WO2013148162A1 - Nouveaux procédés de détection et d'identification de micro-organismes - Google Patents

Nouveaux procédés de détection et d'identification de micro-organismes Download PDF

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WO2013148162A1
WO2013148162A1 PCT/US2013/030510 US2013030510W WO2013148162A1 WO 2013148162 A1 WO2013148162 A1 WO 2013148162A1 US 2013030510 W US2013030510 W US 2013030510W WO 2013148162 A1 WO2013148162 A1 WO 2013148162A1
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sample
peaks
data set
microorganism
mass spectral
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PCT/US2013/030510
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English (en)
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Jane E. HILL
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University Of Vermont
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/02—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
    • C12Q1/04—Determining presence or kind of microorganism; Use of selective media for testing antibiotics or bacteriocides; Compositions containing a chemical indicator therefor

Definitions

  • Detection and identification of microorganisms such as those involved in pathological conditions, generally require time-consuming growth of the microorganism in vitro or sequencing of the microorganism's genetic material.
  • In situ detection of infectious agents has the potential for more rapid diagnosis than use of these biochemical methods, particularly for slow-growing or difficult to isolate species. Such detection also has the potential to reduce false-negative results caused by the unsuccessful recovery of microorganisms from infection sites.
  • One alternative approach to use of standard biochemical methods of characterizing microorganisms is the analysis and characterization of volatile metabolites released by microorganisms as part of their metabolic cycle.
  • Bacteria produce a complex mixture of volatile metabolites, the whole of which can be used as unique chemical fingerprints of each species, and possibly the strain or serovar of the bacteria (Zhu et al, 2010, J. Clin. Microbiol. 48:4426-4431). This information may allow the diagnosis of infections in situ - for example, from a wound, urine sample, or breath - without requiring the time-consuming recovery of the microbes or their genetic material.
  • Volatile compound-based analyses may also be applied to the phenotypic characterization of bacteria in situ, which has important implications for patient care. For instance, bacterial anaerobiosis in the infection site impacts antibiotic susceptibility, and cannot be characterized in vitro. Volatile metabolomics may also be used in non-invasive breath analysis of lung, sinus, or gastrointestinal infections (Preti et ah, 2009, J.
  • metabolomics are currently employed in the diagnosis of H. pylori stomach infections (Schuman et al, 1995, Gastroenterology 108:A215-A215).
  • the applications for microbial volatile metabolomics extend beyond human health, and can be used for monitoring air quality, detecting biofouling, or for tracking bioconversions in bioengineering processes.
  • tuberculosis (caused by the bacillus Mycobacterium tuberculosis) is a major public health concern, with active infection affecting as many as 23 million people worldwide.
  • the bacterium is also rapidly evolving resistance to antibiotic treatment.
  • One of today's major tuberculosis healthcare challenges is to provide a fast and effective diagnosis.
  • Present clinical methods take as long as two months to determine a positive or negative result, relying extensively on cultivation of the causative agent from patient sputum.
  • High-abundance compounds can mask co-eluting low-intensity peaks, and this is especially problematic for co-eluting isomers that cannot be deconvoluted because of similar mass fragmentation patterns. Late-eluting compounds are also difficult to identify by 1D-GC, as their signal can be obscured by column bleed. Pre-concentration of the volatiles may address the problems caused by column bleed, but it does not facilitate isomer deconvolution. Generally, due to the immense variety of human pathogens, and the close relatedness of some of these bacteria, the robust identification of the bacterium based on its volatile metabolome is likely to require a large number of volatile compounds for each species.
  • GCxGC In GCxGC analysis, two GC columns of orthogonal character are connected in series, facilitating the separation of peaks that are closely eluting in the first dimension. The most frequently used column sets separate compounds by boiling point on the first column, and polarity on the second. The additional resolving power of the second dimension of chromatography improves the spectral purity, facilitating the detection and identification of low abundance compounds via matches to spectral databases (e.g., NIST Mass Spectral Libraries).
  • GCxGC has been used for characterizing complex samples (Blomberg et al, 1997, J. High Res. Chrom. 20:539-544; Humston et al, 2011, Anal. Bioanal. Chem.
  • SESI-MS Secondary electrospray ionization mass spectrometry
  • the invention includes a method of determining the presence or absence of a microorganism in a sample.
  • the method comprises the steps of providing the headspace volatile phase of the sample; performing the mass spectral analysis of the headspace volatile phase of the sample to generate a sample data set; comparing the sample data set with a standard data set, wherein the standard data set is representative of the mass spectral analysis of the headspace volatile phase of a culture medium
  • each data set comprises at least one mass spectral peak characterized by a mass/charge ratio value, and wherein the mass spectral analysis comprises using a technique selected from the group consisting of GCxGC-MS, SESI-MS or a combination thereof; and, determining the presence or absence of the microorganism in the sample.
  • the microorganism is a bacterium.
  • the bacterium comprises at least one organism selected from the group consisting of Escherichia coli, Pseudomonas aeruginosa, Pseudomonas fluorescens, Pseudomonas mendocina, Pseudomonas putida, Pseudomonas stutzeri, Staphylococcus aureus, Legionella pneumophila, Salmonella enterica serovar
  • the sample is from a subject, preferably, the subject is a mammal, and even more preferably, the mammal is selected from the group consisting of a dog, a cat, a cow, a horse, a monkey, and an ape. More preferably, the mammal is human.
  • the sample is derived from a material selected from the group comprising of blood, lymphatic fluid, wound, urine, breath, sputum, flatus, epidemiologic surveillance swab, and combinations thereof.
  • the headspace volatile phase of the sample is concentrated before the mass spectral analysis is performed.
  • concentrating the headspace volatile phase of the sample comprises using solid-phase micro-extraction.
  • the comparing of the sample data set with the standard data set is automated.
  • the presence of the microorganism in the sample is determined if at least one mass spectral peak present in the standard data set is also present in the sample data set.
  • the absence of the microorganism in the sample is determined if at least one mass spectral peak present in the standard data set is not present in the sample data set.
  • the invention also includes a method of generating a standard mass spectral data set for a microorganism grown in a culture medium.
  • the method comprises the steps of providing the headspace volatile phase of the culture medium comprising the microorganism; and, performing the mass spectral analysis of the headspace volatile phase of the culture medium, thus generating the standard mass spectral data set for the microorganism grown in the medium; wherein the data set comprises at least one mass spectrum peak characterized by a mass/charge ratio value, and wherein the mass spectral analysis comprises using a technique selected from the group consisting of GCxGC-MS, SESI-MS or a combination thereof.
  • the microorganism is a bacterium
  • the bacterium comprises at least one organism selected from the group consisting of Escherichia coli, Pseudomonas aeruginosa, Pseudomonas fluorescens, Pseudomonas mendocina, Pseudomonas putida, Pseudomonas stutzeri, Staphylococcus aureus, Legionella pneumophila, Salmonella enterica serovar Typhimurium,
  • Staphylococcus aureus Streptococcus pneumonia, Klebsiella pneumoniae and
  • the headspace volatile phase is concentrated before the mass spectral analysis is performed.
  • concentrating the headspace volatile phase comprises using solid-phase micro-extraction.
  • the invention additionally includes a kit for determining the presence or absence of a microorganism in a sample.
  • the kit comprises at least one standard mass spectral data set for the microorganism grown in a culture medium; a reagent or device for preparing and performing a mass spectral analysis of the headspace volatile phase of the sample; and instructions for the set-up, performance, monitoring, and interpretation of the mass spectral analysis to determine the presence or absence of the microorganism in the sample, wherein the mass spectral analysis comprises using a technique selected from the group consisting of GCxGC-MS, SESI-MS or a combination thereof; wherein comparison of at least one standard mass spectral data set and the result of the mass spectral analysis of the headspace volatile phase of the sample is indicative of the presence or absence of the microorganism in the sample.
  • Figure 1 comprises a set of graphs illustrating the analysis and characterization of the headspace volatile phase of P. aeruginosa PA14 grown
  • Top panel One-dimensional (ID) gas chromatogram of the headspace volatiles.
  • Middle panel Two-dimensional (2D) gas chromatogram of the headspace volatiles, excluding regions containing only column bleed (2 nd RT ⁇ 1.0). The ID and 2D chromatograms were aligned using the measured retention indices of the headspace volatiles.
  • Bottom panel Bubble plot of the two-dimensional chromatogram, after chromatographic artifacts were removed.
