WO2012143901A1 - Method of distinguishing between different pathogens - Google Patents

Method of distinguishing between different pathogens Download PDF

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Publication number
WO2012143901A1
WO2012143901A1 PCT/IB2012/051995 IB2012051995W WO2012143901A1 WO 2012143901 A1 WO2012143901 A1 WO 2012143901A1 IB 2012051995 W IB2012051995 W IB 2012051995W WO 2012143901 A1 WO2012143901 A1 WO 2012143901A1
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pathogens
group
carbon based
based compounds
pathogen
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PCT/IB2012/051995
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French (fr)
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Du Toit LOOTS
Ilse OLIVIER
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North-West University
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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
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56911—Bacteria
    • G01N33/5695—Mycobacteria
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • 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
    • 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
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • This invention relates to a method of distinguishing between different pathogens in a biological sample.
  • Tuberculosis is a disease mainly caused by Mycobacterium tuberculosis (M. tuberculosis) in humans and infects approximately one third of the world's population.
  • M. tuberculosis Mycobacterium tuberculosis
  • NTM non- tuberculous mycobacteria
  • Detection limits of diagnostic techniques further play an important role in the early diagnostic phase of TB when a sputum sample is used, as only a small amount of bacteria are present, which are often undetected and consequently cause the misdiagnosis of TB in patients. Additionally, TB patients who are co-infected with HIV and childhood infections are frequently left undiagnosed due to lower sputum bacterial loads and lack of TB diagnostic methods with improved detection limits.
  • smear microscopy is still regarded as the TB diagnostic method most commonly used worldwide, due to its simplicity and affordability.
  • WHO World Health Organization
  • a disadvantage associated with the smear microscopy method is that it has a detection limit of 5 000 to 10 000 bacteria ml "1 sputum, consequently detecting only 60 to 70% of all TB incidences in adults, and thus requires a bacteriological culture method to confirm the diagnosis.
  • a further disadvantage associated with the smear microscopy method is that the number of identified cases can be as low as 20 to 30%, when performed under sub-optimal conditions, attributable to inexperienced personnel, for example.
  • a further disadvantage associated with this method is that it is unable to differentiate between various mycobacterial species in the sputum, hence, it can only detect the presence of mycobacteria in general, but cannot establish which mycobacterial species is responsible for infection.
  • Another known TB diagnostic method which has hitherto been regarded as the golden standard for TB diagnostics, is the bacteriological culture method. This method is far more sensitive than the smear microscopy method, and has a detection limit of 10 to 100 bacteria ml "1 using solid or liquid media.
  • a major disadvantage associated with this method is that it requires two to six weeks before bacterial colonies can visually be detected, hence leading to a long waiting period prior to initial treatment of the patient.
  • a further disadvantage associated with the bacteriological culture method is that 15 to 20% of all adult TB incidences are reported to have negative sputum cultures, consequently leading to a false negative result.
  • a more recently developed method for TB diagnostics is nucleic acid amplification, which can obtain a positive result with less than 10 bacteria ml "1 , accompanied by a sensitivity of virtually 100% in positive sputum smears.
  • this method also has the ability to differentiate various mycobacterial species, it has the disadvantage of having a sensitivity of only 60 to 70% in smear-negative, culture-positive samples.
  • a further disadvantage associated with the nucleic acid amplification method is that there is a high incidence of false positive results due to cross contamination.
  • a further disadvantage associated with this method is that due to the residual genetic material in the patients, positive results may still be obtained after effective treatment.
  • WO 2004/059280 discloses a method for measuring a plurality of different organisms in a sample (mucus, nasal, pharyngeal or genital discharge sample). The measurement includes detecting the presence or absence of a proteinaceous, lipid, carbohydrate and/or nucleic acid marker indicative of an organism using specific antibodies.
  • WO 2004/059280 discloses an extraction method for extracting one or more markers from the matrixes of one or more samples and/or rendering the samples more suitable for analysis.
  • WO 2006/079846 discloses a method of detecting and identifying bacteria comprising the steps of collecting volatile bacterial products, subjecting the volatile products to a gas chromatography system employing a surface acoustic wave detector, establishing chromatographic profiles for different bacteria, and comparing the chromatographic profile of the biological sample (including sputum, breath and blood) with profiles in the library.
  • the system is said to be suitable for use in the detection of TB and indicates that mycolic acids could be potential biomarkers. However, no specific markers for TB are described.
  • WO 2009/045116 discloses a method for detecting a mycobacterium by analysing a gas mixture for one or more identified biomarkers selected from a group consisting of methyl phenyl acetate, methyl p-anisate, methyl micotinate, and o-phenylanisole.
  • WO 2009/091375 relates to a method for identifying the presence of M. tuberculosis in a sample by detecting at least one volatile organic compound indicative of the presence of M. tuberculosis in the sample.
  • a reference system for distinguishing between different pathogens in biological samples including:
  • PCA principal component analysis
  • an output device for expressing the benchmark values graphically, characterised in that the output device is programmed to prepare a scatter plot of the respective single representative benchmark values of the replicate samples of each of the said pathogens from the PCA data and identify a list of biomarkers, being the carbon based compounds contributing most to the natural separation in the PCA in addition to those carbon based compounds with relative high VIP (variable influence on the projection) values identified using partial least squares discriminant analysis (PLS-DA) and applying effect size calculations to said indentified biomarkers to determine possible strain comparisons within a specific group of pathogens.
  • PLS-DA partial least squares discriminant analysis
  • the output device may be programmed to build a discriminant model, using the said identified biomarkers, for predicting a pathogen strain membership of a new biological sample, wherein the discriminant model may be selected from the group consisting of ordinary discriminant models, canonical discriminant models, partial least squares discriminant models, support vector machines, neural networks, logistic regression models and discriminant models based on kernel density estimations.
  • the discriminant model may be selected from the group consisting of ordinary discriminant models, canonical discriminant models, partial least squares discriminant models, support vector machines, neural networks, logistic regression models and discriminant models based on kernel density estimations.
  • the output device may be programmed to prepare a scatter plot of the predicted pathogen strain membership for each of the benchmark pathogen samples using only the biomarkers processed via the discriminant model selected.
  • the output device may further be programmed to express a Mahalanobis distance measure limit of each predicted benchmark pathogen strain sample, to the particular pathogen strain centre, as an indication of true strain membership within a specific group of pathogens.
  • the carbon based compounds may be selected from the group consisting of fatty acids, sugars, esters, ketones, amines, sulphur compounds, alcohols, carboxylic acids, and hydrocarbons such as alkenes, alkanes and aromatic compounds, and combinations thereof.
  • the pathogens may be selected from the group consisting of bacterial species, fungal species and yeast species, preferably bacterial species.
  • the pathogens may be selected from the group consisting of TB causing mycobacteria including M. tuberculosis, M. bovis, non-tuberculous mycobacteria (NTM) such as M. avium and M. kansasii, and other lung pathogens including, Pseudomonas aeruginosa.
  • mycobacteria including M. tuberculosis, M. bovis, non-tuberculous mycobacteria (NTM) such as M. avium and M. kansasii, and other lung pathogens including, Pseudomonas aeruginosa.
  • a biological sample both specific and non-specific
  • a reference system in accordance with the first aspect of the invention, which method may include the steps of:
  • the step of extracting carbon based compounds from the pathogens may include the step of neutralising microorganisms other than the said pathogens, isolating the surviving pathogens from the neutralised microorganisms and from the remainder of the biological sample, culturing the pathogens and pyrolysing harvested cells.
  • the step of extracting carbon based compounds from the pathogens may include the step of extracting the compounds directly from the biological sample containing a plurality of microorganisms and the said pathogens.
  • the step of extracting carbon based compounds from the pathogens may include the step of neutralising microorganisms other than the said pathogen, and isolating the neutralised microorganisms and surviving pathogens from the biological sample.
  • the step of extracting at least one group of carbon based compounds may include the steps of:
  • the step of extracting at least one group of carbon based compounds may include a further step of drying the liquid fraction followed by derivatisation by adding a derivatisation solvent.
