Artificial insemination in the pig industry requires frozen semen of better freezability and post-thaw quality. Several recent metabolomics studies were carried out to discover sperm freezability-related biomarkers. However, only a limited number of pig breeds were examined and results remain obscure. Here, boar semen samples were collected and quality evaluated for 3 Western breeds (Duroc, Landrace and Yorkshire), originating from a nucleus farm and a boar station. Post-thaw sperm from the nucleus farm exhibited significantly higher motility and vitality rates (p < 0.01), and also better acrosome integrity and plasma membrane integrity (p < 0.05). For the high and low freezability groups (36 samples in total), ultra high-performance liquid chromatography-mass spectrometry (UHPLC-MS) on spermatozoa identified significantly differential metabolites (p < 0.05, including betaine), with 258 metabolites in Duroc, 126 in Landrace, and 215 in Yorkshire, which were significantly enriched in 11, 8 and 13 metabolic pathways (KEGG) (p < 0.05), respectively. Besides breed-specific pathways (Duroc: cysteine and methionine metabolism; Landrace: arginine and proline metabolism, tryptophan metabolism, lysine degradation, galactose metabolism and pyruvate metabolism; Yorkshire: steroid hormone biosynthesis, riboflavin metabolism and linoleic acid metabolism), two pathways common to 3 breeds (alanine, aspartate and glutamate metabolism, and pentose phosphate pathway) were found. Betaine was confirmed to be at a significantly higher level in the semen of high freezability for all three pig breeds (p < 0.05). Taken together, metabolites and metabolic pathways common and specific to commercial Western breeds were identified. Betaine was related to better spermatozoa freezability. Our findings provide the basis and insights into better understanding the role of metabolic molecules and pathways important to boar spermatozoa freezability.

Identifying causal genetic variants and candidate genes underlying complex traits remains a central challenge in animal breeding and genetics. Genome-wide association studies (GWAS) are widely used for this purpose. However, their reliance on marginal variant effects and sensitivity to linkage disequilibrium (LD) can lead to redundant and less accurate identification of variants or genes of biological relevance. Here, we propose SNP prioritization (GWAS-based and fine-mapping-based) strategies within a unified framework, designed to improve the selection of more informative variants and candidate genes by explicitly modeling LD structure and genetic architectures of three pig teat-related traits (total teat number, teat symmetry, and teat adequacy). While GWAS prioritization favored variants with strong marginal effects, fine-mapping substantially improved joint explanatory performance and prediction accuracy. For total teat number, the best-performing fine-mapping-derived SNP subset achieved a mean PCC of 0.6599 across 10-fold cross-validation, compared with 0.3755 for GWAS-based prioritization. Similarly, for teat adequacy, the highest mean AUC increased from 0.7012 (GWAS) to 0.8547 (fine-mapping). Moreover, fine-mapping-derived SNP sets identified more coherent and trait-specific biological pathways and functionally relevant candidate genes. Taken together, our findings demonstrate that fine-mapping provides a more accurate and biologically meaningful framework for SNP and candidate gene prioritization, supporting its integration into genetic analysis and breeding applications.

Porcine Sertoli cells (SCs) treated by acute heat stress (HS) (43°C, 0.5 h) have significantly decreased taurine level. Taurine treatment of porcine SCs could promote proliferation, inhibit apoptosis, enhance mitochondrial function and modulate protein profile. However, whether taurine can alleviate damages of porcine SCs caused by acute HS is unknown. We here showed that treatment of porcine SCs using taurine (5.7 μM) for 12 h before acute HS (HS0.5-B12-Taurine) significantly rescued damages induced by acute HS (HS0.5-Control), including cell viability, proliferation, apoptosis, intracellular reactive oxygen species (ROS) levels, mitochondrial number, and lactate content. Transcriptome sequencing identified 18 differentially expressed genes (DEGs) (HS0.5-B12-Taurine vs. HS0.5-Control), mainly enriched in Gene Ontology (GO) terms of apoptosis, transmembrane transport, inward rectifier potassium channel activity, and 2 iron/2 sulfur cluster binding. RT-qPCR validated expression of 5 DEGs (GRIA4, KCNJ13, PTER, RAB44 and SLC12A8) and 7 other genes (AFF4, CXCL8, DENND5B, EPM2AIP1, SLC6A6, SSH2 and WNK3), most of them showing change trend consistent with RNA-seq results. Moreover, HDAC5 was confirmed to be significantly reduced in HS0.5-B12-Taurine (Western blotting: P < 0.05; Immunofluorescence: P < 0.01). Collectively, these findings suggest that taurine protects porcine SCs against damages induced by acute HS.

