Tuning Fe3O4 for sustainable cathodic heterogeneous electro-Fenton catalysis by acetylated chitosan.

Peroxidase-like catalysts are safe and low-cost candidates to tackle the dilemma in constructing sustainable cathodic heterogeneous electro-Fenton (CHEF) catalysts for water purification, but the elusive structure-property relationship of enzyme-like catalysts constitutes a pressing challenge for the advancement of CHEF processes in practically relevant water and wastewater treatment. Herein, we probe the origins of catalytic efficiency in the CHEF process by artificially tailoring the peroxidase-like activity of Fe3O4 through a series of acetylated chitosan-based hydrogels, which serve as ecofriendly alternatives to traditional carbon shells. The optimized acetylated chitosan wrapping Fe3O4 hydrogel on the cathode shows an impressive activity and stability in CHEF process, overcoming the complicated and environmentally unfavored procedures in the electro-Fenton-related processes. Structural characterizations and theoretical calculations reveal that the amide group in chitosan can modulate the intrinsic redox capacity of surficial Fe sites on Fe3O4 toward CHEF catalysis via the neutral hydrogen bond. This work provides a sustainable path and molecule-level insight for the rational design of high-efficiency CHEF catalysts and beyond.

Discriminating the Active Ru Species Towards the Selective Generation of Singlet Oxygen from Peroxymonosulfate: Nanoparticles Surpass Single-Atom Catalysts.

Singlet oxygen (1O2) is an exceptional reactive oxygen species in advanced oxidation processes for environmental remediation. Despite single-atom catalysts (SACs) representing the promising candidate for the selective generation of 1O2 from peroxymonosulfate (PMS), the necessity to meticulously regulate the coordination environment of metal centers poses a significant challenge in the precisely-controlled synthetic method. Another dilemma to SACs is their high surface free energy, which results in an inherent tendency for the surface migration and aggregation of metal atoms. We here for the first time reported that Ru nanoparticles (NPs) synthesized by the facile pyrolysis method behave as robust Fenton-like catalysts, outperforming Ru SACs, towards efficient activation of PMS to produce 1O2 with nearly 100 % selectivity, remarkably improving the degradation efficiency for target pollutants. Density functional theory calculations have unveiled that the boosted PMS activation can be attributed to two aspects: (i) enhanced adsorption of PMS molecules onto Ru NPs, and (ii) decreased energy barriers by offering adjacent sites for promoted dimerization of *O intermediates into adsorbed 1O2. This study deepens the current understanding of PMS chemistry, and sheds light on the design and optimization of Fenton-like catalysts.

A composite strategy of genome-wide association study and copy number variation analysis for carcass traits in a Duroc pig population.

BACKGROUND: Carcass traits are important in pig breeding programs for improving pork production. Understanding the genetic variants underlies complex phenotypes can help explain trait variation in pigs. In this study, we integrated a weighted single-step genome-wide association study (wssGWAS) and copy number variation (CNV) analyses to map genetic variations and genes associated with loin muscle area (LMA), loin muscle depth (LMD) and lean meat percentage (LMP) in Duroc pigs. RESULTS: Firstly, we performed a genome-wide analysis for CNV detection using GeneSeek Porcine SNP50 Bead chip data of 3770 pigs. A total of 11,100 CNVs were detected, which were aggregated by overlapping 695 CNV regions (CNVRs). Next, we investigated CNVs of pigs from the same population by whole-genome resequencing. A genome-wide analysis of 21 pigs revealed 23,856 CNVRs that were further divided into three categories (851 gain, 22,279 loss, and 726 mixed), which covered 190.8 Mb (~ 8.42%) of the pig autosomal genome. Further, the identified CNVRs were used to determine an overall validation rate of 68.5% for the CNV detection accuracy of chip data. CNVR association analyses identified one CNVR associated with LMA, one with LMD and eight with LMP after applying stringent Bonferroni correction. The wssGWAS identified eight, six and five regions explaining more than 1% of the additive genetic variance for LMA, LMD and LMP, respectively. The CNVR analyses and wssGWAS identified five common regions, of which three regions were associated with LMA and two with LMP. Four genes (DOK7, ARAP1, ELMO2 and SLC13A3) were highlighted as promising candidates according to their function. CONCLUSIONS: We determined an overall validation rate for the CNV detection accuracy of low-density chip data and constructed a genomic CNV map for Duroc pigs using resequencing, thereby proving a value genetic variation resource for pig genome research. Furthermore, our study utilized a composite genetic strategy for complex traits in pigs, which will contribute to the study for elucidating the genetic architecture that may be influenced and regulated by multiple forms of variations.

