Antibiotic-induced alterations in the gut microbiota are implicated in many metabolic and inflammatory diseases, increase the risk of secondary infections and contribute to the emergence of antimicrobial resistance. Here we report the design and in vivo performance of an engineered strain of Lactococcus lactis that altruistically degrades the widely used broad-spectrum antibiotics β-lactams (which disrupt commensal bacteria in the gut) through the secretion and extracellular assembly of a heterodimeric β-lactamase. The engineered β-lactamase-expression system does not confer β-lactam resistance to the producer cell, and is encoded via a genetically unlinked two-gene biosynthesis strategy that is not susceptible to dissemination by horizontal gene transfer. In a mouse model of parenteral ampicillin treatment, oral supplementation with the engineered live biotherapeutic minimized gut dysbiosis without affecting the ampicillin concentration in serum, precluded the enrichment of antimicrobial resistance genes in the gut microbiome and prevented the loss of colonization resistance against Clostridioides difficile. Engineered live biotherapeutics that safely degrade antibiotics in the gut may represent a suitable strategy for the prevention of dysbiosis and its associated pathologies.
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All code for the 16S rDNA analysis and for the metagenomics analysis are available on GitHub at https://github.com/maalcantar/eLBP-prevents-dysbiosis-16s-analysis and https://github.com/maalcantar/eLBP-prevents-dysbiosis-metagenomics-analysis.
Becattini, S., Taur, Y. & Pamer, E. G. Antibiotic-induced changes in the intestinal microbiota and disease. Trends Mol. Med. 22, 458–478 (2016).
Karachalios, G. & Charalabopoulos, K. Biliary excretion of antimicrobial drugs. Chemotherapy 48, 280–297 (2002).
Ghibellini, G., Leslie, E. M. & Brouwer, K. L. Methods to evaluate biliary excretion of drugs in humans: an updated review. Mol. Pharm. 3, 198–211 (2006).
Stecher, B., Maier, L. & Hardt, W. D. ‘Blooming’ in the gut: how dysbiosis might contribute to pathogen evolution. Nat. Rev. Microbiol. 11, 277–284 (2013).
Modi, S. R., Lee, H. H., Spina, C. S. & Collins, J. J. Antibiotic treatment expands the resistance reservoir and ecological network of the phage metagenome. Nature 499, 219–222 (2013).
Kent, A. G., Vill, A. C., Shi, Q., Satlin, M. J. & Brito, I. L. Widespread transfer of mobile antibiotic resistance genes within individual gut microbiomes revealed through bacterial Hi-C. Nat. Commun. 11, 4379 (2020).
Modi, S. R., Collins, J. J. & Relman, D. A. Antibiotics and the gut microbiota. J. Clin. Investig. 124, 4212–4218 (2014).
WHO Report on Surveillance of Antibiotic Consumption: 2016–2018 Early Implementation (World Health Organization, 2018).
Draper, K., Ley, C. & Parsonnet, J. Probiotic guidelines and physician practice: a cross-sectional survey and overview of the literature. Benef. Microbes 8, 507–519 (2017).
Suez, J., Zmora, N., Segal, E. & Elinav, E. The pros, cons, and many unknowns of probiotics. Nat. Med. 25, 716–729 (2019).
Hempel, S. et al. Safety of probiotics to reduce risk and prevent or treat disease. Evid. Rep. Technol. Assess. 200, 1–645 (2011).
Suez, J. et al. Post-antibiotic gut mucosal microbiome reconstitution is impaired by probiotics and improved by autologous FMT. Cell 174, 1406–1423.e16 (2018).
Bermudez-Humaran, L. G. et al. Engineering lactococci and lactobacilli for human health. Curr. Opin. Microbiol. 16, 278–283 (2013).
Fisher, J. F. & Mobashery, S. β-Lactam resistance mechanisms: Gram-positive bacteria and Mycobacterium tuberculosis. Cold Spring Harb. Perspect. Med. 6, a025221 (2016).
Wright, G. D. Bacterial resistance to antibiotics: enzymatic degradation and modification. Adv. Drug Deliv. Rev. 57, 1451–1470 (2005).
Bush, K. Past and present perspectives on β-lactamases. Antimicrob. Agents Chemother. 62, e01076–18 (2018).
