Fat tail is a unique trait in sheep acquired during domestication. Several genomic analyses have been conducted in sheep breeds from limited geographic origins to identify the genetic factors underlying this trait. Nevertheless, these studies obtained different candidates. The results of these regional studies were easily biased by the breed structures.FST) and difference of derived allele frequency (ΔDAF). The results showed that platelet derived growth factor D (PDGFD) exhibited the highest genetic differentiation between fat- and thin-tailed sheep breeds. Analysis of sequence variation identified that a 6.8-kb region within the first intron of PDGFD is likely the target of positive selection and contains regulatory mutation(s) in fat-tailed sheep. Histological and gene expression analyses demonstrated that PDGFD expression is associated with maturation and hemostasis of adipocytes. Further retrospective analysis of public transcriptomic datasets revealed that PDGFD expression is down-regulated during adipogenesis in both human and mouse, and is higher in fat tissues of obese individuals than that in lean individuals.To minimize the bias and distinguish the true candidates, we used an extended data set of 968 sheep representing 18 fat-tailed breeds and 14 thin-tailed breeds from around the world, and integrated two statistical tests to detect selection signatures, including Genetic Fixation Index (PDGFD is the predominant factor for the fat tail phenotype in sheep by contributing to adiopogenesis and maintaining the hemostasis of mature adipocytes. This study provides insights into the selection of fat-tailed sheep and has important application to animal breeding, as well as obesity-related human diseases.These results reveal that The online version contains supplementary material available at 10.1186/s12864-020-07210-9. Following domestication in the Fertile Crescent approximately 8000–9000 years ago , sheep sToday, the fat-tail phenotype is largely undesirable for both producers and consumers, partially because that fat tails have a significant influence on fat deposition in other parts of the body , mating FST) method, to identify candidate genes. FST-based approach is widely used to identify the region with high divergence between populations; however, it lacks the power of detecting the direction of selection. To clarify this, we performed a comprehensive genomic analysis of 18 thin-tailed and 14 fat-tailed sheep breeds from around the world. We integrated two selection tests, including FST and the difference of derived allele frequency (ΔDAF) (referred to as DAFFat-tailed sheep – DAFThin-tailed sheep), to identify positively selected genes specifically in fat-tailed sheep. Whole genome-sequencing data of the candidate genes were further analyzed to detect the potentially casual variants. Additionally, histological and gene expression analysis of sheep tail tissues were carried out to understand the association of the expression of the most predominant candidate gene PDGFD with fat deposition in sheep tail during embryonic development. Finally, re-analysis of public transcriptomic datasets were performed to examine the change of PDGFD expression during adipogenesis in human and mouse, as well as between the fat tissues of obese and lean individuals.Several efforts have been made aiming to identify genes or genomic regions associated with fat tail phenotype by genome-wide scans –11. Howe; Additional file 1: Table S1). The geographic distributions of the sheep breeds in this report include South Asia, East Asia, Middle East, Europe, Africa, North America, and South America . After a series of quality control filters, a total of 45,337 SNPs and 828 individuals from 30 diverse sheep breeds worldwide were retained for further analysis.SNP Beadchip data for a total of 968 individual sheep was collected and used in this study. These samples contained eight wild Mouflon sheep individuals, 18 thin-tailed sheep breeds and 14 representative fat-tailed sheep breeds from different countries or regions within countries  vs South Asian thin-tailed sheep (SAT), MEF vs European thin-tailed sheep (EUT), and Chinese fat-tailed sheep (CHF) vs SAT. In total, 213, 202 and 157 positively selected SNPs were identified by both FST and ΔDAF methods in the three comparisons, respectively . Among them, 16 loci were common to all the three group-pair comparisons . The remaining eight SNPs are highly diverged between thin- and fat-tailed sheep, and keep ancestral allele in most thin-tailed sheep breeds while derived allele in most fat-tailed sheep breeds worldwide  and further supported the strong divergence of these loci. Gene annotation of 150 kb-long genomic region surrounding the eight SNPs revealed 24 genes and PDGFD corresponding SNP OAR15_3091174 among them that is located on chromosome 15 was most highly differentiated  in more than 30% of the thin-tailed (or fat-tailed) sheep breeds, after examining the DAF of these SNPs in additional three American thin-tailed sheep breeds and three African fat-tailed sheep breeds (See Methods) . A total of 17,124 SNPs were identified in the selected regions among these individual sheep (MAF > 0.05). We calculated the absolute difference of allele frequency (ΔAF) of each SNP and confirmed that the PDGFD gene is the most divergent locus between fat- and thin-tailed sheep  family members, which play an important role in regulation of adipocyte development and function. The expression pattern across multiple issues obtained from Human Protein Atlas database (http://www.proteinatlas.org/) showed that PDGFD is relatively more abundantly expressed in pancreas, pituitary, ovary and adipose tissue than that in other tissues . These already known involvements of PDGFD in lipid metabolism provide further support for the hypothesis that this gene is an ideal candidate for fat tail in sheep.To better investigate these selected genes, we extracted their genome sequence variants of 16 thin-tailed and 13 fat-tailed individual sheep with diverse geographic origins from NextGen project  . Haplotype comparison analysis using all the SNPs (n = 122) within this region revealed a consistent differentiation between sheep breeds with different tail types  in NPs n = 12 within PDGFD, several other selected genes identified in this study have known functions that are associated with lipid metabolism, such as ENPP2, ANAPC5, RNF34, KDM2B, CAMKK2, TSHR, and CDH1. The genetic differentiation for these genes was not as pronounced as for the PDGFD loci, implying that these loci likely contribute to the phenotypic difference with relatively small effects. We also examined the DAF distribution of the top candidate SNPs that were proposed in previous studies and the results showed that the majority of these SNPs are not consistently diverged between thin- and fat-tailed sheep populations .In addition to To gain insight into tail fat deposition in fat-tailed sheep breeds, tail tissues from a fat-tailed Chinese sheep breed, namely Tan sheep, at four different embryonic time points, including embryonic day 60 (E60), E70, E80 and E90, were collected. It was observed that the tail is thinnest at E60 and E70 and, by E80 and E90, is much larger Fig. a, indicaPDGFD in the development of fat tail in sheep, we performed gene expression analysis by RNA-seq for different embryonic stages. The results showed that the expression of some marker genes which are known to be down-regulated  and up-regulated  during adipogenesis had no obvious difference between E60 and E70 while significantly decreased and increased at E80 compared to earlier stages, respectively . Gene expression correlation analysis revealed that PDGFD expression is highly positively correlated with markers enriched in preadipocytes and negatively correlated with markers up-regulated in mature adipocytes  underlying the fat tail phenotype of sheep, herein we comprehensively analyzed genomic variation data from a large cohort of sheep breeds with different tail types from around the world and integrated two different selection tests to detect genomic regions with signals of selection. We demonstrated that ogenesis , 13, exhBMP2, which have been reported by several independent studies [BMP2 locus, exhibited relatively higher DAF in fat-tailed sheep than that in thin-tailed . Unfortunately, the genetic differentiation level of the former two SNPs is below the genome-wide threshold in one (MEF vs SAT) and two group-pair comparison(s) , respectively . A possible reason for this is the low abundance of derived allele in some fat-tailed sheep populations, such as Afshari, LocalAwassi and EthiopianMenz . Therefore, BMP2 has lower priority than the PDGFD locus as the candidate for fat-tailed phenotype. Furthermore, a recent study based on whole-genome re-sequencing data also found that PDGFD is the most highly differentiated loci between fat-tail sheep populations from China as well as Middle East, and thin-tailed Tibetan sheep, followed by BMP2 as the second top highly divergent gene [PDGFD is the predominant candidate for fat tail in sheep, although our study suffers limitation that only genes covered by the Ovine 50 K Beadchip were investigated.Compared to several existing papers that have studied sheep breeds from limited geographical areas and reported quite different candidates associated with fat tail phenotype –11, this studies , 8, 11. PDGFD expression is negatively associated with adipocyte accumulation  to adult stage  to mature adipocytes as well as expansion of adipocyte size . This fiion Fig. a-d. Conslls Fig. e-f [15, age Fig. h-i. Suppan sheep . Furthermulation . The higues Fig. j-k [19, e tissue , we propPDGFD and narrowed down the candidate casual mutations to 13 SNPs which display high frequency in fat-tail sheep while low abundance in thin-tailed sheep . Since intronic mutations mainly affect the transcriptional efficiency of genes by creating or disrupting binding sites for transcriptional factors [PDGFD in fat-tailed sheep is attributed to some/one of these mutations. Future mutagenesis studies are warranted to determine the effect of mutated allele of these candidate SNPs identified in fat-tailed sheep on PDGFD expression to pinpoint the true casual mutation(s).We discovered a list of mutations that are located within the first intron of  factors , 23, it PDGFD gene is the predominant factor underlying the fat tail phenotype in sheep and is a potentially novel regulator for adiopogenesis and maintaining hemostasis of mature adipocytes. Mutations occurring within the first intron of PDGFD is likely associated with PDGFD transcriptional activity and fat deposition. These findings are important for understanding the selection of the fat tail in sheep and useful in genome-based animal improvement, as well as obesity-related diseases in human.In conclusion, we have demonstrated that This study followed the recommendations of the “Regulations for the Management of Affairs Concerning Experimental Animals” . The study was approved by the ethics committees of all the participating institutes .http://www.sheephapmap.org/hapmap.php) [1: Table S1. All the SNP data were generated using the Illumina Ovine 50 K