Characterization of Eukaryotic Microbiome Using 18S Amplicon Sequencing

Challenges for pathologists in implementing clinical microbiome diagnostic testing

Recent research has established that the microbiome plays potential roles in the pathogenesis of numerous chronic diseases, including carcinomas. This discovery has led to significant interest in clinical microbiome testing among physicians, translational investigators, and the lay public. As novel, inexpensive methodologies to interrogate the microbiota become available, research labs and commercial vendors have offered microbial assays. However, these tests still have not infiltrated the clinical laboratory space. Here, we provide an overview of the challenges of implementing microbiome testing in clinical pathology. We discuss challenges associated with preanalytical and analytic sample handling and collection that can influence results, choosing the appropriate testing methodology for the clinical context, establishing reference ranges, interpreting the data generated by testing and its value in making patient care decisions, regulation, and cost considerations of testing. Additionally, we suggest potential solutions for these problems to expedite the establishment of microbiome testing in the clinical laboratory.

Merging and concatenation of sequencing reads: a bioinformatics workflow for the comprehensive profiling of microbiome from amplicon data

A comprehensive profiling of microbial diversity is essential to understand the ecosystem functions. Universal primer sets such as the 515Y/926R could amplify a part of 16S and 18S rRNA and infer the diversity of prokaryotes and eukaryotes. However, the analyses of mixed sequencing data pose a bioinformatics challenge; the 16S and 18S rRNA sequences need to be separated first and analysed individually/independently due to variations in the amplicon length. This study describes an alternative strategy, a merging and concatenation workflow, to analyse the mixed amplicon data without separating the 16S and 18S rRNA sequences. The workflow was tested with 24 mock community (MC) samples, and the analyses resolved the composition of prokaryotes and eukaryotes adequately. In addition, there was a strong correlation (cor = 0.950; P-value = 4.754e-10) between the observed and expected abundances in the MC samples, which suggests that the computational approach could infer the microbial proportions accurately. Further, 18 samples collected from the Sundarbans mangrove region were analysed as a case study. The analyses identified Proteobacteria, Bacteroidota, Actinobacteriota, Cyanobacteria, and Crenarchaeota as dominant bacterial phyla and eukaryotic divisions such as Metazoa, Gyrista, Cryptophyta, Chlorophyta, and Dinoflagellata were found to be dominant in the samples. Thus, the results support the applicability of the method in environmental microbiome research. The merging and concatenation workflow presented here requires considerably less computational resources and uses widely/commonly used bioinformatics packages, saving researchers analyses time (for equivalent sample numbers, compared to the conventional approach) required to infer the diversity of major microbial domains from mixed amplicon data at comparable accuracy.

Exploring the Potential of Metatranscriptomics to Describe Microbial Communities and Their Effects in Molluscs

Metatranscriptomics has emerged as a very useful technology for the study of microbiomes from RNA-seq reads. This method provides additional information compared to the sequencing of ribosomal genes because the gene expression can also be analysed. In this work, we used the metatranscriptomic approach to study the whole microbiome of mussels, including bacteria, viruses, fungi, and protozoans, by mapping the RNA-seq reads to custom assembly databases (including the genomes of microorganisms publicly available). This strategy allowed us not only to describe the diversity of microorganisms but also to relate the host transcriptome and microbiome, finding the genes more affected by the pathogen load. Although some bacteria abundant in the metatranscriptomic analysis were undetectable by 16S rRNA sequencing, a common core of the taxa was detected by both methodologies (62% of the metatranscriptomic detections were also identified by 16S rRNA sequencing, the Oceanospirillales, Flavobacteriales and Vibrionales orders being the most relevant). However, the differences in the microbiome composition were observed among different tissues of Mytilus galloprovincialis, with the fungal kingdom being especially diverse, or among molluscan species. These results confirm the potential of a meta-analysis of transcriptome data to obtain new information on the molluscs' microbiome.

Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research

Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.

