A gene co-association network regulating gut microbial communities in a Duroc pig population

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.

Berberine improved the microbiota in lung tissue of colon cancer and reversed the bronchial epithelial cell changes caused by cancer cells

Objective

The lung is a common organ for colon cancer metastasis, and the objective of this experiment was to explore the protective effect of berberine on lung tissue or alveolar epithelial cells induced by colon cancer.

Methods

Thirty-six BALB/c nude mice were used to establish a xenograft model of colon cancer with the HT29 cell line and were treated with berberine and probiotics. Human bronchial epithelial BEAS-2B cells were induced by conditioned medium (CM) from the colon cancer cell lines HT29 and RKO and were treated with berberine. Lung tissues were collected to detect the changes in the microbiota using 16S rDNA sequencing and the expression of inflammatory cytokines. The expression of E-cadherin and N-cadherin in BEAS-2B cells was detected by cellular immunofluorescence. The changes in cell proliferation were detected by the CCK-8 assay. Western blotting was used to detect E-cadherin, N-cadherin, collagen I, fibronectin, PDGF-β, and RAD51 expression in BEAS-2B cells.

Results

The richness and evenness of the microbiota in the lung tissues of mice with colon cancer were significantly lower than those of the control group. Berberine significantly increased the abundances of Bacteroidetes, Bacteroidia, Bacteroidales, Lactobacillaceae, Lactobacillus and Acinetobacter in the lung tissue of mice with colon cancer, with reduced abundances of Actinobacteria, Bacillales, Staphylococcaceae and Staphylococcus. Berberine or probiotics significantly increased the alpha diversity of the lung microbiota. Compared with probiotics, berberine significantly enhanced the abundance of microbiota involved in the metabolism of lysosomes, flavone and flavonol biosynthesis, glycosaminoglycan degradation, and glycosphingolipid biosynthesis-ganglio. Berberine increased IL-6 and IL-10 and decreased IL-17 and IFN-γ expression in lung tissue (P > 0.05), but berberine-probiotics significantly decreased IL-17 and IFN-γ and increased IL-10 expression (P < 0.05). Colon cancer cells could not induce BEAS-2B proliferation but decreased the expression of the epithelial marker E-cadherin and altered the expression of extracellular matrix-related proteins (collagen I, fibronectin, and PDGF-β), which were reversed by berberine. Berberine increased RAD51 expression in BEAS-2B cells, which had been decreased by HT29 and RKO CM treatment.

Conclusion

Berberine can selectively regulate the abundance of some microbiomes of lung tissue in colon cancer, improve the inflammatory response in lung tissue, and antagonize the cancerous stimulation of colon cancer cells to lung tissue cells by regulating the bronchial epithelial cell phenotype, extracellular matrix remodelling and the expression of the repair gene RAD51.

The Genetic Selection of HSPD1 and HSPE1 Reduce Inflammation of Liver and Spleen While Restraining the Growth and Development of Skeletal Muscle in Wuzhishan Pigs

Wuzhishan (WZS) pigs, which are minipigs native to Hainan Province in China, are characterized by strong resistance to extreme hot temperatures and humidity. The relationship between their immune response and growth still needs to be clarified. In this study, we used whole genome sequencing (WGS) to detect variations within 37 WZS pigs, 32 Large White (LW) pigs, and 22 Xiangxi black (XXB) pigs, and ~2.49 GB of SNPs were obtained. These data were combined with those of two other pig breeds, and it was found that most of the genes detected (354) were located within the distinct genetic regions between WZS pigs and LW pigs. The network that was constructed using these genes represented a center including 12 hub genes, five of which had structural variations (SVs) within their regulatory regions. Furthermore, RNA-seq and RT-qPCR data for 12 genes were primarily consistent in liver, spleen, and LDM tissues. Notably, the expression of HSPs (HSPD1 and HSPE1) was higher while that of most genes involved in the JAK3-STAT pathway were lower in liver tissue of WZS pigs, compared with LW pigs. This likely not only reduced inflammation-related immune response but also impaired their growth. Our findings demonstrated the role of HSPs in the connection between inflammation and growth rate, while also providing the fundamental genetic selection of the adaptability of WZS pigs.