Cycloleucine (CL) is a methyl donor inhibitor. Our previous findings showed that CL could affect meiotic maturation and developmental potency of porcine oocytes by reducing nucleic acid N6-methyladenosine (m6A) epigenetic modification level. However, the effects of CL on porcine male reproduction are unclear. Here, we showed that CL treatment of porcine testicular Sertoli cell line (SCL) could reduce viability in a dose-dependent manner. CL treatment (40 mM, 36h) of porcine SCL also inhibited proliferation, promoted late apoptosis, increased level of intracellular reactive oxygen species (ROS), reduced mitochondrial function, suppressed levels of nucleic acid N6-methyladenosine (m6A), H3K4me3, H3K27ac and H4K16ac, and increased H3K9me2 level. ELISA assays showed that CL (40 mM, 36h) decreased lactate production, but unaltered levels of anti-müllerian hormone. RNA-seq and metabolomics identified 1212 differentially expressed genes (DEGs) (536 up- and 676 down-) and 67 significantly different metabolites (SDMs) (45 up- and 22 down-) to be altered by CL (40 mM, 36h) treatment of porcine SCL. These DEGs and SDMs were involved in multiple pathways, including PI3K-Akt, cell cycle, ferroptosis, FoxO, aminoacyl-tRNA biosynthesis. Validation of some DEGs showed that CL (40 mM, 36h) significantly decreased expression of HMGCS1 and P4HA1, but increased IGFBP5 abundance at both mRNA and protein levels. Combined analysis of transcriptome and metabolome identified the high correlations between some DEGs and SDMs. Collectively, these findings indicate that CL could affect functions of porcine SCL by modifying epigenetic modifications to alter gene expression and metabolism, providing insights into understanding the mechanisms by which epigenetic modification influences the functions of porcine SCL, as well as aiding references for regulating porcine male reproduction.

Dopamine receptors (DARs) are a type of evolutionarily conserved G-protein-coupled receptors, which are capable of coordinating the interactions between neuroendocrine and immune systems. However, their roles in the systemic antibacterial immune of invertebrates remains unclear. This study aimed to elucidate the immunomodulatory functions and the underlying mechanisms of Dopamine receptor D1-like receptor (DAR) in the process of bacterial infection in P. clarkii. In this study, it was found that the expression levels were significantly up-regulated in hemocytes, hepatopancreas, intestines, and gills post V. harveyi stimulation. Pc-DAR regulated the expression of antimicrobial peptides (AMPs) genes by promoting the expression of Relish to maintain the homeostasis of the hemolymph microbiota. Moreover, Pc-DAR regulated the melanization process to clear bacteria through the phenoloxidase system. Mechanistically, the inhibition of DAR could regulate the concentrations of downstream cAMP and PKA, thereby influencing intracellular genes expression and enzymes activities. Subsequently, our results revealed that Pc-dar were involved in the cellular immune process. It executed this function by upregulating the expression of the scavenger receptor protein gene (Pc-SRB) and two small GTPases (Pc-rab 7 and Pc-rab 11), which were related to the transportation of phagosomes to lysosomes in the cytoplasm. By detecting changes in the content of antioxidant enzymes, we speculated that Pc-DAR might regulate the reactive oxygen species (ROS) balance in the body by modulating antioxidant enzymes (SOD, CAT, GST). Overall, our results revealed that Pc-DAR plays an important immunomodulatory role in crayfish. We speculated that Pc-DAR is necessary for the effective maintaining of antibacterial immune responses during bacterial infection. And its function is associated with the cAMP-PKA signaling pathway.