Bush, K. & Bradford, P. A. Epidemiology of β-lactamase-producing pathogens. Clin. Microbiol. Rev. 33, e00047–19 (2020).
Teuber, M. in The Genera of Lactic Acid Bacteria (eds Wood, B. J. B. & Holzapfel, W. H.) 173–234 (Springer, 1995).
Limaye, S. A. et al. Phase 1b, multicenter, single blinded, placebo-controlled, sequential dose escalation study to assess the safety and tolerability of topically applied AG013 in subjects with locally advanced head and neck cancer receiving induction chemotherapy. Cancer 119, 4268–4276 (2013).
Braat, H. et al. A phase I trial with transgenic bacteria expressing interleukin-10 in Crohn’s disease. Clin. Gastroenterol. Hepatol. 4, 754–759 (2006).
Zhang, C. et al. Ecological robustness of the gut microbiota in response to ingestion of transient food-borne microbes. ISME J. 10, 2235–2245 (2016).
Galarneau, A., Primeau, M., Trudeau, L.-E. & Michnick, S. W. β-Lactamase protein fragment complementation assays as in vivo and in vitro sensors of protein–protein interactions. Nat. Biotechnol. 20, 619–622 (2002).
Zakeri, B. et al. Peptide tag forming a rapid covalent bond to a protein, through engineering a bacterial adhesin. Proc. Natl Acad. Sci. USA 109, E690–E697 (2012).
Nielsen, J. B. & Lampen, J. O. Membrane-bound penicillinases in Gram-positive bacteria. J. Biol. Chem. 257, 4490–4495 (1982).
Forsberg, K. J. et al. The shared antibiotic resistome of soil bacteria and human pathogens. Science 337, 1107–1111 (2012).
Jernberg, C., Lofmark, S., Edlund, C. & Jansson, J. K. Long-term ecological impacts of antibiotic administration on the human intestinal microbiota. ISME J. 1, 56–66 (2007).
Alcock, B. P. et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 48, D517–D525 (2020).
Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).
Schubert, A. M., Sinani, H. & Schloss, P. D. Antibiotic-induced alterations of the murine gut microbiota and subsequent effects on colonization resistance against Clostridium difficile. mBio 6, e00974 (2015).
Crobach, M. J. T. et al. Understanding Clostridium difficile colonization. Clin. Microbiol Rev. 31, e00021–17 (2018).
Theriot, C. M. et al. Antibiotic-induced shifts in the mouse gut microbiome and metabolome increase susceptibility to Clostridium difficile infection. Nat. Commun. 5, 3114 (2014).
Wong, J. M. W., de Souza, R., Kendall, C. W. C., Emam, A. & Jenkins, D. J. A. Colonic health: fermentation and short chain fatty acids. J. Clin. Gastroenterol. 40, 235–243 (2006).
Theriot, C. M., Bowman, A. A. & Young, V. B. Antibiotic-induced alterations of the gut microbiota alter secondary bile acid production and allow for Clostridium difficile spore germination and outgrowth in the large intestine. mSphere 1, e00045–15 (2016).
Lewis, B. B., Carter, R. A. & Pamer, E. G. Bile acid sensitivity and in vivo virulence of clinical Clostridium difficile isolates. Anaerobe 41, 32–36 (2016).
Steidler, L. et al. Biological containment of genetically modified Lactococcus lactis for intestinal delivery of human interleukin 10. Nat. Biotechnol. 21, 785–789 (2003).
Schwartz, D. J., Langdon, A. E. & Dantas, G. Understanding the impact of antibiotic perturbation on the human microbiome. Genome Med. 12, 82 (2020).
Harmoinen, J. et al. Enzymic degradation of a β-lactam antibiotic, ampicillin, in the gut: a novel treatment modality. J. Antimicrob. Chemother. 51, 361–365 (2003).
Kaleko, M. et al. Development of SYN-004, an oral β-lactamase treatment to protect the gut microbiome from antibiotic-mediated damage and prevent Clostridium difficile infection. Anaerobe 41, 58–67 (2016).
Kokai-Kun, J. F. et al. Use of ribaxamase (SYN-004), a β-lactamase, to prevent Clostridium difficile infection in β-lactam-treated patients: a double-blind, phase 2b, randomised placebo-controlled trial. Lancet Infect. Dis. 19, 487–496 (2019).