Unraveling the roles of coastal bacterial consortia in degradation of various lignocellulosic substrates

Lignocellulose, as the most abundant natural organic carbon on earth, plays a key role in regulating the global carbon cycle, but there have been only few studies in marine ecosystems. Little information is available about the extant lignin-degrading bacteria in coastal wetlands, limiting our understanding of their ecological roles and traits in lignocellulose degradation. We utilized in situ lignocellulose enrichment experiments coupled with 16S rRNA amplicon and shotgun metagenomics sequencing to identify and characterize bacterial consortia attributed to different lignin/lignocellulosic substrates in the southern-east intertidal zone of East China Sea. We found the consortia enriched on woody lignocellulose showed higher diversity than those on herbaceous substrate. This also revealed substrate-dependent taxonomic groups. A time-dissimilarity pattern with increased alpha diversity over time was observed. Additionally, this study identified a comprehensive set of genes associated with lignin degradation potential, containing 23 gene families involved in lignin depolymerization, and 371 gene families involved in aerobic/anaerobic lignin-derived aromatic compound pathways, challenging the traditional view of lignin recalcitrance within marine ecosystems. In contrast to similar cellulase genes among the lignocellulose substrates, significantly different ligninolytic gene groups were observed between consortia under woody and herbaceous substrates. Importantly, we not only observed synergistic degradation of lignin and hemi-/cellulose, but also pinpointed the potential biological actors at the levels of taxa and functional genes, which indicated that the alternation of aerobic and anaerobic catabolism could facilitate lignocellulose degradation. Our study advances the understanding of coastal bacterial community assembly and metabolic potential for lignocellulose substrates. IMPORTANCE It is essential for the global carbon cycle that microorganisms drive lignocellulose transformation, due to its high abundance. Previous studies were primarily constrained to terrestrial ecosystems, with limited information about the role of microbes in marine ecosystems. Through in situ lignocellulose enrichment experiment coupled with high-throughput sequencing, this study demonstrated different impacts that substrates and exposure times had on long-term bacterial community assembly and pinpointed comprehensive, yet versatile, potential decomposers at the levels of taxa and functional genes in response to different lignocellulose substrates. Moreover, the links between ligninolytic functional traits and taxonomic groups of substrate-specific populations were revealed. It showed that the synergistic effect of lignin and hemi-/cellulose degradation could enhance lignocellulose degradation under alternation of aerobic and anaerobic conditions. This study provides valuable taxonomic and genomic insights into coastal bacterial consortia for lignocellulose degradation.

Copy Number Variation on ABCC2-DNMBP Loci Affects the Diversity and Composition of the Fecal Microbiota in Pigs

Genetic variation in the pig genome partially modulates the composition of porcine gut microbial communities. Previous studies have been focused on the association between single nucleotide polymorphisms (SNPs) and the gut microbiota, but little is known about the relationship between structural variants and fecal microbial traits. The main goal of this study was to explore the association between porcine genome copy number variants (CNVs) and the diversity and composition of pig fecal microbiota. For this purpose, we used whole-genome sequencing data to undertake a comprehensive identification of CNVs followed by a genome-wide association analysis between the estimated CNV status and the fecal bacterial diversity in a commercial Duroc pig population. A CNV predicted as gain (DUP) partially harboring ABCC2-DNMBP loci was associated with richness (P = 5.41 × 10-5, false discovery rate [FDR] = 0.022) and Shannon α-diversity (P = 1.42 × 10-4, FDR = 0.057). The in silico predicted gain of copies was validated by real-time quantitative PCR (qPCR), and its segregation, and positive association with the richness and Shannon α-diversity of the porcine fecal bacterial ecosystem was confirmed in an unrelated F1 (Duroc × Iberian) cross. Our results advise the relevance of considering the role of host-genome structural variants as potential modulators of microbial ecosystems and suggest the ABCC2-DNMBP CNV as a host-genetic factor for the modulation of the diversity and composition of the fecal microbiota in pigs. IMPORTANCE A better understanding of the environmental and host factors modulating gut microbiomes is a topic of greatest interest. Recent evidence suggests that genetic variation in the pig genome partially controls the composition of porcine gut microbiota. However, since previous studies have been focused on the association between single nucleotide polymorphisms and the fecal microbiota, little is known about the relationship between other sources of genetic variation, like the structural variants and microbial traits. Here, we identified, experimentally validated, and replicated in an independent population a positive link between the gain of copies of ABCC2-DNMBP loci and the diversity and composition of pig fecal microbiota. Our results advise the relevance of considering the role of host-genome structural variants as putative modulators of microbial ecosystems and open the possibility of implementing novel holobiont-based management strategies in breeding programs for the simultaneous improvement of microbial traits and host performance.