  • the colors reflect the compound classes detected for the sample and blank: aliphatic hydrocarbons (red), alcohols (pink), aldehydes (purple), ketones (blue), esters (green), aromatic hydrocarbons (cyan), heteroaromatics (yellow), functionalized benzenes (orange), and all other classes
  • FIG. 2 is a diagram illustrating the classifications of P. aeruginosa PA14 headspace volatile compounds, represented by their relative abundance in area percent.
  • Column bleed and co-eluting compounds in ID chromatography obscured the mass spectrum of the low-abundance compound 2-AA.
  • 2D-GC afforded higher chromatographic purity, making it possible to identify low- abundance compounds by library matching.
  • the NIST library spectrum of 2-AA was provided for reference (bottom panel).
  • Figure 5 is a series of graphs illustrating spectrum comparisons and principal component analysis (PCA) score plots of E. coli
  • FIG. 5a-5c spectrum from headspace volatile phase of EC 0157:H7, SA and ST grown in ( Figure 5a) MEM, ( Figure 5b) VEM, and ( Figure 5c) AEM. Nine replicates were used to generate the average spectrum here.
  • Figures 5d-5f VOC biomarkers profiling of EC 0157:H7, SA and ST in (Figure 5d) MEM, (Figure 5e) VEM, and (Figure 5f) AEM, by principal component analysis score plot using spectrum data from Figures 5a-5c. Data ovals were generated for
  • Figure 6 comprising Figures 6a-6d, illustrates the presence/absence analysis of SESI-MS peaks produced by foodborne pathogen bacteria grown in three food modeling medium: MEM, VEM and AEM (37 °C, 16 h, 200 rpm).
  • MEM foodborne pathogen bacteria grown in three food modeling medium
  • VEM vanadium-oxide-semiconductor
  • AEM AEM-oxide-semiconductor
  • the analyzed data are presented in four panels: Figure 6a - E. coli 0157:H7, Figure 6b - S. aureus, Figure 6c - S. Typhimurium, and Figure 6d - core peaks from EC, SA and ST.
  • Figure 7 illustrates the average SESI-MS peak intensity (left panel, six replicates) comparison of E. coli strains E. coli K12, 06, 026, 045, 084, 0103, 0111, 0121, 0145, 0157:H7 and 0157: NM, and SA and ST after growth on MEM (37 °C, 16 h, 200 rpm). Color bar represents the common logarithm value of each peaks' relative intensity.
  • the right panel illustrates the PCA loading plot of data from the left panel. Data ovals were generated for visualization purpose only.
  • Figure 8 illustrates E. coli core peak intensity change (bar chart, left y-axis, standard error indicated) during EC 0157:H7 growth on MEM (37°C, 16 h, 200 rpm). The plate count data for each time -point are also shown (right y-axis, standard error indicated).
  • Figure 9 illustrates experiments with distinct strains of E. coli.
  • Figure 9a SESI-MS spectra of E. coli 0157:H7, E. coli K12 and their co-culture in MEM (37°C, 16h, 200 rpm).
  • Figure 9b Presence/absence analysis of peaks from EC 0157:H7, EC K12 and their co-culture spectrum.
  • Figure 9c Principal component analysis of EC 0157:H7, EC K12 and their co-culture spectrum data (grown in MEM). Six replicates were included in each group.
  • FIG. 10 illustrates the PCA score plot (left) and loading plot (right) of
  • FIG 11 illustrates SESI-MS breathprints of mice with P. aeruginosa PAOl, P. aeruginosa FRD1, or S. aureus RN450 lung infections, or uninfected lungs.
  • Each spectrum is the average of breath from five mice after a 24 h lung infection.
  • Figure 12 illustrates SESI-MS spectra of P. aeruginosa PAOl, FRD1 and S. aureus RN450, grown in vitro in TSB (24 h, 37 °C).
  • Figure 13 comprising Figures 13a-13f, illustrates experiments with postexposure to Pseudomonas aeruginosa (PA) and S. aureus (SA).
  • Figure 13a White blood cell numbers post-exposure to PA.
  • Figure 13b Total cell counts of
  • polymorphonuclear neutrophils post-exposure to PA.
  • Figure 13c Lactose dehydrogenase (LDH) activity post-exposure to PA.
  • Figure 13d White blood cell numbers post-exposure to SA.
  • Figure 13e Total cell counts of polymorphonuclear neutrophils (PMNs) post-exposure to SA.
  • Figure 13f Lactose dehydrogenase (LDH) activity post-exposure to SA. Data was measured from mouse bronchoalveolar lavage fluid (BALF) samples collected after 6, 12, 24, 48, 72 and 120 h post-exposure to PA and SA.
  • BALF mouse bronchoalveolar lavage fluid
  • Figure 14 illustrates SESI-MS spectra breathprints of P. aeruginosa- infected mice, as they change over 120 h. Each spectrum is the average breathprint from five biological replicates.
  • Figure 15 illustrates SESI-MS breathprints of S. aureus-infected mice, as they change over 120 h. Each spectrum is the average breathprint from five biological replicates.
  • Figure 16 illustrates discriminant analysis using prediction formulae from partial least squares regression for the separation of breathprints from mice with P.
  • Figure 17 comprising Figures 17a- 17b, illustrates bacterial cell counts from 6 h to 12 0 h infection.
  • Figure 17a P. aeruginosa
  • Figure 17b S. aureus.
  • Figure 18 illustrates a variable importance plot (VIP) showing the individual peak's contribution for projecting the data to PLS scores. It summarizes the contribution that a variable makes to the model. A pink dotted line is drawn on the plot at 0.8, a standardly accepted minimum for significant contributions to the model.
  • VIP variable importance plot
  • Figure 19 illustrates the total number of polymorphonuclear neutrophils (PMNs) in bronchoalveolar lavage fluid (BALF). Statistical significance determined by t- test (3 h infection) or one-way ANOVA (24 and 48 h infections); ***p ⁇ 0.0001, **p ⁇ 0.001 compared to the corresponding PBS-treated mice (control) as per Table 6. Values represent the mean ⁇ standard error of the mean of all replicates in each group.
  • Figure 20 illustrates SESI-MS breathprints from mice with H. influenzae, K. pneumoniae, L. pneumophila, M. catarrhalis, P. aeruginosa, S. aureus, or S.
  • Figure 21 illustrates principal components analysis of spectral breathprints from mice with lung infections caused by H. influenzae (HI), K. pneumoniae (KP), L. pneumophila (LP), M. catarrhalis (MC), P. aeruginosa (PA), S. aureus (SA), or S.
  • H. influenzae HI
  • KP K. pneumoniae
  • LP L. pneumophila
  • MC M. catarrhalis
  • PA P. aeruginosa
  • SA S. aureus
  • SP pneumoniae
  • the present invention relates to the discovery of improved methods for detection and identification of microorganisms so that the organisms are detected and identified in a time-effective, sensitive and accurate manner by analyzing the volatile metabolites released from the microorganisms (and present in the headspace volatile phase) using a mass spectrometry method.
  • the mass spectrometry method useful in the methods of the invention comprises GCxGC-MS.
  • the mass spectrometry method useful in the methods of the invention comprises SESI-MS.
  • the methods of the invention allow for the identification of the species of the bacteria.
  • the methods of the invention allow for the identification of the strain or serovar of the bacteria.
  • the causative agent of an infectious disease typically requires a 1-3 day turnaround time for clinical samples. In the case of some agents, such as Mycobacterium, this testing time can extend up to 6 weeks. Clearly a clinician cannot make an informed treatment decision without these data, so guesses as to the microorganism identity are made, leading to increased risk for the patient and general waste of time and money.
  • the methods of the present invention allow substantial reduction in time-to-treatment, thus reducing hospital labor cost and bed-stay time, improving the ability of physicians' to diagnose and treat, and increasing the quality of patient care overall through more rapid diagnosis and assessment of treatment success.
  • the method is implemented with a mass spectrometry system, into which a patient would breathe for the diagnosis of lung infection.
  • GCxGC-MS is a highly reliable and sensitive method to analyze the volatile mixture produced by a bacterium.
  • GCxGC-MS was used to identify the headspace volatile phase of P. aeruginosa grown for 24 hours.
