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. 2017 Sep 14;12(9):e0183564.
doi: 10.1371/journal.pone.0183564. eCollection 2017.

Identification of a mouse Lactobacillus johnsonii strain with deconjugase activity against the FXR antagonist T-β-MCA

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Identification of a mouse Lactobacillus johnsonii strain with deconjugase activity against the FXR antagonist T-β-MCA

Michael DiMarzio et al. PLoS One. .

Abstract

Bile salt hydrolase (BSH) activity against the bile acid tauro-beta-muricholic acid (T-β-MCA) was recently reported to mediate host bile acid, glucose, and lipid homeostasis via the farnesoid X receptor (FXR) signaling pathway. An earlier study correlated decreased Lactobacillus abundance in the cecum with increased concentrations of intestinal T-β-MCA, an FXR antagonist. While several studies have characterized BSHs in lactobacilli, deconjugation of T-β-MCA remains poorly characterized among members of this genus, and therefore it was unclear what strain(s) were responsible for this activity. Here, a strain of L. johnsonii with robust BSH activity against T-β-MCA in vitro was isolated from the cecum of a C57BL/6J mouse. A screening assay performed on a collection of 14 Lactobacillus strains from nine different species identified BSH substrate specificity for T-β-MCA only in two of three L. johnsonii strains. Genomic analysis of the two strains with this BSH activity revealed the presence of three bsh genes that are homologous to bsh genes in the previously sequenced human-associated strain L. johnsonii NCC533. Heterologous expression of several bsh genes in E. coli followed by enzymatic assays revealed broad differences in substrate specificity even among closely related bsh homologs, and suggests that the phylogeny of these enzymes does not closely correlate with substrate specificity. Predictive modeling allowed us to propose a potential mechanism driving differences in BSH activity for T-β-MCA in these homologs. Our data suggests that L. johnsonii regulates T-β-MCA levels in the mouse intestinal environment, and that this species may play a central role in FXR signaling in the mouse.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Visual comparisons of L. johnsonii NCK88 and LB1 draft genomes to the reference strain NCC533.
(a) WebACT visualization of genome similarity of NCK88 and LB1 to NCC533 based on BlastN analysis with a minimum match size of 100 nucleotides. Red bars indicate matches in the same orientation, and blue bars indicate matches in the reverse orientation. (b and c) Dot plots representing the maximum unique matches (MUMs) of the six frame amino acid translations of LB1(b) and NCK88 (c) draft genomes relative to NCC533. Forward MUMs are plotted as red lines/dots while reverse MUMs are plotted as blue lines/dots. A line of dots with slope of 1 represents an undisturbed segment of conservation between the two sequences and a line with a slope of -1 represents an inverted segment of conservation between the two sequences.
Fig 2
Fig 2. Phylogenetic analysis of Lactobacillus BSH sequences by maximum likelihood method.
The evolutionary history of BSHs encoded by Lactobacillus strains screened in our in vitro assay was inferred by using the Maximum Likelihood method based on the Le Gascuel 2008 model [17]. The tree with the highest log likelihood (-8040.5390) is shown. Initial trees for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then selecting the topology with superior log likelihood value. A discrete Gamma distribution was used to model evolutionary rate differences among sites (3 categories (+G, parameter = 2.2994)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 4.7893% sites). The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 19 amino acid sequences. All positions containing gaps and missing data were eliminated. There were a total of 297 positions in the final dataset. Evolutionary analyses were conducted in MEGA6 [18].
Fig 3
Fig 3. Visualizations of model differences in BSH substrate interactions with T-β-MCA.
(a) Depiction of residue side chains within a 4Å radius of the T-β-MCA ring structure in the substrate binding pocket of enzymes with activity against T-β-MCA. LB1 BSHC is in blue, LB1 BSHB is in green, NCK88 BSHB is in yellow, and T-β-MCA is in grey. Stick depictions of T-β-MCA and amino acid side chains indicate oxygen atoms in red, and nitrogen atoms in dark blue. Hydrogens have been removed to improve visual clarity. (b and c) Cartoon comparisons of LB1 BSHC (b) and NCK88 BSHC (c) with T-β-MCA in the binding pocket. YASSARA models energetically minimized to T-β-MCA are depicted in blue (LB1 BSHC) and magenta (NCK88 BSHC), and the range of loop movements for each model based on CABS-flex simulations is depicted in grey. Occlusion of the substrate binding pocket by the loop structure in NCK88 BSHC is indicated in a yellow box in (c).

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References

    1. Young VB. The role of the microbiome in human health and disease: an introduction for clinicians. BMJ. 2017;356: j831 doi: 10.1136/bmj.j831 - DOI - PubMed
    1. Geng W, Lin J. Bacterial bile salt hydrolase: an intestinal microbiome target for enhanced animal health. Anim Heal Res Rev. 2017/02/03. Cambridge University Press; 2016;17: 148–158. doi: 10.1017/S1466252316000153 - DOI - PubMed
    1. Ley RE, Bäckhed F, Turnbaugh P, Lozupone C a, Knight RD, Gordon JI. Obesity alters gut microbial ecology. Proc Natl Acad Sci U S A. 2005;102: 11070–5. doi: 10.1073/pnas.0504978102 - DOI - PMC - PubMed
    1. Ley R, Turnbaugh P, Klein S, Gordon J. Microbial ecology: human gut microbes associated with obesity. Nature. 2006;444: 1022–1023. doi: 10.1038/4441022a - DOI - PubMed
    1. Turnbaugh PJ, Ley RE, Mahowald M a, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444: 1027–31. doi: 10.1038/nature05414 - DOI - PubMed

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