Gut microbiome mediates host genomic effects on phenotypes: a case study with fat deposition in pigs

Applying recursive modelling to assess the role of the host genome and the gut microbiome on feed efficiency in pigs

The gut microbiome plays an important role in the performance and health of swine by providing essential nutrients and supporting the immune system. Recent studies have demonstrated that the gut microbiome can explain part of the variation observed in growth, health, and meat quality. Feed efficiency is crucial in swine production, as feed cost account for more than 60% of total production costs. This study aimed to assess the relationships between the host genome, gut microbiome, and feed efficiency in Iberian pigs raised under intensive conditions. The specific objectives were to assess the mediating effects of the gut microbiome on feed efficiency and to estimate the direct and total heritability of feed efficiency. The data set included the feed conversion ratio (FCR) and residual feed intake (RFI) from 587 Iberian pigs, as well as the 16S rRNA gut microbial abundance from 151 of those pigs raised in a nucleus of selection. We reparametrised variance components from standard bivariate mixed models into recursive models to disentangle the microbiome's mediating effect on feed efficiency. In our models, the host genome has direct effects on both the phenotype (G→P) and the gut microbiome (G→M). Additionally, there is an indirect effect of the host genome on the phenotype mediated by the microbiome (G→M→P). We identified a total of 14 taxa with relevant effects on FCR and 16 taxa with relevant effects on RFI. We categorised the gut microbiome into groups for potential practical application in pig farming. The gut microbes with relevant causal effects and low heritability can be manipulated through management interventions, while those microbes with relevant causal effects and moderate heritability can be targeted through selective breeding. Our findings indicate that incorporating microbiome data leads to a reduction in total heritability for both FCR and RFI. This study provides new insights into the link between the gut microbiome and feed efficiency, presenting practical methods to target microbes that can be influenced through selective breeding or management interventions.

Multiple-trait genomic prediction for swine meat quality traits using gut microbiome features as a correlated trait

Traits such as meat quality and composition are becoming valuable in modern pork production; however, they are difficult to include in genetic evaluations because of the high phenotyping costs. Combining genomic information with multiple-trait indirect selection with cheaper indicator traits is an alternative for continued cost-effective genetic improvement. Additionally, gut microbiome information is becoming more affordable to measure using targeted rRNA sequencing, and its applications in animal breeding are becoming relevant. In this paper, we investigated the usefulness of microbial information as a correlated trait in selecting meat quality in swine. This study incorporated phenotypic data encompassing marbling, colour, tenderness, loin muscle and backfat depth, along with the characterization of gut (rectal) microbiota through 16S rRNA sequencing at three distinct time points of the animal's growth curve. Genetic progress estimation and cross-validation were employed to evaluate the utility of utilizing host genomic and gut microbiota information for selecting expensive-to-record traits in crossbred individuals. Initial steps involved variance components estimation using multiple-trait models on a training dataset, where the top 25 associated operational taxonomic units (OTU) for each meat quality trait and time point were included. The second step compared the predictive ability of multiple-trait models incorporating different numbers of OTU with single-trait models in a validation set. Results demonstrated the advantage of including genomic information for some traits, while in some instances, gut microbial information proved advantageous, namely, for marbling and pH. The study suggests further investigation into the shared genetic architecture between microbial features and traits, considering microbial data's compositional and high-dimensional nature. This research proposes a straightforward method to enhance swine breeding programs for improving costly-to-record traits like meat quality by incorporating gut microbiome information.

Harnessing meta-omics to unveil and mitigate methane emissions in ruminants: Integrative approaches and future directions

Methane emissions from enteric fermentation present a dual challenge globally: they not only contribute significantly to atmospheric greenhouse gases but also represent a considerable energy loss for ruminant animals. Utilizing high-throughput omics technologies to analyze rumen microbiome samples (meta-omics, i.e., metagenomics, metatranscriptomics, metaproteomics, metabolomics) holds vast potential for uncovering the intricate interplay between diet, microbiota, and methane emissions in these animals. The primary obstacle is the effective integration of diverse meta-omic approaches and their broader application across different ruminant species. Genetic variability significantly impacts methane production in ruminants, suggesting that genomic selection could be a viable strategy to reduce emissions. While substantial research has been conducted on the microbiological aspects of methane production, there remains a critical need to delineate the specific genetic interactions between the host and its microbiome. Advancements in meta-omics technologies are poised to shed light on these interactions, enhancing our understanding of the genetic factors that govern methane output. This review explores the potential of meta-omics to accelerate genetic advancements that could lead to reduced methane emissions in ruminants. By employing a systems biology approach, the integration of various omics technologies allows for the identification of key genomic regions and genetic markers linked to methane production. These markers can then be leveraged in selective breeding programs to cultivate traits associated with lower emissions. Moreover, the review addresses current challenges in applying genomic selection for this purpose and discusses how omics technologies can overcome these obstacles. The systematic integration and analysis of diverse biological data provide deeper insights into the genetic underpinnings and overall biology of methane production traits in ruminants. Ultimately, this comprehensive approach not only aids in reducing the environmental impact of agriculture but also contributes to the sustainability and efficiency of livestock management.