Genotype imputation (GI) plays a critical role in predicting missing genetic information for genomic studies and breeding applications. Although recent reference-free deep learning approaches have demonstrated promising performance, they often fail to exploit local genomic information, which limits further improvements in prediction accuracy and stability. In this study, we developed ALGI, a novel method based on a sparse convolutional denoising autoencoder, which uniquely integrates local genomic window information with group-specific feature learning. Unlike conventional convolutional or autoencoder-based approaches, ALGI first applies K-means clustering to group samples according to local genomic windows, then learns hidden genotype configurations specific to each group, capturing fine-scale local patterns and complex haplotype structures. Systematic evaluation was conducted across yeast, human, and pig MHC regions under multiple scenarios, including different window sizes, missing rates, sample sizes, and numbers of variants. Results show that ALGI demonstrates consistent improvements over conventional methods (Beagle) and state-of-the-art deep learning approaches (AE, SCDA) under the evaluated settings, with enhanced accuracy, stability, and robustness. In addition, ALGI is user-friendly and publicly available. While evaluated on highly polymorphic MHC regions, its strong performance suggests applicability to less complex regions, though broader genome-wide validation is needed. This approach provides a powerful tool for genomic selection and advancing complex trait genetics in livestock and other species.

This study focused on peroxiredoxin (Prx) as a core target to investigate its regulatory mechanism in the innate immunity of Procambarus clarkii (crayfish). We observed that a reduction in the expression of prx 4 via injection of double-stranded RNA (dsRNA) induced early upregulation of H2O2 levels in the hepatopancreas of crayfish. When the crayfish were subsequently infected with Vibrio harveyi, the H2O2 levels further increased, and the antioxidant enzyme system was activated. After injecting dsPc-Prx 4 and subsequently stimulating the crayfish with V. harveyi, we observed upregulated expression of genes related to the melanization pathway, reactive oxygen species (ROS) pathway, apoptosis pathway, and Toll pathway. Furthermore, after the injection of dsPc-Prx 4 followed by V. harveyi and subsequent N-acetylcysteine (NAC) exposure, the expression of genes related to the melanization, apoptosis, and Toll pathways was downregulated; moreover, the melanization phenomenon was significantly weakened, and the survival rate of the crayfish decreased. The abovementioned experimental results demonstrate that Pc-Prx 4 is an important regulatory enzyme in the antioxidant system of crayfish. It can influence the antibacterial innate immune response of P. clarkii by modulating H2O2 levels. This study provides a significant addition to the fundamental theory of antibacterial innate immunity in invertebrates and offers a theoretical basis for the prevention and control of bacterial diseases in P. clarkii.

Melatonin (MT), a neurohormone synthesized and secreted primarily by the pineal gland, is of vital function to animal reproduction. However, the effects of gene expression and metabolism exerted by MT on porcine immature Sertoli cells (iSCs) remain unclear. Here, MT treatment (10 nM, 36h) elevated mitochondrial function and reduced oxidative stress, to promote proliferation and inhibit apoptosis of porcine iSCs. Transcriptome profiling identified 39 differentially expressed genes (DEGs) (33 known and 6 novel) (MT vs. Control), mainly involved in the steroid and glutamine metabolic processes, oxidoreductase activity and G protein coupled receptor binding (GO terms), and steroid biogenesis, pyruvate metabolism and AMPK signal pathways, etc (KEGG pathways). RT-qPCR validated 6 DEGs (Phgdh, Scd, Hmgcs1, Cytb, Pck2 and Sqle), with similar expression pattern to RNA-seq. Metabolomics further showed that 14 metabolites were significantly altered. The HMGCS1 protein abundance and the estradiol level were confirmed to be significantly decreased by MT (10 nM, 36h) treatment, and direct inhibition of HMGCS1 could also significantly reduce the estradiol level. However, levels of cholesterol and lactate were unchanged. Collectively, through integrated transcriptomics and metabolomics analysis, MT is demonstrated to inhibit the HMGCS1-estradiol pathway, to enhance the function of porcine iSCs.

