Multi-omics analysis reveals substantial linkages between the oral-gut microbiomes and inflamm-aging molecules in elderly pigs.

Introduction: The accelerated aging of the global population has emerged as a critical public health concern, with increasing recognition of the influential role played by the microbiome in shaping host well-being. Nonetheless, there remains a dearth of understanding regarding the functional alterations occurring within the microbiota and their intricate interactions with metabolic pathways across various stages of aging. Methods: This study employed a comprehensive metagenomic analysis encompassing saliva and stool samples obtained from 45 pigs representing three distinct age groups, alongside serum metabolomics and lipidomics profiling. Results: Our findings unveiled discernible modifications in the gut and oral microbiomes, serum metabolome, and lipidome at each age stage. Specifically, we identified 87 microbial species in stool samples and 68 in saliva samples that demonstrated significant age-related changes. Notably, 13 species in stool, including Clostridiales bacterium, Lactobacillus johnsonii, and Oscillibacter spp., exhibited age-dependent alterations, while 15 salivary species, such as Corynebacterium xerosis, Staphylococcus sciuri, and Prevotella intermedia, displayed an increase with senescence, accompanied by a notable enrichment of pathogenic organisms. Concomitant with these gut-oral microbiota changes were functional modifications observed in pathways such as cell growth and death (necroptosis), bacterial infection disease, and aging (longevity regulating pathway) throughout the aging process. Moreover, our metabolomics and lipidomics analyses unveiled the accumulation of inflammatory metabolites or the depletion of beneficial metabolites and lipids as aging progressed. Furthermore, we unraveled a complex interplay linking the oral-gut microbiota with serum metabolites and lipids. Discussion: Collectively, our findings illuminate novel insights into the potential contributions of the oral-gut microbiome and systemic circulating metabolites and lipids to host lifespan and healthy aging.

An imputed whole-genome sequence-based GWAS approach pinpoints causal mutations for complex traits in a specific swine population.

Sequencing-based genome-wide association studies (GWAS) have facilitated the identification of causal associations between genetic variants and traits in diverse species. However, it is cost-prohibitive for the majority of research groups to sequence a large number of samples. Here, we carried out genotype imputation to increase the density of single nucleotide polymorphisms in a large-scale Swine F2 population using a reference panel including 117 individuals, followed by a series of GWAS analyses. The imputation accuracies reached 0.89 and 0.86 for allelic concordance and correlation, respectively. A quantitative trait nucleotide (QTN) affecting the chest vertebrate was detected directly, while the investigation of another QTN affecting the residual glucose failed due to the presence of similar haplotypes carrying wild-type and mutant allelesin the reference panel used in this study. A high imputation accuracy was confirmed by Sanger sequencing technology for the most significant loci. Two candidate genes, CPNE5 and MYH3, affecting meat-related traits were proposed. Collectively, we illustrated four scenarios in imputation-based GWAS that may be encountered by researchers, and our results will provide an extensive reference for future genotype imputation-based GWAS analyses in the future.

Imputation-Based Whole-Genome Sequence Association Study Reveals Constant and Novel Loci for Hematological Traits in a Large-Scale Swine F2 Resource Population.

The whole-genome sequences of progenies with low-density single-nucleotide polymorphism (SNP) genotypes can be imputed with high accuracy based on the deep-coverage sequences of key ancestors. With this imputation technology, a more powerful genome-wide association study (GWAS) can be carried out using imputed whole-genome variants and the phenotypes of interest to overcome the shortcomings of low-power detection and the large confidence interval derived from low-density SNP markers in classic association studies. In this study, 19 ancestors of a large-scale swine F2 White Duroc × Erhualian population were deeply sequenced for their genome with an average coverage of 25×. Considering 98 pigs from 10 different breeds with high-quality deep sequenced genomes, we imputed the whole genomic variants of 1020 F2 pigs genotyped by the PorcineSNP60 BeadChip with high accuracy and obtained 14,851,440 sequence variants after quality control. Based on this, 87 novel quantitative traits loci (QTLs) for 18 hematological traits at three different physiological stages of the F2 pigs were identified, among which most of the novel QTLs have been repeated in two of the three stages. Literature mining pinpointed that the FGF14 and LCLAT1 genes at SSC11 and SSC3 may affect the MCH at day 240 and MCV at day 18, respectively. The present study shows that combining high-quality imputed genomic variants and correlated phenomic traits into GWAS can improve the capability to detect QTL considerably. The large number of different QTLs for hematological traits identified at multiple growth stages implies the complexity and time specificity of these traits.

Imputation-Based Whole-Genome Sequence Association Study Rediscovered the Missing QTL for Lumbar Number in Sutai Pigs.

Resequencing a number of individuals of various breeds as reference population and imputing the whole-genome sequences of individuals that were genotyped with medium-density chips to perform an association study is a very efficient strategy. Previously, we performed a genome-wide association study (GWAS) of lumbar number using 60K SNPs from the porcine Illumina chips in 418 Sutai pigs and did not detect any significant signals. Therefore, we imputed the whole-genome sequences of 418 Sutai individuals from 403 deeply resequenced reference individuals and performed association tests. We identified a quantitative trait locus (QTL) for lumbar number in SSC1 with a P value of 9.01E-18 that was close to the potential causative gene of NR6A1. The result of conditioning on the top SNP association test indicated that only one QTL was responsible for this trait in SSC1. The linkage disequilibrium (LD) drop test result for the condition of the reported potential causative mutation (c.575T > C missense mutation of NR6A1) indicated that this mutation was probably not the underlying mutation that affected lumbar number in our study. As the first trial of imputed whole-genome sequence GWAS in swine, this approach can be also powerful to investigate complex traits in pig like in human and cattle.

Uncovering the composition of microbial community structure and metagenomics among three gut locations in pigs with distinct fatness.

Uncovering the phylogenetic composition of microbial community and the potential functional capacity of microbiome in different gut locations is of great importance to pig production. Here we performed a comparative analysis of gut microbiota and metagenomics among jejunum, ileum and cecum in pigs with distinct fatness. 16S rRNA gene sequencing revealed dramatic differences of microbial composition, diversity and species abundance between small intestine and cecum. Clostridium and SMB53 were enriched in the small intestine, while Prevotella, Treponema, Ruminococcus and Faecalibacterium showed a higher abundance in the cecum. Functional capacity analysis of gut microbiome revealed that the microbiome of small intestine plays important roles in the metabolism of small molecule nutrients, while the microbiome of cecum has the stronger ability to degrade xylan, pectin and cellulose. We identified tens of fatness associated-bacterial species including Escherichia spp. that showed a notable increase of relative abundance in all three gut locations of high fatness pigs. We further suggested that the potential pathogens, inflammation process, and microbial metabolism and nutrient sensing are involved in the high fatness of pigs. These results improve our knowledge about microbiota compositions in different gut locations, and give an insight into the effect of gut microbiota on porcine fatness.