Letter | Published:

Engineered bacteria can function in the mammalian gut long-term as live diagnostics of inflammation

Nature Biotechnology volume 35, pages 653658 (2017) | Download Citation


Bacteria can be engineered to function as diagnostics or therapeutics in the mammalian gut but commercial translation of technologies to accomplish this has been hindered by the susceptibility of synthetic genetic circuits to mutation and unpredictable function during extended gut colonization. Here, we report stable, engineered bacterial strains that maintain their function for 6 months in the mouse gut. We engineered a commensal murine Escherichia coli strain to detect tetrathionate, which is produced during inflammation. Using our engineered diagnostic strain, which retains memory of exposure in the gut for analysis by fecal testing, we detected tetrathionate in both infection-induced and genetic mouse models of inflammation over 6 months. The synthetic genetic circuits in the engineered strain were genetically stable and functioned as intended over time. The durable performance of these strains confirms the potential of engineered bacteria as living diagnostics.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


Primary accessions


Sequence Read Archive

Referenced accessions

NCBI Reference Sequence


  1. 1.

    , & Microbiome therapeutics - advances and challenges. Adv. Drug Deliv. Rev. 105 Pt A, 44–54 (2016).

  2. 2.

    et al. Biodistribution and genetic stability of the novel antitumor agent VNP20009, a genetically modified strain of Salmonella typhimurium. J. Infect. Dis. 181, 1996–2002 (2000).

  3. 3.

    , , & Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat. Methods 12, 415–418 (2015).

  4. 4.

    & Visualization of evolutionary stability dynamics and competitive fitness of Escherichia coli engineered with randomized multigene circuits. ACS Synth. Biol. 2, 519–528 (2013).

  5. 5.

    , , & Designing and engineering evolutionary robust genetic circuits. J. Biol. Eng. 4, 12 (2010).

  6. 6.

    et al. Genetic circuit performance under conditions relevant for industrial bioreactors. ACS Synth. Biol. 1, 555–564 (2012).

  7. 7.

    , , , & Using synthetic biological parts and microbioreactors to explore the protein expression characteristics of Escherichia coli. ACS Synth. Biol. 3, 129–139 (2014).

  8. 8.

    , & Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000).

  9. 9.

    , & Engineered E. coli that detect and respond to gut inflammation through nitric oxide sensing. ACS Synth. Biol. 1, 451–457 (2012).

  10. 10.

    et al. Permanent genetic memory with >1-byte capacity. Nat. Methods 11, 1261–1266 (2014).

  11. 11.

    & Synthetic biology. Genomically encoded analog memory with precise in vivo DNA writing in living cell populations. Science 346, 1256272 (2014).

  12. 12.

    et al. Programmable bacteria detect and record an environmental signal in the mammalian gut. Proc. Natl. Acad. Sci. USA 111, 4838–4843 (2014).

  13. 13.

    , , & Programming a Human Commensal Bacterium, Bacteroides thetaiotaomicron, to Sense and Respond to Stimuli in the Murine Gut Microbiota. Cell Syst. 1, 62–71 (2015).

  14. 14.

    et al. Gut inflammation provides a respiratory electron acceptor for Salmonella. Nature 467, 426–429 (2010).

  15. 15.

    et al. Genetic and Metabolic Signals during Acute Enteric Bacterial Infection Alter the Microbiota and Drive Progression to Chronic Inflammatory Disease. Cell Host Microbe 19, 21–31 (2016).

  16. 16.

    , , , & The genetic basis of tetrathionate respiration in Salmonella typhimurium. Mol. Microbiol. 32, 275–287 (1999).

  17. 17.

    , & The dynamics of gut-associated microbial communities during inflammation. EMBO Rep. 14, 319–327 (2013).

  18. 18.

    , , & The alternative electron acceptor tetrathionate supports B12-dependent anaerobic growth of Salmonella enterica serovar typhimurium on ethanolamine or 1,2-propanediol. J. Bacteriol. 183, 2463–2475 (2001).

  19. 19.

    , , , & Tetrathionate stimulated growth of Campylobacter jejuni identifies a new type of bi-functional tetrathionate reductase (TsdA) that is widely distributed in bacteria. Mol. Microbiol. 88, 173–188 (2013).

  20. 20.

    et al. Gut microbiome composition and function in experimental colitis during active disease and treatment-induced remission. ISME J. 8, 1403–1417 (2014).

  21. 21.

    et al. Pretreatment of mice with streptomycin provides a Salmonella enterica serovar Typhimurium colitis model that allows analysis of both pathogen and host. Infect. Immun. 71, 2839–2858 (2003).

  22. 22.

    et al. Fecal lipocalin 2, a sensitive and broadly dynamic non-invasive biomarker for intestinal inflammation. PLoS One 7, e44328 (2012).

  23. 23.