Single-cell transcriptome sequencing (scRNA-seq) is widely used in the fields of animal and plant developmental biology and important trait analysis by obtaining single-cell transcript abundance data in high throughput, which can deeply reveal cell types, subtype composition, specific gene markers and functional differences. However, scRNA-seq data are often accompanied by problems such as high noise, high dimensionality and batch effect, resulting in a large number of low-expressed genes and variants, which seriously affect the accuracy and reliability of data analysis. This not only increases the complexity of data processing, but also limits the effectiveness of feature selection and downstream analysis. Although several statistical inference and machine learning methods have been used to address these challenges, the existing methods still have limitations in cell type identification, feature selection, and batch effect correction, which are difficult to meet the needs of complex biological research. In this study, we proposes an innovative single-cell classification method, scIC (single-cell image classification), which converts scRNA-seq data into image form and combines it with deep learning techniques for cell classification. Through this image conversion, we are able to capture complex patterns in the data more efficiently, and then construct efficient classification models using convolutional neural networks (CNN) and residual networks (ResNet). After testing scRNA-seq data from four cell types (mouse skin basal cells, mouse lymphocytes, human neuronal cells, and mouse spinal cord cells), the accuracy of the classification models exceeded 94%, with the mouse skin basal cell dataset achieving a classification accuracy of 99.8% when using the ResNet50 model. These results indicate that image transformation of scRNA-seq data and combining it with deep learning techniques can significantly improve the classification accuracy, providing new ideas and effective tools for solving key challenges in single-cell data analysis. The code for this study is publicly available at: https://github.com/Bingxi-Gao/SCImageClassify.

Neuropeptide F (NPF), a key component of the neuroendocrine-immune (NEI) system, is widely distributed in the central nervous system and peripheral immune cells and is involved in various physiological processes. In this study, the role of NPF in the NEI system of Procambarus clarkii was investigated, with a specific focus on its regulatory functions in innate immunity. We found that Pc-NPF is expressed in multiple tissues of P. clarkii, including hemocytes, hepatopancreas, gills, intestine, heart, and muscle, with higher expression levels in the hepatopancreas. Upon bacterial challenge with Staphylococcus aureus and Edwardsiella ictaluri, Pc-NPF expression levels were significantly increased, indicating its involvement in immune responses. Using RNA interference (RNAi) to knock down Pc-NPF, we observed reduced bacterial clearance and significantly decreased survival rates of P. clarkii, highlighting the critical role of Pc-NPF in defending against bacterial infections. Further investigation revealed that Pc-NPF knockdown inhibited the expression of key genes in the Toll and Imd signaling pathways, including receptor genes (Pc-toll 1 and Pc-toll 3), transcription factor genes (Pc-dorsal, Pc-relish 2, and Pc-relish 3), and antimicrobial peptide genes (Pc-crustin 1, Pc-crustin 2, Pc-ALF 1, etc.). These findings suggest that Pc-NPF regulates innate immune responses through the Toll and Imd pathways, providing new insights into the NEI system of P. clarkii and offering a theoretical basis for disease control and the development of immune strategies involving P. clarkii.

Context Sertoli cells in testis constitute the microenvironment and produce essential substances, to protect and support spermatogenic cells. Previously, we found that acute heat stress damaged the function and reduced taurine concentrations of porcine Sertoli cells (SCs). Exogenous supplementation of taurine could enhance function of porcine SCs. However, how taurine induces the proteomics change to affect the function of SCs remains unknown.Aims To identify differentially expressed proteins (DEPs) in porcine SCs as induced by taurine.Methods Four-dimensional data-independent acquisition (4D-DIA) quantitative proteomics and western blotting was used to profile and validate DEPs.Key results In total, 109 DEPs (74 up and 35 down-regulated) were identified. Further enrichment analyses showed multiple signaling pathways, including long-chain fatty acid biosynthetic process, oxidative phosphorylation, steroid hormone biosynthesis, ubiquitin-protein transferase activity, regulation of protein dephosphorylation and regulation of TOR signal etc. Two DEPs (GAS6 and HDAC5) were validated by western blotting, and had the same abundance trend as detected by proteomics.Conclusions Taurine could modulate various important proteins and associated signaling pathways of porcine SCs.Implications These findings help understand how taurine induces phenotypic changes of porcine SCs, providing critical insights into usage of taurine to mitigate acute heat stress and regulate male reproductive function.

