Untargeted accurate identification of highly pathogenic bacteria directly from blood culture flasks

Journal Pre-proof Untargeted accurate identification of highly pathogenic bacteria directly from blood culture flasks Erwin M. Berendsen, Evgeni Levin, Rene´ Braakman, Andrei Prodan, Hans C. van Leeuwen, Armand Paauw

PII:

S1438-4221(19)30317-0

DOI:

https://doi.org/10.1016/j.ijmm.2019.151376

Reference:

IJMM 151376

To appear in:

International Journal of Medical Microbiology

Received Date:

12 June 2019

Revised Date:

22 August 2019

Accepted Date:

29 October 2019

Please cite this article as: Berendsen EM, Levin E, Braakman R, Prodan A, van Leeuwen HC, Paauw A, Untargeted accurate identification of highly pathogenic bacteria directly from blood culture flasks, International Journal of Medical Microbiology (2019), doi: https://doi.org/10.1016/j.ijmm.2019.151376

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Untargeted accurate identification of highly pathogenic bacteria directly from blood culture flasks

Erwin M. Berendsen1, Evgeni Levin2,3, René Braakman1, Andrei Prodan2,3, Hans C. van Leeuwen1, Armand Paauw1*

Netherlands Organization for Applied Scientific Research TNO, Department of CBRN

Protection, Rijswijk, The Netherlands. 2

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HORAIZON Technology BV., Rotterdam, The Netherlands.

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Amsterdam Diabetes Center, Department of Internal Medicine, Academic Medical Center,

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VU University Medical Center, Amsterdam, The Netherlands

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* Corresponding author: Armand Paauw, Department of CBRN protection, TNO, P.O. Box 45, 2280 AA Rijswijk, The Netherlands, Phone:+31 888663766 E-mail:

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[email protected]

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Abstract

To improve the preparedness against exposure to highly pathogenic bacteria and to anticipate

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the wide variety of bacteria that can cause bloodstream infections (BSIs), a safe, unbiased and highly accurate identification method was developed. Our liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based method can identify highly pathogenic bacteria, their near-neighbors and bacteria that are common causes of BSIs directly from positive blood culture flasks. The developed Peptide-Based Microbe Detection Engine (http://proteome2pathogen.com/app/) relies on a two-step workflow: a genus-level search

followed by a species-level search. This strategy enables the rapid identification of microorganisms based on the analyzed proteome. This method was successfully used to identify strains of Bacillus anthracis, Brucella abortus, Brucella melitensis, Brucella suis, Burkholderia pseudomallei, Burkholderia mallei, Francisella tularensis, Yersinia pestis and closely related species from simulated blood culture flasks. This newly developed LCMS/MS method is a safe and rapid method for accurately identifying bacteria directly from

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positive blood culture flasks.

Keywords: blood stream infection, identification, pathogens, mass spectrometry, proteome,

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proteomics, diagnostics

Introduction Recognizing the danger in case of an incident with highly pathogenetic bacteria (HPB) is crucial for protecting the health and well-being of the public. Despite the progress made in clinical microbiological diagnostics, a major challenge remains to rapidly recognize incidents with HPBs. Highly accurate and confident identification results are especially essential in cases involving HPBs (Harch et al., 2019). Direct identification of unforeseen HPBs from positive blood cultures is often not trivial; thus, it can be more time consuming than the

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identification of commonly encountered BSI infectious agents, as emphasized by Rudrik et al. (Rudrik et al., 2017). One of the major challenges in microbiological diagnostics is to be able to rely and act on the first test result, even if it is unusual or unexpected. In cases

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involving HPBs, confidence in the diagnostic results is important because the consequences of a confirmed HPB infection can be enormous (Harch et al., 2019).

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In cases of suspected bacterial sepsis, the initial concentration of bacterial cells is low (< 1

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CFU/mL) (Lamy et al., 2016). Therefore, blood from a patient is injected into a blood culture bottle. When microbial growth is detected in the blood culture flask, the identification process of the causative bacterial pathogen begins. Workflows for determining the identity of

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the bacteria differ per laboratory. Direct identification is often pursued via Gram staining, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF

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MS) tests or multiplex PCR. Next, different types of agar plates are inoculated to obtain pure

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colonies or pure culture(s) of the bacteria that grew in the blood culture flask for further testing. Direct identification is often limited by the number of targets used in the multiplex PCRs and by the lack of discriminatory power of MALDI-TOF MS when applied directly to positive blood cultures (Rudrik et al., 2017). For commonly encountered BSI pathogens, the correct identification rate from blood cultures with MALDI-TOF MS ranges from 66% to 85% (La Scola and Raoult, 2009; Lin et al., 2018; Simon et al., 2019; Yonetani et al., 2016).

Clearly, there is a need for rapid and precise identification of bacteria directly from positive blood cultures. Proteomics using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) can be used for accurate identification of bacterial pathogens from plates (Boulund et al., 2017; Jabbour et al., 2010; Jabbour et al., 2011; Tracz et al., 2013) and directly from positive blood cultures (Berendsen et al., 2017; Fleurbaaij et al., 2017). An LC-MS/MS proteomics-based approach was previously demonstrated to be able to correctly identify 33

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tested microorganisms, commonly encountered in BSIs (Berendsen et al., 2017). To make the method universally useful for identifying bacteria in all bacteremia cases, including those caused by HPBs, this method was redesigned. A filter-aided sample preparation (FASP)

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method was introduced to increase safety. To maximize the number of identified peptides per sample, a 90-minute gradient over a nanoflow LC system was used. However, the biggest

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improvement was the development of a new data analysis approach that makes identification

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certain. This new data analysis approach is accessible via a web-based data analysis tool using a curated database (Figure 1). Clearly, this new approach can reduce the time needed to identify the causative agents of bacteremia, and it can quickly confirm the identities of HPBs

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with a high level of certainty and accuracy.