Driving gut microbiota enterotypes through host genetics

BACKGROUND

Population stratification based on interindividual variability in gut microbiota composition has revealed the existence of several ecotypes named enterotypes in humans and various animal species. Enterotypes are often associated with environmental factors including diet, but knowledge of the role of host genetics remains scarce. Moreover, enterotypes harbor functionalities likely associated with varying abilities and susceptibilities of their host. Previously, we showed that under controlled conditions, 60-day-old pig populations consistently split into two enterotypes with either Prevotella and Mitsuokella (PM enterotype) or Ruminococcus and Treponema (RT enterotype) as keystone taxa. Here, our aim was to rely on pig as a model to study the influence of host genetics to assemble enterotypes, and to provide clues on enterotype functional differences and their links with growth traits.

RESULTS

We established two pig lines contrasted for abundances of the genera pairs specifying each enterotype at 60 days of age and assessed them for fecal microbiota composition and growth throughout three consecutive generations. Response to selection across three generations revealed, per line, an increase in the prevalence of the selected enterotype and in the average relative abundances of directly and indirectly selected bacterial genera. The PM enterotype was found less diverse than the RT enterotype but more efficient for piglet growth during the post-weaning period. Shotgun metagenomics revealed differentially abundant bacterial species between the two enterotypes. By using the KEGG Orthology database, we show that functions related to starch degradation and polysaccharide metabolism are enriched in the PM enterotype, whereas functions related to general nucleoside transport and peptide/nickel transport are enriched in the RT enterotype. Our results also suggest that the PM and RT enterotypes might differ in the metabolism of valine, leucin, and isoleucine, favoring their biosynthesis and degradation, respectively.

CONCLUSION

We experimentally demonstrated that enterotypes are functional ecosystems that can be selected as a whole by exerting pressure on the host genetics. We also highlight that holobionts should be considered as units of selection in breeding programs. These results pave the way for a holistic use of host genetics, microbiota diversity, and enterotype functionalities to understand holobiont shaping and adaptation. Video Abstract.

A first characterization of the microbiota-resilience link in swine

BACKGROUND

The gut microbiome plays a crucial role in understanding complex biological mechanisms, including host resilience to stressors. Investigating the microbiota-resilience link in animals and plants holds relevance in addressing challenges like adaptation of agricultural species to a warming environment. This study aims to characterize the microbiota-resilience connection in swine. As resilience is not directly observable, we estimated it using four distinct indicators based on daily feed consumption variability, assuming animals with greater intake variation may face challenges in maintaining stable physiological status. These indicators were analyzed both as linear and categorical variables. In our first set of analyses, we explored the microbiota-resilience link using PERMANOVA, α-diversity analysis, and discriminant analysis. Additionally, we quantified the ratio of estimated microbiota variance to total phenotypic variance (microbiability). Finally, we conducted a Partial Least Squares-Discriminant Analysis (PLS-DA) to assess the classification performance of the microbiota with indicators expressed in classes.

RESULTS

This study offers four key insights. Firstly, among all indicators, two effectively captured resilience. Secondly, our analyses revealed robust relationship between microbial composition and resilience in terms of both composition and richness. We found decreased α-diversity in less-resilient animals, while specific amplicon sequence variants (ASVs) and KEGG pathways associated with inflammatory responses were negatively linked to resilience. Thirdly, considering resilience indicators in classes, we observed significant differences in microbial composition primarily in animals with lower resilience. Lastly, our study indicates that gut microbial composition can serve as a reliable biomarker for distinguishing individuals with lower resilience.

