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Volume 7, Issue 9

Metagenomic evidence for a polymicrobial signature of sepsis Open Access

Abstract

Our understanding of the host component of sepsis has made significant progress. However, detailed study of the microorganisms causing sepsis, either as single pathogens or microbial assemblages, has received far less attention. Metagenomic data offer opportunities to characterize the microbial communities found in septic and healthy individuals. In this study we apply gradient-boosted tree classifiers and a novel computational decontamination technique built upon SHapley Additive exPlanations (SHAP) to identify microbial hallmarks which discriminate blood metagenomic samples of septic patients from that of healthy individuals. Classifiers had high performance when using the read assignments to microbial genera [area under the receiver operating characteristic (AUROC=0.995)], including after removal of species ‘culture-confirmed’ as the cause of sepsis through clinical testing (AUROC=0.915). Models trained on single genera were inferior to those employing a polymicrobial model and we identified multiple co-occurring bacterial genera absent from healthy controls. While prevailing diagnostic paradigms seek to identify single pathogens, our results point to the involvement of a polymicrobial community in sepsis. We demonstrate the importance of the microbial component in characterising sepsis, which may offer new biological insights into the aetiology of sepsis, and ultimately support the development of clinical diagnostic or even prognostic tools.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): bacteraemia , blood metagenomics , contamination , kitome , machine learning , metagenomics , sepsis and SHAP
Funding
This study was supported by the:
  • University College London Hospitals NHS Foundation Trust (Award AI in Healthcare Funding Call 2019)
    • Principle Award Recipient: FrancoisBalloux
  • University College London Hospitals NHS Foundation Trust (Award AI in Healthcare Funding Call 2019)
    • Principle Award Recipient: Lucyvan Dorp
© 2021 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2021-09-03
2024-04-23
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References

  1. Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. Lancet 2020; 395:200–211 [View Article] [PubMed]
     [Google Scholar]
  2. Kwizera A, Baelani I, Mer M, Kissoon N, Schultz MJ et al. The long sepsis journey in low-and middle-income countries begins with a first step… but on which road?. Springer 2018
     [Google Scholar]
  3. Bone RC, Balk RA, Cerra FB, Dellinger RP, Fein AM et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Chest 1992; 101:1644–1655 [View Article]
     [Google Scholar]
  4. Levy MM, Fink MP, Marshall JC, Abraham E, Angus D et al. 2001 sccm/esicm/accp/ats/sis international sepsis definitions conference. Intensive Care Med 2003; 29:530–538 [View Article] [PubMed]
     [Google Scholar]
  5. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D et al. The third international consensus definitions for sepsis and septic shock (Sepsis-3. Jama 2016; 315:801–810 [View Article] [PubMed]
     [Google Scholar]
  6. van der Poll T, van de Veerdonk FL, Scicluna BP, Netea MG. The immunopathology of sepsis and potential therapeutic targets. Nat Rev Immunol 2017; 17:407–420 [View Article] [PubMed]
     [Google Scholar]
  7. Venet F, Monneret G. Advances in the understanding and treatment of sepsis-induced immunosuppression. Nat Rev Nephrol 2018; 14:121–137 [View Article] [PubMed]
     [Google Scholar]
  8. Ammer-Herrmenau C, Kulkarni U, Andreas N, Ungelenk M, Ravens S et al. Sepsis induces long-lasting impairments in CD4+ T-cell responses despite rapid numerical recovery of T-lymphocyte populations. PLoS One 2019; 14:e0211716 [View Article] [PubMed]
     [Google Scholar]
  9. Mao Q, Jay M, Hoffman JL, Calvert J, Barton C et al. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open 2018; 8:e017833 [View Article] [PubMed]
     [Google Scholar]