  • the derivatisation solvent may be selected from the group consisting of methoxyamine hydrochloride, pyridine, A/-Methyl-/V- trimethylsilyltrifluoroacetamide (MSTFA), (N,0-bis(trimethylsilyl) trifluoroacetamide (BSTFA), trimethylchlorosilane (TMCS), methanolic HCI and other derivatisation reagents or combinations thereof.
  • MSTFA A/-Methyl-/V- trimethylsilyltrifluoroacetamide
  • BSTFA N,0-bis(trimethylsilyl) trifluoroacetamide
  • TMCS trimethylchlorosilane
  • the extraction solvent may be selected from the group consisting of acetic acid, acetone, benzene, 1 -butanol, 2-butanol, chloroform, chlorobenzene, cyclohexane, 1 ,2-dichloroethane, diethyl ether, diethyl glycol, diethylene glycol dimethyl ether, dimethyl-formamide (DMF), dimethyl sulfoxide (DMSO), ethanol, ethyl acetate, ethylene glycol, glycerine, hexane, methanol, pentane, 1 -propanol, toluene, tetrahydrofuran (THF), pyridine and water.
  • acetic acid acetone
  • benzene 1 -butanol
  • 2-butanol chloroform
  • chlorobenzene cyclohexane
  • diethyl ether diethyl glyco
  • figure 1 is a three dimensional Principal Component Analysis (PCA) scores plot of Principal Components (PCs) 1 to 3 illustrating the natural differentiation of the various pathogen groups using the extracted group of carbon based compounds having a relative low molecular weight, wherein 1 represents M. tuberculosis, 2 represents M. kansasii, 3 represents M. avium, 4 represents M. bovis, and 5 represents P. aeruginosa; figure 2: is a three dimensional PCA scores plot of PCs 1 to 3 illustrating the natural differentiation of the respective pathogens using a combination of biomarkers selected wherein 1 represents M. tuberculosis, 2 represents M. kansasii, 3 represents M. avium, 4 represents M.
  • PCA Principal Component Analysis
  • figure 3 is a scatter plot of the individual predicted pathogen strains for each of the benchmark pathogen sample in each of the individual pathogen group, using only the biomarkers processed via the discriminant model based on kernel density estimates (the majority of samples analysed falling within a 90% probability for belonging to the correct pathogen group).
  • Group 1 represents M. tuberculosis
  • 2 represents M. kansasii
  • 3 represents M. avium
  • 4 represents M. bovis
  • 5 represents P.
  • figure 4 is a scatter plot of a Mahalanobis distances measure limit for each of the predicted benchmark pathogen class samples, relative to the particular class centre, as an indication of true class membership within a specific group of pathogens.
  • the line at the Mahalanobis distance of 4 is the square root of the 99.9% chi-square quantile with 3 degrees of freedom, and thus indicates the distance control limit for correct identification within each particular group.
  • Group 1 represents M. tuberculosis
  • 2 represents M. kansasii
  • 3 represents M. avium
  • 4 represents M. bovis
  • 5 represents P.
  • figure 5 is a scatter plot of the predicted pathogen class probabilities as determined by the aforementioned discriminant model (figure 3) based on kernel density estimates, where the previously unknown pathogen samples are correctly assigned to their respective pathogen groups (indicated by X).
  • Group 1 represents M. tuberculosis
  • 2 represents M. kansasii
  • 3 represents M. avium
  • 4 represents M. bovis
  • 5 represents P. aeruginosa
  • the line at the Mahalanobis distance of 4 is the square root of the 99.9% chi-square quantile with 3 degrees of freedom, and thus indicates the distance limit for correct identification of previously unknown pathogen samples within each particular pathogen group (unknown samples indicated by X).
  • Group 1 represents M. tuberculosis
  • 2 represents M. kansasii
  • 3 represents M. avium
  • 4 represents M. bovis
  • 5 represents P. Aeruginosa
  • a reference system for distinguishing between different pathogens in biological samples including:
  • PCA principal component analysis
  • an output device for expressing the benchmark values graphically, characterised in that the output device is programmed to prepare a scatter plot of the respective single representative benchmark values of the replicate samples of each of the said pathogens from the PCA data and identify a list of biomarkers, being the carbon based compounds contributing most to the natural separation in the PCA in addition to those carbon based compounds with relative high VIP (variable influence on the projection) values identified using partial least squares discriminant analysis (PLS-DA) and applying effect size calculations to said indentified biomarkers to determine possible strain comparisons within a specific group of pathogens.
  • PLS-DA partial least squares discriminant analysis
  • the output device is further programmed to build a discriminant model, using the said identified biomarkers, for predicting the pathogen strain membership of a new biological sample, wherein the discriminant model is selected from the group consisting of ordinary discriminant models, canonical discriminant models, partial least squares discriminant models, support vector machines, neural networks, logistic regression models and discriminant models based on kernel density estimations.
  • the discriminant model is selected from the group consisting of ordinary discriminant models, canonical discriminant models, partial least squares discriminant models, support vector machines, neural networks, logistic regression models and discriminant models based on kernel density estimations.
  • the output device is yet further programmed to prepare a scatter plot of the predicted pathogen strain membership for each of the benchmark pathogen samples using only the biomarkers processed via the discriminant model selected.
  • the output device is yet further programmed to express a Mahalanobis distance measure limit of each predicted benchmark pathogen strain sample, to the particular pathogen strain centre, as an indication of true strain membership within a specific group of pathogens.
  • the carbon based compounds is selected from the group consisting of fatty acids, sugars, esters, ketones, amines, sulphur compounds, alcohols, carboxylic acids, and hydrocarbons such as alkenes, alkanes and aromatic compounds, and combinations thereof.
  • the pathogens are selected from the group consisting of bacterial species, fungal species and yeast species, preferably bacterial species. More specifically, but not exclusively, the pathogens are selected from the group consisting of TB causing mycobacteria including M. tuberculosis, M. bovis, non-tuberculous mycobacteria (NTM) such as M. avium and M. kansasii, and other lung pathogens including, Pseudomonas aeruginosa.
  • mycobacteria including M. tuberculosis, M. bovis, non-tuberculous mycobacteria (NTM) such as M. avium and M. kansasii, and other lung pathogens including, Pseudomonas aeruginosa.
  • a biological sample both specific and non-specific
  • a reference system in accordance with the first embodiment of the invention, which method includes the steps of:
  • the step of extracting carbon based compounds from the pathogen includes the step of neutralising microorganisms other than the said pathogens, isolating the surviving pathogens from the neutralised microorganisms and from the remainder of the biological sample, culturing the pathogens and pyrolysing harvested cells.
  • the step of extracting carbon based compounds from the pathogens includes the step of extracting the compounds directly from the biological sample containing a plurality of microorganisms and the said pathogens.
  • the step of extracting carbon based compounds from the pathogens includes the step of neutralising microorganisms other than the said pathogen, and isolating the neutralised microorganisms and surviving pathogens from the biological sample.
  • the step of extracting at least one group of carbon based compounds includes the steps of:
  • the step of extracting at least one group of carbon based compounds includes a further step of drying the liquid fraction followed by derivatisation by adding a derivatisation solvent.
  • the derivatisation solvent is selected from the group consisting of methoxyamine hydrochloride, pyridine, A/-Methyl-A/- trimethylsilyltrifluoroacetamide (MSTFA), (N,0-bis(trimethylsilyl) trifluoroacetamide (BSTFA), trimethylchlorosilane (TMCS), methanolic HCI and other derivatisation reagents or combinations thereof.
  • the extraction solvent is selected from the group consisting of acetic acid, acetone, benzene, 1 -butanol, 2-butanol, chloroform, chlorobenzene, cyclohexane, 1 ,2-dichloroethane, diethyl ether, diethyl glycol, diethylene glycol dimethyl ether, dimethyl-formamide (DMF), dimethyl sulfoxide (DMSO), ethanol, ethyl acetate, ethylene glycol, glycerine, hexane, methanol, pentane, 1 -propanol, toluene, tetrahydrofuran (THF), pyridine and water.
  • Methoxyamine hydrochloride purchased from Sigma-Aldrich (St. Louis, Mo., USA);
  • the step of obtaining a biological sample containing at least one pathogen includes:
  • the carbon based compound extraction can be done directly after bacterial cell isolation following the liquefying step, provided that an internal standard is added.