Mao, N., Cubillos-Ruiz, A., Cameron, D. E. & Collins, J. J. Probiotic strains detect and suppress cholera in mice. Sci. Transl. Med. 10, eaao2586 (2018).
Edwards, A. N. & McBride, S. M. Isolating and purifying clostridium difficile spores. Methods Mol. Biol. 1476, 117–128 (2016).
Salverda, M. L., De Visser, J. A. & Barlow, M. Natural evolution of TEM-1 β-lactamase: experimental reconstruction and clinical relevance. FEMS Microbiol. Rev. 34, 1015–1036 (2010).
Cameron, D. E. & Collins, J. J. Tunable protein degradation in bacteria. Nat. Biotechnol. 32, 1276–1281 (2014).
Theriot, C. M. et al. Cefoperazone-treated mice as an experimental platform to assess differential virulence of Clostridium difficile strains. Gut Microbes 2, 326–334 (2011).
Winston, J. A., Thanissery, R., Montgomery, S. A. & Theriot, C. M. Cefoperazone-treated mouse model of clinically-relevant Clostridium difficile strain R20291. J. Vis. Exp. 10, 54850 (2016).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).
McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).
Kuraku, S., Zmasek, C. M., Nishimura, O. & Katoh, K. aLeaves facilitates on-demand exploration of metazoan gene family trees on MAFFT sequence alignment server with enhanced interactivity. Nucleic Acids Res. 41, W22–W28 (2013).
Katoh, K., Rozewicki, J. & Yamada, K. D. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. 20, 1160–1166 (2019).
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663 (2015).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Joshi, N. A. & Fass, J. N. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files. https://github.com/najoshi/sickle (2011).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Parnanen, K. et al. Maternal gut and breast milk microbiota affect infant gut antibiotic resistome and mobile genetic elements. Nat. Commun. 9, 3891 (2018).
Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S-PLUS (Springer Science & Business Media, 2013).
Miller, R. G. in Simultaneous Statistical Inference (ed. Miller, R. G.) 1–35 (Springer, 1981).
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
We are grateful to A. Graveline for help with the animal protocol setup and A. Vernet and M. Sanchez-Ventura for assistance with animal experiments. We thank X. Tan, M. A. English, R. Gayet, D. Morales and J. Cubillos-Ruiz for helpful comments and discussions. We also thank K. Pärnänen for helpful discussion on metagenomic data processing and calculating antibiotic resistance gene abundances from metagenomic data. Additionally, we thank S. Blomquist for help with running analysis pipelines via the Commonwealth Computational Cloud for Data Driven Biology (C3DDB) cluster. This work was supported by funding from the Defense Threat Reduction Agency grant HDTRA1-14-1-0006 (to J.J.C.), Wyss Institute funding (to J.J.C.) and the Paul G. Allen Frontiers Group (to J.J.C.) and Wyss Institute validation project funding (to A.C.-R.). M.A.A. was supported by a National Science Foundation graduate research fellowship (award no. 1122374).
J.J.C. is co-founder and SAB chair of Synlogic and EnBiotix. A.C.-R. and J.J.C. have filed a patent application for this work. The other authors declare no competing interests.
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Extended Data Fig. 1 Schematic of the antibiotic survival landscape of the bacterial strains in this study.
a. In wildtype cells, the survival to the antibiotic is dictated by the minimum inhibitory concentration (MIC), which is the lowest concentration of an antibiotic that achieves inhibition of growth. In a population of antibiotic-sensitive cells, the landscape partition between survival and death is independent of cell density. b. Antimicrobial resistance factors (that is, native β-lactamase) decrease the susceptibility of the bacterial cell to the antibiotic, increasing the MIC. In a population of antibiotic-resistant cells, the partition between survival and death is also independent of cell density. c. Density-dependent survival effect in spTEM1-expressing strains. Secretion and extracellular assembly of the spTEM1 β-lactamase preclude self-protection in producer cells and makes the partition of the antibiotic survival landscape a function of the cell density. Below the critical cell density threshold, the engineered cells are as susceptible to the antibiotic as wildtype cells. Above the cell density threshold, the engineered cells can survive the antibiotic to the same extent as their neighboring cells, offering population-wide protection.
