Identification of a novel prognostic cuproptosis-associated LncRNA signature for predicting prognosis and immunotherapy response in patients with esophageal cancer.

Nowadays, effective prognostic models for esophageal cancer (ESCA) are still lacking. Long noncoding RNAs (lncRNAs) are commonly utilized as indicators for diagnosing cancer and forecasting patient outcomes. Cuproptosis is regulated by multiple genes and is crucial to the progression of ESCA. However, it is not yet clear what role the cuproptosis-associated lncRNAs (CuALs) play in ESCA. To tackle this problem, a prognostic signature incorporating three CuALs was created. This signature was constructed by the use of the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Subsequently, the signature effectively stratified ESCA samples into a high-risk group and a low-risk group. Those in the low-risk group demonstrated extended overall survival (OS), as well as increased infiltration of T cells, macrophages, and NK cells, suggesting a potentially enhanced response to immunotherapy. The ROC curve analysis demonstrated that this prognostic signature outperformed conventional clinical factors in predicting patient prognosis (AUC = 0.708). K-M survival analysis and correlation analysis identified UGDH-AS1 (a CuAL) as a protective factor positively associated with patient prognosis. The results of RT-qPCR and wound healing assays indicated that UGDH-AS1 is overexpressed in ESCA and could inhibit cancer cell migration. In general, the prognostic signature of CuALs demonstrated a robust capability in forecasting the immune environment and patient prognosis, highlighting its potential as a tool for enhancing personalized treatment strategies in ESCA.

Effects of Spermine Synthase Deficiency in Mesenchymal Stromal Cells Are Rescued by Upstream Inhibition of Ornithine Decarboxylase.

Despite the well-known relevance of polyamines to many forms of life, little is known about how polyamines regulate osteogenesis and skeletal homeostasis. Here, we report a series of in vitro studies conducted with human-bone-marrow-derived pluripotent stromal cells (MSCs). First, we show that during osteogenic differentiation, mRNA levels of most polyamine-associated enzymes are relatively constant, except for the catabolic enzyme spermidine/spermine N1-acetyltransferase 1 (SAT1), which is strongly increased at both mRNA and protein levels. As a result, the intracellular spermidine to spermine ratio is significantly reduced during the early stages of osteoblastogenesis. Supplementation of cells with exogenous spermidine or spermine decreases matrix mineralization in a dose-dependent manner. Employing N-cyclohexyl-1,3-propanediamine (CDAP) to chemically inhibit spermine synthase (SMS), the enzyme catalyzing conversion of spermidine into spermine, also suppresses mineralization. Intriguingly, this reduced mineralization is rescued with DFMO, an inhibitor of the upstream polyamine enzyme ornithine decarboxylase (ODC1). Similarly, high concentrations of CDAP cause cytoplasmic vacuolization and alter mitochondrial function, which are also reversible with the addition of DFMO. Altogether, these studies suggest that excess polyamines, especially spermidine, negatively affect hydroxyapatite synthesis of primary MSCs, whereas inhibition of polyamine synthesis with DFMO rescues most, but not all of these defects. These findings are relevant for patients with Snyder-Robinson syndrome (SRS), as the presenting skeletal defects-associated with SMS deficiency-could potentially be ameliorated by treatment with DFMO.

Application of neoadjuvant chemoradiotherapy and neoadjuvant chemotherapy in curative surgery for esophageal cancer: A meta-analysis.