  • the carbon based compound extraction can be done on patient collected sputum without a prior liquefying step, provided sample is homogenised using 30% ethanol, and dried in a speedvac, prior to the carbon based compound extraction. This is accomplished by adding 450 ⁇ _ pure ethanol and 550 pL H 2 0 to the collected patient sputum sample, followed by a shaking 50 Hz for 2 min in an MM 400 vibration mill (Retsch GmbH & co. KG, Haan, Germany). Hereafter, the homogenised sputum suspension is completely dried in a speedvac and the dried sputum sample is extracted.
  • the step of extracting at least one group of carbon based compounds from the pathogens in the sample using at least one extraction solvent includes the following:
  • a protein determination can also be used in addition to or in combination with the internal standard for quantification purposes. • adding 1 ,25 mL of an extraction solvent mixture made up of chloroform/Methanol/ddH 2 0 (2:6:2);
  • the volumes of the aforementioned derivatisation reagents can be modified in accordance to the protein determination for each sample.
  • the step of analysing the extracted group of carbon based compounds to prepare a data matrix comprising a set of values representing the relative concentrations of the said carbon based compounds in the sample includes the following:
  • AMDIS Automated Mass Spectral Deconvolution and Identification System V2.65
  • AMDIS being used as a deconvolution tool
  • other software options could also be considered for deconvolution and alignment of data generated by the GC-MS or other hyphenated chromatographic techniques.
  • the step of comparing the compound profile with benchmark profiles to identify the respective microorganisms in the sample includes:
  • the data matrix comprise a set of values of replicate samples of the said pathogens, each set representing the relative concentrations of the said carbon based compounds in a sample of each of the said pathogens;
  • PCA principal component analysis
  • the said discriminant model will always assign an unknown sample to one of the benchmark pathogen groups. Due to this occurrence, the Mahalanobis distance measure of the unknown sample to the centre of the predicted benchmark pathogen group is calculated.
  • the line at the Mahalanobis distance of 4 is the square root of the 99.9% chi-square quantile with 3 degrees of freedom. In a case where the unknown sample lies within the square root of an appropriate chi-square quantile for the predicted benchmark group, it is indicative of a correct classification. In a case where the unknown sample lies beyond the square root of an appropriate chi-square quantile from the predicted group, it would most likely be indicative for an unknown pathogen to the model; and
  • the sample with the lowest number of cells which does not overlap with the blank is considered the lowest number of cells required for a diagnosis, and hence, the detection limit of the analysis is predicted to be 250 cells as seen in figure 7.
  • the detection limit was confirmed by analysing the various pathogen groups (M. tuberculosis, M. avium, M. bovis, M. kansasii, and P. aeruginosa), each at the detection limit concentration of 250 cells per analysis, using the extracted group of carbon based compounds.
  • pathogen groups M. tuberculosis, M. avium, M. bovis, M. kansasii, and P. aeruginosa
  • Example 1 In implementing the method of Example 1 in distinguishing between different microorganisms in a biological sample, the method is repeated, using the unknown biological sample as the source of the microorganisms instead.
  • This diagnostic method uses the unique metabolome profiles of the most characteristic biomarker compounds and their comparative ratios, for differentiating or identifying various infectious pathogens and particularly disease causing mycobacterial species (e.g. M. bovis, M. avium, M. kansasi, and M. tuberculosis).
  • mycobacterial species e.g. M. bovis, M. avium, M. kansasi, and M. tuberculosis.
  • This diagnostic approach also has an advantage for identifying unique disease specific biomarkers, which may in turn lead to further refinement of the diagnostic method, leading to even faster, simpler, more sensitive, and inexpensive diagnostic kits, which can be used without the need of an analytical or diagnostic laboratory.
  • the biomarkers may also be used for understanding the mechanisms underlying disease progression, drug resistance and co-infection.

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Abstract

This invention relates to a method of distinguishing between different pathogens in a biological sample (both specific and non-specific) in a reference system, including the steps of obtaining a biological sample containing at least one pathogen, extracting at least one group of carbon based compounds having a relatively low molecular weight, from at least one pathogen in the sample using at least one extraction solvent, analysing the extracted group of carbon based compounds to prepare a data matrix comprising a set of values representing the relative concentrations of the said biomarker compounds in the sample; and applying a discriminant models to predict the pathogen strain membership within a specific group of pathogens and calculate the Mahalanobis distance measure of the patient obtained biological sample using only the selected biomarkers compounds.

Description

METHOD OF DISTINGUISHING BETWEEN DIFFERENT PATHOGENS
INTRODUCTION AND BACKGROUND TO THE INVENTION
This invention relates to a method of distinguishing between different pathogens in a biological sample.
Tuberculosis (TB) is a disease mainly caused by Mycobacterium tuberculosis (M. tuberculosis) in humans and infects approximately one third of the world's population. A relatively high prevalence of TB causing mycobacteria, other than M. tuberculosis, such as M. bovis and non- tuberculous mycobacteria (NTM) such as M. avium and M. kansasii, are frequently encountered in clinical specimens, such as sputum.
In 2008, there were an estimated 9.4 million newly identified TB incidences and 1 .8 million deaths globally attributable to TB. Different treatment regimes are followed for different mycobacterial infections and it is becoming more important for diagnostic techniques to differentiate between various mycobacterial species.
Detection limits of diagnostic techniques further play an important role in the early diagnostic phase of TB when a sputum sample is used, as only a small amount of bacteria are present, which are often undetected and consequently cause the misdiagnosis of TB in patients. Additionally, TB patients who are co-infected with HIV and childhood infections are frequently left undiagnosed due to lower sputum bacterial loads and lack of TB diagnostic methods with improved detection limits.
In accordance with a report by the World Health Organisation (WHO) on TB diagnostics, smear microscopy is still regarded as the TB diagnostic method most commonly used worldwide, due to its simplicity and affordability. However, a disadvantage associated with the smear microscopy method is that it has a detection limit of 5 000 to 10 000 bacteria ml"1 sputum, consequently detecting only 60 to 70% of all TB incidences in adults, and thus requires a bacteriological culture method to confirm the diagnosis.
A further disadvantage associated with the smear microscopy method is that the number of identified cases can be as low as 20 to 30%, when performed under sub-optimal conditions, attributable to inexperienced personnel, for example. A further disadvantage associated with this method is that it is unable to differentiate between various mycobacterial species in the sputum, hence, it can only detect the presence of mycobacteria in general, but cannot establish which mycobacterial species is responsible for infection.
Another known TB diagnostic method, which has hitherto been regarded as the golden standard for TB diagnostics, is the bacteriological culture method. This method is far more sensitive than the smear microscopy method, and has a detection limit of 10 to 100 bacteria ml"1 using solid or liquid media.
A major disadvantage associated with this method is that it requires two to six weeks before bacterial colonies can visually be detected, hence leading to a long waiting period prior to initial treatment of the patient.
A further disadvantage associated with the bacteriological culture method is that 15 to 20% of all adult TB incidences are reported to have negative sputum cultures, consequently leading to a false negative result.
Yet a further disadvantage associated with this method is that 1 to 4% of all incidences present a false positive, due to poorly executed anti- contamination procedures.
A more recently developed method for TB diagnostics is nucleic acid amplification, which can obtain a positive result with less than 10 bacteria ml"1, accompanied by a sensitivity of virtually 100% in positive sputum smears. Although this method also has the ability to differentiate various mycobacterial species, it has the disadvantage of having a sensitivity of only 60 to 70% in smear-negative, culture-positive samples.
A further disadvantage associated with the nucleic acid amplification method is that there is a high incidence of false positive results due to cross contamination.
A further disadvantage associated with this method is that due to the residual genetic material in the patients, positive results may still be obtained after effective treatment.
Yet a further disadvantage associated with this method is that it cannot be used in patients with previous mycobacterial infections and for monitoring of a patient's response to therapy.
Yet a further disadvantage of this method is that in the clinical practice environment, this method requires two days before a diagnostic result can be obtained, and in most cases, additionally requires prior culturing of the organism, in order to overcome the previously mentioned disadvantages. As a result, an additional two to six weeks diagnostic time is required.
According to the WHO, no serology TB diagnostic assays have yet been approved for clinical use due to a lack of adequate sensitivity and specificity of these methods.