CONCLUSION

Our comprehensive analyses have highlighted the host-microbiota and resilience connection, contributing valuable insights to the existing scientific knowledge. The practical implications of PLS-DA and microbiability results are noteworthy. PLS-DA suggests that host-microbiota interactions could be utilized as biomarkers for monitoring resilience. Furthermore, the microbiability findings show that leveraging host-microbiota insights may improve the identification of resilient animals, supporting their adaptive capacity in response to changing environmental conditions. These practical implications offer promising avenues for enhancing animal well-being and adaptation strategies in the context of environmental challenges faced by livestock populations. Video Abstract.

Life at the borderlands: microbiomes of interfaces critical to One Health

Microbiomes are foundational components of the environment that provide essential services relating to food security, carbon sequestration, human health, and the overall well-being of ecosystems. Microbiota exert their effects primarily through complex interactions at interfaces with their plant, animal, and human hosts, as well as within the soil environment. This review aims to explore the ecological, evolutionary, and molecular processes governing the establishment and function of microbiome-host relationships, specifically at interfaces critical to One Health-a transdisciplinary framework that recognizes that the health outcomes of people, animals, plants, and the environment are tightly interconnected. Within the context of One Health, the core principles underpinning microbiome assembly will be discussed in detail, including biofilm formation, microbial recruitment strategies, mechanisms of microbial attachment, community succession, and the effect these processes have on host function and health. Finally, this review will catalogue recent advances in microbiology and microbial ecology methods that can be used to profile microbial interfaces, with particular attention to multi-omic, advanced imaging, and modelling approaches. These technologies are essential for delineating the general and specific principles governing microbiome assembly and functions, mapping microbial interconnectivity across varying spatial and temporal scales, and for the establishment of predictive frameworks that will guide the development of targeted microbiome-interventions to deliver One Health outcomes.

The potential of microbiota information to better predict efficiency traits in growing pigs fed a conventional and a high-fiber diet

BACKGROUND

Improving pigs' ability to digest diets with an increased dietary fiber content is a lever to improve feed efficiency and limit feed costs in pig production. The aim of this study was to determine whether information on the gut microbiota and host genetics can contribute to predict digestive efficiency (DE, i.e. digestibility coefficients of energy, organic matter, and nitrogen), feed efficiency (FE, i.e. feed conversion ratio and residual feed intake), average daily gain, and daily feed intake phenotypes. Data were available for 1082 pigs fed a conventional or high-fiber diet. Fecal samples were collected at 16 weeks, and DE was estimated using near‑infrared spectrometry. A cross-validation approach was used to predict traits within the same diet, for the opposite diet, and for a combination of both diets, by implementing three models, i.e. with only genomic (Gen), only microbiota (Micro), and both genomic and microbiota information (Micro+Gen). The predictive ability with and without sharing common sires and breeding environment was also evaluated. Prediction accuracy of the phenotypes was calculated as the correlation between model prediction and phenotype adjusted for fixed effects.

RESULTS

Prediction accuracies of the three models were low to moderate (< 0.47) for growth and FE traits and not significantly different between models. In contrast, for DE traits, prediction accuracies of model Gen were low (< 0.30) and those of models Micro and Micro+Gen were moderate to high (> 0.52). Prediction accuracies were not affected by the stratification of diets in the reference and validation sets and were in the same order of magnitude within the same diet, for the opposite diet, and for the combination of both diets. Prediction accuracies of the three models were significantly higher when pigs in the reference and validation populations shared common sires and breeding environment than when they did not (P < 0.001).

CONCLUSIONS

The microbiota is a relevant source of information to predict DE regardless of the diet, but not to predict growth and FE traits for which prediction accuracies were similar to those obtained with genomic information only. Further analyses on larger datasets and more diverse diets should be carried out to complement and consolidate these results.

The gut microbiota links disease to human genome evolution

A large number of studies have established a causal relationship between the gut microbiota and human disease. In addition, the composition of the microbiota is substantially influenced by the human genome. Modern medical research has confirmed that the pathogenesis of various diseases is closely related to evolutionary events in the human genome. Specific regions of the human genome known as human accelerated regions (HARs) have evolved rapidly over several million years since humans diverged from a common ancestor with chimpanzees, and HARs have been found to be involved in some human-specific diseases. Furthermore, the HAR-regulated gut microbiota has undergone rapid changes during human evolution. We propose that the gut microbiota may serve as an important mediator linking diseases to human genome evolution.