  • the analytical purity and resolution afforded by this chromatographic method facilitated the identification of 28 new P. aeruginosa-derived volatile compounds (including alcohols, aldehydes, ketones, functionalized benzenes, and heteroaromatic molecules), nearly doubling the volatile compound list for this species.
  • bacteria strains which volatile metabolites may be analyzed according to the methods of the invention include Pseudomonas aeruginosa, Pseudomonas fluorescens, Pseudomonas mendocina, P. putida, Pseudomonas stutzeri, Staphylococcus aureus, Legionella pneumopila, Escherichia coli, and Salmonella enterica serovar Typhimurium, Streptococcus pneumonia, Klebsiella pneumoniae and Mycobacterium tuberculosis.
  • the invention includes use of secondary electrospray ionization mass spectrometry (SESI-MS), which allows for implementation of chemical fingerprinting and identification of protonatable or deprotonatable metabolites found in volatile phases of biological samples, such as bacterial cultures, mixed cultures of flask-grown bacteria, breath and sputum.
  • SESI-MS secondary electrospray ionization mass spectrometry
  • This technique allows for rapid profiling of volatile compounds (as little as 10-60 seconds per sample), and fragmentation of fingerprint peaks (through the triple quadrupole system of the instrument) enhances the user's ability to confirm biomarkers within the system of interest.
  • this technology may be complemented with the use of comprehensive two dimensional gas chromatography time-of- flight mass spectrometry (GCxGC TOF-MS) data to identify the volatile compounds produced in their entirely (in as much as analytically possible).
  • GCxGC TOF-MS comprehensive two dimensional gas chromatography time-of- flight mass spectrometry
  • the methods described herein are useful for the analysis of volatile organic compounds (VOCs) in the breath as a rapid diagnostic tool for tuberculosis.
  • respiratory bacterial infections generate a distinctive pattern of volatiles in the host's breath, and once identified, that these patterns may serve as diagnostic signatures.
  • a database may thus be generated comprising known volatile compounds associated with a given infection, using sources such as bacteria grown in the flask to human breath studies.
  • the infection is tuberculosis infection. Evaluation of volatile compounds from bacterial cultures may be performed by assessing volatile chemical stability in Tedlar bags.
  • the breath of infected mice and infected humans is analyzed for headspace volatile compounds originated by Mycobacteria (including M. tuberculosis), and this information is used for identifying a diagnostic signature for this important human pathogen.
  • the methods of the invention allow for the quantification of bacteria in the sample.
  • the present invention also includes a kit comprising a device and/or reagent that allows for the identification of volatile compounds derived from bacteria, wherein such volatile compounds may be isolated from clinical specimens and other samples, and further including instructions for use of the kit on at least one make/model of a commercially available MS system.
  • an element means one element or more than one element.
  • the term "headspace volatile phase” of a sample refers to the volatile compounds formed or observed in the immediate vicinity of the sample, or a representative aliquot or fraction thereof.
  • the volatile headspace phase of a sample may be isolated by sampling the gaseous phase located in the immediate vicinity of the sample.
  • the volatile headspace phase of a sample may be isolated by applying vacuum to the sample.
  • 1D-GC refers to one-dimensional gas chromatography
  • 2-AA refers to 2-aminoacetophenone
  • GCxGC refers to comprehensive two- dimensional gas chromatography.
  • RI refers to retention index
  • SESI secondary electrospray ionization
  • MEM meat extract medium
  • VEM vegetable extract medium
  • AEM apple extract medium
  • the term "PCA” refers to principal component analysis.
  • the term “mass spectrometry (MS)” refers to an analytical technique that measures the mass-to-charge ratio of charged particles. It is used for determining masses of particles, for determining the elemental composition of a sample or molecule, and for elucidating the chemical structures of molecules, such as peptides and other chemical compounds.
  • the MS principle consists of ionizing chemical compounds to generate charged molecules or molecule fragments and measurement of their mass-to-charge ratios.
  • a sample is loaded onto the MS instrument, and undergoes vaporization; the components of the sample are ionized by one of a variety of methods (e.g., by impacting them with an electron beam), which results in the formation of charged particles (ions); the ions are separated according to their mass- to-charge ratio in an analyzer by electromagnetic fields; the ions are detected, usually by a quantitative method; and the ion signal is processed into mass spectra.
  • ions charged particles
  • a “mass spectrophotometer” comprises three modules: an ion source, which can convert gas phase sample molecules into ions (or, in the case of electrospray ionization, move ions that exist in solution into the gas phase); a mass analyzer, which sorts the ions by their masses by applying electromagnetic fields; and a detector, which measures the value of an indicator quantity and thus provides data for calculating the abundances of each ion present.
  • Bacterium or "bacteria,” as these terms are used herein, refer to a single- celled prokaryotic organism or organisms, respectively. The use of the singular
  • bacteria or plural “bacteria” should not necessarily be construed to imply any clonality in the population of organisms described, but should also not exclude clonality of the organisms under certain circumstances.
  • sample includes a clinical sample obtained from a patient, such as, but not limited to, wound, breath, blood, lymph, urine, spinal or synovial fluid, or a tissue sample obtained from any organ or region of the body.
  • a sample may also include a non-clinical sample, such as a surface of some type, or a nonclinical liquid, such as a solution or other liquid that might be used in a clinical setting.
  • a sample can also be a culture, mixed or pure, of bacteria.
  • a "disease” is a state of health of an animal, preferably a mammal and more preferably, a human, wherein the animal cannot maintain homeostasis, and wherein if the disease is not ameliorated then the animal's health continues to deteriorate.
  • a disorder in an animal is a state of health in which the animal is able to maintain homeostasis, but in which the animal's state of health is less favorable than it would be in the absence of the disorder. Left untreated, a disorder does not necessarily cause a further decrease in the animal's state of health.
  • the terms "patient,” “subject,” “individual,” and the like, are used interchangeably, and refer to any animal, or cells thereof whether in vitro or in situ, amenable to the methods described herein.
  • the patient, subject or individual is a mammal, and more preferably a dog , cat, a horse, a cow, a monkey, and ape, or any other mammal whether a domestic or non-domestic mammal.
  • the mammal is a human.
  • treatment as used within the context of the present invention is meant to include therapeutic treatment as well as prophylactic, or suppressive measures for the disease or disorder.
  • treatment includes the administration of an agent prior to or following the onset of a disease or disorder thereby reducing, preventing or removing all signs of the disease or disorder.
  • administration of the agent after clinical manifestation of the disease to combat the symptoms of the disease comprises “treatment” of the disease.
  • a “therapeutic” treatment is a treatment administered to a subject who exhibits signs of pathology, for the purpose of diminishing or eliminating those signs.
  • treating a disease or disorder means reducing the frequency with which a symptom of the disease or disorder is experienced by a patient.
  • Disease and disorder are used interchangeably herein.
  • a disease or disorder is "alleviated” if the severity of a symptom of the disease or disorder, the frequency with which such a symptom is experienced by a patient, or both, is reduced.
  • isolated means altered or removed from the natural state.
  • a nucleic acid or a peptide naturally present in a living animal is not “isolated,” but the same nucleic acid or peptide partially or completely separated from the coexisting materials of its natural state is “isolated.”
  • An isolated nucleic acid or protein can exist in substantially purified form, or can exist in a non-native environment such as, for example, a host cell.
  • Naturally occurring as used herein describes a composition that can be found in nature as distinct from being artificially produced.
  • a nucleotide sequence present in an organism which can be isolated from a source in nature and which has not been intentionally modified by a person in the laboratory, is naturally occurring.
  • an "instructional material” includes a publication, a recording, a diagram, or any other medium of expression that can be used to
  • the instructional material can describe one or more methods of for use of the kit of the invention.
  • the instructional material of the kit of the invention can, for example, be affixed to a container that contains the kit, or be shipped together with a container that contains the kit. Alternatively, the instructional material can be shipped separately from the container with the intention that the instructional material and the kit be used cooperatively by the recipient.
  • range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.
  • the volatile metabolites of a sample are released from the sample at the temperature at which the sample was originally obtained, such as but not limited to room temperature, body temperature, or temperature of the culture medium.
  • the volatile metabolites of a sample are released from the sample by heating the sample above the temperature at which the sample was originally obtained, such as but not limited about 100 °C, about 90 °C, about 80 °C, about 70 °C, about 60 °C, about 50 °C, about 40 °C, about 30 °C, or any other temperature that allows for the volatile metabolites to be released from the sample.
