DESCRIPTION

BIOMARKERS OF REDUCED INSULIN ACTION

BACKGROUND OF THE INVENTION

This application claims benefit of priority to U.S. Provisonal Application Serial No. 61/406,895, filed October 26, 2010, the entire contents of which are hereby incorporated by reference.

Funding for the work described herein was provided by the federal government under grant number ROl DK41973 and R21/33 DK70179 awarded by National Institute of Health.

1. Field of the Invention

This document relates to methods and materials involved in assessing metabolic disorders resulting from reduced insulin action (e.g., insulin resistance or insulin deficiency) in a mammal (e.g., human). For example, this document provides methods and materials for determining a mammal's risk of developing insulin resistance. In some aspects, the document relates to methods and kits useful for diagnosing, classifying, profiling, and treating insulin resistance. For example, this document provides methods and materials for assessing treatment efficacy on insulin action in metabolic disorders resulting from reduced insulin action (e.g., diabetes).

2. Description of the Related Art

Diabetes mellitus, or simply, diabetes, is a group of diseases characterized by high blood glucose levels that result from defects in the body's ability to produce and/or use insulin. The main types include Type 1 diabetes (TD1), Type 2 diabetes (TD2), and gestational diabetes. TD1 is a form of diabetes mellitus that results from autoimmune destruction of insulin-producing beta cells of the pancreas and is characterized by absence of insulin if not provided exogenously. TD2 is characterized by increased hepatic glucose output, increased peripheral resistance to insulin action, and impaired insulin secretion. Gestational diabetes is a type of diabetes that is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. Insulin is the principal hormone that regulates uptake of glucose from the blood into most cells and its disposal either by oxidation or by storage as glycogen. Therefore deficiency of insulin or the insensitivity of its action plays a central role in all forms of diabetes mellitus. Absolute insulin deficiency in TID causes profound alterations in carbohydrate, lipid and protein metabolism (Tessari et al, 1986; Rizza et al., 2001). Insulin plays a key regulatory role in the transcription (Melloul et al., 2002; Sreekumar et al., 2002), translation (Kimball and Jefferson, 1988) and post-translational modification of proteins (Jaleel et al., 2010; Kahn, 1995). Metabolites are the downstream end product of genome, transcriptome and proteome variability of a biological system (Bain et al., 2009). Therefore, the metabolite fingerprint should give a direct specific measure of an altered physiological phenomenon (Wang et al., 2008; Gehlenborg et al, 2010; Connor et al, 2010).

Animal and human studies have shown the effects of the alterations in glucose tolerance and insulin sensitivity on plasma and urine metabolites (Zhang et al, 2009; Zhang et al, 2008; Wang et al, 2011). NMR based non-targeted metabolomic profiling of human serum failed to distinguish between prediabetic individuals with impaired glucose tolerance (IGT) and those with normal glucose tolerance (NGT) (Zhang et al, 2009; Zhang et al, 2008). In contrast, an ultra performance liquid chromatography quadruple time-of-flight mass spectrometry (UPLC- qTOF-MS) based comprehensive metabolomic profiling approach was found to discriminate between IGT and NGT (Zhao et al, 2010). These emerging technologies enabled researchers to identify biomarkers (Wang et al, 2011) to predict the risk for onset of diabetes that will help to develop strategies to prevent this disease and its complications.

Using a model of insulin deficiency in TID, alterations in specific metabolic pathways due to insulin deficiency have been reported (Zhang et al, 2009; Nair et al, 1984; Nair et al, 1995; Felig et al, 1976; Polonsky and Rubenstein, 1984). Although systemic insulin treatment normalizes glucose, it remains unclear whether other metabolic abnormalities are also corrected. It is well known that systemic insulin treatment not only causes relative hyperinsulinemia, but also alters the normal hepatic and peripheral insulin ratio of 2: 1 that is normally present in non- diabetic individuals (Polonsky and Rubenstein, 1984). It would therefore be very important to determine whether systemic insulin treatment normalizes all metabolic alterations in TID and how. SUMMARY OF THE INVENTION

This document relates to methods and materials involved in assessing metabolic disorders resulting from reduced insulin action (e.g., insulin resistance or insulin deficiency) in a mammal (e.g., human). For example, this document provides methods and materials for determining a mammal's risk of developing insulin resistance. In some aspects, the document relates to methods and kits useful for diagnosing, classifying, profiling, and treating insulin resistance. For example, this document provides methods and materials for assessing treatment efficacy on insulin action in metabolic disorders resulting from reduced insulin action (e.g. , diabetes)

As described herein, blood samples were collected from diabetic patients on insulin treatment and following insulin deprivation, and compared to matched non-diabetic controls. Blood samples were analyzed and metabolic profiling was performed to generate a list of metabolites altered by insulin deprivation. The results provided herein demonstrate that metabolites act as markers of insulin resistance. This can allow physicians to develop a clinical assay to determine insulin resistance or the likelihood of developing insulin resistance, as well as to monitor the efficacy of a particular treatment for this condition.

In general, one aspect of this document features a method of assessing insulin action in a mammal, the method comprising, (a) determining whether or not a sample obtained from the mammal has an insulin action metabolic signature, and (b) classifying the mammal as having insulin resistance if the sample has reduced insulin action metabolic signature and classifying the mammal as not having insulin resistance if the sample does not contain an insulin action metabolic signature. The mammal can be a human. The sample can be a body fluid. The body fluid can be blood.

In one aspect, this document provides methods for diagnosing or determining likelihood

(or risk) of developing insulin resistance in a mammal. In another aspect, this document features a method of monitoring the efficacy of insulin resistance treatment in a mammal, the method comprising, (a) determining whether or not a first sample obtained from the mammal has an insulin action metabolic signature, (b) treating the mammal for insulin resistance if the first sample has an insulin action metabolic signature, (c) analyzing a second sample from the mammal for an insulin resistance metabolic signature at a second time point after treatment, and (d) comparing the insulin action metabolic signature from the first sample with the second sample to assess the efficacy of the treatment. The mammal can be a human. The sample can be a body fluid. The body fluid can be blood. The treatment can comprise administering a therapeutic agent to the subject. The therapeutic agent can be an insulin sensitizer. The insulin sensitizer can be a thiazolidinedione. The insulin sensitizer can be metformin. The treatment can comprise a lifestyle modification of the subject. The lifestyle modification can include a modification of the nutrition, diet, or exercise routine of the subject.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting. Other features and advantages of the invention will be apparent from the following detailed description, and from the claims.

BRIEF DESCRIPTION OF THE FIGURES

The following drawings form part of the present specification and are included to further demonstrate certain aspects of the present invention. The invention may be better understood by reference to one or more of these drawings in combination with the detailed description of specific embodiments presented herein.

FIG. 1. Heatmap analysis of plasma metabolites in type 1 diabetes (TIP) during insulin deficiency (I-) and insulin treatment (1+) and comparison with non-diabetes (ND). Metabolite perturbations in plasma were calculated based on the median for each metabolite level of three independent biological replicates of plasma samples from each study participant. Each row represents a metabolite and each column depicts a subject. The study groups are color coded; insulin deprived (I-) TID is denoted in blue, insulin treated (1+) TID in red and non-diabetic (ND) groups in maroon. The fold change in metabolite levels is also color coded: red pixels means up regulation, down regulation in blue; yellow means no significant change. Metabolites such as acetate, lactate, acetoacetate, hydroxybutyrate, gluconate, hydroxy adipate, carnitines, glucosamine, taurocholate including AA (e.g. leucine, isoleucine, valine, N-methyl histidine, keto-glutarate, glutamate, alanine, phenylalanine) were all found to be elevated in IT ID (Table S4). A consistent decrease was observed in other metabolites e.g. hydroxypyridine, niacotinamide, hydroxyl nicotinic acid, adipate, methylthioribose, uridine, xanthine, hypoxanthine, methylguanosine, N-acetyl tryptophan, pipecolate, homoserine, aldosterone, arachidonyl lysolecithin, phosphoethanolamine etc.

FIGS. 2A-B. (FIG. 2A) The effect of differential regulation of metabolites on the canonical pathways during insulin deficiency (I-) in TIP in comparison with insulin treated (1+) TIP (orange bar) and non-diabetes (ND) (blue bar). The significance of the pathways was evaluated using p-values and false discovery rate (FDR) < 0.05. (FIG. 2B) Altered canonical pathways following insulin treatment in TIP in comparison to ND. The metabolic pathways which were observed exclusively after systemic insulin treatment are marked with asterisks (*). The significance of the pathways was evaluated using p-values and false discovery rate (FPR) < 0.05.

FIGS. 3A-B. Comparison of plasma metabolome with transcriptome of (FIG. 3A) insulin deprived (I-) vs non-diabetes (NP) and (FIG. 3B) insulin treated (1+) vs non-diabetes (NP). The coclustering between the metabolomic changes and the transcripts of the corresponding muscle genes showed similar directional changes on the canonical pathways although the statistical significance were different. The microarray/ transcriptome data and the metabolome data are marked as orange bar and blue bar respectively. The pathways with asterisks (*) were used to build metabolic networks as shown in FIGS. 4A-B.

FIGS. 4A-B . Integration of the metabolomics with transcriptomics data and their superimposition to build metabolic networks. (FIG. 4A) Metabolic network of peroxisome proliferative activated receptor (PPAR) transcription pathway which is connected to other metabolic processes such as lipid homeostasis, glucose, fatty acid metabolism, inflammatory response. (FIG. 4B) Network model of downstream of insulin signaling pathways. The metabolites and the gene names shown in red are up regulated and the same shown in blue are down regulated during insulin deficiency. The legends used in the above figures are as the following: generic receptors; generic binding protein; G-protein alfa; generic enzyme; receptor ligand; transporters; transcription factor; reaction; physiological reaction processes; metabolites; predicted metabolites; complex bunit; positive regulatory effect; negative regulatory effect on biological processes. The abbreviations mentioned in metabolic signaling network are depicted as B-binding, C-cleavage, IE- influence on expression, +P- phosphorylation, -P- dephosphorylation, T- transformation, TR- transcription regulation, Z-catalysis. DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENTS

As described herein, this document relates to methods and materials involved in assessing reduced insulin action (e.g., insulin resistance or insulin deficiency) in a mammal. For example, this document provides methods and materials for determining a mammal's risk of developing insulin resistance. A mammal can be any type of mammal including, without limitation, a mouse, rat, dog, cat, horse, sheep, goat, cow, pig, monkey, or human.

A. Disorders

As used herein, metabolic disorders resulting from reduced insulin action refers broadly to any disorder, disease, or syndrome characterized by a reduction in the regulation of glucose homeostasis (e.g., hyperglycemia). Typically a metabolic disorder resulting from reduced insulin action is associated with abnormal insulin levels (either low or high levels), insulin activity, and/or sensitivity to insulin (e.g., insulin resistance). As used herein, diabetes (also referred to as diabetes mellitus), refers to any one of a number of exemplary classes (or types) of glucose- related metabolic disorders. Diabetes includes, but is not limited to the following classes (or types): type I diabetes mellitus (TD1), type II diabetes mellitus (TD2), gestational diabetes, secondary diabetes and other specific types of diabetes. Metabolic disorders resulting from reduced insulin action also include prediabetic conditions, such as those associated with impaired fasting glycemia and impaired glucose tolerance. B. Metabolic Signature

This document relates to the discovery of a plurality of biomarkers that are useful for characterizing a metabolic disorder resulting from reduced insulin action such an insulin resistant state or an insulin deficient sate. As described herein, expression of one or more (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, or 104) of the biomarkers or metabolites listed in Table III in a sample can be assessed to determine whether or not a mammal will proceed to develop a metabolic disorder resulting from reduced insulin action. In some cases, a mammal can be classified as having insulin resistance based on the presence in a sample of an insulin action metabolic signature. For the purpose of this document, the term "insulin action metabolic signature" as used herein refers to a set of occurrences or levels of a plurality of (e.g., two or more, three or more, four or more) of metabolites (biomarkers) listed in Table III or IV expressed in a manner (e.g. , over- or under-expressed) indicative of a sample obtained from a mammal who has developed insulin resistance. Any combination of the biomarkers or metabolites listed in Table III or IV can be used.