WO 2004/059280 discloses a method for measuring a plurality of different organisms in a sample (mucus, nasal, pharyngeal or genital discharge sample). The measurement includes detecting the presence or absence of a proteinaceous, lipid, carbohydrate and/or nucleic acid marker indicative of an organism using specific antibodies.
Furthermore, WO 2004/059280 discloses an extraction method for extracting one or more markers from the matrixes of one or more samples and/or rendering the samples more suitable for analysis.
WO 2006/079846 discloses a method of detecting and identifying bacteria comprising the steps of collecting volatile bacterial products, subjecting the volatile products to a gas chromatography system employing a surface acoustic wave detector, establishing chromatographic profiles for different bacteria, and comparing the chromatographic profile of the biological sample (including sputum, breath and blood) with profiles in the library. The system is said to be suitable for use in the detection of TB and indicates that mycolic acids could be potential biomarkers. However, no specific markers for TB are described.
WO 2009/045116 discloses a method for detecting a mycobacterium by analysing a gas mixture for one or more identified biomarkers selected from a group consisting of methyl phenyl acetate, methyl p-anisate, methyl micotinate, and o-phenylanisole.
WO 2009/091375 relates to a method for identifying the presence of M. tuberculosis in a sample by detecting at least one volatile organic compound indicative of the presence of M. tuberculosis in the sample.
A disadvantage associated with the abovementioned methods is that they do not differentiate between various disease causing species, including those belonging to the genus Mycobacterium. OBJECT OF THE INVENTION
It is accordingly an object of the present invention to provide a method for distinguishing between different pathogens in a biological sample with which the aforesaid disadvantages could be overcome or at least minimised.
SUMMARY OF THE INVENTION
According to a first aspect of the invention, there is provided a reference system for distinguishing between different pathogens in biological samples (both specific and non-specific) including:
- a reference library of the relative concentrations of carbon based compounds having a relatively low molecular weight, which may be present in extracts of the said different pathogens in combination;
- a data matrix comprising a set of values of replicate samples of the said pathogens, each set representing the relative concentrations of the said carbon based compounds in a sample of each of the said pathogens;
- a processing device programmed to convert each set of values into a single representative benchmark value applying principal component analysis (PCA) calculations; and
- an output device for expressing the benchmark values graphically, characterised in that the output device is programmed to prepare a scatter plot of the respective single representative benchmark values of the replicate samples of each of the said pathogens from the PCA data and identify a list of biomarkers, being the carbon based compounds contributing most to the natural separation in the PCA in addition to those carbon based compounds with relative high VIP (variable influence on the projection) values identified using partial least squares discriminant analysis (PLS-DA) and applying effect size calculations to said indentified biomarkers to determine possible strain comparisons within a specific group of pathogens.
Further according to the invention the output device may be programmed to build a discriminant model, using the said identified biomarkers, for predicting a pathogen strain membership of a new biological sample, wherein the discriminant model may be selected from the group consisting of ordinary discriminant models, canonical discriminant models, partial least squares discriminant models, support vector machines, neural networks, logistic regression models and discriminant models based on kernel density estimations.
Yet further according to the invention the output device may be programmed to prepare a scatter plot of the predicted pathogen strain membership for each of the benchmark pathogen samples using only the biomarkers processed via the discriminant model selected.
The output device may further be programmed to express a Mahalanobis distance measure limit of each predicted benchmark pathogen strain sample, to the particular pathogen strain centre, as an indication of true strain membership within a specific group of pathogens.
The carbon based compounds may be selected from the group consisting of fatty acids, sugars, esters, ketones, amines, sulphur compounds, alcohols, carboxylic acids, and hydrocarbons such as alkenes, alkanes and aromatic compounds, and combinations thereof.
The pathogens may be selected from the group consisting of bacterial species, fungal species and yeast species, preferably bacterial species.
More specifically, but not exclusively, the pathogens may be selected from the group consisting of TB causing mycobacteria including M. tuberculosis, M. bovis, non-tuberculous mycobacteria (NTM) such as M. avium and M. kansasii, and other lung pathogens including, Pseudomonas aeruginosa.
According to a second aspect of the invention there is provided a method of distinguishing between different pathogens in a biological sample (both specific and non-specific) in a reference system in accordance with the first aspect of the invention, which method may include the steps of:
- obtaining a patient biological sample containing at least one pathogen;
- extracting at least one group of said carbon based compounds from at least one pathogen in the sample using at least one extraction solvent; - analysing the extracted group of carbon based compounds to prepare a data matrix comprising a set of values representing the relative concentrations of the said biomarker compounds in the sample; and
- applying one of the discriminant models in accordance with the first aspect of the invention to predict the pathogen strain membership within a specific group of pathogens and calculate the Mahalanobis distance measure of the patient obtained biological sample using only the selected biomarkers compounds.
The step of extracting carbon based compounds from the pathogens may include the step of neutralising microorganisms other than the said pathogens, isolating the surviving pathogens from the neutralised microorganisms and from the remainder of the biological sample, culturing the pathogens and pyrolysing harvested cells.
Alternatively, the step of extracting carbon based compounds from the pathogens may include the step of extracting the compounds directly from the biological sample containing a plurality of microorganisms and the said pathogens.
Further alternatively, the step of extracting carbon based compounds from the pathogens may include the step of neutralising microorganisms other than the said pathogen, and isolating the neutralised microorganisms and surviving pathogens from the biological sample. Further according to the invention, the step of extracting at least one group of carbon based compounds may include the steps of:
adding one or more extraction solvents to the sample to form an extraction mixture;
breaking up the cell walls of the pathogens to release and extract the carbon based compounds from the pathogens; and
subjecting the extraction mixture to centrifugation to separate the liquid fraction from the solid fraction.
Further according to the invention, the step of extracting at least one group of carbon based compounds may include a further step of drying the liquid fraction followed by derivatisation by adding a derivatisation solvent.
The derivatisation solvent may be selected from the group consisting of methoxyamine hydrochloride, pyridine, A/-Methyl-/V- trimethylsilyltrifluoroacetamide (MSTFA), (N,0-bis(trimethylsilyl) trifluoroacetamide (BSTFA), trimethylchlorosilane (TMCS), methanolic HCI and other derivatisation reagents or combinations thereof.
The extraction solvent may be selected from the group consisting of acetic acid, acetone, benzene, 1 -butanol, 2-butanol, chloroform, chlorobenzene, cyclohexane, 1 ,2-dichloroethane, diethyl ether, diethyl glycol, diethylene glycol dimethyl ether, dimethyl-formamide (DMF), dimethyl sulfoxide (DMSO), ethanol, ethyl acetate, ethylene glycol, glycerine, hexane, methanol, pentane, 1 -propanol, toluene, tetrahydrofuran (THF), pyridine and water.