  • the volatile metabolites of a sample are released from the sample by applying vacuum to the sample.
  • vacuum may be combined with application of heat to the sample, if necessary.
  • the volatile metabolites of a given sample are directly analyzed according to the analytical methods useful in the invention.
  • the volatile metabolites of the sample are concentrated prior to their analysis by the analytical methods useful in the invention.
  • the volatile metabolites may be concentrated using a solid-phase micro-extraction filter, such as but not limited to a divinylbenzene/ carboxen/polydimethylsiloxana system.
  • the samples useful in the methods of the invention may be analyzed by mass spectrometry, in order to characterize and identify at least one volatile compound present in the samples.
  • mass spectrometry in order to characterize and identify at least one volatile compound present in the samples.
  • GCxGC-MS and SESI-MS are useful in the methods of the invention.
  • GCxGC analysis two GC columns of orthogonal character are connected in series. In one embodiment, one column separates compounds by boiling point. In another embodiment, one column separates compounds by polarity. In yet another embodiment, the first column in the GCxGC analysis separates compounds by boiling point, and the second column separates compounds by polarity. In yet another embodiment, the first column in the GCxGC analysis separates compounds by polarity, and the second column separates compounds by boiling point.
  • the mass spectrum data set for a sample comprises one or more peaks, each of which is characterized by mass/charge (m/z) ratio and intensity value. Within a data set, the highest intensity peak may be assigned 100% intensity for simplicity of comparison. Since one skilled in the art may control the mass/charge detection range of the mass spectrometer, one skilled in the art may vary the mass/charge detection range of interest for each analysis performed.
  • the mass spectrum data set for a sample may be manipulated, for example by removing (or "filtering") peaks related to, for example, impurities, solvents, contaminants or molecules of no interest.
  • the mass spectrum data may be also processed to remove background signals. Any of these variations on mass spectrum data set are useful in the methods of the invention.
  • a standard mass spectrum data set for a microorganism may be collected from the headspace volatile phase of a culture medium comprising the microorganism, following the methods described herein.
  • the data set is independent of the culture medium selected.
  • the data set is dependent on the culture medium selected.
  • the data set is dependent on the animal selected.
  • the data set is independent of the animal selected.
  • Comparison between the sample data set and the standard data set may be performed manually or using any kind of computer system.
  • Data sets may be compared as to absence or presence of at least one peak (as characterized by its m/z ratio), absence or presence of a defined combination of peaks (as characterized by their m/z ratios), or the relative intensities of the peaks present, or any combination thereof.
  • comparison between the data sets give rise to a binary (yes/no) decision on the presence of a microorganism in the sample.
  • comparison between the data sets gives rise to a probability (or probability range) for the presence of a microorganism in the sample.
  • aspects included in the methods and procedures of the present invention include use and control of specific growth media, internal standards, incubation procedures, and mass spectrometric methods, for example. Any or all of these aspects may be optimized or otherwise altered to create a uniform method, or to create specialized methods for detecting the presence or absence of a specific microorganism in a sample.
  • the methods and procedures of the present invention may include any or all of the following non-limiting steps.
  • specific parameters on a GCxGC-MS or SESI-MS system can be set on the instrument, including type of column used (matrix, length, bead size), mobile and stationary phases/solutions, injection volume, flow rate, wash steps, run duration and sample diversion (i.e. solvent delay), for example.
  • type of column used matrix, length, bead size
  • mobile and stationary phases/solutions injection volume, flow rate, wash steps, run duration and sample diversion (i.e. solvent delay)
  • injection volume injection volume
  • flow rate i.e. solvent delay
  • run duration and sample diversion i.e. solvent delay
  • sample diversion i.e. solvent delay
  • interpretive guidelines may be provided based on the GC retention time and relative signal intensities for the controls and internal standards. These may specify the conditions necessary for a run to be considered valid, including expected values for control reactions and expected values for internal standards.
  • interpretive criteria may be provided for test results based on the relative or absolute intensity of signals of interest. These interpretative criteria may alternatively be implemented in the form of software or a macro that provides preliminary interpretation of assay validity and results pending technologist input.
  • the present invention further includes an assay kit comprising a reagent and/or device for detecting a microorganism in a sample, wherein the detection is based on the mass spectrometry-based analysis of the headspace volatile phase of the sample for characteristic volatile compounds.
  • the mass spectrometry-based analysis comprises GCxGC-MS or SESI-MS
  • the kit comprises reagents, devices and instructions for the set-up, performance, monitoring, and interpretation of the methods of the present invention.
  • the analysis of headspace volatile phase may be performed by any type of commercially available GCxGC-MS or SESI-MS system as disclosed elsewhere herein.
  • the present invention may include novel software developed to assist and automate assay performance and interpretation.
  • P. aeruginosa PA14 was cultured aerobically at 37°C in 50 mL of LB- Lennox. After 24 h of growth, the bacterial culture was cooled on ice, centrifuged at 5000 rpm at 4°C for 20 min to pellet the cells, and the supernatant was filter sterilized through a 0.22 ⁇ PES membrane. Ten milliliters of the supernatant and a stir bar were sealed into sterilized 20 mL GC headspace vials with PTFE/silicone caps. Two PA14 cultures and one LB blank were prepared. All samples were stored at -20°C until analyzed.
  • microextraction fiber SPME; divinylbenzene/carboxen/ polydimethylsiloxane, 50/30 ⁇ ; Supelco/Sigma-Aldrich, St. Louis, MO
  • SPME divinylbenzene/carboxen/ polydimethylsiloxane
  • 50/30 ⁇ Supelco/Sigma-Aldrich, St. Louis, MO
  • the GCxGC-TOF system (Pegasus 4D, Leco Corp., St. Joseph, MI) was fitted with a two-dimensional column set consisting of an Rtx-5 (5% diphenyl/95% dimethyl polysiloxane; 30 m x 180 ⁇ x 0.18 ⁇ ; Restek, Bellefonte, PA) in the first dimension, and an Rtx-17 (50% diphenyl/50% dimethyl polysiloxane; 1 m x 100 ⁇ x 0.1 ⁇ ; Restek) in the second dimension, joined by a press fit connection.
  • Rtx-5 5% diphenyl/95% dimethyl polysiloxane
  • Restek Bellefonte, PA
  • the columns were heated independently; the primary oven containing column 1 was initiated at 30°C (0.2 min hold), then heated at 10°C/min to 230°C (0.8 min hold); the secondary oven containing column 2 was initiated at 35°C (0.2 min hold), then heated at 10°C/min to 235°C (0.8 min hold).
  • a quad-jet modulator was used with a 4 s modulation period (0.4 s hot pulses, 1.6 s cold pulses) and a 30°C temperature offset relative to the secondary oven.
  • the carrier gas flow rate was 1 mL/min.
  • a pulsed split injection was used, with a 10: 1 split at 30 s.
  • the inlet and transfer line temperatures were 250°C.
  • Mass spectra were acquired over the range of 25-500 Da at the rate of 200 spectra/s. Data acquisition and analysis was performed using ChromaTOF software, Version 4.22 (LECO Corp.).
  • One-dimensional GC-MS was performed on an Rxi-5MS (5% diphenyl/95% dimethyl polysiloxane; 30 m x 250 ⁇ x 0.25 ⁇ ; Restek) under conditions identical to the GCxGC parameters described above. Mass spectra were acquired over the range of 25-500 Da using mass selective detection.
  • Retention indices (RI) for the bacterial headspace volatiles were calculated using internal alkane standards for ID- and GCxGC analyses. After the SPME fiber had been exposed to the bacterial headspace volatiles for 10 min at 50°C, the fiber was exposed for one second at room temperature to the headspace of a mixture of alkanes (C5-C20), then analyzed by the chromatographic methods described above.
  • ChromaTOF parameters The baseline was drawn through the middle of the noise and the signal-to-noise (S/N) cutoff was set at 250. The resulting peaks were identified by forward search of the NIST 08 Mass Spectral Library. Similarity scores were calculated by ChromaTOF and peaks with scores below 700 were designated as unknowns (labeled as "Unidentified” in Table 1). The number of unknown peaks was unrestricted during data processing. The identity of the peaks with similarity scores > 700 were further evaluated by their RIs, when available. Observed RIs that fall within the range of previously-reported values in the NIST database for an Rtx-5 column (or equivalent) were recorded in bold in Table 1. With the exception of acetic acid, all of the un-bolded RIs have insufficient data in the NIST database for comparison.