A meta-analysis of genome-wide association studies for average daily gain and lean meat percentage in two Duroc pig populations.

BACKGROUND: Average daily gain (ADG) and lean meat percentage (LMP) are the main production performance indicators of pigs. Nevertheless, the genetic architecture of ADG and LMP is still elusive. Here, we conducted genome-wide association studies (GWAS) and meta-analysis for ADG and LMP in 3770 American and 2090 Canadian Duroc pigs. RESULTS: In the American Duroc pigs, one novel pleiotropic quantitative trait locus (QTL) on Sus scrofa chromosome 1 (SSC1) was identified to be associated with ADG and LMP, which spans 2.53 Mb (from 159.66 to 162.19 Mb). In the Canadian Duroc pigs, two novel QTLs on SSC1 were detected for LMP, which were situated in 3.86 Mb (from 157.99 to 161.85 Mb) and 555 kb (from 37.63 to 38.19 Mb) regions. The meta-analysis identified ten and 20 additional SNPs for ADG and LMP, respectively. Finally, four genes (PHLPP1, STC1, DYRK1B, and PIK3C2A) were detected to be associated with ADG and/or LMP. Further bioinformatics analysis showed that the candidate genes for ADG are mainly involved in bone growth and development, whereas the candidate genes for LMP mainly participated in adipose tissue and muscle tissue growth and development. CONCLUSIONS: We performed GWAS and meta-analysis for ADG and LMP based on a large sample size consisting of two Duroc pig populations. One pleiotropic QTL that shared a 2.19 Mb haplotype block from 159.66 to 161.85 Mb on SSC1 was found to affect ADG and LMP in the two Duroc pig populations. Furthermore, the combination of single-population and meta-analysis of GWAS improved the efficiency of detecting additional SNPs for the analyzed traits. Our results provide new insights into the genetic architecture of ADG and LMP traits in pigs. Moreover, some significant SNPs associated with ADG and/or LMP in this study may be useful for marker-assisted selection in pig breeding.

Genome-wide detection of CNV regions and their potential association with growth and fatness traits in Duroc pigs.

BACKGROUND: In the process of pig breeding, the average daily gain (ADG), days to 100 kg (AGE), and backfat thickness (BFT) are directly related to growth rate and fatness. However, the genetic mechanisms involved are not well understood. Copy number variation (CNV), an important source of genetic diversity, can affect a variety of complex traits and diseases and has gradually been thrust into the limelight. In this study, we reported the genome-wide CNVs of Duroc pigs using SNP genotyping data from 6627 animals. We also performed a copy number variation region (CNVR)-based genome-wide association studies (GWAS) for growth and fatness traits in two Duroc populations. RESULTS: Our study identified 953 nonredundant CNVRs in U.S. and Canadian Duroc pigs, covering 246.89 Mb (~ 10.90%) of the pig autosomal genome. Of these, 802 CNVRs were in U.S. Duroc pigs with 499 CNVRs were in Canadian Duroc pigs, indicating 348 CNVRs were shared by the two populations. Experimentally, 77.8% of nine randomly selected CNVRs were validated through quantitative PCR (qPCR). We also identified 35 CNVRs with significant association with growth and fatness traits using CNVR-based GWAS. Ten of these CNVRs were associated with both ADG and AGE traits in U.S. Duroc pigs. Notably, four CNVRs showed significant associations with ADG, AGE, and BFT, indicating that these CNVRs may play a pleiotropic role in regulating pig growth and fat deposition. In Canadian Duroc pigs, nine CNVRs were significantly associated with both ADG and AGE traits. Further bioinformatic analysis identified a subset of potential candidate genes, including PDGFA, GPER1, PNPLA2 and BSCL2. CONCLUSIONS: The present study provides a necessary supplement to the CNV map of the Duroc genome through large-scale population genotyping. In addition, the CNVR-based GWAS results provide a meaningful way to elucidate the genetic mechanisms underlying complex traits. The identified CNVRs can be used as molecular markers for genetic improvement in the molecular-guided breeding of modern commercial pigs.

Nomogram for predicting advanced liver fibrosis and cirrhosis in patients with chronic liver disease.