Beadchip and were thus readily merged together. The final dataset included 968 sheep individuals and 47,415 common autosomal SNPs (based on genome Oar_v3.1).Genome-wide SNP data of 968 individual sheep from five South Asian thin-tailed sheep breeds , six European thin-tailed sheep breeds , three American thin-tailed sheep breeds , six Middle East fat-tailed sheep breeds , four African fat-tailed sheep breeds  and eight Mouflon sheep individuals (wild sheep) were downloaded from Ovine HapMap project (map.php) . SNP datmap.php) . Tail tyAncestral allele information for a subset of 33,059 SNPs were obtained from Ovine HapMap . For thehttp://evolution.genetics.washington.edu/phylip.html) on the basis of allele frequency data. After PCA analysis, a total of 45,337 SNPs for 828 individuals from 30 diverse sheep breeds were retained for downstream analyses .A pruned set of 32,450 SNPs were used to investigate the genetic relationship among these sheep breeds from different geographic locations. PCA was performed with the GCTA software  and the Three previous studies compared the genomic variations between Middle East/European thin-tailed versus (v.s.) Middle East fat-tailed sheep , EuropeaFST and ΔDAF were applied to evaluate the genetic differentiation of each SNP between thin- and fat-tailed sheep populations. The FST analysis was conducted using Genepop 4.3 software [Fat-tailed sheep – DAFThin-tailed sheep) using R version 3.3.3 (https://www.r-project.org/). For each group-pair comparison, FST and ΔDAF at each SNP marker was calculated between each thin-tailed breed against each fat-tailed breed and averaged across breed-pairs to produce an overall value for each SNP. The top 1% SNPs with large overall FST or ΔDAF value were considered as the significant SNPs in each test and the significant SNPs overlapped in both tests were considered as positively selected SNPs in fat-tailed sheep. Finally, positively selected SNPs that were identified in all three group-pair comparisons were considered as the loci under positive selection in fat-tailed sheep and genes within 150 kb of these loci were retrieved from Ensembl BioMart database (http://useast.ensembl.org/biomart/martview/625a397ecca62a421509935f099cdaa7). Because the high average value of FST or ΔDAF may have resulted from the extremely large values in several specific breed-pairs due to population structure, we additionally examined the DAF of the candidate SNPs in three thin-tailed sheep breeds from Americas and three fat-tailed sheep breeds from Africa and further filtered the promising candidate list by removing SNPs with DAF > 0.5 in more than five thin-tailed sheep breeds or DAF < 0.5 in more than four fat-tailed sheep breeds .Two statistics, including the software . ΔDAF wahttp://projects.ensembl.org/nextgen/) . The VCF file was converted to PLINK PED file using VCFtools [Genomic variations stored in Variant Call Format (VCF) file for 16 thin-tailed sheep individuals and 13 fat-tailed sheep individuals from 17 different breeds from around the world were downloaded from NextGen of Ensembl Projects  and derived allele frequency larger than 0.8 in fat-tailed sheep were selected for further validation in this expanded cohort. The genotyping of these 13 SNPs was carried out with Sequenom MassArray system. Briefly, primers for PCR and for locus-specific single-base extension were designed with MassArray Assay Design 4.0. The PCR products were applied for locus-specific single-base extension reactions. The final products were desalted and transferred to a matrix chip. The resultant mass spectrograms and genotypes were analyzed with MassArray Typer software.Ear tissues of 200 Tibetan sheep (Thin-tailed sheep) 184 Tan sheep (fat-tailed sheep) were collected from farms operated by the Research Institute of Animal Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, and Ningxia Yanchi Tan sheep breeding farm, respectively. Animals were released after collecting the samples. Genomic DNA was isolated and was quantified using a Nanodrop 2000 . A total of 13 intronic SNPs of A total of 16 pregnant tan sheep individuals (fat-tailed sheep) subjected to artificial insemination were randomly chosen at farm of Institute of Animal Science, Ningxia Academy of Agricultural and Forestry Sciences in China. At each of the expected embryonic stages including embryonic day 60 (E60), E70, E80 and E90, four animals were euthanized by captive bolt stunning followed by exsanguination and embryonic tail tissues were collected and subjected to histology analysis, RNA-seq analysis or qRT-PCR. For Histology analysis, tissues were fixed with 4% paraformaldehyde over-night at 4 °C and then embedded in paraffin. Sections were cut at 8-um thickness. For HE staining, sections were deparaffinized with two changes of Xylene (10 min each) and were rehydrated with 100, 95 and 80% ethanol rinses (5 min each). After a brief wash in distilled water, sections were stained in hematoxylin solution for 5 min, washed in running tap water for 5 min, differentiated in 1% acid alcohol for 30 s and washed again with running tap water for 1 min. Following bluing in 0.2% ammonia water for 30 s and a wash in running tap water for 5 min, sections were rinsed in 95% alcohol and stained in eosin-phloxine solution for 30 s. Sections were then dehydrated with a series of rinses in 80% alcohol (5 s), 95% alcohol , 100% alcohol , and then mounted. For Oil Red O staining, 0.5 g Oil Red O powder (Sigma) was evenly dissolved in 100 mL of isopropanol to prepare stock solution, which was then diluted with distilled water at the ratio of 3:2 and filtrated to make working solution. The sections were rinsed with 60% isopropanol and then stained with Oil Red O working solution for 15 min. Sections were then mounted and imaged for evaluation of droplet formation.Tail tissues from Tan sheep at three different developmental stages as described above including E60, E70, E80 were used for RNA-seq analysis. Tail tissues were flash frozen in liquid nitrogen and stored at − 80 °C for subsequent RNA extraction. Total RNA was extracted by RNeasy Lipid Tissue Mini Kit (Qiagen) and the RNA-seq library was constructed with Dynabeads mRNA DIRECT Kit (invitrogen) for whole transcriptome RNA-seq analysis. Pair-end RNA-sequencing was performed on a X-ten system (Illumina) in 150-bp length.After removing adaptor sequence and low-quality reads, pass-filtered reads were then mapped to UCSC sheep reference genome Oar_v4.0 using Tophat 2.1.1 . The genPDGFD expression during development. Four additional tail samples from each of Suffolk sheep (thin-tailed sheep) at E70 and Tan sheep at adult stage  were collected from farm operated by the Institute of Animal Science, Ningxia Academy of Agricultural and Forestry Sciences in China. Tail samples from four Bhyanglung sheep individuals at adult stage  were collected from farm of National Animal Science Institute, Nepal Agricultural Research Council in Nepal. For embryonic sample collection, animals were euthanized by captive bolt stunning followed by exsanguination and embryonic tail tissues were harvested. For adult sample collection, animals were released right after harvesting samples. Total RNA from tail tissues were isolated with RNeasy Lipid Tissue Mini Kit (Qiagen). 0.8 μg of total RNA was utilized as template for RT with random hexamer primers using PrimeScript RT reagent Kit (Takara). qRT-PCR was performed with respective gene-specific primers . All samples were amplified in triplicate and all experiments were repeated at least 3 independent times. Relative gene expression was converted using the 2-∆∆CT method against the internal control house-keeping gene ACTB where ∆∆CT =  - (CTcontrol gene - CTcontrol ACTB). The relative gene expression in control group was set to 1. Student t test was performed to determine significance.Tail tissues of Tan sheep (fat-tailed) at different developmental stages including E60, E70, E80, E90 described above were used for qRT-PCR analysis of Micro-array data of different cell types isolated from human white adipose tissues by fluorescence-activated cell sorting (FACS) including CD45−/CD34+/CD31- (progenitors), CD45+/CD14+/CD206+ , CD45+/CD14 + CD206+/CD11c + (M1 macrophages), CD45+/CD14+/CD206+/CD11c- (M2 macrophages), CD45+/CD3+ , CD45+/CD3+/CD4+/CD8- (Th T-cells), CD45+/CD3+/CD4−/CD8+ (Tc T-cells) (GSE100795)  were obtAdditional file 1 Table S1. Information of samples used in this study.Additional file 2 Table S2. Positively selected SNPs identified in comparison of Middle East fat-tailed sheep vs South Asian thin-tailed sheep.Additional file 3 Table S3. Positively selected SNPs identified in comparison of Middle East fat-tailed sheep vs European thin-tailed sheep.Additional file 4 Table S4. Positively selected SNPs identified in comparison of Chinese fat-tailed sheep vs South Asian thin-tailed sheep.Additional file 5 Figure S1. The distribution of derived allele frequency (DAF) of the 16 candidate SNPs in each breed. SNPs were sorted according to the average value of FST and ΔDAF among the three comparisons indicated in Fig. Additional file 6 Figure S2. Phylogenetic analysis of the studied sheep breeds. (A) Principle Component Analysis (PCA) results. The left panel shows the PCA plot generated based on genome wide SNPs. The right panel shows the PCA plot generated based on the 8 candidate SNPs. (B) Phylogenetic tree results. The left panel shows the phylogenetic tree constructed according to genome wide SNPs. The right panel shows the phylogenetic tree constructed according to the 8 candidate SNPs.Additional file 7 Table S5. Information of samples used for genome sequence variation analysis.Additional file 8 Figure S3. The expression level across different tissues of PDGFD gene obtained from FANTOM5 dataset from The Human Protein Atlas database.Additional file 9 Table S6. Highly differentiated SNPs (ΔAF > 0.60) within the putative targeted region of positive selection in PDGFD gene .Additional file 10 Figure S4. Replication of the top candidate SNPs proposed by three previous studies in all sheep breeds used in this study. (A) Derived allele frequency (DAF) of the top candidate SNPs previously proposed. (B) The top panel of “A” indicates the the derived allele frequency (DAF) of 10 top SNPs on chromosome 5 and 7 proposed in Table 3 in study of Moradi et al.. The s553221.1 locus on chromosome 5 in their study was not included because it did not pass the quality control in this study. (B) The middle panel of “A” shows the DAF of four SNPs corresponding to BMP2 and two SNPs corresponding to VRTN gene proposed in Table 2 in study of Moioli et al.. The s73063.1 locus annotated to VRTN was not was not included because it did not pass the quality control in this study. (C) The bottom panel of “A” represents the DAF of SNPs annotated to the key genes proposed in Table 1 and Table 2 in study of Yuan et al. The OAR4_73050615.1 locus is from Table 1 and other loci are from Table 2 in their study.Additional file 11 Figure S5. Expression of the top candidate genes in tail tissues of fat-tailed sheep during embryonic development revealed by RNA-seq. E60: embryonic day 60. E70: embryonic day 70. E80: embryonic day 80.


