WO2011012553A1 - Method for predicting the likelihood of an onset of an inflammation associated organ failure - Google Patents
Method for predicting the likelihood of an onset of an inflammation associated organ failure Download PDFInfo
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- WO2011012553A1 WO2011012553A1 PCT/EP2010/060745 EP2010060745W WO2011012553A1 WO 2011012553 A1 WO2011012553 A1 WO 2011012553A1 EP 2010060745 W EP2010060745 W EP 2010060745W WO 2011012553 A1 WO2011012553 A1 WO 2011012553A1
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- G—PHYSICS
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- 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
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- 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
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- 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
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- 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
- G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
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- 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
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y10T436/00—Chemistry: analytical and immunological testing
- Y10T436/17—Nitrogen containing
- Y10T436/173845—Amine and quaternary ammonium
Definitions
- the present invention relates to a method for predicting the likelihood of an onset of an inflammation or infection associated organ failure from a biological sample of a mammalian subject in vitro in accordance with claim 1.
- the invention generally relates to biomarkers for organ failure as tools in clinical diagnosis for early detection of organ failure, therapy monitoring and methods based on the same biomarkers.
- Organ failure in acute pancreatitis was predicted by using a combination of plasma interleukin 10 and serum calcium measurements (Early Prediction of Organ Failure by Combined Markers in Patients With Acute Pancreatitis Mentula P, Kylanpaa M-L, Kemppainen E, Br J Surg, 92, 68 - 75, 2005).
- interleukin 6 and interleukin 10 were used for multiple OF prediction (Lausevic Z, Lausevic M, Trbojevic-Stankovic J, Krstic S, Stojimirovic, Predicting multiple organ failure in patients with severe trauma B Can J Surg. 2008, 51 , 97-102).
- Severe sepsis also includes OF and occurs when one or more vital organs are compromised. It can lead to septic shock, which is marked by low blood pressure that does not respond to standard treatment, problems in vital organs, and oxygen
- CRP C-reactive protein
- PCT procalcitonin
- Sepsis by definition comprises systemic inflammatory response syndrome (SIRS) and infection with pathogens.
- SIRS systemic inflammatory response syndrome
- SIRS Systemic inflammatory response syndrome
- the quantitative metabolomics profile of the endogenous organ failure predictive target metabolites can be combined with any of the above classical clinical laboratory parameters.
- Organ failure includes a systemic inflammatory response syndrome (SIRS) together with an infection.
- Sepsis commonly called a "blood stream infection” denotes the presence of bacteria (bacteremia) or other infectious organisms or their toxins in the blood (septicemia) or in other tissue of the body and the immune response of the host.
- Organ failure due to sepsis is currently thought to start with the interaction between the host response and the presence of micro-organisms and/or their toxins within the body.
- the observed host responses include immune, coagulation, pro and anti-inflammatory responses.
- Septic organ failure 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, hypoxaemia, 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
- at least one of the following indications of altered organ function altered mental status, hypoxemia, increased serum lactate level.
- 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.
- US 2009/0104596 A1 discloses methods and kits for diagnosing a disease state of cachexia by measuring biomarker profiles.
- the biomarkers concerned are those known from the energy metabolism, namely lactate, citrate, formate, acetoacetate, 3-hydroxy butyrate and some amino acids.
- Organ failure of any kind is not addressed.
- Freund et al., Ann. Surg. (1979), 190, 571 -576 desclose the use of a plasma amino acid pattern as predictors of the severity and outcome of sepsis for discriminating between septic encephalopathy and no encephalopathy, wherein the degree of encephalopathy of a patient is considered an expression for the severity of the septic process.
- 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.
- the present invention provides a solution to these problems based on the application of a new technology in this context and on an unknown list of endogenous metabolites 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 OF through a measurement of a plurality of endogenous metabolic biomarker (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 OF, having OF, or suspected of having OF, and comparing the biomarker profile from the individual to reference biomarker values or scores.
- the reference biomarker values may be obtained from a population of individuals (a "reference population") who are, for example, afflicted with OF or who are suffering from either the onset of OF or a particular stage in the progression of OF.
- 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 OF, as being afflicted with OF or as being at the particular stage in the progression of OF as the reference population.
- the present invention provides, inter alia, methods of predicting the likelihood of an onset of OF 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 to a reference biomarker profile. Comparison of the biomarker profiles can predict the onset of OF in the individual preferably with an accuracy of at least about . This method may be repeated again at any time prior to the onset of OF.
- the present invention further provides a method of determining the progression (i.e., the stage) of sepsis in an individual towards OF.
- This method comprises obtaining a biomarker profile 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 provides a method of diagnosing OF in an individual having or suspected of having OF. 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 OF 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 OF or diagnosing OF 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 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 OF or diagnosing OF in an individual comprising obtaining a biomarker score from a biological sample taken from the individual and comparing the individual's biomarker score to a reference 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 OF or diagnoses OF in the individual.
- the present invention provides, inter alia, a method of determining the status of OF or diagnosing OF in an individual.
- the method comprises comparing a measurable characteristic of at least one biomarker between a 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 OF or diagnoses OF in the individual.