  10. Taylor RA, Pare JR, Venkatesh AK, Mowafi H, Melnick ER et al. Prediction of in‐hospital mortality in emergency department patients with sepsis: a local big data–driven, machine learning approach. Acad Emerg Med 2016; 23:269–278 [View Article] [PubMed]
     [Google Scholar]
  11. Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med 2018; 46:547–553 [View Article] [PubMed]
     [Google Scholar]
  12. Henry KE, Hager DN, Pronovost PJ, Saria S. A targeted real-time early warning score (TREWScore) for septic shock. Sci Transl Med 2015; 7:299ra122 [View Article] [PubMed]
     [Google Scholar]
  13. Smith GB, Prytherch DR, Meredith P, Schmidt PE, Featherstone PI. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation 2013; 84:465–470 [View Article] [PubMed]
     [Google Scholar]
  14. Rhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med 2017; 43:304–377 [View Article] [PubMed]
     [Google Scholar]
  15. Klaerner HG, Eschenbach U, Kamereck K, Lehn N, Wagner H et al. Failure of an automated blood culture system to detect nonfermentative gram-negative bacteria. J Clin Microbiol 2000; 38:1036–1041 [View Article] [PubMed]
     [Google Scholar]
  16. Benjamin RJ, Wagner SJ. The residual risk of sepsis: modeling the effect of concentration on bacterial detection in two‐bottle culture systems and an estimation of false‐negative culture rates. Transfusion 2007; 47:1381–1389 [View Article] [PubMed]
     [Google Scholar]
  17. Tay WH, Chong KKL, Kline KA. Polymicrobial–host interactions during infection. J Mol Biol 2016; 428:3355–3371 [View Article] [PubMed]
     [Google Scholar]
  18. Westh H, Lisby G, Breysse F, Böddinghaus B, Chomarat M et al. Multiplex real-time PCR and blood culture for identification of bloodstream pathogens in patients with suspected sepsis. Clin Microbiol Infect 2009; 15:544–551 [View Article] [PubMed]
     [Google Scholar]
  19. Salter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol 2014; 12:87 [View Article] [PubMed]
     [Google Scholar]
  20. Glassing A, Dowd SE, Galandiuk S, Davis B, Chiodini RJ. Inherent bacterial DNA contamination of extraction and sequencing reagents may affect interpretation of microbiota in low bacterial biomass samples. Gut Pathog 2016; 8:24 [View Article] [PubMed]
     [Google Scholar]
  21. Weiss S, Amir A, Hyde ER, Metcalf JL, Song SJ et al. Tracking down the sources of experimental contamination in microbiome studies. Genome Biol 2014; 15:564 [View Article] [PubMed]
     [Google Scholar]
  22. Blauwkamp TA, Thair S, Rosen MJ, Blair L, Lindner MS et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease. Nat Microbiol 2019; 4:663–674 [View Article]
     [Google Scholar]
  23. Grumaz S, Stevens P, Grumaz C, Decker SO, Weigand MA et al. Next-generation sequencing diagnostics of bacteremia in septic patients. Genome Med 2016; 8:73 [View Article] [PubMed]
     [Google Scholar]
  24. Gosiewski T, Ludwig-Galezowska AH, Huminska K, Sroka-Oleksiak A, Radkowski P et al. Comprehensive detection and identification of bacterial DNA in the blood of patients with sepsis and healthy volunteers using next-generation sequencing method-the observation of DNAemia. Eur J Clin Microbiol Infect Dis 2017; 36:329–336 [View Article] [PubMed]
     [Google Scholar]
  25. Grumaz S, Grumaz C, Vainshtein Y, Stevens P, Glanz K et al. Enhanced performance of next-generation sequencing diagnostics compared with standard of care microbiological diagnostics in patients suffering from septic shock. Crit Care Med 2019; 47:e394–e402 [View Article] [PubMed]
     [Google Scholar]
  26. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article] [PubMed]
     [Google Scholar]
  27. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 2009; 10:R25 [View Article] [PubMed]
     [Google Scholar]
  28. Bushnell B. BBMap: a Fast, Accurate, Splice-Aware Aligner Berkeley, CA (United States: Lawrence Berkeley National Lab (LBNL; 2014
     [Google Scholar]
  29. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet j 2011; 17:10–12 [View Article]
     [Google Scholar]
  30. Aronesty E. Comparison of sequencing utility programs. Open Bioinforma J 2013; 7: [View Article]
     [Google Scholar]