BRIEF DESCRIPTION OF THE FIGURES
The invention will now be described further, by way of non-limiting example only, with reference to the accompanying figures wherein:
figure 1 : is a three dimensional Principal Component Analysis (PCA) scores plot of Principal Components (PCs) 1 to 3 illustrating the natural differentiation of the various pathogen groups using the extracted group of carbon based compounds having a relative low molecular weight, wherein 1 represents M. tuberculosis, 2 represents M. kansasii, 3 represents M. avium, 4 represents M. bovis, and 5 represents P. aeruginosa; figure 2: is a three dimensional PCA scores plot of PCs 1 to 3 illustrating the natural differentiation of the respective pathogens using a combination of biomarkers selected wherein 1 represents M. tuberculosis, 2 represents M. kansasii, 3 represents M. avium, 4 represents M. bovis, and 5 represents P. aeruginosa; figure 3: is a scatter plot of the individual predicted pathogen strains for each of the benchmark pathogen sample in each of the individual pathogen group, using only the biomarkers processed via the discriminant model based on kernel density estimates (the majority of samples analysed falling within a 90% probability for belonging to the correct pathogen group). Group 1 represents M. tuberculosis, 2 represents M. kansasii, 3 represents M. avium, 4 represents M. bovis, and 5 represents P. aeruginosa; figure 4: is a scatter plot of a Mahalanobis distances measure limit for each of the predicted benchmark pathogen class samples, relative to the particular class centre, as an indication of true class membership within a specific group of pathogens. The line at the Mahalanobis distance of 4, is the square root of the 99.9% chi-square quantile with 3 degrees of freedom, and thus indicates the distance control limit for correct identification within each particular group. Group 1 represents M. tuberculosis, 2 represents M. kansasii, 3 represents M. avium, 4 represents M. bovis, and 5 represents P. aeruginosa; figure 5: is a scatter plot of the predicted pathogen class probabilities as determined by the aforementioned discriminant model (figure 3) based on kernel density estimates, where the previously unknown pathogen samples are correctly assigned to their respective pathogen groups (indicated by X). Group 1 represents M. tuberculosis, 2 represents M. kansasii, 3 represents M. avium, 4 represents M. bovis, and 5 represents P. aeruginosa; is a scatter plot of the individual Mahalanobis distances for each of the benchmark pathogen samples, relative to the centre of the predicted pathogen group as described in figure 4. The line at the Mahalanobis distance of 4, is the square root of the 99.9% chi-square quantile with 3 degrees of freedom, and thus indicates the distance limit for correct identification of previously unknown pathogen samples within each particular pathogen group (unknown samples indicated by X). Group 1 represents M. tuberculosis, 2 represents M. kansasii, 3 represents M. avium, 4 represents M. bovis, and 5 represents P. Aeruginosa; is a three dimensional PCA scores plot of PC 1 to 3 illustrating the natural differentiation of the various M. tuberculosis concentration groups using the extracted group of carbon based compounds wherein: 0 = blank or 0 cells, 1 = 2.5, 2 = 2.5 X 101, 3 = 2.5 X 102; 4 = 2.5 X 103; 5 = 2.5 X 104 and 6 = 2.5 X 105 cells, indicating that the detection limit of this approach is 250 bacteria cells; and is a three dimensional PCA scores plot of PCs 1 to 3 illustrating the natural differentiation of the various pathogen groups using the extracted group of carbon based compounds, at the detection limit concentration of 250 cells, wherein 1 represents M. tuberculosis, 2 represents M. kansasii, 3 represents M. avium, 4 represents M. bovis, and 5 represents P. aeruginosa.
DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION
According to preferred embodiments of the invention there are provided a system and methods for distinguishing between different pathogens in a biological sample.
According to a first preferred embodiment of the invention, there is provided a reference system for distinguishing between different pathogens in biological samples (both specific and non-specific) including:
- a reference library of the relative concentrations of carbon based compounds having a relatively low molecular weight, which are present in extracts of the said different pathogens in combination;
- a data matrix comprising a set of values of replicate samples of the said pathogens, each set representing the relative concentrations of the said carbon based compounds in a sample of each of the said pathogens;
- a processing device programmed to convert each set of values into a single representative benchmark value applying principal component analysis (PCA) calculations; and
- an output device for expressing the benchmark values graphically, characterised in that the output device is programmed to prepare a scatter plot of the respective single representative benchmark values of the replicate samples of each of the said pathogens from the PCA data and identify a list of biomarkers, being the carbon based compounds contributing most to the natural separation in the PCA in addition to those carbon based compounds with relative high VIP (variable influence on the projection) values identified using partial least squares discriminant analysis (PLS-DA) and applying effect size calculations to said indentified biomarkers to determine possible strain comparisons within a specific group of pathogens.
The output device is further programmed to build a discriminant model, using the said identified biomarkers, for predicting the pathogen strain membership of a new biological sample, wherein the discriminant model is selected from the group consisting of ordinary discriminant models, canonical discriminant models, partial least squares discriminant models, support vector machines, neural networks, logistic regression models and discriminant models based on kernel density estimations.
The output device is yet further programmed to prepare a scatter plot of the predicted pathogen strain membership for each of the benchmark pathogen samples using only the biomarkers processed via the discriminant model selected.
The output device is yet further programmed to express a Mahalanobis distance measure limit of each predicted benchmark pathogen strain sample, to the particular pathogen strain centre, as an indication of true strain membership within a specific group of pathogens.
The carbon based compounds is selected from the group consisting of fatty acids, sugars, esters, ketones, amines, sulphur compounds, alcohols, carboxylic acids, and hydrocarbons such as alkenes, alkanes and aromatic compounds, and combinations thereof.
The pathogens are selected from the group consisting of bacterial species, fungal species and yeast species, preferably bacterial species. More specifically, but not exclusively, the pathogens are selected from the group consisting of TB causing mycobacteria including M. tuberculosis, M. bovis, non-tuberculous mycobacteria (NTM) such as M. avium and M. kansasii, and other lung pathogens including, Pseudomonas aeruginosa.
According to a second preferred embodiment of the invention there is provided a method of distinguishing between different pathogens in a biological sample (both specific and non-specific) in a reference system in accordance with the first embodiment of the invention, which method includes the steps of:
- obtaining a patient biological sample containing at least one pathogen; - extracting at least one group of said carbon based compounds from at least one pathogen in the sample using at least one extraction solvent;
- analysing the extracted group of carbon based compounds to prepare a data matrix comprising a set of values representing the relative concentrations of the said biomarker compounds in the sample; and
- applying one of the discriminant models in accordance with the first aspect of the invention to predict the strain membership within a specific group of pathogens and calculate the Mahalanobis distance measure of the patient obtained biological sample using only the selected biomarkers compounds.
The step of extracting carbon based compounds from the pathogen includes the step of neutralising microorganisms other than the said pathogens, isolating the surviving pathogens from the neutralised microorganisms and from the remainder of the biological sample, culturing the pathogens and pyrolysing harvested cells.
Alternatively, the step of extracting carbon based compounds from the pathogens includes the step of extracting the compounds directly from the biological sample containing a plurality of microorganisms and the said pathogens.
Further alternatively, the step of extracting carbon based compounds from the pathogens includes the step of neutralising microorganisms other than the said pathogen, and isolating the neutralised microorganisms and surviving pathogens from the biological sample.
The step of extracting at least one group of carbon based compounds includes the steps of:
adding one or more extraction solvents to the sample to form an extraction mixture;
breaking up the cell walls of the pathogens to release and extract said carbon based compounds from the pathogens; and
subjecting the extraction mixture to centrifugation to separate the liquid fraction from the solid fraction;
The step of extracting at least one group of carbon based compounds includes a further step of drying the liquid fraction followed by derivatisation by adding a derivatisation solvent.
The derivatisation solvent is selected from the group consisting of methoxyamine hydrochloride, pyridine, A/-Methyl-A/- trimethylsilyltrifluoroacetamide (MSTFA), (N,0-bis(trimethylsilyl) trifluoroacetamide (BSTFA), trimethylchlorosilane (TMCS), methanolic HCI and other derivatisation reagents or combinations thereof.
The extraction solvent is selected from the group consisting of acetic acid, acetone, benzene, 1 -butanol, 2-butanol, chloroform, chlorobenzene, cyclohexane, 1 ,2-dichloroethane, diethyl ether, diethyl glycol, diethylene glycol dimethyl ether, dimethyl-formamide (DMF), dimethyl sulfoxide (DMSO), ethanol, ethyl acetate, ethylene glycol, glycerine, hexane, methanol, pentane, 1 -propanol, toluene, tetrahydrofuran (THF), pyridine and water.
It will be appreciated, however, that the system and method could be adapted to distinguish between most known organisms.
The above system, method and individual steps of the method are described in more detail below, by way of examples.
EXAMPLE 1
In accordance with a first non-limiting example of a preferred embodiment of the invention according to the invention, the following reagents and chemicals are provided:
• MSTFA, TMCS, pyridine, potassium hydroxide, 3-phenyl butyric acid, ethanol and trimethylpentane purchased from Merck (Darmstadt, Germany);
• Methoxyamine hydrochloride purchased from Sigma-Aldrich (St. Louis, Mo., USA); and
• organic solvents are ultra pure Burdick & Jackson brands (Honeywell International Inc., Muskegon, Ml, USA).