BACKGROUND: The effectiveness of neoadjuvant therapy in esophageal cancer (EC) treatment is still a subject of debate. AIM: To compare the clinical efficacy and toxic side effects between neoadjuvant chemoradiotherapy (nCRT) and neoadjuvant chemotherapy (nCT) for locally advanced EC (LAEC). METHODS: A comprehensive search was conducted using multiple databases, including PubMed, EMBASE, MEDLINE, Science Direct, The Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, Chinese Science and Technology Journal Database, and Chinese Biomedical Literature Database Article. Studies up to December 2022 comparing nCRT and nCT in patients with EC were selected. RESULTS: The analysis revealed significant differences between nCRT and nCT in terms of disease-free survival. The results indicated that nCRT provided better outcomes in terms of the 3-year overall survival rate (OSR) [odds ratio (OR) = 0.95], complete response rate (OR = 3.15), and R0 clearance rate (CR) (OR = 2.25). However, nCT demonstrated a better 5-year OSR (OR = 1.02) than nCRT. Moreover, when compared to nCRT, nCT showed reduced risks of cardiac complications (OR = 1.15) and pulmonary complications (OR = 1.30). CONCLUSION: Overall, both nCRT and nCT were effective in terms of survival outcomes for LAEC. However, nCT exhibited better performance in terms of postoperative complications.

Ranging analysis of a moving target based on the dynamic instrument response function.

A ranging high-speed moving target with a high accuracy is challenging for a single-photon ranging system (SPRS). In this Letter, the dynamic instrument response function (IRF) is proposed to establish a dynamic discrete model (DDM) by introducing a velocity and a system timing resolution, which leads to better accuracy of cross-correlation results. And with the data of a dynamic Monte Carlo (DMC), the ranging accuracy can be improved with DDM.

Factors affecting the accuracy of genomic prediction in joint pig populations.

Genomic prediction (GP) has greatly advanced animal and plant breeding over the past two decades. GP in joint populations is a feasible method to improve the accuracy of genomic estimated breeding values in small populations. However, there is still a need to understand the factors that influence GP in joint populations. This study used simulated data and real data from Duroc pig populations to examine the impact of linkage disequilibrium (LD), causal variants effect sizes (CVESs), and minor allele frequencies (MAF) of SNPs on the accuracy of genomic prediction in joint populations. Three prediction methods were used: genomic best linear unbiased prediction (GBLUP), single-step GBLUP and multi-trait GBLUP. Results from the simulated datasets showed that the accuracies of GP in joint populations were always higher than those in a single population when only LD inconsistencies existed. However, single-step GBLUP accuracy in joint populations decreased as the correlation of MAF between populations decreased, while the accuracy of GBLUP is consistently higher in joint populations than in a single population. As the correlation of CVES between populations decreased, the accuracy of both GBLUP and single-step GBLUP in joint populations declined. Analysis of real Duroc populations showed low genetic correlation, similar to the simulated relationship between the most distant populations. In most cases in Duroc populations, GP have higher accuracies in joint populations than in individual population. In conclusion, the consistency of CVES plays a more important role in multi-population GP. The genetic relatedness of the Duroc populations is so weak that the prediction accuracy of GP in joint populations is reduced in some traits. Multi-trait GBLUP is a competitive method for the joint breeding evaluation.

Underwater single photon 3D imaging with millimeter depth accuracy and reduced blind range.

Mono-static system benefits from its more flexible field of view and simplified structure, however, the backreflection photons from mono-static system lead to count loss for target detection. Counting loss engender range-blind, impeding the accurate acquisition of target depth. In this paper, count loss is reduced by introducing a polarization-based underwater mono-static single-photon imaging method, and hence reduced blind range. The proposed method exploits the polarization characteristic of light to effectively reduce the count loss of the target, thus improving the target detection efficiency. Experiments demonstrate that the target profile can be visually identified under our method, while the unpolarization system can not. Moreover, the ranging precision of system reaches millimeter-level. Finally, the target profile is reconstructed using non-local pixel correlations algorithm.

The Role of SOCS3 in Regulating Meat Quality in Jinhua Pigs.