In some cases, the combination may include at least 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150 or more biomarkers or metabolites selected from Table III or IV, and can be assessed to determine whether they are up-regulated in a sample from a mammal. For example, metabolites such as Symmetric dimethylarginine, hydroxychloroquine, lactaldehyde, hexadecanedioic acid mono-L-carnitine ester, 2- oxoisocaproic acid, 5-aminopentanoic acid, isobutyryl carnitine, 3-methyladipic acid, 20a- dihydroprednisolone, 2-methoxyestrone 3-glucuronide, and 2-hydroxy-4-methylvaleric acid are up-regulated in samples from mammals who have reduced insulin action or who have a greater risk of developing insulin resistance as compared to those mammals who do not have insulin resistance and are not at risk for developing insulin resistance.

In some cases, the combination may include at least 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150 or more biomarkers or metabolites selected from Table III or IV, and can be assessed to determine whether they are down-regulated in a sample from a mammal. For example, metabolites such as pyroglutamic acid, glycerophosphoethanolamine, dihydrolevobunolol glucuronide, myristoyl L-a- lysophosphatidylcholine, digoxigenin monodigitoxoside, benzene, tyramine, cctanol, desethyletomidate and 2,3-dihydroxy-3-methylvaleric acid are down-regulated in samples from mammals who have reduced insulin action or who have a greater risk of developing insulin resistance as compared to those mammals who do not have insulin resistance and are not at risk for developing insulin resistance. In some cases, the combination can comprise biomarkers or metabolites selected from different metabolic pathways. For example, the combination of biomarkers or metabolites can comprise at least one metabolite or biomarker selected from each of the canonical pathways listed in Table II and the putative pathways of Table III.

C. Samples

A sample can be any biological specimen (e.g., a blood sample) useful for characterizing the metabolic disorders resulting from reduced insulin action (e.g., insulin resistance). Typically, a sample contains one or more metabolites. The sample can be a body fluid. Examples of body fluids can include blood, serum, plasma, or urine.

D. Analysis

In one aspect, the invention provides methods for diagnosing or determining likelihood (or risk) of developing insulin resistance in a mammal. The methods include determining levels or occurrences of a plurality of biomarkers in a sample obtained from the mammal, wherein the plurality of biomarkers are selected from Table III or IV, and performing a comparison between the levels or occurrences of the plurality of biomarkers in the sample and an insulin resistance metabolic signature, wherein the comparison is indicative of whether or not the subject has an increased likelihood (or risk) of developing insulin resistance.

The levels of the metabolites for a subject can be obtained by any art recognized method.

Typically, the level is determined by measuring the level of the metabolite in a body fluid (clinical sample), e.g., blood, plasma, or urine. The level can be determined by any method known in the art, e.g., immunoassays, enzymatic assays, spectrophotometry, liquid chromatography, gas chromatography, mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), LC-MS/MS, tandem MS); high pressure liquid chromatography (HPLC), HPLC-MS, Ultra Performance Liquid Chromatography (UPLC) and nuclear magnetic resonance spectroscopy or other known techniques for determining the presence and/or quantity of a metabolite. Conventional methods include sending a clinical sample(s) to a commercial laboratory for measurement or the use of commercially available assay kits.

The insulin action metabolic signature can be determined for many reasons. In one aspect of this document, the insulin action metabolic signature can be used in combination with current testing methods to determine individuals with reduced insulin action. Currently, no sensitive measures are available to identify subjects with reduced insulin action. Blood glucose levels can be normal in individuals with severe insulin resistance if the beta cells of their pancreas produce sufficient insulin to keep the glucose levels normal. These individuals have abnormally high levels of insulin and are at greater risk of developing metabolic disorders resulting from reduced insulin action. For example, determining the insulin action metabolic signature in this subset of individuals in combination with blood glucose testing can provide a better measure of the risk of developing metabolic disorders resulting from reduced insulin action.

In one aspect of this document, the insulin action metabolic signature can be determined to select an appropriate treatment for a mammal. In one aspect of the document, the methods further include selecting a treatment (i.e., a treatment for diabetes) for the subject based on the comparison of the levels of the metabolic biomarkers with the insulin resistance metabolic signature. In some cases, the methods further include administering the selected treatment to the subject. A care giver, e.g., a physician, will readily be able to select an appropriate treatment for the subject. In some cases, the treatment is administering to the subject an effective amount of at least one anti-diabetes compound, and/or instructing the subject to adopt at least one lifestyle change. Treatments for metabolic disorders resulting from reduced insulin action can include administration of metformin and or thiazolidinediones (e.g., pioglitazone). Treatment can also include low calorie diet and/or aerobic exercise.

Blood glucose levels can fluctuate during treatment if dosage or appropriate treatment has not been optimized. These fluctuations can result in periods of abnormal levels of glucose or insulin in the blood of a mammal. In some cases, determining the insulin action metabolic signature can be used to monitoring treatment response (e.g. , response to a particular drug or therapy regimen) and predicting phenotype (e.g. , the likelihood of developing diabetes or its complications). In some cases, the insulin resistance metabolic signature can be used to optimize the dosages of a particular drug given to the mammal. In addition, the insulin action metabolic signature can be used to determine treatment efficacy. Treatment efficacy is based on the normalization of the metabolites measured in the insulin action metabolic signature as compared to the insulin action metabolic signature of a "control" individual. A control individual is an individual with no deficiencies in insulin action. Thus, the applications of the invention are numerous and are not limited to the specific examples described herein. There are a variety of methods that can be used to assess for metabolites. One such approach is with the use of antibodies. As used herein, the term "antibody" is intended to refer broadly to any immunologic binding agent such as IgG, IgM, IgA, IgD and IgE. Generally, IgG and/or IgM are preferred because they are the most common antibodies in the physiological situation and because they are most easily made in a laboratory setting. The term "antibody" also refers to any antibody-like molecule that has an antigen binding region, and includes antibody fragments such as Fab', Fab, F(ab') ₂ , single domain antibodies (DABs), Fv, scFv (single chain Fv), and the like. The techniques for preparing and using various antibody-based constructs and fragments are well known in the art. Means for preparing and characterizing antibodies, both polyclonal and monoclonal, are also well known in the art (See, e.g., Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, 1988; incorporated herein by reference). In particular, antibodies to calcyclin, calpactin I light chain, astrocytic phosphoprotein PEA- 15 and tubulin-specific chaperone A are contemplated.

In accordance with the present invention, immunodetection methods are provided. Some immunodetection methods include enzyme linked immunosorbent assay (ELISA), radioimmunoassay (RIA), immunoradiometric assay, fluoroimmunoassay, chemiluminescent assay, bioluminescent assay, and Western blot to mention a few. The steps of various useful immunodetection methods have been described in the scientific literature, such as, e.g., Doolittle & Ben-Zeev O, 1999; Gulbis & Galand, 1993; De Jager et al, 1993; and Nakamura et al, 1987, each incorporated herein by reference.

In general, the immunobinding methods include obtaining a sample suspected of containing a relevant analyte, and contacting the sample with a first antibody under conditions effective to allow the formation of immunocomplexes. In terms of antigen detection, the biological sample analyzed may be any sample that is suspected of containing an analyte, such as, for example, a tissue section or specimen, a homogenized tissue extract, a cell, or even a biological fluid.

Contacting the chosen biological sample with the antibody under effective conditions and for a period of time sufficient to allow the formation of immune complexes (primary immune complexes) is generally a matter of simply adding the antibody composition to the sample and incubating the mixture for a period of time long enough for the antibodies to form immune complexes with, i.e., to bind to, any analytes present. After this time, the sample-antibody composition, such as a tissue section, ELISA plate, dot blot or western blot, will generally be washed to remove any non-specifically bound antibody species, allowing only those antibodies specifically bound within the primary immune complexes to be detected.

In general, the detection of immunocomplex formation is well known in the art and may be achieved through the application of numerous approaches. These methods are generally based upon the detection of a label or marker, such as any of those radioactive, fluorescent, biological and enzymatic tags. Patents concerning the use of such labels include U.S. Patents 3,817,837; 3,850,752; 3,939,350; 3,996,345; 4,277,437; 4,275,149 and 4,366,241, each incorporated herein by reference. Of course, one may find additional advantages through the use of a secondary binding ligand such as a second antibody and/or a biotin/avidin ligand binding arrangement, as is known in the art.

The antibody employed in the detection may itself be linked to a detectable label, wherein one would then simply detect this label, thereby allowing the amount of the primary immune complexes in the composition to be determined. Alternatively, the first antibody that becomes bound within the primary immune complexes may be detected by means of a second binding ligand that has binding affinity for the antibody. In these cases, the second binding ligand may be linked to a detectable label. The second binding ligand is itself often an antibody, which may thus be termed a "secondary" antibody. The primary immune complexes are contacted with the labeled, secondary binding ligand, or antibody, under effective conditions and for a period of time sufficient to allow the formation of secondary immune complexes. The secondary immune complexes are then generally washed to remove any non-specifically bound labeled secondary antibodies or ligands, and the remaining label in the secondary immune complexes is then detected.

Further methods include the detection of primary immune complexes by a two step approach. A second binding ligand, such as an antibody, that has binding affinity for the antibody is used to form secondary immune complexes, as described above. After washing, the secondary immune complexes are contacted with a third binding ligand or antibody that has binding affinity for the second antibody, again under effective conditions and for a period of time sufficient to allow the formation of immune complexes (tertiary immune complexes). The third ligand or antibody is linked to a detectable label, allowing detection of the tertiary immune complexes thus formed. This system may provide for signal amplification if this is desired. Another known method of immunodetection takes advantage of the immuno-PCR (Polymerase Chain Reaction) methodology. The PCR method is similar to the Cantor method up to the incubation with biotinylated DNA, however, instead of using multiple rounds of streptavidin and biotinylated DNA incubation, the DNA/biotin/streptavidin/antibody complex is washed out with a low pH or high salt buffer that releases the antibody. The resulting wash solution is then used to carry out a PCR reaction with suitable primers with appropriate controls. At least in theory, the enormous amplification capability and specificity of PCR can be utilized to detect a single antigen molecule.

As detailed above, immunoassays are in essence binding assays. Certain immunoassays are the various types of enzyme linked immunosorbent assays (ELISAs) and radioimmunoassays (RIA) known in the art. However, it will be readily appreciated that detection is not limited to such techniques, and Western blotting, dot blotting, FACS analyses, and the like may also be used.

In one exemplary ELISA, the antibodies of the invention are immobilized onto a selected surface exhibiting protein affinity, such as a well in a polystyrene microti ter plate. Then, a test composition suspected of containing the analytes, such as a clinical sample, is added to the wells. After binding and washing to remove non-specifically bound immune complexes, the bound analytes may be detected. Detection is generally achieved by the addition of another antibody that is linked to a detectable label. This type of ELISA is a simple "sandwich ELISA." Detection may also be achieved by the addition of a second antibody, followed by the addition of a third antibody that has binding affinity for the second antibody, with the third antibody being linked to a detectable label. In another exemplary ELISA, the samples suspected of containing the analytes are immobilized onto the well surface and then contacted with the anti-antibody.

Another ELISA in which the antigens are immobilized, involves the use of antibody competition in the detection. In this ELISA, labeled antibodies against an analytes are added to the wells, allowed to bind, and detected by means of their label. The amount of an analyte in an unknown sample is then determined by mixing the sample with the labeled antibodies against the analyte during incubation with coated wells. The presence of an analyte in the sample acts to reduce the amount of antibody against the analyte available for binding to the well and thus reduces the ultimate signal. This is also appropriate for detecting antibodies against an analyte in an unknown sample, where the unlabeled antibodies bind to the analyte-coated wells and also reduces the amount of antigen available to bind the labeled antibodies.