- the biomarkers in one embodiment, are selected from the group of biomarkers shown in any one of TABLES 1 to 3.
- the present invention provides methods for predicting organ failure, which is clinically deary to be distinguished from methods of diagnosing sepsis, SIRS, and the like.
- Such methods comprise the steps of: analyzing a biological sample from a subject to determine the level(s) of a purality of biomarkers for organ failure in the sample, where the plurality of biomarkers are selected from Table 1 and comparing the level(s) of the the plurality of biomarkers - respectively a composed value / score generated by subjecting the concentrations of individual biomarkers in the sample to a classification method such as affording an equation processing single concentration values - to obtain a separation between both (diseased and healthy) groups or comparing the level(s) of the the plurality of biomarkers in the sample to organ failure positive or organ failure negative reference levels of the the plurality of biomarkers in order to determine at a very early state whether the subject is developing organ failure or not, so that suitable therapeutic measures can be started.
- 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 by but not limited to mass spectrometry (MS), in particular MS- technologies such as MALDI, ESI, atmospheric pressure 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 MALDI, ESI, atmospheric pressure 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.
- concentrations of the individual markers, 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 diagnosing organ failure and/or duration/severity 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) of organ failure specific metabolites in a sample
- organ failure based on the presence of the organ failure specific metabolite.
- the present invention further provides a method of screening compounds, comprising: contacting an animal, a tissue, a cell containing a organ failure-specific metabolite with a test compound; and detecting the level of the organ failure specific metabolite.
- the method further comprises the step of comparing the level of the organ failure specific metabolite in the presence of the test compound or therapeutic intervention to the level of the organ failure specific metabolite in the absence of the organ failure specific metabolite.
- the cell is in vitro, in a non- human mammal, or ex vivo.
- the test compound is a small molecule or a nucleic acid (e.g., antisense nucleic acid, a siRNA, or a miRNA) or oxygen/xenon or any neuroprotective drug that inhibits the expression of an enzyme involved in the synthesis or breakdown of an organ failure specific metabolite.
- the method is a high throughput method.
- the present invention relates to:
- a method for predicting the likelihood of onset of an inflammation associated organ failure from a biological sample of a mammalian subject in vitro wherein a. the subject's quantitative metabolomics profile comprising a plurality of endogenous metabolites, 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 organ failure predictive target metabolites in order to predict whether the subject is likely or unlikely to develop an organ failure; and c.
- said endogenous organ failure predictive target metabolites have a molecular mass less than 1500 Da and are selected from the group consisting of: Amino acids, in particular, arginine, aspartic acid, citrulline, glutamic acid (glutamate), glutamine, leucine, isoleucine, histidine, ornithine, proline, phenylalanine, serine, tryptophane, tyrosine, valine, kynurenine; phenylthio carbamyl amino acids (PTC-amino acids), in particular, PCT-arginine, PTC- glutamine, PTC-histidine, PTC-methionine, PTC-ornithine, PTC-phenylalanine, PTC- proline, PTC-serine, PTC-tryptophane, PTC-tyrosine, PTC-valine; dimethylarginine, in particular N,N-dimethyl-L-arginine; carboxylic acids
- Ceramides with an N-acyl residue having from 2 to 30 Carbon atoms in the acyl residue and having from 0 to 5 double bonds and having from 0 to 5 hydroxy groups; carnitine; acylcarnitines having from 1 to 20 carbon atoms in the acyl residue; acylcarnitines having from 3 to 20 carbon atoms in the acyl residue and having 1 to 4 double bonds in the acyl residue; acylcarnitines having from 1 to 20 carbon atoms in the acyl residue and having from 1 to 3 OH-groups in the acyl residue; acylcarnitines having from 3 to 20 carbon atoms in the acyl residue with 1 to 4 double bonds and 1 to 3 OH- groups in the acyl residue; phospholipides, in particular lysophosphatidylcholines (monoacylphospha-tidylcholines) having from 1 to 30 carbon atoms in the acyl residue; lysophosphatidylcho
- prostaglandines namely 6-keto-prostaglandin F1 alpha, prostaglandin D2, thromboxane
- oxysterols namely 22-R-hydroxycholesterol, 24-S-hydroxycholesterol, 25- hydroxycholesterol, 27- hydroxycholesterol, 20 ⁇ - hydroxycholesterol, 22-S- hydroxycholesterol, 24,25- epoxycholesterol,3 ⁇ ,5 ⁇ ,6 ⁇ - trihydroxycholesterol, 7 ⁇ - hydroxycholesterol, 7-Ketocholesterol, 53,63- epoxycholesterol, 5 ⁇ ,6 ⁇ - epoxycholesterol, 43- hydroxycholesterol, desmosterol (vitamin D3), 7- dehydrocholesterol, cholestenone, lanosterol, 24-dehydrolanosterol; bile acids, namely cholic acid, chenodeoxycholic acid, deoxycholic acid, glycocholic acid, glycochenodeoxycholic acid, glycodeoxycholic acid, glycolithocholic acid, glycolithocholic acid sulfate, glycoursodeoxycholic acid, lithocholic acid
- inflammation associated organ failure comprises “infection associated organ failure” and/or “sepsis associated organ failure”.
