The structure of microbial populations in Nelore GIT reveals inter-dependency of methanogens in feces and rumen

Background:The success of different species of ruminants in the colonization of a diverse range of environments is due to their ability to digest and absorb nutrients from cellulose,a complex polysaccharide found in leaves and grass.Ruminants rely on a complex and diverse microbial community,or microbiota,in a unique compartment known as the rumen to break down this polysaccharide.Changes in microbial populations of the rumen can affect the host’s development,health,and productivity.However,accessing the rumen is stressful for the animal.Therefore,the development and use of alternative sampling methods are needed if this technique is to be routinely used in cattle breeding.To this end,we tested if the fecal microbiome could be used as a proxy for the rumen microbiome due to its accessibility.We investigated the taxonomic composition,diversity and inter-relations of two different GIT compartments,rumen and feces,of 26 Nelore(Bos indicus)bulls,using Next Generation Sequencing(NGS)metabarcoding of bacteria,archaea and ciliate protozoa.Results:We identified 4265 Amplicon Sequence Variants(ASVs)from bacteria,571 from archaea,and 107 from protozoa,of which 143(96 bacteria and 47 archaea)were found common between both microbiomes.The most prominent bacterial phyla identified were Bacteroidetes(41.48%)and Firmicutes(56.86%)in the ruminal and fecal microbiomes,respectively,with Prevotella and Ruminococcaceae UCG-005 the most relatively abundant genera identified in each microbiome.The most abundant archaeal phylum identified was Euryarchaeota,of which Methanobrevibacter gottschalkii,a methanogen,was the prevalent archaeal species identified in both microbiomes.Protozoa were found exclusively identified in the rumen with Bozasella/Triplumaria being the most frequent genus identified.Co-occurrence among ruminal and fecal ASVs reinforces the relationship of microorganisms within a biological niche.Furthermore,the co-occurrence of shared archaeal ASVs between microbiomes indicates a dependency of the predominant fecal methanogen population on the rumen population.Conclusions:Co-occurring microorganisms were identified within the rumen and fecal microbiomes,which revealed a strong association and inter-dependency between bacterial,archaeal and protozoan populations of the same microbiome.The archaeal ASVs identified as co-occurring between GIT compartments corresponded to the methanogenic genera Methanobrevibacter and Methanosphaera and represented 26.34%of the overall archaeal sequencesdiversity in the rumen and 42.73%in feces.Considering that these archaeal ASVs corresponded to a significant part of the overall diversity of both microbiomes,which is much higher if one includes the interactions of these co-occurring with other rumen archaea ASVs,we suggest that fecal methanogens could be used as a proxy of ruminal methanogens.

Towards a Comprehensive Search of Putative Chitinases Sequences in Environmental Metagenomic Databases

Chitinases catalyze the hydrolysis of chitin, a linear homopolymer of β-(1,4)-linked N-acetylglucosamine. The broad range of applications of chitinolytic enzymes makes their identification and study very promising. Metagenomic approaches offer access to functional genes in uncultured representatives of the microbiota and hold great potential in the discovery of novel enzymes, but tools to extensively explore these data are still scarce. In this study, we develop a chitinase mining pipeline to facilitate the comprehensive search of these enzymes in environmental metagenomic databases and also to explore phylogenetic relationships among the retrieved sequences. In order to perform the analyses, UniprotKB fungal and bacterial chitinases sequences belonging to the glycoside hydrolases (GH) family-18, 19 and 20 were used to generate 15 reference datasets, which were then used to generate high quality seed alignments with the MAFFT program. Profile Hidden Markov Models (pHMMs) were built from each seed alignment using the hmmbuild program of HMMER v3.0 package. The best-hit sequences returned by hmmsearch against two environmental metagenomic databases (Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis—CAMERA and Integrated Microbial Genomes—IMG/M) were retrieved and further analyzed. The NJ trees generated for each chitinase dataset showed some variability in the catalytic domain region of the metagenomic sequences and revealed common sequence patterns among all the trees. The scanning of the retrieved metagenomic sequences for chitinase conserved domains/signatures using both the InterPro and the RPS-BLAST tools confirmed the efficacy and sensitivity of our pHMM-based approach in detecting putative chitinases sequences. These analyses provide insight into the potential reservoir of novel molecules in metagenomic databases while supporting the chitinase mining pipeline developed in this work. By using our chitinase mining pipeline, a larger number of previously unannotated metagenomic chitinase sequences can be classified, enabling further studies on these enzymes.