Meat quality is an important economic trait that influences the development of the pig industry. Skeletal muscle development and glycolytic potential (GP) are two crucial aspects that significantly impact meat quality. It has been reported that abnormal skeletal muscle development and high glycogen content results in low meat quality. However, the genetic mechanisms underlying these factors are still unclear. Compared with intensive pig breeds, Chinese indigenous pig breeds, such as the Jinhua pig, express superior meat quality characteristics. The differences in the meat quality traits between Jinhua and intensive pig breeds make them suitable for uncovering the genetic mechanisms that regulate meat quality traits. In this study, the Jinhua pig breed and five intensive pig breeds, including Duroc, Landrace, Yorkshire, Berkshire, and Pietrain pig breeds, were selected as experimental materials. First, the FST and XP-EHH methods were used to screen the selective signatures on the genome in the Jinhua population. Then, combined with RNA-Seq data, the study further confirmed that SOCS3 could be a key candidate gene that influences meat quality by mediating myoblast proliferation and glycometabolism because of the down-regulated expression of SOCS3 in Jinhua pigs compared with Landrace pigs. Finally, through SOCS3 knockout (KO) and overexpression (OE) experiments in mouse C2C12 cells, the results showed that SOCS3 regulated the cell proliferation of myoblasts. Moreover, SOCS3 is involved in regulating glucose uptake by the IRS1/PI3K/AKT signaling pathway. Overall, these findings provide a basis for the genetic improvement of meat quality traits in the pig industry.

Integrated microbiome-metabolome-genome axis data of Laiwu and Lulai pigs.

Excessive fat deposition can trigger metabolic diseases, and it is crucial to identify factors that can break the link between fat deposition and metabolic diseases. Healthy obese Laiwu pigs (LW) are high in fat content but resistant to metabolic diseases. In this study, we compared the fecal microbiome, fecal and blood metabolome, and genome of LW and Lulai pigs (LU) to identify factors that can block the link between fat deposition and metabolic diseases. Our results show significant differences in Spirochetes and Treponema, which are involved in carbohydrate metabolism, between LW and LU. The fecal and blood metabolome composition was similar, and some anti-metabolic disease components of blood metabolites were different between the two breeds of pigs. The predicted differential RNA is mainly enriched in lipid metabolism and glucose metabolism, which is consistent with the functions of differential microbiota and metabolites. The down-regulated gene RGP1 is strongly negatively correlated with Treponema. Our omics data would provide valuable resources for further scientific research on healthy obesity in both human and porcine.

Perioperative outcomes of neoadjuvant immunotherapy plus chemotherapy and neoadjuvant chemoradiotherapy in the treatment of locally advanced esophageal squamous cell carcinoma: a retrospective comparative cohort study.

Background: Neoadjuvant chemoradiotherapy (nCRT) is recommended as the preferred treatment for locally advanced esophageal squamous cell carcinoma. Recent studies have shown that immune checkpoint inhibitors are beneficial in treating advanced esophageal cancer. Therefore, a growing number of clinical centers are conducting trials of neoadjuvant immunotherapy or neoadjuvant immunotherapy plus chemotherapy (nICT) in patients with locally advanced resectable esophageal cancer. Immunocheckpoint inhibitors are expected to play a role in neoadjuvant therapy for esophageal cancer. However, there were few studies comparing nICT with nCRT. This study compared the efficacy and safety of nICT with that of nCRT administered prior to esophagectomy in patients with resectable locally advanced esophageal squamous cell carcinoma (ESCC). Methods: The study included patients with locally advanced resectable ESCC who were scheduled to receive neoadjuvant therapy at Gaozhou People's Hospital from January 1, 2019, to September 1, 2022. The enrolled patients were divided into 2 groups (nCRT or nICT) according to their neoadjuvant therapy regimen. The 2 groups were compared for their baseline data, the incidence of adverse events during neoadjuvant therapy, the clinical evaluation after neoadjuvant therapy, perioperative indicators, and the incidence of postoperative complications and postoperative pathological remission. Results: A total of 44 patients were enrolled; 23 in the nCRT group and 21 in the nICT group. There were no significant differences between the 2 groups in the baseline data. In the nCRT group, leukopenia occurred more often than in the nICT group, and hemoglobin-decreasing events were rarer (P=0.03<0.05). A significantly higher proportion of patients in the nICT group experienced erythema following neoadjuvant therapy compared to the nCRT group (23.81% vs. 0%; P=0.01<0.05). Neoadjuvant therapy showed no significant difference between the 2 groups for adverse event rates, surgery-related indicators, postoperative pathological remission rates, and postoperative complications. Conclusions: nICT was a safe and feasible treatment for locally advanced ESCC and it may be a potential new treatment modality.