Material and Methods Tested bacterial isolates The microorganisms used for cultivation in blood cultures are listed in Table 1. The identities of all of the isolates were previously confirmed via multilocus variable tandem repeat analysis (MLVA), multilocus sequencing typing (MLST) and/or whole-genome sequencing and biochemical characterization in a previous study conducted by the European Biodefence Laboratory Network (EBLN) (data not shown). Some of the strains included in the study

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belong to the reference collection established by the EBLN under the auspices of the European Defence Agency (EDA). Participating laboratories are or were: NBC &

Environmental Protection Technology Division, Vienna, Austria; Département des

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Laboratoires de la Défense, Brussels, Belgium; National Institute for Nuclear, Chemical and Biologica Protection, Kamenna/University of Defence and Hradec Kralove/Central Military

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Health Institute, Prague, Czech Republic; Centre for Military Medicine CB Defence and

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Environmental Health Centre, Helsinki, Finland; Ministère de la Défense, DGA/MNRBC, Vert le Petit and DGA/MRIS, Bagneux, France; Bundeswehr Institute of Microbiology, Munich, Germany; Army Medical and Veterinary Research Center, Rome, Italy; TNO CBRN

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Protection, Rijswijk, The Netherlands; Norwegian Defence Research Establishment, FFI, Kjeller, Norway; Ministry of National Defence, Science and Military Education Department,

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Warszawa, Poland; La Maranosa Technological Center, Ministry of Defence, Madrid, Spain;

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and Swedish Defence Research Agency, FOI, Umea, Sweden. The objective of the EBLN is to increase Europe’s preparedness against biological warfare agents (BWA), including HPBs. In total, 53 bacterial strains were tested. For each HPB included in this study, their corresponding near neighbors were also tested.

Culture conditions simulated blood cultures Cultivation of microorganisms in blood culture flasks was performed as previously described with some minor modifications (Berendsen et al., 2017). Briefly, BACTEC blood culture flasks (BD Breda, The Netherlands) were injected with 10 mL of sheep blood (Biotrading, Mijdrecht, The Netherlands) and then inoculated with 1 mL of 1·106 CFU/mL of the microorganism tested. This relatively high bacterial concentration was used to guarantee that the injected bacteria would multiply. Because the bacteria reached stationary phase by the

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end of the incubation, the number of injected bacteria had no effect on the total number of bacteria tested.

One aerobic and one anaerobic blood culture flask were inoculated per sample, except for the

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Burkholderia species, for which only aerobic blood culture flasks were inoculated. The

aerobic and anaerobic blood culture flasks injected with Bacillus species and Yersinia species

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were incubated in a standard incubator for 16-18 h at 35 °C. Brucella species and Francisella

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species were incubated for 5 and 7 days, respectively (Doern et al., 1996; Doern, 2000; Yagupsky, 1999). For Burkholderia species, which are aerobic pathogens, only aerobic blood culture flasks were inoculated and incubated in a standard incubator for 16-18 h at 35 °C.

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After cultivation, 1 mL of blood culture was withdrawn from the blood culture flask and used for further sample preparation and analysis. The purity of the cultures was examined via

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inoculation of blood agar plates with a droplet withdrawn from the blood culture flasks.

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These blood agar plates were incubated for two days at 35 °C and visually examined for possible contamination with other bacteria.

Sample preparation for LC-MS/MS analysis 1 mL was withdrawn from each positive blood culture flask and transferred into a tube. The samples were centrifuged for 5 min at 21,000 g. The samples were then treated as previously

described to remove the erythrocytes from the culture medium (Berendsen et al., 2017). Briefly, the following steps were executed twice. Upon removal of the supernatant, 1 mL 2.5% saponin was added, followed by mixing and a 5 min incubation at room temperature. Subsequently, the suspension was centrifuged for 5 min at 21,000 g, followed by washing with Dulbecco's phosphate-buffered saline (DPBS) and demineralized water for 1 min (Berendsen et al., 2017). Next, a sample preparation and digestion protocol was executed consistent with the filter-aided sample preparation (FASP) method with the slight

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modifications described below (Wisniewski et al., 2009). All chemicals were purchased from Sigma Aldrich (Zwijndrecht, The Netherlands) unless stated otherwise. The pellet was

resuspended in 300 µL lysis buffer (4% SDS, 100 mM DTT, 100 mM Tris-HCl, pH 8) and

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transferred to a tube containing 0.1 mm silica glass beads, followed by incubation at 95°C for 30 min. Subsequently, the bacteria were ruptured via bead-beating for 5 min at 3.25 m/s using

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an Omni Bead Ruptor (LA Biosystems, Waalwijk, The Netherlands). The samples were then

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sonicated for 5 min. Subsequently, 30 µL of the obtained lysate was mixed with 200 μl of cleanup buffer (8 M urea in 100 mM Tris-HCl, pH 8). This suspension was transferred to an Ultracel YM-30 filter unit (Merck Chemicals, Amsterdam, The Netherlands). After

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centrifugation (15 min at 21,000 g), 200 μL of cleanup buffer was added followed by centrifugation (5 min at 21,000 g). Next, 50 µL of alkylation buffer (50 mM iodoacetamide, 8

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M urea in 100 mM Tris-HCl, pH 8) was added and mixed for 1 min at 600 rpm in a

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thermomixer (Eppendorf, Nijmegen, The Netherlands). Subsequently, the tubes were incubated for 20 min in the dark. Prior to digestion, the filter was washed twice with 100 μl of cleanup buffer (10 min 21,000 g) and then washed twice with 100 μl of digest buffer (50 mM triethylammonium bicarbonate buffer pH 8.5) (10 min at 21,000 g). For digestion, 50 µL of digest buffer containing 2 µg trypsin was added, followed by incubation at 37°C for 1 hour. The peptides were collected in a new tube via centrifugation (10 min at 21,000 g). To

maximize peptide recovery, an additional 50 µL of digest buffer was added and collected (10 min at 21,000 g). From each protein digest, 10 µL was plated on a blood agar plate. The blood agar plate was incubated for 14 days at 35°C with 5% CO2 to confirm the sterility of the protein digest before the samples were transferred out of the BSL-3 facility.

