WO2011157655A1 - Utilisation des acides de la bile pour la prédiction d'une apparition de sepsie - Google Patents

Utilisation des acides de la bile pour la prédiction d'une apparition de sepsie Download PDF

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WO2011157655A1
WO2011157655A1 PCT/EP2011/059699 EP2011059699W WO2011157655A1 WO 2011157655 A1 WO2011157655 A1 WO 2011157655A1 EP 2011059699 W EP2011059699 W EP 2011059699W WO 2011157655 A1 WO2011157655 A1 WO 2011157655A1
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acid
sepsis
compounds
sample
endogenous
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Hans-Peter Deigner
Matthias Kohl
Therese Koal
Ignatius Emeka Igwe
Guido Dallmann
Michael Bauer
David Enot
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Biocrates Life Sciences Ag
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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
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • 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
    • 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
    • G01N33/92—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving lipids, e.g. cholesterol, lipoproteins, or their receptors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
    • G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/24—Immunology or allergic disorders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/26—Infectious diseases, e.g. generalised sepsis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/50—Determining the risk of developing a disease
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G01N30/7233—Mass spectrometers interfaced to liquid or supercritical fluid chromatograph

Definitions

  • the present invention relates to a method for predicting the likelihood of an onset of a sepsis from a biological sample of a mammalian subject in vitro in accordance with claim 1 , the use of a plurality of compounds belonging of the group of bile acids, as endogenous sepsis predictive target compounds for predicting the likelihood of an onset of a sepsis according to claim 14, and a Kit in accordance with claim 16.
  • the invention generally relates to endogenous sepsis predictive target compounds for predicting the likelihood of an onset of a sepsis as biomarkers for sepsis as tools in clinical diagnosis for early detection of sepsis, sepsis therapy monitoring and methods based on the same biomarkers.
  • Sepsis is a common cause of mortality and morbidity worldwide.
  • the estimated annual incidence of severe sepsis in newborns is 0.3 per 100 live births (Watson et al., 2003), with most mortality occurring within the first 48 hours of infection. (Weinschenk et al., 2000; Stoll et al., 2002).
  • sepsis is a term used to describe symptomatic bacteremia, with or without organ dysfunction. Sustained bacteremia, in contrast to transient bacteremia, may result in a sustained febrile response that may be associated with organ dysfunction.
  • Septicemia refers to the active multiplication of bacteria in the bloodstream, leading to an overwhelming infection.
  • the pathophysiology of sepsis is complex and the roles of inflammation, coagulation, and suppressed fibrinolysis are emerging as important mechanisms in the pathophysiology of sepsis.
  • These mediators of inflammation are often responsible for the clinically observable effects of the bacteremia in the host.
  • impaired pulmonary, hepatic, or renal function may result from excessive release of inflammatory mediators during a septic process.
  • Sepsis by definition comprises systemic inflammatory response syndrome due to an infection with pathogens.
  • SIRS Systemic inflammatory response syndrome
  • Sepsis includes a systemic inflammatory response syndrome (SIRS) together with an infection.
  • SIRS systemic inflammatory response syndrome
  • Sepsis denotes the presence of bacteria (bacteremia) or other infectious organisms or their toxins in the blood (septicaemia) or in other tissue of the body and the immune response of the host.
  • Sepsis is currently thought to result from the interaction between the host response and the presence of micro-organisms and/or their toxins within the body.
  • the observed host responses include the release of pro and anti-inflammatory immune mediators as well as components of the coagulation system.
  • Sepsis thus comprises a systemic response to infection, defined as hypothermia or hyperthermia, tachycardia, tachypnea, a clinically evident focus of infection or positive blood cultures, one or more end organs with either dysfunction or inadequate perfusion, cerebral dysfunction, hypoxemia, increased plasma lactate or unexplained metabolic acidosis, and oliguria.
  • WBC white blood cell
  • SBP systolic blood pressure
  • MAP mean arterial blood pressure
  • SvO2 mixed venous oxygen saturation
  • INR international normalized ratio
  • aPTT activated partial thromboplastin time
  • tachycardia may be absent in hypothermic patients
  • the definition of severe sepsis remained unchanged and refers to sepsis complicated by organ dysfunction.
  • Organ dysfunction is defined using Multiple Organ Dysfunction score (Marshall JC, Cook DJ, Christou NV, et al., "Multiple organ dysfunction score: A reliable descriptor of a complex clinical outcome", Crit Care Med 1995; 23: 1638-1652 or the definitions used for the Sequential Organ Failure Assessment (SOFA) score (Ferreira FL, Bota DP, Brass A, et al., "Serial evaluation of the SOFA score to predict outcome in critically ill patients", JAMA 2002; 286: 1754-1758.
  • Septic shock in adults refers to a state of acute circulatory failure characterized by persistent arterial hypotension unexplained by other causes.
  • Hypotension is defined by a systolic arterial pressure below 90 mm Hg, a MAP ⁇ 70 mmHg, or a reduction in systolic blood pressure of >40 mm Hg from baseline, despite adequate volume resuscitation, in the absence of other causes for hypotension.
  • CRP C-reactive protein
  • PCT procalcitonin
  • the medical practitioner uses a number of diagnostic tools for diagnosing a patient suffering from a certain disease.
  • diagnostic tools for diagnosing a patient suffering from a certain disease.
  • measurement of a series of single routine parameters, e.g. in a blood sample is a common diagnostic laboratory approach.
  • These single parameters comprise for example enzyme activities and enzyme concentration and/or detection
  • WO 2006/071583 A2 describes methods and compositions for determining therapy regimens in systemic inflammatory response syndromes (SIRS), sepsis, severe sepsis, septic shock and/or multiple organ dysfunction syndrome by means of biomarkers.
  • the biomarkers of WO 2006/071583 A2 are selected from the group consisting of at least one of the following matrix metalloproteinase 9 (MMP-9), interleukins-1 ⁇ , interleukin-6, interleukin-8, interleukin-8 6 -77 , interleukin-10, interleukin-22, interleukin-1 receptor agonist, chemokine (C-X-C motif) ligand 6 [CXCL6], CXCL13, CXCL16, chemokine (C- C motif) ligand 8 [CCL8], CCL20, CCL23, CCL26, D-dimer, high mobility group protein-1 (HMG-1 ), tumor necrosis factor-cc, A-type natri
  • biomarkers EP 09167018.2 uses a number of compounds such as amino acids, amino acid dimers, phenylthio carbamyl amino acids; carboxylic acids; ceramides with an N-acyl residue having from 1 to 30 carbon atoms in the acyl residue and having from 0 to 5 double bonds and from 0 to 5 hydroxy groups; carnitine and acylcarnitines having from 1 to 20 carbon atoms in the acyl residue; phospholipides; phosphatidylcholines having a total of 1 to 50 carbon atoms in the acyl residues; sphingolipids; prostaglandines; putrescine; oxysterols; biogenic amines and bile acids.