"Under conditions effective to allow immune complex (antigen/antibody) formation" means that the conditions preferably include diluting the antigens and/or antibodies with solutions such as BSA, bovine gamma globulin (BGG) or phosphate buffered saline (PBS)/Tween. These added agents also tend to assist in the reduction of nonspecific background. The "suitable" conditions also mean that the incubation is at a temperature or for a period of time sufficient to allow effective binding. Incubation steps are typically from about 1 to 2 to 4 hours or so, at temperatures preferably on the order of 25 °C to 27°C, or may be overnight at about 4°C or so.

By exploiting the intrinsic properties of mass and charge, mass spectrometry (MS) can resolved and confidently identified a wide variety of complex compounds. Traditional quantitative MS has used electrospray ionization (ESI) followed by tandem MS (MS/MS) (Chen et al, 2001; Zhong et al., 2001; Wu et al., 2000) while newer quantitative methods are being developed using matrix assisted laser desorption/ionization (MALDI) followed by time of flight (TOF) MS (Bucknall et al, 2002; Mirgorodskaya et al, 2000; Gobom et al, 2000).

ESI. ESI is a convenient ionization technique developed by Fenn and colleagues (Fenn et al, 1989) that is used to produce gaseous ions from highly polar, mostly nonvolatile biomolecules, including lipids. The sample is injected as a liquid at low flow rates (1-10 μΕ/min) through a capillary tube to which a strong electric field is applied. The field generates additional charges to the liquid at the end of the capillary and produces a fine spray of highly charged droplets that are electrostatically attracted to the mass spectrometer inlet. The evaporation of the solvent from the surface of a droplet as it travels through the desolvation chamber increases its charge density substantially. When this increase exceeds the Rayleigh stability limit, ions are ejected and ready for MS analysis.

A typical conventional ESI source consists of a metal capillary of typically 0.1-0.3 mm in diameter, with a tip held approximately 0.5 to 5 cm (but more usually 1 to 3 cm) away from an electrically grounded circular interface having at its center the sampling orifice, such as described by Kabarle et al. (1993). A potential difference of between 1 to 5 kV (but more typically 2 to 3 kV) is applied to the capillary by power supply to generate a high electrostatic field (10 ⁶ to 10 ⁷ V/m) at the capillary tip. A sample liquid carrying the analyte to be analyzed by the mass spectrometer, is delivered to tip through an internal passage from a suitable source (such as from a chromatograph or directly from a sample solution via a liquid flow controller). By applying pressure to the sample in the capillary, the liquid leaves the capillary tip as a small highly electrically charged droplets and further undergoes desolvation and breakdown to form single or multicharged gas phase ions in the form of an ion beam. The ions are then collected by the grounded (or negatively charged) interface plate and led through an the orifice into an analyzer of the mass spectrometer. During this operation, the voltage applied to the capillary is held constant. Aspects of construction of ESI sources are described, for example, in U.S. Patents 5,838,002; 5,788,166; 5,757,994; RE 35,413; and 5,986,258.

ESI/MS/MS. In ESI tandem mass spectroscopy (ESI/MS/MS), one is able to simultaneously analyze both precursor ions and product ions, thereby monitoring a single precursor product reaction and producing (through selective reaction monitoring (SRM)) a signal only when the desired precursor ion is present. When the internal standard is a stable isotope- labeled version of the analyte, this is known as quantification by the stable isotope dilution method. This approach has been used to accurately measure pharmaceuticals (Zweigenbaum et al., 2000; Zweigenbaum et al., 1999) and bioactive peptides (Desiderio et al., 1996; Lovelace et al., 1991). Newer methods are performed on widely available MALDI-TOF instruments, which can resolve a wider mass range and have been used to quantify metabolites, peptides, and proteins. Larger molecules such as peptides can be quantified using unlabeled homologous peptides as long as their chemistry is similar to the analyte peptide (Duncan et al. , 1993; Bucknall et al., 2002). Protein quantification has been achieved by quantifying tryptic peptides (Mirgorodskaya et al., 2000). Complex mixtures such as crude extracts can be analyzed, but in some instances sample clean up is required (Nelson et al., 1994; Gobom et al., 2000).

SIMS. Secondary ion mass spectroscopy, or SIMS, is an analytical method that uses ionized particles emitted from a surface for mass spectroscopy at a sensitivity of detection of a few parts per billion. The sample surface is bombarded by primary energetic particles, such as electrons, ions {e.g., O, Cs), neutrals or even photons, forcing atomic and molecular particles to be ejected from the surface, a process called sputtering. Since some of these sputtered particles carry a charge, a mass spectrometer can be used to measure their mass and charge. Continued sputtering permits measuring of the exposed elements as material is removed. This in turn permits one to construct elemental depth profiles. Although the majority of secondary ionized particles are electrons, it is the secondary ions which are detected and analysis by the mass spectrometer in this method.

LD-MS and LDLPMS. Laser desorption mass spectroscopy (LD-MS) involves the use of a pulsed laser, which induces desorption of sample material from a sample site - effectively, this means vaporization of sample off of the sample substrate. This method is usually only used in conjunction with a mass spectrometer, and can be performed simultaneously with ionization if one uses the right laser radiation wavelength.

When coupled with Time-of-Flight (TOF) measurement, LD-MS is referred to as LDLPMS (Laser Desorption Laser Photoionization Mass Spectroscopy). The LDLPMS method of analysis gives instantaneous volatilization of the sample, and this form of sample fragmentation permits rapid analysis without any wet extraction chemistry. The LDLPMS instrumentation provides a profile of the species present while the retention time is low and the sample size is small. In LDLPMS, an impactor strip is loaded into a vacuum chamber. The pulsed laser is fired upon a certain spot of the sample site, and species present are desorbed and ionized by the laser radiation. This ionization also causes the molecules to break up into smaller fragment-ions. The positive or negative ions made are then accelerated into the flight tube, being detected at the end by a microchannel plate detector. Signal intensity, or peak height, is measured as a function of travel time. The applied voltage and charge of the particular ion determines the kinetic energy, and separation of fragments are due to different size causing different velocity. Each ion mass will thus have a different flight-time to the detector.

One can either form positive ions or negative ions for analysis. Positive ions are made from regular direct photoionization, but negative ion formation require a higher powered laser and a secondary process to gain electrons. Most of the molecules that come off the sample site are neutrals, and thus can attract electrons based on their electron affinity. The negative ion formation process is less efficient than forming just positive ions. The sample constituents will also affect the outlook of a negative ion spectra.

Other advantages with the LDLPMS method include the possibility of constructing the system to give a quiet baseline of the spectra because one can prevent coevolved neutrals from entering the flight tube by operating the instrument in a linear mode. Also, in environmental analysis, the salts in the air and as deposits will not interfere with the laser desorption and ionization. This instrumentation also is very sensitive, known to detect trace levels in natural samples without any prior extraction preparations.

MALDI-TOF-MS. Since its inception and commercial availability, the versatility of MALDI-TOF-MS has been demonstrated convincingly by its extensive use for qualitative analysis. For example, MALDI-TOF-MS has been employed for the characterization of synthetic polymers (Marie et al, 2000; Wu et al, 1998). peptide and protein analysis (Roepstorff et al, 2000; Nguyen et al., 1995), DNA and oligonucleotide sequencing (Miketova et al., 1997; Faulstich et al., 1997; Bentzley et al., 1996), and the characterization of recombinant proteins (Kanazawa et al, 1999; Villanueva et al, 1999). Recently, applications of MALDI-TOF-MS have been extended to include the direct analysis of biological tissues and single cell organisms with the aim of characterizing endogenous peptide and protein constituents (Li et al, 2000; Lynn et al, 1999; Stoeckli et al, 2001 ; Caprioli et al, 1997; Chaurand et al, 1999; Jespersen et al, 1999).

The properties that make MALDI-TOF-MS a popular qualitative tool— its ability to analyze molecules across an extensive mass range, high sensitivity, minimal sample preparation and rapid analysis times— also make it a potentially useful quantitative tool. MALDI-TOF-MS also enables non-volatile and thermally labile molecules to be analyzed with relative ease. It is therefore prudent to explore the potential of MALDI-TOF-MS for quantitative analysis in clinical settings, for toxicological screenings, as well as for environmental analysis. In addition, the application of MALDI-TOF-MS to the quantification of peptides and proteins is particularly relevant. The ability to quantify intact proteins in biological tissue and fluids presents a particular challenge in the expanding area of proteomics and investigators urgently require methods to accurately measure the absolute quantity of proteins. While there have been reports of quantitative MALDI-TOF-MS applications, there are many problems inherent to the MALDI ionization process that have restricted its widespread use (Kazmaier et al, 1998; Horak et al, 2001; Gobom et al, 2000; Wang et al, 2000; Desiderio et al, 2000). These limitations primarily stem from factors such as the sample/matrix heterogeneity, which are believed to contribute to the large variability in observed signal intensities for analytes, the limited dynamic range due to detector saturation, and difficulties associated with coupling MALDI-TOF-MS to on-line separation techniques such as liquid chromatography. Combined, these factors are thought to compromise the accuracy, precision, and utility with which quantitative determinations can be made.

Because of these difficulties, practical examples of quantitative applications of MALDI- TOF-MS have been limited. Most of the studies to date have focused on the quantification of low mass analytes, in particular, alkaloids or active ingredients in agricultural or food products (Wang et al, 1999; Jiang et al, 2000; Wang et al, 2000; Yang et al, 2000; Wittmann et al, 2001), whereas other studies have demonstrated the potential of MALDI-TOF-MS for the quantification of biologically relevant analytes such as neuropeptides, proteins, antibiotics, or various metabolites in biological tissue or fluid (Muddiman et al, 1996; Nelson et al, 1994; Duncan et al, 1993; Gobom et al, 2000; Wu et al, 1997; Mirgorodskaya et al, 2000). In earlier work it was shown that linear calibration curves could be generated by MALDI-TOF-MS provided that an appropriate internal standard was employed (Duncan et al, 1993). This standard can "correct" for both sample-to-sample and shot-to-shot variability. Stable isotope labeled internal standards (isotopomers) give the best result.

With the marked improvement in resolution available on modern commercial instruments, primarily because of delayed extraction (Bahr et al, 1997; Takach et al, 1997), the opportunity to extend quantitative work to other examples is now possible; not only of low mass analytes, but also biopolymers. Of particular interest is the prospect of absolute multi-component quantification in biological samples {e.g., proteomics applications).

The properties of the matrix material used in the MALDI method are critical. Only a select group of compounds is useful for the selective desorption of proteins and polypeptides. A review of all the matrix materials available for peptides and proteins shows that there are certain characteristics the compounds must share to be analytically useful. Despite its importance, very little is known about what makes a matrix material "successful" for MALDI. The few materials that do work well are used heavily by all MALDI practitioners and new molecules are constantly being evaluated as potential matrix candidates. With a few exceptions, most of the matrix materials used are solid organic acids. Liquid matrices have also been investigated, but are not used routinely. E. Kits

The document also provides kits for evaluating biomarkers in a subject. The kits of the invention can take on a variety of forms. Typically, the kits will include reagents suitable for determining levels of a plurality of biomarkers (e.g., those disclosed herein, for example as outlined in Table III or IV) in a sample. For example, the kits may contain one or more control samples. Typically, a comparison between the levels of the biomarkers in the mammal and levels of the biomarkers in the control samples is indicative of a clinical status (e.g. , diagnosis, likelihood assessment, insulin sensitivity, glucose control capacity, etc.). Also, the kits, in some cases, will include written information (indicia) providing a reference (e.g., predetermined values), wherein a comparison between the levels of the biomarkers in the subject and the reference (pre-determined values) is indicative of a clinical status. In some cases, the kits comprise software useful for comparing biomarker levels or occurrences with a reference (e.g., a prediction model). Usually the software will be provided in a computer readable format such as a compact disc, but it also may be available for downloading via the internet. However, the kits are not so limited and other variations with will apparent to one of ordinary skill in the art.

