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Precision identification of diverse bloodstream pathogens in the gut microbiome


A comprehensive evaluation of every patient with a bloodstream infection includes an attempt to identify the infectious source. Pathogens can originate from various places, such as the gut microbiota, skin and the external environment. Identifying the definitive origin of an infection would enable precise interventions focused on management of the source1,2. Unfortunately, hospital infection control practices are often informed by assumptions about the source of various specific pathogens; if these assumptions are incorrect, they lead to interventions that do not decrease pathogen exposure3. Here, we develop and apply a streamlined bioinformatic tool, named StrainSifter, to match bloodstream pathogens precisely to a candidate source. We then leverage this approach to interrogate the gut microbiota as a potential reservoir of bloodstream pathogens in a cohort of hematopoietic cell transplantation recipients. We find that patients with Escherichia coli and Klebsiella pneumoniae bloodstream infections have concomitant gut colonization with these organisms, suggesting that the gut may be a source of these infections. We also find cases where typically nonenteric pathogens, such as Pseudomonas aeruginosa and Staphylococcus epidermidis, are found in the gut microbiota, thereby challenging the existing informal dogma of these infections originating from environmental or skin sources. Thus, we present an approach to distinguish the source of various bloodstream infections, which may facilitate more accurate tracking and prevention of hospital-acquired infections.

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Fig. 1: BSI pathogens are present in the gut microbiota at varying relative abundance prior to BSI.
Fig. 2: Gut and BSI strains from the same patient are more closely related than strains from different patients.
Fig. 3: Antibiotic resistance gene predictions in bloodstream isolate genomes.

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Data availability

All sequencing data sets from the current study have been deposited in the Sequence Read Archive under BioProject PRJNA477326. Accession numbers are listed in Supplementary Table 14.


  1. Costa, S. F., Miceli, M. H. & Anaissie, E. J. Mucosa or skin as source of coagulase-negative staphylococcal bacteraemia? Lancet. Infect. Dis. 4, 278–286 (2004).

    Article  PubMed  Google Scholar 

  2. Mermel, L. A. et al. Clinical practice guidelines for the diagnosis and management of intravascular catheter-related infection: 2009 update by the Infectious Diseases Society of America. Clin. Infect. Dis. 49, 1–45 (2009).

    Article  CAS  PubMed  Google Scholar 

  3. Steinberg, J. P., Robichaux, C., Tejedor, S. C., Reyes, M. D. & Jacob, J. T. Distribution of pathogens in central line-associated bloodstream infections among patients with and without neutropenia following chemotherapy: evidence for a proposed modification to the current surveillance definition. Infect. Control Hosp. Epidemiol. 34, 171–175 (2013).

    Article  PubMed  Google Scholar 

  4. Goto, M. & Al-Hasan, M. N. Overall burden of bloodstream infection and nosocomial bloodstream infection in North America and Europe. Clin. Microbiol. Infect. 19, 501–509 (2013).

    Article  CAS  PubMed  Google Scholar 

  5. Blennow, O., Ljungman, P., Sparrelid, E., Mattsson, J. & Remberger, M. Incidence, risk factors, and outcome of bloodstream infections during the pre-engraftment phase in 521 allogeneic hematopoietic stem cell transplantations. Transpl. Infect. Dis. 16, 106–114 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. Gudiol, C. et al. Etiology, clinical features and outcomes of pre-engraftment and post-engraftment bloodstream infection in hematopoietic SCT recipients. Bone Marrow Transplant. 49, 824–830 (2014).

    Article  CAS  PubMed  Google Scholar 

  7. Mikulska, M. et al. Blood stream infections in allogeneic hematopoietic stem cell transplant recipients: reemergence of Gram-negative rods and increasing antibiotic resistance. Biol. Blood Marrow Transplant. 15, 47–53 (2009).

    Article  CAS  PubMed  Google Scholar 

  8. See, I. et al. Impact of removing mucosal barrier injury laboratory-confirmed bloodstream infections from central line-associated bloodstream infection rates in the National Healthcare Safety Network, 2014. Am. J. Infect. Control 45, 321–323 (2017).