  • bile acids are disclosed in EP 09167018.2 for predicting the likelihood of an onset of an inflammation and/or sepsis related organ failure: cholic acid, chenodeoxycholic acid, deoxycholic acid, glycocholic acid, glycochenodeoxycholic acid, glycodeoxycholic acid, glycolithocholic acid, glycolithocholic acid sulfate, glycoursodeoxycholic acid, lithocholic acid, taurocholic acid, taurochenodeoxycholic acid taurodeoxycholic acid, taurolithocholic acid, taurolithocholic acid sulfate, tauroursodeoxycholic acid, ursodeoxycholic acid.
  • the present invention relates to a method for predicting the likelihood of an onset of a sepsis, comprising the steps of: a) quantitatively detecting in vitro, in at least one biological sample of at least one tissue of a mammalian subject a plurality of endogenous sepsis predictive target compounds having a molecular weight of less than 1500 Dalton, wherein said endogenous sepsis predictive target compounds belong to the group of bile acids and are selected from the group consisting of: cholic acid, glycocholic acid, deoxycholic acid, chenodeoxycholic acid, glycodeoxycholic acid, glycochenodeoxycholic acid, taurochcholic acid, taurochenodeoxycholic acid, taurodeoxycholic acid, taurolithocholic acid, taurolithocholic acid sulfate, tauroursodeoxycholic acid,
  • the present invention provides a solution to the above mentioned problems based on the application of a new technology in this context and on an hitherto unknown list of endogenous sepsis predictive target compounds, also referred to as endogenous sepsis predictive metabolites, respectively, as diagnostic marker. Since metabolite concentration differences in biological fluids and tissues provide links to the various phenotypical responses, metabolites are suitable biomarker candidates.
  • the present invention allows for accurate, rapid, and sensitive prediction and diagnosis of sepsis through a measurement of a plurality of endogenous sepsis predictive target compounds (a plurality of endogenous metabolic biomarkers; a plurality of endogenous sepsis predictive target metabolites) taken from a biological sample at a single point in time. This is accomplished by obtaining a biomarker panel at a single point in time from an individual, particularly an individual at risk of developing sepsis, having sepsis, or suspected of having sepsis, and comparing the biomarker profile from the individual to reference biomarker values or scores.
  • endogenous sepsis predictive target compounds a plurality of endogenous metabolic biomarkers; a plurality of endogenous sepsis predictive target metabolites
  • the reference biomarker values may be obtained from a population of individuals (a "reference population") who are, for example, afflicted with sepsis or who are suffering from either the onset of sepsis or a particular stage in the progression of sepsis. If the biomarker panel values or score from the individual contains appropriately characteristic features of the biomarker values or scores from the reference population, then the individual is diagnosed as having a more likely chance of getting sepsis, as being afflicted with sepsis or as being at the particular stage in the progression of sepsis as the reference population.
  • the present invention provides, inter alia, methods of predicting the likelihood of an onset of sepsis in an individual.
  • the methods comprise obtaining a biomarker score at a single point in time from the individual and comparing the individual's biomarker profile, i.e. the individual's quantitative pattern of the quantitatively detected compounds of a plurality of endogenous sepsis predictive target compounds, to a reference biomarker profile (quantitative reference pattern).
  • Comparison of the biomarker profiles can predict the onset of sepsis in the individual preferably with an accuracy of at least about 90%. This method may be repeated again at any time prior to the onset of sepsis.
  • the present invention further provides a method of determining the progression (i.e., the stage) of sepsis in an individual.
  • This method comprises of a profile of biomarkers composed of bile acids selected from table 1 at a single point in time from the individual and comparing the individual's biomarker profile to a reference biomarker score. Comparison of the biomarker scores can determine the progression of sepsis in the individual preferably with an accuracy of at least about 90 %. This method may also be repeated on the individual at any time.
  • the present invention may be important for diagnosing of sepsis in an individual having or suspected of having sepsis.
  • This method comprises obtaining a biomarker score at a single point in time from the individual and comparing the individual's biomarker score to a reference biomarker score. Comparison of the biomarker profiles can diagnose sepsis in the individual with an accuracy of at least about 90 %. This method may also be repeated on the individual at any time.
  • the invention provides, inter alia, a method of determining the status of sepsis or diagnosing sepsis in an individual comprising applying a decision rule.
  • the decision rule comprises comparing (i) a biomarker score generated from a biological sample taken from the individual at a single point in time with (ii) a biomarker score generated from a reference population.
  • Application of the decision rule determines the status of sepsis or diagnoses of sepsis in the individual.
  • the method may be repeated on the individual at one or more separate, single points in time.
  • the present invention further provides, inter alia, a method of determining the status of sepsis or diagnosing sepsis in an individual comprising obtaining a biomarker score from a biological sample taken from the individual and comparing the individual's bile acid biomarker score to a reference bile acid biomarker score. A single such comparison is capable of classifying the individual as having membership in the reference population. Comparison of the biomarker scores determines the status of sepsis or diagnoses of sepsis in the individual.
  • the present invention provides, inter alia, a method of determining the status of sepsis or diagnosing sepsis in an individual.
  • the method comprises comparing a measurable characteristic of at least one biomarker between a bile acid biomarker panel or biomarker score composed by (processed or unprocessed) values of this panel obtained from a biological sample from the individual and a biomarker score obtained from biological samples from a reference population. Based on this comparison, the individual is classified as belonging to or not belonging to the reference population. The comparison, therefore, determines the likelihood of sepsis or diagnoses of sepsis in the individual.
  • the biomarkers in one embodiment, are selected from the list of bile acid biomarkers shown in Table 1 .
  • the present invention provides methods for predicting onset of a sepsis.
  • Such methods comprise the steps of: analyzing a biological sample from a subject to determine the level(s) of one or more biomarkers for sepsis in the sample, where the one or more biomarkers (endogenous sepsis predictive target compounds or metabolites) are selected from Table 1 and comparing the level(s) of the one or more biomarkers, as well as a composed value / score generated by subjecting the concentrations of individual biomarkers in the sample to a classification method such as affording an equation to process single concentration values - to obtain a separation between both (diseased and healthy) groups or comparing the level(s) of the one or more biomarkers in the sample to sepsis positive or sepsis negative reference levels of the one or more biomarkers in order to determine whether the subject is developing sepsis.
  • the present invention provides a solution to the problem described above, and generally relates to the use of metabolomics data, generated by quantitation of endogenous metabolites, particularly endogenous sepsis predictive target compounds, by but not limited to mass spectrometry (MS), in particular MS-technologies such as Matrix Assisted Laser Desorption/lonisation (MALDI), Electrospray Ionization (ESI), Atmospheric Pressure Chemical Ionization (APCI), and other methods, determination of metabolite concentrations by use of MS-technologies or alternative methods coupled to separation (LC-MS, GC-MS, CE-MS), subsequent feature selection and /or the combination of features to classifiers including molecular data of at least two molecules.
  • MS mass spectrometry
  • MS-technologies such as Matrix Assisted Laser Desorption/lonisation (MALDI), Electrospray Ionization (ESI), Atmospheric Pressure Chemical Ionization (APCI), and other methods, determination of metabolite concentrations by use of MS-
  • concentrations of the individual compounds, biomarkers, analytes, metabolites thus are measured and compared to reference values or data combined and processed to scores, classifiers and compared to reference values thus indicating diseased states etc. with superior sensitivities and specificities compared to known procedures, clinical parameters and biomarkers.