Revealing the composition of the eukaryotic microbiome of oyster spat by CRISPR-Cas Selective Amplicon Sequencing (CCSAS)

BACKGROUND

The microbiome affects the health of plants and animals, including humans, and has many biological, ecological, and evolutionary consequences. Microbiome studies typically rely on sequencing ribosomal 16S RNA gene fragments, which serve as taxonomic markers for prokaryotic communities; however, for eukaryotic microbes this approach is compromised, because 18S rRNA gene sequences from microbial eukaryotes are swamped by contaminating host rRNA gene sequences.

RESULTS

To overcome this problem, we developed CRISPR-Cas Selective Amplicon Sequencing (CCSAS), a high-resolution and efficient approach for characterizing eukaryotic microbiomes. CCSAS uses taxon-specific single-guide RNA (sgRNA) to direct Cas9 to cut 18S rRNA gene sequences of the host, while leaving protistan and fungal sequences intact. We validated the specificity of the sgRNA on ten model organisms and an artificially constructed (mock) community of nine protistan and fungal pathogens. The results showed that > 96.5% of host rRNA gene amplicons were cleaved, while 18S rRNA gene sequences from protists and fungi were unaffected. When used to assess the eukaryotic microbiome of oyster spat from a hatchery, CCSAS revealed a diverse community of eukaryotic microbes, typically with much less contamination from oyster 18S rRNA gene sequences than other methods using non-metazoan or blocking primers. However, each method revealed taxonomic groups that were not detected using the other methods, showing that a single approach is unlikely to uncover the entire eukaryotic microbiome in complex communities. To facilitate the application of CCSAS, we designed taxon-specific sgRNA for ~16,000 metazoan and plant taxa, making CCSAS widely available for characterizing eukaryotic microbiomes that have largely been neglected.

CONCLUSION

CCSAS provides a high-through-put and cost-effective approach for resolving the eukaryotic microbiome of metazoa and plants with minimal contamination from host 18S rRNA gene sequences. Video Abstract.

Interrogating the bovine reproductive tract metagenomes using culture-independent approaches: a systematic review

Undesirable microbial infiltration into the female bovine reproductive tracts, for example during calving or mating, is likely to disturb the commensal microflora. Persistent establishment and overgrowth of certain pathogens induce reproductive diseases, render the female bovine reproductive tract unfavourable for pregnancy or can result in transmission to the foetus, leading to death and abortion or birth abnormalities. This review of culture-independent metagenomics studies revealed that normal microflora in the female bovine reproductive tract is reasonably consistently dominated by bacteria from the phyla Bacteroidetes, Firmicutes, Proteobacteria, following by Actinobacteria, Fusobacteria and Tenericutes. Reproductive disease development in the female bovine reproductive tract was demonstrated across multiple studies to be associated with high relative abundances of bacteria from the phyla Bacteroidetes and Fusobacteria. Reduced bacterial diversity in the reproductive tract microbiome in some studies of cows diagnosed with reproductive diseases also indicated an association between dysbiosis and bovine reproductive health. Nonetheless, the bovine genital tract microbiome remains underexplored, and this is especially true for the male genital tract. Future research should focus on the functional aspects of the bovine reproductive tract microbiomes, for example their contributions to cattle fertility and susceptibility towards reproductive diseases.

An Introduction to Next Generation Sequencing Bioinformatic Analysis in Gut Microbiome Studies

The gut microbiome is a microbial ecosystem which expresses 100 times more genes than the human host and plays an essential role in human health and disease pathogenesis. Since most intestinal microbial species are difficult to culture, next generation sequencing technologies have been widely applied to study the gut microbiome, including 16S rRNA, 18S rRNA, internal transcribed spacer (ITS) sequencing, shotgun metagenomic sequencing, metatranscriptomic sequencing and viromic sequencing. Various software tools were developed to analyze different sequencing data. In this review, we summarize commonly used computational tools for gut microbiome data analysis, which extended our understanding of the gut microbiome in health and diseases.