- 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 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
- 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 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 and/or methods coupled to separation in particular Liquid Chromatography (LC-MS), Gas Chromatography (GC- MS), or Capillary Electrophoresis (CE-MS).
- intensity data of said metabolomics profile are normalized with a set of endogenous housekeeper metabolites by relating detected intensities of the selected endogenous organ failure predictive target metabolites to intensities of said endogenous housekeeper metabolites.
- a particularly preferred method according to the present invention is one, wherein said endogenous housekeeper metabolites are selected from the group consisting of such endogeneous metabolites which show stability in accordance with statistical stability measures being selected from the group consisting of coefficient of variation (CV) of raw intensity data, standard deviation (SD) of logarithmic intensity data, stability measure (M) of geNorm - algorithm or stability measure value (rho) of NormFinder- algorithm.
- CV coefficient of variation
- SD standard deviation
- M stability measure
- rho stability measure value
- said quantitative metabolomics profile comprises the results of measuring at least one of the parameters selected from the group consisting of:
- a panel of reference endogenous organ failure predictive target metabolites or derivatives thereof is established by:
- step a) 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 likelihood to develop an organ failure, in order to select a classifier function to map said preprocessed data to said likelihood; d) applying said trained classifier algorithms of step c) to a preprocesse
- the endogenous organ failure predictive target metabolites for easier and/or more sensitive detection are preferably detected by means of chemically modified derivatives thereof, such as phenylisothiocyanates for amino acids.
- said endogenous organ failure predictive target metabolites are selected from the group consisting of:
- phosphatidylcholines (diacylphosphatidylcholines, PC aa chain length:total number of double bonds or PC ae) in particular, PC aa C28:1 , PC aa C38:0, PC aa C42:0, PC aa C42:1 , PC ae C40:1 , PC ae C40:2, PC ae C40:6, PC ae C42:2, PC ae C42:3, PC ae C42:4, PC ae C44:5, PC ae C44:6, PC aa C36:4, PC aa C38:1 , PC aa C38:2, PC aa C38:4, PC aa C38:5, PC aa C38:6, PC aa C40:5, PC aa C40:6, PC aa C40:7, PC aa C40:8, PC ae C36:4, PC ae C36:5, PC ae C38:4,
- lysophosphatidylcholines (monoacylphosphatidylcholines, PC a chain length:total number of double bonds), in particular, PC a C18:2, PC a C20:4, PC a C20:3, PC a C26:0;
- oxycholesterols in particular, 3 ⁇ ,5 ⁇ ,6 ⁇ -trihydroxycholestan, 7-ketocholesterol, 5 ⁇ ,6 ⁇ - epoxycholesterol;
- lysophosphatidylethanolamins (monoacylphosphatidylcholins, PE a chain length:total number of double bonds), in particular, PE a C18:1 , PE a C18:2, PE a C20:4, PE a C22:5, PE a C22:6;
- phosphatidylethanolamins (diacylphosphatidylcholins, PE aa chain length:total number of double bonds), in particular, PE aa C38:0, PE aa C38:2;
- ceramids (N-chain length:total number of double bonds), in particular, N-C2:0-Cer, N- C7:0-Cer, N-C9:3-Cer, N-C17:1 -Cer, N-C22:1 -Cer, N-C25:0-Cer, N-C27:1 -Cer, N-C5:1 - Cer(2H), N-C7:1 -Cer(2H), N-C8:1 -Cer(2H), N-C1 1 :1 -Cer(2H), N-C20:0-Cer(2H), N- C21 :0-Cer(2H), N-C22:1 -Cer(2H), N-C25:1 -Cer(2H), N-C26:1 -Cer(2H), N-C24:0(OH)- Cer, N-C26:0(OH)-Cer, N-C6:0(OH)-Cer, N
- said plurality of endogenous organ failure predictive target metabolites or derivatives thereof comprises 2 to 80, in particular 2 to 60, preferably 2 to 50, preferred 2 to 30, more preferred 2 to 20, particularly preferred 2 to 10 endogenous metabolites
- 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 organ failure from a biological sample of a mammalian subject in vitro, wherein the metabolites are selected from the group consisting of : Amino acids, in particular, arginine, aspartic acid, citrulline, glutamic acid (glutamate), glutamine, leucine, isoleucine, histidine, ornithine, proline, phenylalanine, serine, tryptophane, tyrosine, valine, kynurenine; phenylthio carbamyl amino acids (PTC-amino acids), in particular, PCT-arginine, PTC- glutamine, PTC-histidine, PTC-methionine, PTC-ornithine, PTC-phenylalanine, PTC- proline, PTC-serine, PTC-tryptophane, PTC-ty