  • the present invention provides a method of predicting the likelihood of an onset of sepsis comprising: detecting the presence or absence of a plurality (e.g., 2 or more, 3 or more, 5 or more, 10 or more, etc. measured together in a multiplex or panel format) endogenous sepsis predictive target metabolites in a sample (e.g., a tissue (e.g., biopsy) sample, a blood sample, a serum sample, or a urine sample) from a subject; and predicting an onset of sepsis based on the presence of the sepsis predictive bile acid metabolites in accordance with the present invention.
  • a sample e.g., a tissue (e.g., biopsy) sample, a blood sample, a serum sample, or a urine sample
  • the present invention further provides a method of screening compounds (biological samples), comprising: an animal, a tissue, a cell containing a sepsis-specific metabolite with a test compound; and detecting the level of the sepsis specific metabolite.
  • the method further comprises the step of comparing the level of the sepsis specific metabolite in the presence of the test compound or therapeutic intervention to the level of the sepsis metabolite in the absence of the sepsis specific metabolite.
  • the cell is in vitro, in a non-human mammal, or ex vivo.
  • the sepsis specific metabolite groups are given in tables 2, and 4 to 9.
  • the method is characterized in that a deproteinization step and/or a separation step is inserted between steps a) and b), wherein said separation step is selected from the group consisting of liquid chromatography (LC), high performance liquid chromatography (HPLC), gas chromatography, liquid-liquid-extraction (LLE).
  • LC liquid chromatography
  • HPLC high performance liquid chromatography
  • LLE liquid-liquid-extraction
  • Said deproteinization step preferably is carried out by mixing said biological sample with organic solvents (e.g. acetonitrile, in particular with acidified acetonitrile, ethanol or acetone).
  • organic solvents e.g. acetonitrile, in particular with acidified acetonitrile, ethanol or acetone.
  • the bile acid compounds are derivatized as esters, amines or amides, wherein said derivatization includes: 2-Hydrazinopyridine (HP), 2- picolylamine (PA); Girard derivatization ; oximation with hydroxylamine first and then silylation with hexamethyldisilazane and trifluoroacetic acid.
  • derivatization includes: 2-Hydrazinopyridine (HP), 2- picolylamine (PA); Girard derivatization ; oximation with hydroxylamine first and then silylation with hexamethyldisilazane and trifluoroacetic acid.
  • the resulting HP- and PA-derivatives were highly responsive in ESI-MS operating in the positive-ion mode and gave characteristic product ions during MS/MS, which enabled the sensitive detection using selected reaction monitoring.
  • PA was of more practical use; the detection responses of the PA-derivatives were increased by 9-1 58-fold over the intact bile acids and the limits of detection were in the low femtomole range (1 .5-5.6 fmol on column).
  • the PA- derivatization was successfully applied to a biological sample analysis; the derivatization followed by LC-ESI-MS/MS enabled the detection of trace amounts of bile acids in human saliva with a simple pretreatment, small sample volume and short analysis time.
  • a further possible derivatization process for bile acids for enhancing sensitivity and separation is a Girard reaction with Girard-reagent T.
  • UPAC synonyms are carboxymethyl)trimethylammonium chloride hydrazide; 2-Hydrazino-N,N,N-trimethyl-2- oxo-ethanaminium chloride; (carboxymethyl) trimethylammoniumchloride hydrazide; 2- hydrazine-N, N, N-trimethyl-2-oxo-ethanaminium chloride.
  • Derivatization of bile acids in particular cholic, lithocholic, chenodeoxycholic, ursodeoxycholic, 3-hydroxy,7-ketocholanic and dehydrocholic acids, can be easily identified, and quantified with significant higher sensitivity as compared to non- derivatized bile acids by a two step derivatization.
  • Derivatizations have been performed with a two-step process of 1 : oximation and 2: silylation varying the time and temperature of both reactions. Optimum responses have been obtained after 30 min oximation with hydroxylamine.HCI and 90 min silylation with hexamethyldisilazane and trifluoroacetic acid at 70 degrees C.
  • step b) selecting at least one suitable classifying algorithm from the group consisting of logistic regression, (diagonal) linear or quadratic discriminant analysis (LDA, QDA, DLDA, DQDA), perceptron, shrunken centroids regularized discriminant analysis (RDA), random forests (RF), neural networks (NN), Bayesian networks, hidden Markov models, support vector machines (SVM), generalized partial least squares (GPLS), partitioning around medoids (PAM), inductive logic programming (ILP), generalized additive models, Gaussian processes, regularized least square regression, self organizing maps (SOM), recursive partitioning and regression trees, K-nearest neighbor classifiers (K-NN), fuzzy classifiers, bagging, boosting, and na ' ive Bayes; and applying said selected classifier algorithm to said preprocessed data of step a); c) said classifier algorithms of step b) being trained on at least one training data set containing preprocessed data from subjects being divided into classes according to their sepsis-related
  • the step of mathematically preprocessing can be carried out e.g. by means of a statistical method on obtained raw data, particularly raw intensity data obtained by a measuring device, wherein said statistical method is selected from the group consisting of: a) in case of raw data obtained by optical spectroscopy (UV, visible, IR,
  • Fluorescence background correction and/or normalization; b) in case of raw data obtained by mass spectrometry or mass spectrometry coupled to liquid or gas chromatography or capillary electrophoresis or by 2D gel electrophoresis, quantitative determination with ELISA or RIA or determination of concentrations/amounts by quantitation of immunoblots or quantitation of amounts of biomolecules bound to aptamers: smoothing, baseline correction, peak picking, optionally, additional further data transformation such as taking the logarithm in order to carry out a stabilization of the variances.
  • said sepsis related disorders are selected from systemic inflammatory response syndrome (SIRS), severe sepsis, and/or septic shock
  • a further step of feature selection is inserted into said preprocessing step, in order to find a lower dimensional subset of features with the highest discriminatory power between classes; and/or said feature selection is carried out by a filter and/or a wrapper approach; and/or
  • said filter approach includes rankers and/or feature subset evaluation methods; and/or wherein
  • wrapper approach is applied, where a classifier is used to evaluate attribute subsets.
  • the method of the present invention is characterized in that said measuring step is carried out by high-throughput mass spectrometry.
  • sepsis specific predictive endogenous compounds are sepsis predictive endogenous metabolites.
  • said mammalian subject is a human being
  • said biological sample is blood wherein raw data of metabolite concentrations are preprocessed using the log transformation
  • RF random forest
  • KNN K nearest neighbours
  • SVM support vector machines
  • LDA linear discriminant analysis
  • said tissue is selected from the group consisting of blood and other body fluids, cerebrospinal fluids, urine; brain tissue, nerve tissue, and/or said sample is a biopsy sample; and/or the method further comprises inclusion of standard lab parameters commonly used in clinical chemistry and critical care units, in particular, blood gases, preferably arterial blood oxygen, blood pH, base status, serum and/or plasma levels of routinely used low molecular weight biochemical compounds, enzymes, enzymatic activities, cell surface receptors and/or cell counts, in particular red and/or white cell counts, platelet counts.