Biological sample preparation
In accordance with this example, the step of obtaining a biological sample containing at least one pathogen includes:
• liquefying the sputum by adding an equal volume of liquefying solution (0.5 N NaOH and 0,0003M N-acetyl-cysteine), followed by vortexing for 60 seconds and incubation for 15 minutes at room temperature. Cells are isolated by centrifugation at 3000 rpm for 15 minutes and washed twice with distilled water;
• obtaining the following pure bacterial cultures, namely M. tuberculosis, M. avium, M. bovis, M. kansasii and P. aeruginosa originally obtained from patient collected sputum, as described above, and then cultured in Middlebrook 7H9 medium with oleic acid-albumin-dextrose-catalase enrichment;
• culturing cells at 37 degrees Celsius while shaking at 120 to 150 rpm until an optical density (420 nm) of 0.30 («2 x 108 bacteria ml"1) is reached; and
• washing the cells once with PBS (phosphate buffered saline), and collection via centrifugation, killing or inactivating the microorganisms in a water bath at 1 10 degrees Celsius for 40 minutes.
Additionally, the carbon based compound extraction can be done directly after bacterial cell isolation following the liquefying step, provided that an internal standard is added.
Furthermore, the carbon based compound extraction can be done on patient collected sputum without a prior liquefying step, provided sample is homogenised using 30% ethanol, and dried in a speedvac, prior to the carbon based compound extraction. This is accomplished by adding 450 μΙ_ pure ethanol and 550 pL H20 to the collected patient sputum sample, followed by a shaking 50 Hz for 2 min in an MM 400 vibration mill (Retsch GmbH & co. KG, Haan, Germany). Hereafter, the homogenised sputum suspension is completely dried in a speedvac and the dried sputum sample is extracted.
Carbon based compound extraction
Further in this example, the step of extracting at least one group of carbon based compounds from the pathogens in the sample using at least one extraction solvent includes the following:
• preparing twelve repeats of an amount of 2,5 x 105 cells of each sputum cultured species in order to prepare the said system;
• determining the detection limit of the system procedure by preparing six repeats of the cultured M. tuberculosis, ranging from 0 to 2.5 x 05 mycobacteria cells, to be extracted and analysed in the same manner;
• adding 50 pL of 3-phenylbutyric acid to the previously mentioned biological samples in a microcentrifuge tube as a reference standard to determine the relative concentrations of the said carbon based compounds in a sample of each of the said pathogens;
• a protein determination can also be used in addition to or in combination with the internal standard for quantification purposes. • adding 1 ,25 mL of an extraction solvent mixture made up of chloroform/Methanol/ddH20 (2:6:2);
• performing the extraction using an MM 400 vibration mill (Retsch GmbH & co. KG, Haan, Germany) at a frequency of 30 Hz s"1 for 5 min;
• adding a 3-mm tungsten carbide bead (Retsch GmbH & co. KG) to each tube for increased extraction efficiency;
• collecting the total liquid fraction (both organic and water fractions) following centrifugation, transferring it to a GC-MS sample vial and drying it under a light stream of nitrogen;
• derivatising the liquid fraction by adding 50 pL of methoxyamine hydrochloride (15 mg mL"1) in pyridine to the sample followed by an incubation period of 90 min at 50 degrees Celsius; and
• trimethylsilylating the extract for 60 min at 50 degrees Celsius after the addition of 50 μΙ_ of MSTFA with 1 % TMCS.
It should be noted that the volumes of the aforementioned derivatisation reagents can be modified in accordance to the protein determination for each sample.
Gas chromatography-mass spectrometry (GC-MS) analysis
Also in this example, the step of analysing the extracted group of carbon based compounds to prepare a data matrix comprising a set of values representing the relative concentrations of the said carbon based compounds in the sample includes the following:
• analysing the prepared extracts by injecting 1 μΐ on an Agilent 7890A gas chromatograph (Agilent, Atlanta, GA) coupled to an Agilent 5975 mass selective detector equipped with a 7683B injector, 7683 auto sampler and VF1-MS capillary column (30 m x 250 pm i.d., 0,25 μιη film thickness) in the splitless mode; and
• maintaining the injector temperature at 270 degrees Celsius for the total run time, with the initial GC oven temperature being maintained at 70 degrees Celsius followed by an increase in oven temperature to a final temperature of 300 degrees Celsius, using helium as a carrier gas and keeping the flow of the helium gas at a constant 1.2 mL/min, and using MS detection in full scan mode.
It will be appreciated that instead of GC-MS analysis, GCxGC-TOFMS analysis or other hyphenated GC or liquid chromatography (LC) techniques could be applied. This could also be applied to these and other hyphenated chromatography techniques without derivatisation.
Further analysis and findings
In executing the method according to the preferred embodiment of the invention, use was made of a so-called Automated Mass Spectral Deconvolution and Identification System V2.65 (AMD!S) in the deconvolution and analysis of the raw GC-MS data. AMDIS is a computer program that deconvolutes the individual components as analysed by GC/MS in order to develop a reference library containing the characteristic mass spectra of a total of 1198 compounds, including: fatty acids, amino acids, silonols, silanes, alcohols, organic acids and monosaccharides, amongst others, which are present in the aforementioned pathogens.
It will be appreciated that instead of AMDIS being used as a deconvolution tool, other software options could also be considered for deconvolution and alignment of data generated by the GC-MS or other hyphenated chromatographic techniques.
Also in this example, the step of comparing the compound profile with benchmark profiles to identify the respective microorganisms in the sample includes:
• characterising the results obtained from the extracted group of carbon based compounds in data matrixes consisting of detected compounds in order to develop a reference library according to the invention for identifying any of the aforementioned pathogens from a patient sample;
• the data matrix comprise a set of values of replicate samples of the said pathogens, each set representing the relative concentrations of the said carbon based compounds in a sample of each of the said pathogens;
• the aforementioned pathogen samples are aligned in this database according to the said carbon based compounds present and retention indexes of the said carbon based compounds in each sample;
analysing the obtained data matrixes via processing device known as principal component analysis (PCA). The processing device converts each set of concentration values for of the said carbon based compounds into a single representative benchmark value termed a principal component (PC) for each benchmark pathogen sample analysed;
(part of PCA) pre-treating data using a non-parametric transformation function to limit variables (metabolites) with high concentrations from dominating the PCA, after which mean centring is applied;
extracting four PCs elucidating 58% (this is variable - unique to this example) of the variation in the scaled data, from the data matrixes obtained from the GC-MS analysed data of the extraction procedure; building a PCA model using the AMDIS generated data matrixes;
preparing a scatter plot of the single representative benchmark values for each of the benchmark pathogen samples, thus providing an overview of the biological samples and how they relate to each other (figure 1 ). Referring to figure 1 , it is evident from the PCA output data that the bacterial groups are clearly differentiated from one another using detected compounds;
extracting VIPs (variables importance in the PC projection), identified using partial least squares discriminant analysis (PLS-DA) from the loadings (a quantitative number representing the contribution of each variable to the separation between samples in the model plane). These included the following fatty acids: 10 Me-C16:0, 10-Me C17:0, 10Me-C15:0, 2,4-DM C14:0, 25-Me C27:0, C10:0, C14:0, 1 -methyl ethylester of C14:0, , C16:0, C16:1 w7c , C16:1 w7t, C17:0, C18:1 w8c, C18:1 w9c, C18:1 w9t, C18:2n6c, C20:0, C20:1 w9c, C22:0, C22:1 w9c, C24:0, C24:0, C24:1 w9c, C25:0, C26:0, C28:0, 2-ethyl hexadecyl ester of C6, benzenepropanoicacid, 2-hexyl- cyclopropaneoctanoic acid, 2-octyl- cyclopropaneoctanoic acid, and tuberculostearic acid; and other compounds including: 05'-Adenylic acid, Butyl phthalate, cadaverine, citric acid, erythritol, heptacosane, hexacosane, indole-acetic acid, inositol, myo-lnositol, purine, putrescine, succinic acid, tetradecane, tricosane, Unknown 268, Unknown 340, Unknown 343, Unknown 367, Unknown 373, Unknown 394, Unknown 394, Unknown 422, Unknown 459, Unknown 466, Unknown 494, Unknown 503, Unknown 541 , valeric acid. (The mass spectra of unknown biomarker compounds are included in Appendix
1 );
• an additional biomarker list was compiled on the basis of effect size calculations and uniqueness to the aforementioned pathogens;
• these included: for M. tuberculosis (Unknown TB1 ); M. avium (Unknown 406); M. kansasii (Glycerine, Unknown 466); P. aeruginosa (2(1 H)-Pyrimidinone, DL-Ornithine, Capric acid, Unknown pseudol , Unknown 341 , Unknown 237, Unknown 315, Unknown 516, and Unknown 319) (The mass spectra of unknown biomarkers compounds are included in Appendix 1 );
• the combined biomarkers were then used to build a biomarker PCA model in order to determine if a natural separation between the said pathogen groups is conserved (figure 2). Referring to figure 2, illustrating the PCA output from data generated using the single representative benchmark values from the selected biomarkers, it is evident that the bacterial groups are clearly differentiated from one another;
• one or more combinations of the above mentioned PCA biomarkers are used to build a discriminant model based on Bayes' theorem in conjunction with multivariate kernel density estimation. This model is developed for the purpose of estimating the class and/or pathogen membership probabilities of an unknown pathogen sample based on the presence of the aforementioned biomarkers (figures 3 to 6);
• it should be noted that the said discriminant model will always assign an unknown sample to one of the benchmark pathogen groups. Due to this occurrence, the Mahalanobis distance measure of the unknown sample to the centre of the predicted benchmark pathogen group is calculated. With reference to figures 4 and 6, the line at the Mahalanobis distance of 4, is the square root of the 99.9% chi-square quantile with 3 degrees of freedom. In a case where the unknown sample lies within the square root of an appropriate chi-square quantile for the predicted benchmark group, it is indicative of a correct classification. In a case where the unknown sample lies beyond the square root of an appropriate chi-square quantile from the predicted group, it would most likely be indicative for an unknown pathogen to the model; and
• in figure 5 the probabilities for the two unknown samples correctly identified in group 1 are: 94% and 95%; for group 2: 100% in both cases; group 3: 93% and 96%; group 4: 98% in both cases; and group 5: 100% in both cases. Referring to figure 6 the previously unknown infectious samples previously shown to be correctly assigned to their respective pathogen groups in figure 5, also fall within the Mahalanobis distance control limit of 4, hence, confirming absolute correct identification for each patient sample.