LC-MS/MS analysis

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The protein digests were analyzed with LC-MS/MS using a nanoflow LC system (Proxeon EASY nLC, Bruker Daltonics GmbH, Bremen, Germany) coupled to a Q-TOF mass

spectrometer (maXis impact, Bruker Daltonics). The samples were injected onto an Acclaim

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PepMap C18 precolumn (75 µm ID × 20 mm, 3 µm, 100 Å, Thermo Fisher Scientific) and

then washed with loading solvent in purified H2O (0.1% formic acid, Fluka, Sigma Aldrich

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Chemie GmbH, Steinheim, Germany) at a flow rate of 5 µL/min for 1 min. Following valve

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switching, the peptides were separated on an Acclaim PepMap C18 analytical column (75 µm ID × 150 mm, 2 µm, 100 Å, Thermo Fisher Scientific) at a constant flow of 300 nL/min with the following binary gradient: from 95% A (purified H2O with 0.1% formic acid) to 35% B

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(80% (v/v) CH3CN and 20% (v/v) purified H2O with 0.1% formic acid) over 90 min, followed by an increase to 95% B for 5 min. The nanoLC system was coupled to the mass

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spectrometer using a CaptiveSpray (Bruker) ionization source. The spray voltage was set at

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1.2-1.5 kV, and the temperature of the heated capillary was set to 150C. The eluted peptides were analyzed using the data-dependent MS/MS mode. The ten most abundant ions (charge state 2+, 3+ and 4+) in each MS spectrum (300-1300 m/z) were selected for data-dependent MS/MS analysis via collision-induced dissociation using nitrogen as the collision gas. MS/MS scans were acquired over a mass range of 100-2000 m/z (2-10 Hz, depending on the signal intensities).

MS/MS data analysis The recorded MS-spectra were analyzed and compared to the MS2peptides-DB database as previously described (Berendsen et al., 2017). Briefly, this database contains annotated protein sequences in FASTA format that were derived from sequenced reference genomes on the NCBI FTP server (extracted on 29-Augustus-2016) supplemented with annotated genomes of species for which no reference genome was available at that time. The database

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contains 442 entries, representing 429 bacterial species, 9 yeast/fungal species and single entries for Ovis aries, Bos taurus, and Homo sapiens as well as the amino acid sequence of

Gallus gallus ovalbumin, which represent potential contaminants (See supplemented Table

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S1 for included entries). Contaminants in the context of this study are nonmicrobial proteins putatively derived from the sample, culture media, or contaminants introduced during sample

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preparation. The MS spectra obtained from each sample were assigned to peptides using

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PEAKS 7.5 (Bioinformatics Solutions Inc., Waterloo, Canada) and the custom database (MS2peptides-DB) (Zhang et al., 2012). Furthermore, filters were used to exclude duplicate peptides and advanced missed cleavage handling (Berendsen et al., 2017). Only peptides with

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a false discovery rate (FDR) ≤ 0.1% were used for further analysis.

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Peptide-Based Microbe Detection Engine analysis

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The peptides were processed via a newly developed web application (app) called the PeptideBased Microbe Detection Engine (http://proteome2pathogen.com/app/), or PBMDE. Figures S1 to S4 demonstrate the PBMDE-app and the results it generates. Furthermore, the PBMDEapp can also be used with own generated peptide lists or with a number of peptide lists used in this study (Table S2; Sample_PBMDE_1.csv to Sample_PBMDE_12.csv). The analysis followed a two-step workflow starting with a genus-level search against a database that

contains proteomic variations in the MS2peptides-DB followed by a species-level search against the relevant species database. Two entries in the MS2peptides-DB-derived DB used for the genus level identification were adjusted. The first adjustment was that Enterobacter aerogenes was included in the genus Klebsiella instead of in Enterobacter. E. aerogenes was recently renamed Klebsiella aerogenes based on its similarity to other Klebsiella species (Tindall et al., 2017). Second, Shigella species are phylogenetically more related to Escherichia coli than E. coli is to other Escherichia species. For the species-level analysis,

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one database adjustment was implemented compared to the previous database. The genetic differences between B. cereus and B. thuringiensis species are plasmid based; therefore, these two species were combined in this analysis (Bacillus cereus group I).

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For all input peptides, the genus/species-level searches determine whether each peptide is a “hit” (i.e., a perfect match for a reference sequence of a microbial genus/species or a

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contaminant). Furthermore, the searches determine whether each peptide is a “discriminative

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hit” (i.e., a peptide that matches exactly one microbial genus/species, while excluding contaminants, rendering it discriminative). Prior to the analysis, all of the isoleucine (I) amino acid residues in the input peptides were replaced with leucine (L) because the identical masses

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(113.084) of these two amino acids make them in indistinguishable from each other with the used method. Duplicate input peptides, including duplicates created by the “I-to-L”

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replacement, were removed.