  31. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20:257 [View Article] [PubMed]
     [Google Scholar]
  32. Lu J, Salzberg S. Ultrafast and accurate 16S microbial community analysis using Kraken 2. bioRxiv 2020
     [Google Scholar]
  33. Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining 2016 pp 785–794
     [Google Scholar]
  34. Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res 2012; 13:281–305
     [Google Scholar]
  35. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B et al. Scikit-learn: Machine learning in Python. J Mach Learn Res 2011; 12:2825–2830
     [Google Scholar]
  36. Cawley GC, Talbot NLC. On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res 2010; 11:2079–2107
     [Google Scholar]
  37. Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One 2015; 10:e0118432
     [Google Scholar]
  38. Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell 2020; 2:56–67 [View Article]
     [Google Scholar]
  39. Shaw LP, Wang AD, Dylus D, Meier M, Pogacnik G et al. The phylogenetic range of bacterial and viral pathogens of vertebrates. Mol Ecol 2020n/a(n/a
     [Google Scholar]
  40. Shaw L. The phylogenetic range of bacterial and viral pathogens of vertebrates: dataset and supplementary material [Internet]; 2020 https://figshare.com/articles/dataset/The_phylogenetic_range_of_bacterial_and_viral_pathogens_of_vertebrates_dataset_and_supplementary_material/8262779
  41. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 2020; 17:261–272 [View Article] [PubMed]
     [Google Scholar]
  42. Friedman J, Alm EJ. Inferring correlation networks from genomic survey data. PLoS Comput Biol 2012; 8:e1002687 [View Article] [PubMed]
     [Google Scholar]
  43. Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol 2015; 11:e1004226 [View Article] [PubMed]
     [Google Scholar]
  44. Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal, complex Syst 2006; 1695:1–9
     [Google Scholar]
  45. Chen T, Yu W-H, Izard J, Baranova O, Lakshmanan A et al. The Human Oral microbiome database: A web accessible resource for investigating oral microbe taxonomic and genomic information. Database (Oxford) 2010; 2010:baq013 [View Article] [PubMed]
     [Google Scholar]
  46. Pytko-Polonczyk J, Konturek SJ, Karczewska E, Bielański W, Kaczmarczyk-Stachowska A. Oral cavity as permanent reservoir of Helicobacter pylori and potential source of reinfection. J Physiol Pharmacol an Off J Polish Physiol Soc 1996; 47:121–129
     [Google Scholar]
  47. Periasamy S, Kolenbrander PE. Mutualistic biofilm communities develop with Porphyromonas gingivalis and initial, early, and late colonizers of enamel. J Bacteriol 2009; 191:6804–6811 [View Article] [PubMed]
     [Google Scholar]
  48. Cephas KD, Kim J, Mathai RA, Barry KA, Dowd SE et al. Comparative analysis of salivary bacterial microbiome diversity in edentulous infants and their mothers or primary care givers using pyrosequencing. PLoS One 2011; 6:e23503 [View Article] [PubMed]
     [Google Scholar]
  49. Chen H, Jiang W. Application of high-throughput sequencing in understanding human oral microbiome related with health and disease. Front Microbiol 2014; 5:508 [View Article] [PubMed]
     [Google Scholar]
  50. Saiman L, Chen Y, San Gabriel P, Knirsch C. Synergistic activities of macrolide antibiotics against Pseudomonas aeruginosa, Burkholderia cepacia, Stenotrophomonas maltophilia, and Alcaligenes xylosoxidans isolated from patients with cystic fibrosis. Antimicrob Agents Chemother 2002; 46:1105–1107 [View Article] [PubMed]
     [Google Scholar]
  51. Sánchez MU, Bello HT, Domínguez MY, Mella SM, Zemelman RZ et al. Transference of extended-spectrum beta-lactamases from nosocomial strains of Klebsiella pneumoniae to other species of Enterobacteriaceae. Rev Med Chil 2006; 134:415–420 [View Article] [PubMed]
     [Google Scholar]
  52. Valm AM, Welch JLM, Rieken CW, Hasegawa Y, Sogin ML et al. Systems-level analysis of microbial community organization through combinatorial labeling and spectral imaging. Proc Natl Acad Sci U S A 2011; 108:4152–4157 [View Article] [PubMed]
     [Google Scholar]
