have manually inspected the SNP clusters to identify and preclude SNP clusters with obvious biases, such as the inclusion of a large number of human isolates that likely represented a single outbreak or a large number of environmental isolates that were derived from a single source population . Importantly, HA and NHA SNP clusters were diverse and did not represent one or a few distinct lineages within irulence . On the l groups , or a laene inlA , 32. TheS. Saintpaul suggested that isolates in HA SNP clusters carried virulence genes  associated with prophages Gifsy-1 and Gifsy-2, which were largely absent from NHA SNP clusters. NTS serovars are known to carry a variety of prophages  resist killing by immune cell-derived superoxide radicals via the activity of SodCI , additional studies (including assessments of null mutants) will be needed to confirm a role for sspH1 in intracellular survival in macrophages. The remaining Gifsy-1/2-borne virulence genes  were not identified as factors significantly affecting the virulence-associated phenotypes tested here. This may be due to (i) a lack of a sufficient number of appropriate strains available for phenotypic testing , (ii) strain genotypes that showed strong correlation of presence/absence patterns for multiple genes , (iii) differences in gene expression of these loci, and/or (iv) inability of tissue culture assays to assess all phenotypes relevant to human virulence.Previous studies have documented a role for SodCI at both cellular and organismal levels, including a study that indicated that SodCI was essential for ic cells . Golubevted mice . Our pheost PKN1 , allowinost PKN1 . While tS. Derby lineage identified a nonsynonymous mutation leading to a PMSC in hilD, which causes the loss of function of the master regulator (HilD) of the Salmonella pathogenicity island 1 (SPI-1) and an associated reduced ability to invade human epithelial cells  oction 47–, 55, . Importantly, genomic characterizations in other nontyphoidal serovars are essential for developing a framework that focuses on the presence of specific genomic signatures, rather than serotyping, to guide risk-based control efforts focusing on Salmonella subtypes that are more frequently associated with human clinical disease.Our understanding of salmonellosis, and more specifically the roles of various genes, has been informed primarily by characterizations of the two model serovars, Another key challenge in implementing the approach outlined here is developing a standardized set of criteria for ensuring the availability of accurate and complete metadata for isolates included in genomic analyses. Currently, the WGS data uploaded to the NCBI PD database have limited accompanying metadata, such as whether the isolate is associated with a human or nonhuman source. In particular, human clinical isolates lack information on the date and location of collection, due primarily to confidentiality reasons. Conversely, nonhuman isolates tend to be associated with more complete associated metadata. However, there is currently no standardization for how dates are reported , and if a geographical location is provided, there is no standardization for what level  these data are provided; similar challenges may be observed for isolation source, as “environmental/other” is used to capture all isolates that are not from human clinical cases, but they may still represent isolates causing animal clinical infections. In our study, we used a combination of year of isolation, geographic location, and source, in addition to SNP distances, to ensure that comparisons were based on a sufficiently diverse collection of isolates. However, this proved challenging, as a number of metadata terms were missing or provided incomplete records. Therefore, standardization of metadata is important for the implementation of the approach described here.S. Saintpaul as a model to implement and test a proposed framework, which leverages WGS and associated metadata available in the NCBI PD database, as well as phenotypic experiments to identify and study genomic and phenotypic characteristics that may be responsible for human hypo- or hypervirulence. While our findings illustrate the challenge of identifying and characterizing human hypo- or hypervirulent Salmonella strains across different nontyphoidal Salmonella serovars, our experiments were able to identify virulence genes and core SNPs leading to PMSCs associated with NCBI PD SNP clusters with over- or underrepresentation of human clinical isolates, including genes  that were associated with an enhanced ability to survive in human macrophage-like cells. Further development of this framework, which ultimately can be used to predict and quantify virulence potential for a risk-based approach to controlling Salmonella in the food supply, may need to include a focus on (i) using additional phenotypic approaches  to assess virulence characteristics , (ii) constructing and characterizing appropriate mutants to mechanistically establish the role of genomic signatures associated with human and environmentally associated SNP clusters and clonal groups, and (iii) improving metadata availability for Salmonella isolates deposited in the NCBI PD database.In conclusion, we used Salmonella enterica subsp. enterica (abbreviated “S.”) serovars having the greatest number of isolates in the NCBI pathogen detection (PD) database (https://www.ncbi.nlm.nih.gov/pathogens/) were first assessed (accessed 20 February 2020) to determine the proportion of isolates in each SNP cluster with the source type “clinical”  (see S. Saintpaul was selected as the model serovar for use in this study because (i) S. Saintpaul has SNP clusters with high and low proportions of human clinical isolates, suggesting that some SNP clusters may be more likely to be associated with human clinical illness . Specifically, from each SNP cluster, the isolate whose genome assembly had the highest N50 value and/or the lowest number of contigs was selected as the representative isolate for the given SNP cluster. Genome assemblies of the representative isolates of the SNP clusters were downloaded from the NCBI PD database, followed by using SISTR v1.0.2 . To comprehensively assess the phylogeny of S. Saintpaul, a diverse collection of reference isolates was included in the phylogenetic analysis; these reference isolates, selected based on their inclusion in previous data sets defining the phylogenetic structure of Salmonella enterica  serovars associated with human and animal clinical salmonellosis. Core SNPs among the representative isolates of S. Saintpaul SNP clusters and the reference isolates were identified using kSNP3 v 3.1 , followed by a one-sided Fisher’s exact test (with the Benjamini-Hochberg [BH] correction for