On chromosome one alone, 51 significant SNPs associated with isovalerylcarnitine and propionylcarnitine were found to be in strong linkage disequilibrium (LD). SNPs in strong LD annotated to FBXL4, and CCNC consisted of two haplotype blocks where three SNPs  were in the intron regions of FBXL4 and CCNC. The interaction network revealed that CCNC and FBXL4 were linked by the hub gene N6AMT1 that was associated with isovalerylcarnitine and propionylcarnitine. Moreover, three metabolites  were clustered in one group based on the low-high RFI pigs. This study performed a comprehensive metabolite-based genome-wide association study (GWAS) analysis for pigs with differences in feed efficiency and provided significant metabolites for which there is significant genetic variation as well as biological interaction networks. The identified metabolite genetic variants, genes, and networks in high versus low feed efficient pigs could be considered as potential genetic or biomarkers for feed efficiency.Metabolites represent the ultimate response of biological systems, so metabolomics is considered the link between genotypes and phenotypes. Feed efficiency is one of the most important phenotypes in sustainable pig production and is the main breeding goal trait. We utilized metabolic and genomic datasets from a total of 108 pigs from our own previously published studies that involved 59 Duroc and 49 Landrace pigs with data on feed efficiency ), genotype (PorcineSNP80 BeadChip) data, and metabolomic data . Utilizing these datasets, our main aim was to identify genetic variants (single-nucleotide polymorphisms (SNPs)) that affect 45 different metabolite concentrations in plasma collected at the start and end of the performance testing of pigs categorized as high or low in their feed efficiency . Genome-wide significant genetic variants could be then used as potential genetic or biomarkers in breeding programs for feed efficiency. The other objective was to reveal the biochemical mechanisms underlying genetic variation for pigs’ feed efficiency. In order to achieve these objectives, we firstly conducted a metabolite genome-wide association study (mGWAS) based on mixed linear models and found 152 genome-wide significant SNPs ( Large populations are generally essential for genome-wide association study (GWAS) to obtain sufficient statistical power for the identification of genetic polymorphisms . HoweverFind significant SNP markers associated with all the metabolites in the metabolomics dataset using mGWAS method and then reveal the biochemical mechanisms underlying genetic variation for porcine feed efficiency using 108 Danish pigs in low and high RFI conditions, genotyped by 68K PorcineSNP80 BeadChip array.Annotate identified significant SNP markers to porcine genes.Annotate metabolites and identify enriched metabolic pathways and gene-metabolite networks to find the potential biomarkers that were strongly associated with feed efficiency.In this study, we aimed to identify genetic variants (SNP markers) affecting concentrations of metabolites and to reveal the biochemical mechanisms underlying genetic variation for pigs’ feed efficiency. Our study is based on two of our previously published papers and datasets used therein ,11. BrieThe partial least squares-discriminant analysis (PLS-DA) results revealed that the first component score (component 1) explained more than 75% variation of all 45 metabolites A. It shop-values ≥ 2.78 × 10−6) and metabolites enriched in the significant metabolic pathways ; thus, GWAS results of these two scenarios were not listed. Manhattan plots of genome-wide association for isovalerylcarnitine and propionylcarnitine are shown in Metabolite based GWAS for first, second, and combined two sampling times revealed 152 genome-wide significant SNPs  in assocFBXL4 and CCNC , we found that 90 significant SNPs were within a 500-kb window of 52 neighboring genes  and thatand CCNC . These rand CCNC . In Tablp-value = 2.79 × 10−8) and propionylcarnitine  from second sampling time were in strong LD (LOC780435 (NM_001078684), FHL5 (NM_001243314), FBXL4 (NM_001171752), CCNC (NM_001190160)/MCHR2 (NM_001044609), and SIM1 (NM_001172585) were in block 2, block 4, block 6, block 8, and block 9/10, respectively. Furthermore, ALGA0004000 in the 6th intron of FBXL4 was in LD of block 6, together with another five SNPs  that were located in the intergenic regions of FBXL4. Especially, three SNPs in strong LD consisted of block 8 with two SNPs (ALGA0004041 and ALGA0004042) located in the second and ninth intron of CCNC (SFXN1 (NM_001098602) on chromosome two and ALGA0061605 in the 5th intron region of MTRF1 (NM_001243580) on chromosome eleven were not in the LD with other SNPs. However, ASGA0093565 in the 8th intron region of DNAJC6 (NM_001145378) was in strong LD with WU_10.2_6_135312468 that was annotated to LEPROT (NM_001145388) (The linkage disequilibrium (LD) pattern for all significant SNPs is shown in the trong LD . Among ttrong LD , so they of CCNC . The num of CCNC . Notably1145388) .CCND2, CTNNB1, JAK1, LAMC1, and NFKB1), followed by the PI3K-Akt signaling pathway (ssc04151) with five genes  and the hepatitis C (ssc05160) with four genes   (The most significantly enriched gene-based pathways were the human papillomavirus infection (ssc05165) with five genes  A. Based to FBXL4 B. Unfortto FBXL4 , we founto FBXL4 C. Resultto FBXL4 C. AfterwC, SFXN1 . General > 0.05) D.The previous study reported that different breed types performed differently in RFI variation , so RFI-The arginine biosynthesis pathway ssc00220), where arginine, aspartic acid, citrulline, glutamic acid, and ornithine were significantly enriched in our study , plays a0, where The previous GWAS for Duroc pigs identified two pleiotropic QTLs on chromosome one and seven for feed efficiency . Do et aCCNC, FBXL4, FHL5, LOC780435, MAT2B, MCHR2, PNISR, RRAGD, and SIM1) on chromosome one , but the associated metabolites only mapped to 1-hexadecyl-sn-glycero-3-phosphocholine (1.7%), 1-myristoyl-sn-glycero-3-phosphocholine (1.7%), isovalerylcarnitine (47.0%), isoleucyl proline (0.9%), propionylcarnitine (47.0%), and lysoPC(16:0) (1.7%). Obviously, isovalerylcarnitine and propionylcarnitine primarily derived from amino acid catabolism were the major metabolites that associated with nine significant SNP-related genes . However, the top SNP was DRGA0008061 , and we found five genes  in which the top ten SNPs were annotated  associated with RFI and other feed efficiency traits [To investigate the direct association between SNP genotypes and RFIs, we also conducted GWAS for RFI  in the mixed linear model. Unfortunately but as expected due to small sample size, the results showed that no genome-wide significant associations were found between SNPs and RFI values  , all the14 to 17 . The pre14 to 17 . The ove14 to 17 .https://www.ebi.ac.uk/metabolights/MTBLS1384, and were collected in two sampling times  for each pig by the previous study [Metabolite data was downloaded by accessing MetaboLights accession ID MTBLS1384 with a link, us study . Finallyhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144064, that was issued by the previous study [−7) and minor allele frequency (MAF ≥ 0.001) by PLINK software (version 1.9) [The genotype data was downloaded from NCBI GEO database with accession number: GSE144064 with a link, us study . After rus study , as the us study ,46. Qualion 1.9) .−7) and 5275 (MAF < 0.001) markers, while the genotypes for the combined two sampling times retained 42,393 SNP markers after removing unqualified 14,053 (HWE ≤ 10−7) and 5275 (MAF < 0.001) markers.In this study, we also combined the metabolite values from the first sampling time and the second sampling time as an integrated dataset, so each pig had two values in one metabolite. However, the genotypes for two metabolite values were the same if one metabolite value was from the first sampling time of one pig while the other metabolite value was from the second sampling time of the same pig. In other words, each pig had two different metabolite values but the same genotypes; thus, QC results of the integrated dataset (n = 206) were different from the unintegrated dataset (n = 108), especially for HWE but not for MAF. Finally, the genotypes for the first sampling time and the second sampling time retained 47,109 SNP markers after removing unqualified 9337  [Homo sapiens as the library. Fishers’ exact test and relative betweenness centrality were used for the overrepresented analysis and the pathway impact value calculation , respectively [The partial least squares-discriminant analysis (PLS-DA) and metabolic pathway analysis for the 45 metabolites were performed by ion 4.0)  using Hoectively . The firIn this study, we considered other environmental factors  the same among all the pigs, so we only used breed and RFI as the fixed effects to directly link metabolites with genotypes. GWAS for 45 single metabolites and transformed metabolites  was conducted by mixed linear model based association analysis in GCTA software (version 1.93.0) . The mixp-values were less than the threshold after Bonferroni correction for multiple hypothesis testing on genome level. The threshold for metabolites from the first and second sampling times was 1.06 × 10−6 , while the threshold for combined two sampling times was 1.18 × 10−6 . Then, the significant SNPs were annotated to the genes and gene components  of porcine RefSeq database (Sscrofa10.2/susScr3) downloaded from University of California Santa Cruz (UCSC) genome browser (https://genome.ucsc.edu/cgi-bin/hgTables), where a window of 500 kb was used for the annotation of intergenic regions to neighboring genes. In addition, we used the reference SNP (rsfSNP) ID  of significant SNPs to annotate them to the genes and gene components of latest porcine RefSeq database (Sscrofa11.1/susScr11).The significant SNPs for GWAS were defined when the Linkage disequilibrium (LD) analysis to display the potential haplotypes for the significant SNPs was performed using Haploview software (version 4.2) . SNPs wiKEGG.db (version 3.2.3) of Sus scrofa species to annotate SNP-related genes in the gene-based