  • Peaks that were assigned as P. aeruginosa headspace volatile compounds were present in both samples at concentrations at least two-fold greater than the blank.
  • the fold-increase over the blank ( ⁇ in Table 1) was calculated by unique masses for each compound, and the reported value is the least of the two samples, if different.
  • the area percent (Area % in Table 1) was calculated using the TICs of the 181 sample peaks (excluding artifacts).
  • SPME Solid-phase microextraction
  • P. aeruginosa strain PA 14 grown for 24 h in LB-Lennox.
  • the volatile and semi-volatile P. aeruginosa metabolites, which are dissolved in the growth medium, are predicted to achieve equilibrium between the liquid medium, headspace, and SPME stationary phase within minutes at 50°C with stirring (Zhang & Pawliszyn, 1993, Anal. Chem. 65: 1843- 1852).
  • the amounts of 2-amino- acetophenone (2-AA) and indole that were adsorbed onto the triphase SPME fiber were measured as a function of exposure time, temperature, and concentration in water.
  • 2-AA is a low-abundance, but highly specific semi-volatile biomarker responsible for the iconic grape-like fragrance of P. aeruginosa, and indole is a high-abundance semi-volatile compound that gives E. coli its mothball-like scent (Labows et ah, 1980, J. Clin.
  • aeruginosa headspace compounds was designed for qualitative volatile discovery, based on the range of Henry's (Ku) and octanol-water partition (K ow ) coefficients of the compounds observed in this study, the majority of the P. aeruginosa headspace volatiles were estimated to be sampled at or near equilibrium concentrations (Zhang & Pawliszyn, 1993, Anal. Chem. 65: 1843-1852).
  • the superior resolving power of GCxGC provided data with improved signal-to-noise (S/N) and analytical purity relative to ID-GC, revealing low-abundance compounds in densely populated regions of one-dimensional chromato grams.
  • S/N signal-to-noise
  • an approach to bacterial identification via their volatile metabolome lies in assigning unique patterns of volatile biomarkers to individual bacterial species. Due to the sheer abundance of microbial species and infectious agents to identify, and the close relatedness of some species within their genus, selective and specific fingerprinting methods may require a large selection of volatiles to be used in combination for correct identifications. Therefore, bacterial volatile profiles should be analyzed beyond the obvious (e.g., easy to smell), high-abundance, and common bacterial volatiles, and begin identifying the low-abundance, unknown peaks in the
  • E. coli 0157:H7 (EC 0157:H7), S. aureus ATCC 25923 (SA), and S. Typhimurium ST5383 (ST) were grown aerobically at 37 °C for 16 h in meat extract medium (MEM), vegetable extract medium (VEM) or apple extract medium (AEM).
  • MEM meat extract medium
  • VOM vegetable extract medium
  • AEM apple extract medium
  • EC 0157:H7 had its dominant peak at m/z of 118 (100% relative intensity (RI)), with m/z of 91 (38% RI) as its second highest peak.
  • the peak m/z of 118 had much higher intensities in EC 0157:H7 than the other two spectra under the test condition.
  • S A had m/z of 43 ( 100% RI) and m/z of 59 (71 % RI) as its top two peaks, and had peak m/z of 61 and peak m/z of 103 among its high intensity peaks - an unique feature as compared to other species.
  • ST had m/z of 109 (100% RI) and m/z of 55 (36% RI) as its top two peaks, and had peak m/z of 105, which was not produced by the other two species..
  • the multivariable analysis method - principal component analysis (PCA) was employed to establish statistical evidence for VOC metabolite profile distinction of the spectra data.
  • PCA principal component analysis
  • the principal component analysis score of the absolute intensities for all peaks (40-150 m/z) of mass spectra from the headspace volatiles of the three bacteria from MEM, VEM and AEM were plotted in Figures 5d-5f. A clear separation between the species could be seen in each medium.
  • Non-0157:H7 E. coli strains have been associated with foodborne outbreaks.
  • SESI-MS was used to profile the VOCs in the headspace of nine outbreak EC strains (E. coli 06, 026, 045, 084, O103, 0111, 0121, 0145, and 0157:NM strains, then compare them to E. coli K12 and 0157:H7 after growth on MEM.
  • Figure 7a illustrates the heat map analysis of peak relative intensity.
  • the six major peaks (m/z of 65, 91, 92, 117, 118 and 119) remained present and at a similar intensity in all eleven EC strains.
  • SESI-MS VOC headspace analysis for VEM and AEM also showed that all six major peaks remain present and at a similar intensity (data not shown). These peaks may be considered as crude VOC biomarkers for Escherichia coli species.
  • FIG. 8 A time series study was performed to determine how early E. coli strains could be detected when grown in MEM.
  • EC 0157:H7 headspace VOC profile time- series data is illustrated in Figure 8 as a non-limiting example for all the ECs included in this study.
  • the six major EC VOC biomarkers were monitored every 2 h, up to 8 h, the average peaks intensities were plotted on Figure 8. At two-hour incubation, four of the six major EC VOC biomarkers showed up; all six major peaks became observable after a four-hour incubation for EC 0157:H7. This result was also true for other ECs grown on MEM.
  • SESI-MS secondary electrospray ionization mass spectrometry
  • VOC volatile organic compound
  • EC E. coli
  • Salmonella Typhimurium a group of eleven E. coli (EC) strains were detected and separated from two major foodborne bacteria, Staphylococcus aureus and Salmonella Typhimurium in three food modeling media.
  • Heatmap analysis of relative peak intensity demonstrated that there are six core peaks (m/z of 65, 91, 92, 117, 118 and 119) present and at a similar intensity in all eleven E. coli strains at the experimental conditions. These peaks can be considered conserved VOC biomarkers for E.
  • Bacterial Strains, Medium, and Growth Condition The strains used in this study are listed in Table 2. Biochemical tests (BD Enterotube II Prepared Media Tubes, Franklin Lakes, NJ) and antigen-specificity stereotyping (Oxoid Dryspot Seroscreen, Oxoid, Cambridge, UK; Remel Wellcolex E. coli 0157:H7 Kit, Remel, Lenexa, KS; Adsorbed monovalent O single antisera, Statens Serum Institut, Copenhagen S Denmark) were used to confirm the genus and serotype of strains. Unless otherwise indicated in the text, strains were cultured aerobically for 16 h at 37 °C in 50 mL of food modeling medium (final OD >1 for all samples).
  • Meat extract medium (Sigma, St. Louis, MO)
  • VEM vegetable extract medium
  • AEM apple extract medium
  • VOC mass spectra were collected using SESI-MS, using previously described methods (Zhu et al, 2010, Clin. Microbiol. 48:4426-4431). Briefly, bacterial culture headspace VOCs were introduced into a customized SESI-MS reaction chamber for 1 min via displacement by CO 2 (99.99%; 2 L/min) at ambient temperature. Formic acid (0.1% (v/v)) was used as the electrospray solution, delivered at a flow rate of 5 nL/s through a non-conductive silica capillary (40 ⁇ ID) with a sharpened needle tip. The operation voltage was ⁇ 3.5 kV. Spectra were collected within 1 min as an accumulation of 20 scans in single-quadrupole positive-ion mode. The system was flushed with C0 2 between samples until the spectrum returned to background levels.
  • Peaks between 40 and 150 mlz (mass-to- charge ratio) and greater than a 1% relative intensity threshold (after blank subtraction) were used as variables for PCA, while all experimental replicates were used as observations.
  • the heatmap was generated using Matlab (Math Works Inc.). Common logarithm values of peak relative intensities from the average spectra of each bacterial strain were used for the generation of this graphical representation of VOC spectra profile data.
  • E. coli 0157:H7 (EC 0157:H7), S. aureus ATCC 25923 (SA), and S. enterica serovar Typhimurium ST5383 (ST) were grown aerobically at 37 °C for 16 h in meat extract medium (MEM), vegetable extract medium (VEM), and apple extract medium (AEM).
  • MEM meat extract medium
  • VEM vegetable extract medium
  • AEM apple extract medium
  • the full scan spectra (40-150 mlz) of bacterial culture headspace VOCs from these three bacteria in the three different food modeling media are shown in Figures 5a-5c. It was observed that the different bacterial species produce a unique global VOC profile consisting of dominant peaks, which are labeled in each spectrum, as well as bacterium-specific peaks.
