- Open Access
Next-generation sequencing diagnostics of bacteremia in septic patients
- Silke Grumaz†1,
- Philip Stevens†2, 4,
- Christian Grumaz1,
- Sebastian O. Decker3,
- Markus A. Weigand3,
- Stefan Hofer3,
- Thorsten Brenner3,
- Arndt von Haeseler4, 5 and
- Kai Sohn1, 2Email author
© The Author(s). 2016
- Received: 27 January 2016
- Accepted: 10 June 2016
- Published: 1 July 2016
Bloodstream infections remain one of the major challenges in intensive care units, leading to sepsis or even septic shock in many cases. Due to the lack of timely diagnostic approaches with sufficient sensitivity, mortality rates of sepsis are still unacceptably high. However a prompt diagnosis of the causative microorganism is critical to significantly improve outcome of bloodstream infections. Although various targeted molecular tests for blood samples are available, time-consuming blood culture-based approaches still represent the standard of care for the identification of bacteria.
Here we describe the establishment of a complete diagnostic workflow for the identification of infectious microorganisms from seven septic patients based on unbiased sequence analyses of free circulating DNA from plasma by next-generation sequencing.
We found significant levels of DNA fragments derived from pathogenic bacteria in samples from septic patients. Quantitative evaluation of normalized read counts and introduction of a sepsis indicating quantifier (SIQ) score allowed for an unambiguous identification of Gram-positive as well as Gram-negative bacteria that exactly matched with blood cultures from corresponding patient samples. In addition, we also identified species from samples where blood cultures were negative. Reads of non-human origin also comprised fragments derived from antimicrobial resistance genes, showing that, in principle, prediction of specific types of resistance might be possible.
The complete workflow from sample preparation to species identification report could be accomplished in roughly 30 h, thus making this approach a promising diagnostic platform for critically ill patients suffering from bloodstream infections.
- Next-generation sequencing
- Circulating nucleic acids
Sepsis remains a challenge in intensive care medicine, its incidence increasing continuously over the past decades [1, 2]. Despite massive efforts in sepsis research, new therapeutic approaches are rare and mortality in patients with septic shock still remains unacceptably high [1, 2]. In addition to an early focus on control, recent guidelines recommend the initiation of an empiric antibiotic therapy as early as possible (preferably within 1 h) following diagnosis of sepsis . However, the identification of the causative pathogen as well as its resistance pattern is crucial for early optimization of the antimicrobial treatment regime. In this context, culture-based diagnostic procedures (e.g., blood cultures) represent the standard of care, although they are associated with relevant limitations [3, 4]: (i) depending on microbiological growth, it often takes up to 5 days for results to become available; and (ii) culture-based diagnostic procedures often reveal false negative results due to the administration of an empiric antibiotic therapy. Accordingly, patients suffering from septic disease are at high risk for antimicrobial overtreatment, antibiotics-related toxicity, and the selection of multi-drug resistant pathogens due to the inadequate and prolonged use of broad spectrum antibiotics. In this context, culture-independent molecular diagnostic procedures (e.g., PCR-based techniques) have already been introduced for the identification of the causative pathogen in infected patients [5–9]. However, the occurrence of ambiguous results as well as limitations in the quantitative measurement of the bacterial load in patients’ samples and detection of antibiotic resistance markers are known limitations of these PCR-based diagnostic approaches. Therefore, the concept of our work was to develop an alternative diagnostic platform for the identification of infectious microorganisms based on unbiased sequence analyses of circulating cell-free DNA (cfDNA) from plasma samples of septic patients by next-generation sequencing (NGS). In addition, we demonstrate the applicability of NGS for the detection of antibiotic resistance markers in plasma.
Human data are from a secondary analysis of a subset of patients participating in the RAMMSES trial (German Clinical Trials Register, DRKS00000505) . An amendment to the existing approval (Trial-Code-Nr. S123-2009) was written and approved by the local ethics committee.