multiple testing) for determining statistical significance. SNP clusters were classified into different epidemiology types (epi-types) based on the proportion of human clinical isolates among all isolates assigned to them. SNP clusters with significantly high or low proportions of human clinical isolates were considered human-associated (HA) and non-human-associated (NHA) SNP clusters, respectively, while the other SNP clusters were not assigned an epi-type. Ten SNP clusters (n = 5 for each of HA and NHA SNP clusters) were selected for use in the comparative genomic analyses and the phenotypic characterizations based on (i) the significance of the BH-corrected P values , (ii) the availability of genomic assemblies for at least 50 isolates in the SNP cluster, and (iii) multiple geographic locations and collection (or creation) years associated with the isolates in the SNP clusters .For each A total of 10 isolates were selected from each selected HA and NHA SNP cluster  to represent the genomic diversity of all isolates assigned to the SNP cluster. Specifically, isolates assigned to each SNP cluster were ranked based on the min-same (minimum SNP distance from the isolate to another isolate in the SNP cluster with the same source category) and min-diff (minimum SNP distance from the isolate to another isolate in the SNP cluster from the opposite source category) values from highest to lowest, followed by calculating the average of the two ranks (average rank). The isolates with the lowest average ranks  were prioritized. For each of the 10 SNP clusters, 10 isolates were selected to represent (i) the lowest average rank, (ii) a diversity of geographical location, and (iii) a range of years of collection , tab 2.S. Saintpaul isolates representing each of the selected SNP clusters, if available, were downloaded from the NCBI PD database. For the isolates whose assemblies were not available, raw data were retrieved from the NCBI Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra/), checked for quality using FastQC v0.11.8  using Prokka v1.14.5  was first performed using Scoary v1.6.14  were further annotated using InterProScan v5.44-79.0  of one isolate (PDT000217923.2) from an HA SNP cluster (PDS000006321) was designated the reference in the program for core SNP annotation, as this genome assembly had the lowest number of contigs (18 contigs). Core SNPs in protein-coding genes were categorized into synonymous and nonsynonymous mutations . To identify core SNPs that have alleles overrepresented among isolates assigned to HA/NHA SNP clusters, pan-GWAS was performed using the same method as described for the gene presence/absence analysis. In cases where a core SNP had more than one alternative allele , each alternative allele was assessed separately. Genes disrupted by a premature stop codon (PMSC) that were overrepresented among HA/NHA isolates were screened out by manually inspecting the annotation of the core SNPs.A k-mer-based SNP variant calling was performed using kSNP3 v3.1  to identin silico identification of putative plasmid sequences was first performed using Platon; through the program, a plasmid identifier (ID) was assigned to each of these sequences if a BLAST+ search against the RefSeq plasmid sequence database  and searched against the genome assembly of the isolate via BLAST+. Plasmids with ≥80% query coverage and ≥80% percentage sequence identity were considered “candidate plasmids” present in the genome of the isolate. Candidate plasmids were manually inspected to identify groups of plasmids that match to the same regions in the genome assembly, in which cases only the one with the best match  was retained for further analyses. For a specific isolate, a contig was considered plasmid-borne if (i) it was identified by Platon as a putative plasmid sequence or (ii) it matched to the candidate plasmids with ≥80% total sequence coverage and percent identity. Finally, to enumerate plasmid-borne genes overrepresented among isolates assigned to HA/NHA SNP clusters, indices of the genes assigned by Prokka were matched to the contigs, and the total number of the genes that were matched to the plasmid-borne contigs were enumerated.For each assembly, contigs were classified into putative chromosome or plasmid sequences using Platon –76, foll–database –80 indicatabase – using BLFor each assembly, putative prophage sequences in the genome assembly were identified using PHASTER , 83. TheS. Saintpaul strains, representing 4 HA  and 3 NHA  SNP clusters included in the comparative genomic analyses, were selected for use in the phenotypic experimentations. The representative strains for all four HA SNP clusters and one NHA SNP cluster (PDS000029303) were selected by identifying strains associated with these SNP clusters from a collection of S. Saintpaul strains in the Food Microbe Tracker (https://www.foodmicrobetracker.net/) and randomly selecting one strain from each of these SNP clusters. S. Saintpaul strains associated with an additional two NHA SNP clusters (PDS000004383 and PDS000004385) were procured from the New York State Department of Health (NYDOH), and one strain was randomly selected to represent each SNP cluster. The susceptibility of all representative strains to gentamicin was confirmed by (i) the absence of gentamicin resistance genes in their genomes and (ii) the growth inhibition observed for each strain in the presence of 20 μg/mL gentamicin (data not shown). Detailed information for the S. Saintpaul strains included in the phenotypic experiments is shown in A total of 7 S. Saintpaul strains  broth containing 5 g NaCl/L. Human intestinal epithelial cells (HIEC-6 cells) and human acute monocytic leukemia cells (THP-1 cells), both purchased from the American Type Culture Collection (ATCC), were preserved in cell culture medium supplemented with 10% (vol/vol) dimethyl sulfoxide in liquid nitrogen. Cells were routinely cultured in T75 flasks  by growing the cells at 37°C with 5% CO2. HIEC-6 cells were cultured in Opti-MEM medium supplemented with 10 ng/μL recombinant epidermal growth factor (EGF)  and 10% (vol/vol) fetal bovine serum  (Gibco-Invitrogen). THP-1 cells were cultured in RPMI 1640 medium supplemented with 2 mM l-glutamine, 10 mM HEPES, 0.01 mM phenol red, 1 mM sodium pyruvate, 25 mM d-glucose, 18 mM sodium bicarbonate, and 10% (vol/vol) FBS (not heat inactivated). Cell lines were routinely checked and confirmed to be free of Mycoplasma infection using the VenorGEM mycoplasma detection kit .Stock cultures for  strains  were preSalmonella from freshly streaked plates (within 7 days) were inoculated into 5 mL LB broth, followed by incubation at 37°C and 200 rpm  for 18 h. Subsequently, bacterial cultures were transferred (1 mL) into 1.5-mL Eppendorf tubes , followed