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The “Exact Mode” mode of the app was applied in this study, and it uses Ag (Boyer and Moore, 1977) for both the genus- and the species-level searches. Ag is a highly speed-optimized command-line tool that uses a Boyer-Moore algorithm to execute fast text searches (https://github.com/ggreer/the_silver_searcher). The app uses Ag to search for matches with the query peptide sequence in the protein sequences database. Species-level searches were performed on all genera, yielding at least 15 discriminatory hits. This mode also outputs genus-

level results alongside the species-level results, as well as a separate output file containing a list of hits for some of the most common contaminants (human, cow, sheep) that might be present in a given sample. The species with the most species-level discriminative hits (from each genus with at least 15 genus-level discriminative hits) was deemed to have been present in the sample. The results are displayed by the app and are available for download in “csv” format spreadsheets. The app was written in R (version 3.4) using the “shiny” package for web development (Chang et al., 2017). In addition to the peptides identified in this study, the

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previously retrieved peptide lists (the “microLC” dataset) from blood culture flasks spiked with BSI-related pathogens were re-analyzed with this data analysis tool (Berendsen et al., 2017).

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Results

One of the major challenges in microbiological diagnostics is to discriminate between HPBs

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and genetically closely related species (often called near neighbors). Therefore, for each HPB

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included in this study, the identification of each specific HPB and their tested corresponding near neighbors analysis results are described. No more than one genus was detected with >15

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genus discriminative peptides in any of the tested flasks.

Identification of B. anthracis and its near neighbors

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All 26 blood culture flasks (13 aerobic and 13 anaerobic) spiked with a Bacillus species were

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correctly identified via nLC-MS/MS (Table 2). Moreover, all 5 B. anthracis strains (2 isolates tested in duplicate), 3 B. cereus strains (1 isolate tested in duplicate), 1 B. thuringiensis strain and 1 B. atrophaeus strain were correctly identified. On average, out of the 4503 and 4213 assigned peptides from the aerobic and anaerobic culture flasks, 23.7% and 27.4%, respectively, of these peptides could be traced back to the cumulative proteomes of the Bacillus species included in the database. The bacilli cultured in all of the flasks were identified to the

genus level based on at least 496 and 551 discriminative peptides in the aerobic and anaerobic blood culture flasks, respectively. The B. anthracis strains were identified to the species level based on at least 13 discriminative peptides analyzed from the aerobic blood culture flasks and at least eight discriminative peptides analyzed from the anaerobic blood culture flasks (Table 2). B. cereus and B. thuringiensis (identified as B. cereus group I) were identified based on at least 62 and 69 discriminative peptides analyzed from the aerobic and anaerobic blood culture flasks, respectively (Table 2). Of the flasks inoculated with B. atropheus, 279 and 291 species-

(Table 2).

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Identification of B. melitensis, B. abortus and B. suis

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discriminative peptides were identified from the aerobic and anaerobic flasks, respectively

Correct species identification was obtained for 19 blood culture flasks (9 aerobic and 10

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anaerobic) spiked with a Brucella species via this LC-MS/MS approach (Table 3). One aerobic

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blood culture spiked with B. abortus (strain 010-00609) was contaminated and overgrown with Staphylococcus warneri (a commensal of the human skin) and was not analyzed further. All of the Brucella isolates, B. melitensis (4x), B. abortus (3x) and B. suis (3x) spiked in blood

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culture flasks with 10 mL sheep blood were correctly identified. On average, out of the 3746 and 2727 assigned peptides from the aerobic and anaerobic culture flasks, 20.9% and 8.5%,

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respectively, were traced back to the cumulative proteomes of the Brucella species included in

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the database. The Brucella isolates tested were identified to the genus level based on at least 646 and 146 discriminative peptides and to the species level based on at least 13 and 3 discriminative peptides from the aerobic and anaerobic blood culture flasks, respectively (Table 3).

Identification of B. pseudomallei, B. mallei and near-neighbors

All eleven aerobic flasks containing seven different Burkholderia species were correctly identified using LC-MS/MS and the data analysis method (Table 4). On average, out of the 3575 assigned peptides from the aerobic culture flasks, 25.7% were derived from the cumulative proteomes of the Burkholderia species included in the database. Of the three different B. mallei isolates tested, at least 316 genus discriminative peptides were detected (Table 4). These three isolates were identified to the species level based on between 7 and 13 discriminative peptides. The three different B. pseudomallei isolates were identified to the

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genus level based on at least 535 genus discriminative peptides and to the species level based on between 13 and 53 discriminative peptides. Blood culture flasks spiked with B. gladioli, B. glumae, B. oklahomensis, B. plantarii or B. thailandensis were identified on the genus level

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based on at least 246 discriminative peptides and to the species level based on at least 67

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discriminative peptides.

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Identification of F. tularensis and F. philomiragia

Using LC-MS/MS, correct species identification was obtained for 18 blood culture flasks (9 aerobic and 9 anaerobic) spiked with a Francisella species (Table 5). 2 F. philomiragia and 7

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F. tularensis isolates spiked in blood culture flasks with 10 mL sheep blood were correctly identified. On average, 2844 and 2664 peptides were identified from the aerobic and anaerobic

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culture flasks, respectively. From both the aerobic and anaerobic flasks, 3.5% of the peptides

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were derived from the cumulative proteomes of the Francisella species included in the database. The Francisella isolates spiked in the aerobic blood culture flasks were identified to the genus level based on at least 17 discriminative peptides, followed by species identification based on at least two discriminative peptides (Table 5). In one anaerobic blood culture flask, the number of discriminative peptides was too low for the genus level identification. Therefore, F. tularensis could only be identified based on the results of the aerobic blood culture flask.