  • PCA principal component analysis
  • Non-0157:H7 E. coli strains have also been associated with foodborne outbreaks.
  • SESI-MS was used to profile VOCs in the headspace of nine outbreak, Shig; toxin-producing EC strains (STEC; E. coli 06, 026, 045, 084, O103, Ol l l, 0121, 0145, and 0157: NM), as well as the non-pathogenic K12 strain to determine whether or not these ten strains can also be differentiated from SA and ST, based on their VOC profile.
  • the eleven EC strains tested in this study have six core peaks in common, there are some peaks that can be used to differentiate between these bacteria at the serovar level.
  • the heatmap ( Figure 7) shows that EC 0145 uniquely produced peak 59, and EC 0157:H7 produced peak 70, which is not present in other EC strains when grown on MEM.
  • Figure 10 shows the PC A score plot and loading plot constructed based on the spectral data from all eleven EC strains grown on MEM. Three out of eleven strains, EC 0157:H7, EC 0145, and EC K12, are differentiable based on the VOC profiles generated in MEM (p ⁇ 0.0001 for both PC 1 and PC 2 for EC
  • peaks such as 41,42, 43, 53, 55 and 59 provide information to separate EC 0145 from other strains, while peaks 70, 120 and 147 drive EC 0157:H7 clustering.
  • EC 0157:H7 and EC 0145 are also distinguishable from each other after growth on VEM and AEM. While in MEM, the EC K12 VOC fingerprint is distinguishable from all STECs. However, EC K12 is not distinguishable from the STECs in VEM and AEM.
  • Example 8 Detecting Bacterial Lung Infections: In Vivo Evaluation of In Vitro Volatile Fingerprints
  • SESI-MS is capable of differentiating infected versus uninfected mice, specifically P. aeruginosa- fected versus S. aureus-infected mice. It was also demonstrated that SESI-MS is capable of distinguishing between infections caused by P. aeruginosa strains PAOl versus FRD1, with statistical significance (p ⁇ 0.05).
  • in vitro and in vivo volatiles were compared and it was observed that only 25-34% of peaks are shared between the in vitro and in vivo SESI-MS fingerprints. To the best of knowledge, these are the first breath volatiles measured for P. aeruginosa PAOl, FRD1, and S. aureus R 450, and the first comparison of in vivo and in vitro volatile profiles from the same strains using the murine infection model.
  • strains used in this study were P. aeruginosa PAOl-UW, P.
  • aeruginosa FRD1 and S. aureus RN450 (courtesy of Professor G L Archer, Virginia Commonwealth University).
  • TTB tryptic soy broth
  • strains were incubated aerobically in TSB (16 h, 37 °C, 200 rpm) whereafter 50 ⁇ , was used to inoculate 50 mL TSB for 24 h (16 h, 37 °C, 200 rpm, OD 600 > 3.0).
  • mice Jackson Laboratories (Bar Harbor, ME). All mice were housed in the Association for Assessment and Accreditation of Laboratory Animal Care-accredited animal facility at the University of Vermont (Burlington, VT). The protocol for animal infection and respiratory physiology measurements was approved by the Institutional Animal Care and Use Committee, in accordance with Association for Assessment and Accreditation of Laboratory Animal Care guidelines.
  • mice were exposed to 40 PBS as a negative control. Five mice per group were exposed and testing was conducted on two different days. After breath collection, the lungs were harvested and homogenized, and the lung bacterial cell counts were obtained by plating on selective media, yielding averages of 1 x 10 5 CFU/lung for PAO 1 , 5 x 10 5 CFU/lung for FRD 1 and 2 x 10 6 CFU/lung for RN450.
  • mice were anesthetized with pentobarbital 24 h after infection and their tracheas were cannulated.
  • the mice were placed on the ventilators (Flexivent, SCIREQ, Montreal, QC, Canada) and paralyzed with intraperitoneal pancuronium bromide (0.5 mg kg 1 ), and an ECG was applied to monitor heart rate to ensure proper anesthesia.
  • Breath coming out of the ventilator was collected in Tedlar bags (SKC, Eighty Four, PA) at 180 breaths mi 1 with a positive end-expiratory pressure (PEEP) of 3 cm H 2 0 for 1 h.
  • PEEP positive end-expiratory pressure
  • Formic acid (0.1% (v/v) was used as the electrospray solution, delivered at a flow rate of 5 nL s _1 through a non-conductive silica capillary (40 ⁇ ID) with a sharpened needle tip.
  • the operation voltage was -3.5 kV.
  • Spectra were collected within 30 s as an accumulation of 10 scans in positive- ion mode. The system was flushed with C0 2 between samples until the spectrum returned to background levels.
  • PCA is a statistical tool used to compress complex information and is typically applied when the measurements have a large number of observed variables (e.g., m/z from the mass spectra). Peaks between 20 and 200 m z ⁇ l (mass-to-charge ratio) and greater than 1% relative intensity (after blank subtraction) were used as variables, with their absolute intensities used for the calculations in PCA. All experimental replicates were used as observations. SAS version 9.2 and JMP version 9 (SAS Institute Inc., Cary, NC, USA) were used to generate Spearman rank correlation coefficient, conduct PCA, as well as to determine the statistical significance of observed PCA score differences.
  • Breath was collected for 1 h from 15 mice infected with either P.
  • aeruginosa PAOl aeruginosa PAOl, FRDl, or S. aureus RN450 (five per group), and from five uninfected controls.
  • the breath volatile compounds were fingerprinted using SESI-MS ( Figure 11), yielding 32 peaks from mice infected with P. aeruginosa PAOl, 61 peaks from mice infected with P. aeruginosa FRDl and 63 peaks from the S. aureus -infected mice.
  • PAOl an acute infection isolate
  • FRD1 a chronic infection isolate
  • the PCA showed that the volatile breathprint from the three bacteria-infected mice groups and the uninfected control group can all be separated with two principal components (p ⁇ 0.05). Therefore, for the strains investigated in this study, SESI-MS analysis of the breath of mice can distinguish between infected and uninfected animals, and identify the infectious species to the strain level.
  • the in vivo and in vitro SESI-MS data were pooled together to generate a SESI-MS volatilome for the three bacterial strains in this study, which yielded a total of 52 peaks for PAOl, 78 peaks for FRD1 and 131 peaks for R 450 (Table 4).
  • the high degree of variation observed between in vitro volatile fingerprints and in vivo breathprints could be attributed to a combination of factors, such as a change in bacterial metabolism, in response to a new environment, particularly when infecting a new host.
  • FRD1 a chronic lung infection isolate of P. aeruginosa
  • FRD1 adaptations are the loss of catabolic repression control (i.e. looser metabolic regulation), which may account for the large number of in v vo-specific peaks it produces.
  • hallmark volatiles that are present in vitro may not be measurable in breath. For instance, the data described herein showed that S.
  • aureus RN450 loses almost two-thirds of its in vitro SESI-MS fingerprint when compared to the murine lung infection breathprint (Table 4).
  • a second set of factors influencing the in vivo breathprint of infection are the volatiles the host produces in response to the pathogen, which are challenging to predict based on in vitro bacterial data alone.
  • Example 9 Robust detection of P. aeruginosa and S. aureus during acute lung infection via SESI-MS
  • SESI-MS secondary electrospray ionization-mass spectrometry
  • PLS-DA partial least squares- discriminant analysis
  • breathprinting was shown to robustly classify acute P. aeruginosa and S. aureus mouse lung infections at any time during the 120 h infection/clearance process.
  • the variable importance plot from PLS indicated that multiple peaks from the SESI-MS breathprints were utilized for discriminating the bacterial infections. Therefore, by utilizing the entire breathprint rather than single biomarkers, infectious agents can be diagnosed by SESI- MS independently of the point at which breath is tested during the infection.
