Exploring the Interplay between the Hologenome and Complex Traits in Bovine and Porcine Animals Using Genome-Wide Association Analysis.

Genome-wide association studies (GWAS) significantly enhance our ability to identify trait-associated genomic variants by considering the host genome. Moreover, the hologenome refers to the host organism's collective genetic material and its associated microbiome. In this study, we utilized the hologenome framework, called Hologenome-wide association studies (HWAS), to dissect the architecture of complex traits, including milk yield, methane emissions, rumen physiology in cattle, and gut microbial composition in pigs. We employed four statistical models: (1) GWAS, (2) Microbial GWAS (M-GWAS), (3) HWAS-CG (hologenome interaction estimated using COvariance between Random Effects Genome-based restricted maximum likelihood (CORE-GREML)), and (4) HWAS-H (hologenome interaction estimated using the Hadamard product method). We applied Bonferroni correction to interpret the significant associations in the complex traits. The GWAS and M-GWAS detected one and sixteen significant SNPs for milk yield traits, respectively, whereas the HWAS-CG and HWAS-H each identified eight SNPs. Moreover, HWAS-CG revealed four, and the remaining models identified three SNPs each for methane emissions traits. The GWAS and HWAS-CG detected one and three SNPs for rumen physiology traits, respectively. For the pigs' gut microbial composition traits, the GWAS, M-GWAS, HWAS-CG, and HWAS-H identified 14, 16, 13, and 12 SNPs, respectively. We further explored these associations through SNP annotation and by analyzing biological processes and functional pathways. Additionally, we integrated our GWA results with expression quantitative trait locus (eQTL) data using transcriptome-wide association studies (TWAS) and summary-based Mendelian randomization (SMR) methods for a more comprehensive understanding of SNP-trait associations. Our study revealed hologenomic variability in agriculturally important traits, enhancing our understanding of host-microbiome interactions.

Holo-omics analysis reveals the influence of gut microbiota on obesity indicators in Jinhua pigs.

BACKGROUND: The mechanisms behind obesity are complex and multi-faceted, involving the interplay of both host genomics and gut microbiome. In recent years, research has largely focused on these factors separately, but rarely from the viewpoint of holo-omics, which considers the host and microbiome as an integrated entity. To address this gap in knowledge, the present study aimed to investigate the holo-omics basis of obesity in Jinhua pigs, a Chinese indigenous breed known for its high degree of fat deposition and superior meat quality. METHODS: Six pigs with extreme obesity phenotype were selected from a larger cohort of eighteen Jinhua pigs, and the contents of the jejunum, cecum, and colon regions were collected after slaughter at 240 days of age. The data obtained was processed, denoised, and annotated using QIIME2, with expression differences being analyzed using edgeR software. RESULTS: The results showed significant differences in jejunal microbial diversity and composition between the two groups, with gut transcriptomics also indicating that differentially expressed genes in the jejunum were enriched in lipid metabolism pathways. These findings provide further evidence of the influence of the gut microbiome and host gene expression on fat deposition in Jinhua pigs. CONCLUSIONS: This study provides valuable insights into the mechanisms of fat deposition in Jinhua pigs from the viewpoint of holo-omics. The integration of host transcriptomics and microbiome data helps shed light on the complex interactions between the host and gut microbiome, and highlights the importance of considering both factors in our understanding of obesity.

Molecular Characterization and Expression of Vitellogenin and Vitellogenin Receptor of Thitarodes pui (Lepidoptera: Hepialidae), an Insect on the Tibetan Plateau.

Vitellogenin (Vg) and vitellogenin receptor (VgR) play important roles in the vitellogenesis of insects. In this study, we cloned and characterized the two corresponding genes (TpVg and TpVgR) in an economically important insect, Thitarodes pui (Lepidoptera: Hepialidae), from the Tibetan plateau. The full length of TpVg is 5566 bp with a 5373 bp open reading frame (ORF) encoding 1,790 amino acids. Sequence alignment revealed that TpVg has three conserved domains: a Vitellogenin_N domain, a DUF1943 domain, and a von Willebrand factor type D domain (VWD). The full length of TpVgR is 5732 bp, with a 5397 bp ORF encoding 1798 amino acids. BLASTP showed that TpVgR belongs to the low-density lipoprotein receptor (LDLR) gene superfamily. Structural analysis revealed that TpVgR has a group of four structural domains: a ligand-binding domain (LBD), an epidermal growth factor (EGF)-precursor homology domain, a transmembrane (TM) domain, and a cytoplasmic domain. In addition, TpVgR has four cysteine-rich LDL repeats in the first ligand-binding site and seven in the second. Quantitative real-time polymerase chain reaction analysis revealed that the expression levels of TpVg and TpVgR are much higher in later pupa than in either the larval or adult stage, implying that the synthesis and uptake of Vg in T. pui occurs in the later pupal stage. These results will help us to understand the molecular mechanism of the reproductive capacity and will provide new insight into the mass rearing and utilization of T. pui.

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.

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.

Applications of Support Vector Machine in Genomic Prediction in Pig and Maize Populations.

Genomic prediction (GP) has revolutionized animal and plant breeding. However, better statistical models that can improve the accuracy of GP are required. For this reason, in this study, we explored the genomic-based prediction performance of a popular machine learning method, the Support Vector Machine (SVM) model. We selected the most suitable kernel function and hyperparameters for the SVM model in eight published genomic data sets on pigs and maize. Next, we compared the SVM model with RBF and the linear kernel functions to the two most commonly used genome-enabled prediction models (GBLUP and BayesR) in terms of prediction accuracy, time, and the memory used. The results showed that the SVM model had the best prediction performance in two of the eight data sets, but in general, the predictions of both models were similar. In terms of time, the SVM model was better than BayesR but worse than GBLUP. In terms of memory, the SVM model was better than GBLUP and worse than BayesR in pig data but the same with BayesR in maize data. According to the results, SVM is a competitive method in animal and plant breeding, and there is no universal prediction model.

Molecular characterization and expression of six heat shock protein genes in relation to development and temperature in Trichogramma chilonis.

Trichogramma is a kind of egg parasitoid wasp that is widely used to control lepidopterous pests. Temperature is one of the main factors that determines the various life activities of this species, including development, reproduction and parasitism efficiency. Heat shock proteins (HSPs) are highly conserved and ubiquitous proteins that are best known for their responsiveness to temperature and other stresses. To explore the potential role of HSPs in Trichogramma species, we obtained the full-length cDNAs of six HSP genes (Tchsp10, Tchsp21.6, Tchsp60, Tchsp70, Tchsc70-3, and Tchsp90) from T. chilonis and analyzed their expression patterns during development and exposure to temperature stress. The deduced amino acid sequences of these HSP genes contained the typical signatures of their corresponding protein family and showed high homology to their counterparts in other species. The expression levels of Tchsp10, Tchsp21.6 and Tchsp60 decreased during development. However, the expression of Tchsc70-3 increased from the pupal stage to the adult stage. Tchsp70 and Tchsp90 exhibited the highest expression levels in the adult stage. The expression of six Tchsps was dramatically upregulated after 1 h of exposure to 32 and 40°C but did not significantly change after 1 h of exposure to 10 and 17°C. This result indicated that heat stress, rather than cold stress, induced the expression of HSP genes. Furthermore, the expression of these genes was time dependent, and the expression of each gene reached its peak after 1 h of heat exposure (40°C). Tchsp10 and Tchsp70 exhibited a low-intensity cold response after 4 and 8 h of exposure to 10°C, respectively, but the other genes did not respond to cold at any time points. These results suggested that HSPs may play different roles in the development of this organism and in its response to temperature stress.