BACKGROUND: We aimed to formulate a novel predictive nomogram to discriminate liver fibrosis stage in patients with chronic liver disease. METHODS: Nomograms were established based on the results of multivariate analysis. The predictive accuracy of the nomograms was assessed by ROC analysis and calibration. Decision curve analysis (DCA) was used to determine the clinical benefit of the nomograms. RESULTS: INR, platelets, and N-terminal propeptide type III collagen (PIIINP) were independent predictors for advanced liver fibrosis (≥ S3) and cirrhosis (S4) in patients with chronic liver disease in the training cohort. In the training set, the areas under the ROCs (AUROCs) of nomogram S3S4, APRI, FIB-4, and GPR for stage ≥ S3 were 0.83, 0.71, 0.68, and 0.74, respectively; the AUROCs of nomogram S4, APRI, FIB-4, and GPR for stage S4 were 0.88, 0.74, 0.78, and 0.79, respectively. The calibrations showed optimal agreement between the prediction by the established nomograms and actual observation. In the validation set, the AUROCs of nomogram S3S4, APRI, FIB-4, and GPR for stage ≥ S3 were 0.86, 0.79, 0.78, and 0.81, respectively; the AUROCs of nomogram S4, APRI, FIB-4, and GPR for stage S4 were 0.88, 0.77, 0.81, and 0.83, respectively. Furthermore, the decision curve analysis suggested that the nomograms represent better clinical benefits in both independent cohorts than APRI, FIB-4, and GPR. CONCLUSION: The constructed nomograms could be a superior tool for discriminating advanced fibrosis and cirrhosis in chronic liver disease.

Predictive performance of eLIFT for liver inflammation and fibrosis in chronic liver diseases.

Objective: The easy liver fibrosis test (eLIFT) is a novel predictor of liver fibrosis in chronic liver disease (CLD). This study aimed to evaluate the predictive value of the eLIFT for liver inflammation and fibrosis in CLD patients. Methods: We enrolled 1125 patients with CLD who underwent liver biopsy. The predictive accuracy for liver inflammation and fibrosis of the eLIFT was assessed and compared to that of the aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis-4 score (FIB-4), and gamma-glutamyl transpeptidase-to-platelet ratio (GPR) by ROC (Receiver Operating Characteristic) analysis and decision curve analysis (DCA). Results: The areas under the ROC curves (AUROCs) of the eLIFT for assessing liver inflammation G ≥ 2 and G ≥ 3 were 0.77 (0.75-0.80) and 0.81 (0.79-0.84), with cut-offs of 8.0 and 11.0, respectively. The AUROCs of the eLIFT for predicting fibrosis stages S ≥ 2 and S4 were 0.72 (0.70-0.76) and 0.76 (0.72-0.80), with cut-offs of 9.0 and 10.0, respectively. In discriminating G≥2 inflammation, the AUROC of the eLIFT was better than that of the FIB-4, with no difference compared with the GPR, but lower than that of the APRI. When discriminating G≥3 inflammation, the AUROC of the eLIFT was comparable to that of the APRI and GPR but superior to that of the FIB-4. There were no significant differences between the four indexes for predicting S≥2 and S4. Conclusion: The eLIFT is a potentially useful noninvasive predictor of liver inflammation and fibrosis in patients with CLD.

INR-to-platelet ratio (INPR) as a novel noninvasive index for predicting liver fibrosis in chronic hepatitis B.

Objective: We aimed to investigate whether a novel noninvasive index, i.e., the international normalized ratio-to-platelet ratio (INPR), was a variable in determining liver fibrosis stage in patients with chronic hepatitis B (CHB). Methods: A total of 543 treatment-naïve CHB patients were retrospectively enrolled. Liver histology was assessed according to the Metavir scoring scheme. All common demographic and clinical parameters were analyzed. Results: Based on routine clinical parameters (age, sex, HBeAg status, HBV DNA, hematological parameters, coagulation index, and liver biochemical indicators), a novel index, i.e., the INR-to-platelet ratio (INPR), was developed to magnify the unfavorable effects of liver fibrosis on INR and platelets. The AUCs of INPR for predicting significant fibrosis, advanced fibrosis, and cirrhosis were 0.74, 0.76 and 0.86, respectively. Compared with APRI, FIB-4, and GPR, the INPR had comparable predictive efficacy for significant fibrosis and better predictive performance for advanced fibrosis and cirrhosis. Conclusion: INPR could be an accurate, easily calculated and inexpensive index to assess liver fibrosis in patients with CHB. Further studies are needed to verify this indicator and compare it with other noninvasive methods for predicting liver fibrosis in CHB patients.