  • blood gases preferably arterial blood oxygen, blood pH, base status, serum and/or plasma levels of routinely used low molecular weight biochemical compounds, enzymes, enzymatic activities, cell surface receptors and/or cell counts, in particular red and/or white cell counts, platelet counts.
  • a further embodiment of the present invention is a use of a plurality of compounds being selected from the group consisting of bile acids, in particular, cholic acid, glycocholic acid, deoxycholic acid, chenodeoxycholic acid, glycodeoxycholic acid, glycochenodeoxycholic acid, taurochcholic acid, taurochenodeoxycholic acid, taurodeoxycholic acid, taurolithocholic acid, taurolithocholic acid sulfate, tauroursodeoxycholic acid, ursodeoxycholic acid, glycoursodeoxycholic acid, lithocholic acid, glycolithocholic acid, glycolithocholic acid sulfate; and glucuronidated compounds thereof; for carrying out a method for predicting the likelihood of an onset of sepsis and/or disorders related thereto in a mammalian subject.
  • bile acids in particular, cholic acid, glycocholic acid, deoxycholic acid, chenodeoxycholic acid, glycodeoxycholic acid
  • Kits for carrying out a method for predicting the likelihood of an onset of sepsis and/or disorders related thereto in a mammalian subject in a biological sample comprising: a) detection agents for the detection of sepsis specific endogenous metabolites, wherein said metabolites are selected from the group consisting of: bile acids, in particular, cholic acid, glycocholic acid, deoxycholic acid, chenodeoxycholic acid, glycodeoxycholic acid, glycochenodeoxycholic acid, taurochcholic acid, taurochenodeoxycholic acid, taurodeoxycholic acid, taurolithocholic acid, taurolithocholic acid sulfate, tauroursodeoxycholic acid, ursodeoxycholic acid, glycoursodeoxycholic acid, lithocholic acid, glycolithocholic acid, glycolithocholic acid sulfate; and glucuronidated compounds thereof;
  • bile acids in particular, cholic acid, glycocholic acid, deoxy
  • the method is a high throughput method.
  • the present invention also relates to:
  • a method for predicting the likelihood of onset of an inflammation associated sepsis from a biological sample of a mammalian subject in vitro wherein a. the subject's quantitative metabolomics profile comprising a plurality of endogenous bile acids, is detected in the biological sample by means of quantitative metabolomics analysis, and b. the quantitative metabolomics profile of the subject's sample is compared with a quantitative reference metabolomics profile of a plurality of endogenous sepsis predictive target metabolites (bile acids) in order to predict whether the subject is likely or unlikely to develop a sepsis; and c. wherein said endogenous sepsis predictive target metabolites have a molecular mass less than 1500 Da and are selected from the group of bile acids
  • the term "inflammation associated sepsis” comprises "infection associated (e.g., bacteria, fungi, viruses, parasites, and other infectious agents) sepsis” and / or sepsis due to non infectious agents like toxins, cellular components of bacteria, fungi, viruses and parasites such as DNA or RNA fragments.
  • a preferred method is one, wherein the biological sample is selected from the group consisting of stool, body fluids, in particular blood, liquor, cerebrospinal fluid, urine, ascitic fluid, seminal fluid, saliva, puncture fluid, cell content, tissue samples, in particular liver biopsy material; or a mixture thereof.
  • a preferred embodiment of the method according to the present invention is one, wherein said quantitative metabolomics profile of bile acids is achieved by a quantitative metabolomics profile analysis method comprising the generation of intensity data for the quantitation of endogenous metabolites by mass spectrometry (MS), in particular, by high- throughput mass spectrometry, preferably by MS- technologies such as Matrix Assisted Laser Desorption/lonisation (MALDI), Electro Spray Ionization (ESI), Atmospheric Pressure Chemical Ionization (APCI), 1 H-, 13 C- and/or 31 P- Nuclear Magnetic Resonance spectroscopy (NMR), optionally coupled to MS, determination of metabolite concentrations by use of MS-technologies and/or methods coupled to separation, in particular Liquid Chromatography (LC-MS), Gas Chromatography (GC-MS), or Capillary Electrophoresis (CE-MS).
  • MS- technologies such as Matrix Assisted Laser Desorption/lonisation (MALDI), Electro Spray Ionization (ESI),
  • said quantitative metabolomics profile comprises the results of measuring at least one of the parameters selected from the group consisting of:
  • the endogenous sepsis predictive target metabolites for easier and/or more sensitive detection are preferably detected by means of chemically modified derivatives thereof, such as the use of esters, amines or amides.
  • said endogenous sepsis predictive target metabolites are selected from the group consisting of:
  • bile acids namely cholic acid, glycocholic acid, deoxycholic acid, chenodeoxycholic acid, glycodeoxycholic acid, glycochenodeoxycholic acid, taurochcholic acid, taurochenodeoxycholic acid, taurodeoxycholic acid, taurolithocholic acid,
  • taurolithocholic acid sulfate tauroursodeoxycholic acid, ursodeoxycholic acid, glycoursodeoxycholic acid, lithocholic acid, glycolithocholic acid, glycolithocholic acid sulfate; and glucuronidated compounds thereof.
  • said plurality of endogenous sepsis predictive target metabolites or derivatives thereof comprise 2 to 20, in particular 2 to 17, 2 to 3, 2 to 4, 2 to 5, particularly preferred 2 to 6; 3 to 17, 4 to 17, 5 to 17, 6 to 17, 7 to 17, 8 to 17, 9 to 17, 10 to 17, preferred 2 to 15, 3 to 15, 4 to 15, preferably 5 to 15, 6 to 15, 7 to 15, 8 to 15, 9 to 15, 10 to 15, particularly preferred 2 to 10, 3 to 10, 4 to 10, 5 to 10, 6 to 10, 7 to 10, 8 to 10, 9 to 10, endogenous metabolites of cholic acid, compounds according to table 1 , respectively.