The observational clinical study was conducted in the surgical intensive care unit (ICU) of Heidelberg University Hospital, Germany. Study and control patients or their legal designees signed a written informed consent. In total, 120 patients in three groups were consecutively enrolled into the study from August 2009 to July 2010. The three groups included: (1) 60 patients with septic shock according to the criteria of the International Sepsis Definitions Conference  and due to documented or suspected infection according to the criteria of the International Sepsis Forum Consensus Conference on Definitions of Infection in the Intensive Care Unit ; (2) 30 postoperative controls following major abdominal surgery without any evidence of infection; and (3) 30 healthy volunteers. Plasma samples from patients with septic shock were collected at sepsis onset (T0) and 24 h (T1), 4 days (T2), 7 days (T3), 14 days (T4), and 28 days (T5) later. Plasma samples from the postoperative group were collected prior to surgery (T0), immediately following the end of the surgical procedure (T1), and 24 h later (T2). Plasma samples from the volunteer group were collected once (T0).
For this secondary analysis, patients’ electronic medical records were retrospectively screened for results from blood culture testing during septicemia and patients were selected according to availability of microbiological data for similar time points, which led to the smaller sample size within this proof-of-concept study.
Blood culture testing in Heidelberg University Hospital is routinely performed as described . Whole blood samples are obtained via direct venipuncture (e.g., antecubital vein) applying sterile techniques and 10 mL blood is inoculated to both an aerobic and an anaerobic liquid culture medium (BACTEC PLUS, BD Biosciences, Heidelberg, Germany). Cultures are incubated for 5 days (BACTEC, BD Biosciences, Heidelberg, Germany) and positive cultures are analyzed according to approved in-house hospital standard techniques, including identification by VITEK2 (Biomerieux, Nuertingen, Germany) or MALDI TOF (Bruker, Madison, WI, USA) and automated antimicrobial susceptibility testing (VITEK 2). Quantification of HSV1 DNA and cytomegalovirus DNA from plasma or tracheal secretion was performed via quantitative real time PCR as previously described . Cultivation of wound swabs and catheter and stool samples was carried out as previously described [15, 16].
Plasma preparation and nucleic acid isolation
Plasma was prepared from blood samples by centrifugation for 10 min at 292 × g and 4 °C, snap frozen, and stored at −80 °C until further processing. Nucleic acids were isolated from thawed plasma after a centrifugation step of 5 min at 1000 × g with the Circulating Nucleic Acid Kit (Qiagen) according to the manufacturer’s protocol with the following exceptions: plasma volumes after centrifugation (from 130 to 790 μl) were adjusted to 1 ml with sterile phosphate buffered saline. Final elution of the nucleic acids from the spin column was carried out with 30 μl molecular biology grade water (5 Prime, Germany). Contamination controls were prepared following the same procedure, starting from 1 ml molecular biology grade water (5 Prime, Germany) and 1 ml of sterile phosphate buffered saline, which was prepared using the Circulating Nucleic Acid Kit (Qiagen). The cfDNA was quantified with the Qubit dsDNA HS Assay Kit (Life Technologies) and quality was assessed by the High Sensitivity DNA Kit on a Bioanalyzer (Agilent).
Preparation of NGS libraries and sequencing
Libraries for NGS were prepared from 1 ng cfDNA using the Nextera XT library preparation Kit (Illumina) according to the manufacturer’s protocol, with the exception that the final elution after bead clean-up was carried out in 34 μl of resuspension buffer (Illumina). A further contamination control was added by using 5 μl of molecular biology grade water (5 Prime, Germany) as template for the Nextera XT Library Preparation Kit (Illumina). Sequencing of the libraries was performed on a HiSeq2500 (Illumina), resulting in 25–30 million 100-bp single end reads, on average, per sample. Since samples V6, V22, and P6 were initially sequenced with considerably higher numbers of reads, those samples were randomly reduced in silico to 30 million representative subsampled reads.
Raw reads were cleared from potential adapter contamination, quality controlled, and, if necessary, trimmed using BBDuk (https://sourceforge.net/projects/bbmap/). To pass the quality filter, read quality needed to surpass a Phred score of 20 and achieve a minimal length of 50 bp after trimming of low quality and adapter bases. Subsequently, NextGenMap was used to align quality-controlled reads to the human reference genome (hg19) requiring a minimum identity between read and reference genome of 80 %. Reads mapping to the human reference genome and reads with low complexity (consecutive stretches of di- and trinucleotides along the whole read sequence) were excluded from further analysis . Finally, Kraken was used to assign reads to systematic classification using the RefSeq database (release version 68) comprising 35,749 bacterial and 4340 viral genomes complemented by 12 selected fungal genomes (Additional file 1: Table S1). Since several Xanthomonas species are described as well-known contaminants, we excluded Xanthomonas reads as well as the Illumina sequencing spike-in PhiX .