Inferring causal structures of gut microbiota diversity and feed efficiency traits in poultry using Bayesian learning and genomic structural equation models

Feed and phosphorus (P) efficiency are of increasing importance in poultry breeding. It has been shown recently that these efficiency traits are influenced by the gut microbiota composition of the birds. The efficiency traits and the gut microbiota composition are partly under control of the host genome. Thus, the gut microbiota composition can be seen as a mediator trait between the host genome and the efficiency traits. The present study used data from 749 individuals of a Japanese quail F2 cross. The birds were genotyped for 4k single-nucleotide polymorphism (SNP) and trait recorded for P utilization (PU) and P retention (PR), body weight gain (BWG), and feed per gain ratio (F:G). The gut microbiota composition was characterized by targeted amplicon sequencing. The alpha diversity was calculated as the Pielou's evenness index (J'). A stable Bayesian network was established using a Hill-Climbing learning algorithm. Pielou's evenness index was placed as the most upstream trait and BWG as the most downstream trait, with direct and indirect links via PR, PU, and F:G. The direct and indirect effects between J', PU, and PR were quantified with structural equation models (SEM), which revealed a causal link from J' to PU and from PU to PR. Quantitative trait loci (QTL) linkage mapping revealed three genome-wide significant QTL regions for these traits with in total 49 trait-associated SNP within the QTL regions. SEM association mapping separated the total SNP effect for a trait into a direct effect and indirect effects mediated by upstream traits. Although the indirect effects were in general small, they contributed to the total SNP effect in some cases. This enabled us to detect some shared genetic effects. The method applied allows for the detection of shared genetic architecture of quantitative traits and microbiota compositions.

Disentangling the causal relationship between rabbit growth and cecal microbiota through structural equation models

BACKGROUND

The effect of the cecal microbiome on growth of rabbits that were fed under different regimes has been studied previously. However, the term "effect" carries a causal meaning that can be confounded because of potential genetic associations between the microbiome and production traits. Structural equation models (SEM) can help disentangle such a complex interplay by decomposing the effect on a production trait into direct host genetics effects and indirect host genetic effects that are exerted through microbiota effects. These indirect effects can be estimated via structural coefficients that measure the effect of the microbiota on growth while the effects of the host genetics are kept constant. In this study, we applied the SEM approach to infer causal relationships between the cecal microbiota and growth of rabbits fed under ad libitum (ADG_AL) or restricted feeding (ADG_R).

RESULTS

We identified structural coefficients that are statistically different from 0 for 138 of the 946 operational taxonomic units (OTU) analyzed. However, only 15 and 38 of these 138 OTU had an effect greater than 0.2 phenotypic standard deviations (SD) on ADG_AL and ADG_R, respectively. Many of these OTU had a negative effect on both traits. The largest effects on ADG_R were exerted by an OTU that is taxonomically assigned to the Desulfovibrio genus (- 1.929 g/d, CSS-normalized OTU units) and by an OTU that belongs to the Ruminococcaceae family (1.859 g/d, CSS-normalized OTU units). For ADG_AL, the largest effect was from OTU that belong to the S24-7 family (- 1.907 g/d, CSS-normalized OTU units). In general, OTU that had a substantial effect had low to moderate estimates of heritability.

CONCLUSIONS

Disentangling how direct and indirect effects act on production traits is relevant to fully describe the processes of mediation but also to understand how these traits change before considering the application of an external intervention aimed at changing a given microbial composition by blocking/promoting the presence of a particular microorganism.

Host genetic control on rumen microbiota and its impact on dairy traits in sheep

BACKGROUND

Milk yield and fine composition in sheep depend on the volatile and long-chain fatty acids, microbial proteins, vitamins produced through feedstuff digestion by the rumen microbiota. In cattle, the host genome has been shown to have a low to moderate genetic control on rumen microbiota abundance but a high control on dairy traits with heritabilities higher than 0.30. There is little information on the genetic correlations and quantitative trait loci (QTL) that simultaneously affect rumen microbiota abundance and dairy traits in ruminants, especially in sheep. Thus, our aim was to quantify the effect of the host genetics on rumen bacterial abundance and the genetic correlations between rumen bacterial abundance and several dairy traits, and to identify QTL that are associated with both rumen bacterial abundance and milk traits.