  • Pseudomonas aeruginosa PAOl-UW and Staphylococcus aureus RN450 were cultured aerobically in 5 mL tryptic soy broth (TSB; 16 h; 37°C; final cell counts >10 9 CFU/mL) before the bacteria were inoculated into the mice airways. After ventilation and breath collection, the mice lungs were harvested and homogenized, and the lung bacterial cell counts were obtained by plating on Pseudomonas isolation agar or Chapman stone medium (BD Diagnostics, Franklin Lakes, NJ, USA). Mice
  • mice Six- to eight- week-old male C57BL/6J mice were purchased from The Jackson Laboratories (Bar Harbor, ME). Before experiments, all mice were housed in the Association for Assessment and Accreditation of Laboratory Animal Care-accredited animal facility at the University of Vermont (Burlington, VT). The protocols for animal infection and respiratory physiology measurements were approved by the Institutional Animal Care and Use Committee, in accordance with Association for Assessment and Accreditation of Laboratory Animal Care guidelines. Microbial airway exposure protocol
  • mice Overnight cultures of bacteria were measured for cell density, centrifuged at 13,000 rpm for 1 min, washed twice with phosphate buffered saline (PBS), and resuspended in 40 PBS to give the desired concentration of bacteria (lxlO 7 CFU/lung for P. aeruginosa infections and lxl 0 8 CFU/lung for S. aureus infections).
  • Mice were briefly anesthetized (isoflurane by inhalation) and infected by oropharyngeal aspiration as described previously (Allard et al, 2009, Eur. J. Immunol. 39:776-788; Wargo et al, 2011, Crit. Care Med. 184:345-354). Additional groups of mice were exposed to 40 PBS as a negative control for each time point. Five mice per group were exposed and tests were conducted on different days to measure data reproducibility.
  • PBS phosphate buffered saline
  • mice were anesthetized with pentobarbital and their tracheas were cannulated. The mice were placed on the ventilator and paralyzed with intraperitoneal pancuronium bromide (0.5 mg/kg), and an ECG was applied to monitor heart rate to ensure proper anesthesia. Breath from the ventilator was collected in 5 L Tedlar bags (SKC, Eighty Four, PA) at 180 breaths/min with a positive end- expiratory pressure (PEEP) of 3 cm H 2 0 for 40-60 min. Breath samples were analyzed within one hour of collection by SESI-MS in positive ion mode. Bronchoalveolar Lavage Fluid: Hematology and Lung Damage Assays
  • BALF cells were enumerated using an AD VIA cell counter (Bayer, Terrytown, NY) and were then fixed onto glass slides (2xl0 4 cells/slide), stained with Hema-3 (Biochemical Sciences, Swedesboro, NJ), counted (300/slide) and categorized as macrophages, eosinophils, neutrophils, or lymphocytes based on characteristic morphology and staining. Lactose dehydrogenase activity (LDH) in BALF samples was measured to determine in vivo lung tissue damage (CytoTox 96 Non-Radioactive Cytotoxicity Assay, Promega, Madison, WI, USA).
  • SESI-MS breath analysis and measurement was performed as previously described (Zhu et al, 2010, J. Clin. Microbiol. 48:4426-4431; Martinez-Lozano and de la Mora, 2009, J. Am. Soc. Mass Spectrom. 20: 1060-1063). Briefly, a stainless steel SESI reaction chamber is used to replace the original electrospray ionization source on a commercially available API 3000 mass spectrometer (SCIEX, Concord, ON, Canada; Bean et al., 2011, J. Vis. Exp. 52:2664). A gas flow of 5 L/min was driven by a mechanical pump connected to the sampling gas outlet of SESI-MS reaction chamber.
  • the breath sample was introduced into the reaction chamber for 30 s at a flow rate of 3 L/min, supplemented with 2 L/min C0 2 (99.99 %) at ambient temperature.
  • Formic acid 0.1 % (v/v)
  • the electrospray solution was used as the electrospray solution, delivered at a flow rate of 5 nL/s through a non-conductive silica capillary (40 ⁇ ID) with a sharpened needle tip.
  • the operation voltage was - 3.5 kV.
  • the declustering, focusing, and entrance potentials for the mass spectrometer were set to 5 V, 350 V, and 2 V, respectively, for efficient molecular ion generation. Spectra were collected for 30 s as an accumulation of 10 scans. The system was flushed with C0 2 between samples until the spectrum returned to background levels. Data Analysis and Statistics
  • PLS-DA Partial least squares-discriminant analysis
  • SESI-MS Breathprints can be used to Robustly Distinguish Acute P. aeruginosa and S. aureus Lung Infections at All Time Points from 6-120 h
  • PLS-DA partial least squares-discriminant analysis
  • the observable variables (the SESI-MS peak intensities) were compressed into a few latent variables, called PLS factors, and then discriminant analyses were applied using these factors to predict the classification (the bacterial species that infected the mice) of each tested sample, as previously described (Cheung et al, 2009, Analyst 134:557-563).
  • the three test groups are clearly separated based on their breathprints using only the first two PLS factors (32.13% variation explained, p ⁇ 0.0001), and all of the replicates for any infection group, regardless of the time point, can be clustered.
  • the results from the PLS-DA suggest that there are a subset of peaks that were consistently detectable during the 120 h time course of each infection (or control), driving the clustering of each group. Therefore, the contributions of each individual peak to the separation of the groups were examined and are reported in the variable importance plot (VIP; Figure 18).
  • VIP variable importance plot
  • variable importance plot from PLS may indicate that discriminating the treatment groups by SESI-MS relies on a large portion of the breathprint data rather than just one or two volatile biomarkers, and therefore incorporating more volatile information into the breath-based diagnostics may improve their sensitivity and specificity.
  • breathprints from lung infections likely arise from a combination of bacterial metabolites and the host volatiles that are related to infection and inflammation.
  • the relative contributions of host and pathogen metabolomes to the breathprint may change over the course of the infection, and may also carry information that could be used to predict the outcome of infections. For example, in both the P.
  • SESI-MS Secondary Electrospray Ionization-Mass Spectrometry
  • breathprints can be used to distinguish mice that are infected with one of seven lung pathogens: H. influenzae, K. pneumoniae, L. pneumophila, M. catarrhalis, P.
  • strains used in this study were Haemophilus influenzae ATCC 51907, Pseudomonas aeruginosa PAOl-UW, Staphylococcus aureus R 450 (courtesy of Professor G. L. Archer, Virginia Commonwealth University), Legionella pneumophila ATCC 33152, Streptococcus pneumoniae ATCC 6301, Moraxella catarrhalis ATCC 43628, and Klebsiella pneumoniae ATCC 13883. Before the bacteria were inoculated into the mice airways, strains were incubated aerobically in tryptic soy broth (TSB; 16 h, 37 °C; final cell counts >109 CFU/mL). After breath collection, the lungs were harvested and homogenized in 1 mL PBS, and lung bacterial cell counts were obtained by plating.
  • TTB tryptic soy broth
  • mice Six- to eight- week-old male C57BL/6J mice were purchased from The Jackson Laboratories (Bar Harbor, ME). The protocols for animal infection and respiratory physiology measurements were approved by the Institutional Animal Care and Use Committee, in accordance with Association for Assessment and Accreditation of Laboratory Animal Care guidelines. All mice were housed in the Association for Assessment and Accreditation of Laboratory Animal Care-accredited animal facility at the University of Vermont (Burlington, VT). Overnight cultures of bacteria were measured for optical density, centrifuged at 13,000 rpm for 1 min, washed twice with phosphate buffer saline (PBS), and resuspended in 40 PBS to give the desired concentration of bacteria (listed in Table 6).
  • PBS phosphate buffer saline
  • mice were briefly anesthetized (isoflurane by inhalation) and infected by oropharyngeal aspiration using previously described methods (Allard et al, 2009, Eur. J. Immunol. 39:776-788; Wargo et al, 2011, Am. J. Respir. Crit. Care Med. 184:345-354). Additional mice were exposed to 40 PBS as a negative control. Six mice per group were exposed and tests were conducted over several days to ensure data reproducibility.
  • mice were anesthetized with pentobarbital and their tracheas were cannulated.
  • the mice were placed on the ventilator and paralyzed with intraperitoneal pancuronium bromide (0.5 mg/kg), and an ECG was applied to monitor heart rate to ensure proper anesthesia.
  • Breath coming out of the ventilator was collected in 5 L Tedlar bags (SKC, Eighty Four, PA) at 180 breaths/min with a positive end-expiratory pressure (PEEP) of 3 cm H 2 O for 40-60 min.
  • PEEP positive end-expiratory pressure
  • BALF bronchoalveolar lavage fluid
  • BALF cells were fixed onto glass slides (2xl0 4 cells/slide), stained with Hema-3 (Biochemical Sciences, Swedesboro, NJ), and the leukocytes were counted (300/slide) and categorized as macrophages, eosinophils, polymorphonuclear neutrophils (PMNs), or lymphocytes based on characteristic morphology and staining.