Identification of parameters in routine blood and coagulation tests related to the severity of COVID-19.

Objective: This study aimed to identify the predictive value of simple markers in routine blood and coagulation tests for the severity of coronavirus disease 2019 (COVID-19). Methods: A total of 311 consecutive COVID-19 patients, including 281 patients with mild/moderate COVID-19 and 30 patients with severe/life-threatening COVID-19, were retrospectively enrolled. Logistic modeling and ROC curve analyses were used to assess the indexes for identifying disease severity. Results: Lymphocyte and eosinophil counts of COVID-19 patients in the severe/life-threatening group were significantly lower than those of patients in the mild/moderate group (P < 0.001). Coagulation parameters, high-sensitivity C-reactive protein (hsCRP) levels and procalcitonin levels were higher in the severe/life-threatening group compared with the mild/moderate group (all P < 0.05). Univariate and multivariate logistic models revealed that hsCRP and fibrinogen degradation products (FDPs) were predictors of severe COVID-19 (OR = 1.072, P = 0.036; and OR = 1.831, P = 0.036, respectively). The AUROCs of hsCRP and FDP for predicting severe/life-threatening COVID-19 were 0.850 and 0.766, respectively. The optimal cutoffs of hsCRP and FDP for the severe/life-threatening type of COVID-19 were 22.41 mg/L and 0.95 µg/ml, respectively. Conclusion: Serum CRP and FDP levels are positively related to the severity of COVID-19. This finding indicates that CRP and FDP levels may potentially be used as early predictors for severe illness and help physicians triage numerous patients in a short time.

Genome-wide association analyses identify known and novel loci for teat number in Duroc pigs using single-locus and multi-locus models.

BACKGROUND: More teats are necessary for sows to nurse larger litters to provide immunity and nutrient for piglets prior to weaning. Previous studies have reported the strong effect of an insertion mutation in the Vertebrae Development Associated (VRTN) gene on Sus scrofa chromosome 7 (SSC7) that increased the number of thoracic vertebrae and teat number in pigs. We used genome-wide association studies (GWAS) to map genetic markers and genes associated with teat number in two Duroc pig populations with different genetic backgrounds. A single marker method and several multi-locus methods were utilized. A meta-analysis that combined the effects and P-values of 34,681 single nucleotide polymorphisms (SNPs) that were common in the results of single marker GWAS of American and Canadian Duroc pigs was conducted. We also performed association tests between the VRTN insertion and teat number in the same populations. RESULTS: A total of 97 SNPs were found to be associated with teat number. Among these, six, eight and seven SNPs were consistently detected with two, three and four multi-locus methods, respectively. Seven SNPs were concordantly identified between single marker and multi-locus methods. Moreover, 26 SNPs were newly found by multi-locus methods to be associated with teat number. Notably, we detected one consistent quantitative trait locus (QTL) on SSC7 for teat number using single-locus and meta-analysis of GWAS and the top SNP (rs692640845) explained 8.68% phenotypic variance of teat number in the Canadian Duroc pigs. The associations between the VRTN insertion and teat number in two Duroc pig populations were substantially weaker. Further analysis revealed that the effect of VRTN on teat number may be mediated by its LD with the true causal mutation. CONCLUSIONS: Our study suggested that VRTN insertion may not be a strong or the only candidate causal mutation for the QTL on SSC7 for teat number in the analyzed Duroc pig populations. The combination of single-locus and multi-locus GWAS detected additional SNPs that were absent using only one model. The identified SNPs will be useful for the genetic improvement of teat number in pigs by assigning higher weights to associated SNPs in genomic selection.

Genome-wide association study reveals genetic loci and candidate genes for average daily gain in Duroc pigs.