  • a particular embodiment of the present invention is the use of a plurality of endogenous metabolites for predicting of an onset of an infection associated sepsis from a biological sample of a mammalian subject in vitro, wherein the metabolites are selected from the group consisting of: bile acids, namely cholic acid, glycocholic acid, deoxycholic acid, chenodeoxycholic acid, glycodeoxycholic acid, glycochenodeoxycholic acid, taurochcholic acid,
  • taurochenodeoxycholic acid taurodeoxycholic acid, taurolithocholic acid
  • taurolithocholic acid sulfate tauroursodeoxycholic acid, ursodeoxycholic acid, glycoursodeoxycholic acid, lithocholic acid, glycolithocholic acid, glycolithocholic acid sulfate; and glucuronidated compounds thereof;
  • the present invention includes a kit for carrying out a method for predicting the likelihood of an onset of a sepsis from a biological sample of a
  • mammalian subject in vitro, in a biological sample comprising:
  • a) detection agents for the detection of sepsis specific endogenous metabolites wherein said metabolites are selected from the group consisting of: bile acids, in particular, cholic acid, glycocholic acid, deoxycholic acid, chenodeoxycholic acid, glycodeoxycholic acid, glycochenodeoxycholic acid, taurochcholic acid, taurochenodeoxycholic acid, taurodeoxycholic acid, taurolithocholic acid, taurolithocholic acid sulfate, tauroursodeoxycholic acid, ursodeoxycholic acid, glycoursodeoxycholic acid, lithocholic acid, glycolithocholic acid, glycolithocholic acid sulfate; and glucuronidated compounds thereof;
  • bile acids in particular, cholic acid, glycocholic acid, deoxycholic acid, chenodeoxycholic acid, glycodeoxycholic acid, glycochenodeoxycholic acid, taurochcholic acid, taurochenodeoxycholic acid, tau
  • Classifiers are typically deterministic functions that map a multi-dimensional vector of biological measurements to a binary (or n-ary) outcome variable that encodes the absence or existence of a clinically-relevant class, phenotype, distinct physiological state or distinct state of disease.
  • classification methods such as, but not limited to, logistic regression, (diagonal) linear or quadratic discriminant analysis (LDA, QDA, DLDA, DQDA), perceptron, shrunken centroids regularized discriminant analysis (RDA), random forests (RF), neural networks (NN), Bayesian networks, hidden Markov models, support vector machines (SVM), generalized partial least squares (GPLS), partitioning around medoids (PAM), inductive logic programming (ILP), generalized additive models, Gaussian processes, regularized least square regression, self organizing maps (SOM), recursive partitioning and regression trees, K-nearest neighbour classifiers (K-NN), fuzzy classifiers, bagging, boosting, and na ' ive Bayes and many more can be used.
  • logistic regression logistic regression
  • QDA linear or quadratic discriminant analysis
  • DLDA linear or quadratic discriminant analysis
  • DQDA perceptron
  • RDA shrunken centroids regularized discriminant analysis
  • RDA random forests
  • RF
  • a patient with sepsis has a clinical presentation that is classified as sepsis as defined above, but is not clinically deemed to have sepsis. Individuals who are at risk of developing sepsis
  • sepsis includes all stages of sepsis including, but not limited to, the onset of sepsis and multi organ dysfunction (MOD), particular liver failure in association with other organs, e.g. associated with the end stages of sepsis.
  • MOD multi organ dysfunction
  • Sepsis refers to a sepsis-positive condition that is associated with a confirmed infectious process. Clinical suspicion of sepsis arises from the suspicion that the sepsis- positive condition of a sepsis patient is a result of an infectious process.
  • the "onset of sepsis” refers to an early stage of sepsis, i.e., prior to a stage when the clinical manifestations are sufficient to support a clinical suspicion of sepsis. Because the methods of the present invention are used to detect sepsis prior to a time that sepsis would be suspected using conventional techniques, the patient's disease status at early sepsis can only be confirmed retrospectively, when the manifestation of sepsis is more clinically obvious. The exact mechanism by which a patient acquires sepsis is not a critical aspect of the invention. The methods of the present invention can detect changes in the biomarker score independent of the origin of the sepsis.
  • the methods of the present invention allow for determining the status of a patient having, or suspected of having, sepsis, as classified by previously used criteria.
  • endogenous sepsis predictive target compound or metabolite refers to a compound/metabolite that is differentially present or differentially concentrated in septic organisms compared to non-septic organisms.
  • sepsis predictive metabolites are present in septic tissues but not in non-septic tissues.
  • sepsis-specific metabolites are absent in septic tissues but present in non-septic cells, tissues, body liquids. In still further embodiments, sepsis specific metabolites are present at different levels (e.g., higher or lower) in septic tissue/cells as compared to non-septic tissue/cells.
  • a sepsis specific metabolite may be differentially present at any level, but is generally present at a level that is increased by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 1 10%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more; or is generally present at a level that is decreased by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%
  • a sepsis -specific bile acid metabolite is preferably differentially present at a level that is statistically significant (e.g., an adjusted p-value less than 0.05 as determined using either Analysis of Variance, Welch's t-test or its non parametric equivalent versions). Exemplary sepsis-specific metabolites are described in the detailed description and experimental sections below.
  • sample in the present specification and claims is used in its broadest sense. On the one hand it is meant to include a specimen or culture. On the other hand, it is meant to include both biological and environmental samples.
  • a sample may include a specimen of synthetic origin. Biological samples may be animal, including human, fluid, solid (e.g., stool) or tissue, such biological samples may be obtained from all of the various families of domestic animals, as well as feral or wild animals, including, but not limited to, such animals as ungulates, bear, fish, rodents, etc.
  • a biological sample may contain any biological material suitable for detecting the desired biomarkers, and may comprise cellular and/or non-cellular material from a subject. The sample can be isolated from any suitable biological tissue or fluid such as, for example, tissue, blood, blood plasma, urine.
  • a “reference level” of a metabolite means a level of the metabolite that is indicative of a particular disease state, phenotype, or lack thereof, as well as combinations of disease states, phenotypes, or lack thereof.
  • a "positive" reference level of a metabolite means a level that is indicative of a particular disease state or phenotype.
  • a “negative” reference level of a metabolite means a level that is indicative of a lack of a particular disease state or phenotype.
  • a "sepsis -positive reference level" of a metabolite means a level of a metabolite that is indicative of a positive diagnosis of sepsis in a subject
  • an "sepsis -negative reference level" of a metabolite means a level of a metabolite that is indicative of a negative diagnosis of sepsis in a subject.
  • a “reference level" of a metabolite may be an absolute or relative amount or concentration of the metabolite, a presence or absence of the metabolite, a range of amount or concentration of the metabolite, a minimum and/or maximum amount or concentration of the metabolite, a mean amount or concentration of the metabolite, and/or a median amount or concentration of the metabolite; and, in addition, “reference levels” of combinations of metabolites may also be ratios of absolute or relative amounts or concentrations of two or more metabolites with respect to each other or a composed value / score obtained by classification.
  • Appropriate positive and negative reference levels of metabolites for a particular disease state, phenotype, or lack thereof may be determined by measuring levels of desired metabolites in one or more appropriate subjects, and such reference levels may be tailored to specific populations of subjects (e.g., a reference level may be age- matched so that comparisons may be made between metabolite levels in samples from subjects of a certain age and reference levels for a particular disease state, phenotype, or lack thereof in a certain age group). Such reference levels may also be tailored to specific techniques that are used to measure levels of metabolites in biological samples (e.g., LC-MS, GC-MS, etc.), where the levels of metabolites may differ based on the specific technique that is used.
  • cell refers to any eukaryotic or prokaryotic cell (e.g., bacterial cells such as E. coli, yeast cells, mammalian cells, avian cells, amphibian cells, plant cells, fish cells, and insect cells), whether located in vitro or in vivo.
  • bacterial cells such as E. coli, yeast cells, mammalian cells, avian cells, amphibian cells, plant cells, fish cells, and insect cells
  • processor refers to a device that performs a set of steps according to a program (e.g., a digital computer).