Integration of Selection Signatures and Protein Interactions Reveals NR6A1, PAPPA2, and PIK3C2B as the Promising Candidate Genes Underlying the Characteristics of Licha Black Pig.

Licha black (LI) pig has the specific characteristics of larger body length and appropriate fat deposition among Chinese indigenous pigs. Body length is one of the external traits that affect production performance, and fat deposition influences meat quality. However, the genetic characteristics of LI pigs have not yet been systematically uncovered. Here, the genomic information from 891 individuals of LI pigs, commercial pigs, and other Chinese indigenous pigs was used to analyze the breed characteristics of the LI pig with runs of homozygosity, haplotype, and FST selection signatures. The results showed the growth traits-related genes (i.e., NR6A1 and PAPPA2) and the fatness traits-related gene (i.e., PIK3C2B) were the promising candidate genes that closely related to the characteristics of LI pigs. In addition, the protein-protein interaction network revealed the potential interactions between the promising candidate genes and the FASN gene. The RNA expression data from FarmGTEx indicated that the RNA expression levels of NR6A1, PAPPA2, PIK3C2B, and FASN were highly correlated in the ileum. This study provides valuable molecular insights into the mechanisms that affect pig body length and fat deposition, which can be used in the further breeding process to improve meat quality and commercial profitability.

Molecular Design-Based Breeding: A Kinship Index-Based Selection Method for Complex Traits in Small Livestock Populations.

Genomic selection (GS) techniques have improved animal breeding by enhancing the prediction accuracy of breeding values, particularly for traits that are difficult to measure and have low heritability, as well as reducing generation intervals. However, the requirement to establish genetic reference populations can limit the application of GS in pig breeds with small populations, especially when small populations make up most of the pig breeds worldwide. We aimed to propose a kinship index based selection (KIS) method, which defines an ideal individual with information on the beneficial genotypes for the target trait. Herein, the metric for assessing selection decisions is a beneficial genotypic similarity between the candidate and the ideal individual; thus, the KIS method can overcome the need for establishing genetic reference groups and continuous phenotype determination. We also performed a robustness test to make the method more aligned with reality. Simulation results revealed that compared to conventional genomic selection methods, the KIS method is feasible, particularly, when the population size is relatively small.

PyAGH: a python package to fast construct kinship matrices based on different levels of omic data.

BACKGROUND: Construction of kinship matrices among individuals is an important step for both association studies and prediction studies based on different levels of omic data. Methods for constructing kinship matrices are becoming diverse and different methods have their specific appropriate scenes. However, software that can comprehensively calculate kinship matrices for a variety of scenarios is still in an urgent demand. RESULTS: In this study, we developed an efficient and user-friendly python module, PyAGH, that can accomplish (1) conventional additive kinship matrces construction based on pedigree, genotypes, abundance data from transcriptome or microbiome; (2) genomic kinship matrices construction in combined population; (3) dominant and epistatic effects kinship matrices construction; (4) pedigree selection, tracing, detection and visualization; (5) visualization of cluster, heatmap and PCA analysis based on kinship matrices. The output from PyAGH can be easily integrated in other mainstream software based on users' purposes. Compared with other softwares, PyAGH integrates multiple methods for calculating the kinship matrix and has advantages in terms of speed and data size compared to other software. PyAGH is developed in python and C + + and can be easily installed by pip tool. Installation instructions and a manual document can be freely available from https://github.com/zhaow-01/PyAGH . CONCLUSION: PyAGH is a fast and user-friendly Python package for calculating kinship matrices using pedigree, genotype, microbiome and transcriptome data as well as processing, analyzing and visualizing data and results. This package makes it easier to perform predictions and association studies processes based on different levels of omic data.

Characterization of the Roles of Suppressor of Cytokine Signaling-3 in Esophageal Carcinoma.