Mammalian peroxiredoxin (Prx) maintains redox equilibrium and protects cells from oxidative stress by eliminating the accumulation of intracellular reactive oxygen species (ROS). Studies have revealed that members of the Prx family play important roles in multiple processes, including oxidative defense, redox signaling, protein folding, cell cycle progression, DNA integrity, inflammation, and carcinogenesis. However, research on Prx molecules in invertebrates has been insufficient to draw definite conclusions. In particular, studies on their role in the regulation of innate immunity are scarce. In this study, the prx 6 gene was used as the target to carry out a series of biochemical and molecular biological studies in P. clarkii. These studies focused on recombinant expression, the identification of antioxidant effects in vitro, the study of temporal and spatial expression profiles, RNAi in vivo, and the detection of critical innate immune responses. The results indicated that rPc-Prx 6 significantly enhanced the protective effect of the MCO reaction system on plasmid DNA and had clear antioxidant effects. It could strongly combine with S. aureus and E. catarrhalis. Compared with those in the 1 × PBS injection group, the relative expression levels of Pc-prx 6 were clearly increased in certain major immunity-related tissues after challenge with V. harveyi. In addition, the direct knockdown of Pc-prx 6 directly resulted in the upregulation of H2O2 content in the crayfish hepatopancreas. Consequently, the hepatopancreatic histomorphology was altered, and the MDA content increased. Moreover, the increase in the expression of NF-κB and some immune effector genes was clearly inhibited. Ultimately, the crayfish survival rate was significantly reduced. Taken together, these findings suggest that Prx 6 plays an important role in antibacterial innate immunity by regulating the H2O2 content in invertebrates.

【Objective】Autoencoder, as one of the deep learning algorithms, offers unique advantages in reducing dimensionality and conducting interpolation analysis on single-cell transcriptome data. The study aims to assess the feasibility of applying the autoencoder AutoClass to early porcine embryo single-cell transcriptome data, and to investigate the impacts of different embryonic activation methods on key genes and signaling pathways.【Method】Single-cell transcriptome data were collected from early porcine embryos with 3 different activation modes (in vivo fertilization, in vitro fertilization, and orphaned females), and AutoClass was used to perform data quality control and interpolation analyses and evaluate AutoClass performance in conjunction with downstream analyses. In addition, an in-depth comparison of key genes and signaling pathways in the three types of embryos was performed by differential expression genes (DEGs) and functional enrichment analysis.【Result】After quality control and autoencoder data interpolation, the accuracy of clustering analysis was enhanced, leading to clear clustering among early embryos with different activation methods. QC alone only screened out 1 287 DEGs, while the number of DEGs increased to 11 523 after autocoder interpolation. Functional enrichment analysis further unearthed key biological processes and signaling pathways that were significantly different among the three different types of embryos, such as basic biological processes and cellular metabolism in porcine in vivo fertilized embryos, gonadal development and sex determination in porcine in vitro fertilized embryos and immune defenses and regulation of secretory pathways in orphaned embryos.【Conclusion】The autoencoder was adopted to improve the analytical accuracy of single-cell transcriptome data, whereby the key genes and signaling pathways affecting early embryonic development in three embryonic activation modes were revealed, which provided new ideas for further in-depth study of the molecular mechanisms underlying early embryonic development in porcine.

