We carried out a comparative analysis between the bacterial microbiota composition of zoo-housed western lowland gorillas and their wild counterparts through 16S rRNA gene amplicon sequencing. In addition, we characterised the carbohydrate-active and methanogenic potential of the zoo-housed gorilla (ZHG) microbiome through shotgun metagenomics and RNA sequencing. The ZHG microbiota showed increased alpha diversity in terms of bacterial species richness and a distinct composition from that of the wild gorilla microbiota, including a loss of abundant fibre-degrading and hydrogenic Chloroflexi. Metagenomic analysis of the CAZyome indicated predominant oligosaccharide-degrading activity, while RNA sequencing revealed diverse cellulase and hemi-cellulase activities in the ZHG gut, contributing to a total of 268 identified carbohydrate-active enzymes. Metatranscriptome analysis revealed a substantial contribution of 38% of the transcripts from anaerobic fungi and archaea to the gorilla microbiome. This activity originates from cellulose-degrading and hydrogenic fungal species belonging to the class Neocallimastigomycetes, as well as from methylotrophic and hydrogenotrophic methanogenic archaea belonging to the classes Thermoplasmata and Methanobacteria, respectively. Our study shows the added value of RNA sequencing in a multiomics approach and highlights the contribution of eukaryotic and archaeal activities to the gut microbiome of gorillas.

The identification of functional processes taking place in microbiome communities augment traditional microbiome taxonomic studies, giving a more complete picture of interactions taking place within the community. While there are applications that perform functional annotation on metagenomes or metatranscriptomes, very few of these are able to link taxonomic identity to function or are limited by their input types or databases used. Here we present MetaFunc, a workflow which takes RNA sequences as input reads, and from these (1) identifies species present in the microbiome sample and (2) provides gene ontology annotations associated with the species identified. In addition, MetaFunc allows for host gene analysis, mapping the reads to a host genome, and separating these reads, prior to microbiome analyses. Differential abundance analysis for microbe taxonomies, and differential gene expression analysis and gene set enrichment analysis may then be carried out through the pipeline. A final correlation analysis between microbial species and host genes can also be performed. Finally, MetaFunc builds an R shiny application that allows users to view and interact with the microbiome results. In this paper, we showed how MetaFunc can be applied to metatranscriptomic datasets of colorectal cancer.

Trichomonads, anaerobic microbial eukaryotes members of the phylum Parabasalia, are common obligate extracellular symbionts that can lead to pathological or asymptomatic colonization of various mucosal surfaces in a wide range of animal hosts. Results from previous in vitro studies have suggested a number of intriguing mucosal colonization strategies by Trichomonads, notably highlighting the importance of interactions with bacteria. However, in vivo validation is currently lacking. A previous metatranscriptomics study into the cause of idiopathic chronic diarrhoea in macaques reported the presence of an unidentified protozoan parasite related to Trichomonas vaginalis. In this work, we performed a reanalysis of the published data in order to identify the parasite species present in the macaque gut. We also leveraged the information-rich metatranscriptomics data to investigate the parasite behaviour in vivo. Our results indicated the presence of at least 3 genera of Trichomonad parasite; Tetratrichomonas, Pentatrichomonas and Trichomitus, 2 of which had not been previously reported in the macaque gut. In addition, we identified common in vivo expression profiles shared amongst the Trichomonads. In agreement with previous findings for other Trichomonads, our results highlighted a relationship between Trichomonads and mucosal bacterial diversity which could be influential in health and disease.

Rumen microbiome profiling uses 16S rRNA (18S rRNA, internal transcribed spacer) gene sequencing, a method that usually sequences a small portion of a single gene and is often biased and varies between different laboratories. Functional information can be inferred from this data, but only for those that are closely related to known annotated species, and even then may not truly reflect the function performed within the environment being studied. Genome sequencing of isolates and metagenome-assembled genomes has now reached a stage where representation of the majority of rumen bacterial genera are covered, but this still only represents a portion of rumen microbial species. The creation of a microbial genome (bins) database with associated functional annotations will provide a consistent reference to allow mapping of RNA-Seq reads for functional gene analysis from within the rumen microbiome. The integration of multiple omic analytics is linking functional gene activity, metabolic pathways and rumen metabolites with the responsible microbiota, supporting our biological understanding of the rumen system. The application of these techniques has advanced our understanding of the major microbial populations and functional pathways that are used in relation to lower methane emissions, higher feed efficiencies and responses to different feeding regimes. Continued and more precise use of these tools will lead to a detailed and comprehensive understanding of compositional and functional capacity and design of techniques for the directed intervention and manipulation of the rumen microbiota towards a desired state.