pathway enrichments. Based on the adjusted p-values (p.adjust) < 0.2 under FDR control, the gene-based pathways were finally realized by R package clusterprofiler (version 3.12.0) [GeneMANIA server [Homo sapiens as the library. Then, the gene–metabolite networks for interactions between SNP-related genes and phenotype-related metabolites were created by MetaboAnalyst tool [Homo sapiens. Significant SNP-associated metabolites based on the low-high RFI pigs were hierarchically clustered by Ward’s method in Euclidean distance [MetaboAnalyst tool [We used R package  3.12.0) . The genA server ,54 with yst tool  with defdistance . Then, ayst tool .p-value < 1.06 × 10−6) in association with 17 metabolites that included 90 significant SNPs annotated to 52 genes. On chromosome one alone, we found SNPs in strong LD that could be annotated to FBXL4 and CCNC; it consisted of two haplotype blocks, where three SNPs  were in the intron regions of FBXL4 and CCNC. The interaction network analyses revealed that CCNC and FBXL4 were linked to each other by N6AMT1 gene and were associated with compounds isovalerylcarnitine and propionylcarnitine. The identified genetic variants and genes affecting important metabolites in high versus low feed efficient pigs could be considered as potential genetic or biomarkers, but we recommend that these results are validated in much higher sample size.We utilized metabolic and genomic datasets from a total of 108 pigs that were made available for this study from our own previously published studies ,11 in pu


FGFR2, MAP3K1 and BABAM1. Next, using SDCP-MS and AIDP-Wb, we rapidly identify multiple regulatory factors that specifically bind in an allele-imbalanced manner to the fSNPs on the FGFR2 locus. We finally demonstrate that the factors identified by SDCP-MS can regulate risk gene expression. These data suggest that the sequential application of Reel-seq, SDCP-MS, and AIDP-Wb can greatly help to translate large sets of GWAS data into biologically relevant information.GWAS cannot identify functional SNPs (fSNP) from disease-associated SNPs in linkage disequilibrium (LD). Here, we report developing three sequential methodologies including Reel-seq (Regulatory element-sequencing) to identify fSNPs in a high-throughput fashion, SDCP-MS (SNP-specific DNA competition pulldown-mass spectrometry) to identify fSNP-bound proteins and AIDP-Wb  to detect allele-specific protein:fSNP binding. We first apply Reel-seq to screen a library containing 4316 breast cancer-associated SNPs and identify 521 candidate fSNPs. As proof of principle, we verify candidate fSNPs on three well-characterized loci:  It is often difficult to identify functional SNPs from disease-associated SNPs in linkage disequilibrium. Here, the authors present Reel-seq, SDCP-MS and AIDP-Wb, three sequential methodologies for fSNP identification and characterization. BC is strongly correlated to age, and for US women, the median age of diagnosis is 62 . Based on available molecular and genetic information, BC can be divided into five main intrinsic or molecular subtypes. These include luminal A, luminal B, triple-negative/basal-like breast cancer (TNBC), HER2-enriched and normal-like BC2. Among these, TNBC is the most aggressive form of the disease.Breast cancer (BC) is the most common cancer in women both in the developed and less developed world, accounting for 11.6% of all new cancer cases and 6.6% of the total cancer deaths in 2018 BRCA1 and BRCA2, although these variants account for only 5−10% of BC cases in the general population4. In addition, many common genetic variants, mainly in the form of single nucleotide polymorphism (SNP) in the noncoding region of human genome, also contribute to the disease susceptibility. In general, although these more common variants each confers small individual risk, combinations of these SNPs, together with environmental factors, are believed to contribute significantly to the etiology of the disease7. However, the mechanisms underlying these genetic contributions remain incompletely characterized.BC risk is strongly influenced by rare coding variants in susceptibility genes such as 9. However, in general, translating GWAS data into biological insights has remained challenging. There are a number of reasons for this, but one major hurdle is that SNPs used by GWAS to pinpoint genetic loci are simply a proxy for large stretches of DNA (haplotypes) that contains many other SNPs in linkage disequilibrium (LD), most of which reside in noncoding DNA. Therefore, identifying a functional SNP (fSNP) as a causal factor for a disease among all the other disease-associated SNPs in LD is often difficult. While in silico methods exist, these strategies are by nature computational, and therefore often lack experimental validation. Even where a fSNP is established, it often remains quite difficult to define the mechanisms by which a fSNP regulates the expression of its disease-associated target gene. Indeed, while several thousand GWAS have combined to identify thousands of disease-associated loci as bona fide disease risk factors, there are relatively few studies that have identified and completely characterized fSNPs11.Since 2005, genome-wide association studies (GWAS) have identified more than 170 genetic loci that are associated with BC12. In this study, we introduce another unbiased HTP technique referred to as Reel-seq (regulatory element-sequencing) to identify fSNPs in an even more efficient way. We also herein describe two additional tools we call SNP-specific DNA competition pulldown-mass spectrometry (SDCP-MS) and allele-imbalanced DNA pulldown-Western blot (AIDP-Wb). These assays are developed because of the general belief that noncoding fSNPs influence disease susceptibility by binding regulatory factors involved in gene expression, and do so, in an allele-specific fashion. As such, these new techniques provide a convenient methodology to identify the regulatory proteins that bind to fSNPs and ascertain allele-specific protein:fSNP binding. We apply these techniques to rapidly identify 521 candidate fSNPs from a library containing 4316 BC-associated SNPs located on 177 risk loci in LD with R2 > 0.8. Furthermore, by coupling Reel-seq with SDCP-MS and AIDP-Wb, we provide a strategy to delineate which regulatory proteins bind to a fSNP, and demonstrate how this binding is allele-specific and examine how these regulatory proteins alter risk gene expression.To address this question, previously we developed an unbiased high-throughput (HTP) technique we termed SNP-seq7. The regulatory elements containing these SNPs presumably exert their function by binding to specific regulatory proteins that modulate risk gene expression16. In brief, Reel-seq is an electrophoresis mobility shift assay (EMSA)-based, unbiased HTP screening technique. For screening, a SNP sequence library is generated by massive parallel oligonucleotide synthesis containing disease-associated SNP constructs with both the risk and non-risk alleles. In each of the constructs, a 31 bp SNP centered sequence is placed between two primers for PCR amplification, as well as for next-generation sequencing (NGS)  isolated from a disease-relevant cell type and then analyzed on a TBE native polyacrylamide gel for gel shifting  Fig. . Since aing Fig. , left. Aing Fig. , right. ing Fig. .Fig. 1ReR2 > 0.8 located on 177 BC-associated loci based on GWAS Catalog (2015) as outlined in Fig. 18.To demonstrate the feasibility of Reel-seq, we initially screened a synthetic DNA library that contains 4316 SNPs in LD with 8 reads, among which we identified 1.91 × 108 reads with a perfect sequence match to the library template with a rate of 40%. Quality control was then performed to eliminate all the SNPs that did not have a completed set of reads on the two alleles from cycles 1 to 4, 7, and 10. By doing so, we recovered 6785 sequences representing a total of 3436 SNPs (Source data are provided as a Source Data file). The quality of this screen was evidenced by high reproducibility among the five repeats with all correlation coefficient demonstrating an R2 > 0.8  between the ratio of the five buffer controls and the five NE-containing samples measured at cycle 10. Subsequently, we applied a second filter to these 1719 SNPs by determining whether the ratio between the five controls and the five samples progressively increased across cycles 1, 4, 7 and 10 with an empirical Slope > 0.05 or <−0.05 as would be predicted if a SNP is functional , five from the MAP3K1 locus  and two from the BABAM1 locus (rs79321361 and rs8101691). Consistent with the designation of these 12 SNPs as candidate fSNPs, we could demonstrate that all these 12 SNPs exhibited allele-imbalanced gel shifting using NE from MDA-MB-468 cells and all the shifted allele-imbalanced bands could be specifically competed away with an increased amount of the corresponding unlabeled probes . To further demonstrate the fidelity of Reel-seq, we first performed an allele-imbalanced EMSA with the 13 putative fSNPs, together with the 2 SNPs that did not pass the quality control filter. We identified that in this pool of 15 SNPs, 13 SNPs showing an allele-imbalanced gel shift pattern . The proteins that nonspecifically bind to 3′ and 5′ double-stranded DNAs, to single-stranded DNAs, and to the beads can therefore be largely removed. As such, only the proteins released with the fSNP sequence are collected for complex identification by MS analysis. Second, we employ a unique negative competitor that has the same 31 bp fSNP sequence, but with a 3 bp deletion on either side of the SNP site, including the SNP. We refer to this reagent as the 7d competitor. By adding an excessive amount of this negative competitor in the bead-DNA-NE incubation, we can further reduce nonspecific protein binding. Third, we perform the pulldown reaction with multiple different fSNP sequences in parallel so that we can use each fSNP sequence as a negative control. Finally, we performed the pulldown assay with each fSNP in duplicate. By doing these four steps, we could greatly improve the specificity and fidelity of the assay.To further understand how BC-associated fSNPs