by centrifuging at 8,000 rpm for 5 min. Supernatants were removed, and the cell pellets were resuspended in the complete HIEC-6 cell culture medium. HIEC-6 cells were grown to 90 to 100% confluence by seeding them into a 24-well plate (4 × 105 cells per well) , followed by incubation at 37°C with 5% CO2 for 20 to 24 h. S. Saintpaul strains were randomly assigned a processing order for infection of HIEC-6 cells seeded in the 24-well plate. Bacterial cultures suspended in HIEC-6 cell culture medium were used to infect HIEC-6 cells at a multiplicity of infection (MOI) of 100 bacteria per HIEC-6 cell. Infected HIEC-6 cells were incubated at 37°C with 5% CO2 for 1 h, after which the cell culture medium was removed and the HIEC-6 cells were washed three times with 1× phosphate-buffered saline (PBS), followed by incubation with medium supplemented with 20 μg/mL gentamicin at 37°C with 5% CO2 for 1 h. After the 1 h of incubation, the gentamicin-containing medium was removed and the HIEC-6 cells were washed three times with 1× PBS. Subsequently, the HIEC-6 cells were detached with 0.25% trypsin-EDTA  at 37°C for 3 min, followed by lysis with 1% Triton X-100 in 1× PBS on ice for 5 to 6 min. The HIEC-6 cell lysates were serially diluted in 1× PBS and spread-plated (100 μL) on LB agar plates in duplicate , followed by incubation at 37°C for 20 to 24 h. The bacterial inoculums were serially diluted in 1× PBS and spot-plated (20 μL) on LB agar plates in triplicate , followed by incubation at 30°C for 16 h. Colonies were enumerated with the SphereFlash automated colony counter . Four independent experiments  were performed.Single colonies of Salmonella strains were grown as described for the invasion of HIEC-6 cells. Bacterial cultures were transferred (1 mL) into 1.5-mL Eppendorf tubes and diluted (1:10) once in 1× PBS. The diluted bacterial cultures were centrifuged at 8,000 rpm for 5 min, followed by removal of supernatants and resuspension of the cell pellets in the complete THP-1 cell culture medium. Prior to each experiment, THP-1 cells were seeded in three 24-well plates (6 × 105 cells per well), designated for 2, 6, and 24 hpi, respectively, followed by differentiation into macrophage-like cells with treatment with 20 ng/mL phorbol 12-myristate-12-acetate (PMA)  for 3 days at 37°C with 5% CO2. The condition for THP-1 cell differentiation was determined based on (i) suggestions of Starr et al.  on LB agar plates in duplicate , followed by incubation at 37°C for 20 to 24 h. The bacterial inoculums were serially diluted in 1× PBS and spot-plated (20 μL) on LB agar plates in triplicate , followed by incubation at 30°C for 16 h. Colonies were enumerated with the SphereFlash automated colony counter. Three independent experiments  were performed.r et al.  and (ii)P value of 0.05.All statistical analyses were performed in R Statistical Programming Environment v4.0.2 . The thrS. Saintpaul strains used for phenotypic experiments for the virulence genes and core SNPs leading to PMSCs identified in the comparative genomic analyses  their genotype did not vary across the S. Saintpaul strains  or (ii) their genotypic pattern correlated with the lineage . Additionally, genomic signatures with identical genotypic patterns were grouped together , and each group was treated as a single factor (see S. Saintpaul strains).To identify the genotypic pattern across the analyses  and 4, t10.1128/mSphere.00730-21.10DATA SET S6S. Saintpaul strains included in phenotypic experiments. Download Data Set S6, XLSX file, 0.01 MB.Genotypic pattern of selected genomic signatures across Copyright © 2022 Chen et al.2022Chen et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the S. Saintpaul strains was represented by the decimal logarithm (log10)-transformed recovery rate log10N/N0, where N0 refers to the number of bacterial cells used for infection and N refers to the number of bacterial cells recovered from the HIEC-6 cell lysates . A linear regression model (designated the INV model) was constructed, using the stats package v4.0.5 (S. Saintpaul strains representing different epi-types and lineages and (ii) identify genomic signatures (HA virulence genes and HA/NHA core SNPs leading to PMSCs) that might contribute to the ability of invasion. The response variable of the model was log10N/N0, and the biological replicate was included as a blocking effect to account for the variation in log10N/N0 across different biological replicates. A feature selection procedure was performed on the candidate factors, including epi-type, phylogenetic lineage, and unique genotypic patterns specific for one or multiple genomic signatures, to identify the factors that should be included in the model as fixed effects. Specifically, the correlation of each genotypic pattern with the response variable was inferred based on the R value, using the stats package v4.0.5  using the same package. Genotypic patterns (and thus the corresponding genomic signatures) were excluded from the model if they were correlated with other genotypic patterns with a higher R value. Subsequently, the “best subset selection” method, implemented with the leaps package v3.1 (S. Saintpaul strains into HIEC-6 cells based on (i) Bayesian information criterion, (ii) Mallows’ pC-statistic and (iii) adjusted R squared. If the three metrics suggested different models, the final model was determined by comparing the models (i) using the likelihood ratio test, implemented with the stats package v4.0.5  between strains assigned to SNP clusters representing different epi-types, from different lineages, and/or with different genotypes of a given genomic signature, using the emmeans package v1.4. 8  e v4.0.5 , and theage v3.1 , was pere v4.0.5 , for nese v4.0.5 , for mod v1.4. 8 .S. Saintpaul strains in macrophage-like cells were represented by the log10-transformed CFU per mL. The difference in log10 CFU/mL was used to infer the ability of S. Saintpaul strains to survive in macrophage-like cells during different time periods, including 0 to 2, 2 to 6, and 6 to 24 hpi, and was calculated for each strain by subtracting the log10 CFU/mL at the beginning from the log10 CFU/mL at the end of each time period. Separate linear regression models were constructed for different time periods  with the difference in log10 CFU/mL as the response variable and biological replicate as a blocking effect. For each model, feature selection, ANOVA, and post hoc pairwise comparisons were performed as described for the invasion assay to identify and investigate factors, including epi-type, lineage, and genomic signatures, which had a significant impact on the survival of S. Saintpaul strains in macrophage-like cells.For the intracellular survival assay, the intracellular levels of the S. Saintpaul representative isolates used for constructing the phylogeny and selected from the 5 HA and 5 NHA SNP clusters with the most significant association with the corresponding isolation sources are listed in https://github.com/FSL-MQIP/Pew_SalmonellaSaintpaul_HumanVirulence.git). These include (i) the metadata of Salmonella enterica isolates available in the NCBI PD database (last accessed 23 June 2020), (ii) the experimental data obtained from the tissue culture assays, (iii) template codes for running BLAST+, kSNP3, Panaroo, Platon, Prokka, RAxML, Scoary, SISTR, and SKESA, (iv) codes for calculating odds ratios and performing Fisher’s exact tests, and (v) codes for statistical analyses of the tissue culture assay data.Genome assembly accession numbers for the