- hydroxysphingoyelines having a total number of carbon atoms in the acyl residues from 10 to 30 and 1 to 5 double bonds; prostaglandines, namely 6-keto-prostaglandin F1 alpha, prostaglandin D2, thromboxane B2; putrescine; oxysterols, namely 22-R-hydroxycholesterol, 24-S-hydroxycholesterol, 25- hydroxycholesterol, 27- hydroxycholesterol, 20 ⁇ - hydroxycholesterol, 22-S- hydroxycholesterol, 24,25- epoxycholesterol,3 ⁇ ,5 ⁇ ,6 ⁇ - trihydroxycholesterol, 7 ⁇ - hydroxycholesterol, 7-Ketocholesterol, 53,63- epoxycholesterol, 5 ⁇ ,6 ⁇ - epoxycholesterol, 43- hydroxycholesterol, desmosterol (vitamin D3), 7- dehydrocholesterol, cholestenone, lanosterol, 24-dehydrolanosterol; bile acids, namely
- Particularly preferred endogenous organ failure predictive target metabolites are selected from the group consisting of:
- Carnitin acylcarnitines (C chain length :total number of double bonds), in particular, C12-DC, C14:1 , C14:1 -OH, C14:2, C14:2-OH, C18, C6:1 ;
- sphingomyelins (SM chain length :total number of double bonds), in particular, SM C16:0, SM C17:0, SM C18:0, SM C19:0, SM C21 :1 , SM C21 :3, SM C22:2, SM C23:0, SM C23:1 , SM C23:2, SM C23:3, SM C24:0, SM C24:1 , SM C24:2, SM C24:3, SM C24:4, SM C26:4, SM C3:0, SM (OH) C22:1 , SM (OH) C22:2, SM (OH) C24:1 , SM C26:0, SM C26:1 ;
- phosphatidylcholines (diacylphosphatidylcholines, PC aa chain length:total number of double bonds or PC ae) in particular, PC aa C28:1 , PC aa C38:0, PC aa C42:0, PC aa C42:1 , PC ae C40:1 , PC ae C40:2, PC ae C40:6, PC ae C42:2, PC ae C42:3, PC ae C42:4, PC ae C44:5, PC ae C44:6, PC aa C36:4, PC aa C38:1 , PC aa C38:2, PC aa C38:4, PC aa C38:5, PC aa C38:6, PC aa C40:5, PC aa C40:6, PC aa C40:7, PC aa C40:8, PC ae C36:4, PC ae C36:5, PC ae C38:4,
- oxycholesterols in particular, 3 ⁇ ,5 ⁇ ,6 ⁇ -trihydroxycholestan, 7-ketocholesterol, 5 ⁇ ,6 ⁇ - epoxycholesterol;
- lysophosphatidylethanolamins (monoacylphosphatidylcholins, PE a chain length:total number of double bonds), in particular, PE a C18:1 , PE a C18:2, PE a C20:4, PE a C22:5, PE a C22:6;
- phosphatidylethanolamins (diacylphosphatidylcholins, PE aa chain length:total number of double bonds), in particular, PE aa C38:0, PE aa C38:2;
- ceramids (N-chain length:total number of double bonds), in particular, N-C2:0-Cer, N- C7:0-Cer, N-C9:3-Cer, N-C17:1 -Cer, N-C22:1 -Cer, N-C25:0-Cer, N-C27:1 -Cer, N-C5:1 - Cer(2H), N-C7:1 -Cer(2H), N-C8:1 -Cer(2H), N-C1 1 :1 -Cer(2H), N-C20:0-Cer(2H), N-
- the present invention includes a kit for carrying out a method for predicting the likelihood of an onset of an infection associated organ failure from a biological sample of a mammalian subject in vitro, in a biological sample, comprising: a) calibration agents for the quantitative detection of endogenous organ failure predictive target metabolites, wherein said metabolites are selected from the group consisting of: Amino acids, in particular, arginine, aspartic acid, citrulline, glutamic acid
- PTC-amino acids phenylthio carbamyl amino acids
- PCT-arginine PTC- glutamine, PTC-histidine, PTC-methionine, PTC-ornithine, PTC-phenylalanine, PTC- proline, PTC-serine, PTC-tryptophane, PTC-tyrosine, PTC-valine
- dimethylarginine in particular N,N-dimethyl-L-arginine
- carboxylic acids namely 15(S)-hydroxy-5Z,8Z,1 1 Z,13E-eicosatetraenoic acid [(5Z,8Z,1 1 Z,13E,15S)-15-Hydroxyicosa-5
- Ceramides with an N-acyl residue having from 2 to 30 Carbon atoms in the acyl residue and having from 0 to 5 double bonds and having from 0 to 5 hydroxy groups; carnitine; acylcarnitines having from 1 to 20 carbon atoms in the acyl residue; acylcarnitines having from 3 to 20 carbon atoms in the acyl residue and having 1 to 4 double bonds in the acyl residue; acylcarnitines having from 1 to 20 carbon atoms in the acyl residue and having from 1 to 3 OH-groups in the acyl residue; acylcarnitines having from 3 to 20 carbon atoms in the acyl residue with 1 to 4 double bonds and 1 to 3 OH- groups in the acyl residue; phospholipides, in particular lysophosphatidylcholines (monoacylphospha-tidylcholines) having from 1 to 30 carbon atoms in the acyl residue; lysophosphatidylcho
- prostaglandines namely 6-keto-prostaglandin F1 alpha, prostaglandin D2, thromboxane
- oxysterols namely 22-R-hydroxycholesterol, 24-S-hydroxycholesterol, 25- hydroxycholesterol, 27- hydroxycholesterol, 20 ⁇ - hydroxycholesterol, 22-S- hydroxycholesterol, 24,25- epoxycholesterol,3 ⁇ ,5 ⁇ ,6 ⁇ - trihydroxycholesterol, 7 ⁇ - hydroxycholesterol, 7-Ketocholesterol, 53,63- epoxycholesterol, 5 ⁇ ,6 ⁇ - epoxycholesterol, 43- hydroxycholesterol, desmosterol (vitamin D3), 7- dehydrocholesterol, cholestenone, lanosterol, 24-dehydrolanosterol; bile acids, namely cholic acid, chenodeoxycholic acid, deoxycholic acid, glycocholic acid, glycochenodeoxycholic acid, glycodeoxycholic acid, glycolithocholic acid, glycolithocholic acid sulfate, glycoursodeoxycholic acid, lithocholic acid
- step c) classification software for generating the quantitative metabolomics profiles achieved with said calibration agents of step a) and classifying the results based on the processed data of step b).