modulate risk gene expression, we developed SDCP-MS to identify fSNP-bound proteins Fig. . SNP-speFGFR2 locus. Briefly, 15 μg of the purified SDCP construct DNA for each SNP was incubated with 1 mg NE in a buffer containing 600 µg of its 7d competitor. After a 2 h incubation, the protein-bound DNA-beads were washed and digested with EcoRI to remove the 3′ end of DNA and a host of nonspecific proteins. After washing, the protein-bound DNA-beads were further digested with BamHI to release the 31 bp fSNP DNA together with its binding proteins for subsequent MS analysis. In total, we recovered 400 proteins from MS analysis. In all, 283 were filtered for their nonspecific binding to all these three SNPs and 98 for binding to SNPs without reproducibility. By removing these 381 proteins, we identified a total of 19 unique binding proteins that bound to these three fSNPs showed by peptide spectrum counts  and the heavy strand promoter 1 (HSP1); however, none of these sequences match the sequence on rs7895676. For PARP-2, there is no report found to describe the binding consensus sequences.To further demonstrate the specific binding of these proteins to their corresponding fSNPs, we also investigated the binding motif for these five proteins. We discovered the TEAD family binding sequence with the core sequence 5′-N[A/T/G]G[T/A/C]ATNT-3′To further validate a functional role of the identified regulatory proteins on FGFR2 expression, we performed shRNA knockdown on these five genes in MDA-MB-468 cells and assessed the subsequent effects on FGFR2 expression. Using shRNA lentiviruses, we were able to generate polyclonal cell lines that show reduced expression of PARP-2 and TFAM Fig. , left, TTFAM gene, located on chromosome 10, encodes mitochondrial transcription factor A that is a key activator of mitochondrial transcription34. We are aware of no report demonstrating that TFAM is a transcription factor regulating nuclear DNA expression although it has been observed that nuclear TFAM negatively regulates itself without modulating mitochondrial activity in HT22 hippocampal neuronal cells35. To further demonstrate that TFAM is a nuclear transcription factor regulating FGFR2 expression, we performed two additional experiments. First, we overexpressed TFAM in MDA-MB-468 cells and we observed a decreased expression of FGFR2 at both the protein and mRNA level  on each cycle of allele-imbalanced gel shifting, this greatly increases the sensitivity of this method. Indeed, out of 3436 BC-associated SNPs, using Reel-seq we identified 1719 SNPs with significant allele-imbalanced gel shifting . This is a recovery rate of 50%. In contrast, using a pool of SNPs that were enriched for fSNPs by functional annotations, the rate of recovery for SNPs-seq was only 27% (101 out of 374)36. Nonetheless, in the future, it will be important to directly compare these two screening techniques using the same SNP library.Similar to Reel-seq, SNPs-seq was recently developed by also detecting allele-specific binding between proteins and SNPsFGFR2 locus with that obtained by functional annotations from Haploreg 4.1. We did not observe any obvious correlation between these two approaches. The same conclusions were reached with several other Reel-seq screens carried out in our lab (unpublished data). Based on these observations, currently we do not believe that it is useful to pre-screen disease-associated SNPs with functional annotations before using Reel-seq.In addition, as we present in Supplementary Table 12. Here, we have described SDCP-MS, which uses excess 7d competitor during the incubation, and thereby greatly increases the specificity and fidelity of FREP-MS to identify fSNP-bound proteins. In addition, as we described above, SDCP-MS is fundamentally different from other DNA pulldown assays, as well as antibody pulldown assays. Instead of eluting proteins from DNA conjugated beads that have been pulled down, it collects proteins specifically by releasing the SNP sequence from the pulldown using a sequential restriction enzyme digestion. By doing so, it eliminates proteins that bind to 3′ and 5′ dsDNA, to single-stranded DNA as well as to the beads. This, together with the 7d competitor, reduces the nonspecific protein burden.We previously described FREP-MS as a method to identify regulatory proteins that potentially bind to fSNPsConventionally, validation of the specific binding of a regulatory protein to a fSNP can be carried out by gel supershift assay and ChIP assay for protein:DNA binding. However, both gel supershifting and ChIP assays generally require antibodies with extremely high quality and specificity. To overcome this difficulty, we developed the AIDP-Wb that allows us to detect specific protein:fSNP binding, instead of protein:DNA binding, using conventional Western blot analysis. Moreover, since AIDP-Wb detects the binding of a regulatory protein to a particular fSNP, we can validate both the regulatory protein and its binding to the fSNP simultaneously.P value < 0.05 and a Slope < −0.05 and >0.05) on the BC-associated FGFR2 locus. In 2008 and 2013, Meyer et al. first dissected the BC-associated FGFR2 locus and identified rs2981578, together with rs7895676, rs35054928, and rs45631563 as candidate fSNPs by using functional annotation and gel shift assay38. Of note, both rs2981578 and rs7895676 were identified by Reel-seq as candidate fSNPs, while we identified rs35054928 as a putative fSNP  and MCF7 (Cat#: ATCC HTB-22) were purchased from ATCC and were free of mycoplasma. MDA-MB-468 cells were cultured in F12:DMEM (1:1) medium and MCF7 cells in RPMI 1640 medium. Both were supplemented with 10% fetal bovine serum.All primers are listed in Supplementary Table 12. Primer bioseq and G3 were used for library amplification and regeneration during the screen. For screening, ~10 µg NE (buffer for control) was mixed with ~50 ng library DNA in the binding buffer from the LightShift™ Chemiluminescent EMSA Kit (Thermo Fisher Scientific) and incubated at RT for 2 h. The reaction was performed with five buffer-treated controls and five NE-treated samples. These samples were then run on a 6% TBE native gel for gel shifting. After the completion of electrophoresis, unshifted bands at 75 bp from each of the controls and samples were cut and isolated. The isolated library DNA was amplified by PCR with bioseq and G3 primers using Accuprime Taq polymerase (Invitrogen) and the regenerated libraries were used for the next cycle of the Reel-seq screen. A total of ten cycles were performed. After the screen, NGS was performed by NextSeq 500 system with the PCR product from cycles 1, 4, 7 and 10 analyzed.The Reel-seq construct for generating the BC library was the same as the SNP-seq construct described previouslyt test with a P value < 0.05. Second, we identified SNPs for which allele-imbalanced gel shift differences increased with increasing cycle number, indicating progressive enrichment. For each SNP, we calculated the average ratio of the sequence count between alleles from the five buffer-treated controls and the five NE-treated samples and normalized the ratio of the sample with the control. We next used the normalized ratio from cycles 1, 4, 7 and 10 to calculate for Slope and identified SNPs with a Slope > 0.05 and < −0.05 as an empirical cutoff point. In this way, we identified candidate fSNPs with a P value < 0.05 and a Slope > 0.05 and < −0.05 and putative fSNP with a P value < 0.05 and a Slope < 0.05 and > −0.05.For quality control, we eliminated SNPs for which complete sequencing data were not available across cycles 1, 4, 7 and 10. To identify fSNPs, we first calculated the ratio of the sequence count between the two alleles for each SNP, and for each of the five replicates at cycle 10. We identified SNPs demonstrating allele-imbalanced gel shifting at cycle 10 with a significant difference in the ratio between the five controls and the five samples using a Student’s Electrophoretic mobility shift assay was performed using the LightShift Chemiluminescent EMSA Kit (Thermo Fisher) according to the manufacturer’s instructions. For the probe, a 31 bp SNP fragment with the SNP centered in the middle was made by annealing two oligos. The double-stranded oligos were then biotinylated using the Biotin 3′ End DNA Labeling Kit (Thermo Fisher Scientific). Nuclear extract from MDA-MB-468 was isolated using NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo Scientific) according to the manufacturer’s instructions. For competition assay in EMSA, 2, 4, and 6 µg cold competitors were added together with NE. The data represent three biological repeats.12 by the addition of 40-fold excess of a 7d competitor, a negative competitor that has the same fSNP sequence, but contains a 3 bp deletion on both sides of the SNP site, including the SNP. In brief, ~15 µg of the purified SDCP construct DNA was conjugated to 150 µl streptavidin-coupled Dynabeads (Life Technologies) according to the manufacturer’s instruction. The DNA-beads were then washed and mixed with 1 mg NE in a buffer containing 40-fold excess of 7d competitor at RT for 1 h. After separation and washing, the protein-DNA-beads were digested with 5 µl EcoRI (100 units/µl NEB) at 37 °C for 30 min to remove the 3′ DNA plus the proteins that bound to this non-SNP region. After separation and washing, the protein-DNA-beads were subsequently digested with 5 µl BamHI (100 units/µl NEB) at 37 °C for 45 min to release the fSNP sequence plus the fSNP-bound proteins. The supernatant was run on an 8% short SDS-PAGE gel (http://www.bidmcmassspec.org/) and then collected for protein complex identification by mass spectrometry. For mass spectrometry analysis, two technical replicates were performed in parallel for each SNP.SNP-specific DNA competition pulldown-mass spectrometry was modified from our previously described FREP-MSTo identify fSNP-specific binding proteins, we first eliminated all the proteins that have peptide counts in all the samples. We also eliminated all the proteins that had peptide spectrum counts only in one of the two replicates for each fSNP. fSNP-specific bound proteins were identified as those proteins that have protein peptide spectrum counts in both of the two replicates for one fSNP, but, not in the two replicates for the controls performed in parallel.Nuclear extract was isolated as described above. A 31 bp biotinylated SNP sequence centered with either the risk or non-risk allele was generated by annealing biotinylated primers purchased from IDT. Approximately 1 µg DNA was attached to Dynabeads™ M-280 Streptavidin. DNA-beads were mixed with ~100 µg NE at RT for 1 h with rotation. After separation and washing, the DNA-bound proteins were eluted for Western blot analysis. The data represent three biological replicates.Whole-cell proteins were isolated with RIPA buffer (Sigma). Cytosolic proteins and nuclear proteins were isolated using NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo Fisher Scientific) according to the manufacturer’s instructions. The data represent three biological replicates.Total RNA was isolated using RNeasy Mini Kit (Qiagen). cDNA was synthesized with SuperScript® III Reverse Transcriptase (Invitrogen) after the RNA sample was treated with DNase I (Invitrogen). All procedures were performed in accordance with the manufacturer’s protocols. qPCR was done with the StepOne real-time PCR system according to the protocol provided for the power SYBR green PCR master mix (Applied Biosystems). All the data represent a combination of three biological replicates unless as indicated.