EPHA6) showing an association with litter size in Polish Mountain Sheep. We suggest that the EPHA6 gene can be a candidate gene for prolificacy trait in selected breeds of sheep; however, it needs further functional data for validation.Reproductive traits  are usually characterized by low heritability, and thus, phenotypic selection is often ineffective and slow. In order to improve fertility characteristics such as ovulation rate and litter size, it seems more effective to select breeding animals based on their genotype. The aim of the study was to use genome-wide association study (GWAS) in three sheep breeds to identify the genetic variants affecting the litter size in sheep. The study allowed us to identify one genome-wide significant SNP (rs402032081—located in ephrin type-A receptor 6, The online version contains supplementary material available at 10.1007/s13353-021-00615-6. ODZ1 and ODZ3, LTBP3, and DSCAM as a potential gene with impact on fertility traits. The same author identified genes from the ephrin family which shows a significant correlation with backfat  , Polish Mountain Sheep , and Colored Mountain Sheep . For each sheep, prolificacy data was recorded as the number of lambs per litter (litter size). The average fertility is calculated on the basis of based on the first three litters for each sheep. DNA was purified using the QuickGene DNA Whole Blood Kit (Kurabo). The obtained DNA was quality controlled and analyzed with the use of OvineSNP50 BeadChip  which targets 54,241 SNPs, following standard Infinium Ultra Protocol. The obtained genotypes were controlled for quality by evaluation of CallRate, and only samples with values >95% were further analyzed. The individual SNPs have also been filtered across the population, and only SNPs with minor allele frequency (MAF) >0.01, missing genotypes <20%, and not deviating from Hardy-Weinberg Equilibrium (p>0.0001) have been analyzed. Additionally, SNPs without a known position in the Oar_v3.1 genome build have been removed.Blood samples obtained from 155 randomly selected female sheep belonging to three native Polish breeds were analyzed. Animal procedures were approved by the Local Animal Care Ethics Committee No. II in Kraków - permission number 1293/2016 in accordance with EU regulations. The breeds included in the study included Podhale Zackel  gene and in the vicinity of the U6 spliceosomal RNA (ENSOART00000025351) gene and other provisional ENSEMBL genes (ENSOART00000018925).The association analysis was performed on a final filtered set of 49,204 SNPs with an average inter-marker distance of 52.38 kb and mean MAF ranging from 0.277 ±0.138) to 0.290 (±0.131) in PMS and PZ, respectively. The average observed heterozygosity for the filtered SNPs raged from 0.373 in PMS to 0.383 in PZ. The average prolificacy for the studied breeds was the highest for CMS (1.369 ±0.23) and the lowest for PMS (1.2 ±0.29). The performed linear regression for all breeds did not result in any significant SNPs after correction for multiple testing (Supplementary File 38 to 0.2EPHA6) showing an association with litter size in Polish Mountain Sheep. EPHA6 in mouse embryos is highly expressed during the development of the central nervous system, in adult animals in the hypothalamus, thalamus, and amygdala  and Development Slit-Robo signaling (https://www.genecards.org/cgi-bin/carddisp.pl?gene=EPHA6). Both these pathways have great potential to regulate early development and cell proliferation and thus affect prolificacy. The ERK cascade is activated by a variety of extracellular agents, including growth factors, hormones, and also cellular stresses to induce cellular processes that include mainly proliferation and differentiation, so the important processes for developing an embryo Supplementary File 2.Results of GWAS across breeds. (XLSX 5181 kb)Supplementary File 3.Results of GWAS within separate breeds. (XLSX 14986 kb)


AA6060 ingots were pierced in a two-high screw rolling mill (MISIS-130D) with guiding shoes (Mannesmann mill type). Three different plugs, i.e., a conventional entire plug, a plug with a cavity, and a hollow plug, were used for piercing. We established that the grain size decreases after piercing, by order of magnitude, compared to the initial non-pierced annealed bill, with a grain size of 100–400 μm, and the hollow shell grains are elongated along the piercing direction. The produced hollow shells had 30% higher hardness than the initial billet. The highest hardness values were obtained after piercing the conventional entire plug. The most uniform hardness distribution through the hollow shell’s volume was obtained after piercing the hollow plug. The cross and longitudinal section hardness measurements demonstrate that the hardness decreases from the outer surface to the inner surface of the hollow shells. One of the ways of producing hollow shells is piercing in a two-high screw rolling mill. This technique has a range of advantages. For instance, screw piercing has higher productivity than press piercing . DiffereRegarding the review of two-high screw rolling research publications, it is worth noting articles connected with hollow shell microstructure formation under different deformation regimes ,22,23,24The research objective was to investigate the hardness and microstructure of aluminum alloy hollow shells after two-high screw rolling in a mill with guiding shoes, while using an entire plug, a hollow plug, and a plug with a cavity. The idea of using such plugs was to estimate how a difference in metal flow character influences microstructure and hardness. In the case of the traditional entire plug, metal only flows along the surface of the plug. In the case of the hollow plug, metal not only flows along the surface of the plug, but also in it. In the case of the plug with a cavity, there is metal flow along the surface of the plug and limited flow inside the plug. Regarding the flow inside the plug, it was found to be limited by the depth of the cavity.2Cl6) at the