- 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 neighbor classifiers (K-NN), fuzzy classifiers, bagging, boosting, and na ⁇ ve 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 neural
- Fig.1 is a Venn diagram showing the agreement between adjusted p value
- Fig. 2 is a graph showing classifier accuracy for support vector machines (SVM) with linear kernel, diagonal linear discriminant analysis (DLDA) and k nearest neighbors (KNN) with k equal to one where the features are selected using a ranker which ranks the metabolites combining adjusted p value, fold change and AUC;
- SVM support vector machines
- DLDA diagonal linear discriminant analysis
- KNN k nearest neighbors
- Fig. 3 is a graph showing classifier accuracy for support vector machines (SVM) with linear kernel, diagonal linear discriminant analysis (DLDA) and k nearest neighbors (KNN) with k equal to one where the features are selected by a so-called wrapper using boosted regression trees;
- SVM support vector machines
- DLDA diagonal linear discriminant analysis
- KNN k nearest neighbors
- Fig. 4 is a Venn diagram showing the agreement between adjusted p value
- Sese sepsis refers to sepsis associated with organ dysfunction, hypoperfusion abnormalities, or sepsis-induced hypotension. Hypoperfusion abnormalities include, but are not limited to, lactic acidosis, oliguria, or an acute alteration in mental status.
- Stress refers to sepsis-induced hypotension that is not responsive to adequate intravenous fluid challenge and with manifestations of peripheral hypoperfusion.
- a "converter patient” refers to a SIRS-positive patient who progresses to clinical suspicion of sepsis during the period the patient is monitored, typically during an ICU stay.
- non- converter patient refers to a SIRS-positive patient who does not progress to clinical suspicion of sepsis during the period the patient is monitored, typically during an ICU stay.
- a patient with OF has a clinical presentation that is classified as OF, as defined above, but is not clinically deemed to have OF.
- Individuals who are at risk of developing OF include patients in an ICU and those who have otherwise suffered from a physiological trauma, such as a burn or other insult.
- organ failure includes all stages of OF including, but not limited to, the onset of OF and multi organ failure (MOD), e.g. associated with the end stages of sepsis.
- Sepsis refers to a SIRS-positive condition that is associated with a confirmed infectious process. Clinical suspicion of sepsis arises from the suspicion that the SIRS- positive condition of a SIRS patient is a result of an infectious process.
- the "onset of OF” refers to an early stage of OF, i.e., prior to a stage when the clinical manifestations are sufficient to support a clinical suspicion of OF. Because the methods of the present invention are used to detect OF prior to a time that OF would be suspected using conventional techniques, the patient's disease status at early OF can only be confirmed retrospectively, when the manifestation of OF is more clinically obvious. The exact mechanism by which a patient acquires OF 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 OF. Regardless of how OF arises, the methods of the present invention allow for determining the status of a patient having, or suspected of having, OF, as classified by previously used criteria.
- organ failure specific metabolite refers to a metabolite that is differentially present or differentially concentrated in septic organisms compared to non- septic organisms.
- organ failure specific metabolites are present in septic tissues but not in non- in septic tissues.
- organ failure-specific metabolites are absent in septic tissues but present in non-septic cells, tissues, body liquids. In still further embodiments, organ failure specific metabolites are present at different levels (e.g., higher or lower) in septic tissue/cells as compared to non-septic cells.
- an organ failure 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%, by at least 40%
- An organ failure-specific 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 organ failure-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, or cerebral spinal fluid (CSF).
- CSF cerebral spinal fluid
- 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 "organ failure-positive reference level" of a metabolite means a level of a metabolite that is indicative of a positive diagnosis of organ failure in a subject
- an "organ failure-negative reference level" of a metabolite means a level of a metabolite that is indicative of a negative diagnosis of organ failure 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 that involves fragmenting a target molecule, then analyzing the fragments, based on 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 ration 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.hmdb.ca/) / and other databases and literature. This includes any substance produced by metabolism or by a metabolic process and any substance involved in metabolism.
- Methodomics 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.
- 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 buildup 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.
- clinical failure refers to a negative outcome following organ failure treatment.