The Francisella isolates in the other eight anaerobic blood culture flasks were identified to the genus level based on at least 35 discriminative peptides, followed by species level identification based on at least two discriminative peptides.

Identification of Y. pestis and its near neighbors Using LC-MS/MS, correct species identification was obtained for all 28 blood culture flasks (14 aerobic and 14 anaerobic) spiked with Yersinia species. Moreover, all 5 Y. pestis, 4 Y.

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pseudotuberculosis, 1 Y. intermedia (twice), 1 Y. enterocolitica, 1 Y. similis and 1 Y. wautersii strains spiked in aerobic and anaerobic blood culture flasks with 10 mL sheep blood were correctly identified (Table 6). On average, out of the 4289 and 3795 assigned peptides from

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the aerobic and anaerobic culture flasks, 13.1% and 11.7%, respectively, could be traced back to the cumulative proteomes of the Yersinia species included in the database. Yersinia was

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identified to the genus level based on 17 and 15 discriminative peptides in one aerobic and one

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anaerobic blood culture flask, respectively. In the other blood culture flasks injected with Yersinia species, at least 124 genus-discriminative peptides were identified. All 5 Y. pestis isolates were identified. The sample withdrawn from the aerobic blood culture spiked with

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isolate 6/69 was identified to the species level based on 18 discriminatory peptides, while the sample withdrawn from the anaerobic blood culture was identified based on 1 discriminatory

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peptide. Y. pestis NCTC 10030 was identified to the species level based on 1 discriminatory

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peptide in the sample withdrawn from the aerobic blood culture and 42 discriminative peptides detected in the anaerobic blood culture flask. The three other Y. pestis strains tested were identified to the species level based on at least 16 and 11 discriminatory peptides from aerobic and anaerobic blood culture flasks, respectively. The four Y. pseudotuberculosis strains tested were identified to the species level based on at least 8 and 4 discriminatory peptides from the aerobic and anaerobic blood culture flasks, respectively. Other Yersinia species were identified

based on at least 30 discriminatory peptides from the aerobic or anaerobic blood culture flasks (Table 6).

Nonbacterial peptides (contaminants) The analyzed samples mimic positive blood cultures, which also contain culture media (which contains Bos taurus proteins ) and Ovis aries blood in addition to the microorganisms. Despite the sample preparation steps intended to remove nonbacterial proteins, a large fraction of the

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analyzed peptides were derived from “contaminants”. Table S3 provides an overview of the total number of identified peptides and the number of peptides identified from “contaminants”.

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Safety of the used sample preparation

No growth was detected in any of the 103 trypsin digests (from 57 aerobic and 46 anaerobic

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blood culture flasks) after the incubation period.

Discussion

BSIs are life threatening events, and the development of techniques for rapid, safe, reliable and

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accurate identification of the causative pathogen would improve clinical diagnostics. In addition, identification of HPBs from blood culture flasks could potentially be the first moment

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in time when a biological threat incident is revealed, or it could be a confirmation of an ongoing

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HPB incident. Here, we developed and evaluated an LC-MS/MS-based method for identifying HPBs directly from positive blood cultures based on multiple discriminatory peptides. The results demonstrate that it is feasible to identify the HPBs B. anthracis, B. abortus, B. melitensis, B. suis, B. pseudomallei, B. mallei, F. tularensis and Y. pestis directly from positive blood culture flasks. Using the developed LC-MS/MS-based method and the PBMDE analysis tool, the HPBs and their near neighbors (n=53) could be distinguished from each other based

on discriminatory peptides. In one case, F. tularensis could only be identified based on the aerobic blood culture flask, while the analysis of the anaerobic blood culture yielded less than 15 discriminatory peptides at the genus level. Furthermore, upon reanalyzing a previously obtained dataset (“microLC”) of simulated BSIs using more commonly encountered bacteria (Berendsen et al., 2017), all of the samples were correctly identified to the bacterial species level (Table S4). This newly developed LC-MS/MS-based analysis pipeline for bacterial identification has

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several advantages over conventional microbiological identification methodologies. All microorganisms can be identified using this approach, given that their genera and species are included in the database. The high number of different species currently included in the

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database increases the chance of timely diagnosis in case of uncommon pathogens (e.g., bacteria that are uncommon in the region or are an exceptional cause of BSIs).

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In this proof-of-principle study, not all diversity within bacterial species is fully covered in the

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current database; nevertheless, species identification was not hampered. Our database can be extended and adjusted to cover a large diversity of microorganisms. Bacterial identification using this LC-MS/MS pipeline is based on multiple discriminatory

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peptides. Therefore, in contrast to targeted methods (e.g., PCR or immune-based assays), this method is resistant to mutations in the pathogen genomes, thus increasing the identification

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reliability. Correct identification is based on the presence of unique amino acid sequences

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(peptides). Support for identification of these amino acid sequences is based on a large number of explainable measured masses from the original peptide from which the peptide fragments were derived (see Figure S5 for an example). As a result, the identification of a microorganism is based not only on one mass of a single peptide but also on the identified unique amino acid sequence of this peptide.

The safety of the developed method and the reliable identification of microorganisms helps to prevent exposure of health care workers to unexpectedly encountered pathogens, of which some HPBs are known to pose a hazard (Lasch et al., 2015; Rudrik et al., 2017). At the start of the protocol, the cells are already inactivated, which is a critical step to limit exposure risk for technicians. The use of FASP represents yet another precautionary safety measure. The use of a filter, as a physical safety barrier, prevents cells from accidentally ending up in the samples intended for LC-MS/MS measurement.