® shRNA Library (Sigma) . The luciferase activity was measured by the Dual-Glo® Luciferase Reporter Assay System . All experiments were performed according to the manufacturer’s protocol. Insert target sequences are listed in Supplementary Table P value was calculated using Student’s t test with two tails without correction for multiple hypothesis testing. Error bars represent the median with SE. For EMSA and Western blot, the data in each case represent three biological independent experiments. For qPCR, the data represent a combination of three biological independent samples. For luciferase reporter assay, the data represent biological independent samples as indicated.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Mangifera indica L.), known as the king of fruits, has an attractive taste and fragrance and high nutritional value. Mango is commercially important in India, where ~55% of the global crop is produced. The fruit has three main parts: pulp, peel, and kernel. The pulp is the most-consumed part, while the peel and kernel are usually discarded. Mango pulp is a source of a variety of reducing sugars, amino acids, aromatic compounds, and functional compounds, such as pectin, vitamins, anthocyanins, and polyphenols. Mango processing generates peels and kernels as bio-wastes, though they also have nutraceutical significance. Functional compounds in the peel, including protocatechuic acids, mangiferin and β-carotene are known for their antimicrobial, anti-diabetic, anti-inflammatory, and anti-carcinogenic properties. The mango kernel has higher antioxidant and polyphenolic contents than the pulp and peel and is used for oil extraction; it’s possible usage in combination with corn and wheat flour in preparing nutraceuticals is being increasingly emphasized. This review aims to provide nutraceutical and pharmacological information on all three parts of mango to help understand the defense mechanisms of its functional constituents, and the appropriate use of mangoes to enhance our nutrition and health.Mango ( Mango has been cultivated in India for more than 4000 years. Among the other tropical fruits, mango is the most popular dessert worldwide. Mango belongs to the Anacardiaceae family, which includes a number of deadly poisonous plants. It has a delicious taste (delightfully blended sweetness and acidity) and aroma, and high nutritional value. The production of mango continues to rank it as the predominant tropical fruit in the 21st century . Mango iMangifera indica, many other species such as M. khasiana, M. andamanica, M. camptosperma and M. sylvatica have been reported in India . Si. Si37]. The lipid and fatty acid proportion in mango pulp is lower than the protein content and much lower than the carbohydrates. The fatty acid composition of pulp was studied in many mango varieties, and triglycerols were found to be the major constituent along with minor levels of mono- and diglycerols. The total content ranges from 0.8% to 1.36% . Mango pMango pulp contains various organic acids including citric acid, malic acid, oxalic acid, succinic acid, ascorbic acid, and tartaric acid. Organic acids are generally weak acids with low molecular weight. The presence of organic acids provides fruits their characteristic tastes and flavors, playing an important role in their organoleptic quality . Among oThe mango pulp is known to have excellent bioactive compounds like carotenoids , phenolic acids, polysaccharides, sterols, and alkaloids. Mango is included in the TRAMIL list  as it is used by the indigenous people to treat diarrhea, fever, gastritis, and ulcers . Phenolic compounds are the important secondary metabolite and can be categorized into phenolic acids and polyphenols. These are mainly present in combination with sugar moieties, linked to one or more phenolic groups, or can occur as ester or methyl-ester derivatives. Mango is one of the rich sources of these phenolic compounds. Phenolic acids are the important secondary metabolites that help to protect against various diseases and play pivotal role in management of human health. Pulp contains mainly hydroxycinnamic- and hydroxybenzoic-derivative phenolic acids, which are present in free form or conjugated with glucose or quinic acid or both. Gallic, syringic, vanillic, and protocatechuic acids are the main constituents of the hydroxybenzoic acid group, and p-coumaric, ferulic, chlorogenic, and caffeic acids are the main constituents of the hydroxycinnamic acid group . The pheHu et al.  identifiThe maturation stage of the fruit can be determined by changes in the color and texture of the fruit peel and flesh. Raw mango is generally green in color, changing to yellow or orange when fully ripe. The color change depends on the type of cultivar, and can also be an indicator of the quality of the fruit. There are several pigments, including chlorophylls, carotenoids, and flavonoids, that are responsible for the change in the color and metabolism of the mango fruit .The green color of mango is due to the presence of chlorophyll. Two types of chlorophylls are found in mango fruit: chlorophyll a provides a blue-green color and chlorophyll b provides a yellow-green; these are present in a 3:1 ratio . The chltrans-β-carotene is the most abundant  followed by the all-trans and 9-cis-violaxanthin  mg/100 g mg/100 gSimilar to the pulp and the peel, the MSK is also considered a prospective source of polyphenols with potent antioxidant activity. Ahmed et al.  estimateIn the MSK, the total flavonoid content was estimated as about 3325 ± 120 mg catechin equivalent (CE)/100 g seed . The repIn India, the whole mango tree, including the stem, bark, leaves, flowers, and fruit, has been widely used as an ancient traditional medicine to treat various diseases and discomforts. All parts of the mango tree contain essential bioactive compounds, such as mangiferin, quercetin, catechins, and kaempferol. In recent years, many researchers have explored the ethnopharmacological and pharmacological efficacies of the various bioactive constituents of mango fruit. This evidence emphasizes the importance of mango byproducts in the treatment of various chronic diseases, including diabetes, cancer, asthma, hypertension, and hemorrhage in the lungs and intestine, having higher efficiency and fewer side effects . DiffereSo far, about 500 research articles have been published on the presence, structure, chemical synthesis, and therapeutic properties of mangiferin. The majority of these studies are from India and Bangladesh. Countries like Brazil, Nigeria, and Iran also contributed significantly to reporting the therapeutic effects of mango contents. Mangiferin has been shown to restore the learning and memory impairments and to decrease hippocampal injury in a mouse model . A recenM. indica, such as mangiferin, gallic acid, catechin, quercetin, β-carotene, shikimic acid, and kaempferol, have been reported to have antioxidant effects. These compounds are also well-known for their anticancer, anti-diabetic, anti-inflammatory, skin-protecting, neuron-protecting, antimicrobial, and anti-aging effects. However, more pharmacokinetics, pharmacodynamics, and clinical trials are required to assess the toxicity or the safe dose of these molecules. Nevertheless, mango is considered safe and even beneficial for cellular functioning, and regular consumption of fresh mango fruit and/or use of its byproducts could promote overall health. Mango is the main fruit produced in India, accounting for almost 55% of the global production . Mango i