same temperature. The refined melt was kept for 15 min and poured into a mold at 740 °C. The mold was preheated up to 150 °C. The mold’s material was an A356 aluminum alloy. Ingots of 60 mm diameter and 210 mm height were cast. Before pouring, the mold was covered using Cillolin Al285 coating [AA6060 aluminum alloy scrap was used as a raw material. Melting was realized using a clay graphite crucible in a resistance furnace. The alloy was treated with the ARSAL 2125 refining flux at 760 °C, and degassing was performed using hexachloroethane , Electrostal city, Moscow Region, Russian Federation) ,26. The Three plugs were used for piercing, namely, an entire plug, a plug with a cavity, and a hollow plug , with Tixomet software. Magnification was ×50–×1000; etching was performed using HF:How shell . The linVickers microhardness was measured using a MicroMet 5101  . The microhardness measurements were obtained in the hollow shell cross-section at the stationary piercing stage a. The arThe initial non-pierced billet  has a coarse-grained structure, with a grain size of 100–400 μm . The iniThe grain size in the hollow shells’ structure decreases by order of magnitude com-pared to the non-pierced billet. In the longitudinal section, grains elongated along the piercing direction are observed . Such elThe elongated chain of dynamically recrystallized small grains, 5–10 μm in size with rough edges, is observed near the subgrains’ boundaries . These cThe hardness variation along the walls of the hollow shells pierced by the different plugs is given in According to The hardness fluctuations may be due to the presence of deformed areas with a high concentration of crystal defects that have higher density. They could also be due to dynamically recrystallized areas that have low hardness values. At the same time, the structure type after piercing has little effect on the alloy’s microhardness. One of the possible reasons for this is that the subgrain structure is formed because of the piercing. It is known that the subgrain size affects the alloy’s strength less than the grain size .The hardness measurements in the longitudinal section are presented in 2) and for the plug with a cavity piercing (2.1 HV2) are 1.8 and 2.1 times less than the hardness variance value for the entire plug piercing (4.5 HV2), respectively.The quantitative estimation of the data shown in The mean values of hardness in the longitudinal section of the hollow shells  do not dIt is worth noting that the mean value of hardness increases compared to the mean value of the non-pierced billet. This increase is 33% for the entire plug piercing, 28% for the plug with a cavity piercing, and 21% for the hollow plug piercing.The average grain size of the hollow shells decreases by order of magnitude (up to 20 times) compared to the non-pierced billet. Notably, the hollow shells’ grains (subgrains) are elongated in the piercing direction. The elongated chains of dynamically recrystallized grains, 5–10 μm size, with rough edges, are visible near the subgrain boundaries.The hardness for all hollow shells decreases from the outer surface to the inner surface. The mean value of hardness is the highest after the entire plug piercing (44 HV), followed by the plug with a cavity piercing (42–43 HV), then the hollow plug piercing (40–41 HV), and the mean value is the lowest for the non-pierced billet (33 HV).The hardness measurements for the cross and longitudinal sections of the hollow shells reveal a common trend; namely, the hardness decreases from the outer surface to the inner surface of the hollow shell.The experiments demonstrated that the traditional entire plug allows the achievement of the highest values of hardness and, hence, the highest material strength of all three investigated piercing cases. The hollow plug provides the most uniform hardness distribution through the hollow shells’ volume. Hence, it is possible to deduce that when metal only flows between the rolls and plug (as in the entire plug case), one can gain higher hardness and, hence, strength, but with a decrease in hardness distribution uniformity. Whereas, when metal not only flows between the rolls and plug, but also inside the plug, the uniformity of hardness distribution is higher, but the hardness values are less compared to those achieved with the traditional entire plug. For the plug with a cavity, the outcome is somewhere between those for the entire plug and the hollow plug. The estimated effect of plug construction on the hollow shell’s properties is important. Even though a hollow shell is a semi-finished product, it sufficiently defines the properties of the final seamless tube, which is produced from the hollow shell by an elongation process. Hence, plug construction is sufficient, in terms of defining the quality of the final seamless tubes.The piercing of AA6060 aluminum alloy ingots was conducted in a two-high screw rolling mill MISIS-130D with guiding shoes. A traditional entire plug, a plug with a cavity, and a hollow plug were used for piercing. The study made it possible to establish the following:


MYOD1, were detected. By performing genome-wide association study for the BrW trait, we defined a major haplotype and two conserved SNPs that affected BrW. By integrated genomic and transcriptomic analysis, IGF2BP1 was identified as the crucial gene associated with BrW. In conclusion, these results offer a new insight into chicken directional selection and provide target genetic markers by which to improve chicken BrW.Breast muscle weight (BrW) is one of the most important economic traits in chicken, and directional breeding for that results in both phenotypic and genetic changes. The Jingxing yellow chicken, including an original (without human-driven selection) line and a selected line (based on selection for increased intramuscular fat content), were used to dissect the genetic architecture and key variants associated with BrW. We detected 1069 high-impact single nucleotide polymorphisms (SNPs) with high conserved score and significant frequency difference between two lines. Based on the annotation result, the ECM-receptor interaction and fatty acid biosynthesis were enriched, and muscle-related genes, including  For poultry, the weight and size of breast muscle are crucial to production efficiency and economic benefit by affecting carcass appearance, especially for chicken. Usually, the ratio of breast muscle weight (BrW) is as high as 20% or even 25% in fast-growing chickens  ,2, whileModern farm animals have been genetically adapted due to intensive human-driven selection, resulting in a genetically separate population that differs from the original population with regard to morphology, physiology, and behavior ,7,8. ForNCO17, TPD52L1, FABP7, CJA1, and