Skip to main content
Top
Published in: Journal of Translational Medicine 1/2020

Open Access 01-12-2020 | Septicemia | Research

Rapid diagnosis and comprehensive bacteria profiling of sepsis based on cell-free DNA

Authors: Pei Chen, Shuo Li, Wenyuan Li, Jie Ren, Fengzhu Sun, Rui Liu, Xianghong Jasmine Zhou

Published in: Journal of Translational Medicine | Issue 1/2020

Login to get access

Abstract

Background

Sepsis remains a major challenge in intensive care units, causing unacceptably high mortality rates due to the lack of rapid diagnostic tools with sufficient sensitivity. Therefore, there is an urgent need to replace time-consuming blood cultures with a new method. Ideally, such a method also provides comprehensive profiling of pathogenic bacteria to facilitate the treatment decision.

Methods

We developed a Random Forest with balanced subsampling to screen for pathogenic bacteria and diagnose sepsis based on cell-free DNA (cfDNA) sequencing data in a small blood sample. In addition, we constructed a bacterial co-occurrence network, based on a set of normal and sepsis samples, to infer unobserved bacteria.

Results

Based solely on cfDNA sequencing information from three independent datasets of sepsis, we distinguish sepsis from healthy samples with a satisfactory performance. This strategy also provides comprehensive bacteria profiling, permitting doctors to choose the best treatment strategy for a sepsis case.