    Article  PubMed  Google Scholar 

  9. Satlin, M. J. et al. Emergence of carbapenem-resistant Enterobacteriaceae as causes of bloodstream infections in patients with hematologic malignancies. Leuk. Lymphoma 54, 799–806 (2012).

  10. Samet, A. et al. Leukemia and risk of recurrent Escherichia coli bacteremia: genotyping implicates E. coli translocation from the colon to the bloodstream. Eur. J. Clin. Microbiol. Infect. Dis. 32, 1393–1400 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Snitkin, E. S. et al. Genome-wide recombination drives diversification of epidemic strains of Acinetobacter baumannii. Proc. Natl Acad. Sci. USA 108, 13758–13763 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lieberman, T. D. et al. Genetic variation of a bacterial pathogen within individuals with cystic fibrosis provides a record of selective pressures. Nat. Genet. 46, 82–87 (2013).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  13. Snitkin, E. S. et al. Tracking a hospital outbreak of carbapenem-resistant Klebsiella pneumoniae with whole-genome sequencing. Sci. Transl Med. 4, 148ra116 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Kaysen, A. et al. Integrated meta-omic analyses of the gastrointestinal tract microbiome in patients undergoing allogeneic hematopoietic stem cell transplantation. Transl Res. 186, 79–94.e1 (2017).

    Article  PubMed  Google Scholar 

  15. Costea, P. I. et al. metaSNV: a tool for metagenomic strain level analysis. PLoS ONE 12, e0182392 (2017).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  16. Nayfach, S., Rodriguez-Mueller, B., Garud, N. & Pollard, K. S. An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res. 26, 1612–1625 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Truong, D. T., Tett, A., Pasolli, E., Huttenhower, C. & Segata, N. Microbial strain-level population structure & genetic diversity from metagenomes. Genome Res. 27, 626–638 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. National Healthcare Safety Network. Patient Safety Component Manual (Center for Disease Control, 2016).

  19. Minot, S. S., Krumm, N. & Greenfield, N. B. One Codex: a sensitive and accurate data platform for genomic microbial identification. Preprint at (2015).

  20. Ubeda, C. et al. Vancomycin-resistant Enterococcus domination of intestinal microbiota is enabled by antibiotic treatment in mice and precedes bloodstream invasion in humans. J. Clin. Invest. 120, 4332–4341 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Taur, Y. et al. Intestinal domination and the risk of bacteremia in patients undergoing allogeneic hematopoietic stem cell transplantation. Clin. Infect. Dis. 55, 905–914 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Nesher, L. et al. Fecal colonization and infection with Pseudomonas aeruginosa in recipients of allogeneic hematopoietic stem cell transplantation. Transpl. Infect. Dis. 17, 33–38 (2014).

    Article  PubMed  Google Scholar 

  23. Wade, J. C., Schimpff, S. C., Newman, K. A. & Wiernik, P. H. Staphylococcus epidermidis: an increasing cause of infection in patients with granulocytopenia. Ann. Intern. Med. 97, 503–508 (1982).

    Article  CAS  PubMed  Google Scholar 

  24. Rotstein, C., Higby, D., Killion, K. & Powell, E. Relationship of surveillance cultures to bacteremia and fungemia in bone marrow transplant recipients with Hickman or Broviac catheters. J. Surg. Oncol. 39, 154–158 (1988).

    Article  CAS  PubMed  Google Scholar 

  25. MacFie, J. et al. Gut origin of sepsis: a prospective study investigating associations between bacterial translocation, gastric microflora, and septic morbidity. Gut 45, 223–228 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Costa, S. F. et al. Colonization and molecular epidemiology of coagulase-negative staphylococcal bacteremia in cancer patients: a pilot study. Am. J. Infect. Control 34, 36–40 (2006).

