Host, microbiome, and complex space: applying population and landscape genetic approaches to gut microbiome research in wild populations

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.

Progress on network modeling and analysis of gut microecology: a review

The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota's impact on human health.

Genetic diversity and population structure of wild Macrobrachium nipponense populations across China: Implication for population management

BACKGROUND

Macrobrachium nipponense, is an important economic indigenous prawn and is widely distributed in China. However, most these genetic structure analysis researches were focused on a certain water area, systematic comparative studies on genetic structure of M. nipponense across China are not yet available.

METHODS AND RESULTS

In this study, D-loop region sequences was used to investigate the genetic diversity and population structure of 22 wild populations of M. nipponense through China, containing the major rivers and lakes of China. Totally 473 valid D-loop sequences with a length of 1110 bp were obtained, and 348 variation sites and 221 haplotypes were detected. The haplotype diversity (h) was ranged from 0.1630 (Bayannur) ~ 1.0000 (Amur River) and the nucleotide diversity π value ranged from 0.001164 (Min River) ~ 0.037168 (Nen River). The pairwise genetic differentiation index (F_ST) ranged from 0.00344 to 0.91243 and most pair-wised F_ST was significant (P < 0.05). The lowest F_ST was displayed in Min River and Jialing River populations and the highest was between Nandu River and Nen River populations. The phylogenetic tree of genetic distance showed that all populations were divided into two branches. The Dianchi Lake, Nandu River, Jialing River and Min River populations were clustered into one branch. The neutral test and mismatch distribution results showed that M. nipponense populations were not experienced expanding and kept a steady increase.

CONCLUSIONS

Taken together, a joint resources protection and management strategy for M. nipponense have been suggested based on the results of this study for its sustainable use.

Fine-scale spatial variation shape fecal microbiome diversity and composition in black-tailed prairie dogs (Cynomys ludovicianus)

BACKGROUND

Host associated gut microbiota are important in understanding the coevolution of host-microbe, and how they may help wildlife populations to adapt to rapid environmental changes. Mammalian gut microbiota composition and diversity may be affected by a variety of factors including geographic variation, seasonal variation in diet, habitat disturbance, environmental conditions, age, and sex. However, there have been few studies that examined how ecological and environmental factors influence gut microbiota composition in animals' natural environments. In this study, we explore how host habitat, geographical location and environmental factors affect the fecal microbiota of Cynomys ludovicianus at a small spatial scale. We collected fecal samples from five geographically distinct locations in the Texas Panhandle classified as urban and rural areas and analyzed them using high throughput 16S rRNA gene amplicon sequencing.

RESULTS

The results showed that microbiota of these fecal samples was largely dominated by the phylum Bacteroidetes. Fecal microbiome diversity and composition differed significantly across sampling sites and habitats. Prairie dogs inhabiting urban areas showed reduced fecal diversity due to more homogenous environment and, likely, anthropogenic disturbance. Urban prairie dog colonies displayed greater phylogenetic variation among replicates than those in rural habitats. Differentially abundant analysis revealed that bacterial species pathogenic to humans and animals were highly abundant in urban areas which indicates that host health and fitness might be negatively affected. Random forest models identified Alistipes shahii as the important species driving the changes in fecal microbiome composition. Despite the effects of habitat and geographic location of host, we found a strong correlation with environmental factors and that- average maximum temperature was the best predictor of prairie dog fecal microbial diversity.

CONCLUSIONS

Our findings suggest that reduction in alpha diversity in conjunction with greater dispersion in beta diversity could be indicative of declining host health in urban areas; this information may, in turn, help determine future conservation efforts. Moreover, several bacterial species pathogenic to humans and other animals were enriched in prairie dog colonies near urban areas, which may in turn adversely affect host phenotype and fitness.

Best practice for wildlife gut microbiome research: A comprehensive review of methodology for 16S rRNA gene investigations

Extensive research in well-studied animal models underscores the importance of commensal gastrointestinal (gut) microbes to animal physiology. Gut microbes have been shown to impact dietary digestion, mediate infection, and even modify behavior and cognition. Given the large physiological and pathophysiological contribution microbes provide their host, it is reasonable to assume that the vertebrate gut microbiome may also impact the fitness, health and ecology of wildlife. In accordance with this expectation, an increasing number of investigations have considered the role of the gut microbiome in wildlife ecology, health, and conservation. To help promote the development of this nascent field, we need to dissolve the technical barriers prohibitive to performing wildlife microbiome research. The present review discusses the 16S rRNA gene microbiome research landscape, clarifying best practices in microbiome data generation and analysis, with particular emphasis on unique situations that arise during wildlife investigations. Special consideration is given to topics relevant for microbiome wildlife research from sample collection to molecular techniques for data generation, to data analysis strategies. Our hope is that this article not only calls for greater integration of microbiome analyses into wildlife ecology and health studies but provides researchers with the technical framework needed to successfully conduct such investigations.

How longitudinal data can contribute to our understanding of host genetic effects on the gut microbiome

A key component of microbiome research is understanding the role of host genetic influence on gut microbial composition. However, it can be difficult to link host genetics with gut microbial composition because host genetic similarity and environmental similarity are often correlated. Longitudinal microbiome data can supplement our understanding of the relative role of genetic processes in the microbiome. These data can reveal environmentally contingent host genetic effects, both in terms of controlling for environmental differences and in comparing how genetic effects differ by environment. Here, we explore four research areas where longitudinal data could lend new insights into host genetic effects on the microbiome: microbial heritability, microbial plasticity, microbial stability, and host and microbiome population genetics. We conclude with a discussion of methodological considerations for future studies.