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Abstract Background Lactobacillus rhamnosus GG (LGG) is the most widely used probiotic, but the mechanisms underlying its beneficial effects remain unresolved. Previous studies typically inoculated LGG in hosts with established gut microbiota, limiting the understanding of specific impacts of LGG on host due to numerous interactions among LGG, commensal microbes, and the host. There has been a scarcity of studies that used gnotobiotic animals to elucidate LGG-host interaction, in particular for gaining specific insights about how it modifies the metabolome. To evaluate whether LGG affects the metabolite output of pathobionts, we inoculated with LGG gnotobiotic mice containing Propionibacterium acnes, Turicibacter sanguinis, and Staphylococcus aureus (PTS). Results 16S rRNA sequencing of fecal samples by Ion Torrent and MinION platforms showed colonization of germ-free mice by PTS or by PTS plus LGG (LTS). Although the body weights and feeding rates of mice remained similar between PTS and LTS groups, co-associating LGG with PTS led to a pronounced reduction in abundance of P. acnes in the gut. Addition of LGG or its secretome inhibited P. acnes growth in culture. After optimizing procedures for fecal metabolite extraction and metabolomic liquid chromatography-mass spectrometry analysis, unsupervised and supervised multivariate analyses revealed a distinct separation among fecal metabolites of PTS, LTS, and germ-free groups. Variables-important-in-projection scores showed that LGG colonization robustly diminished guanine, ornitihine, and sorbitol while significantly elevating acetylated amino acids, ribitol, indolelactic acid, and histamine. In addition, carnitine, betaine, and glutamate increased while thymidine, quinic acid and biotin were reduced in both PTS and LTS groups. Furthermore, LGG association reduced intestinal mucosal expression levels of inflammatory cytokines, such as IL-1α, IL-1β and TNF-α. Conclusions LGG co-association had a negative impact on colonization of P. acnes, and markedly altered the metabolic output and inflammatory response elicited by pathobionts.

Abstract Background An approach utilizing the long-read capability of the Oxford Nanopore MinION to rapidly sequence bacterial ribosomal operons of complex natural communities was developed. Microbial fingerprinting employs domain-specific forward primers (16S rRNA subunit), reverse primers (23S rRNA subunit), and a high-fidelity Taq polymerase with proofreading capabilities. Amplicons contained both ribosomal subunits for broad-based phylogenetic assignment (~ 3900 bp of sequence), plus the intergenic spacer (ITS) region (~ 300 bp) for potential strain-specific identification. Results To test the approach, bacterial rRNA operons (~ 4200 bp) were amplified from six DNA samples employing a mixture of farm soil and bioreactor DNA in known concentrations. Each DNA sample mixture was barcoded, sequenced in quadruplicate (n = 24), on two separate 6-h runs using the MinION system (R7.3 flow cell; MAP005 and 006 chemistry). From nearly 90,000 MinION reads, roughly 33,000 forward and reverse sequences were obtained. This yielded over 10,000 2D sequences which were analyzed using a simplified data analysis pipeline based on NCBI Blast and assembly with Geneious software. The method could detect over 1000 operational taxonomic units in the sample sets in a quantitative manner. Global sequence coverage for the various rRNA operons ranged from 1 to 1951x. An iterative assembly scheme was developed to reconstruct those rRNA operons with > 35x coverage from a set of 30 operational taxonomic units (OTUs) among the Proteobacteria, Actinobacteria, Acidobacteria, Firmicutes, and Gemmatimonadetes. Phylogenetic analysis of the 16S rRNA and 23S rRNA genes from each operon demonstrated similar tree topologies with species/strain-level resolution. Conclusions This sequencing method represents a cost-effective way to profile microbial communities. Because the MinION is small, portable, and runs on a laptop, the possibility of microbiota characterization in the field or on robotic platforms becomes realistic.