  • processors for example, include Central Processing Units ("CPUs"), electronic devices, or systems for receiving, transmitting, storing and/or manipulating data under programmed control.
  • CPUs Central Processing Units
  • electronic devices or systems for receiving, transmitting, storing and/or manipulating data under programmed control.
  • memory device refers to any data storage device that is readable by a computer, including, but not limited to, random access memory, hard disks, magnetic (floppy) disks, compact discs, DVDs, magnetic tape, flash memory, and the like.
  • Mass Spectrometry is a technique for measuring and analyzing molecules or molecule fragments of a target compound, by measuring their mass/charge ratios, to produce a mass spectrum that serves as a "molecular fingerprint". Determining the mass/charge ratio of an object is done through means of determining the wavelengths at which electromagnetic energy is absorbed by that object. There are several commonly used methods to determine the mass to charge ratio of an ion, some measuring the interaction of the ion trajectory with electromagnetic waves, others measuring the time an ion takes to travel a given distance, or a combination of both. The data from these fragment mass measurements can be searched against databases to obtain definitive identifications of target molecules. Mass spectrometry is also widely used in other areas of chemistry, like petrochemistry or pharmaceutical quality control, among many others.
  • the term "metabolite” denotes endogenous organic compounds of a cell, an organism, a tissue or being present in body liquids and in extracts obtained from the aforementioned sources with a molecular weight typically below 1500 Dalton.
  • Typical examples of metabolites are carbohydrates, lipids, phospholipids, sphingolipids and sphingophospholipids, amino acids, cholesterol, steroid hormones and oxidized sterols and other compounds such as collected in the Human Metabolite database [Wishart DS et al., HMDB: the Human Metabolome Database. Nucleic Acids Res. 2007 Jan;35 (Database issue):D521-6 (see http ://www. h mdb.ca / and other databases and literature.
  • Methodabolomics designates the comprehensive quantitative measurement of several (2-thousands) metabolites by, but not limited to, methods such as mass spectroscopy, coupling of liquid chromatography, gas chromatography and other separation methods chromatography with mass spectroscopy.
  • separation refers to separating a complex mixture into its component proteins or metabolites.
  • Common laboratory separation techniques include gel electrophoresis and chromatography.
  • capillary electrophoresis refers to an automated analytical technique that separates molecules in a solution by applying voltage across buffer-filled capillaries. Capillary electrophoresis is generally used for separating ions, which move at different speeds when the voltage is applied, depending upon the size and charge of the ions.
  • the solutes (ions) are seen as peaks as they pass through a detector and the area of each peak is proportional to the concentration of ions in the solute, which allows quantitative determinations of the ions.
  • Chromatographic refers to a physical method of separation in which the components to be separated are distributed between two phases, one of which is stationary (stationary phase) while the other (the mobile phase) moves in a definite direction. Chromatographic output data may be used for manipulation by the present invention.
  • An “ion” is a charged object formed by adding electrons to or removing electrons from an atom.
  • a “mass spectrum” is a plot of data produced by a mass spectrometer, typically containing m/z values on x-axis and intensity values on y-axis.
  • a “peak” is a point on a mass spectrum with a relatively high y-value.
  • the term “m/z” refers to the dimensionless quantity formed by dividing the mass number of an ion by its charge number. It has long been called the "mass-to-charge” ratio.
  • metabolism refers to the chemical changes that occur within the tissues of an organism, including “anabolism” and “catabolism”. Anabolism refers to biosynthesis or the build up of molecules and catabolism refers to the breakdown of molecules.
  • post-surgical tissue refers to tissue that has been removed from a subject during a surgical procedure. Examples include, but are not limited to, biopsy samples, excised organs, and excised portions of organs.
  • detect may describe either the general act of discovering or discerning or the specific observation of a detectably labeled composition.
  • a biomarker in this context is a characteristic, comprising data of at least one metabolite that is measured and evaluated as an indicator of biologic processes, pathogenic processes, or responses to a therapeutic intervention associated with sepsis or related to sepsis treatment.
  • a combined biomarker as used here may be selected from at least two small endogenous molecules and metabolites.
  • the present invention relates to markers of sepsis and its duration/severity.
  • the present invention provides metabolites that are differentially present in sepsis.
  • Experiments conducted during the course of development of the embodiments of the present invention identified a series of metabolites as being differentially present in subjects in sepsis in comparison to those without sepsis, with non-systemic inflammatory reaction, non-systemic infection or systemic inflammatory response syndrome without infection. Diagnostic Applications
  • the present invention provides methods and compositions for diagnosing sepsis, including but not limited to, characterizing risk of sepsis, stage of sepsis, duration and severity etc. based on the presence of sepsis specific metabolites or their derivatives, precursors, metabolites, etc. Exemplary diagnostic methods are described below.
  • a method of diagnosing (or aiding in diagnosing) whether a subject has sepsis comprises (1 ) detecting the presence or absence or a differential level of one or more sepsis specific bile acid metabolites selected from table 1 and b) diagnosing sepsis based on the presence, absence or differential level of the sepsis specific metabolite.
  • the results of the method may be used along with other methods (or the results thereof) useful in the clinical determination of whether a subject has sepsis.
  • the sample may be tissue (e.g., a biopsy sample or post-surgical tissue), blood, urine, or a fraction thereof (e.g., plasma, serum, urine supernatant, urine cell pellet).
  • tissue e.g., a biopsy sample or post-surgical tissue
  • blood e.g., blood, urine, or a fraction thereof (e.g., plasma, serum, urine supernatant, urine cell pellet).
  • the patient sample undergoes preliminary processing designed to isolate or enrich the sample for sepsis specific metabolites or cells that contain sepsis specific metabolites.
  • preliminary processing designed to isolate or enrich the sample for sepsis specific metabolites or cells that contain sepsis specific metabolites.
  • Metabolites may be detected using any suitable method including, but not limited to, liquid and gas phase chromatography, alone or coupled to mass spectrometry (See e.g., experimental section below), NMR, immunoassays, chemical assays, spectroscopy and the like. In some embodiments, commercial systems for chromatography and NMR analysis are utilized.
  • metabolites are detected using optical imaging techniques such as magnetic resonance spectroscopy (MRS), magnetic resonance imaging (MRI), computed axial tomography (CAT) scans, ultra sound, MS-based tissue imaging or X-ray detection methods (e.g., energy dispersive x-ray fluorescence detection).
  • optical imaging techniques such as magnetic resonance spectroscopy (MRS), magnetic resonance imaging (MRI), computed axial tomography (CAT) scans, ultra sound, MS-based tissue imaging or X-ray detection methods (e.g., energy dispersive x-ray fluorescence detection).
  • MRS magnetic resonance spectroscopy
  • MRI magnetic resonance imaging
  • CAT computed axial tomography
  • ultra sound MS-based tissue imaging
  • X-ray detection methods e.g., energy dispersive x-ray fluorescence detection
  • Suitable methods include chromatography (e.g., HPLC, gas chromatography, liquid chromatography), mass spectrometry (e.g., MS, MS-MS), enzyme-linked immunosorbent assay (ELISA), antibody linkage, other immunochemical techniques, biochemical or enzymatic reactions or assays, and combinations thereof.