F. Examples

The following examples are included to demonstrate particular embodiments of the invention. It should be appreciated by those of skill in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function well in the practice of the invention. However, those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit and scope of the invention.

EXAMPLE 1 - MATERIALS AND METHODS

Study design and protocol. Seven C-peptide negative T1D were studied on two occasions; one during insulin treatment and the other following withdrawal of insulin for 8h and compared with matched healthy ND participants (Table I). All study volunteers were screened with a detailed medical history, physical exam, hematological and biochemical profile (Jaleel et al., 2009; Lanza et al., 2010; Karakelides et al, 2007). On the insulin-treated study day, insulin was infused into a forearm vein to maintain blood glucose between 4.44 and 5.56 mmol/1 overnight until 1200h of the next day. On the insulin-deprived study day, insulin was discontinued for 8.6 + 0.6h starting at 0400h. ND participants were kept on a saline infusion from the evening following their meal. Arterialized venous blood was obtained from a catheterized hand vein maintained at 60 °C using a hot box for the duration of the study. Plasma samples were stored at -80 °C until analysis. Percutaneous needle biopsies were performed under local anesthesia as previously described (25) with the muscle specimens immediately frozen in liquid nitrogen and stored at -80 °C until analysis.

Table I - Characteristics of Study Participants

Variables ND T1D

n=7 n=7

Age, yr 29.7+3 30+3

Weight, Kg 81+6.4 78.2+5

BMI kg/m ² 25+1.1 26.2+1.3

Fat mass % 33.2+4 31.6+4.1

HbAlc % 5.0+0.05 7.2+0.5*

Duration of type

1 diabetes, yr 18.7+4

T1D (I-) T1D (1+)

Glucose, mmol/1 4.9+0.1 17.0+0.6 ^a ' ^b 5.2+0.2

Glucagon ng/1 99.0+19.0 82.6+17.3 50.3+6.8 ^a

Insulin pmol/1 23.4+4.52 3.9+1.36 ^{a b} 69.8+17.8 ^a

Data are means + SE for 7 participants (3 women and 4 men)/group. ND, non-diabetic study participants; T1D, type 1 diabetic participants; I-, insulin-deprived state; I+, insulin-treated state. P<0.05 vs. ND ( ^a ) and vs. T1D (1+) ( ^b )

Metabolomic profiling. Sample Preparation: Plasma quality control samples used in the study were prepared from pooled plasma spiked with a selection of metabolites to mimic elevated levels of metabolites in during I- condition. Plasma was spiked with a standard mixture (3: 1 ratio of plasma to spiking solution) containing 100 g ml of niacin, hypoxanthine, leucine, isoleucine, phenylalanine, tryptophan, citric acid, glucose, hippuric acid, taurocholic acid dissolved in 1: 1 acetonitrile/water. All plasma samples (200 μΐ) were thawed on ice at 4 °C followed by deproteinization with methanol (1 :4 ratio of plasma to methanol), vortexed for 10s and followed by incubation at -20 °C for 2 h. The samples were then centrifuged at 15,871 g for 30 min at 4 °C. The supernatants were lyophilized (Savant, NY, USA) and stored at -20 °C prior to analysis. The samples were reconstituted in 50% H20/acetonitrile and passed through a Microcon YM3 filter (Millipore Corporation, USA). The supernatants were transferred to analytical vials, stored in the auto sampler at 4 °C and analyzed within 48h of reconstitution in buffer.

The LC platform consisted of an AcquityTM UPLC system (Waters Corporation., MA,

USA). Plasma metabolite separation was achieved using both hydrophilic interaction chromatography (HILIC) (BEH, 2.1X 150 mm, 1.7 μπι, Waters, MA) and reversed-phase liquid chromatography (RPLC) C18 (HSS 2.1X 150 mm, 1.8 μιη, Waters, MA). For each column the run time was 20 min at a flow rate of 400 μΐ/min. Reverse phase chromatography was performed using 99% Solvent A (5 mM NH4 acetate + 0.1% formic acid +1% acetonitrile, ACN) to 100% solvent B (95% ACN with 0.1% formic acid). The gradient was 0 min, 0% B; 1 min, 0% B; 3 min, 5% B; 13.0 min, 100% B; 16 min, 100% B; 16.5 min, 0% B; 20 min, 0% B. The HILIC gradient was as follows: 0 min, 100% B; 1 min, 100% B; 5 min, 90% B; 13.0 min, 0% B; 16 min, 0% B; 16.5 min, 100% B; 20 min, 100% B. Other LC parameters were injection volume, 5 μΐ, and column temperature, 50 °C. Each sample was injected in triplicate with blank injections between each sample. QCs and standards were run at the beginning and end of the sequence.

Mass spectrometry: A 6220 ToF MS (Agilent Technologies Inc., USA) was operated in both positive and negative electrospray ionization (ESI) modes using a scan range of m/z 50- 1200. The mass accuracy and mass resolution were <5 ppm and ~ 20,000 respectively. The instrument settings were as follows: nebulizer gas temperature 325 °C, capillary voltage 3.5 kV, capillary temperature 300 °C, fragmentor voltage 150 V, skimmer voltage 58 V, octapole voltage 250 V and cycle time 0.5 s and runtime 15.0 min.

Data Preprocessing: All raw data files were converted to compound exchange file (CEF) format using Masshunter DA reprocessor software (Agilent, USA). Mass Profiler Professional (Agilent, USA) was used for data alignment and to convert each metabolite feature (m/z x intensity x time) into a matrix of detected peaks vs compound identification. Each sample was normalized to the median of the baseline and log2 transformed. Default settings were used with the exception of S/N threshold (3), mass limit (0.0025 u) and time limit (9 s).

The resulting metabolites were identified against the METLIN metabolite database using a detection window of .5 ppm. Putative identification of each metabolite was made based on mass accuracy (m/z) chemical abstract service (CAS), kyoto encyclopedia of genes and genomes (KEGG), human microbiome project (HMP) and lipid maps (LIPID) identifiers (Chen et al, 2008; Sana et al, 2010).

Method performance was evaluated for the ten metabolite standards with respect to limit of detection (LOD), linearity, reproducibility, and mass accuracy. Linearity was evaluated using the linear regression of the observed signal with respect to concentration, with a lower limit of 10 ng/ml being set as the LOD. In addition, the 10 ng/ml mix was run three times each day over five days during a period of one month to determine inter and intra assay variation.

Analysis of gene transcripts using GeneChips. RNA was extracted from frozen muscle samples (50 mg) using the RNeasy Fibrous Tissue kit (Qiagen) following the manufacturer's instructions. Gene transcript profiles were measured using high-density oligonucleotide microarrays containing probes for 54,675 transcripts and expressed sequence tags (HGU133 plus 2.0 GeneChip arrays; Affymetrix, Santa Clara, CA). GeneChip data were subjected to invariant probe set normalization (Karakelides et al., 2007). Differences between the I- TID and 1+ TID/ ND groups were evaluated by paired t test (Karakelides et al., 2007; Asmann et al., 2006). The inventors opted to focus on significantly altered pathways and functional gene sets rather than individual genes.

To validate the findings of the Gene-array results and to quantify other genes of interest, transcript levels of selected genes were analyzed by real-time quantitative PCR (Applied Biosystems 7900) as previously described (Karakelides et al., 2007; Balagopal et al., 2001). The primers and probes used were cytochrome-c oxidase subunits COX5B and COX 10, ubiquinol cytochrome-c reductase (UQCR) 6.4-kDa subunit, uncoupling protens 2 and 3 (UCP2 and UCP3 and ATP5F1 subunit) (Nair et al., 1988). The abundance of the target gene was normalized to 28S (Balagopal et al, 2001).

Statistical Analysis. One of the challenges in the analysis of metabolomics data resides in the substantial missing values in the dataset. The compounds detected in at least > 50% of the samples in any treatment group were selected for differential expression analyses. Then two approaches based on two different assumptions were used to handle missing values. First, missing values were assumed to be of very low abundance (at the limit of experimental detection) or zero abundance and were replaced by 1.0 before applying log2 transformation. The second approach involved the use of parametric imputation models to provide estimates of the missing values. Imputation leverages the pattern of expression between a set of correlated metabolite features to predict the missing values of metabolites. In order to reduce the uncertainty associated with estimating missing values, only metabolites present in at least 50% of all the samples and at least 5 samples in each study group were used for imputation. Each metabolite feature was independently imputed from the 10 most closely correlated metabolites from the same experimental mode. Missing value estimation was carried out using Markov Chain Monte Carlo (MCMC) methods included in R software with 100 imputations and 50 iterations. Imputation models were set to account for treatment groups and replicate samples.

The complete datasets resulting from both approaches were analyzed using random intercept models to account for the multiple measurements that were taken from each sample. Separate models were fitted for each pairwise treatment group comparison. Results from multiple imputations were combined with methods proposed by Rubin (1987). All statistical calculations were performed using the statistics software package, 'R'. In particular, the 'mi' and 'me' packages were used for multiple imputation and random effect model fitting, respectively. Identified compounds that were differentially expressed across treatment groups were used for pathway analysis.

Hierarchical cluster analysis (HCA) of metabolites was performed to reveal associations between replicate biological samples within a group based on the similarity of their mass abundance profiles. HCA was performed on the log2 transformed, oneway ANOVA data set. A heat map was generated where each column depicts a sample and each row represents a metabolite, the relative change being color coded (FIG. 1).

Pathway analysis. The differentially expressed metabolites were analyzed for pathway enrichment using MetaCoreTM (Genego, MI) (Schuierer et al., 2010). Metabolite identifiers (CAS, KEGG) were used for each metabolite including name, molecular weight in addition to fold change and differential p-value. The p-value from the hypergeometric test, generated by Metacore, represents the enrichment of certain metabolites in a pathway. A p-value < 0.05 is indicative of significant enrichment. The ratio of significantly changed metabolites in the pathway over total number of metabolites in a pathway was also calculated. A false discovery rate (FDR) of <0.15 was also applied. A comparison of canonical pathways was also made between the metabolome and transcriptome studies.

EXAMPLE 2 - RESULTS

Clinical and biochemical characteristics of participants are given in Table I.

Significantly higher levels of HbAlc, were noted in T1D. Plasma glucose levels remained significantly higher in I- but insulin concentration was significantly lower. Insulin concentration was higher in 1+ than in ND. Glucagon concentration was significantly lower in 1+ than ND with similar glucose levels. Bicarbonate levels were not significantly different between the two groups indicating that I- T1D were not in metabolic acidosis although β-hydroxy butyrate (βΗΒ) concentration was higher in I- than in 1+ showing that fatty acid (FA) metabolism was elevated. No difference was detected in the other physiological parameters reflecting kidney function, total protein and albumin (data not shown) in plasma.

Plasma metabolome. The coefficient of variation of retention times of the standard compounds was less than 5% and the mass accuracy was <5 ppm. Metabolic profiling was able to identify a total of 402 compounds including metabolites, peptide fragments and drug molecules (data not shown). Of these, 330 metabolites were detected and identified in all three study groups: I-, 1+ and ND. 69 metabolites that were confirmed based on comparison with standards and retention time are enlisted in. The identification of the other 261 metabolites was based on accurate mass measurements using database searches.

Heatmap. Heatmap was generated using identified and unidentified metabolites (FIG. 1). The heatmap revealed considerable differences between I-, 1+ and ND showing alterations in the natural abundance of several metabolites in plasma. The heatmap demonstrated that replicates samples belonging to the different study groups are clustered.