On the holobiont 'predictome' of immunocompetence in pigs

BACKGROUND

Gut microbial composition plays an important role in numerous traits, including immune response. Integration of host genomic information with microbiome data is a natural step in the prediction of complex traits, although methods to optimize this are still largely unexplored. In this paper, we assess the impact of different modelling strategies on the predictive capacity for six porcine immunocompetence traits when both genotype and microbiota data are available.

METHODS

We used phenotypic data on six immunity traits and the relative abundance of gut bacterial communities on 400 Duroc pigs that were genotyped for 70 k SNPs. We compared the predictive accuracy, defined as the correlation between predicted and observed phenotypes, of a wide catalogue of models: reproducing kernel Hilbert space (RKHS), Bayes C, and an ensemble method, using a range of priors and microbial clustering strategies. Combined (holobiont) models that include both genotype and microbiome data were compared with partial models that use one source of variation only.

RESULTS

Overall, holobiont models performed better than partial models. Host genotype was especially relevant for predicting adaptive immunity traits (i.e., concentration of immunoglobulins M and G), whereas microbial composition was important for predicting innate immunity traits (i.e., concentration of haptoglobin and C-reactive protein and lymphocyte phagocytic capacity). None of the models was uniformly best across all traits. We observed a greater variability in predictive accuracies across models when microbiability (the variance explained by the microbiome) was high. Clustering microbial abundances did not necessarily increase predictive accuracy.

CONCLUSIONS

Gut microbiota information is useful for predicting immunocompetence traits, especially those related to innate immunity. Modelling microbiome abundances deserves special attention when microbiability is high. Clustering microbial data for prediction is not recommended by default.

Genome-Wide Association Study on Reproductive Traits Using Imputation-Based Whole-Genome Sequence Data in Yorkshire Pigs

Reproductive traits have a key impact on production efficiency in the pig industry. It is necessary to identify the genetic structure of potential genes that influence reproductive traits. In this study, a genome-wide association study (GWAS) based on chip and imputed data of five reproductive traits, namely, total number born (TNB), number born alive (NBA), litter birth weight (LBW), gestation length (GL), and number of weaned (NW), was performed in Yorkshire pigs. In total, 272 of 2844 pigs with reproductive records were genotyped using KPS Porcine Breeding SNP Chips, and then chip data were imputed to sequencing data using two online software programs: the Pig Haplotype Reference Panel (PHARP v2) and Swine Imputation Server (SWIM 1.0). After quality control, we performed GWAS based on chip data and the two different imputation databases by using fixed and random model circulating probability unification (FarmCPU) models. We discovered 71 genome-wide significant SNPs and 25 potential candidate genes (e.g., SMAD4, RPS6KA2, CAMK2A, NDST1, and ADCY5). Functional enrichment analysis revealed that these genes are mainly enriched in the calcium signaling pathway, ovarian steroidogenesis, and GnRH signaling pathways. In conclusion, our results help to clarify the genetic basis of porcine reproductive traits and provide molecular markers for genomic selection in pig breeding.

Employing pigs to decipher the host genetic effect on gut microbiome: advantages, challenges, and perspectives

The gut microbiota is a complex and diverse ecosystem comprised of trillions of microbes and plays an essential role in host's immunity, metabolism, and even behaviors. Environmental and host factors drive the huge variations in the gut microbiome among individuals. Here, we summarize accumulated evidences about host genetic effect on the gut microbial compositions with emphases on the correlation between host genetic kinship and the similarity of microbial compositions, heritability estimates of microbial taxa, and identification of genomic variants associated with the gut microbiome in pigs as well as in humans. A proportion of bacterial taxa have been reported to be heritable, and numerous variants associated with the diversity of the gut microbiota or specific taxa have been identified in both humans and pigs. LCT and ABO gene have been replicated in multiple studies, and its mechanism have been elucidated clearly. We also discuss the main advantages and challenges using pigs as experimental animals in exploring host genetic effect on the gut microbial composition and provided our insights on the perspectives in this area.

Dietary grape pomace extract supplementation improved meat quality, antioxidant capacity, and immune performance in finishing pigs

In pig production, reducing production costs and improving immunity are important. Grape pomace, a good agricultural by-product, has been thrown away as food waste for a long time. Recently, we found that it could be used as a new source of pig feed. We investigated the effect of grape pomace on inflammation, gut barrier function, meat quality, and growth performance in finishing pigs. Our results indicated that treatment samples showed a significant decrease in water loss, IL-1β, DAO, ROS, and MDA content (p < 0.05). IgA, IgG, IgM, CAT, T-AOC, SOD, and IFN-γ significantly increased compared with those in control samples (p < 0.05). Meanwhile, the relative mRNA expression of the tight junction protein occludin showed a significant difference (p < 0.05). Analysis of metagenomic sequencing indicated that grape pomace significantly decreased the relative abundance of Treponema and Streptococcus (p < 0.05). In summary, our results demonstrated that grape pomace could improve meat quality, alleviate inflammation, and decrease oxidative stress.