    Article  PubMed  Google Scholar 

  27. Brown, C. T., Olm, M. R., Thomas, B. C. & Banfield, J. F. Measurement of bacterial replication rates in microbial communities. Nat. Biotechnol. 34, 1256–1263 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Petersen, K. R., Streett, D. A., Gerritsen, A. T., Hunter, S. S. & Settles, M. L. Super deduper, fast PCR duplicate detection in fastq files. In Proc. 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics—BCB ’15 491–492 (ACM, 2015).

  29. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).

    Article  Google Scholar 

  30. Krueger, F. Trim Galore! (Babraham Bioinformatics, 2017).

  31. Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. GAEMR v.1.0.1 (GAEMR, 2012).

  33. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522 (2012).

    Article  PubMed  CAS  Google Scholar 

  35. Roach, D. J. et al. A year of infection in the intensive care unit: prospective whole genome sequencing of bacterial clinical isolates reveals cryptic transmissions and novel microbiota. PLoS Genet. 11, e1005413 (2015).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  36. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  38. Barnett, D. W., Garrison, E. K., Quinlan, A. R., Strömberg, M. P. & Marth, G. T. BamTools: a C++ API and toolkit for analyzing and managing BAM files. Bioinformatics 27, 1691–1692 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Touchon, M. et al. Organised genome dynamics in the Escherichia coli species results in highly diverse adaptive paths. PLoS Genet. 5, e1000344 (2009).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  41. Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  43. Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).

    Article  CAS  PubMed  Google Scholar 

  44. Schliep, K. P. phangorn: Phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).

    Article  CAS  PubMed  Google Scholar 

  45. Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T.-Y. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2016).

    Article  Google Scholar 

  46. Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. PubMLST (PubMLST, accessed 20 April 2018).

  48. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  PubMed  Google Scholar 

  49. Inouye, M. et al. SRST2: rapid genomic surveillance for public health and hospital microbiology labs. Genome Med. 6, 90 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  50. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  51. Gibson, M. K., Forsberg, K. J. & Dantas, G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J. 9, 207–216 (2015).

    Article  CAS  PubMed  Google Scholar 

  52. HMMER v3.2.1 (HMMER, 2016).

  53. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, New York, 2016).

  54. Wickham, H. Reshaping data with the reshape package. J. Stat. Softw. 21, 1–20 (2007).

    Article  Google Scholar 

  55. Wickham, H. et al. dplyr: A Grammar of Data Manipulation. R package version 0.7.4. (2017).

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We thank J. Kang for her assistance with stool sample processing, as well as the other members of the Bhatt laboratory for providing feedback on the study design, bioinformatics pipeline and manuscript revisions. We also thank N. Greenfield and the One Codex team for help with using their platform. We appreciate M. Kelly, C. Severyn and D. Ward for their feedback on the manuscript. We especially thank the patients and nurses on the Blood and Marrow Transplantation service for their enthusiastic participation in this project. This work was supported in part by the National Science Foundation Graduate Research Fellowship (F.B.T.), the National Institutes of Health (NIH), National Center for Advancing Translational Science, Clinical and Translational Science Awards KL2 TR001083 and UL1 TR001085 and the American Society of Blood and Marrow Transplantation New Investigator Award (T.M.A.). A.S.B. was funded in part by the National Cancer Institute NIH K08 award, no. CA184420, the Damon Runyon Clinical Investigator Award and the Amy Strelzer Manasevit Award. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Authors and Affiliations



F.B.T. generated the bloodstream isolate sequencing libraries, developed the StrainSifter pipeline and performed the sequencing data analysis. T.M.A. developed the stool biospecimen collection, assisted in study design, extracted clinical metadata from the electronic medical record and generated the stool sample sequencing libraries. E.T. contributed to the generation of stool sample sequencing libraries. F.S. and N.B. provided blood culture isolates. A.S.B. was responsible for study design and manuscript feedback. T.M.A., F.B.T. and A.S.B. wrote and edited the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ami S. Bhatt.

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Tamburini, F.B., Andermann, T.M., Tkachenko, E. et al. Precision identification of diverse bloodstream pathogens in the gut microbiome. Nat Med 24, 1809–1814 (2018).

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