Patient characteristics, cfDNA concentration, and sequencing statistics
cfDNA (ng/ml plasma)
Human reads (%)
Average S T0
Average S all
Average P T0
Average P T1–T2
Average P all
To test this assumption for each species a standard χ 2 goodness of fit test is performed.
If this p value is small, then one would reject the hypothesis that the read count of species j in the patient sample follows the Poisson distribution derived from the healthy individuals and conclude that the respective species occurs too often in the patient.
The SIQ score now gives rise to a quantitative and probabilistic assessment of every detected microbe in the respective sample.
In order to identify resistance genes, potential microbial reads were mapped against the downloaded CARD resistance gene database using NextGenMap with the following parameters: sensitivity (-s) and minimal number of mapped residues (-R) of 0.9. We required 100 % identity between read sequence and target sequence in the CARD database.
Elevated levels of cfDNA in septic patients reveal microbial DNA fragments
Establishment of the SIQ score as a quantitative score for pathogen calling
Clinical relevance of the SIQ score
Identification of resistance genes
In recent years, several reports have been published on the use of metagenomics for the identification of viruses or microbes in patient samples. Most of these reports describe the identification of different viruses, including influenza A, norovirus, Ebola, arenaviruses, or emerging viruses from various specimens like nasopharyngeal swabs, serum, or solid tissue [23–29]. In addition, metagenomics, amongst other methods, has also been applied for identification of bacteria from urine, vaginal swabs, or sputum [30–33]. However, to the best of our knowledge, we report here the first proof-of-concept for quantitative NGS-based diagnosis of septicemia using plasma samples from septic patients and uninfected controls based on free circulating DNA. Several attempts have been made to use cfDNA quantification as a diagnostic and prognostic marker for sepsis outcome [34–36]. However, elevated cfDNA levels resulting from apoptotic or necrotic processes have also been described in other physiologic or pathologic conditions, including pregnancy, exercise, inflammatory diseases, trauma due to stroke, cancer, and surgery. Consequently, it is controversial whether elevated levels of circulating cfDNA are of diagnostic and prognostic value per se [36, 37]. Five out of seven septic patients (S9, S10, S19, S23, S60) underwent major abdominal surgery within 2 days of sepsis onset, which likely contributed to elevated cfDNA concentrations. However, septic patients showed a significantly higher share of cfDNA that could be assigned to microbial origin, suggesting a direct release of DNA from bacteria or co-release from phagocytic cells. This cfDNA could originate from both live and dead pathogens. However, as cfDNA is subject to considerable turnover, with a half-life in the range of minutes [38, 39], and obtained SIQ scores are very dynamic during the time course of infection, this suggests that cfDNA levels from microbial origin seem to precisely reflect the dynamics of infection, irrespective of whether cfDNA is released by dead or living cells. Due to the very short half-life of cfDNA, we therefore would also not expect to face a significant false positive rate. However, as our study only comprises a limited number of cases, further research and more comprehensive cohorts are needed to elucidate the source and mechanism of cfDNA release and its impact on false positive rates.
In patients suffering from severe sepsis or septic shock, positive blood cultures are obtained in only a fraction of cases despite a proven underlying rate of bacterial infection of 33 % [40–42]. This is partially attributable to technical shortfalls in blood culture acquisition but is also due to fastidious organisms or very low rates of viable microorganisms in the blood stream . A molecular approach with higher sensitivity for sepsis might, therefore, overcome the aforementioned limitations of classic microbiological diagnostics. However, state of the art molecular approaches based on PCR amplification of target sequences suffer from limited power to discriminate between contaminations, colonization, and infections, often revealing ambiguous or invalid results. In this context, NGS-based diagnostic testing might offer several advantages over PCR assays: (i) NGS is an open platform, providing the opportunity to detect bacterial, fungal, and viral pathogens in a single assay; (ii) NGS is quantitative through counting of sequence reads and calculation of statistical significance; (iii) NGS is unbiased and untargeted so benefits from any DNA sequence information within patient specimens, potentially delivering higher sensitivity and specificity. However, although NGS is becoming increasingly important in clinical microbiology (e.g., for strain typing), only sporadic reports of NGS-analyzed clinical specimens have been published to date, including one actionable single-case report of NGS-based diagnosis of Leptospira from cerebrospinal fluid [44–49]. Another recent paper, by De Vlaminck and coworkers , reports the identification of predominantly viral pathogens in cfDNA from patients after lung transplantation. Nevertheless, we currently lack NGS-based diagnostic approaches that, by combining quantitative values with statistical significance, provide a measure for relevance. Especially in specimens like plasma, where only the lowest amounts of microbial DNA can be expected, the specificity is challenged by the detection of microbial contaminants from laboratory reagents, workflows, or specimen sampling [18, 51]. We therefore developed a SIQ score to discriminate signal reads from noise caused by contaminant or commensal species. Since the incidence of viral and fungal infections in sepsis is less frequent, the SIQ scores for fungi and viruses will generally be higher than those for bacteria (as seen in patient S23; Additional file 11: Figure S6) as they are also less often detected in control samples. Consequently, the robustness of the SIQ score will be increased by using the most complete database of relevant genomes and a control group large enough to capture even small variances in the microbiome of healthy individuals.