A combination of information from blood glucose (BG) and heart rate (HR) measurements has been proposed to investigate the HR changes related to nocturnal hypoglycemia (NH) episodes in pediatric subjects with type 1 diabetes (T1D), examining whether they could improve hypoglycemia prediction. We enrolled seventeen children and adolescents with T1D, monitored on average for 194 days. BG was detected by flash glucose monitoring devices, and HR was measured by wrist-worn fitness trackers. For each subject, we compared HR values recorded in the hour before NH episodes (before-hypoglycemia) with HR values recorded during sleep intervals without hypoglycemia (no-hypoglycemia). Furthermore, we investigated the behavior after the end of NH. Nine participants (53%) experienced at least three NH. Among these nine subjects, six (67%) showed a statistically significant difference between the before-hypoglycemia HR distribution and the no-hypoglycemia HR distribution. In all these six cases, the before-hypoglycemia HR median value was higher than the no-hypoglycemia HR median value. In almost all cases, HR values after the end of hypoglycemia remained higher compared to no-hypoglycemia sleep intervals. This exploratory study support that HR modifications occur during NH in T1D subjects. The identification of specific HR patterns can be helpful to improve NH detection and prevent fatal events. Hypoglycemia is a common and serious side effect of insulin therapy in subjects with type 1 diabetes (T1D) ,3,4. EarDead-in-bed syndrome occurs in about 5% of deaths in childhood-onset diabetes ; hypoglyRecently, a combination of information from continuous glucose monitor (CGM) and HR measurements have been proposed to predict hypoglycemia in both bedbound and active T1D subjects ,14. ThisBaseline characteristics of the participants at the time of recruitment are presented in Nine of the seventeen participants experienced at least three episodes of hypoglycemia during sleep. A total of 516 valid sleep intervals remained after the preprocessing step, split into 68 intervals (13%) with the occurrence of hypoglycemic episodes and 448 intervals (87%) without hypoglycemic episodes. p-value < 0.01. Interestingly, in all these six cases (67%), the HR median value of the before-hypoglycemia dataset was higher than the HR median value of the no-hypoglycemia dataset. On average, the before-hypoglycemia HR values were higher than the HR median value of the respective no-hypoglycemia dataset in 75% of cases (± standard deviation of 14%). Subject 1 also showed a higher HR median value before hypoglycemic episodes than in sleep intervals without hypoglycemia, but the difference is not statistically significant, while for Subjects 10 and 16, the HR median values were comparable. For each of the nine selected subjects, the distribution of HR values in the before-hypoglycemia dataset was compared to the distribution of HR values in the no-hypoglycemia dataset . All subT1D is a common chronic disease in children and adolescents, caused by auto-immune destruction of beta-pancreatic cells and characterized by chronic hyperglycemia ,18. A T1A reduction in the incidence and progression of diabetes-related complications is associated with tight glycemic control ,21; neveIn experimental studies, the morphological changes in electrocardiographic repolarization, the QTc prolongation, and cardiac rate/rhythm disturbances have been described during hypoglycemia in T1D ,8,32.Abnormal cardiac repolarization during hypoglycemia appears to be mediated by both the direct effect of sympathoadrenal stimulation and catecholamine- and insulin-induced hypokalemia on cardiac ion channels . HypoglyIn this study, we confirmed that symptomatic or asymptomatic hypoglycemia continues to frequently occur in young people with T1D during sleep. Considering subjects with at least three episodes of NH, in 67% of cases, there was a statistically significant difference between the distribution of the HR values collected in the hour before a hypoglycemic episode and the distribution of the HR values collected in sleep intervals without hypoglycemia. In addition, for all the subjects that showed a significant difference between the two HR distributions, the HR median value of the before-hypoglycemia dataset was higher than the HR median value of the no-hypoglycemia dataset. In addition, after the end of the hypoglycemic episode, in all cases but one, the HR values remained higher compared to those collected in the no-hypoglycemia sleep intervals. The presence of higher HR values in the before-hypoglycemia dataset supports the sympathetic response to hypoglycemia , while aOur results suggest that HR may be an interesting parameter to discern the episodes of hypoglycemia. Simultaneous monitoring of various physiological parameters could offer unexplored opportunities to build algorithms for early detection of hypoglycemic events, as described by Bertachi et al. in a cohort of adult subjects . The intWe acknowledge several limitations of our study. The number of participants is relatively small, not enough for the development of a predictive model; in the future, increased sample sizes are recommended. We selected a group of T1D subjects who were free of complications since older participants and those with early complications could have more evident HR alterations. In addition, we have not data about morphological changes in electrocardiographic repolarization and/or the QTc prolongation, which could be useful to study the pathogenetic mechanisms of cardiac arrhythmias. Finally, the FGM systems may be less accurate than direct glucose measurements, although the Abbott FreeStyle Libre sensor has been validated in children and adolescents with T1D . In conclusion, this exploratory study supports that HR modifications occur during NH in T1D subjects. The identification of specific HR patterns could be helpful to improve the early detection of hypoglycemic episodes and prevent fatal events. The creation of a NH prediction algorithm may represent a challenge for the management of diabetes in children and adolescents.A group of seventeen children and adolescents with T1D under multiple daily injections, using a rapid-acting insulin analog such as prandial insulin and a basal insulin analog, was recruited from the Pediatric Diabetology Unit at Fondazione IRCCS Policlinico San Matteo Hospital in Pavia, Italy, between August and December 2018. Subjects with retinopathy, nephropathy, established macrovascular disease, and those on drugs likely to affect cardiac function or rhythm were excluded.®, Abbott Diabetes Care, Alameda, CA, USA) [®, San Francisco, CA, USA) [Anthropometric data were recorded at the enrollment. A FGM sensor   and a PFCA, USA)  were delThe study was performed in accordance with the Declaration of Helsinki, and the protocol was approved by the Institutional Review Board of the hospital. Children’s caregivers (or subjects aged ≥ 18 years) provided written consent for inclusion after they were given information about the nature of the study.Physical examination of subjects included anthropometric measurements of weight and height, Body Mass Index (BMI) calculation, and assessment of the pubertal stage.Weight was measured with participants in light clothing without shoes, standing upright in the center of the scale platform facing the recorder, hands at sides, and looking straight ahead. For standing height, subjects were instructed to stand as tall as possible in an upright position without shoes, with heels together and toes apart, hands at sides, aligning the head in the Frankfort horizontal plane, and taking a deep breath. Measurements were performed using a Harpenden stadiometer with a fixed vertical backboard and an adjustable headpiece.BMI was calculated by dividing the subjects’ weight in kilograms by the square of the height in meters. The Marshall and Tanner scale was used to evaluate the pubertal staging, with the pre-pubertal characteristics corresponding to Tanner Stage 1 ,44.AID-GM is a web application developed at the Department of Electrical, Computer and Biomedical Engineering of the University of Pavia, Italy, in collaboration with the Pediatric Diabetology outpatient service of the Fondazione IRCCS Policlinico San Matteo Hospital in Pavia . The AIDThe AID-GM application offers a wide range of visualization and innovative analytical tools. In particular, it supports advanced temporal data analysis functionalities for the extraction of qualitative patterns from time-series data. This is possible thanks to the integration with the Java Time Series Abstractor (JTSA), a framework for processing temporal data and extracting knowledge-based patterns . JTSA moThe application is designed to analyze both an individual subject and a group of subjects. This can be valuable, especially considering the huge amount of longitudinal data that has become available with the use of monitoring devices, which will not allow easy manual identification of critical situations. Statistics on a group of patients can help the physician to quickly identify subjects who need closer control and possible therapy adjustments.Exploiting the synchronization between PFT and FGM, the AID-GM platform allowed both identifying the start and endpoints of sleep intervals and detecting those intervals characterized by the occurrence of hypoglycemic patterns, i.e., where BG < 70 mg/dL (BG < 3.9 mmol/L) . In the preprocessing step, a filter was applied to exclude all the sleep intervals that were preceded by hypoglycemic episodes in the previous two hours since these episodes could alter the succeeding BG and HR measurements. Therefore, only the HR measures recorded in the remaining sleep intervals were considered in the study. In addition, subjects with less than three overall hypoglycemic patterns during sleep were excluded.For each selected subject three HR datasets were created, called before-hypoglycemia, after-hypoglycemia, and no-hypoglycemia. The first two datasets included HR measurements collected during sleep intervals with hypoglycemic episodes, while the last one related to sleep intervals without hypoglycemic episodes. Regarding sleep intervals with hypoglycemia, the before-hypoglycemia dataset was limited to HR measurements recorded in the hour before the hypoglycemic episode, whereas the after-hypoglycemia dataset included HR measurements recorded in the hour after the hypoglycemic episode. For nights without hypoglycemia, the analysis was performed on HR measurements collected during the whole sleep period, excluding the first hour after falling asleep and the last hour before waking up. Finally, the HR measurements from each dataset were aggregated into separate five-minute time intervals to capture the average values.For each subject, the non-parametric Mann-Whitney U test was performed to compare whether the HR distribution was the same for before-hypoglycemia and no-hypoglycemia datasets or there was a difference between them, setting the significance threshold to 0.05. In addition, by comparing the HR distribution between after-hypoglycemia and no-hypoglycemia datasets, we investigated what happened after the end of hypoglycemic episodes. All the analyses were implemented with the R for statistical computing, version 3.5.1. Mann–Whitney U test was implemented through the function “wilcox.test” available in the R package called “stats” .


