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

Short-read full-length 16S rRNA amplicon sequencing for characterisation of the respiratory bacteriome of captive and free-ranging African elephants (Loxodonta africana)

Hypervariable region sequencing of the 16S ribosomal RNA (rRNA) gene plays a critical role in microbial ecology by offering insights into bacterial communities within specific niches. While providing valuable genus-level information, its reliance on data from targeted genetic regions limits its overall utility. Recent advances in sequencing technologies have enabled characterisation of the full-length 16S rRNA gene, enhancing species-level classification. Although current short-read platforms are cost-effective and precise, they lack full-length 16S rRNA amplicon sequencing capability. This study aimed to evaluate the feasibility of a modified 150 bp paired-end full-length 16S rRNA amplicon short-read sequencing technique on the Illumina iSeq 100 and 16S rRNA amplicon assembly workflow by utilising a standard mock microbial community and subsequently performing exploratory characterisation of captive (zoo) and free-ranging African elephant (Loxodonta africana) respiratory microbiota. Our findings demonstrate that, despite generating assembled amplicons averaging 869 bp in length, this sequencing technique provides taxonomic assignments consistent with the theoretical composition of the mock community and respiratory microbiota of other mammals. Tentative bacterial signatures, potentially representing distinct respiratory tract compartments (trunk and lower respiratory tract) were visually identified, necessitating further investigation to gain deeper insights into their implication for elephant physiology and health.

Next-generation data filtering in the genomics era

Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology, evolution and human health. However, these datasets often contain noise or errors and are missing information that can affect the accuracy and reliability of subsequent computational analyses and conclusions. A key step in genomic data analysis is filtering - removing sequencing bases, reads, genetic variants and/or individuals from a dataset - to improve data quality for downstream analyses. Researchers are confronted with a multitude of choices when filtering genomic data; they must choose which filters to apply and select appropriate thresholds. To help usher in the next generation of genomic data filtering, we review and suggest best practices to improve the implementation, reproducibility and reporting standards for filter types and thresholds commonly applied to genomic datasets. We focus mainly on filters for minor allele frequency, missing data per individual or per locus, linkage disequilibrium and Hardy-Weinberg deviations. Using simulated and empirical datasets, we illustrate the large effects of different filtering thresholds on common population genetics statistics, such as Tajima's D value, population differentiation (FST), nucleotide diversity (π) and effective population size (Ne).

Exploring the Role of Gut-Lung Interactions in COPD Pathogenesis: A Comprehensive Review on Microbiota Characteristics and Inflammation Modulation

Chronic obstructive pulmonary disease (COPD) is a paramount contributor to global morbidity and mortality. Over the past decade, the concept of the "gut-lung axis" has emerged, offering a lens through which to examine the intricate interplay between the host, microbiome, and respiratory diseases, including COPD. An expanding body of evidence underscores that the composition of both the gastrointestinal and respiratory microbiome deviates in COPD patients compared to healthy individuals, leading to distinct host immune responses and clinical manifestations. The objective of this review is to provide a concise overview of the role both gut and respiratory microbiome play in the development of COPD. This was accomplished by compiling current literature on the microbiome profile in stable and exacerbated cases of COPD, as well as exploring the biological mechanisms through a discussion of relevant experiments conducted on murine models. Hallmark characteristics of the microbial profile in COPD encompass reduced Prevotella species in the respiratory microbiome, culminating in a loss of anti-inflammatory protection, and diminished Bacteroidetes in the gut microbiome, leading to a decrease in protective short-chain fatty acids. The proliferation of Proteobacteria, particularly the Haemophilus species, Moraxellaspecies, and Pseudomonas species contribute to COPD pathologies via recognition of proinflammatory lipopolysaccharide via Toll-like receptors. As a consequence, deteriorated pulmonary function, enhanced severity, increased onset of exacerbations, and elevated mortality were observed.

Genetic Insights into the Gut-Lung Axis: Mendelian Randomization Analysis on Gut Microbiota, Lung Function, and COPD

Background

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder with a complex etiology involving genetic and environmental factors. The dysbiosis of gut microbiota has been implicated in COPD. Mendelian Randomization (MR) provides a tool to investigate causal links using genetic variants as instrumental variables. This study aims to employ MR analysis to explore the causal relationship between gut microbiota, lung function, and COPD.