RESULTS

Our results in Lacaune sheep show that the heritability of rumen bacterial abundance ranges from 0 to 0.29 and that the heritability of 306 operational taxonomic units (OTU) is significantly different from 0. Of these 306 OTU, 96 that belong mainly to the Prevotellaceae, Lachnospiraceae and Ruminococcaceae bacterial families show strong genetic correlations with milk fatty acids and proteins (absolute values ranging from 0.33 to 0.99). Genome-wide association studies revealed a QTL for alpha-lactalbumin concentration in milk on Ovis aries chromosome (OAR) 11, and six QTL for rumen bacterial abundances i.e., for two OTU belonging to the genera Prevotella (OAR3 and 5), Rikeneleaceae_RC9_gut_group (OAR5), Ruminococcus (OAR5), an unknown genus of order Clostridia UCG-014 (OAR10), and CAG-352 (OAR11). None of these detected regions are simultaneously associated with rumen bacterial abundance and dairy traits, but the bacterial families Prevotellaceae, Lachnospiraceae and F082 show colocalized signals on OAR3, 5, 15 and 26.

CONCLUSIONS

In Lacaune dairy sheep, rumen microbiota abundance is partially controlled by the host genetics and is poorly genetically linked with milk protein and fatty acid compositions, and three main bacterial families, Prevotellaceae, Lachnospiraceae and F082, show specific associations with OAR3, 5, 15 and 26.

Applications of Omics Technology for Livestock Selection and Improvement

Conventional animal selection and breeding methods were based on the phenotypic performance of the animals. These methods have limitations, particularly for sex-limited traits and traits expressed later in the life cycle (e.g., carcass traits). Consequently, the genetic gain has been slow with high generation intervals. With the advent of high-throughput omics techniques and the availability of multi-omics technologies and sophisticated analytic packages, several promising tools and methods have been developed to estimate the actual genetic potential of the animals. It has now become possible to collect and access large and complex datasets comprising different genomics, transcriptomics, proteomics, metabolomics, and phonemics data as well as animal-level data (such as longevity, behavior, adaptation, etc.,), which provides new opportunities to better understand the mechanisms regulating animals' actual performance. The cost of omics technology and expertise of several fields like biology, bioinformatics, statistics, and computational biology make these technology impediments to its use in some cases. The population size and accurate phenotypic data recordings are other significant constraints for appropriate selection and breeding strategies. Nevertheless, omics technologies can estimate more accurate breeding values (BVs) and increase the genetic gain by assisting the section of genetically superior, disease-free animals at an early stage of life for enhancing animal productivity and profitability. This manuscript provides an overview of various omics technologies and their limitations for animal genetic selection and breeding decisions.

Composition of the ileum microbiota is a mediator between the host genome and phosphorus utilization and other efficiency traits in Japanese quail (Coturnix japonica)

BACKGROUND

Phosphorus is an essential nutrient in all living organisms and, currently, it is the focus of much attention due to its global scarcity, the environmental impact of phosphorus from excreta, and its low digestibility due to its storage in the form of phytates in plants. In poultry, phosphorus utilization is influenced by composition of the ileum microbiota and host genetics. In our study, we analyzed the impact of host genetics on composition of the ileum microbiota and the relationship of the relative abundance of ileal bacterial genera with phosphorus utilization and related quantitative traits in Japanese quail. An F2 cross of 758 quails was genotyped with 4k genome-wide single nucleotide polymorphisms (SNPs) and composition of the ileum microbiota was characterized using target amplicon sequencing. Heritabilities of the relative abundance of bacterial genera were estimated and quantitative trait locus (QTL) linkage mapping for the host was conducted for the heritable genera. Phenotypic and genetic correlations and recursive relationships between bacterial genera and quantitative traits were estimated using structural equation models. A genomic best linear unbiased prediction (GBLUP) and microbial (M)BLUP hologenomic selection approach was applied to assess the feasibility of breeding for improved phosphorus utilization based on the host genome and the heritable part of composition of the ileum microbiota.

RESULTS

Among the 59 bacterial genera examined, 24 showed a significant heritability (nominal p ≤ 0.05), ranging from 0.04 to 0.17. For these genera, six genome-wide significant QTL were mapped. Significant recursive effects were found, which support the indirect host genetic effects on the host's quantitative traits via microbiota composition in the ileum of quail. Cross-validated microbial and genomic prediction accuracies confirmed the strong impact of microbial composition and host genetics on the host's quantitative traits, as the GBLUP accuracies based on the heritable microbiota-mediated components of the traits were similar to the accuracies of conventional GBLUP based on genome-wide SNPs.