Conclusions

The combination of sepsis identification and bacteria-inferring strategies is a success for noninvasive cfDNA-based diagnosis, which has the potential to greatly enhance efficiency in disease detection and provide a comprehensive understanding of pathogens. For comparison, where a culture-based analysis of pathogens takes up to 5 days and is effective for only a third to a half of patients, cfDNA sequencing can be completed in just 1 day and our method can identify the majority of pathogens in all patients.
Appendix
Available only for authorised users
Literature
2.
go back to reference Walkey AJ, Wiener RS, Lindenauer PK. Utilization patterns and outcomes associated with central venous catheter in septic shock: a population-based study. Crit Care Med. 2013;41(6):1450–7.PubMedPubMedCentralCrossRef Walkey AJ, Wiener RS, Lindenauer PK. Utilization patterns and outcomes associated with central venous catheter in septic shock: a population-based study. Crit Care Med. 2013;41(6):1450–7.PubMedPubMedCentralCrossRef
3.
go back to reference Dellinger RP, Levy MM, Rhodes A, Annane D, Gerlach H, Opal SM, Sevransky JE, Sprung CL, Douglas IS, Jaeschke R, et al. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shoc, 2012. Crit Care Med. 2013;41(2):580–637.PubMedCrossRef Dellinger RP, Levy MM, Rhodes A, Annane D, Gerlach H, Opal SM, Sevransky JE, Sprung CL, Douglas IS, Jaeschke R, et al. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shoc, 2012. Crit Care Med. 2013;41(2):580–637.PubMedCrossRef
4.
go back to reference Wang HE, Shapiro NI, Angus DC, Yealy DM. National estimates of severe sepsis in United States emergency departments. Crit Care Med. 2007;35:1928–36.PubMedCrossRef Wang HE, Shapiro NI, Angus DC, Yealy DM. National estimates of severe sepsis in United States emergency departments. Crit Care Med. 2007;35:1928–36.PubMedCrossRef
5.
go back to reference Vincent JL, Brealey D, Libert N, Abidi NE, O’Dwyer M, Zacharowski K, Mikaszewska-Sokolewicz M, Schrenzel J, Simon F, Wilks M, et al. Rapid diagnosis of infection in the critically ill, a multicenter study of molecular detection in bloodstream infections, pneumonia, and sterile site infections. Crit Care Med. 2015;43(11):2283–91.PubMedPubMedCentralCrossRef Vincent JL, Brealey D, Libert N, Abidi NE, O’Dwyer M, Zacharowski K, Mikaszewska-Sokolewicz M, Schrenzel J, Simon F, Wilks M, et al. Rapid diagnosis of infection in the critically ill, a multicenter study of molecular detection in bloodstream infections, pneumonia, and sterile site infections. Crit Care Med. 2015;43(11):2283–91.PubMedPubMedCentralCrossRef
7.
go back to reference Ulz P, Thallinger GG, Auer M, et al. Inferring expressed genes by whole-genome sequencing of plasma DNA. Nat Genet. 2016;48(10):1273.PubMedCrossRef Ulz P, Thallinger GG, Auer M, et al. Inferring expressed genes by whole-genome sequencing of plasma DNA. Nat Genet. 2016;48(10):1273.PubMedCrossRef
8.
go back to reference Schwarzenbach H, Hoon DS, Pantel K. Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer. 2011;11(6):426.PubMedCrossRef Schwarzenbach H, Hoon DS, Pantel K. Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer. 2011;11(6):426.PubMedCrossRef
9.
go back to reference Long Y, Zhang Y, Gong Y, et al. Diagnosis of sepsis with cell-free DNA by next-generation sequencing technology in ICU patients. Arch Med Res. 2016;47(5):365–71.PubMedCrossRef Long Y, Zhang Y, Gong Y, et al. Diagnosis of sepsis with cell-free DNA by next-generation sequencing technology in ICU patients. Arch Med Res. 2016;47(5):365–71.PubMedCrossRef
10.
11.
13.
go back to reference Kang S, Li Q, Chen Q, et al. CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biol. 2017;18(1):53.PubMedPubMedCentralCrossRef Kang S, Li Q, Chen Q, et al. CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biol. 2017;18(1):53.PubMedPubMedCentralCrossRef
14.
go back to reference Li W, Li Q, Kang S, et al. CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data. Nucleic Acids Res. 2018;46(15):e89.PubMedPubMedCentralCrossRef Li W, Li Q, Kang S, et al. CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data. Nucleic Acids Res. 2018;46(15):e89.PubMedPubMedCentralCrossRef
15.