Methods

We utilized genome-wide association study (GWAS) data from MiBioGen, UK Biobank and FinnGen, which were related to gut microbial taxa, lung function parameters including forced vital capacity in one second (FEV1), forced vital capacity (FVC), and percentage of predicted FEV1 (FEV1%pred), as well as GWAS data for COPD. MR analysis was conducted to assess the causal effects of gut microbiota on lung function and the risk of COPD. Sensitivity analysis was utilized to examine the stability of the causal relationships. Multiple testing and reverse analysis were employed to evaluate the robustness of these relationships.

Results

Using the IVW method, 64 causal correlations were identified. Through conducting sensitivity analysis, multiple testing, and reverse analysis, we identified 14 robust and stable causal relationships. The bacterial taxa that showed a positive association with lung function included Desulfovibrionaceae, Erysipelotrichales, Desulfovibrionales, Clostridiales, Clostridia, Deltaproteobacteria and Erysipelotrichia, while Selenomonadales and Negativicutes showed a negative association with lung function. The abundance of Holdemanella were positively correlated with the risk of COPD, while FamilyXIII exhibited a negative correlation with the risk of COPD.

Conclusion

Several microbial taxa were discovered to have a positive causal correlation with lung function, offering potential insights into the development of probiotics. The presence of microbial taxa negatively correlated with lung function and positively correlated with COPD emphasized the potential impact of gut microbiota dysbiosis on respiratory health.

Habitat shapes the gut microbiome diversity of Malayan tigers (Panthera tigris jacksoni) as revealed through metabarcoding 16S rRNA profiling

The gut microbiome refers to the microorganism community living within the digestive tract. The environment plays a crucial role in shaping the gut microbiome composition of animals. The gut microbiome influences the health and behavior of animals, including the critically endangered Malayan tiger (Panthera tigris jacksoni). However, the gut microbiome composition of Malayan tigers, especially those living in their natural habitats, remains poorly understood. To address this knowledge gap, we used next-generation sequencing DNA metabarcoding techniques to analyze the gut microbiome of wild Malayan tigers using fecal samples collected from their natural habitats and in captivity. Our aim was to determine the gut microbiota composition of the Malayan tiger, considering the different types of habitat environments. The results revealed a diverse microbial community within the gut microbiome of Malayan tigers. The prominent phyla that were observed included Firmicutes, Proteobacteria, Actinobacteriota, Fusobacteriota and Bacteroidota. Beta diversity analysis revealed significant differences in gut microbiome composition of Malayan tigers that inhabited oil palm plantations, in villages and protected areas. Diversity analysis also revealed significant difference in the gut microbiome between wild and captive Malayan tigers. However, the distinctions of gut microbiome between wild and captive alpha diversity did not yield significant differences. The differences in microbiome diversity resulted from the interplay of dietary intake and environmental factors. This information will facilitate the establishment of focused conservation approaches and enhance our understanding of the effect of microbiome composition on Malayan tiger health.

Genetic evidence on the causality between gut microbiota and various asthma phenotypes: a two-sample Mendelian randomization study

Introduction

Asthma is a multifarious disease that manifests in various phenotypes. Among the various factors that contribute to the development of asthma, the gut microbiota has recently emerged as a compelling area of investigation. This study aims to investigate the causal relationships between gut microbiota and distinct asthma phenotypes.

Methods

The genome-wide association study (GWAS) summary statistics for 211 gut microbial taxa were used as study exposure. Five traits pertaining to various asthma phenotypes (asthma, allergic asthma, childhood asthma, suggestive for eosinophilic asthma and obesity-related asthma) were included as study outcome. We conducted Mendelian randomization (MR) analysis and sensitivity analysis for each bacterial taxa and asthma phenotypes.

Result

We discovered a total of 58 associations that exhibited evidence of causality. Out of these, 4 associations remained significant even after applying multiple correction. An increased risk of asthma was causally associated with higher abundance of genus Holdemanella (OR = 1.11; CI: 1.05-1.17; p = 0.027), genus Oxalobacter (OR = 1.09; CI: 1.04-1.15; p = 0.025) and genus Butyricimonas (OR = 1.14; CI: 1.06-1.22; p = 0.027). Order NB1n was causally linked with an increased risk of obesity-related asthma (OR = 1.17; CI: 1.07-1.29; p = 0.015). There was limited overlap among the taxa that exhibited potential causal relationships with distinct asthma phenotypes.

Conclusion

Our research has provided genetic evidence that establishes multiple causal relationships between the gut microbiota and distinct asthma phenotypes, supporting the role of the gut microbiota in various asthma phenotypes. It is possible that different taxa play a role in the development of distinct asthma phenotypes. The causal relationships identified in this study require further investigation.