OBJECTIVE: Average daily gain (ADG) is an important target trait of pig breeding programs. We aimed to identify single nucleotide polymorphisms (SNPs) and genomic regions that are associated with ADG in the Duroc pig population. METHODS: We performed a genome-wide association study involving 390 Duroc boars and by using the PorcineSNP60K Beadchip and two linear models. RESULTS: After quality control, we detected 3,5971 SNPs, which included seven SNPs that are significantly associated with the ADG of pigs. We identified six quantitative trait loci (QTL) regions for ADG. These QTLs included four previously reported QTLs on Sus scrofa chromosome (SSC) 1, SSC5, SSC9, and SSC13, as well as two novel QTLs on SSC6 and SSC16. In addition, we selected six candidate genes (general transcription factor 3C polypeptide 5, high mobility group AT-hook 2, nicotinamide phosphoribosyltransferase, oligodendrocyte transcription factor 1, pleckstrin homology and RhoGEF domain containing G4B, and ENSSSCG00000031548) associated with ADG on the basis of their physiological roles and positional information. These candidate genes are involved in skeletal muscle cell differentiation, diet-induced obesity, and nervous system development. CONCLUSION: This study contributes to the identification of the casual mutation that underlies QTLs associated with ADG and to future pig breeding programs based on marker-assisted selection. Further studies are needed to elucidate the role of the identified candidate genes in the physiological processes involved in ADG regulation.

Multi-omic characterization of allele-specific regulatory variation in hybrid pigs.

Hybrid mapping is a powerful approach to efficiently identify and characterize genes regulated through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we perform a comprehensive multi-omic characterization of regulatory variation across the brain, liver, muscle, and placenta through four developmental stages. We produce one of the largest multi-omic datasets in pigs to date, including 16 whole genome sequenced individuals, as well as 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We develop a read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We show that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identify 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. We integrate methylation, chromatin accessibility, and gene expression data to show that allele specific expression can be explained in great part by allele specific methylation and/or chromatin accessibility. This study provides a comprehensive characterization of regulatory variation across multiple tissues and developmental stages in pigs.

A pan-cancer analysis identifies SOAT1 as an immunological and prognostic biomarker.

Sterol o-acyltransferase1 (SOAT1) is an enzyme that regulates lipid metabolism. Nevertheless, the predictive value of SOAT1 regarding immune responses in cancer is not fully understood. Herein, we aimed to expound the predictive value and the potential biological functions of SOAT1 in pan-cancer. Raw data related to SOAT1 expression in 33 different types of cancer were acquired from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. SOAT1 expression was significantly increased in most cancers and showed a distinct correlation with prognosis. This enhanced expression of the SOAT1 gene was confirmed by evaluating SOAT1 protein expression using tissue microarrays. In addition, we found significant positive associations between SOAT1 expression levels and infiltrating immune cells, including T cells, neutrophils, and macrophages. Moreover, the co-expression analysis between SOAT1 and immune genes showed that many immune-related genes were increased with enhanced SOAT1 expression. A gene set enrichment analysis (GSEA) revealed that the expression of SOAT1 correlated with the tumor microenvironment, adaptive immune response, interferon signaling, and cytokine signaling. These findings indicate that SOAT1 is a potential candidate marker for predicting prognosis and a promising target for tumor immunotherapy in cancers.

Ferrous ion enhanced Fenton-like degradation of emerging contaminants by sulfidated nanosized zero-valent iron with pH insensitivity.

In this study, the performance and mechanism of the integrated sulfidated nanosized zero-valent iron and ferrous ions (S-nZVI/Fe2+) system for oxygen activation to remove emerging contaminants (ECs) were comprehensively explored. The S-nZVI/Fe2+ system exhibited a 2.4-8.2 times of increase in the pseudo-first order kinetic rate constant for the oxidative degradation of various ECs compared to the S-nZVI system under aerobic conditions, whereas negligible removal was observed in both nZVI and nZVI/Fe2+ systems. Moreover, remarkable EC mineralization efficiency and benign detoxification capacity were also demonstrated in the S-nZVI/Fe2+ system. We revealed that dosing Fe2+ promoted the corrosion of S-nZVI by maintaining an acidic solution pH, which was conducive to O2 activation by dissolved Fe2+ and surface-absorbed Fe(II) to produce •OH. Furthermore, the generation of H* was enhanced for the further reduction of Fe(III) and H2O2 to Fe(II) and •O2-, resulting in the improvement of consecutive single-electron O2 activation for •OH production. Additionally, bisphenol A (BPA) degradation by S-nZVI/Fe2+ was positively correlated with the S-nZVI dosage, with an optimum S/Fe molar ratio of 0.15. The Fenton-like degradation process by S-nZVI/Fe2+ was pH-insensitive, indicating its robust performance over a wide pH range. This study provides valuable insights for the practical implementation of nZVI-based technology in achieving high-efficiency removal of ECs from water.