Impact of Insulin deprivation. The comprehensive profiling approach of paired analysis (I- vs 1+ and I- vs. ND) showed alterations of 302 known plasma metabolites in T1D (I-) individuals of which 176 were significant (p<0.05). The metabolite classes that were found to be significantly altered between I- and 1+ include plasma amino acids (AA), branch chain amino acids (BCAA), lipid metabolites, bile acids, purines, pyrimidines, Krebs (TCA) cycle and carbohydrate metabolites, transcription of peroxisome proliferative activated receptor (PPAR), vitamins including steroids and ecosanoids. Consistent changes were also observed when the metabolite plasma levels in I- were compared with ND (Table S6).

Pathway enrichment analysis. Pathway enrichment analysis of identified metabolites showed that more than 33 canonical pathways (p<0.05) were perturbed during insulin deficiency (FIG. 2A). Table II shows a shortlist of selected metabolic pathways that were affected by differential regulation of metabolites during insulin deficiency in comparison to 1+ and ND. The comparison of pathway enrichment analysis before and following multiple imputation analysis of paired study groups was also performed and showed that the overall pathway findings remained the same but the statistical inference was changed. The p-values of the pathways involving andamide, prostaglandin (PGE), γ-amino butyrate (GABA), cortisone biosynthesis and metabolism were decreased whereas HETE and HPETE biosynthesis, leukotriene metabolism including tryptophan and butanoate metabolism were increased significantly after imputation (data not shown).

Table II. The implicated canonical pathways affected by differential regulation of metabolites during insulin deficiency (I-) in type 1 diabetes (T1D) in comparison with insulin treated (1+) and non-diabetes (ND). The regulation was calculated on log 2 scaled values of I- with respect to 1+ and ND individuals. P-value is the significance of the pathway and ratio is the number of compound identified to the total number of metabolites present in a pathway.

Maps Regulation p-value Ratio

1. Leucine, isoleucine and valine metabolism (BCAA) up 3.036e ^"y 10/54

2. Prostaglandin, HETE and HPETE biosynthesis and up 6.892e ^"8 12/80 metabolism

3. Histidine-glutamate-glutamine metabolism up 5.862e ^"5 8/96

4. Aminoacyl-tPvNA biosynthesis in up 6.315e ^"5 8/97 cytoplasm/mitochondria

5. Propionate metabolism up 6.459e ^"5 7/72

6. Neurophysiological process_GABAergic up 6.680e ^"5 6/50 neurotransmission

7. Gamma-aminobutyrate (GABA) biosynthesis and up 1.151e ⁴ 6/55 metabolism Maps Regulation p-value Ratio

Proline metabolism up 1.022e ³ 5/55

(L)-Alanine, (L)-cysteine, and (L)-methionine up 1.109e ^"3 5/56 metabolism

Beta-alanine metabolism up 1.412e ^"3 4/35

Galactose metabolism up 1.758e ^"3 5/62

Fructose metabolism up 3.608e ^"3 5/73

Anandamide biosynthesis and metabolism up 3.906e ^"3 4/46

Glycolysis and gluconeogenesis up 3.906e ^"3 4/46

Tyrosine metabolism up 8.347e ^"3 5/89

Cortisone biosynthesis and metabolism up 1.187e ² 4/63

N-Acylethanolamines. N-Acyltransferase pathway up 1.195e ² 3/34

Phenylalanine metabolism up 1.539e ^"2 4/68

Regulation of lipid metabolism_PPAR regulation of up 2.114e ^"2 3/42 lipid metabolism

Methionine-cysteine-glutamate metabolism up 2.250e ^"2 3/43

Regulation of lipid metabolism_Insulin regulation of up 3.561e ^"2 4/88 fatty acid methabolism

Taurine and hypotaurine metabolism up 3.802e ^"2 3/22

Mitochondrial ketone bodies biosynthesis and up 5.521e ^"2 3/27 metabolism

Muscle contraction_eNOS Signaling in Skeletal up 5.521e ^"2 3/27 Muscle

Butanoate metabolism up 6.385e ^"2 3/65

Pyruvate metabolism up 6.623e ^"2 3/66

Acetylcholine biosynthesis and metabolism up 7.457e ^"2 2/32 Regulation of lipid metabolism_Alpha ^"1 via up 7.614e ^"2 3/70 arachidonic acid adrenergic receptors signaling

Urea cycle up 7.614e ^"2 3/70

Fatty Acid Omega Oxidation up 8.284e ^"2 2/34

Aspartate and asparagine metabolism up 8.398e ^"2 3/73

Nitrogen metabolism up 8.708e ^"2 5/35

Aminoacyl-tRNA biosynthesis in down 5.104e ^"4 6/97 cytoplasm/mitochondria

Catecholamine metabolism down 2.100e ^"1 2/74

CTP/UTP metabolism down 4.537e ^"2 3/108

Regulation of lipid metabolism_Alpha ^_1 via arachidonic down 7.636e ³ 6/34 acid adrenergic receptors signaling

Glycine, serine, cysteine and threonine metabolism down 6.482e ^"3 3/122

GTP-XTP metabolism down 2.758e ^"3 3/90

Histidine-glutamate-glutamine metabolism down 3.664e ^"2 3/96

Neurophysiological process_Role of CDK5 in down 8.510e ^"2 3/28 presynaptic signaling

Niacin-HDL metabolism down 1.388e ^"2 3/47

Pentose phosphate pathway down 1.525e ^"2 3/52

Bile acid biosynthesis down 5.23e ^"2 4/94 Maps Regulation p-value Ratio

44. Phenylalanine metabolism down 2.268e ^"3 3/68

45. Tryptophan metabolism down 4.238e ^"2 4/104

46. Tyrosine metabolism down 2.552e ^"2 3/64

47. Glutathione metabolism down 4.25e ^"2 3/72

48. Ubiquinone metabolism down 2.264e ^"2 3/74

The effect of systemic insulin treatment. In order to identify whether insulin treatment ameliorates the metabolic pattern in T1D, the metabolic fingerprint of 1+ was compared with that of ND. Paired analysis of 1+ vs ND identified 241 altered metabolites, of which 71 were significant (p<0.05). This perturbation of pathways include ecosanoid metabolism, BCAA, immune response, PGE-2 response and the corresponding signaling pathways (FIG. 2B). The abnormalities in these pathways indicated that insulin treatment in T1D did not restore the metabolic alterations completely. In addition, systemic insulin treatment in T1D compared to ND showed a differential effect on 7 metabolic pathways which were not observed in comparison to the I- state, which are marked with asterisks (*) in FIG. 2B.

Correlation of metabolomics and transcriptomics in altered insulin action. Consistent differences between I- and 1+ diabetic individuals based on Q-PCR based mRNA levels of (COX5B, COX 10, UQCR, ATP5F1) and mRNAs based of gene array were reported previously (Sana et al., 2010). A total of 40,438 transcripts from gene array were included in the analysis, of which 2,355 and 1775 transcripts were differentially expressed between I- vs ND and 1+ vs ND subjects respectively (p<0.05) (Karakelides et al., 2007). These genes were used as "focus genes" for pathway analysis.

Both transcriptome and metabolome levels were found to be affected by insulin deprivation in T1D. Therefore, the muscle gene transcriptome was compared with the plasma metabolome of the same participants under identical study conditions. Analysis of I- vs. ND for microarray and metabolomics profile identified several canonical pathways indicating similar directional changes as shown in FIG. 3A. These implicated pathways, namely transcription of PPAR, immune response related to PGE-2, muscle contraction, lipid and carbohydrate metabolism and inhibitory actions of lipoxins showed a direct correlation of metabolites with transcriptome and their differential regulation during low insulin action. These pathways are clustered based on the gene-metabolite pathway associations. In general many pathways agreed between the ones based on metabolomics and transcriptiome (FIGS. 3A-B). As expected many substrate metabolism pathways showed more clear cut alterations based on metabolomic analysis. In contrast the pathway analysis based on transcriptiome showed more robust changed were noted for many other pathways such as PGE, PPAR, immune response and muscle contraction.

To obtain further information on the effect of such altered pathways on the biological processes during insulin deficiency, metabolomics data were overlaid with the transcriptomics data to depict a metabolic network as illustrated in FIGS. 4A-B. The network model was built using canonical pathways highlighted with (*) in FIGS. 3A-B. The association of genes and metabolites involved in PPAR transcription pathway was found to be one of the significant pathways affected both at the metabolic and transcript levels during insulin deficiency (FIGS. 3 A and 4A). PPAR network showed differential expression of transcripts viz. PKA, Akt, PTGIS, XIAP, TAKl and metabolites such as long chain FAs, PGE-2, leukotriene, hydroperoxylinoleic acid (HPODE) (FIG. 4A). The association of prostaglandins, HEPTE, FA metabolites and arachidonic acid biosynthesis are all interconnected thus affecting other metabolic responses such as carbohydrate, lipid homeostasis, insulin receptor signaling, TGF-β signaling. A molecular network was also generated for the insulin signaling pathways which also showed co- clustering of gene transcripts, e.g., PKA, PDE3B, Akt (PKB), ACSL1 and metabolites like glucose, glucose

6- phosphate, citric acid and others (FIG. 4B).

EXAMPLE 3 - DISCUSSION

A non-targeted mass spectrometry based metabolic profiling in T1D showed that insulin deficiency altered several known and unknown metabolites and various putative metabolic pathways. Many but not all these alterations were normalized by insulin treatment. Moreover, additional metabolites and pathways were found to be affected by insulin treatment. The confirmation of previously reported metabolic pathways affected by insulin based on multiple analytical approaches support the validity of the metabolomic analysis of plasma samples. The revelation of previously unknown pathways affected by insulin is likely to stimulate novel hypotheses based research. The integrated analysis of the altered metabolic pathways based on plasma metabolomics and skeletal muscle microarray based gene transcriptomics showed significant concordance.

Insulin withdrawal in T1D caused elevation of levels of many known metabolites such as ketogenic and gluconeogenic AA, BCAA, glycerol and β-hydroxybutyrate suggesting an increased rate of proteolysis, lipolysis, and ketogenesis. These results are consistent with previous reports based on both NMR spectroscopy and LC coupled with tandem mass spectroscopy in T1D and insulin resistant state (Lanza et al., 2010; Shaham et al., 2008). The current approach also showed that major molecular and cellular functions affected by insulin deficiency as expected were carbohydrate (p<0.00001), lipid (p<0.0004), molecular transport and AA (p<0.0004), nucleic acid metabolism (p<0.04). In addition, many other known pathways such as TCA cycle and mitochondrial ketone bodies biosynthesis and degradation were also found to be affected. In general, many altered pathways that switch from anabolic to catabolic mode whereas insulin secretion following a glucose meal exhibited a reverse effect (Shaham et al, 2008).

The current study showed various metabolites and pathways that were previously not recognized as being affected by insulin action. Among such affected pathways include PGE metabolism with its wide range of impacts on various functions including platelet aggregation that may lead to vascular complications. A previous study reported high levels of plasma PGEE2 and PGE-F2 and low levels of serum dihomo-y-linolenic acid and arachidonic acid in association with increased platelet aggregation in diabetic children (Arisaka et al., 1986). The oxidative metabolism of arachadonic acid was found to promote insulin release from pancreatic beta cells (Robertson, 1986; Persaud et al., 2007) and PGE was postulated to play a potential role in the pathophysiology of T2D (Robertson and Chen, 1977; Robertson, 1983). A recent report showed that insulin deficiency affected the arachidonic metabolism pathway (Xie et al., 2010; Ma et al., 2010). Arachidonic acid are converted to PGEs, leukotrienes and lipoxins of which PGE-E and leukotrienes are potent pro-inflammmatory lipid mediators and are also linked to hepatic steatosis (Li et al., 2009; Takahashi et al., 2010). Altered regulation of the oxidized lipids such as HPTE and its hydroxylated form HETE were observed in insulin deficiency in the present study. 5 -HPTE is an intermediate product of leukotriene-A4 and might alter immune response and leuokotiene b4 biosynthesis. They are also involved in mediating inflammation and induced the adhesion and activation of leukocytes on the endothelium, allowing them to bind to and cross into tissue (takahashi et al, 2010). The complications of diabetes involving inflammation and endothelial dysfunction may be mediated by alterations in the PGE pathways (Robertson, 1983; Metz et al, 1983).