A cost-effective strategy to explore the complete DNA sequence in animals for genetic evaluation purposes is to sequence key ancestors of a population, followed by imputation mechanisms to infer marker genotypes that were not originally reported in a target population of animals genotyped with single nucleotide polymorphism (SNP) panels. The feasibility of this process relies on the accuracy of the genotype imputation in that population, particularly for potential causal mutations which may be at low frequency and either within genes or regulatory regions. The objective of the present study was to investigate the imputation accuracy to the sequence level in a Nellore beef cattle population, including that for variants in annotation classes which are more likely to be functional.Information of 151 key sequenced Nellore sires were used to assess the imputation accuracy from bovine HD BeadChip SNP (~ 777 k) to whole-genome sequence. The choice of the sires aimed at optimizing the imputation accuracy of a genotypic database, comprised of about 10,000 genotyped Nellore animals. Genotype imputation was performed using two computational approaches: FImpute3 and Minimac4 (after using Eagle for phasing). The accuracy of the imputation was evaluated using a fivefold cross-validation scheme and measured by the squared correlation between observed and imputed genotypes, calculated by individual and by SNP. SNPs were classified into a range of annotations, and the accuracy of imputation within each annotation classification was also evaluated.High average imputation accuracies per animal were achieved using both FImpute3 (0.94) and Minimac4 (0.95). On average, common variants  > 0.03) were more accurately imputed by Minimac4 and low-frequency variants (MAF ≤ 0.03) were more accurately imputed by FImpute3. The inherent Minimac4 Rsq imputation quality statistic appears to be a good indicator of the empirical Minimac4 imputation accuracy. Both software provided high average SNP-wise imputation accuracy for all classes of biological annotations.Our results indicate that imputation to whole-genome sequence is feasible in Nellore beef cattle since high imputation accuracies per individual are expected. SNP-wise imputation accuracy is software-dependent, especially for rare variants. The accuracy of imputation appears to be relatively independent of annotation classification.The online version contains supplementary material available at 10.1186/s12711-021-00622-5. Compared to the use of genotypes from single nucleotide polymorphism (SNP) panels, genotypic information from whole-genome sequencing may improve prediction accuracies of breeding values for economically relevant traits since it potentially includes causal mutations for all phenotypes –3. In spBos indicus populations. Compared to taurine breeds, B. indicus presents, in general, lower levels of linkage disequilibrium (LD) between genetic markers at short distances [Besides the potential benefit of improving the prediction accuracy of breeding values, sequence information can improve quantitative trait loci (QTL) mapping in genome-wide association studies , 5. Howeistances  and a hiistances , which cBos indicus) is the predominant breed used for beef production, and various Nellore breeding programs started independently and have created reference populations based on high-density SNP arrays [In Brazil, Nellore  to high-density (~ 777 k) SNP panels. For this, a k-means cluster analysis was performed using the genomic relationship matrix of the genotyped animals. The number of clusters was set equal to 151 and, within each cluster, the sire with the largest number of genotyped progenies was chosen for sequencing. A genomic relationship representation of the sequenced and genotyped animals is provided in [Additional file The whole-genome sequencing of the sires was performed using the Illumina sequencing-by-synthesis technology at an overall average sequence coverage, after quality control (described below), of 14.5×, ranging from 7.8 to 26.3×. Fifty-two animals were sequenced using the Illumina HiSeq X™ Ten platform and 99 animals were sequenced using the Illumina NovaSeq™ platform.http://www.1000bullgenomes.com/doco/1000bullsGATK3.8pipelineSpecifications_Run8_Revision_20191101.docx. Both SNPs and insertion/deletion mutations were identified, but only SNPs were considered for this imputation study. After generating a variant call format file containing SNP information for each of the 151 sires, a quality control filtering step was implemented, using the VariantFiltration tool from the GATKv3.8 software [Variant calling procedures were carried out following the guidelines provided by the 1000 bull Genomes Project, available at software , using tsoftware : qualitysoftware  was usedhttps://genome.sph.umich.edu/wiki/Minimac4). FImpute, Eagle and Minimac were run with default parameters in an Intel® Xeon® server with 1 TB of RAM memory and 72-core processors running at 2.70 GHz. To evaluate the software processing time efficiency in a similar multi-core system, each software was run parallelizing the 29 chromosomes in 58 processors (2 processors per chromosome).Imputation for the sequence level variants was carried out using two software: FImpute v3  and MiniThe accuracy of imputation was investigated using a fivefold cross-validation scheme. The 151 animals with sequence information were randomly divided into five groups. Thus, five imputations were performed in such a way that a different group (target population) has all their genotypes masked except those that overlapped with the high-density (HD) SNP panel (~ 777 K). Table https://genome.sph.umich.edu/wiki/Minimac3_Info_File). According to [The squared Pearson’s correlation between observed and imputed genotypes  was computed by using Plink v1.9 . EnsemblImputation accuracies per animal were high and consistent across methods and statistics Table . The aven et al. .Table2ImOne of the main issues in imputing sequence from low- or even high-density SNP panels in any population is the huge number of SNPs that have to be imputed. The accuracy of imputation tends to decrease as the number of SNPs from the lower density SNP panel decreases. This leads to an increased distance between the SNP to be imputed and the nearest SNP on the lower density marker panel. In this sense, there is a consensus in the literature , 25 thatConsidering the total number of imputed variants , the average imputation accuracy per SNP indicated by the PERC and The imputation of rare variants is one of the most important issues that affect the average imputation accuracy in a specific population. This is especially true for whole-genome sequence imputation since it usually relies on imputing a high proportion of rare variants . Here, ttated by , PERC ishan PERC , hencefoPlotting the Minimac4 Rsq statistic together with the empirical imputation accuracy  approach, FImpute captures first the more accurate information from the close relatives by moving long windows along a chromosome and then exploits information from more distant relationships by gradually shrinking the window size in each chromosomal sweep . EssentiIn terms of computational performance, FImpute is known to be an extremely fast software. In their study , Pausch Achieving high imputation accuracy is crucial for an effective use of imputed sequence genotypes in genetic evaluations of a specific population and the first priority is to choose a group of animals for which the number of haplotypes present in the reference population is maximized . EfficieRegardless of the imputation approach, quality control of pre-imputation genotypes plays an important role for reaching a high imputation accuracy of sequenced variants. As shown in [Additional file As in , 25, aveIn Fleckvieh cattle, Pausch et al.  detectedFigure The observed high median with a relatively low interquartile range Fig.  highlighAs shown by Druet et al. , compareHigh imputation accuracy to whole-genome sequence was achieved in Nellore beef cattle. In general, common variants were imputed with higher accuracy by using Eagle-Minimac4, but, in terms of computational efficiency and higher imputation accuracy for low-frequency variants, there were advantages in using FImpute3.Additional file 1: Figure S1. Principal component analysis based on genomic relationship matrix showing the genetic structure of the sequenced sires (in red) relative to our Nellore reference population of about 10.000 genotyped animals.Additional file 2: Figure S2. Smoothed conditional means (see http://search.r-project.org/library/ggplot2/html/geom_smooth.html) of the SNP-wise imputation accuracies by minor allele frequency (MAF). Additional file 3: Figure S3. Smoothed conditional means (see http://search.r-project.org/library/ggplot2/html/geom_smooth.html) of the SNP-wise imputation accuracies by minor allele frequency (MAF), comparing analyses using a more or less strict genotype filtering before imputations. FImpute3 and Minimac4 methods correspond to the squared Pearson’s correlation between observed and imputed genotypes; and Rsq_Minimac4 method is an estimate of the squared correlation between imputed genotypes and true, unobserved genotypes (see https://genome.sph.umich.edu/wiki/Minimac3_Info_File).Additional file 4: Figure S4. SNP-wise imputation accuracies by successive 1-Mb windows across all autosomes. FImpute3 and Minimac4 methods correspond to the squared Pearson’s correlation between observed and imputed genotypes; and Rsq_Minimac4 method is an estimate of the squared correlation between imputed genotypes and true, unobserved genotypes (see https://genome.sph.umich.edu/wiki/Minimac3_Info_File).Additional file 5: Figure S5. Number of variants in the high-density (HD) SNP panel and in the whole-genome sequencing (WGS) reference panel per successive 1-Mb windows across all autosomes. The red line represents the number of SNPs per Mb included in HD and the blue line represents the number of SNPs (× 100) included in WGS.Additional file 6: Figure S6. SNP-wise imputation accuracy for the intergenic, intronic, missense, up- and down-stream, and UTR variants.