  • the level(s) of the one or more metabolites may be measured indirectly, for example, by using an assay that measures the level of a compound (or compounds) that correlates with the level of the biomarker(s) that are desired to be measured.
  • the levels of one or more of the recited metabolites may be determined in the methods of the present invention. For example, the level(s) of one metabolites, two or more metabolites, three or more metabolites, four or more metabolites, five or more metabolites, six or more metabolites, seven or more metabolites, eight or more metabolites, nine or more metabolites, ten or more metabolites, etc., including a combination of some or all of the metabolites including, but not limited to those listed in table 1 , may be determined and used in such methods.
  • Determining levels of combinations of the metabolites may allow greater sensitivity and specificity in the methods, such as diagnosing sepsis and aiding in the diagnosis of sepsis, and may allow better differentiation or characterization of sepsis from other disorders or other sepsis that may have similar or overlapping metabolites to sepsis (as compared to a subject not having sepsis). For example, ratios of the levels of certain metabolites in biological samples may allow greater sensitivity and specificity in diagnosing sepsis and aiding in the diagnosis of sepsis.
  • a computer-based analysis program is used to translate the raw data generated by the detection assay (e.g., the presence, absence, or amount of a sepsis specific metabolite) into data of predictive value for a clinician.
  • the clinician can access the predictive data using any suitable means.
  • the present invention provides the further benefit that the clinician, who is not likely to be trained in metabolite analysis, need not understand the raw data.
  • the data is presented directly to the clinician in its most useful form. The clinician is then able to immediately utilize the information in order to optimize the care of the subject.
  • the present invention contemplates any method capable of receiving, processing, and transmitting the information to and from laboratories conducting the assays, information provides, medical personal, and subjects.
  • a sample e.g., a biopsy or a blood, urine or serum sample
  • a profiling service e.g., clinical lab at a medical facility, etc.
  • any part of the world e.g., in a country different than the country where the subject resides or where the information is ultimately used
  • the subject may visit a medical center to have the sample obtained and sent to the profiling center, or subjects may collect the sample themselves (e.g., a plasma sample) and directly send it to a profiling center.
  • the sample comprises previously determined biological information
  • the information may be directly sent to the profiling service by the subject (e.g., an information card containing the information may be scanned by a computer and the data transmitted to a computer of the profiling center using an electronic communication systems).
  • the profiling service Once received by the profiling service, the sample is processed and a profile (i.e., metabolic profile), specific for the diagnostic or prognostic information desired for the subject is produced.
  • the profile data is then prepared in a format suitable for interpretation by a treating clinician.
  • the prepared format may represent a diagnosis or risk assessment (e.g., likelihood of sepsis being present) for the subject, along with recommendations for particular treatment options.
  • the data may be displayed to the clinician by any suitable method.
  • the profiling service generates a report that can be printed for the clinician (e.g., at the point of care) or displayed to the clinician on a computer monitor.
  • the information is first analyzed at the point of care or at a regional facility.
  • the raw data is then sent to a central processing facility for further analysis and/or to convert the raw data to information useful for a clinician or patient.
  • the central processing facility provides the advantage of privacy (all data is stored in a central facility with uniform security protocols), speed, and uniformity of data analysis.
  • the central processing facility can then control the fate of the data following treatment of the subject. For example, using an electronic communication system, the central facility can provide data to the clinician, the subject, or researchers.
  • the subject is able to directly access the data using the electronic communication system.
  • the subject may chose further intervention or counseling based on the results.
  • the data is used for research use.
  • the data may be used to further optimize the inclusion or elimination of markers as useful indicators of a particular condition or stage of disease.
  • the amount(s) or level(s) of the one or more metabolites in the sample may be compared to sepsis metabolite-reference levels, such as sepsis-positive and/or sepsis-negative reference levels to aid in diagnosing or to diagnose whether the subject has sepsis.
  • sepsis metabolite-reference levels such as sepsis-positive and/or sepsis-negative reference levels to aid in diagnosing or to diagnose whether the subject has sepsis.
  • Levels of the one or more metabolites in a sample corresponding to the sepsis -positive reference levels e.g., levels that are the same as the reference levels, substantially the same as the reference levels, above and/or below the minimum and/or maximum of the reference levels, and/or within the range of the reference levels
  • Levels of the one or more metabolites in a sample corresponding to the sepsis -negative reference levels are indicative of a diagnosis of no sepsis in the subject.
  • levels of the one or more metabolites that are differentially present (especially at a level that is statistically significant) in the sample as compared to sepsis-negative reference levels are indicative of a diagnosis of sepsis in the subject.
  • Levels of the one or more metabolites that are differentially present (especially at a level that is statistically significant) in the sample as compared to sepsis -positive reference levels are indicative of a diagnosis of no sepsis in the subject.
  • the level(s) of the one or more metabolites may be compared to sepsis -positive and/or sepsis -negative reference levels using various techniques, including a simple comparison (e.g., a manual comparison) of the level(s) of the one or more metabolites in the biological sample to sepsis -positive and/or sepsis -negative reference levels.
  • the level(s) of the one or more metabolites in the biological sample may also be compared to sepsis and/or sepsis -negative reference levels using one or more statistical analyses (e.g., t-test, Welch's t-test, Wilcoxon's rank sum test, random forests, support vector machines, linear discriminant analysis, k nearest neighbours).
  • compositions for use include reagents for detecting the presence or absence of sepsis specific metabolites. Any of these compositions, alone or in combination with other compositions of the present invention, may be provided in the form of a kit. Kits may further comprise appropriate controls and/or detection reagents.
  • Embodiments of the present invention provide for multiplex or panel assays that simultaneously detect a plurality (at least two) of the markers of the present invention depicted in tables 1 and 2, alone or in combination with additional sepsis markers known in the art.
  • panel or combination assays are provided that detected 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 15 or more markers in a single assay.
  • assays are automated or high throughput.
  • a preferred embodiment of the present invention is the use of markers listed in tables 1 and 2 for prediction/diagnosis of sepsis and its duration/severity where said mammalian subject is a human being, said biological sample blood and/or blood cells.
  • additional sepsis markers are included in multiplex or panel assays. Markers are selected for their predictive value alone or in combination with the metabolic markers described herein.
  • Plasma samples were prepared by standard procedures and stored at (-70 °C). To enable analysis of all samples simultaneously within one batch, samples were thawed on ice (1 h) on the day of analysis and centrifuged at 18000 g at 2°C for 5 min. All tubes were prepared with 0.001 % BHT (butylated hydroxytoluene; Sigma-Aldrich, Vienna, Austria) to prevent artificial autooxidation.
  • BHT butylated hydroxytoluene
  • Tissue Liver and lung tissue samples were homogenized using a Precellys® 24 homogenizer (Peqlab Biotechnologie GmbH, Er Weg, Germany) with Cryolys cooling module before analysis. Frozen liver tissue samples were weighted into 2.0 ml Precellys tubes (Peqlab Biotechnologie GmbH, Er Weg, Germany) equipped with ceramic beads. Homogenates were prepared by adding ethanol: 10 mM phosphate buffer 85:15 (v/v) to the tissue sample (typically 50 mg), ratio 3:1 (w/v).