Using our current workflow, a good correlation of NGS results with blood culture and other patient specimens was observed for all patients except patient S60. Moreover, this agreement was observed not only for bacteria but also for viruses and fungi. For patient S60 we found E. coli and E. faecium in plasma, which is in good agreement with wound swab findings but in contrast to blood culture, where S. epidermidis was identified one day before. Clinical records suggest that this patient suffered from gut complications, indicating that the NGS-based approach might be more sensitive and specific than blood culture, which has to be systematically evaluated by further studies.
Importantly, we could also demonstrate the potential of quantitative NGS-based diagnosis to identify genes conferring antibiotic resistance in plasma cfDNA. We unambiguously identified from a septic patient that underwent liver transplantation a number of reads that match with 100 % identity to vanB and vanS B , known to confer resistance to vancomycin. Indeed, we could identify VRE as the infecting strain as confirmed by microbiological testing. Meanwhile, we were also able to detect mecA from methicillin-resistant S. aureus in other patient plasma samples (data not shown), indicating that the identification of major resistance genes is feasible with our approach. However, the applicability of the CARD antibiotic resistance database for diagnostic purposes is currently limited to genes which, per se, confer resistance to antibiotics, whereas the heterogeneity of resistance mechanisms also comprises point mutations, gene expression changes, and posttranslational modifications, for example. Consequently, using the current setup of the database and workflows, it is possible to identify resistance mechanisms based on acquisition of specific resistance genes like mecA, vanA/B, KPC, etc. but not necessarily to detect all resistance mechanisms comprehensively. Furthermore, it should be noted that mapping of reads to resistance-related genes relies on high read coverage for the respective species. For routine detection of resistance genes, enrichment strategies and complementation with expression data might be helpful in elucidating additional resistance mechanisms.
Finally, using our workflow to quantitatively identify microbial and viral nucleic acids in patient plasma (Additional file 15: Figure S8), the current total turnaround time starting from plasma adds up to approximately 31 h, where the most time-consuming step is sequencing, which was accomplished in rapid run mode on a HiSeq2500 within 16 h. This is competitive with standard blood culture with a time to positivity of 24–120 h, where automated blood culture systems are routinely incubated for 5 days . However, recent developments in sequencing technologies (i.e., Oxford Nanopore’s MinION device) indicate that it will be possible to sequence even larger amounts of DNA in shorter times in a bedside setup. Using this technology it has already been reported that strain identification of clinically relevant strains and the corresponding resistance profiles is possible in approximately 2 h . Furthermore, the identification of resistance genes has been shown to be manageable in 2 to 12 h, which makes a “same day” diagnosis possible . Nanopore sequencing would, therefore, allow a diagnostic decision within 3–4 h after initial diagnosis of sepsis using our workflow, providing a reasonable timeframe for effective therapeutic intervention. It should also be mentioned that current sequencing costs are of considerable importance for diagnostics. Although costs per base are constantly dropping, diagnostic approaches based on NGS are still comparatively expensive. Therefore, we expect that such a diagnostic workflow will be in a cost range similar to that of noninvasive prenatal testing (NIPT) when it was introduced several years before. This might limit a corresponding approach for diagnosis in critically ill patients in the beginning, for whom overall costs per patient and day are relatively high. But as seen for NIPT, costs per assay might significantly drop in short order following a broader application of NGS in clinics, thus making such an approach conceivable also from a financial point of view.