This study was aimed to analyze the diagnostic, therapeutic, and prognostic value of the suppressor of cytokine signaling 3 (SOCS3) in pancancer, especially in esophageal carcinoma (ESCA), and investigate the role of SOCS3 in the tumorigenesis and progression of ESCA. We used a variety of bioinformatics methods to explore the expression of SOCS3 in 33 kinds of cancers and evaluate its potential role in the pathogenesis, prognosis, immune microenvironment, immune evasion, and therapeutic response of cancers. The results indicated that SOCS3 was upregulated in 10 cancers, downregulated in 12 cancers, and upregulated in ESCA. Mutation and amplification were the main causes of abnormal expression of SOCS3 in pancancer. In ESCA, expression of SOCS3 was negatively correlated with methylation. The analysis showed that ESCA patients with low SOCS3 levels had better overall survival. Furthermore, the SOCS3 level was positively related to the ESTIMATE score, immune score, stromal score, and negatively related to tumor purity. In ESCA, a significant association was found between SOCS3 and several immune checkpoint genes. In addition, SOCS3 was associated with sensitivity to 59 drugs. Next, the role of SOCS3 in ESCA was investigated in ECA109, EC9706 cells, and in xenografted mouse model. SOCS3 was confirmed to be upregulated in ESCA cells. Knockdown of SOCS3 decreased the proliferation, migration, and invasion of ESCA cells while increasing apoptosis. Meanwhile, downregulation of SOCS3 activated the nuclear factor kappa-B signaling pathway and inhibited ESCA tumorigenesis in vivo. In conclusion, high SOCS3 expression is closely related to the occurrence and progression of ESCA and can be used as a therapeutic target and prognostic biomarker for ESCA.

A web tool for the global identification of pig breeds.

BACKGROUND: Natural and artificial selection for more than 9000 years have led to a variety of domestic pig breeds. Accurate identification of pig breeds is important for breed conservation, sustainable breeding, pork traceability, and local resource registration. RESULTS: We evaluated the performance of four selectors and six classifiers for breed identification using a wide range of pig breeds (N = 91). The internal cross-validation and external independent testing showed that partial least squares regression (PLSR) was the most effective selector and partial least squares-discriminant analysis (PLS-DA) was the most powerful classifier for breed identification among many breeds. Five-fold cross-validation indicated that using PLSR as the selector and PLS-DA as the classifier to discriminate 91 pig breeds yielded 98.4% accuracy with only 3K single nucleotide polymorphisms (SNPs). We also constructed a reference dataset with 124 pig breeds and used it to develop the web tool iDIGs ( http://alphaindex.zju.edu.cn/iDIGs_en/ ) as a comprehensive application for global pig breed identification. iDIGs allows users to (1) identify pig breeds without a reference population and (2) design small panels to discriminate several specific pig breeds. CONCLUSIONS: In this study, we proved that breed identification among a wide range of pig breeds is feasible and we developed a web tool for such pig breed identification.

The effect of high-density genotypic data and different methods on joint genomic prediction: A case study in large white pigs.

Joint genomic prediction (GP) is an attractive method to improve the accuracy of GP by combining information from multiple populations. However, many factors can negatively influence the accuracy of joint GP, such as differences in linkage disequilibrium phasing between single nucleotide polymorphisms (SNPs) and causal variants, minor allele frequencies and causal variants' effect sizes across different populations. The objective of this study was to investigate whether the imputed high-density genotype data can improve the accuracy of joint GP using genomic best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP), multi-trait GBLUP (MT-GBLUP) and GBLUP based on genomic relationship matrix considering heterogenous minor allele frequencies across different populations (wGBLUP). Three traits, including days taken to reach slaughter weight, backfat thickness and loin muscle area, were measured on 67 276 Large White pigs from two different populations, for which 3334 were genotyped by SNP array. The results showed that a combined population could substantially improve the accuracy of GP compared with a single-population GP, especially for the population with a smaller size. The imputed SNP data had no effect for single population GP but helped to yield higher accuracy than the medium-density array data for joint GP. Of the four methods, ssGLBUP performed the best, but the advantage of ssGBLUP decreased as more individuals were genotyped. In some cases, MT-GBLUP and wGBLUP performed better than GBLUP. In conclusion, our results confirmed that joint GP could be beneficial from imputed high-density genotype data, and the wGBLUP and MT-GBLUP methods are promising for joint GP in pig breeding.