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Fastidious growing bacteria such as Brucella spp. and Francisella spp. can be directly identified from positive blood culture flasks, thus eliminating the need for plate cultivation. This feature is advantageous as it reduces the time required to diagnosis these types of bacteria

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by days. However, for certain species, e.g., F. tularensis, it is important to know the subspecies, as this has implications for the medical treatment and the potential risk it poses to the infected

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individual. Based on the combined results from the aerobic and anaerobic blood culture flasks,

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all seven F. tularensis isolates were successfully identified to the species level based on at least two discriminative peptides. It was not possible to discriminate the F. tularensis tularensis, F. tularensis holartica and F. tularensis novicida subspecies based on the discriminative peptides

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found in this analysis. Additional testing would be required to identify the subspecies of these F. tularensis strains.

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These assay results represent a proof-of-principle study. This approach will be further validated

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using true clinical samples in clinical situations to determine its added value in comparison with other diagnostic assays that identify microorganisms in blood. Using positive blood culture bottles as starting material, its sensitivity in identifying bacteria in positive blood cultures was the same as that of other tests, i.e., approximately 1-5 CFU/mL (Lamy et al., 2016).

The required skills to execute this sample preparation method and the data analysis are at the level of an experienced laboratory engineer. The time needed to identify a microorganism is approximately eight hours per sample. However, because the run time of the LC-MS/MS is 90 minutes per sample, the time-to-identification increases rapidly with each additional sample measured. The main limiting factor of this newly developed LC-MS/MS-based method is that it is currently too complex to use as a high-throughput platform. However, the method is robust, safe, gives highly accurate results and has, for an unbiased identification method, a short time-

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to-answer. Therefore, we think this LC-MS/MS-based method can be initially used for prioritized and difficult samples. For example, this approach might be useful for blood culture flasks taken from patients or travelers with unpredictable causes of sepsis (e.g., severe immune-

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compromised patients or an unexpected community-acquired sepsis). Added value for this method is expected in cases of suspected HPBs, for reliably confirming the results of other

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identification methods (e.g., PCR) and for serving as a backup in cases where the causative

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agent isn’t reliably identified via another method (Harch et al., 2018; Harch et al., 2019). We also see an added value for this method in cases where blood culture flasks (or another enrichment media) become positive after more than five days of incubation. In these cases, re-

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growing the bacteria is too time consuming and this LC-MS/MS method can be used directly. In the current analysis, the amino acid sequences of the analyzed peptides were used to identify

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the microorganism to the species level. The identified peptide amino acid sequences indicate

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which proteins are expressed by the analyzed bacterium. Therefore, the identified peptides can also provide additional information regarding phenotypic characteristics, e.g., the expression of ESBLs or carbapenemases. The potential for LC-MS/MS to assess the presence and type of β-lactamase and carbapenemases has been previously demonstrated (Fleurbaaij et al., 2017; Trip et al., 2015).

In conclusion, this LC-MS/MS method is a safe and rapid method for identifying bacteria directly from positive blood culture flasks. The high accuracy of identification, based on multiple peptides, renders the results highly reliable; furthermore, together with additional information, such as therapy resistance, this approach will facilitate and support decision making. Reliable identification has been demonstrated with several HPBs, including B. anthracis, B. abortus, B. melitensis, B. suis, B. pseudomallei, B. mallei, F. tularensis and Y. pestis, their near neighbors and bacteria that are common causes of BSIs. Implementation of

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this LC-MS/MS-based method for identifying the causes of BSIs would be a safe and adequate manner to anticipate the increased diversity in bacteria that can cause BSIs and to improve the

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preparedness against HPBs in particular.

Acknowledgements

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This work was financially supported by the Dutch Ministry of Defence, grant number V1408.

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This work was part of the European Defence Agency (EDA) EBLN project B0060 involving 12 biodefence research institutions from Europe. We would like to thank the participants of the European Defence Agency (EDA) EBLN project B0060 namely; NBC & Environmental

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Protection Technology Division, Vienna, Austria; Département des Laboratoires de la Défense, Brussels, Belgium; National Institute for Nuclear, Chemical and Biologica Protection,

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Kamenna/University of Defence and Hradec Kralove/Central Military Health Institute, Prague,

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Czech Republic; Centre for Military Medicine CB Defence and Environmental Health Centre, Helsinki, Finland; Ministère de la Défense, DGA/MNRBC, Vert le Petit and DGA/MRIS, Bagneux. France; Bundeswehr Institute of Microbiology, Münich, Germany; Army Medical and Veterinary Research Center, Rome, Italy; TNO CBRN Protection, Rijswijk, The Netherlands; Norwegian Defence Research Establishment, FFI, Kjeller, Norway; Ministry of National Defence, Science and Military Education Department, Warszawa, Poland; La

Maranosa Technological Center, Ministry of Defence, Madrid, Spain; and Swedish Defence Research Agency, FOI, Umea, Sweden. Furthermore, the authors would like to thank Debora van der Riet-van Oeveren, Norbert Sedee, Ingrid Voskamp-Visser, Ton van der Laaken and

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Marcel Alblas for technical assistance.

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26. Harch, S. A. J.; Currie, B. J.; Papanicolas, L.; Rigas, V.; Baird, R.; Bastian, I. Utility of a rapid lateral flow assay to resolve erroneous identification of Burkholderia pseudomallei as Burkholderia thailandensis by Matrix-Assisted Laser Desorption Ionization-Time of Flight (MALDI-TOF) Mass Spectrometry. J. Clin. Microbiol. 2018, 56, 10.1128/JCM.01437-18. Print 2018 Dec.