This work shows for the first time that identification of viruses, bacteria, and fungi from cfDNA in plasma of septic patients not only is technically feasible but also provides a basis to differentiate the relevant infecting organisms by establishing a quantitative score. These characteristics go beyond state-of-the-art molecular approaches for the diagnosis of infecting organisms in septic specimens, which are not open but instead based on PCR amplification of defined targets and are, in most cases, just qualitative in nature. Therefore, this approach allows for an unbiased analysis of bloodstream infections, which might be especially useful for the diagnosis of cases where classic microbiological or molecular diagnostic approaches fail. However, although we think that our approach might be more sensitive and specific than state-of-the-art technologies, additional clinical trials will be needed to exactly define the sensitivity and specificity as well as the positive and negative predictive value as this case study was limited by the low number of patients.
cfDNA, cell-free DNA; HSV, herpes simplex virus; NGS, next generation sequencing; qPCR, quantitative PCR; SIQ, sepsis indicating quantifier; VRE, vancomycin-resistant E. faecium.
We thank all patients and referring physicians and study nurses who submitted samples. We also acknowledge Karolina Glanz and Ute Krauser for excellent technical support.
This study was supported by the Fraunhofer Future Foundation. The Fraunhofer society had no role in study design, collection, analysis, or interpretation of data.
Availability of data and materials
The data supporting the conclusions of this article are available in the European Nucleotide Archive (ENA) repository under accession PRJEB13247 (http://www.ebi.ac.uk/ena/data/view/PRJEB13247).
SG, PS, SOD, SH, TB, MAW, and KS designed the study. SG, PS, CG, AvH, and KS acquired, analyzed, or interpreted the data. SG, PS, CG, TB, and KS wrote the manuscript. All authors critically revised the manuscript for important intellectual content and approved the final manuscript. PS and AvH provided bioinformatical data processing and analyses. Funding was obtained by MAW and KS. Further administrative, technical, or material support was provided by SG, PS, CG, SOD, SH, TB, and KS. The whole study was supervised by SG, MAW, AvH, and KS.
SG, PS, AvH, and KS are co-inventors of a pending international patent on diagnostic algorithms. CG, SOD, MAW, SH, and TB declare that they have no competing interests.
Ethics approval and consent to participate
Human data result from a secondary analysis of a subset of patients participating in the RAMMSES trial (German Clinical Trials Register DRKS00000505). An amendment to the existing approval (Trial-Code-Nr. S123-2009) was written and approved by the Heidelberg ethics committee.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Mayr FB, Yende S, Angus DC. Epidemiology of severe sepsis. Virulence. 2014;5(1):4–11.View ArticlePubMedGoogle Scholar
- 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.View ArticlePubMedPubMed CentralGoogle Scholar
- 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 shock: 2012. Crit Care Med. 2013;41(2):580–637.Google Scholar
- 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.Google Scholar
- Bacconi A, Richmond GS, Baroldi MA, Laffler TG, Blyn LB, Carolan HE, Frinder MR, Toleno DM, Metzgar D, Gutierrez JR, et al. Improved sensitivity for molecular detection of bacterial and Candida infections in blood. J Clin Microbiol. 2014;52(9):3164–74.Google Scholar
- Ecker DJ, Sampath R, Li H, Massire C, Matthews HE, Toleno D, Hall TA, Blyn LB, Eshoo MW, Ranken R, et al. New technology for rapid molecular diagnosis of bloodstream infections. Expert Rev Mol Diagn. 2010;10(4):399–415.Google Scholar