go back to reference Jiang P, Chan CW, Chan KA, Cheng SH, Wong J, Wong VW, Wong GL, Chan SL, Mok TS, Chan HL, Lai PB. Lengthening and shortening of plasma DNA in hepatocellular carcinoma patients. Proc Natl Acad Sci. 2015;112(11):E1317–25.PubMedCrossRefPubMedCentral Jiang P, Chan CW, Chan KA, Cheng SH, Wong J, Wong VW, Wong GL, Chan SL, Mok TS, Chan HL, Lai PB. Lengthening and shortening of plasma DNA in hepatocellular carcinoma patients. Proc Natl Acad Sci. 2015;112(11):E1317–25.PubMedCrossRefPubMedCentral
16.
go back to reference Altrichter J, Zedler S, Kraft R, et al. Neutrophil-derived circulating free DNA (cf-DNA/NETs), a potential prognostic marker for mortality in patients with severe burn injury. Eur J Trauma Emerg Surg. 2010;36(6):551–7.PubMedCrossRef Altrichter J, Zedler S, Kraft R, et al. Neutrophil-derived circulating free DNA (cf-DNA/NETs), a potential prognostic marker for mortality in patients with severe burn injury. Eur J Trauma Emerg Surg. 2010;36(6):551–7.PubMedCrossRef
17.
go back to reference Eshoo MW, Crowder CD, Li H, Matthews HE, Meng S, Sefers SE, Sampath R, Stratton CW, Blyn LB, Ecker DJ, et al. Detection and identification of Ehrlichia species in blood by use of PCR and electrospray ionization mass spectrometry. J Clin Microbiol. 2010;48(2):472–8.PubMedCrossRef Eshoo MW, Crowder CD, Li H, Matthews HE, Meng S, Sefers SE, Sampath R, Stratton CW, Blyn LB, Ecker DJ, et al. Detection and identification of Ehrlichia species in blood by use of PCR and electrospray ionization mass spectrometry. J Clin Microbiol. 2010;48(2):472–8.PubMedCrossRef
18.
go back to reference Kaleta EJ, Clark AE, Cherkaoui A, Wysocki VH, Ingram EL, Schrenzel J, Wolk DM. Comparative analysis of PCR-electrospray ionization/mass spectrometry (MS) and MALDI-TOF/MS for the identification of bacteria and yeast from positive blood culture bottles. Clin Chem. 2011;57(7):1057–67.PubMedCrossRefPubMedCentral Kaleta EJ, Clark AE, Cherkaoui A, Wysocki VH, Ingram EL, Schrenzel J, Wolk DM. Comparative analysis of PCR-electrospray ionization/mass spectrometry (MS) and MALDI-TOF/MS for the identification of bacteria and yeast from positive blood culture bottles. Clin Chem. 2011;57(7):1057–67.PubMedCrossRefPubMedCentral
19.
go back to reference Grumaz S, Stevens P, Grumaz C, et al. Next-generation sequencing diagnostics of bacteremia in septic patients. Genome Med. 2016;8(1):1–13.CrossRef Grumaz S, Stevens P, Grumaz C, et al. Next-generation sequencing diagnostics of bacteremia in septic patients. Genome Med. 2016;8(1):1–13.CrossRef
20.
go back to reference Ulz P, Heitzer E, Speicher MR. Co-occurrence of MYC amplification and TP53 mutations in human cancer. Nat Genet. 2016;48(2):104.PubMedCrossRef Ulz P, Heitzer E, Speicher MR. Co-occurrence of MYC amplification and TP53 mutations in human cancer. Nat Genet. 2016;48(2):104.PubMedCrossRef
21.
go back to reference Blauwkamp TA, Thair S, Rosen MJ, Blair L, Lindner MS, Vilfan ID, Kawli T, Christians FC, Venkatasubrahmanyam S, Wall GD, Cheung A. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease. Nat Microbiol. 2019;4(4):663.PubMedCrossRef Blauwkamp TA, Thair S, Rosen MJ, Blair L, Lindner MS, Vilfan ID, Kawli T, Christians FC, Venkatasubrahmanyam S, Wall GD, Cheung A. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease. Nat Microbiol. 2019;4(4):663.PubMedCrossRef
23.
24.
go back to reference Svetnik V, Liaw A, Tong C, et al. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003;43(6):1947.PubMedCrossRef Svetnik V, Liaw A, Tong C, et al. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003;43(6):1947.PubMedCrossRef
25.
go back to reference Zou J, Wang E. eTumorType, An algorithm of discriminating cancer types for circulating tumor cells or cell-free DNAs in blood. Genom Proteom Bioinform. 2017;15(2):130–40.CrossRef Zou J, Wang E. eTumorType, An algorithm of discriminating cancer types for circulating tumor cells or cell-free DNAs in blood. Genom Proteom Bioinform. 2017;15(2):130–40.CrossRef
26.
go back to reference Huang L, Jin Y, Gao Y, Thung KH, Shen D, Alzheimer’s Disease Neuroimaging Initiative. Longitudinal clinical score prediction in Alzheimer’s disease with soft-split sparse regression based random forest. Neurobiol Aging. 2016;46:180–91.PubMedPubMedCentralCrossRef Huang L, Jin Y, Gao Y, Thung KH, Shen D, Alzheimer’s Disease Neuroimaging Initiative. Longitudinal clinical score prediction in Alzheimer’s disease with soft-split sparse regression based random forest. Neurobiol Aging. 2016;46:180–91.PubMedPubMedCentralCrossRef
27.