  • Hema-3 Biochemical Sciences, Swedesboro, NJ
  • PMNs polymorphonuclear neutrophils
  • lymphocytes based on characteristic morphology and staining.
  • LDH lactose dehydrogenase activity
  • the breath sample was introduced into the reaction chamber for 30 s at a flow rate of 3 L/min, and supplemented with 2 L/min C0 2 (99.99 %) at ambient temperature.
  • Formic acid 0.1 % (v/v)
  • the operation voltage was approximately 3.5 kV, and the declustering, focusing, and entrance potentials for the mass spectrometer were set to 5 V, 350 V, and 2 V, respectively.
  • Spectra were collected for 30 s as an accumulation of 10 scans. The system was flushed with C0 2 between samples until the spectrum returned to background levels.
  • a murine lung infection model using seven different bacteria was employed, establishing a 3 h infection with Moraxella catarrhalis, 24 h infections with Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus, and
  • the utility of SESI-MS breathprinting relies on high inter-group differences between breathprints from different infections, coupled with high intra-group reproducibility.
  • the average Spearman correlation coefficients between biological replicate breathprints were calculated within a single group to assess the reproducibility of SESI-MS.
  • the reproducibility of the breathprints is high, ranging from 0.81 to 0.94 (standard deviation ⁇ 0.09).
  • the exception is M. catarrhalis (0.64 ⁇ 0.14), possibly because of its quick clearance rate (typically less than four hours) coupled with the short time-scale (3 h) between infecting inoculation and breath measurement.
  • the intensities of the common peaks in the spectra also carry information, as seen by the patterns generated in Table 7.
  • the patterns of peak intensities across the breathprint mass range also confer unique information for bacterial identification.
  • MS/MS fragmentation of breath volatiles which can be used for compound identification and peak verification, is a capability that is afforded by SESI- MS, unlike similar mass spectrometry methods such as selected ion flow tube-mass spectrometry (SIFT-MS) and proton transfer reaction-mass spectrometry (PTR-MS). More than 200 MS/MS product ion scans were conducted to obtain peak fragmentation data on the most abundant peaks from each breath sample. NIST 08 MS software was then used to compare the fragmentation patterns between biological replicates as well as between bacterial groups. It was confirmed that all peaks with the same m/z have similar fragmentation patterns (match score > 700), and therefore should be recognized as the same compound or group of compounds.
  • SIFT-MS selected ion flow tube-mass spectrometry
  • PTR-MS proton transfer reaction-mass spectrometry

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Abstract

Cette invention concerne un procédé permettant d'identifier un micro-organisme dans un échantillon, ledit procédé comprenant l'analyse par des procédés de spectrométrie de masse des métabolites volatils libérés par le micro-organisme dans l'échantillon. Dans un mode de réalisation, le procédé de spectrométrie de masse utile dans les procédés décrits comprend GCxGC ou SESI. Dans un autre mode de réalisation, les procédés décrits permettent d'identifier l'espèce de la bactérie, et dans un autre, la souche ou le sérovar de la bactérie. Dans d'autres modes de réalisation encore, les procédés décrits permettent d'identifier l'état métabolique de la bactérie et/ou de quantifier la bactérie dans l'échantillon.
PCT/US2013/030510 2012-03-29 2013-03-12 Nouveaux procédés de détection et d'identification de micro-organismes WO2013148162A1 (fr)

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US10777397B2 (en) 2015-03-06 2020-09-15 Micromass Uk Limited Inlet instrumentation for ion analyser coupled to rapid evaporative ionisation mass spectrometry (“REIMS”) device
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US11139156B2 (en) 2015-03-06 2021-10-05 Micromass Uk Limited In vivo endoscopic tissue identification tool
US20210310927A1 (en) * 2018-08-10 2021-10-07 Industry-Academic Cooperation Foundation, Yonsei University Real time continuous measurement apparatus for airborne microbial
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US11342170B2 (en) 2015-03-06 2022-05-24 Micromass Uk Limited Collision surface for improved ionisation
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US20090230300A1 (en) * 2007-10-19 2009-09-17 Jose Miguel Trevejo Rapid detection of volatile organic compounds for identification of bacteria in a sample
US20120064591A1 (en) * 2010-09-15 2012-03-15 Wisconsin Alumni Research Foundation Recombinant yeast with improved ethanol tolerance and related methods of use

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US11139156B2 (en) 2015-03-06 2021-10-05 Micromass Uk Limited In vivo endoscopic tissue identification tool
US10777397B2 (en) 2015-03-06 2020-09-15 Micromass Uk Limited Inlet instrumentation for ion analyser coupled to rapid evaporative ionisation mass spectrometry (“REIMS”) device
US10777398B2 (en) 2015-03-06 2020-09-15 Micromass Uk Limited Spectrometric analysis
US10916415B2 (en) 2015-03-06 2021-02-09 Micromass Uk Limited Liquid trap or separator for electrosurgical applications
GB2554206B (en) * 2015-03-06 2021-03-24 Micromass Ltd Spectrometric analysis of microbes
US10978284B2 (en) 2015-03-06 2021-04-13 Micromass Uk Limited Imaging guided ambient ionisation mass spectrometry
US11031222B2 (en) 2015-03-06 2021-06-08 Micromass Uk Limited Chemically guided ambient ionisation mass spectrometry
US11367605B2 (en) 2015-03-06 2022-06-21 Micromass Uk Limited Ambient ionization mass spectrometry imaging platform for direct mapping from bulk tissue
US11037774B2 (en) 2015-03-06 2021-06-15 Micromass Uk Limited Physically guided rapid evaporative ionisation mass spectrometry (“REIMS”)
US11367606B2 (en) 2015-03-06 2022-06-21 Micromass Uk Limited Rapid evaporative ionisation mass spectrometry (“REIMS”) and desorption electrospray ionisation mass spectrometry (“DESI-MS”) analysis of swabs and biopsy samples
US10026599B2 (en) 2015-03-06 2018-07-17 Micromass Uk Limited Rapid evaporative ionisation mass spectrometry (“REIMS”) and desorption electrospray ionisation mass spectrometry (“DESI-MS”) analysis of swabs and biopsy samples
US11289320B2 (en) 2015-03-06 2022-03-29 Micromass Uk Limited Tissue analysis by mass spectrometry or ion mobility spectrometry
US11342170B2 (en) 2015-03-06 2022-05-24 Micromass Uk Limited Collision surface for improved ionisation
GB2554206A (en) * 2015-03-06 2018-03-28 Micromass Ltd Spectrometric analysis of microbes
US11239066B2 (en) 2015-03-06 2022-02-01 Micromass Uk Limited Cell population analysis
US11264223B2 (en) 2015-03-06 2022-03-01 Micromass Uk Limited Rapid evaporative ionisation mass spectrometry (“REIMS”) and desorption electrospray ionisation mass spectrometry (“DESI-MS”) analysis of swabs and biopsy samples
WO2016142681A1 (fr) * 2015-03-06 2016-09-15 Micromass Uk Limited Analyse spectrométrique de microbes
US11031223B2 (en) 2015-09-29 2021-06-08 Micromass Uk Limited Capacitively coupled REIMS technique and optically transparent counter electrode
US11133164B2 (en) 2015-09-29 2021-09-28 Micromass Uk Limited Capacitively coupled REIMS technique and optically transparent counter electrode
US11454611B2 (en) 2016-04-14 2022-09-27 Micromass Uk Limited Spectrometric analysis of plants
US11828694B2 (en) * 2018-08-10 2023-11-28 Industry-Academic Cooperation Foundation, Yonsei University Apparatus for real-time continuous measurement of airborne microorganisms
US20210310927A1 (en) * 2018-08-10 2021-10-07 Industry-Academic Cooperation Foundation, Yonsei University Real time continuous measurement apparatus for airborne microbial
CN113345522A (zh) * 2021-06-10 2021-09-03 上海美吉生物医药科技有限公司 基于一代测序技术的自动化菌种鉴定方法、系统、终端及介质
CN113345522B (zh) * 2021-06-10 2023-11-24 上海美吉生物医药科技有限公司 基于一代测序技术的自动化菌种鉴定方法、系统、终端及介质
CN113984948A (zh) * 2021-10-28 2022-01-28 上海交通大学 基于voc标志物的幽门螺旋杆菌感染的联合诊断模型及其建立方法和应用

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