  • the Precellys-24 homogenizer uses a figure-eight motion to rapidly gyrate beats to grind up to 24 samples temperature controlled (0 - 4 °C) at one time with the following program (frequency, cycles, cycle time, pause between cycles): 5800 rpm, 3 x 30 sec, 25 sec. Unsolved material and beads for tissue disintegration were removed by centrifugation at 10 OOOg at 2 °C for 5 minutes. Bile acids (LC-MS/MS) assay
  • LC-MS/MS system consisted of an API 4000TM triple quadrupole mass spectrometer (AB Sciex) equipped with a Turbo VTM ESI source and an Agilent 1200 hplc system (Agilent Technologies). Chromatographic separation was performed using an Agilent Zorbax Eclipse XDB C18 column (100 x 3.0 mm, 3.5 ⁇ ) with guard column (C 18, 4 x 2 mm in Security Guard Cartridge, Phenomenex). AnalystTM software (version 1 .4.2, Applied Biosystems) was used for data acquisition and processing. For comprehensive statistical analysis the data were exported.
  • the autosampler temperature was set at 10 °C and the columns were maintained at 40 °C during the whole analysis.
  • Gradient elution was applied with a constant flow rate of 300 ⁇ _ ⁇ starting with 30% mobile-phase A (100% water containing 0.012% formic acid and 5 mM ammonium acetate), and 70% mobile-phase B (100% methanol containing 0.012% formic acid and 5 mM ammonium acetate) followed by a linear increase toward 95% mobile-phase B which is achieved in 7 min and maintained for 2.50 min.
  • the gradient changed linear to the initial setting which is maintained for 5.40 min giving a total run time of 15 minutes.
  • the ESI source was operated in negative ion mode and an ion-spray voltage of -3 kV was applied. Heater temperature was set at 400 °C.
  • Other analysis parameters such as declustering potential (DP) or collision energy (CE), were optimized for each bile acid separately to ensure maximum product ion formation for each compound. See Table 1 for details concerning analyte-specific MS parameters.
  • Table 1 List of endogenous sepsis predictive target compounds (analytes) and their corresponding analyte-specific MS parameters.
  • Figure 1 is a flow chart showing the sample preparation as used for the present invention.
  • serum specimens were obtained at day 1 , 3, and 5 and were stored at - 80 Q C for subsequent assaying.
  • the ImFit function in the package Limma (Limma: linear models for microarray data, Smyth G.K. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, Springer, New York, pp 397-420, R package version 2.16.5) is used to compute the moderated statistics for pairwise comparisons between measurements from control samples and samples with sepsis. Resulting p values are adjusted by the method described in Benjamini and Hochberg (Benjamini Y. and Hochberg Y., Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B, 1995, 57, 289-300) leading to so-called q values.
  • Sensitivity/specificity properties of a classifier comprising one analyte or a combination of analytes are summarised in terms of Area Under the Receiver Operating Characteristic Curve (AUC).
  • AUC Area Under the Receiver Operating Characteristic Curve
  • the function colAUC (caTools: Tools: moving window statistics, GIF, Base64, ROC AUC, etc., Tuszynski J., 2008, R package version 1 .9) is used to compute AUC values.
  • Table 3 AUC (area under the curve), accuracy, sensitivity and specificity for various classifiers. The numbers in brackets are the results for .632-bootstrap.
  • Tissue samples were homogenized using a Precellys® 24 homogenizer with Cryolys cooling module before analysis. Typically 50 mg of tissue were homogenized in ethanol: phosphate buffer 9:1 (v/v) for 30 min and unsolved material and beads for tissue disintegration removed by 5 min centrifugation at 1 0OOOg.
  • the effective dose of the extract (to induce either sepsis or organ failure) has to be predetermined for each batch (of stool from one individual human subject).
  • sepsis can be induced within 24 h with a complete recovery of the animals > 48 h or septic organ failure can be induced by applying a higher dosage; for instance sepsis can be induced by injection of 0.5 ml of extract and organ failure by injection of 1 .0 ml intraperitoneal ⁇ . All samples of liver tissue were drawn 6h and 24 h after intraperitoneal injection of the extract.
  • the ImFit function in the package limma (limma: linear models for microarray data, Smyth G.K. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor, Springer, New York, pp 397-420, R package version 2.16.5) is used to compute the moderated statistics for pair wise comparisons between measurements from control samples and samples with sepsis. Resulting p values are adjusted by the method described in Benjamini and Hochberg (Benjamini Y. and Hochberg Y., Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B, 1995, 57, 289-300) leading to so-called q values.
  • Sensitivity/specificity properties of a classifier comprising one analyte or a combination of analytes are summarised in terms of Area Under the Receiver Operating Characteristic Curve (AUC).
  • AUC Area Under the Receiver Operating Characteristic Curve
  • the function colAUC (caTools: Tools: moving window statistics, GIF, Base64, ROC AUC, etc., Tuszynski J., 2008, R package version 1 .9) is used to compute AUC values.
  • Typical cut-off values for the absolute value of the log2-FC are 0.58 and 1 .0 representing a change of 50% and 100%, respectively.
  • the q value represents the expected proportion of false positive test results in a multiple testing setup where one typically uses cut-off values of 0.05 or 0.10 but also larger values may be acceptable.
  • the values range from 0 to 1 , with a value of 1 representing perfect prediction and a value of 0.5 representing chance prediction.
  • the relevant parameter space is 0.5-1 .0. Values between 0.7 and 0.8 indicate an acceptable discrimination, values between 0.8 and 0.9 are considered as excellent and values larger than 0.9 are outstanding and are only rarely observed.

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Abstract

La présente invention concerne un procédé servant à prédire in vitro l'apparition d'une sepsie, en détectant quantitativement dans au moins un échantillon biologique d'un patient une pluralité de composés cibles prédictifs d'une sepsie endogène dont le poids moléculaire est inférieur à 1500 daltons, dont les étapes consistent : à sélectionner lesdits composés parmi le groupe des acides de la bile, à mesurer la concentration, le niveau ou la quantité de chaque composé cible prédictif d'une sepsie endogène de ladite pluralité de composés dans ledit échantillon, à établir un motif quantitatif ; à utiliser et à mémoriser l'ensemble de valeurs obtenu dans une bases de données ; et à étalonner lesdites valeurs en comparant des paramètres de référence positifs d'une sepsie confirmés cliniquement et/ou négatifs d'une sepsie confirmés cliniquement, et à comparer lesdites valeurs mesurées dans l'échantillon aux valeurs calibrées afin d'évaluer si le patient risque de développer une sepsie ou non.
PCT/EP2011/059699 2010-06-15 2011-06-10 Utilisation des acides de la bile pour la prédiction d'une apparition de sepsie WO2011157655A1 (fr)

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WO2016038377A1 (fr) * 2014-09-10 2016-03-17 The University Of Warwick Biomarqueur
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