ASF1A identified as candidate genes regulating BrW [LCORL and MAPT), and both of these two genes were expressed at significantly higher levels in chickens with larger breast muscle [IGF2BP1 gene has been studied widely in muscle development and lipid metabolism in animals [IGF2BP1 had a predominant role in body size as judged by GWAS and quantitative trait locus (QTL) mapping in duck and chicken, respectively [IGF2BP1, GIP, and other genes) that affected growth traits [IGF2BP1 in chicken breast muscle development.Genome-wide association study (GWAS) is an efficient and precise method to inspect the genetic markers associated with complex traits  in farm animals, especially for chicken due to the large-scale sequencing data ,14. For ting BrW . Kang etting BrW . Liu et t muscle . These r animals . Zhou etectively ,20. In ch traits . Taken tCollectively, due to the changes of genetic architecture and BrW trait after selection of multi-generations ,10, we aAll the experiments associated with chicken were conducted under the guidelines for experimental animals established by the Ministry of Science and Technology . Ethical approval on animal survival was given by the animal welfare and ethics committee of the Institute of Animal Sciences  and the Chinese Academy of Agricultural Sciences  with the following reference number: IAS2019-21.n = 520) were obtained from the Chinese Academy of Agricultural Sciences, Institute of Animal Sciences . Of these, 264 JX chickens (the selected line) were used. This line had been propagated for 16 generations for enhanced breast intramuscular fat content [Female JX chickens  was removed, weighed, and BrW was recorded . Venous n = 520) by the phenol-chloroform method. After quality control by gel electrophoresis, eligible DNA (presenting obvious strap and no degradation) was used to construct a pair-end library of 300–500 bp of average insert size. Each library was sequenced with a Novaseq 6000 sequencing platform  to acquire raw reads with an average depth of 10× coverage. The data can be accessed at CRA002643 and CRA002650 (https://bigd.big.ac.cn/gsa/) (accessed on 10 October 2021) [http://ftp.ensembl.org/pub/release-104/fasta/gallus_gallus/dna/Gallus_gallus.GRCg6a.dna_sm.toplevel.fa.gz) (accessed on 10 October 2021). The output was converted to bam files and sorted by coordinates based on Samtools software [Genomic DNA was isolated from blood samples (er 2021) ,23. Brieer 2021)  was usedsoftware . PCR dupThe SNPs located in the autosome and the Z chromosome were reserved for next analysis. Quality control was implemented with the particular parameters  in PLINK 1.9 software . After qhttp://hgdownload.soe.ucsc.edu/goldenPath/galGal6/phastCons77way/galGal6.77way.phastCons/) (accessed on 10 October 2021). To compare the allele frequency difference between original and selected chickens, we calculated the frequency of missense variants in original and selected lines, respectively. And Fisher’s exact test was used to calculate the p values [SnpEff (v.5.0) was used to annotate variants based on their location categorization . Conservp values , and they indicates the BrW phenotype, b represents the vector of batch effect, a represents the vector of random additive genetic effects, e represents the vector of random residual effect. X and Z are design matrices related to the corresponding fixed and random effects, respectively.To estimate the heritability for BrW, we firstly calculated the kinship matrix by the method of VanRaden using the GCTA software ,33. And y represents the vector of BrW record; An univariate linear mixed model (LMM) was used to perform association analysis based on all SNPs for BrW in GEMMA (0.98.4) software . Within −9  and 1.03 × 10−7 , respectively. Genomic inflation factor (λ) was calculated to estimate bias and stratification. The proportion of variance explained by lead SNP was evaluated as the equation: To reduce the false positive probability, a Bonferroni correction was used in association analysis. A genome-wide and suggestive threshold were defined as 5.14 × 102pqβ2σg2 , where pironment .2 for the lead SNP was calculated in PLINK 1.9 [2 > 0.8 and the 2-LOD drop-off method [Pairwise rLINK 1.9 . A narrof method . A signif method .http://ftp.ensembl.org/pub/release-104/fasta/gallus_gallus/dna/Gallus_gallus.GRCg6a.dna_sm.toplevel.fa.gz) (accessed on 10 October 2021) using HISAT2 [p value < 0.05. Stage-based expression level for candidate genes were presented as adjusted count value using the data from generation 15.The transcriptome data were acquired with accession number CRA001334 (sampling from the generation 15 of JX chickens)  and CRA0g HISAT2 . Then, wg HISAT2 . Here, wg HISAT2 , and DEGp value of 0.05 was regarded as the threshold for significant enrichment.KEGG pathway enrichment analysis was performed by ClusterProfiler  to exploGLM was used to estimate the significance of the effect of lead SNP on BrW. Pearson correlation analysis was used to evaluate the relationship between gene expression and BrW.Bioinformatics analysis using the described pipeline detected over 9.7 million high-quality bi-allelic SNPs in autosome and Z chromosome after filtration. The retained variants were distributed in the genome with an average density of 1 SNP every ~110 bases. Among those, about 58.08% of the SNPs were located in intron region, with only 1.73% of SNPs anchored in exons A. Approxp < 1.54 × 10−6). A total of 5352 significant missense variants were detected, primarily in 1–4 chromosomes   . 2 = 0.35) and higher BrW was observed in the selected population (p < 1.03 × 10−7) were found. The proportion of variance in genotypes was explained by the lead SNP (chr27_6115361) at up to 18.93% . In addition to the peak in chromosome 27, only one significant SNP greater than threshold was observed on chromosome 3. Located 10 kb up-stream of this site, lncRNA (ENSGALG00000034564) is the only annotated gene in this region by the current genome build  between two lines . By enriJX is a widely known domestic chicken breed due to its excellent meat quality. Regarding pectoral IMF content plays an essential role in meat quality, we constructed a 16th generation IMF selected line ,10,21. Fp < 1.54 × 10−6, Fisher’s exact test for allele difference, PhastCon score > 0.98), 1069 missense SNPs and 912 protein-coding genes were found to influence chicken traits during intensive selection. This result indicates that only a small percentage of genomic sites responded to artificial selection or possible environmental effect [Long-term artificial selection of farm animals can produce biased genetic variants or genomic signatures that distinguished the selected animals from the original population, which may be caused by pleiotropy of genes under