CONCLUSIONS

Our results revealed a significant effect of host genetics on composition of the ileal microbiota and confirmed that host genetics and composition of the ileum microbiota have an impact on the host's quantitative traits. This offers the possibility to breed for improved phosphorus utilization based on the host genome and the heritable part of composition of the ileum microbiota.

A Probabilistic Structural Equation Model to Evaluate Links between Gut Microbiota and Body Weights of Chicken Fed or Not Fed Insect Larvae

Simple Summary

Feeding poultry with insects could reduce production costs, but the impact of this diet on their gut microbiota and growth is little known because the network of relationships between their weights, the composition of their microbiota and their diet is complex and potentially biased by confounding factors (such as the gut compartment, age and sex of the birds). In this study, we were able to unravel these relationships in local breed chickens fed or not fed with black soldier fly larvae thanks to a technique of artificial intelligence (the probabilistic structural equation model). Bacteria were grouped into few entities with distinctive metabolic attributes and were probably linked nutritionally. Birds’ age influenced body weights and bacterial composition. The proposed methodology was thus able to simplify the complex dependencies among bacteria present in the gut and to highlight links potentially important in the response of chicken to insect feed.

Abstract

Feeding chicken with black soldier fly larvae (BSF) may influence their rates of growth via effects on the composition of their gut microbiota. To verify this hypothesis, we aim to evaluate a probabilistic structural equation model because it can unravel the complex web of relationships that exist between the bacteria involved in digestion and evaluate whether these influence bird growth. We followed 90 chickens fed diets supplemented with 0%, 5% or 10% BSF and measured the strength of the relationship between their weight and the relative abundance of bacteria (OTU) present in their cecum or cloaca at 16, 28, 39, 67 or 73 days of age, while adjusting for potential confounding effects of their age and sex. Results showed that OTUs (62 genera) could be combined into ten latent constructs with distinctive metabolic attributes. Links were discovered between these constructs that suggest nutritional relationships. Age directly influenced weights and microbiotal composition, and three constructs indirectly influenced weights via their dependencies on age. The proposed methodology was able to simplify dependencies among OTUs into knowledgeable constructs and to highlight links potentially important to understand the role of insect feed and of microbiota in chicken growth.

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.

Chicken Authentication and Discrimination via Live Weight, Body Size, Carcass Traits, and Breast Muscle Fat Content Clustering as Affected by Breed and Sex Varieties in Malaysia

Nowadays, the high demand for village chickens in Malaysia leads to the fraudulent substitution of indigenous chickens with other cheaper counterparts. Discriminating different chicken breeds based on their phenotypic characteristics is one strategy to avoid chicken adulteration. The main objective of this study was to authenticate and group dominant chicken breeds in Malaysia, including commercial chickens (Cobb, Hubbard, DeKalb) and cross-bred village chickens (Ayam Kampung, Akar Putra). The further discrimination of village chickens from underaged colored broilers (UCBs) (Hubbard, Sasso) was performed based on phenotype traits. The results showed that the breed had a significant effect (p < 0.05) on phenotypic characteristics, while the sex effect was not significant for some characteristics. In the first phase, the most remarkable discriminating factors were abdominal fat weight, breast muscle weight, chest circumference, shank length, and wingspan. However, in the second phase, notable variations in phenotypic characteristics between village chickens and UCBs were not detected. Principal component analysis (PCA) showed the successful separation of village chickens from high-performance breeds (broiler and colored broiler). Nevertheless, there was overlap among observations for Sasso and village chickens, which approved the possible similarities in their phenotypic characteristics. This study showed clear breed clustering, which leads to the chicken authentication based on their phenotypic characteristics.

Causal Inference in Microbiome Medicine: Principles and Applications

Microorganisms that colonize the mammalian skin and cavity play critical roles in various physiological functions of the host. Numerous studies have revealed strong associations between the microbiota and multiple diseases. However, association does not mean causation. To clarify the mechanisms underlying microbiota-mediated diseases, research is moving from associative analyses to causation studies. In this article, we first introduce the principles of the computational methods for causal inference, and then discuss the applications of these methods in microbiome medicine. Furthermore, we examine the reliability of theoretically inferred causality by the interventionist framework. Finally, we show the potential of confirmed causality in microbiota-targeted therapy, especially in personalized dietary intervention. We conclude that a comprehensive understanding of the causal relationships between diets, microbiota, host targets, and diseases is critical to future microbiome medicine.