a. Treatment regimen. Mice were orally gavaged with two doses of 1010 CFU of L. lactis. The first dose occurred 2 hours prior to the ampicillin injection and the second simultaneous with the 200 mg/kg ampicillin injection. Intestinal load was evaluated each day for L. lactis spTEM1 and the empty-vector control strain L. lactis EV. b. Enumeration of viable L. lactis cells in feces in each day of treatment demonstrates no difference in the fecal loads of L. lactis spTEM1 (n = 8) and L. lactis EV (n = 8). Box plots display minimum, 25th percentile, median, 75th percentile, and maximum of biological replicate measurements. Unpaired two-sided Wilcoxon test: Day 1, p = 0.7944; Day 2, p = 0.0511; Day 3, p = 0.4093.
a. Fecal samples were collected 2 hours after a single dose of L. lactis spTEM1 or delivery vehicle control. b. Nitrocefin hydrolysis assay for the detection of β-lactamase activity. Means and standard deviations of biological replicates are shown. n = 10 mice in the L. lactis spTEM1 and control groups.
Extended Data Fig. 4 Mouse model for parenteral ampicillin-induced dysbiosis and the disruption of colonization resistance against C. difficile.
a. A 3-day intraperitoneal ampicillin administration regimen is evaluated for its effects in abolishing colonization resistance against 5×103 spores of C. difficile at 24 hours after the last ampicillin dose. C. difficile density in feces is evaluated 24 hours after the infection. b. Evaluation of single- or double-dose (8 hours apart) administration regimens for intraperitoneal ampicillin injection indicates that a single dose of ampicillin for 3 days is enough to sensitize the mouse gut to robust C. difficile colonization. n = 5 mice in each treatment group. Means and standard deviations of biological replicates are shown. c. Dose-dependency of single daily intraperitoneal ampicillin injections in the disruption of colonization resistance against C. difficile. n = 5 mice in each treatment group. Means and standard deviations of biological replicates are shown.
Extended Data Fig. 5 Diversity and composition of the gut microbiota during the experiment of preservation of colonization resistance against C. difficile.
a. Determination of the Shannon diversity index for gut microbial communities in mice pre- and post-treatment. The p-values correspond to FDR-corrected Wilcoxon tests between the groups that received L. lactis spTEM1 and L. lactis EV. Adjusted P-values below boxes correspond to FDR-corrected two-sided Wilcoxon tests comparing diversity values against baseline diversity at the pre-treatment timepoint. Box plots display minimum, 25th percentile, median, 75th percentile, and maximum of biological replicate measurements. n = 8 mice in each treatment group. b. Principal coordinates analysis of gut microbial communities in ampicillin-treated mice receiving L. lactis spTEM1 and L. lactis EV control reveals differences in the resulting composition of the community. Close clustering to pre-ampicillin conditions indicates smaller alterations in the community structure. n = 8 mice in each treatment group.
Extended Data Fig. 6 Metagenomic analysis of antimicrobial resistance genes (ARG) during the experiment of preservation of colonization resistance against C. difficile.
a. Analysis of the abundance of ARG reveals significant enrichment in ampicillin-treated mice receiving L. lactis EV but not in mice receiving L. lactis spTEM1. Stacked bar data are presented as reads mapping the different CARD database categories and normalized to the size of the read pool in each sample. Vector-derived ARG in the β-lactam and chloramphenicol classes are presented as a different category to differentiate them from endogenous ARG. Adjusted p-values were calculated with a negative binomial generalized linear model with Tukey’s post hoc test between spTEM1-treated and EV-treated groups. b. Abundance of endogenous and L. lactis spTEM1-derived β-lactamases in the mouse. Elimination of the ampicillin selective pressure by the spTEM1 β-lactamases reduces the enrichment of endogenous β-lactamases in the mouse gut. Rapid elimination from the system of the spTEM1 gene fragments compared to endogenous β-lactamase genes suggests lack of competitive advantage in the spTEM1 strain. n = 8 mice in each L. lactis spTEM1 and L. lactis EV groups. p-value significance corresponds to FDR-corrected two-sided Wilcoxon tests comparing ARG abundance values against baseline values at the pre-treatment timepoint. Means and standard deviations of biological replicates are shown.
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Cubillos-Ruiz, A., Alcantar, M.A., Donghia, N.M. et al. An engineered live biotherapeutic for the prevention of antibiotic-induced dysbiosis. Nat. Biomed. Eng 6, 910–921 (2022). https://doi.org/10.1038/s41551-022-00871-9
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