- 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 organ failure or related to organ failure 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 Organ failure and its duration/severity as well of the effect of therapeutic interventions.
- the present invention provides metabolites that are differentially present in Organ failure.
- Experiments conducted during the course of development of embodiments of the present invention identified a series of metabolites as being differentially present in
- Tables 2 and 3 provide additional metabolites present in plasma serum or other body liquids.
- the disclosed markers find use as diagnostic and therapeutic targets.
- the present invention provides methods and compositions for diagnosing organ failure, including but not limited to, characterizing risk of organ failure, stage of organ failure, duration and severity etc. based on the presence of organ failure 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 organ failure comprises (1 ) detecting the presence or absence or a differential level of a plurality of organ failure specific metabolites selected from tables 1 *** and b) diagnosing organ failure based on the presence, absence or differential level of the organ failure 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 organ failure.
- 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 organ failure specific metabolites or cells that contain organ failure specific metabolites.
- preliminary processing designed to isolate or enrich the sample for organ failure specific metabolites or cells that contain organ failure specific metabolites.
- a variety of techniques known to those of ordinary skill in the art may be used for this purpose, including but not limited: centrifugation; immunocapture; and cell lysis.
- 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), 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), CAT scans, ultra sound, MS-based tissue imaging or X-ray detection methods (e.g., energy dispersive x-ray fluorescence detection).
- any suitable method may be used to analyze the biological sample in order to determine the presence, absence or level(s) of the the plurality of metabolites in the sample. 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. Further, the level(s) of the the plurality of 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.
- chromatography e.g., HPLC, gas chromatography, liquid chromatography
- mass spectrometry e.g., MS, MS-MS
- ELISA enzyme-linked immunosorbent assay
- antibody linkage other immunochemical techniques, biochemical or enzymatic reactions or as
- the levels of the plurality 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 2, 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 organ failure and aiding in the diagnosis of organ failure, and may allow better differentiation or characterization of organ failure from other disorders or other organ failure that may have similar or overlapping metabolites to organ failure (as compared to a subject not having organ failure). For example, ratios of the levels of certain metabolites in biological samples may allow greater sensitivity and specificity in diagnosing organ failure and aiding in the diagnosis of organ failure and allow better differentiation or characterization of organ failure from other organ failure or other disorders of the that may have similar or overlapping metabolites to organ failure (as compared to a subject not having organ failure).
- 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 an organ failure 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 is produced (i.e., metabolic profile), specific for the diagnostic or prognostic information desired for the subject.
- 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 organ failure 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 plurality of metabolites in the sample may be compared to organ failure metabolite- reference levels, such as -organ failure-positive and/or organ failure-negative reference levels to aid in diagnosing or to diagnose whether the subject has organ failure.
- organ failure metabolite- reference levels such as -organ failure-positive and/or organ failure-negative reference levels to aid in diagnosing or to diagnose whether the subject has organ failure.
- Levels of the plurality of metabolites in a sample corresponding to the organ failure-positive reference levels are indicative of a diagnosis of organ failure in the subject.
- Levels of the plurality of metabolites in a sample corresponding to the organ failure-negative reference levels are indicative of a diagnosis of no organ failure in the subject.
- levels of the plurality of metabolites that are differentially present (especially at a level that is statistically significant) in the sample as compared to organ failure-negative reference levels are indicative of a diagnosis of organ failure in the subject.
- Levels of the plurality of metabolites that are differentially present (especially at a level that is statistically significant) in the sample as compared to organ failure-positive reference levels are indicative of a diagnosis of no organ failure in the subject.
- the level(s) of the plurality of metabolites may be compared to organ failure-positive and/or organ failure-negative reference levels using various techniques, including a simple comparison (e.g., a manual comparison) of the level(s) of the plurality of metabolites in the biological sample to organ failure-positive and/or organ failure- negative reference levels.
- the level(s) of the plurality of metabolites in the biological sample may also be compared to organ failure-positive and/or organ failure-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 organ failure 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 of the markers of the present invention depicted in tables 1 to 3, alone or in combination with additional organ failure 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, or 20 or more, 30 or more, 40 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 2 and 3 for prediction/diagnosis of organ failure and its duration/severity where said mammalian subject is a human being, said biological sample blood and/or blood cells.
- additional organ failure 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.
- the present invention provides therapeutic methods (e.g., that target the organ failure specific metabolites described herein).
- the therapeutic methods target enzymes or pathway components of the organ failure specific metabolites described herein.
- the present invention provides compounds that target the organ failure specific metabolites of the present invention.
- the compounds may decrease the level of organ failure specific metabolite by, for example, interfering with synthesis of the organ failure specific metabolite (e.g., by blocking transcription or translation of an enzyme involved in the synthesis of a metabolite, by inactivating an enzyme involved in the synthesis of a metabolite (e.g., by post translational modification or binding to an irreversible inhibitor), or by otherwise inhibiting the activity of an enzyme involved in the synthesis of a metabolite) or a precursor or metabolite thereof, by binding to and inhibiting the function of the organ failure specific metabolite, by binding to the target of the organ failure specific metabolite (e.g., competitive or non competitive inhibitor), or by increasing the rate of break down or clearance of the metabolite.