Efficacy of sternocleidomastoid muscle flap in reducing anastomotic mediastinal/pleural cavity leak.

BACKGROUND: Anastomotic mediastinal/pleural cavity leak (AMPCL) is a life-threatening postoperative complication after esophagectomy. The objective of this study was to find a safe and effective surgical method to reduce the incidence of AMPCL. METHODS: A total of 223 patients who underwent surgery in Fujian Medical University Union Hospital from May 2020 to October 2021 were enrolled in this study. Data for preoperative and postoperative test indices, postoperative complications, perioperative treatment were collected. After using 1:1 propensity score matching (PSM) to match two cohort (caliper = 0.1), the relationship between various factors and the incidence of AMPCL were analyzed. RESULTS: 209 patients were included for further analysis in the end. There were 95 patients in the sternocleidomastoid muscle flap embedding group (intervention group) and 114 in the routine operation group (control group). There was a significant difference in mean age between two groups. Gender, age, body mass index, diabetes, American society of anesthesiologists score, preoperative neoadjuvant therapy, pathological stage were included in performing 1:1 PSM, and there were no significant differences between two groups. Median operative time was significantly less in intervention group. Anastomotic leak (AL) did not present significant difference between two groups (8 [8.6] vs. 13 [14.0], p = 0.247), however, the AMPCL in intervention group was significantly lower than control group (0 [0] vs. 6 [6.5], p = 0.029). CONCLUSIONS: The sternocleidomastoid muscle flap embedding could significantly reduce the incidence of AMPCL. This additional procedure is safe, and effective without increase in the occurrence of postoperative complications and hospital expenses.

Identification of PTK2 as an adverse prognostic biomarker in breast cancer by integrated bioinformatics and experimental analyses.

PTK2 is highly expressed in many cancers and is involved in cell growth, survival, migration, and invasion. However, the prognostic value of PTK2 and its potential function remain unclear in breast cancer. Therefore, we performed a comprehensive analysis of multiple public databases to explore the roles of PTK2. By integrating multiple datasets, we found that PTK2 mRNA expression in breast cancer tissue was higher than that in normal breast tissue or adjacent tissue. High PTK2 expression was associated with lymph node metastasis stage, tumor stage, breast cancer type, age, TP53 mutation, and gender and significantly predicted a poor survival outcome in breast cancer patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) results suggested that PTK2 and co-expressed genes participated in the cell cycle. Immune infiltration analysis clarified that high PTK2 expression was positively correlated with infiltrating levels of CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. The DNA methylation of PTK2 in breast cancer tissues was higher than that in normal tissues, and high PTK2 methylation was correlated with poor prognosis in breast cancer patients. Furthermore, 16 possible ceRNA networks related to PTK2 were constructed for breast cancer. Additionally, PTK2 knockdown could suppress the proliferation and migration ability of MCF-7 cells. These results suggest that PTK2 can be used as a prognostic biomarker for breast cancer.

The involvement of high succinylation modification in the development of prostate cancer.

Objective: Succinylation modification of the lysine site plays an important role in tumorigenesis and development, but it is rarely reported in prostate cancer (PCa), so this study aims to elucidate its expression in and clinical correlation with PCa. Methods: A total of 95 tumor, 3 normal and 52 paired adjacent tissue of PCa were involved for succinylation stanning. 498 PCa samples with 20 succinylation modification-related genes from TCGA were downloaded for model construction. Statistical methods were employed to analyze the data, including Non-Negative Matrix Factorization (NMF) algorithm, t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm and Cox regression analysis. Results: The pan-succinyllysine antibody stanning indicated that tumor tissues showed higher succinyllysine level than adjacent tissues (p<0.001). Gleason grade and PDL1 expression levels were significantly different (p<0.001) among the high, medium and low succinylation staining scores. The types of PCa tissue were divided into four clusters using RNA-seq data of 20 succinylation-related genes in TCGA database. Clinical characterize of age, PSA level, and pathological stage showed differences among four clusters. The expression of succinylation-related genes (KAT5, SDHD and GLYATL1) and PCa related genes (PDL1, AR and TP53) were significantly different in 52 matched tumor and adjacent tissues (p<0.001). GLYATL1 and AR gene expression was significantly related to the pathological stage of PCa. Conclusion: Succinylation was significantly increased in PCa tissues and was closely related to Gleason grade and PD-L1 expression. Model construction of 20 genes related to succinylation modification showed that the later the pathological stage of PCa, the higher the level of succinylation modification.