selection, hitch-hiking of unfavorable alleles with the alleles under selection ,8. Therel effect . Based ol effect . MYO1D, MYO1H, and MYO19), MYH7, MYOD1, MYF5, and other genes , and a deletion mutation in the promoter region has been validated as a causal variant [IGF2BP1 was shown to affect body size in ducks [IGF2BP1, which is consistent with the previous report [GIP is important to bone size and growth traits in chicken [HOXB5 was differentially expressed between two lines, but the correlation with BrW was relatively low. The genes HOXB5, HOXB6, and HOXB7 were involved in the morphogenesis of chicken respiratory tract [HOXB7 and HOXB8 were also shown to be associated with beard trait in chicken [HOXB genes in breast muscle development. Although HOXB2 was positively correlated with BrW, the expression level of which is relatively low in muscle tissue. In addition, we detected no expression of HOXB1, HOXB9, HOXB13, SKAP1 in muscle tissue, as well as the lncRNA (ENSGALG00000034564). According to expression atlas, the microRNAs gga-mir-10a and gga-mir-196 were not expressed in skeletal muscle [SNF8, UBE2Z, CALCOCO2, and ATP5MC1 have been shown a core function in metabolic disease and cancer. Partial QTLs or SNPs have been associated with coronary artery disease (SNF8 and UBE2Z) [CALCOCO2 is associated with autophagy-related genes, influencing tumorigenesis and progression of osteosarcoma [ATP5MC1 was confirmed to perturb glucose metabolism and inhibit the oncogenic K-Ras signaling [In a previous study, it has been demonstrated that the BrW was elevated after selection of IMF . Here weer genes . Therefo variant . Similars report . Further chicken ,50, but ry tract , HOXB7 a chicken , but fewl muscle . SNF8, Ud UBE2Z) ,55. CALCignaling . But no Current knowledge of these genes makes identification of causal variants and associated genes difficult. To narrow candidate genomic regions and ensure accuracy, we combined the linkage status of the lead SNP with the 2-LOD cutoff method for fine-signal mapping . A 51-kbIGF2BP1 was negatively related to BrW. Wang et al. reported higher transcription level of IGF2BP1 was detected in Ross compared with that in Gushi chickens [IGF2BP1. But actually, cellular assay demonstrated that reinforced proliferation of myocytes was observed with siIGF2BP1 treatment in C2C12 myoblasts, and a decreasing trend was captured during the prenatal stage (E33~E95) of skeletal muscle development [IGF2BP1 was differentially expressed between original and selected chicken lines. But regrettably, there was insufficient RNA-sequencing data to compare the transcriptomic difference between individuals with different genotypes, the existing samples are heterozygous for haplotype or SNPs. In a word, the results of this study indicated that IGF2BP1 or its upstream regulatory element was directionally selected during the IMF enhancement, with confirmation by transcriptional activity differences between the two chicken lines. In this region, the frequency of the major haplotype and two conserved SNPs were consistently changed toward directional selection. These results provide evidence for functional selection of an animal genome within a limited number of years . Howeverchickens . This inelopment . Those rIGF2BP1 was confirmed as a major gene affecting BrW in JX chickens. It’s worth noting that a refined haplotype and two conserved SNPs located in the up-stream of IGF2BP1 have a strong effect on BrW, and the frequency change of which was consistent with the selection process. These results suggest novel targets for investigation of the genetic mechanisms that impact breast muscle development. Such an investigation will serve as the foundation upon which to improve chicken BrW.Collectively, intensive selection for IMF in chickens has produced a pattern of genomic variant, reflected in lipid- and muscle-related pathways and genes that differed from unselected chickens.


A total of 115,706 single-nucleotide polymorphisms (SNPs) of 122,415 were retained to identify quantitative trait loci (QTL) associated to HVP in chicken. The GWAS results based on the logistic mixed model (LMM) revealed that a narrow genomic location on chromosomes 1 (49.2–51.3 Mb) was significantly associated (p ≤ 4.32 × 10−7) with HVP, which contained 23 SNP makers related to 14 functional genes . Four highly associated (p < 10−5) haplotype blocks of 0.80 kb (two SNPs), 0.06 kb (two SNPs), 0.95 kb (two SNPs), and 0.03 kb (two SNPs) were identified with two, two, four, and four distinct haplotypes, respectively. As a melanoma-associated gene, CYP2D6 were also possibly involved in the development of HVP occurring in chicken with two significant variations (rs314284996 and rs317955795) in the promoter regions. Further tests revealed that the expression of CYP2D6 was obviously higher in the visceral peritoneum tissue of chicken with HVP than that in the normal group (p < 0.05). Our results provide a novel clue to understand the genetic mechanism of HVP generation in chicken, and the mapped QTL or candidate genes might serve for genomic selection to improve carcass quality in the yellow-feathered chicken industry.Hyperpigmentation of the visceral peritoneum (HVP) has been becoming one of the most challenging problems in yellow-feathered chicken production, which seriously affected chicken carcass quality traits. Detecting which genes dominantly impact pigmentation in the peritoneum tissues is of great benefit to the genetic improvement of HVP. To investigate the genetic mechanism of HVP in yellow-feathered broilers, genome-wide association studies (GWASs) were conducted in the F All breeds of domestic fowl may have hyperpigmentation in the skin of the shanks and abdominal fascia, resulting in a decline in the appearance of the carcass . Hyperpi2 resource population was conducted to identify two novel candidate genes related to three single-nucleotide polymorphisms (SNPs) associated with avian influenza antibody titers  were proved to be a viable and successful approach to explore phenotype-to-genotype connections , which hy titers . Anotherpulation . For thepulation .EDN3 expression  and Use Committee (GAAS-IAS-2009-73).2 population were used for GWAS, including 212 cocks and 183 hens from eight half-sib families. At 91 days of age, venous blood was collected and slaughtered. As shown in The experimental population was a three-generation cross-breeding population construction from two different meat-type chicken lines. The first founder line was Huiyang Bearded Chicken (HB), which is an indigenous breed in China. The second founder line was the commercial broiler breed “High Quality Chicken A Line” (HQLA). The ratio of HVP of Huiyang Bearded Chicken is much higher than that of HQLA. A total of 395 individuals from the Fftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/002/315/GCF_000002315.4_Gallus_gallus-5.0) was used as the reference genome. The qualifying DNA of the sampled individuals was digested using HaeIII restriction enzymes. The length of paired-end reads was 125 bp, while the sequencing insert size was 364–444 bp. The consistent part of the variation obtained by GATK ; Gi is the genotype of animal i; and β is the genotype effect. It was assumed that K is the additive genetic relationship matrix based on genomic information. GWAS was implemented in the GMMAT (GWAS was performed using the logistic mixed model (LMM); the statistical model ishe GMMAT  package,p = 0.05/N, where N is the number of SNP loci. In this study, we used a genome-wide significance threshold p-value of 4.32*10−7 . Quantile–quantile (QQ) plots and Manhattan plots were plotted using the R package CMplot  translated the significant SNPs from Gallus gallus 5.0 to Gallus gallus 6.0.The Bonferroni correction  was usede CMplot . Use of The population structure was analyzed using Admixture software , and admLinkage disequilibrium (LD) of SNP loci significantly associated with HVP was performed with the software haploview . After thttp://archive.ensembl.org/Gallus_gallus/Info/Index).The nearest genes present within 50 kb upstream or 50 kb downstream of the SNP loci significantly associated with HVP were identified as potential candidate genes based on the Gallus gallus 6.0 reference genome (n = 5) (absent HVP control group) and five HVP-positive (severe HVP) chickens. The total RNA was extracted with RNA TRIzol . qRT–PCR primers were designed using NCBI Primer-Blast. The reactions of qRT–PCR were performed in 20 µL volumes. Parameters of RT-PCR were as follows: 3 min 95°C, followed by 40 cycles of 30 s at 95°C, 30 s at 60°C, and 45 s at 72°C. The relative gene expression levels of the target genes MFNG, MYH9, RANGAP1, and CYP2D6 were calculated using the 2−△△CT method and normalized with housekeeping genes: GAPDH. The differential expression of MFNG, MYH9, RANGAP1, and CYP2D6 in the visceral peritoneum tissue, between the normal and HVP-positive, was determined using a t-test with the software Graphpad Prism 8  for the first three principal components (PCs) . The fir2 population of yellow chicken, which illustrated that the HVP trait could be potentially improved by selective breeding. GWAS performed on HVP showed that 23 consistent SNPs were identified as the most significantly associated loci with HVP performance, which was analyzed by the logistic mixed model . Interestingly, CYP2D6 was identified as a biomarker associated with increased susceptibility to melanoma, which is coded for another enzyme that may play a role in detoxifying potentially carcinogenic compounds . As shown in p < 0.01), and the GT haplotype has a great plus effect on HVP (p < 0.01). However, a total of two haplotypes were generated in Block 2, and the two haplotypes were significantly associated with HVP. The AT haplotype has a significant minus effect on HVP (p < 0.01), and the GC haplotype has a great plus effect on HVP (p < 0.01). The haplotypic Block 3 included four significant SNPs, with only two SNP haplotypes constructed. The TG haplotype has a significant minus effect on HVP (p < 0.01), and the CA haplotype displayed a plus effect on HVP (p < 0.01). However, a total of four haplotypes were generated in Block 4 with two haplotypes significantly associated with HVP traits. The CT haplotype has a significant minus effect on HVP (p < 0.01), and the TC haplotype has a great plus effect on HVP (p < 0.01).There were four haplotype blocks, which spanned 0.80, 0.09, 0.06, and 0.03 kb genomic regions , and theCYP2D6, MYH9, RANGAP1, and MFNG by qRT-PCR. The primers are listed in CYP2D6 gene was greatly differentially expressed between the birds from the absent and severe HVP groups (p < 0.05), and CYP2D6 had a much high level in the server individuals, which was consistent with the studies in humans that CYP2D6 genotypes were associated with the hair color, Breslow thickness, and malignant melanoma . For the MYH9 gene, which encodes the non-muscle myosin heavy-chain IIA protein (p > 0.05). For the RANGAP1 gene, a crucial component of the RanGTPase system (p > 0.05).Exploring expression level changes in candidate genes was proved to be helpful to identify major genes following GWAS analysis. Here, five birds were randomly chosen from both the severe HVP and absent HVP groups and were utilized for detecting transcriptional expression profiles of melanoma . For theignaling , there w protein , there we system , there w2 cross population, indicating that it is possible to improve HVP performance through molecular breeding selection with the precious understanding knowledge of the genetic mechanism on this trait. A recent study found that designed populations, such as F2 populations for GWASs, were very beneficial in enhancing QTL mapping accuracy .The broiler chicken population used in this study was constructed by crossing a Huiyang Beard chicken and a Chinese commercial broiler breed. Huiyang Beard chickens are one of the indigenous Chinese chicken breeds. There were several problems for breeding in most local breeds, such as smaller effective population numbers, which resulted in lower diversity in breeds and a higher LD level and larger fraction of SNPs with fixed alleles . HoweverImprovements in the density of SNPs at the whole genome level have allowed more accuracy in mapping resolution of common variant associations in GWAS. Based on the same broiler population, CYP2D6, MYH9, RANGAP1, and MFNG. Finally, the gene expression pattern between case-control groups was utilized to explore the candidate genes. CYP2D6 was identified as a major candidate gene for HVP by integrating all these tests. Moreover, the functional of CYP2D6 also contributed to our conclusion. CYP2D6 encodes a highly polymorphic gene in the family of cytochrome P450, and its function is highly variable  on chromosome 1 (GGA1) was identified clearly associated with HVP, related to 23 significantly associated SNPs. Gene annotations revealed that 14 genes  could be primary candidate genes for HVP occurring in chicken. Joint analysis of haplotype association and gene expression pattern changes indicated that CYP2D6 was a novel potential genetic marker for HVP performance in chicken with much higher expression in the birds of severe HVP, which was in agreement with previous studies in humans that CYP2D6 genotypes were highly associated with tumor thickness in cutaneous melanoma. Our results provide a new insight to understand the genetic basis of HVP generation in chicken and improve the accuracy of genomic selection for carcass quality in the poultry breeding.In this study, GWAS in the F