The Lipoxin pathway was also found to be affected by insulin deficiency and inhibitory action of lipoxins and superoxide production in the neutrophil which might be manifested by a diminished inflammatory response in T1D (Filep et al., 1999). The current study also noted a substantial up regulation of aldosterone biosynthesis and metabolic pathways. This is of considerable interest as aldosterone excess has been extensively studied and shown to be a major cause of cardiovascular complications in many insulin resistant conditions and may contribute to vascular complications in T1D.

The current metabolomics approach measure metabolites not only derived from endogenous cellular metabolism but also those exogenously from drugs, foods and cosmetics, etc. Surprisingly, the inventors noted that among the metabolites that showed significant differences between 1+ and I- were morphine and coniine. Since the identification of these compounds are putative based on mass (m/z) it is possible that molecules with identical sequencing (m/z) may be reported as morphine and coniine. It is known that endogenous opioids such as endorphins are measured in plasma in individuals after glycogen depleting aerobic exercise (Gambert et al., 1981 ; Carr et al., 1981). It remains unclear whether insulin deprivation in T1D and associated glycogen depletion may increase endorphin secretion. Endorphin also has substantial structural similarity in terms of AA sequence to adrenocorticotropic hormone (ACTH) and many other alkaloids might have similar structure as coniine. However, the importance of these findings warrant more detailed studies.

Insulin treatment corrected the levels of most of the altered metabolites but some of the metabolites and metabolic pathways (FIG. 2B and Table III) remained unaffected. In addition, insulin treatment showed changes in 7 metabolic pathways which were not previously observed to be affected in T1D during insulin treatment (marked with * in FIG. 2B). The additional group of metabolites altered by insulin treatment (ND vs 1+) included many organic acids, glucogenic AAs, bile acids, purine, pyrimidine, phosphatydylcholine, ethanolamine, carnitine, creatinine. Several of these metabolites are involved in hepatic metabolism and lipid metabolic processes in adipose tissue (Li et al. 2010; Le Bouter et al., 2010). The potential impact of these altered pathways in T1D following insulin treatment needs future investigations. Systemic versus pre- hepatic insulin administration altered energy and protein metabolism in diabetic dogs (Freyse et al., 2006). The inventors have shown the effects of short term tight glycemic control by insulin but it remains to be determined if long-term insulin treatment will show persistent changes in these pathways. Higher glucagon levels in I- compared to 1+ may have contributed to some of the changes (Charlton et al., 1996; Charlton and Nair, 1998) although insulin deficiency is likely to be the predominant factor.

Table III. The putative pathways that are altered after insulin treatment in (1+) type 1 diabetes

(T1D) with respect to non-diabetic (ND) individual.

P-value is the mean of p-values of three pairs of study groups depicting significance of the pathway and ratio is the number of compound identified to the total number of metabolites present in a pathway.

Map Metabolites p-value Ratio

1 HETE and HPETE biosynthesis Arachidonic acid, HETE, HopTE 6.892e ^"s 10/80 and metabolism

2 Prostaglandin biosynthesis and Prostagladin, Arachidonic acid, 1.075e ^"5 9/101 metabolism and immune response HETE, HopTE

5 Leucine, isoleucine and valine Leucine, isoleucine, valine, 4.079e ^"6 9/54 (BCAA) metabolism oxovaline

3 Histidine-glutamate-glutamine Histidine, alanine, oxoglutaric 5.510e ^"5 9/96 metabolism acid

4 L)-Alanine, (L)-cysteine, and (L)- Alanine, cystine, oxoglutarate, 4.215e ^"4 6/56 methionine metabolism glutamate

6 Gamma-aminobutyrate (GABA) Aminobutyrate, glutamate, 9.827e ^"4 6/55 biosynthesis and metabolism glutamine, oxoglutarate

7 Transcription of PPAR Pathway Prostagladin, leukotriene 4, 4.306e ^"3 5/61

HETE, HPODE

8 Muscle contraction_nNOS Acetylcholine, glucose, arginine, 2.618e ^"2 5/43 Signaling in Skeletal Muscle acetate, citrate, phosphoinisitol

9 Leukotriene 4 biosynthesis and Prostagladin, Arachidonic acid, 9.993e ^"4 5/44 metabolism HETE, HopTE

10 Regulation of lipid Hydroxybutyrate, acetoacetate, 3.885e ^"4 6/55 metabolism_insulin regulation of glycerol, GP

fatty acid methabolism

11 Niacin-HDL metabolism Nicotinamide, niacin, glycerol 4.094e ^"3 6/47

12 TCA Citrate, ketoglutarate, pyruvate, 5.496e ^"3 5/51 acetoacetate Previous studies reported that cessation of insulin treatment is associated with higher oxidative metabolism (Nair et ah, 1984) but reduced skeletal muscle ATP production (Karakelides et ah, 2007) thus creating an environment of high oxidative stress. This higher oxidative metabolism and catabolism of many AAs (Charlton and Nair, 1998) were also shown to be at least partially related to hyperglucogonemia (Charlton et ah, 1996). Glucagon has no receptors in skeletal muscle and therefore it is likely that these effects of glucagon may have occurred in liver but not in skeletal muscle. Insulin deficiency in T1D individuals has also been shown to accelerate the catabolism of many AAs especially of BCAA in skeletal muscle (Nair et ah, 1995). The changes in plasma metabolites observed therefore represent not only of those processes occurring in skeletal muscle but also in multiple other organs, especially in liver. The inventors have, however, examined whether plasma metabolite based pathway analysis are in agreement from those derived from skeletal muscle gene transcriptome.

The current study demonstrated concordance of 16 canonical pathways that are altered by insulin deficiency based on metabolomics vs transcriptomics (FIG. 3A). Moreover, similar alterations of the pathways between 1+ T1D and ND were also observed (FIG. 3B). Metabolic response displayed a much higher level of specificity than the transcriptomics data which may be due to the capacity of the metabolites to respond faster to short term insulin deprivation than muscle transcription of genes. Metabolomics of human plasma is a reflection of the spill over from various organs from all over the body whereas the transcriptomics of muscle tissue only depicts the localized changes in mRNA levels. Thus, synergy of metabolites and genes and the canonical correlation approaches enabled us to demonstrate the effect of coordinated changes of the transcriptome and the metabolic processes.

The integration of the data from both analyses allowed us to build metabolic networks of PPAPv and insulin signaling pathway as shown in FIGS. 4A-B. Of interest, pathway analysis based on both plasma metabolites and gene transcriptome demonstrated highly significant (P<0.004) differences of PPAR pathway between I- and 1+ T1D/ ND participants. The different isoforms of PPAR was not distinguished by the pathway enrichment analysis. The metabolites involved in the specific PPAR , β, γ pathways require further investigation.

The current study confirmed the validity of the non-targeted plasma metabolomic profiling by demonstrating that this single plasma analysis could identify most pathways previously reported based on multiple approaches over many years of research. In addition the significant concordance of pathways based on plasma metabolites and skeletal muscle transcriptiome support the notion that plasma metabolites are chemical fingerprint of cellular metabolites and pathways. The novel pathway affected by insulin and the demonstration of alteration of many metabolites and pathways affected by insulin treatment indicate potential therapeutic targets for the high morbidity and mortality in T1D individuals despite improved glycemic control.

Table IV - List of Metabolites for Insulin Deprived Individuals versus Treated Individuals.

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

Symmetric

Dimethylarginine

[C8 H18 N4 02, db=99.72,

overall=99.72, HMP

ID=HMDB03334] 1.22E-19 2.139041 up 202.1428 1.03519 42 +

Hydroxychloroquine

[CI 8 H26 CI N3 O, db=62.74,

overall=62.74, KEGG ID=C07043,

CAS ID=118-42-3] 4.38E-13 7.277993 up 335.1747 2.292048 42 +

Lactaldehyde

C3 H6 02, db=98.97,

overall=98.97, KEGG ID=C00424,

CAS ID=598-35-6 5.45E-09 1.972908 up 74.0371 1.418833 42

Hexadecanedioic acid

mono-L-carnitine ester

C23 H43 N 06, db=75.36,

overall=75.36, HMP

ID=HMDB00712 7.03E-08 6.118299 up 429.3082 7.469119 42 +

2- oxoisocaproic acid

C6 H10 03, db=96.54,

overall=96.54, CAS ID=816-66-0 5.45E-07 1.631858 up 130.063 4.994714 42

5-Aminopentanoic acid

C5 H11 N 02, db=99.75,

overall=99.75, HMP

ID=HMDB03355 1.90E-04 1.416886 up 117.0789 1.1875 42

Isobutyryl carnitine

Cl l H21 N 04, db=98.17,

overall=98.17, CAS ID=25518-49-4 4.25E-04 1.922513 231.1471 5.153524 42 +

3- Methyladipic acid

C7 H12 04, db=79.68, 0.004756 1.400231 160.0733 1.406905 42

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode overall=79.68, CAS ID=3058-01-3

20alpha-Dihydroprednisolone

C21 H30 O5, db=91.70,

overall=91.70, CAS ID=15847-24-2 0.005164 1.53567 up 362.2088 10.19017 42 + 2-Methoxyestrone 3-glucuronide

C25 H32 09, db=88.07,

overall=88.07, HMP

ID=HMDB04482 0.005736 1.706398 up 476.2022 10.17281 42

2-Hydroxy-4- methylvaleric acid

C6 H12 03, db=95.76,

overall=95.76, CAS ID=13748-90-8 0.007835 1.507463 up 132.0782 1.376428 42

2-Oxo-3-hydroxy-4- phosphobutanoate

C4 H7 08 P, db=94.52,

overall=94.52, KEGG ID=C06054 0.013073 1.501854 up 213.9867 7.593359 42

Monoethylglycylxylidide (MEGX)

C12 H18 N2 O, db=99.50,

overall=99.50, CAS ID=7728-40-7 0.045795 1.259835 up 206.142 7.472383 42 + 2,3-Dioxogulonic acid

C6 H8 07, db=96.54,

overall=96.54, CAS ID=3409-57-2 0.049945 1.443292 up 192.027 1.230572 42 + b-D-Glucopyranosiduronic acid

C15 H21 N 08, db=93.49,

overall=93.49, CAS ID=33150-82-2 2.37E-09 2.963354 up 343.1259 1.924659 41 + Dodecanoylcarnitine

C19 H37 N 04, db=92.45,

overall=92.45, HMP

ID=HMDB02250 9.30E-05 2.109583 up 343.271 6.872779 41 + (E)-2-Methylglutaconic

acid

C6 H8 04, db=85.87, 1.27E-04 1.671055 up 144.0424 1.470659 41 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode overall=85.87, HMP

ID=HMDB02266

Chloramphenicol alcohol

C11 H14 N2 06, db=83.61,

overall=83.61, CAS ID=23885-72-5 4.79E-04 1.669037 up 270.0849 2.802855 41 + cis-5-Tetradecenoyl

carnitine

C21 H39 N 04, db=62.83,

overall=62.83, HMP

ID=HMDB02014 9.06E-06 3.24565 up 369.2876 6.730251 40 + Dihydroxyacetone (glycerone)