- 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.Google Scholar
- 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.Google Scholar
- Kaleta EJ, Clark AE, Johnson DR, Gamage DC, Wysocki VH, Cherkaoui A, Schrenzel J, Wolk DM. Use of PCR coupled with electrospray ionization mass spectrometry for rapid identification of bacterial and yeast bloodstream pathogens from blood culture bottles. J Clin Microbiol. 2011;49(1):345–53.Google Scholar
- Brenner T, Fleming T, Uhle F, Silaff S, Schmitt F, Salgado E, Ulrich A, Zimmermann S, Bruckner T, Martin E, et al. Methylglyoxal as a new biomarker in patients with septic shock: an observational clinical study. Crit Care. 2014;18(6):683.Google Scholar
- Levy MM, Fink MP, Marshall JC, Abraham E, Angus D, Cook D, Cohen J, Opal SM, Vincent JL, Ramsay G. 2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference. Crit Care Med. 2003;31(4):1250–6.Google Scholar
- Calandra T, Cohen J. The international sepsis forum consensus conference on definitions of infection in the intensive care unit. Crit Care Med. 2005;33(7):1538–48.View ArticlePubMedGoogle Scholar
- Gumbinger C, Hug A, Murle B, Berger B, Zorn M, Becker KP, Zimmermann S, Dalpke AH, Veltkamp R. Early blood-based microbiological testing is ineffective in severe stroke patients. J Neurol Sci. 2013;325(1-2):46–50.Google Scholar
- Brenner T, Rosenhagen C, Hornig I, Schmidt K, Lichtenstern C, Mieth M, Bruckner T, Martin E, Schnitzler P, Hofer S, et al. Viral infections in septic shock (VISS-trial)-crosslinks between inflammation and immunosuppression. J Surg Res. 2012;176(2):571–82.Google Scholar
- Mischnik A, Mieth M, Busch CJ, Hofer S, Zimmermann S. First evaluation of automated specimen inoculation for wound swab samples by use of the Previ Isola system compared to manual inoculation in a routine laboratory: finding a cost-effective and accurate approach. J Clin Microbiol. 2012;50(8):2732–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Mischnik A, Trampe M, Zimmermann S. Evaluation of the impact of automated specimen inoculation, using Previ Isola, on the quality of and technical time for stool cultures. Ann Lab Med. 2015;35(1):82–8.View ArticlePubMedGoogle Scholar
- Horie M, Honda T, Suzuki Y, Kobayashi Y, Daito T, Oshida T, Ikuta K, Jern P, Gojobori T, Coffin JM, et al. Endogenous non-retroviral RNA virus elements in mammalian genomes. Nature. 2010;463(7277):84–7.Google Scholar
- Salter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO, Moffatt MF, Turner P, Parkhill J, Loman NJ, Walker AW. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 2014;12:87.Google Scholar
- Giacona MB, Ruben GC, Iczkowski KA, Roos TB, Porter DM, Sorenson GD. Cell-free DNA in human blood plasma: length measurements in patients with pancreatic cancer and healthy controls. Pancreas. 1998;17(1):89–97.View ArticlePubMedGoogle Scholar
- Lichtenstein AV, Melkonyan HS, Tomei LD, Umansky SR. Circulating nucleic acids and apoptosis. Ann N Y Acad Sci. 2001;945:239–49.View ArticlePubMedGoogle Scholar
- Martins GA, Kawamura MT, Carvalho MG. Detection of DNA in the plasma of septic patients. Ann N Y Acad Sci. 2000;906:134–40.View ArticlePubMedGoogle Scholar
- Zeerleder S, Zwart B, Wuillemin WA, Aarden LA, Groeneveld AB, Caliezi C, van Nieuwenhuijze AE, van Mierlo GJ, Eerenberg AJ, Lammle B, et al. Elevated nucleosome levels in systemic inflammation and sepsis. Crit Care Med. 2003;31(7):1947–51.Google Scholar
- Yang J, Yang F, Ren L, Xiong Z, Wu Z, Dong J, Sun L, Zhang T, Hu Y, Du J, et al. Unbiased parallel detection of viral pathogens in clinical samples by use of a metagenomic approach. J Clin Microbiol. 2011;49(10):3463–9.Google Scholar