go back to reference Kowarsky M, Camunas-Soler J, Kertesz M, et al. Numerous uncharacterized and highly divergent microbes which colonize humans are revealed by circulating cell-free DNA. Proc Natl Acad Sci USA. 2017;114(36):9623.PubMedCrossRefPubMedCentral Kowarsky M, Camunas-Soler J, Kertesz M, et al. Numerous uncharacterized and highly divergent microbes which colonize humans are revealed by circulating cell-free DNA. Proc Natl Acad Sci USA. 2017;114(36):9623.PubMedCrossRefPubMedCentral
28.
go back to reference Proal AD, Albert PJ, Marshall TG. Inflammatory disease and the human microbiome. Discov Med. 2014;17(95):257.PubMed Proal AD, Albert PJ, Marshall TG. Inflammatory disease and the human microbiome. Discov Med. 2014;17(95):257.PubMed
29.
go back to reference Barberán A, Bates ST, Casamayor EO, et al. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6(2):343.PubMedCrossRef Barberán A, Bates ST, Casamayor EO, et al. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6(2):343.PubMedCrossRef
30.
go back to reference Widder S, Besemer K, Singer GA, et al. Fluvial network organization imprints on microbial co-occurrence networks. Proc Natl Acad Sci USA. 2014;111(35):12799.PubMedCrossRefPubMedCentral Widder S, Besemer K, Singer GA, et al. Fluvial network organization imprints on microbial co-occurrence networks. Proc Natl Acad Sci USA. 2014;111(35):12799.PubMedCrossRefPubMedCentral
32.
go back to reference Gegov E, Gegov A, Gobet F, et al. Cognitive modelling of language acquisition with complex networks[M]//Computational intelligence. Hauppauge: Nova Science Publishers; 2012. Gegov E, Gegov A, Gobet F, et al. Cognitive modelling of language acquisition with complex networks[M]//Computational intelligence. Hauppauge: Nova Science Publishers; 2012.
33.
go back to reference Morueta-Holme N, Blonder B, Sandel B, et al. A network approach for inferring species associations from co-occurrence data. Ecography. 2016;39(12):1139–50.CrossRef Morueta-Holme N, Blonder B, Sandel B, et al. A network approach for inferring species associations from co-occurrence data. Ecography. 2016;39(12):1139–50.CrossRef
34.
go back to reference Rocha EPC. Codon usage bias from tRNA’s point of view: redundancy, specialization, and efficient decoding for translation optimization. Genome Res. 2004;14(11):2279–86.PubMedPubMedCentralCrossRef Rocha EPC. Codon usage bias from tRNA’s point of view: redundancy, specialization, and efficient decoding for translation optimization. Genome Res. 2004;14(11):2279–86.PubMedPubMedCentralCrossRef
35.
go back to reference Couturier E, Rocha EPC. Replication-associated gene dosage effects shape the genomes of fast-growing bacteria but only for transcription and translation genes. Mol Microbiol. 2010;59(5):1506–18.CrossRef Couturier E, Rocha EPC. Replication-associated gene dosage effects shape the genomes of fast-growing bacteria but only for transcription and translation genes. Mol Microbiol. 2010;59(5):1506–18.CrossRef
36.
37.
go back to reference Gyssens I C, Bax H I, Schippers E F, et al. Antibacterial therapy of adult patients with Sepsis. 2010. Gyssens I C, Bax H I, Schippers E F, et al. Antibacterial therapy of adult patients with Sepsis. 2010.
Metadata
Title
Rapid diagnosis and comprehensive bacteria profiling of sepsis based on cell-free DNA
Authors
Pei Chen
Shuo Li
Wenyuan Li
Jie Ren
Fengzhu Sun
Rui Liu
Xianghong Jasmine Zhou
Publication date
01-12-2020
Publisher
BioMed Central
Published in
Journal of Translational Medicine / Issue 1/2020
Electronic ISSN: 1479-5876
DOI
https://doi.org/10.1186/s12967-019-02186-x

Other articles of this Issue 1/2020

Journal of Translational Medicine 1/2020 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

Highlights from the ACC 2024 Congress

Year in Review: Pediatric cardiology

Watch Dr. Anne Marie Valente present the last year's highlights in pediatric and congenital heart disease in the official ACC.24 Year in Review session.

Year in Review: Pulmonary vascular disease

The last year's highlights in pulmonary vascular disease are presented by Dr. Jane Leopold in this official video from ACC.24.

Year in Review: Valvular heart disease

Watch Prof. William Zoghbi present the last year's highlights in valvular heart disease from the official ACC.24 Year in Review session.

Year in Review: Heart failure and cardiomyopathies

Watch this official video from ACC.24. Dr. Biykem Bozkurt discusses last year's major advances in heart failure and cardiomyopathies.