C3 H6 03, db=99.75,

overall=99.75, KEGG ID=C00184,

CAS ID=96-26-4 0.01311 2.361845 up 90.0317 0.900275 40

3-Methylglutaryl

Carnitine

C13 H23 N 06, db=88.94,

overall=88.94, HMP

ID=HMDB00552 4.34E-07 2.67332 up 289.1523 4.494179 39 + 3E-undecenoic acid

Cl l H20 O2, db=85.02,

overall=85.02 6.84E-04 2.319903 up 184.1459 1.353077 39

Ecgonine

C9 H15 N 03, db=71.48,

overall=71.48, CAS ID=481-37-8 6.94E-04 1.580701 up 185.1047 2.242461 39 +

Pantothenic Acid

C9 H17 N 05, db=62.28,

overall=62.28, CAS ID=137-08-6 0.016411 1.295907 up 219.1103 2.636872 39 +

Isopentenyladenine

CIO H13 N5, db=97.41, HMP

ID=HMDB02313 1.80E-06 1.838154 203.1172 8.834027 38 +

Isobutyryl carnitine 0.002057 1.73372 231.1467 8.30108 38 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

Cl l H21 N 04, db=93.46, CAS

ID=25518-49-4

Glycoursodeoxycholic

Acid

C26 H43 N 05, db=90.59,

overall=90.59, HMP

ID=HMDB00708 0.045096 1.988482 up 449.3122 5.656351 37

5-octadecylenic acid

C18 H34 02, db=84.31,

overall=84.31, Lipid

ID=LMFA01030065 6.03E-04 2.072487 up 282.2554 1.04 36

Dodecanedioic acid

C12 H22 04, db=86.36,

overall=86.36, HMP

ID=HMDB00623 0.001388 4.149088 up 230.1507 1.171417 36

Hydroxypentobarbital

C11 H18 N2 04, db=88.36,

overall=88.36, CAS ID=4241-40-l 0.001442 2.684938 up 242.1265 7.397805 36 + 2-methyl-tridecanedioic

Acid

C14 H26 04, db=84.53,

overall=84.53 0.047116 1.62001 up 258.1819 1.147222 36

(E)-2-Methylglutaconic

Acid

C6 H8 04, db=87.45,

overall=87.45, HMP

ID=HMDB02266 1.46E-19 3.021139 up 144.0421 0.912857 35 + clavulone II

C25 H34 07, db=65.49,

overall=65.49, Lipid

ID=LMFA03120002 1.54E-06 2.207437 up 506.253 2.569914 35

b-D-Galactopyranose 9.27E-13 3.213161 up 360.1267 0.873273 33

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

C6 H12 06, db=97.41,

overall=97.41, CAS ID=7296-64-2

(E)-2-Methylglutaconic

acid

C6 H8 04, db=84.44,

overall=84.44, HMP

ID=HMDB02266 1.32E-08 2.341264 up 144.0423 2.875818 33 +

3' ,4' -dihydroxyflurbiprof en

C15 H13 F 04, db=73.24,

overall=73.24, CAS ID=66067-41-2 2.19E-05 1.871724 up 276.0806 0.705406 32

Tuberonic acid

C12 H18 04, db=71.78,

overall=71.78, Lipid

ID=LMFA02020007 9.34E-05 5.542252 up 226.1213 1.257219 32

5' -Methylthioadenosine

C11 H15 N5 03 S, db=71.84,

overall=71.84, KEGG ID=C00170,

CAS ID=2457-80-9 2.28E-04 1.372455 up 297.0898 6.569625 32 +

Adipic acid

C6 H10 04, db=79.51,

overall=79.51„ CAS ID=124-04-9 5.25E-07 6.110991 up 146.0574 3.312484 31 +

Phenylethylamine

C8 H11 N, db=97.26,

overall=97.26, KEGG ID=C02455,

CAS ID=2627-86-3 3.31E-04 1.435718 up 121.0898 3.110871 31 +

Primaquine

C15 H21 N3 O, db=65.48, KEGG

ID=C07627, CAS ID=90-34-6 1.23E-12 3.509552 up 297.1234 2.7945 30 + 2-Hydroxyadipic acid

C6 H10 05, db=93.93,

overall=93.93, HMP

ID=HMDB00321 1.64E-05 2.1859 up 162.0528 0.913833 30 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

(E)-2-Methylglutaconic acid

C6 H8 04, db=81.48, HMP

ID=HMDB02266 2.75E-05 1.627582 up 144.0421 8.833768 30 + Hydantoin-5-propionic acid

C6 H8 N2 04, db=51.75,

overall=51.75, HMP

ID=HMDB01212 7.49E-05 2.754425 up 172.048 1.9332 30

5-(4-hydroxy-2,5- dimethylphenoxy)-2,2-dimethyl- Pentanoic acid

(Gemfibrozil Ml)

C15 H22 04, db=72.73,

overall=72.73, CAS ID=80373-12-2 1.28E-08 2.197184 up 266.1529 1.123107 28

Medroxyprogesterone

Glucuronide

C28 H40 09, db=67.94,

overall=67.94 1.44E-05 2.168423 up 520.2682 2.571607 28

Thyroacetic acid

C14 H12 O4, db=59.60,

overall=59.60, CAS ID=500-79-8 2.30E-05 3.775266 up 244.0744 0.840536 28

L-Formylkynurenine

C11 H12 N2 04, db=83.33,

overall=83.33, KEGG ID=C02700,

CAS ID= 1022-31- 0.001125 3.25056 up 472.1588 2.614929 28

6,9-dioxo-decanoic acid

C10 H16 O4, db=78.18,

overall=78.18 6.87E-06 6.57785 up 200.1046 1.258741 27

Naringin

C27 H32 014, db=57.81,

overall=57.81, KEGG ID=C09789,

CAS ID=10236-47-2 0.010042 1.718305 up 580.1792 2.480296 27

(3-Methylcrotonyl)glycine 0.004838 1.636994 up 171.0894 1.476423 26 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode methyl ester

C8 H13 N 03, db=67.54,

overall=67.54, HMP

ID=HMDB01910

2-Propylglutaric acid

C8 H14 04, db=88.69,

overall=88.69 CAS ID=32806-62-5 5.09E-04 2.808559 up 174.0894 1.30388 25

sebacic acid

C10 H18 O4, db=92.44,

overall=92.44, KEGG ID=C08277,

CAS ID=111-20-6 0.002617 7.627153 up 202.1207 1.2305 24

Isovaleric acid

C5 H10 02, db=83.66,

overall=83.66, KEGG ID=C08262,

CAS ID=503-74-2 0.004403 3.295286 up 102.0679 1.505042 24

Etoposide glucuronide

C35 H40 019, db=69.98,

overall=69.98, KEGG ID=C11245,

CAS ID=100007-55-4 5.96E-13 3.913672 up 764.2174 0.890609 23 + a-hydroxyisovalerate

C5 H10 03, db=87.11,

overall=87.11 0.00349 2.433096 up 118.0631 1.442136 22

2R-aminoheptanoic acid

C7 H15 N 02, db=49.88,

overall=49.88 0.005364 1.555888 up 145.1099 8.794772 22

18-bromo-9Z,17E-Octadecadiene- 7,15-diynoic acid

C18 H23 Br 02, db=66.27,

overall=66.27 16 up 350.0887 2.256429 21 + 5-Aminopentanoic acid

C5 H11 N 02, db=99.72,

overall=99.72, HMP 1.70E-05 1.562072 up 117.0788 1.203667 21 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

ID=HMDB03355

1- Naphthyl beta-D-glucuronide

C16 H16 O7, db=68.06,

overall=68.06 .23E-05 5.152374 up 320.0911 1.557619 21

Homoarginine

C7 H16 N4 02, db=86.79,

overall=86.79, HMP

ID=HMDB00670 6.82E-04 1.586733 up 188.127 0.92 21 +

Dihydrostreptomycin 3'alpha,6- bisphosphate

C21 H43 N7 018 P2, db=61.18,

overall=61.18, KEGG ID=C01280 0.001127 1.792667 up 743.2104 1.126333 21 +

4-Oxo-norfloxacin

C16 H16 F N3 04, db=78.60,

overall=78.60, CAS ID=74011-42-0 0 5.716575 up 333.1138 0.939 20 +

2- Hydroxyadipic acid

C6 H10 05, db=81.95,

overall=81.95, HMP

ID=HMDB00321 8.01E-06 6.915113 up 162.053 2.88355 20 + O-Desmethyloxotolrestat sulfate

C15 H12 F3 N 07 S, db=52.39,

overall=52.39, CAS ID=765867-91- 2 1.09E-04 2.585586 up 407.0299 1.47185 20 +

6-Methoxy-2-naphthylacetic acid

C13 H12 03, db=79.31,

overall=79.31, CAS ID=23981-47-7 1.19E-04 3.913132 up 216.0797 1.420368 19

10-Deoxymethymycin

C25 H43 N 06, db=60.38,

overall=60.38, Lipid

ID=LMPK01000039, KEGG

ID=C11994 0 16 453.3075 7.432111 18 +

(E)-2-Methylglutaconic acid 6.87E-07 3.005034 144.0423 2.768444 18 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p-value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

C6 H8 04, db=92.38,

overall=92.38, HMP

ID=HMDB02266

2-oxo capric acid

C10 H18 O3, db=54.78,

overall=54.78, Lipid

ID=LMFA01060001 0.006744 3.641006 up 186.125 1.239944

Muramic acid

C9 H17 N 07, db=57.11,

overall=57.11, KEGG ID=C06470,

CAS ID=1114-41 -6 16 up 251.1002 0.926 17 + N4-Acetylsulfadoxine

C14 H16 N4 05 S, db=65.24,

overall=65.24, CAS ID=5018-54-2 0 2.162285 up 352.0853 2.306812 16

Ribulose

C5 HIO 05, db=83.82,

overall=83.82, CAS ID=5556-48-9 8.11E-06 2.210106 up 150.0526 2.57125 16

Glucoheptonic acid

C7 H14 08, db=94.33,

overall=94.33, CAS ID=23351-51-1 1.41E-04 2.30306 up 226.0681 2.576313 16

17beta-Estradiol 17-(beta-D- glucuronide)

C24 H31 O8, db=48.50,

overall=48.50, Lipid

ID=LMST05010015, KEGG

ID=C11237 16 up 447.2041 2.317333 15 +

Diethylpropion(metabolite V- glucuronide)

C19 H27 N 08, db=73.97,

overall=73.97 0 16 up 397.1751 1.200333 15 +

Glycerophosphoethanolamine

(18:0/0:0) 0.003937 1.293495 up 481.3162 8.045666 15

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

C23 H48 N 07 P, db=67.81,

overall=67.81, Lipid

ID=LMGP02050001

Hydroxypentobarbital

C11 H18 N2 04, db=83.92,

overall=83.92, CAS ID=4241-40-1 0.01476 2.543024 up 242.1253 6.201667 15

sebacic acid

C10 H18 O4, db=81.13,

overall=81.13, KEGG ID=C08277,

CAS ID=111-20-6 0.031899 2.709987 up 202.1206 9.9398 15

2- Hydroxyadipic acid

C6 H10 05, db=71.74,

overall=71.74, HMP

ID=HMDB00321 0.044817 2.169076 up 162.0534 3.844667 15

5-Hydroxybuspirone

C21 H31 N5 03, db=67.23, CAS

ID=105496-33-l 16 up 401.2416 2.109072 14 + 8,13-dihydroxy-9,ll- octadecadienoic acid

C18 H32 04, db=78.62,

overall=78.62 16 up 312.2294 1.107357 14

Diamorphine (heroin)

C21 H23 N 05, db=71.61,

overall=71.61, KEGG ID=C06534,

CAS ID=561-27-3 16 up 369.1592 4.040928 14 + Glutaral (Glutaraldehyde)

C5 H8 02, db=99.28,

overall=99.28, KEGG ID=C12518,

CAS ID=111-30-8 16 up 100.0522 3.295428 14 +

3- (a-Naphthoxy)lactic acid

Glucuronide

C19 H20 OlO, db=78.69, 16 up 408.1048 0.884615 13 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p-value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode overall=78.69, CAS ID=110300-11- 3