  53. Kumar PS. From focal sepsis to periodontal medicine: a century of exploring the role of the oral microbiome in systemic disease. J Physiol 2017; 595:465–476 [View Article] [PubMed]
     [Google Scholar]
  54. Kumar PS. Oral microbiota and systemic disease. Anaerobe 2013; 24:90–93 [View Article] [PubMed]
     [Google Scholar]
  55. Tan X, Liu H, Long J, Jiang Z, Luo Y et al. Septic patients in the intensive care unit present different nasal microbiotas. Future Microbiol 2019; 14:383–395 [View Article] [PubMed]
     [Google Scholar]
  56. Haak BW, Prescott HC, Wiersinga WJ. Therapeutic potential of the gut microbiota in the prevention and treatment of sepsis. Front Immunol 2018; 9:2042 [View Article] [PubMed]
     [Google Scholar]
  57. Bharucha T, Oeser C, Balloux F, Brown JR, Carbo EC et al. STROBE-metagenomics: a STROBE extension statement to guide the reporting of metagenomics studies. Lancet Infect Dis 2020; 20:e251–e260 [View Article] [PubMed]
     [Google Scholar]
  58. Casadevall A, Pirofski L. Host-pathogen interactions: basic concepts of microbial commensalism, colonization, infection, and disease. Infect Immun 2000; 68:6511–6518 [View Article] [PubMed]
     [Google Scholar]
  59. Balloux F, van Dorp L. Q&A: What are pathogens, and what have they done to and for us?. BMC Biol 2017; 15:1–6
     [Google Scholar]
  60. Jervis-Bardy J, Leong LEX, Marri S, Smith RJ, Choo JM et al. Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data. Microbiome 2015; 3:19 [View Article] [PubMed]
     [Google Scholar]
  61. Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 2018; 6:226 [View Article] [PubMed]
     [Google Scholar]
  62. Grumaz S, Hoffmann A, Vainshtein Y, Kopp M, Grumaz S et al. Rapid next-generation sequencing-based diagnostics of bacteremia in septic patients. J Mol Diagn 2020; 22:405–418 [View Article]
     [Google Scholar]
  63. Sanderson ND, Street TL, Foster D, Swann J, Atkins BL et al. Real-time analysis of nanopore-based metagenomic sequencing from infected orthopaedic devices. BMC Genomics 2018; 19:714 [View Article] [PubMed]
     [Google Scholar]
  64. Salipante SJ, Sengupta DJ, Rosenthal C, Costa G, Spangler J et al. Rapid 16S rRNA next-generation sequencing of polymicrobial clinical samples for diagnosis of complex bacterial infections. PLoS ONE 2013; 8:e65226 [View Article]
     [Google Scholar]
  65. Brenner T, Decker SO, Grumaz S, Stevens P, Bruckner T et al. Next-generation sequencing diagnostics of bacteremia in sepsis (Next GeneSiS-Trial): study protocol of a prospective, observational, noninterventional, multicenter, clinical trial. Medicine (Baltimore) 2018; 97:e9868 [View Article] [PubMed]
     [Google Scholar]
  66. Morfopoulou S, Plagnol V. Bayesian mixture analysis for metagenomic community profiling. Bioinformatics 2015; 31:2930–2938 [View Article] [PubMed]
     [Google Scholar]
  67. McIntyre ABR, Ounit R, Afshinnekoo E, Prill RJ, Hénaff E et al. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers. Genome Biol 2017; 18:182 [View Article] [PubMed]
     [Google Scholar]
  68. Simon HY, Siddle KJ, Park DJ, Sabeti PC. Benchmarking metagenomics tools for taxonomic classification. Cell 2019; 178:779–794 [View Article] [PubMed]
     [Google Scholar]
  69. Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol 2012; 10:538–550 [View Article]
     [Google Scholar]
  70. Ponomarova O, Patil KR. Metabolic interactions in microbial communities: untangling the Gordian knot. Curr Opin Microbiol 2015; 27:37–44 [View Article] [PubMed]
     [Google Scholar]
  71. Freilich S, Zarecki R, Eilam O, Segal ES, Henry CS et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat Commun 2011; 2:1–7 [View Article]
     [Google Scholar]
  72. Kumar M, Ji B, Zengler K, Nielsen J. Modelling approaches for studying the microbiome. Nat Microbiol 2019; 4:1253–1267 [View Article] [PubMed]
     [Google Scholar]
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Metagenomic evidence for a polymicrobial signature of sepsis
M Gen 7, 000642 (2021); https://doi.org/10.1099/mgen.0.000642
/content/journal/mgen/10.1099/mgen.0.000642
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