Dual oxidase (Duox) a member of the nicotinamide adenine dinucleotide phosphate oxidase (NOX) family can induce the production of reactive oxygen species (ROS). In vertebrates, the duox gene was indicated to be associated with the mucosal immunity. The roles of the duox gene in invertebrates were mainly studied in insects for the function of maintaining intestinal flora balance. In recent years, some studies have reported that Duox is involved in regulating the production of ROS and plays an important role in defending against the intestinal pathogen infection. However, the molecular mechanism has not been fully illuminated. In this study, a duox 2 involved in the production of H2O2 was identified for the first time in P. clarkii. Mature Pc-Duox 2 is a 7-transmembrane protein molecule that includes PHD, FAD, and NAD domains. Pc-duox 2 was mainly expressed in hemocytes and intestinal tissue. Its expression levels were obviously upregulated after intramuscular or oral infection with V. harveyi. In the RNAi assay, the upregulated trends of H2O2 and total ROS levels in crayfish intestine were significantly suppressed when Pc-duox 2 was knocked down. Compared with the slightly affected SOD activity, the upregulated CAT activity was suppressed more obviously in the crayfish intestine. Furthermore, Pc-duox 2 had an important effect on the maintenance of the structural stability of crayfish the intestine. Further research revealed that the knockdown of Pc-duox 2 could cause an obvious suppression in the upregulated levels of Toll signalling pathway-related genes, including Pc-toll 1, Pc-toll 3, Pc-dorsal, Pc-ALF 5, Pc-crustin 1, and Pc-lysozyme. Ultimately, these changes triggered the accelerated death of crayfish. Overall, we speculated that Pc-duox 2 played an important role in antibacterial innate immunity in the crayfish intestine by regulating the total ROS level.

The branched-chain amino acids (BCAAs: leucine, isoleucine and valine) are essential for animal growth and metabolic health. However, the effect of valine on male reproduction and its underlying molecular mechanism remain largely unknown. Here, we showed that L-valine supplementation (0.30% or 0.45%, water drinking for 3 weeks) did not change body and testis weights, but significantly altered morphology of sertoli cells and germ cells within seminiferous tubule, and enlarged the space between seminiferous tubules within mouse testis. L- valine treatment (0.45%) increased significantly the Caspase3/9 mRNA levels and CASPASE9 protein levels, therefore induced apoptosis of mouse testis. Moreover, gene expression levels related to autophagy (Atg5 and Lamb3), DNA 5 mC methylation (Dnmt1, Dnmt3a, Tet2 and Tet3), RNA m6A methylation (Mettl14, Alkbh5 and Fto), and m6A methylation binding proteins (Ythdf1/2/3 and Igf2bp1/2) were significantly reduced. Protein abundances of ALKBH5, FTO and YTHDF3 were also significantly reduced, but not for ATG5 and TET2. Testis transcriptome sequencing detected 537 differentially expressed genes (DEGs, 26 up-regulated and 511 down-regulated), involved in multiple important signaling pathways. RT-qPCR validated 8 of 9 DEGs (Cd36, Scd1, Insl3, Anxa5, Lcn2, Hsd17b3, Cyp11a1, Cyp17a1 and Agt) to be decreased significantly, consistent with RNA-seq results. Taken together, L-valine treatment could disturb multiple signaling pathways (autophagy and RNA methylation etc.), and induce apoptosis to destroy the tissue structure of mouse testis.

In recent years, statistics and machine learning methods have been widely used to analyze the relationship between human gut microbial metagenome and metabolic diseases, which is of great significance for the functional annotation and development of microbial communities. In this study, we proposed a new and scalable framework for image enhancement and deep learning of gut metagenome, which could be used in the classification of human metabolic diseases. Each data sample in three representative human gut metagenome datasets was transformed into image and enhanced, and put into the machine learning models of logistic regression (LR), support vector machine (SVM), Bayesian network (BN) and random forest (RF), and the deep learning models of multilayer perceptron (MLP) and convolutional neural network (CNN). The accuracy performance of the overall evaluation model for disease prediction was verified by accuracy (A), accuracy (P), recall (R), F1 score (F1), area under ROC curve (AUC) and 10 fold cross-validation. The results showed that the overall performance of MLP model was better than that of CNN, LR, SVM, BN, RF and PopPhy-CNN, and the performance of MLP and CNN models was further improved after data enhancement (random rotation and adding salt-and-pepper noise). The accuracy of MLP model in disease prediction was further improved by 4%-11%, F1 by 1%-6% and AUC by 5%-10%. The above results showed that human gut metagenome image enhancement and deep learning could accurately extract microbial characteristics and effectively predict the host disease phenotype. The source code and datasets used in this study can be publicly accessed in https://github.com/HuaXWu/GM_ML_Classification.git.