- interfering with synthesis of the organ failure specific metabolite e.g., by blocking transcription or translation of an enzyme involved in the synthesis of a
- the compounds may increase the level of organ failure specific metabolite by, for example, inhibiting the break down or clearance of the organ failure specific metabolite (e.g., by inhibiting an enzyme involved in the breakdown of the metabolite), by increasing the level of a precursor of the organ failure specific metabolite, or by increasing the affinity of the metabolite for its target.
- Dosing is dependent on severity and responsiveness of the disease state to be treated, with the course of treatment lasting from several days to several months, or until a cure is effected or a diminution of the disease state is achieved.
- Optimal dosing schedules can be calculated from measurements of drug accumulation in the body of the patient. The administering physician can easily determine optimum dosages, dosing methodologies and repetition rates.
- the present invention provides drug screening assays (e.g., to screen for anti - organ failure drugs).
- the screening methods of the present invention utilize organ failure specific metabolites described herein.
- test compounds are small molecules, nucleic acids, or antibodies.
- test compounds target organ failure specific metabolites directly. In other embodiments, they target enzymes involved in metabolic pathways of organ failure specific metabolites.
- Plasma samples were prepared by standard procedures and stored at (-70 0 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 formation of prostaglandins caused by autooxidation .
- BHT butylated hydroxytoluene
- Liver 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 desintegration removed by 5 min centrifugation at 10 000g.
- VLM nitrogen evaporator
- Mass spectrometric analysis was performed on an API4000 QTrap® tandem mass spectrometry instrument (Applied Biosystems/MDS Analytical Technologies) equipped with an electro-spray ionization (ESI)-source using the analysis acquisition method as provided in the Absolute/DQ kit.
- the standard FIA-MS/MS method was applied for all measurements with two subsequent 20 ⁇ l_ injections (one for positive and one for negative mode analysis).
- Multiple reaction monitoring (MRM) detection was used for quantification applying the spectra parsing algorithm integrated into the MetlQ software (Biocrates Life Sciences AG). Concentration values for 148 metabolites (all analytes determined with the metabolomics kit besides of the amino acids, which were determined by a different method) obtained by internal calibration were exported for comprehensive statistical analysis.
- Amino acids and biogenic amines were quantitatively analyzed by reversed phase LC- MS/MS to obtain chromatographic separation of isobaric (same MRM ion pairs) metabolites for individual quantitation performed by external calibration and by use of internal standards.
- 10 ⁇ L sample volume (plasma, brain homogenate) is required for the analysis using the following sample preparation procedure. Samples were added on filter spots placed in a 96- solvinert well plate (internal standards were placed and dried down under nitrogen before), fixed above a 96 deep well plate (capture plate). 20 ⁇ L of 5% phenyl-isothiocyanate derivatization reagent was added.
- the derivatized samples were extracted after incubation by aqueous methanol into the capture plate.
- Sample extracts were analyzed by LC-ESI-MS/MS in positive MRM detection mode with an API4000 QTrap® tandem mass spectrometry instrument (Applied Biosystems/MDS Analytical Technologies).
- the analyzed individual metabolite concentrations (Analyst 1.4.2 software, Applied Biosystems) were exported for comprehensive statistical analysis.
- Bile acids (LC-MS/MS) A highly selective reversed phase LC-MS/MS analysis method in negative MRM detection mode was applied to determine the concentration of bile acids in plasma samples. Samples were extracted via dried filter spot technique in 96 well plate format, which is well suitable for high throughput analysis. For highly accurate quantitation internal standards and external calibration were applied. In brief, internal standards and 20 ⁇ l_ sample volume placed onto filter spots were extracted and simultaneously protein precipitated with aqueous methanol. These sample extracts were measured by LC-ESI- MS/MS with an API4000 QTrap® tandem mass spectrometry instrument (Applied Biosystems/MDS Analytical Technologies). Data of bile acids were quantified with Analyst 1.4.2 software (Applied Biosystems) and finally exported for comprehensive statistical analysis.
- Prostanoids - a term summarizing prostaglandins (PG), thromboxanes (TX) and prostacylines - and oxidised fatty acid metabolites were analyzed in plasma extracts by
- Oxysterols are determined after extraction and saponification by HPLC-Tandem mass spectrometer (HPLC-API-MS/MS) in positive detection mode using Multiple Reaction Mode (MRM).
- HPLC-Tandem mass spectrometer HPLC-API-MS/MS
- MRM Multiple Reaction Mode
- Chromatographic separation and detection is performed by using a Zorbax Eclipse XDB C18, 150 x 2.0 mm, 3.5 ⁇ m HPLC-Column at a flow rate of 0.3 mL/min followed by electrospray ionization on the API4000/QTRAP4000 tandem mass spectrometer.
- the Analyst Quantitation software from Applied Bioystems was used.
- hdyrophilic interaction liquid chromatography HILIC-ESI-MS/MS method in highly selective negative MRM detection mode was used.