The Role of Gut Microbiota in the Skeletal Muscle Development and Fat Deposition in Pigs

Pork quality is a factor increasingly considered in consumer preferences for pork. The formation mechanisms determining meat quality are complicated, including endogenous and exogenous factors. Despite a lot of research on meat quality, unexpected variation in meat quality is still a major problem in the meat industry. Currently, gut microbiota and their metabolites have attracted increased attention in the animal breeding industry, and recent research demonstrated their significance in muscle fiber development and fat deposition. The purpose of this paper is to summarize the research on the effects of gut microbiota on pig muscle and fat deposition. The factors affecting gut microbiota composition will also be discussed, including host genetics, dietary composition, antibiotics, prebiotics, and probiotics. We provide an overall understanding of the relationship between gut microbiota and meat quality in pigs, and how manipulation of gut microbiota may contribute to increasing pork quality for human consumption.

Genome Wide Scan to Identify Potential Genomic Regions Associated With Milk Protein and Minerals in Vrindavani Cattle

In this study, genome-wide association study (GWAS) was conducted for identifying significantly associated genomic regions/SNPs with milk protein and minerals in the 96 taurine-indicine crossbred (Vrindavani) cows using 50K SNP Chip. After quality control, a total of 41,427 SNPs were retained and were further analyzed using a single-SNP additive linear model. Lactation stage, parity, test day milk yield and proportion of exotic inheritance were included as fixed effects in GWAS model. Across all traits, 13 genome-wide significant (p < 1.20 x 10⁻⁰⁶) and 49 suggestive significant (p < 2.41 x 10⁻⁰⁵) SNPs were identified which were located on 18 different autosomes. The strongest association for protein percentage, calcium (Ca), phosphorus (P), copper (Cu), zinc (Zn), and iron (Fe) were found on BTA 18, 7, 2, 3, 14, and 2, respectively. No significant SNP was detected for manganese (Mn). Several significant SNPs identified were within or close proximity to CDH13, BHLHE40, EDIL3, HAPLN1, INHBB, USP24, ZFAT, and IKZF2 gene, respectively. Enrichment analysis of the identified candidate genes elucidated biological processes, cellular components, and molecular functions involved in metal ion binding, ion transportation, transmembrane protein, and signaling pathways. This study provided a groundwork to characterize the molecular mechanism for the phenotypic variation in milk protein percentage and minerals in crossbred cattle. Further work is required on a larger sample size with fine mapping of identified QTL to validate potential candidate regions.

Host Genetic Determinants of the Microbiome Across Animals: From Caenorhabditis elegans to Cattle

Animals harbor diverse communities of microbes within their gastrointestinal tracts. Phylogenetic relationship, diet, gut morphology, host physiology, and ecology all influence microbiome composition within and between animal clades. Emerging evidence points to host genetics as also playing a role in determining gut microbial composition within species. Here, we discuss recent advances in the study of microbiome heritability across a variety of animal species. Candidate gene and discovery-based studies in humans, mice, Drosophila, Caenorhabditis elegans, cattle, swine, poultry, and baboons reveal trends in the types of microbes that are heritable and the host genes and pathways involved in shaping the microbiome. Heritable gut microbes within a host species tend to be phylogenetically restricted. Host genetic variation in immune- and growth-related genes drives the abundances of these heritable bacteria within the gut. With only a small slice of the metazoan branch of the tree of life explored to date, this is an area rife with opportunities to shed light into the mechanisms governing host-microbe relationships.