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Figure legends Figure 1. Schematic overview of the liquid chromatography-tandem mass spectrometrybased method for identifying microorganisms from blood cultures. Sample preparation: 1, clean sample with 2.5% saponin; 2, FASP and trypsin digestion; 3, generation of MS-spectra using nano-LC coupled to a Q-TOF mass spectrometer; 4, generation of a list of identified peptides from the MS-spectra; 5, transfer of the peptide list to the Peptide-Based Microbe Detection Engine (BMBE); 6, genus-level pathogen identification; 7, species-level pathogen

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identification.

Tables

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Original name 4-IZSLT carbosap Ames Sterne 7700 Vollum DSMZ 3648 DSMZ 6891 DSMZ 9378 B. globigii a Kurstaki aizawaib 010-00609 010-00633 RB51 16M 63/9 Ether 200204965 004-00526 Thomsen S2 Chine NCTC 12378 DSMZ 9512 NCTC 10245 NCTC 10230 NCTC 12938 DSMZ 21774 DSMZ 7128 BM1355 BM1357 BM1361 DSMZ 13276 ATCC 25015 FSC039 BD11-00177 FSC 022 FSC 237 FSC 054 FSC 200 FSC 604 FSC 040 ATCC 29913 BM1275 BM1253 6/69 Yokohama M23 NCTC 10030 ATCC 29833 2895 GS95 Tytgat BM1267 BM1277

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Strain no. BM1210 BM1213 BM1220 BM1221 BM233 BM628 BM632 BM931 BM013 BM081 BM1339 BM1340 BM377 BM407 BM408 BM410 BM416 BM1341 BM427 BM431 BM640 BM905 BM645 BM646 BM647 BM642 BM903 BM1355 BM1357 BM1361 BM902 BM117 BM1588 BM1363 BM1316 BM1324 BM1320 BM1323 BM1327 BM1318 BM112 BM1275 BM1253 BM1255 BM1265 BM1266 BM490 BM115 BM1597 BM1602 BM1605 BM1267 BM1277

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Species Bacillus anthracis Bacillus anthracis Bacillus anthracis Bacillus anthracis Bacillus anthracis Bacillus cereus Bacillus cereus Bacillus cereus Bacillus atrophaeus Bacillus thuringiensis Brucella abortus Brucella abortus Brucella abortus Brucella melitensis Brucella melitensis Brucella melitensis Brucella melitensis Brucella suis Brucella suis Brucella suis Burkholderia gladioli Burkholderia glumae Burkholderia mallei Burkholderia mallei Burkholderia mallei Burkholderia oklahomensis Burkholderia plantarii Burkholderia pseudomallei Burkholderia pseudomallei Burkholderia pseudomallei Burkholderia thailandensis Francisella philomiragia Francisella philomiragia Francisella tularensis holarctica Francisella tularensis holarctica Francisella tularensis mediasiatica Francisella tularensis tularensis Francisella tularensis tularensis Francisella tularensis tularensis Francisella tularansis Yersinia enterocolitica Yersinia intermedia Yersinia pestis Yersinia pestis Yersinia pestis Yersinia pestis Yersinia pestis Yersinia pseudotuberculosis Yersinia pseudotuberculosis Yersinia pseudotuberculosis Yersinia pseudotuberculosis Yersinia similis Yersinia wautersii

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Table 1. Strains used in this study

a

Isolated from Dugway B. atrophaeus. bCommercial pesticide formulation containing B.

thuringiensis Kurstaki/Aizawai spores (Certis Europe, Utrecht, The Netherlands)

Table 2. Identified peptides from blood culture flasks injected with Bacillus species using liquid chromatography-tandem mass spectrometry.

Injected microorganism Strain

O2 b 4406 3504 4640 Ames 4342 B. anthracis Sterne 7700 5140 B. anthracis Vollumd 4971 B. anthracis 4347 DSMZ 3648 4409 B. cereus DSMZ 6891 4789 B. cereus DSMZ 9378d 5734 B. cereus 4379 3305 B. atrophaeus Dugwaye B. thuringiensis Kurstaki aizawaif 4576 B. anthracis B. anthracis

Øc 3743 3764 4413 3487 2923 3472 4947 4317 5049 5110 5805 3385 4355

Highest nr. of Identified discriminative microorganism peptides on species level

Second best result on (first false positive)a

O2 b 757 496 942 778 1473 1007 625 1238 1330 1864 1374 794 1418

O2 b 27 13 34 32 33 39 25 250 62 361 271 279 293

O2 b 2 4 3 3 3 1 3 3 2 3 2 2 2

Øc 846 862 1250 619 728 551 1613 1502 1387 1832 2354 750 1341

Øc 26 21 36 14 14 8 43 328 69 384 501 291 300

B. anthracis B. anthracis B. anthracis B. anthracis B. anthracis B. anthracis B. anthracis B. cereus groupg B. cereus groupg B. cereus groupg B. cereus groupg B. atrophaeus B. cereus groupg

Øc 1 2 7 2 2 2 2 3 3 3 4 1 3

Second highest number of discriminative peptides on the species level. b Results from

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a

4-IZSLT carbosapd

Highest nr. of discriminative peptides on genus level

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Total nr. of peptides identified

aerobic culture flasks. c Results from anaerobic culture flasks, d Duplicates were

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independently tested, e Isolated from Dugway B. atrophaeus, f Isolated from Turex WP 50, g

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This B. cereus group contains B. cereus and B. thuringiensis.

Table 3. Identified peptides from blood culture flasks injected with Brucella species using liquid chromatography-tandem mass spectrometry.