Vitamin C (Ascorbic acid, AA), as vital micro-nutrient, plays an essential role for male animal reproduction. Previously, we showed that vitamin C reprogrammed the transcriptome and proteome to change phenotypes of porcine immature Sertoli cells (iSCs). Here, we used LC-MS-based non-targeted metabolomics to further investigate the metabolic effects of vitamin C on porcine iSCs. The results identified 43 significantly differential metabolites (DMs) (16 up and 27 down) as induced by vitamin C (L-ascorbic acid 2-phosphate sesquimagnesium salt hydrate, AA2P) treatment of porcine iSCs, which were mainly enriched in steroid related and protein related metabolic pathways. ELISA (Enzyme-Linked ImmunoSorbent Assay) showed that significantly differential metabolites of Dehydroepiandrosterone (DHEA) (involved in steroid hormone biosynthesis) and Desmosterol (involved in steroid degradation) were significantly increased, which were partially consistent with metabolomic results. Further integrative analysis of metabolomics, transcriptomics and proteomics data identified the strong correlation between the key differential metabolite of Dehydroepiandrosterone and 6 differentially expressed genes (DEGs)/proteins (DEPs) (HMGCS1, P4HA1, STON2, LOXL2, EMILIN2 and CCN3). Further experiments validated that HMGCS1 could positively regulate Dehydroepiandrosterone level. These data indicate that vitamin C could modulate the metabolism profile, and HMGCS1-DHEA could be the pathway to mediate effects exerted by vitamin C on porcine iSCs.

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.

Chloroquine (CQ) is widely used in the therapy against malarial, tumor and recently the COVID-19 pandemic, as a lysosomotropic agent to inhibit the endolysosomal trafficking in the autophagy pathway. We previously reported that CQ (20 mu M, 36 h) could reprogram transcriptome, and impair multiple signaling pathways vital to porcine immature Sertoli cells (iSCs). However, whether CQ treatment could affect the metabolomic compositions of porcine iSCs remains unclear. Here, we showed that CQ (20 mu M, 36 h) treatment of porcine iSCs induced significant changes of 63 metabolites (11 up and 52 down) by the metabolomics method, which were involved in different metabolic pathways. Caffeic acid and esculetin, the top two up-regulated metabolites, were validated by ELISA. The combined analysis of metabolomics and transcriptome showed caffeic acid and esculetin to be highly correlated with multiple differentially expressed genes (DEGs), including Ndrg1, S100a8, Sqstm1, S100a12, S100a9, Ill1, Lif, Ntn4 and Peg10. Furthermore, esculetin treatment (53 nM, 36 h) significantly decreased the viability and proliferation, suppressed the mitochondrial function, whereas promoted the apoptosis of porcine iSCs, similar to those by CQ treatment (20 mu M, 36 h). Collectively, our results showed that CQ treatment induces metabolic changes, and its effect on porcine iSCs could be partially mediated by esculetin.

IntroductionThe branched-chain amino acids (BCAAs) are essential to mammalian growth and development but aberrantly elevated in obesity and diabetes. Each BCAA has an independent and specific physio-biochemical effect on the host. However, the exact molecular mechanism of the detrimental effect of valine on metabolic health remains largely unknown.Methods and resultsThis study showed that for lean mice treated with valine, the hepatic lipid metabolism and adipogenesis were enhanced, and the villus height and crypt depth of the ileum were significantly increased. Transcriptome profiling on white and brown adipose tissues revealed that valine disturbed multiple signaling pathways (e.g., inflammation and fatty acid metabolism). Integrative cecal metagenome and metabolome analyses found that abundances of Bacteroidetes decreased, but Proteobacteria and Helicobacter increased, respectively; and 87 differential metabolites were enriched in several molecular pathways (e.g., inflammation and lipid and bile acid metabolism). Furthermore, abundances of two metabolites (stercobilin and 3-IAA), proteins (AMPK/pAMPK and SCD1), and inflammation and adipogenesis-related genes were validated.DiscussionValine treatment affects the intestinal microbiota and metabolite compositions, induces gut inflammation, and aggravates hepatic lipid deposition and adipogenesis. Our findings provide novel insights into and resources for further exploring the molecular mechanism and biological function of valine on lipid metabolism.