Genomic Signatures Reveal Breeding Effects of Lulai Pigs.

In Chinese pig populations in which crossbreeding is used, these animals show a level of weakness compared with their original purebred ancestors. For instance, in the Lulai pig, a newly developed Chinese breed that is raised on the basis of the Laiwu pig (a Chinese indigenous breed with exceptionally high intramuscular fat content) and the Yorkshire pig using a method of systematic crossbreeding, both their market acceptance and performance are inferior. To reveal the practical role of these admixed breeds and traditional systematic crossbreeding methods at the genomic level, we explored population structure, genetic signatures, and introgression. We conducted this study based on the SNP chip data of 381 Lulai pigs, 182 Laiwu pigs, and 127 Yorkshires, which showed deficient genome coverage during our study. Therefore, we further selected the Genotyping by Genome Reducing and Sequencing (GGRS) method, which has a high density and suitable genome coverage as a supplement. We applied the GGRS data of 38 Lulai pigs, 75 Laiwu pigs, and 75 Yorkshires. In terms of the SNP chip data, by Fst analysis, we detected 782 significantly different genes between Lulai pigs and Yorkshires, including 3 major genes associated with growth (LEPR) and meat quality (SCD and TBC1D1), and we detected 426 significantly different genes between Lulai pigs and Laiwu pigs. With rIBD, we detected 12 genomic regions that included 182 genes that Yorkshires introgressed to Lulai pigs, and we detected 27 genomic regions that included 229 genes with a major gene (SCD) that Laiwu pigs introgressed to Lulai pigs. Regarding the GGRS data, we detected 601 significantly different genes between Lulai pigs and Yorkshires by Fst analysis, including 3 major genes associated with growth and fat deposits (IGF2 and FTO) and with hair color (KIT), and we detected 634 significantly different genes between Lulai pigs and Laiwu pigs, including 3 major genes related to their body composition (MYPN), hair color (KIT), and ear size (PPARD). By rIBD, we detected 94 deep sections that included 363 genes that Yorkshires introgressed to Lulai pigs, and we detected 149 deep sections that included 727 genes with a major gene (ESR1) that Laiwu pigs introgressed to Lulai pigs. Altogether, this study provides both insight into the molecular background of synthesized breeds of Lulai pigs and a reference for the evaluation of systematic crossbreeding in China.

Estimation of Complex-Trait Prediction Accuracy from the Different Holo-Omics Interaction Models.

Statistical models play a significant role in designing competent breeding programs related to complex traits. Recently; the holo-omics framework has been productively utilized in trait prediction; but it contains many complexities. Therefore; it is desirable to establish prediction accuracy while combining the host's genome and microbiome data. Several methods can be used to combine the two data in the model and study their effectiveness by estimating the prediction accuracy. We validate our holo-omics interaction models with analysis from two publicly available datasets and compare them with genomic and microbiome prediction models. We illustrate that the holo-omics interactive models achieved the highest prediction accuracy in ten out of eleven traits. In particular; the holo-omics interaction matrix estimated using the Hadamard product displayed the highest accuracy in nine out of eleven traits, with the direct holo-omics model and microbiome model showing the highest prediction accuracy in the remaining two traits. We conclude that comparing prediction accuracy in different traits using real data showed important intuitions into the holo-omics architecture of complex traits.