- Towner JS, Sealy TK, Khristova ML, Albarino CG, Conlan S, Reeder SA, Quan PL, Lipkin WI, Downing R, Tappero JW, et al. Newly discovered ebola virus associated with hemorrhagic fever outbreak in Uganda. PLoS Pathog. 2008;4(11):e1000212.Google Scholar
- Palacios G, Druce J, Du L, Tran T, Birch C, Briese T, Conlan S, Quan PL, Hui J, Marshall J, et al. A new arenavirus in a cluster of fatal transplant-associated diseases. N Engl J Med. 2008;358(10):991–8.Google Scholar
- Nakamura S, Yang CS, Sakon N, Ueda M, Tougan T, Yamashita A, Goto N, Takahashi K, Yasunaga T, Ikuta K, et al. Direct metagenomic detection of viral pathogens in nasal and fecal specimens using an unbiased high-throughput sequencing approach. PLoS One. 2009;4(1):e4219.Google Scholar
- Kohl C, Brinkmann A, Dabrowski PW, Radonic A, Nitsche A, Kurth A. Protocol for metagenomic virus detection in clinical specimens. Emerg Infect Dis. 2015;21(1):48–57.View ArticlePubMedPubMed CentralGoogle Scholar
- Greninger AL, Chen EC, Sittler T, Scheinerman A, Roubinian N, Yu G, Kim E, Pillai DR, Guyard C, Mazzulli T, et al. A metagenomic analysis of pandemic influenza A (2009 H1N1) infection in patients from North America. PLoS One. 2010;5(10):e13381.Google Scholar
- Briese T, Paweska JT, McMullan LK, Hutchison SK, Street C, Palacios G, Khristova ML, Weyer J, Swanepoel R, Egholm M, et al. Genetic detection and characterization of Lujo virus, a new hemorrhagic fever-associated arenavirus from southern Africa. PLoS Pathog. 2009;5(5):e1000455.Google Scholar
- Seth-Smith HM, Harris SR, Skilton RJ, Radebe FM, Golparian D, Shipitsyna E, Duy PT, Scott P, Cutcliffe LT, O'Neill C, et al. Whole-genome sequences of Chlamydia trachomatis directly from clinical samples without culture. Genome Res. 2013;23(5):855–66.Google Scholar
- Doughty EL, Sergeant MJ, Adetifa I, Antonio M, Pallen MJ. Culture-independent detection and characterisation of Mycobacterium tuberculosis and M. africanum in sputum samples using shotgun metagenomics on a benchtop sequencer. PeerJ. 2014;2:e585.View ArticlePubMedPubMed CentralGoogle Scholar
- Andersson P, Klein M, Lilliebridge RA, Giffard PM. Sequences of multiple bacterial genomes and a Chlamydia trachomatis genotype from direct sequencing of DNA derived from a vaginal swab diagnostic specimen. Clin Microbiol Infect. 2013;19(9):E405–8.View ArticlePubMedGoogle Scholar
- Hasman H, Saputra D, Sicheritz-Ponten T, Lund O, Svendsen CA, Frimodt-Moller N, Aarestrup FM. Rapid whole-genome sequencing for detection and characterization of microorganisms directly from clinical samples. J Clin Microbiol. 2014;52(1):139–46.Google Scholar
- Avriel A, Paryente Wiessman M, Almog Y, Perl Y, Novack V, Galante O, Klein M, Pencina MJ, Douvdevani A. Admission cell free DNA levels predict 28-day mortality in patients with severe sepsis in intensive care. PLoS One. 2014;9(6):e100514.Google Scholar
- Dwivedi DJ, Toltl LJ, Swystun LL, Pogue J, Liaw KL, Weitz JI, Cook DJ, Fox-Robichaud AE, Liaw PC, Canadian Critical Care Translational Biology G. Prognostic utility and characterization of cell-free DNA in patients with severe sepsis. Crit Care. 2012;16(4):R151.Google Scholar
- Rhodes A, Wort SJ, Thomas H, Collinson P, Bennett ED. Plasma DNA concentration as a predictor of mortality and sepsis in critically ill patients. Crit Care. 2006;10(2):R60.View ArticlePubMedPubMed CentralGoogle Scholar
- Garnacho-Montero J, Huici-Moreno MJ, Gutierrez-Pizarraya A, Lopez I, Marquez-Vacaro JA, Macher H, Guerrero JM, Puppo-Moreno A. Prognostic and diagnostic value of eosinopenia, C-reactive protein, procalcitonin, and circulating cell-free DNA in critically ill patients admitted with suspicion of sepsis. Crit Care. 2014;18(3):R116.Google Scholar