DETECTION LIMITS
For determining the detection limit of this diagnostic model, a series of samples were prepared including: 0 = blank or 0 Mycobacterium cells, 1 = 2.5 Mycobacterium cells, 2 = 2.5 x 101 Mycobacterium cells, 3 = 2.5 x 102 Mycobacterium cells; 4 = 2.5 x 103 Mycobacterium cells; 5 = 2.5 x 104 Mycobacterium cells; and 6 = 2.5 x 105 Mycobacterium cells, and analysed in the same manner as described above.
The sample with the lowest number of cells which does not overlap with the blank is considered the lowest number of cells required for a diagnosis, and hence, the detection limit of the analysis is predicted to be 250 cells as seen in figure 7.
Referring to figure 8 the detection limit was confirmed by analysing the various pathogen groups (M. tuberculosis, M. avium, M. bovis, M. kansasii, and P. aeruginosa), each at the detection limit concentration of 250 cells per analysis, using the extracted group of carbon based compounds.
EXAMPLE 2
In implementing the method of Example 1 in distinguishing between different microorganisms in a biological sample, the method is repeated, using the unknown biological sample as the source of the microorganisms instead.
The aforementioned discriminant model developed from the biomarker PCA model of the said benchmark pathogen samples in Example 1 was used to predict class membership of two such unknown samples - derived from separately grown cultures - for each of the bacterial species, further validating the model (figures 4 and 5).
Using the methodology explained above, 10 separately cultured samples were extracted in the same manner, and processed as described above in order to validate or test the model. All of these validation samples were correctly identified with very high class membership probabilities, and the additional Mahalanobis distances for each of the unknown samples were within the control limit, i.e. the model was able to correctly diagnose different pathogens, grown from separate patient sputum samples, including various species of Mycobacterium,
It is therefore evident that the above mentioned extraction procedure, and the metabolomics data analysis that followed, has the capacity to be developed into a feasible, quick diagnostic method, for the identification of various pathogens and for differentiating various disease causing mycobacterial species, from culture and potentially directly from sputum also.
The applicant has found that the method according to the invention has the following advantages over the prior art method:
• This diagnostic method uses the unique metabolome profiles of the most characteristic biomarker compounds and their comparative ratios, for differentiating or identifying various infectious pathogens and particularly disease causing mycobacterial species (e.g. M. bovis, M. avium, M. kansasi, and M. tuberculosis).
• Duration for diagnostic identification, excluding the required culturing step, is 7 hours. PCR also requires culturing before hand, however, in practice, once culturing is complete, effectively takes two days to get a diagnostic result.
• The detection limits for this analysis are extremely low only requiring 250 cells for a positive identification or differentiation of an infectious organism, hence, the required culture time prior to analysis, comparative to other methods, would potentially also be less than that required by other methodologies.
• This diagnostic approach also has an advantage for identifying unique disease specific biomarkers, which may in turn lead to further refinement of the diagnostic method, leading to even faster, simpler, more sensitive, and inexpensive diagnostic kits, which can be used without the need of an analytical or diagnostic laboratory.
• The biomarkers may also be used for understanding the mechanisms underlying disease progression, drug resistance and co-infection.
It will be appreciated further that variations in detail are possible with a system and method for distinguishing between different pathogens in a biological sample according to the invention without departing from the scope of this disclosure.

Claims

1. A reference system for distinguishing between different pathogens in biological samples (both specific and non-specific) including:
- a reference library of relative concentrations of carbon based compounds having a relatively low molecular weight, which are present in extracts of the said different pathogens in combination;
- a data matrix comprising a set of values of replicate samples of the said pathogens, each set representing the relative concentrations of the said carbon based compounds in a sample of each of the said pathogens;
- a processing device programmed to convert each set of values into a single representative benchmark value applying principal component analysis (PCA) calculations; and
- an output device for expressing the benchmark values graphically, characterised in that the output device is programmed to prepare a scatter plot of the respective single representative benchmark values of the replicate samples of each of the said pathogens from the PCA data and identify a list of biomarkers, being the carbon based compounds contributing most to the natural separation in the PCA in addition to those carbon based compounds with relative high VIP (variable influence on the projection) values identified using partial least squares discriminant analysis (PLS-DA) and applying effect size calculations to said indentified biomarkers to determine possible strain comparisons within a specific group of pathogens.
2. A reference system according to claim 1 wherein the output device is further programmed to build a discriminant model, using the said identified biomarkers, for predicting the pathogen strain membership of a new biological sample, wherein the discriminant model is selected from the group consisting of ordinary discriminant models, canonical discriminant models, partial least squares discriminant models, support vector machines, neural networks, logistic regression models and discriminant models based on kernel density estimations.
3. A reference system according to claim 2 wherein the output device is further programmed to prepare a scatter plot of the predicted pathogen strain membership for each of the benchmark pathogen samples using only the biomarkers processed via the discriminant model selected.
4. A reference system according to claim 3 wherein the output device is further programmed to express a Mahalanobis distance measure limit of each predicted benchmark pathogen strain sample, to the particular pathogen strain centre, as an indication of true strain membership within a specific group of pathogens.
5. A reference system according to any one of claims 1 to 4 wherein the carbon based compounds are selected from the group consisting of fatty acids, sugars, esters, ketones, amines, sulphur compounds, alcohols, carboxylic acids, and hydrocarbons such as alkenes, alkanes and aromatic compounds, and combinations thereof.
6. A reference system according to any one of claims 1 to 4 wherein the pathogens are selected from the group consisting of bacterial species, such as tuberculosis causing mycobacteria including M. tuberculosis, M. bovis, and non-tuberculous mycobacteria (NTM) such as M. avium and M, kansasii, and other lung pathogens including, Pseudomonas aeruginosa; fungal species and yeast species, preferably bacterial species.
7. A method of distinguishing between different pathogens in a biological sample (both specific and non-specific) in a reference system according to claims 1 to 6, including the steps of:
- obtaining a patient biological sample containing at least one pathogen;
- extracting at least one group of carbon based compounds from at least one pathogen in the sample using at least one extraction solvent;
- analysing the extracted group of carbon based compounds to prepare a data matrix comprising a set of values representing the relative concentrations of the said biomarker compounds in the sample; and
- applying one of the discriminant models of claim 3 to predict the strain membership within a specific group of pathogens and calculate the Mahalanobis distance measure of the patient obtained biological sample using only the selected biomarkers compounds.