Strain 010-00609 010-00633 RB51 16M 63/9 Ether 200204965 004-00526 Thomsen S2 Chine

Highest nr. of discriminative peptides on genus level

Highest nr. of Identified discriminative microorganism peptides on species level

Second best result on (first false positive)a

O2 b -d 3835 4810 2739 3774 3689 3003 3553 4133 4180

O2 b -d 648 1109 690 758 691 646 689 885 907

O2 b -d 24 44 30 36 18 35 14 35 13

O2 b -d 0 1 0 0 0 0 6 0 1

Øc 2871 2649 2465 2229 2853 2899 2903 2802 3033 2564

Øc 294 168 444 253 163 165 161 270 205 146

Øb 11 3 17 11 6 3 6 6 10 4

a

B. abortus B. abortus B. abortus B. melitensis B. melitensis B. melitensis B. melitensis B. suis B. suis B. suis

Øc 0 0 0 0 0 0 0 1 0 0

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Injected microorganism B. abortus B. abortus B. abortus B. melitensis B. melitensis B. melitensis B. melitensis B. suis B. suis B. suis

Total nr. of peptides detected

Second highest number of discriminative peptides on species level. bResults from aerobic

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culture flasks. cResults from anaerobic culture flasks. dContaminated blood culture flask (the grown bacterium was identified a S. warneri (a commensal of the human skin) based on 934

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and 341 genus- and species-discriminative peptides, respectively).

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Table 4. Identified peptides from aerobic blood culture flasks injected with Burkholderia species using liquid chromatography-tandem mass spectrometry. Strain

Total nr. of peptides detected

Highest nr. of discriminative peptides on genus level

Highest nr. of Identified discriminative microorganism peptides on species level

B. gladioli B. glumae B. mallei B. mallei B. mallei B. oklahomensis B. plantarii B. pseudomallei B. pseudomallei B. pseudomallei B. thailandensis

NCTC 12378 DSMZ 9512 NCTC 10245 NCTC 10230 NCTC 12938 DSMZ 21774 DSMZ 7128 BM1355 BM1357 BM1361 DSMZ 13276

3621 4303 2786 3529 2431 4445 2690 3101 3951 3497 4972

925 1544 389 836 316 1566 246 535 1526 1056 1996

449 526 8 13 7 607 67 14 53 25 565

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Injected microorganism

a

B. gladioli B. glumae B. mallei B. mallei B. mallei B. oklahomensis B. plantarii B. pseudomallei B. pseudomallei B. pseudomallei B. thailandensis

Second highest number of discriminative peptides on the species level.

Second best result on (first false positive)a 5 3 2 2 1 2 3 2 3 3 3

Table 5. Identified peptides from blood culture flasks injected with Francisella species using liquid chromatography-tandem mass spectrometry.

a

Strain ATCC 25015 FSC039 BD11-00177 FSC 022 FSC 237 FSC 054 FSC 200 FSC 604 FSC 040

Highest nr. of discriminative peptides on genus level

Highest nr. of Identified discriminative microorganism peptides on species level

Second best result on (first false positive)a

O2 b 2929 3269 2715 2682 2581 2888 2510 2827 3192

O2 b 157 390 33 77 17 31 68 45 128

O2 b 19 67 4 11 2 4 10 6 2

O2 b 2 1 0 0 0 0 0 0 0

Øc 2854 1881 2814 2652 2944 2846 3045 3240 1696

Øc 160 86 <15d 75 35 209 51 82 96

Øb 12 6 8 2 4 7 5 2

F. philomiragia F. philomiragia F. tularensis F. tularensis F. tularensis F. tularensis F. tularensis F. tularensis F. tularensis

Øc 4 1 0 0 0 0 0 0

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Injected microorganism F. philomiragia F. philomiragia F. tularensis F. tularensis F. tularensis F. tularensis F. tularensis F. tularensis F. tularensis

Total nr. of peptides detected

Second highest number of discriminative peptides on the species level. b Results from

aerobic culture flasks. c Results from anaerobic culture flasks. d Less than 15 genus-

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discriminatory peptides were found; therefore, the analysis was not extended to the species

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level.

Table 6. Identified peptides from blood culture flasks injected with Yersinia species using liquid chromatography-tandem mass spectrometry. Total nr. of peptides detected

Y. wautersii a

Øc 4641 2965 2358 3930 2804 4577 4810 2966

O2 b 805 233 349 639 392 632 707 17

Øc 671 124 156 429 15 822 775 308

O2 b 211 61 91 16 18 65 53 1

Øc 187 30 36 11 1 78 53 42

4370

828

802

10

11

2895

4655

2819

813

319

12

5

GS95

4274

4192

698

550

8

Tytgat

4610

4109

772

540

BM1267

4137

3983

649

474

BM1277

5195

4609

736

681

Second best result on (first false positive)a O2 b 3 1 2 1 1 1 1 0

Øc 3 1 1 1 0 2 2 1

Y. pseudotuberculosis Y. pseudotuberculosis Y. pseudotuberculosis Y. pseudotuberculosis Y. similis

2

1

4

2

3

2

1

2

1

0

Y. wautersii

6

7

Y. enterocolitica Y. intermedia Y. intermedia Y. pestis Y. pestis Y. pestis Y. pestis Y. pestis

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Y. pseudotuberculosis Y. pseudotuberculosis Y. pseudotuberculosis Y. pseudotuberculosis Y. similis

Strain O2 b ATCC 29913 5454 BM1275 3627 2938 BM1253 4458 6/69 3671 Yokohama 4515 M23 4928 NCTC 2592 10030 ATCC 29833 4995

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Y. pestis Y. pestis Y. pestis Y. pestis Y. pestis

Highest nr. of Identified discriminative microorganism peptides on species level

11

6

94

70

34

33

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Injected microorganism Y. enterocolitica Y. intermedia

Highest nr. of discriminative peptides on genus level

Second highest number of discriminative peptides on the species level. b Results from

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aerobic culture flasks. c Results from anaerobic culture flasks.