A Co-Association Network Analysis Reveals Putative Regulators for Health-Related Traits in Pigs

In recent years, the increase in awareness of antimicrobial resistance together with the societal demand of healthier meat products have driven attention to health-related traits in livestock production. Previous studies have reported medium to high heritabilities for these traits and described genomic regions associated with them. Despite its genetic component, health- and immunity-related traits are complex and its study by association analysis with genomic markers may be missing some information. To analyse multiple phenotypes and gene-by-gene interactions, systems biology approaches, such as the association weight matrix (AWM), allows combining genome wide association study results with network inference algorithms. The present study aimed to identify gene networks, key regulators and candidate genes associated to immunocompetence in pigs by integrating multiple health-related traits, enriched for innate immune phenotypes, using the AWM approach. The co-association network analysis unveiled a network comprised of 3,636 nodes (genes) and 451,407 edges (interactions), including a total of 246 regulators. From these, five genes (ARNT2, BRMS1L, MED12L, SUPT3H and TRIM25) were selected as key regulators as they were associated with the maximum number of genes with the minimum overlapping (1,827 genes in total). The five regulators were involved in pathways related to immunity such as lymphocyte differentiation and activation, platelet activation and degranulation, megakaryocyte differentiation, FcγR-mediated phagocytosis and response to nitric oxide, among others, but also in immunometabolism. Furthermore, we identified genes co-associated with the key regulators previously reported as candidate genes (e.g., ANGPT1, CD4, CD36, DOCK1, PDE4B, PRKCE, PTPRC and SH2B3) for immunity traits in humans and pigs, but also new candidate ones (e.g., ACSL3, CXADR, HBB, MMP12, PTPN6, WLS) that were not previously described. The co-association analysis revealed new regulators associated with health-related traits in pigs. This approach also identified gene-by-gene interactions and candidate genes involved in pathways related to cell fate and metabolic and immune functions. Our results shed new light in the regulatory mechanisms involved in pig immunity and reinforce the use of the pig as biomedical model.

Leveraging host-genetics and gut microbiota to determine immunocompetence in pigs

Background

The gut microbiota influences host performance playing a relevant role in homeostasis and function of the immune system. The aim of the present work was to identify microbial signatures linked to immunity traits and to characterize the contribution of host-genome and gut microbiota to the immunocompetence in healthy pigs.

Results

To achieve this goal, we undertook a combination of network, mixed model and microbial-wide association studies (MWAS) for 21 immunity traits and the relative abundance of gut bacterial communities in 389 pigs genotyped for 70K SNPs. The heritability (h²; proportion of phenotypic variance explained by the host genetics) and microbiability (m²; proportion of variance explained by the microbial composition) showed similar values for most of the analyzed immunity traits, except for both IgM and IgG in plasma that was dominated by the host genetics, and the haptoglobin in serum which was the trait with larger m² (0.275) compared to h² (0.138). Results from the MWAS suggested a polymicrobial nature of the immunocompetence in pigs and revealed associations between pigs gut microbiota composition and 15 of the analyzed traits. The lymphocytes phagocytic capacity (quantified as mean fluorescence) and the total number of monocytes in blood were the traits associated with the largest number of taxa (6 taxa). Among the associations identified by MWAS, 30% were confirmed by an information theory network approach. The strongest confirmed associations were between Fibrobacter and phagocytic capacity of lymphocytes (r = 0.37), followed by correlations between Streptococcus and the percentage of phagocytic lymphocytes (r = -0.34) and between Megasphaera and serum concentration of haptoglobin (r = 0.26). In the interaction network, Streptococcus and percentage of phagocytic lymphocytes were the keystone bacterial and immune-trait, respectively.

Conclusions

Overall, our findings reveal an important connection between gut microbiota composition and immunity traits in pigs, and highlight the need to consider both sources of information, host genome and microbial levels, to accurately characterize immunocompetence in pigs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-021-00138-9.

The value of gut microbiota to predict feed efficiency and growth of rabbits under different feeding regimes

Gut microbiota plays an important role in nutrient absorption and could impact rabbit feed efficiency. This study aims at investigating such impact by evaluating the value added by microbial information for predicting individual growth and cage phenotypes related to feed efficiency. The dataset comprised individual average daily gain and cage-average daily feed intake from 425 meat rabbits, in which cecal microbiota was assessed, and their cage mates. Despite microbiota was not measured in all animals, consideration of pedigree relationships with mixed models allowed the study of cage-average traits. The inclusion of microbial information into certain mixed models increased their predictive ability up to 20% and 46% for cage-average feed efficiency and individual growth traits, respectively. These gains were associated with large microbiability estimates and with reductions in the heritability estimates. However, large microbiabililty estimates were also obtained with certain models but without any improvement in their predictive ability. A large proportion of OTUs seems to be responsible for the prediction improvement in growth and feed efficiency traits, although specific OTUs taxonomically assigned to 5 different phyla have a higher weight. Rabbit growth and feed efficiency are influenced by host cecal microbiota, thus considering microbial information in models improves the prediction of these complex phenotypes.