8. A method according to claim 7 wherein the step of extracting carbon based compounds from the pathogen include the step of neutralising microorganisms other than the said pathogens, isolating the surviving pathogens from the neutralised microorganisms and from the remainder of the biological sample, culturing the pathogens and pyrolysing harvested cells.
9. A method according to claim 7 wherein the step of extracting carbon based compounds from the pathogens include the step of extracting the compounds directly from the biological sample containing a plurality of microorganisms and the said pathogens.
10. A method according to claim 7 wherein the step of extracting carbon based compounds from the pathogens include the step of neutralising microorganisms other than the said pathogen, and isolating the neutralised microorganisms and surviving pathogens from the biological sample.
1 1 . A method according to any one of claims 7 to 10 wherein the step of extracting at least one group of carbon based compounds includes the steps of:
- adding one or more extraction solvents to the sample to form an extraction mixture;
- breaking up the cell walls of the pathogens to release and extract the carbon based compounds from the pathogens; and
- subjecting the extraction mixture to centrifugation to separate the liquid fraction from the solid fraction;
12. A method according to claim 1 1 wherein the step of extracting at least one group of carbon based compounds include the further step of drying the liquid fraction followed by derivatisation by adding a derivatisation solvent selected from the group consisting of methoxyamine hydrochloride, pyridine, A/-Methyl-A/- trimethylsilyltrifluoroacetamide (MSTFA), (N,O-bis(trimethylsilyl) trifluoroacetamide (BSTFA), trimethylchlorosilane (TMCS), methanolic HCI and other derivatisation reagents or combinations thereof.
13. A method according to any one of claims 7 to 12 wherein the extraction solvent is selected from the group consisting of acetic acid, acetone, benzene, 1 -butanol, 2-butanol, chloroform, chlorobenzene, cyclohexane, 1 ,2-dichloroethane, diethyl ether, diethyl glycol, diethylene glycol dimethyl ether, dimethyl-formamide (DMF), dimethyl sulfoxide (DMSO), ethanol, ethyl acetate, ethylene glycol, glycerine, hexane, methanol, pentane, 1 -propanol, toluene, tetrahydrofuran (THF), pyridine and water.
14. A reference system substantially as herein described and exemplified.
15. A method of distinguishing between different pathogens in a biological sample substantially as herein described and exemplified.
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104020135A (en) * 2014-06-18 2014-09-03 中国科学院重庆绿色智能技术研究院 Calibration model establishing method based on near infrared spectrum
WO2016200773A1 (en) 2015-06-10 2016-12-15 Vuv Analytics, Inc. Method for detailed and bulk classification analysis of complex samples using vacuum ultra-violet spectroscopy and gas chromatography
CN109557148A (en) * 2018-11-15 2019-04-02 四川理工学院 A kind of system and method for quick detection microbiologic population
CN109813683A (en) * 2019-01-31 2019-05-28 南京医科大学 A kind of pathogen high speed detector and its detection method
US20200365260A1 (en) * 2015-04-20 2020-11-19 Cardeya Corporation Pathogen Detection And Display System

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004059280A2 (en) 2002-12-26 2004-07-15 Meso Scale Technologies, Llc Methods, compositions and kits for biomarker extraction
WO2006079846A1 (en) 2005-01-31 2006-08-03 Graf International Limited A method of detecting and identifying bacteria
WO2008124920A1 (en) * 2007-04-12 2008-10-23 The Governors Of The University Of Alberta Urine based detection of a disease state caused by a pneumococcal infection
WO2009045116A1 (en) 2007-10-05 2009-04-09 University Of Otago Detection of volatile compounds as markers for mycobacteria tuberculosis
WO2009091375A2 (en) 2007-10-19 2009-07-23 The Charles Stark Draper Laboratory, Inc. Rapid detection of volatile organic compounds for identification of bacteria in a sample

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004059280A2 (en) 2002-12-26 2004-07-15 Meso Scale Technologies, Llc Methods, compositions and kits for biomarker extraction
WO2006079846A1 (en) 2005-01-31 2006-08-03 Graf International Limited A method of detecting and identifying bacteria
WO2008124920A1 (en) * 2007-04-12 2008-10-23 The Governors Of The University Of Alberta Urine based detection of a disease state caused by a pneumococcal infection
WO2009045116A1 (en) 2007-10-05 2009-04-09 University Of Otago Detection of volatile compounds as markers for mycobacteria tuberculosis
WO2009091375A2 (en) 2007-10-19 2009-07-23 The Charles Stark Draper Laboratory, Inc. Rapid detection of volatile organic compounds for identification of bacteria in a sample

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
ILSE OLIVIER ET AL: "A metabolomics approach to characterise and identify various Mycobacterium species", JOURNAL OF MICROBIOLOGICAL METHODS, vol. 88, no. 3, 1 March 2012 (2012-03-01), pages 419 - 426, XP055033534, ISSN: 0167-7012, DOI: 10.1016/j.mimet.2012.01.012 *
IZQUIERDO-GARCÃA JOSE L ET AL: "A novel R-package graphic user interface for the analysis of metabonomic profiles", BMC BIOINFORMATICS, BIOMED CENTRAL, LONDON, GB, vol. 10, no. 1, 29 October 2009 (2009-10-29), pages 363, XP021061891, ISSN: 1471-2105, DOI: 10.1186/1471-2105-10-363 *
JOHAN TRYGG ET AL: "Chemometrics in Metabonomics", JOURNAL OF PROTEOME RESEARCH, vol. 6, no. 2, 1 February 2007 (2007-02-01), pages 469 - 479, XP055033530, ISSN: 1535-3893, DOI: 10.1021/pr060594q *
RUBEN T'KINDT ET AL: "Metabolomics to Unveil and Understand Phenotypic Diversity between Pathogen Populations", PLOS NEGLECTED TROPICAL DISEASES, vol. 4, no. 11, 30 November 2010 (2010-11-30), pages E904, XP055033532, DOI: 10.1371/journal.pntd.0000904 *
SLUPSKY CAROLYN M ET AL: "Streptococcus pneumoniae and Staphylococcus aureus pneumonia induce distinct metabolic responses", JOURNAL OF PROTEOME RESEARCH, ACS, WASHINGTON, DC, US, vol. 8, no. 6, 5 June 2009 (2009-06-05), pages 3029 - 3036, XP002593642, ISSN: 1535-3893, [retrieved on 20090415], DOI: 10.1021/PR900103Y *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104020135A (en) * 2014-06-18 2014-09-03 中国科学院重庆绿色智能技术研究院 Calibration model establishing method based on near infrared spectrum
CN104020135B (en) * 2014-06-18 2016-09-21 中国科学院重庆绿色智能技术研究院 Calibration model modeling method based near infrared spectrum
US20200365260A1 (en) * 2015-04-20 2020-11-19 Cardeya Corporation Pathogen Detection And Display System
US11862329B2 (en) * 2015-04-20 2024-01-02 Cardeya Corporation Pathogen detection and display system
WO2016200773A1 (en) 2015-06-10 2016-12-15 Vuv Analytics, Inc. Method for detailed and bulk classification analysis of complex samples using vacuum ultra-violet spectroscopy and gas chromatography
EP3308155A4 (en) * 2015-06-10 2019-03-13 Vuv Analytics, Inc. Method for detailed and bulk classification analysis of complex samples using vacuum ultra-violet spectroscopy and gas chromatography
US10302607B2 (en) 2015-06-10 2019-05-28 Vuv Analytics, Inc. Method for detailed and bulk classification analysis of complex samples using vacuum ultra-violet spectroscopy and gas chromatography
CN109557148A (en) * 2018-11-15 2019-04-02 四川理工学院 A kind of system and method for quick detection microbiologic population
CN109813683A (en) * 2019-01-31 2019-05-28 南京医科大学 A kind of pathogen high speed detector and its detection method
CN109813683B (en) * 2019-01-31 2021-07-30 南京医科大学 Pathogen rapid detector and detection method thereof

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