- the MRM detection was performed using an API4000 QTrap® tandem mass spectrometry instrument (Applied Biosystems/MDS Analytical Technologies). 20 ⁇ l_ sample volume (plasma, brain homogenate) was protein precipitated and extracted simultaneously with aqueous methanol in a 96 well plate format. Internal standards (ratio external to internal standard) and external calibration were used for highly accurate quantitation. Data were quantified with Analyst 1.4.2 software (Applied Biosystems) and finally exported for statistical analysis.
- ADMA B.Am total DMA B.Am.
- Table 1 summarizes analyzed metabolites and respective abbreviations; Glycero-phospholipids are further differentiated with respect to the presence of ester (a) and ether (e) bonds in the glycerol moiety, where two letters (aa, ea, or ee) denote that the first and the second position of the glycerol scaffold are bound to a fatty acid residue, whereas a single letter (a or e) indicates a bond with only one fatty acid residue; e.g. PC_ea_33:1 denotes a plasmalogen phosphatidylcholine with 33 carbons in the two fatty acid side chains and a single double bond in one of them.
- ester (a) and ether (e) bonds in the glycerol moiety where two letters (aa, ea, or ee) denote that the first and the second position of the glycerol scaffold are bound to a fatty acid residue, whereas a single letter (a or e) indicates
- missing metabolite concentrations are replaced by the average value of the 6 closest samples to the one where the measurement is missing (impute: Imputation for microarray data, Hastie T., Tibshirani R., Narasimhan B. and Chu G., R package version 1.14.0).
- Imputation for microarray data Hastie T., Tibshirani R., Narasimhan B. and Chu G., R package version 1.14.0.
- FC fold change
- 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 between measurements from septic patients samples and samples from patient developing organ failure. 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 and plot ROC curves. From the three univariate statistics (adjusted p value (q value), fold change and AUC), features are ranked according to a 2 step strategy: 1 ) the 3 measures are first used as input to the multiple objective algorithm described by Chen et al.
- gbm function from gbm R package (gbm: Generalized Boosted Regression Models, Ridgeway G., 2007, R package version 1.6-3) was used to perform tree based gradient boosting specifying a gaussian loss function, a shrinkage parameter of 0.05 and allowing trees with up to 3 trees splits.
- feature relevance score is presented as the average rank calculated by leaving one set out on the training set.
- Performance of single markers as well as of combinations of markers is assessed by three classification algorithms that rely on different mechanisms to ensure that the outcome is not dependent on the modelling technique: support vector machine (SVM) with linear kernel using the R function svm in package e1071 (e1071 : Misc Functions of the Department of Statistics (e1071 ), Dimitriadou E., Hornik k., Leisch F., Meyer D.
- SVM support vector machine
- Table 2 depicts the ranks of the individual analytes and metabolites in terms of discriminatory power for detecting the onset of infection associated organ failure. Ranking was performed using a ranker combining adjusted p values, fold changes and AUCs as well as using a multivariate wrapper which is based on boosted regression trees as described above. For additional information see Fig 1-3.
- the effective dosis of the extract (to induce either sepsis or organ failure) has to be pre- determined 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 24 h after intraperitoneal injection of the extract.
- missing metabolite concentrations are replaced by the average value of the 6 closest samples to the one where the measurement is missing (impute: Imputation for microarray data, Hastie T., Tibshirani R., Narasimhan B. and Chu G., R package version 1.14.0).
- Imputation for microarray data Hastie T., Tibshirani R., Narasimhan B. and Chu G., R package version 1.14.0.
- FC fold change
- 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 between measurements from septic patients samples and samples from patient developing organ failure. 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 and plot ROC curves. From the three univariate statistics (adjusted p value (q value), fold change and AUC), features are ranked according to a 2 step strategy: 1 ) the 3 measures are first used as input to the multiple objective algorithm described by Chen et al.
- Desmosterol 30 5.26E-002 -68,61 0,80
- Table 3 depicts the ranks of the individual analytes and metabolites in terms of discriminatory power for detecting the onset of infection associated organ failure. Ranking was performed using a univariate ranker which combines adjusted p values, fold changes and AUCs. For additional information see Fig 4.
- Table 4 shows the endogenous organ failure predictive targe metabolites as used in the present invention with their abbreviations and chemical names
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AU2010277664A AU2010277664A1 (en) | 2009-07-31 | 2010-07-23 | Method for predicting the likelihood of an onset of an inflammation associated organ failure |
CA2767763A CA2767763A1 (en) | 2009-07-31 | 2010-07-23 | Method for predicting the likelihood of an onset of an inflammation associated organ failure |
JP2012522121A JP2013501215A (en) | 2009-07-31 | 2010-07-23 | A method for predicting the onset probability of inflammation-related organ failure |
CN2010800341097A CN102472756A (en) | 2009-07-31 | 2010-07-23 | Method for predicting the likelihood of an onset of an inflammation associated organ failure |
EP10737041A EP2460014A1 (en) | 2009-07-31 | 2010-07-23 | Method for predicting the likelihood of an onset of an inflammation associated organ failure |
US13/387,572 US20120202240A1 (en) | 2009-07-31 | 2010-07-23 | Method for Predicting the likelihood of an Onset of an Inflammation Associated Organ Failure |
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