    & Inflammatory bowel disease--a radical view. Gut 34, 865–868 (1993).

  24. 24.

    , , & A multihit model: colitis lessons from the interleukin-10-deficient mouse. Inflamm. Bowel Dis. 21, 1967–1975 (2015).

  25. 25.

    , & 129X1/SvJ mouse strain has a novel defect in inflammatory cell recruitment. J. Immunol. 168, 869–874 (2002).

  26. 26.

    et al. Strain and model dependent differences in inflammatory cell recruitment in mice. Inflamm. Res. 57, 457–463 (2008).

  27. 27.

    , , , & A distributed cell division counter reveals growth dynamics in the gut microbiota. Nat. Commun. 6, 10039 (2015).

  28. 28.

    , , & Rates of spontaneous mutation. Genetics 148, 1667–1686 (1998).

  29. 29.

    , , & Rate and molecular spectrum of spontaneous mutations in the bacterium Escherichia coli as determined by whole-genome sequencing. Proc. Natl. Acad. Sci. USA 109, E2774–E2783 (2012).

  30. 30.

    & One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl. Acad. Sci. USA 97, 6640–6645 (2000).

  31. 31.

    Experiments in Molecular Genetics (Cold Spring Harbor Laboratory, 1972).

  32. 32.

    , & A Manual for Genetic Engineering: Advanced Bacterial Genetics (Cold Spring Harbor Laboratory, 1980).

  33. 33.

    et al. Dietary-fat-induced taurocholic acid promotes pathobiont expansion and colitis in Il10−/− mice. Nature 487, 104–108 (2012).

  34. 34.

    et al. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses. Genome Biol. 17, 121 (2016).

  35. 35.

    et al. Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS One 10, e0128036 (2015).

  36. 36.

    & Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq. Methods Mol. Biol. 1151, 165–188 (2014).

Download references


We thank A. Graveline and L. Bry for discussions and assistance with mouse experiments and A. Verdegaal for experimental assistance. S. typhimurium TT22470 was a gift from J. Roth. We thank Dana-Farber/Harvard Cancer Center in Boston for the use of the Rodent Histopathology Core, which provided histology preparation service. Dana-Farber/Harvard Cancer Center is supported in part by a NCI Cancer Center Support Grant # NIH 5 P30 CA06516. D.T.R. was supported by a Human Frontier Science Program Long-Term Fellowship and an NHMRC/RG Menzies Early Career Fellowship from the Menzies Foundation through the Australian National Health and Medical Research Council. T.W.G. was supported by a Leopoldina Research Fellowship (LPDS 2014-05) from the German National Academy of Sciences Leopoldina. The research was funded by Defense Advanced Research Projects Agency Grant HR0011-15-C-0094 (P.A.S.) and the Wyss Institute for Biologically Inspired Engineering.

Author information

Author notes

    • S Jordan Kerns
    •  & Jonathan W Kotula

    Present addresses: Emulate Inc., Boston, Massachusetts, USA (S.J.K.) and SynLogic, Cambridge, Massachusetts, USA (J.W.K.).


  1. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA.

    • David T Riglar
    • , Tobias W Giessen
    • , Michael Baym
    • , S Jordan Kerns
    • , Matthew J Niederhuber
    • , Jonathan W Kotula
    •  & Pamela A Silver
  2. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, Massachusetts, USA.

    • David T Riglar
    • , Tobias W Giessen
    • , S Jordan Kerns
    • , Matthew J Niederhuber
    • , Jonathan W Kotula
    • , Jeffrey C Way
    •  & Pamela A Silver
  3. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.

    • Michael Baym
  4. Department of Microbiology and Immunology, Harvard Medical School, Boston, Massachusetts, USA.

    • Roderick T Bronson
  5. Massachusetts Host-Microbiome Center, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

    • Georg K Gerber


  1. Search for David T Riglar in:

  2. Search for Tobias W Giessen in:

  3. Search for Michael Baym in:

  4. Search for S Jordan Kerns in:

  5. Search for Matthew J Niederhuber in:

  6. Search for Roderick T Bronson in:

  7. Search for Jonathan W Kotula in:

  8. Search for Georg K Gerber in:

  9. Search for Jeffrey C Way in:

  10. Search for Pamela A Silver in:


D.T.R., T.W.G., M.B., S.J.K., G.K.G., J.C.W. and P.A.S. designed experiments. D.T.R. and M.J.N. performed and analyzed in vitro characterization. D.T.R. performed and analyzed all mouse experiments. T.W.G. performed and analyzed tetrathionate mass spectrometry. M.B. performed and analyzed whole genome sequencing. S.J.K. and J.W.K. generated strains and collected preliminary data for the study. R.T.B. performed all histology scoring. D.T.R. and P.A.S. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Pamela A Silver.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–5 and Supplementary Tables 1–2

About this article

Publication history






Further reading