- Rumore P, Muralidhar B, Lin M, Lai C, Steinman CR. Haemodialysis as a model for studying endogenous plasma DNA: oligonucleosome-like structure and clearance. Clin Exp Immunol. 1992;90(1):56–62.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang J, Tong KL, Li PK, Chan AY, Yeung CK, Pang CC, Wong TY, Lee KC, Lo YM. Presence of donor- and recipient-derived DNA in cell-free urine samples of renal transplantation recipients: urinary DNA chimerism. Clin Chem. 1999;45(10):1741–6.Google Scholar
- Brunkhorst FM, Oppert M, Marx G, Bloos F, Ludewig K, Putensen C, Nierhaus A, Jaschinski U, Meier-Hellmann A, Weyland A, et al. Effect of empirical treatment with moxifloxacin and meropenem vs meropenem on sepsis-related organ dysfunction in patients with severe sepsis: a randomized trial. JAMA. 2012;307(22):2390–9.Google Scholar
- Engel C, Brunkhorst FM, Bone HG, Brunkhorst R, Gerlach H, Grond S, Gruendling M, Huhle G, Jaschinski U, John S, et al. Epidemiology of sepsis in Germany: results from a national prospective multicenter study. Intensive Care Med. 2007;33(4):606–18.Google Scholar
- Schmitz RP, Keller PM, Baier M, Hagel S, Pletz MW, Brunkhorst FM. Quality of blood culture testing--a survey in intensive care units and microbiological laboratories across four European countries. Crit Care. 2013;17(5):R248.View ArticlePubMedPubMed CentralGoogle Scholar
- Kirn TJ, Weinstein MP. Update on blood cultures: how to obtain, process, report, and interpret. Clin Microbiol Infect. 2013;19(6):513–20.View ArticlePubMedGoogle Scholar
- Be NA, Allen JE, Brown TS, Gardner SN, McLoughlin KS, Forsberg JA, Kirkup BC, Chromy BA, Luciw PA, Elster EA, et al. Microbial profiling of combat wound infection through detection microarray and next-generation sequencing. J Clin Microbiol. 2014;52(7):2583–94.Google Scholar
- Brown JR, Morfopoulou S, Hubb J, Emmett WA, Ip W, Shah D, Brooks T, Paine SM, Anderson G, Virasami A, et al. Astrovirus VA1/HMO-C: an increasingly recognized neurotropic pathogen in immunocompromised patients. Clin Infect Dis. 2015;60(6):881–8.Google Scholar
- Kommedal O, Wilhelmsen MT, Skrede S, Meisal R, Jakovljev A, Gaustad P, Hermansen NO, Vik-Mo E, Solheim O, Ambur OH, et al. Massive parallel sequencing provides new perspectives on bacterial brain abscesses. J Clin Microbiol. 2014;52(6):1990–7.Google Scholar
- Naccache SN, Peggs KS, Mattes FM, Phadke R, Garson JA, Grant P, Samayoa E, Federman S, Miller S, Lunn MP, et al. Diagnosis of neuroinvasive astrovirus infection in an immunocompromised adult with encephalitis by unbiased next-generation sequencing. Clin Infect Dis. 2015;60(6):919–23.Google Scholar
- Wilson MR, Naccache SN, Samayoa E, Biagtan M, Bashir H, Yu G, Salamat SM, Somasekar S, Federman S, Miller S, et al. Actionable diagnosis of neuroleptospirosis by next-generation sequencing. N Engl J Med. 2014;370(25):2408–17.Google Scholar
- Naccache SN, Federman S, Veeraraghavan N, Zaharia M, Lee D, Samayoa E, Bouquet J, Greninger AL, Luk KC, Enge B, et al. A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples. Genome Res. 2014;24(7):1180–92.Google Scholar
- De Vlaminck I, Martin L, Kertesz M, Patel K, Kowarsky M, Strehl C, Cohen G, Luikart H, Neff NF, Okamoto J, et al. Noninvasive monitoring of infection and rejection after lung transplantation. Proc Natl Acad Sci U S A. 2015;112(43):13336–41.Google Scholar
- Strong MJ, Xu G, Morici L, Splinter Bon-Durant S, Baddoo M, Lin Z, Fewell C, Taylor CM, Flemington EK. Microbial contamination in next generation sequencing: implications for sequence-based analysis of clinical samples. PLoS Pathog. 2014;10(11):e1004437.Google Scholar
- Cao MD, Ganesamoorthy D, Elliot AG, Zhang H, Cooper MA, Coin L. Real-time strain typing and analysis of antibiotic resistance potential using Nanopore MinION sequencing. bioRxiv. 2015. doi: http://0-dx.doi.org.brum.beds.ac.uk/10.1101/019356.