Asymmetrically Coordinated CoB1 N3 Moieties for Selective Generation of High-Valence Co-Oxo Species via Coupled Electron-Proton Transfer in Fenton-like Reactions.

High-valence metal species generated in peroxymonosulfate (PMS)-based Fenton-like processes are promising candidates for selective degradation of contaminants in water, the formation of which necessitates the cleavage of OH and OO bonds as well as efficient electron transfer. However, the high dissociation energy of OH bond makes its cleavage quite challenging, largely hampering the selective generation of reactive oxygen species. Herein, an asymmetrical configuration characterized by a single cobalt atom coordinated with boron and nitrogen (CoB1 N3 ) is established to offer a strong local electric field, upon which the cleavage of OH bond is thermodynamically favored via a promoted coupled electron-proton transfer process, which serves an essential step to further allow OO bond cleavage and efficient electron transfer. Accordingly, the selective formation of Co(IV)O in a single-atom Co/PMS system enables highly efficient removal performance toward various organic pollutants. The proposed strategy also holds true in other heteroatom doping systems to configure asymmetric coordination, thus paving alternative pathways for specific reactive species conversion by rationalized design of catalysts at atomic level toward environmental applications and more.

Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs.

BACKGROUND: Pork quality can directly affect customer purchase tendency and meat quality traits have become valuable in modern pork production. However, genetic improvement has been slow due to high phenotyping costs. In this study, whole genome sequence (WGS) data was used to evaluate the prediction accuracy of genomic best linear unbiased prediction (GBLUP) for meat quality in large-scale crossbred commercial pigs. RESULTS: We produced WGS data (18,695,907 SNPs and 2,106,902 INDELs exceed quality control) from 1,469 sequenced Duroc × (Landrace × Yorkshire) pigs and developed a reference panel for meat quality including meat color score, marbling score, L* (lightness), a* (redness), and b* (yellowness) of genomic prediction. The prediction accuracy was defined as the Pearson correlation coefficient between adjusted phenotypes and genomic estimated breeding values in the validation population. Using different marker density panels derived from WGS data, accuracy differed substantially among meat quality traits, varied from 0.08 to 0.47. Results showed that MultiBLUP outperform GBLUP and yielded accuracy increases ranging from 17.39% to 75%. We optimized the marker density and found medium- and high-density marker panels are beneficial for the estimation of heritability for meat quality. Moreover, we conducted genotype imputation from 50K chip to WGS level in the same population and found average concordance rate to exceed 95% and r2 = 0.81. CONCLUSIONS: Overall, estimation of heritability for meat quality traits can benefit from the use of WGS data. This study showed the superiority of using WGS data to genetically improve pork quality in genomic prediction.

Identification of candidate genes associated with carcass component weights in commercial crossbred pigs through a combined GWAS approach.

In the pork industry chain, carcass cutting is crucial for enhancing the commercial value of pork carcasses. However, the genetic mechanisms underlying carcass component weights remain poorly understood. Here, we used a combined genome-wide association study (GWAS) approach that integrated single- and multi-locus models to map genetic markers and genes associated with the weights of seven carcass components in Duroc × Landrace × Yorkshire (DLY) pigs. As multi-locus GWAS captures more single nucleotide polymorphisms (SNPs) with large effects than single-locus GWAS, the combined GWAS approach detected more SNPs than using the single-locus model alone. We identified 177 nonredundant SNPs associated with these traits in 526 DLY pigs, including boneless butt shoulder (BBS), boneless picnic shoulder (BPS), boneless leg (BL), belly (BELLY), front fat (FF), rear fat (RF), and skin-on whole loin (SLOIN). Using single-locus GWAS, we identified a quantitative trait locus (QTL) for SLOIN on Sus scrofa chromosome 15 (SSC15). Notably, a single SNP (ASGA0069883) in the proximity of this QTL was consistently detected by all GWAS models (one single-locus and four multi-locus models) and explained more than 4% of the phenotypic variance. Our findings suggest that the involved gene, MYO3B, is proposed to be a strong candidate for SLOIN. Further analysis also identified several candidate genes related to BBS (PPP3CA and CPEB4), BPS (ECH1), FF (CACNB2 and ZNF217), BELLY (FGFRL1), BL (CHST11), and RF (LRRK2). The identified SNPs can be used as molecular markers for the genetic improvement of pork carcasses in the molecular-guided breeding of modern commercial pigs.

Carcass cutting is the most effective method for enhancing the commercial value of pork carcasses in the industry chain. However, the genetic mechanisms underlying carcass component weights remain elusive. In this study, we used a combination of single- and multi-locus models to increase the power of genome-wide association analysis. We identified 177 important genetic variants that are potentially promising candidate markers for marker-assisted selection in breeding. Further investigation revealed one quantitative trait locus region and several candidate genes (PPP3CA, CPEB4, ECH1, CACNB2, ZNF217, FGFRL1, CHST11, LRRK2) associated with the weights of seven carcass components in Duroc × Landrace × Yorkshire pigs.

Identification of Candidate Genes for Economically Important Carcass Cutting in Commercial Pigs through GWAS.

During the process of pork production, the carcasses of pigs are divided and sold, which provides better economic benefits and market competitiveness for pork production than selling the carcass as a whole. Due to the significant cost of post-slaughter phenotypic measurement, the genetic architecture of tenderloin weight (TLNW) and rib weight (RIBW)-important components of pig carcass economic value-remain unknown. In this study, we conducted genome-wide association studies (GWAS) for TLNW and RIBW traits in a population of 431 Duroc × Landrace × Yorkshire (DLY) pigs. In our study, the most significant single nucleotide polymorphism (SNP) associated with TLNW was identified as ASGA0085853 (3.28 Mb) on Sus scrofa chromosome 12 (SSC12), while for RIBW, it was Affx-1115046258 (172.45 Mb) on SSC13. Through haplotype block analysis, we discovered a novel quantitative trait locus (QTL) associated with TLNW, spanning a 5 kb region on SSC12, and a novel RIBW-associated QTL spanning 1.42 Mb on SSC13. Furthermore, we hypothesized that three candidate genes, TIMP2 and EML1, and SMN1, are associated with TLNW and RIBW, respectively. Our research not only addresses the knowledge gap regarding TLNW, but also serves as a valuable reference for studying RIBW. The identified SNP loci strongly associated with TLNW and RIBW may prove useful for marker-assisted selection in pig breeding programs.

The SWine IMputation (SWIM) haplotype reference panel enables nucleotide resolution genetic mapping in pigs.

Genetic mapping to identify genes and alleles associated with or causing economically important quantitative trait variation in livestock animals such as pigs is a major goal in animal genetic improvement. Despite recent advances in high-throughput genotyping technologies, the resolution of genetic mapping in pigs remains poor due in part to the low density of genotyped variant sites. In this study, we overcame this limitation by developing a reference haplotype panel for pigs based on 2259 whole genome-sequenced animals representing 44 pig breeds. We evaluated software combinations and breed composition to optimize the imputation procedure and achieved an average concordance rate in excess of 96%, a non-reference concordance rate of 88%, and an r2 of 0.85. We demonstrated in two case studies that genotype imputation using this resource can dramatically improve the resolution of genetic mapping. A public web server has been developed to allow the pig genetics community to fully utilize this resource. We expect this resource to facilitate genetic mapping and accelerate genetic improvement in pigs.

Enhanced reductive reactivity of zero-valent iron (ZVI) for pollutant removal by natural organic matters (NOMs) under aerobic conditions: Correlation between NOM properties and ZVI activity.

While ubiquitous natural organic matters (NOMs) are capable of enhancing zero-valent iron (ZVI) performance under aerobic conditions, there is limited understanding of how the properties of NOMs affect the reactivity of ZVI towards contaminants removal. Here, the corresponding activity of ZVI under aerobic conditions was investigated in the presence of humic acid (HA), fulvic acid (FA), bovine serum albumin (BSA). It was found that three models of NOMs were all effective in promoting diatrizoate (DTA) reduction via depassivating ZVI. Interestingly, fast adsorption of NOM onto ZVI surface initially caused inconspicuous impact or visible inhibition on hydrophilic DTA reduction depending on their hydrophobicity. However, subsequent exposure of more reactive sites with high hydrophilicity arising from the detachment of surfaced NOM-associated iron oxide finally contributed to the enhanced consumption of Fe0 with the ability: HA > FA ≈ BSA, and 1-2 times increase in DTA removal kinetic rate following the order: HA > FA > BSA. It further revealed that there were two key factors in determining DTA removal under aerobic conditions, including the ability of NOMs to boost Fe0 consumption as contributed by their aromaticity degree and amino groups, and the hydrophobicity of NOMs to initially affect the property of ZVI surfaces.