Cystathionine sulfoxide

C7 H14 N2 05 S, db=56.08,

overall=56.08, HMP

ID=HMDB02399 16 up 238.063 2.275538 13 + deschlorobenzoyl Indomethacin

Cl l Hl l N 03, db=43.83,

overall=43.83, CAS ID=32387-22-7 0 1.258471 up 205.0748 9.098154 13 + Gibberellin A51-catabolite

C19 H22 05, db=61.76,

overall=61.76, Lipid

ID=LMPRO 1040071, KEGG

ID=C 11854 0 3.375087 up 330.1468 1.785231 13

Salicin

C13 H18 O7, db=69.09,

overall=69.09, HMP

ID=HMDB03546 0 8.661772 up 286.1041 1.903308 13 +

2- Butanone, 4-[6-(sulfooxy)-2- naphthalenyl]

C14 H14 05 S, db=83.13,

overall=83.13, CAS ID=91488-18-5 16 up 294.0565 0.877667 12

3- Hydroxydodecanedioic acid

C12 H22 05, db=51.30, HMP

ID=HMDB00413 16 up 246.1457 1.60275 12 + 3-Hydroxyisovalerylcarnitine

C12 H23 N 05, db=68.79, HMP

ID=HMDB02138 16 up 261.1573 1.698583 12 + 3-Hydroxysebacic acid

C10 H18 O5, db=67.87,

overall=67.87, HMP

ID=HMDB00350 16 up 218.1152 8.31725 12 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

Traumatic acid

C12 H20 O4, db=97.30,

overall=97.30, HMP

ID=HMDB00933 16 up 228.1362 10.72425 12

Dodecanedioic acid

C12 H22 O4, db=80.68,

overall=80.68, HMP

ID=HMDB00623 0.015756 2.708751 up 230.1513 11.018 12 + 2E,6E,8E,10E-dodecatetraenoic

acid

C12 H16 02, db=82.75,

overall=82.75 16 up 192.1149 10.73145 11 +

2-Methoxyestrone 3-sulfate

C19 H24 06 S, db=83.31,

overall=83.31, Lipid

ID=LMST05020006, KEGG

ID=C08358 16 up 380.1289 2.428454 11

4-undecynoic acid

Cl l H18 O2, db=69.01,

overall=69.01 16 up 182.1289 1.275091 11

griseorhodin A

C25 H16 012, db=53.69,

overall=53.69 16 up 508.0668 2.546273 11

Thyroacetic acid

C14 H12 04, db=61.25,

overall=61.25, CAS ID=500-79-8 16 up 244.0738 0.851182 11 +

9-oxo-2E-decenoic acid

C10 H16 O3, db=84.59,

overall=84.59 16 up 184.1098 1.6932 10

Perphenazine

C21 H26 CI N3 O S, db=51.89,

overall=51.89, KEGG ID=C07427, 16 up 403.1469 2.901 10 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

CAS ID=58-39-9

2-methyl-tridecanedioic acid

C14 H26 04, db=97.31,

overall=97.31 16 up 258.183 1.132445

6,9-dioxo-decanoic acid

C10 H16 O4, db=80.35,

overall=80.35 16 up 200.1052 9.612223

a,b-Dihydroxyisobutyric acid

C4 H8 04, db=98.62,

overall=98.62, CAS ID=21620-60-0 16 up 120.0427 0.898667

Catalpol

C15 H22 O10, db=78.71,

overall=78.71, Lipid

ID=LMPR01020108, KEGG

ID=C09773 16 up 362.121 2.599111

Desmethylmaprotiline

Glucuronide

C25 H29 N 06, db=76.51 0 1.363799 up 439.2004 1.913667 +

Ecgonine-methylester

C10 H17 N O3, db=62.58,

overall=62.58, CAS ID=7143-09-1 0 1.282937 up 199.1208 2.049444 +

Nalbuphine-6-sulfate

C21 H27 N 07 S, db=67.57,

overall=67.57 16 up 437.1517 1.448222 +

Furafylline

C12 H12 N4 03, db=60.12, CAS

ID=80288-49-9 8.42E-06 6.354985 up 260.0919 2.919555 + Hexadecanedioic acid

C16 H30 O4, db=87.61,

overall=87.61, HMP

ID=HMDB00672 0.046117 8.627143 286.2144 1.112111

Tacrolimus + 1.8226666 0.049969 1.793654 803.4801 1.822667 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p-value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

C44 H69 N 012, db=87.52, KEGG

ID=C01375, CAS ID= 104987- 11-3

3-Methylsuberic acid

C9 H16 04, db=83.87,

overall=83.87, CAS ID=34284-35-0 16 up 188.1054 8.711

Pyroglutamic acid

C5 H7 N 03, db=99.45,

overall=99.45, KEGG ID=C01879,

CAS ID=98-79-3 1.49E-05 1.655644 down 129.0425 1.4775 42

Glycerophosphoethanolamine

(6:0/6:0)

C17 H34 N 08 P, db=93.40,

overall=93.40, Lipid

ID=LMGP02010104 0.003059 1.521784 down 411.2021 8.403548 42 +

Dihydrolevobunolol glucuronide

C23 H35 N 09, db=78.39,

overall=78.39 0.006525 1.69428 down 469.2316 1.543429 42 +

Myristoyl L-a- lysophosphatidylcholine

C22 H46 N 07 P, db=99.40, CAS

ID=20559-16-4 0.010642 1.421207 down 467.3008 8.154478 42 +

Glycerophosphoethanolamine

(18:0/0:0)

C23 H48 N 07 P, db=99.43, Lipid

ID=LMGP02050001 0.011139 1.441483 down 481.3164 8.098617 42 + 25-hydroxyvitamin D2 25-(beta- glucuronide) /

25 hydroxyergocalciferol 25- (beta-glucuronide)

C34 H52 08, db=82.21,

overall=82.21, Lipid

ID=LMST05010021 0.015543 1.364606 down 588.3631 1.822762 42 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

Digoxigenin monodigitoxoside

C29 H44 08, db=84.20,

overall=84.20, CAS ID=5352-63-6 0.015801 2.013725 down 520.302 1.329762 42

Benzene

C6 H6, db=68.41, overall=68.41,

HMP ID=HMDB01505 0.017633 1.307086 down 78.0473 6.351214 42 + Tyramine

C8 H11 N O, db=89.19,

overall=89.19, KEGG ID=C00483,

CAS ID=51-67-2 0.018436 1.273037 down 137.084 7.384809 42 + Octanol

C8 H18 O, db=99.45,

overall=99.45, HMP

ID=HMDB01183 0.02083 1.337684 down 130.1364 1.599524 42

Desethyletomidate

C12 H12 N2 02, db=84.42,

overall=84.42, CAS ID=7036-56-8 0.031899 1.356521 down 216.0902 6.656024 42

2,3-Dihydroxy-3-methylvaleric

acid

C6 H12 04, db=83.53,

overall=83.53, CAS ID=562-43-6 0.05119 1.389371 down 148.0735 1.623191 42 + 6-Hydroxynicotinic acid

C6 H5 N 03, db=86.24,

overall=86.24, KEGG ID=C01020,

CAS ID=5006-66-6 0.00371 1.415818 down 139.0266 1.06435 40

Levonorgestrel acetate

C23 H30 03, db=89.26,

overall=89.26, CAS ID=13732-69-9 7.87E-05 2.177429 down 354.2181 1.003513 39

Linolenoyl lysolecithin

C26 H48 N 07 P, db=71.91, CAS

ID=63163-01-9 0.032544 1.457361 down 517.315 8.04877 39 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p-value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

Glycerophosphoethanolamine

(16:0/0:0)

C21 H44 N 07 P, db=84.78,

overall=84.78, Lipid

ID=LMGP02050002 0.00269 1.905124 down 453.285 6.732729 37

Glycerophosphoethanolamine

(6:0/6:0)

C17 H34 N 08 P, db=56.51,

overall=56.51, Lipid

ID=LMGP02010104 0.015382 1.334118 down 411.2024 8.14919 37 +

N-Acetylserine

C5 H9 N 04, db=84.44,

overall=84.44, CAS ID=16354-58-8 9.29E-05 1.348314 down 147.0533 0.908056 36

Octanal

C8 H16 0, db=96.18,

overall=96.18, HMP

ID=HMDB01140 0.008928 1.950205 down 128.1197 1.602083 36

Glycerophosphoethanolamine

(18:0/0:0)

C23 H48 N 07 P, db=83.78, Lipid

ID=LMGP02050001 2.33E-04 1.320349 down 481.3161 7.965514 35 + b-D-Mannose 6-phosphate

C9 H12 CI N 04 S, db=58.84,

overall=58.84, CAS ID=117354-64- 0 3.57E-05 1.47993 down 265.0178 7.095 32

p-Hydroxycarvedilol sulfate

C24 H26 N2 08 S, db=68.87,

overall=68.87, CAS ID=142227- 52-9 0.003585 1.580275 down 502.1429 2.712065 31 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

4,4-Bis(p-fluorophenyl)butyric

acid

C16 H14 F2 02, db=47.59,

overall=47.59, CAS ID=20662-52-6 1.33E-05 1.651333 down 276.0961 1.063286 28 + 4,8-dimethyl-dodecanoic acid

C14 H28 O2, db=70.88,

overall=70.88 2.05E-04 1.445999 down 228.2081 1.617577 26

4-Demethoxydaunomycinone

(Idarubicin aglycone)

C20 H16 O7, db=64.31,

overall=64.31, CAS ID=60660-75-5 0.012432 1.566742 down 368.0891 2.9822 25 + N-Acetyl-DL-tryptophan

C13 H14 N2 03, db=77.58,

overall=77.58, CAS ID=87-32-l 1.37E-04 6.90506 down 246.1002 3.199625 24 + Morphine

C17 H19 N O3, db=76.10,

overall=76.10, KEGG ID=C01516,

CAS ID=57-27-2 0.046525 1.661279 down 285.1368 1.133667 24 + 10,11-epoxy-chlorovulone I

C21 H29 CI 05, db=54.24,

overall=54.24, Lipid

ID=LMFA03120012 5.05E-04 1.39822 down 396.1717 1.187864 22

pipecolic acid

C6 H11 N 02, db=87.80,

overall=87.80, KEGG ID=C00408,

CAS ID=535-75-l 0.040985 1.320686 down 129.0789 1.352889 + DL-2-Aminooctanoic acid

C8 H17 N 02, db=75.06, HMP

ID=HMDB00991 2.06E-04 1.428222 down 159.1256 8.568438 16 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

Tomatidine

C27 H45 N 02, db=75.84, Lipid

ID=LMSTO 1150003 6.14E-04 3.225393 down 415.3439 5.530187 16 + Podophyllotoxin

(Podophyllum)

C22 H22 08, db=70.76,

overall=70.76, KEGG ID=C 10874,

CAS ID=518-28-5 0.021974 1.486208 down 414.1308 1.326438 16 + Salicylic acid

C7 H6 03, db=72.74,

overall=72.74, CAS ID=69-72-7 4.05E-05 1.712841 down 138.0311 0.834067 15

Olsalazine

C14 H10 N2 O6, db=54.09,

overall=54.09, KEGG ID=C07323,

CAS ID=15722-48-2 0.002892 1.710024 down 302.0549 1.208357 14

1,3-Glyceryl dinitrate

C3 H6 N2 07, db=68.40,

overall=68.40, CAS ID=623-87-0 0.003448 1.686685 down 182.0173 0.911572 14 + Benzoic acid

C7 H6 02, db=88.72,

overall=88.72, CAS ID=65-85-0 0.037056 1.532715 down 122.0374 1.209286 14

Amylose

C12 H20 O10, db=72.54,

overall=72.54, HMP

ID=HMDB03403 0 17.68509 down 324.105 0.917385 13 +

Indoleacrylic acid

Cl l H9 N 02, db=47.59, HMP

ID=HMDB00734, CAS ID=1204-

06-4 16 down 187.0634 3.198923 13 +

[deprived] Vs [deprived] [deprived] Vs

[treated] Vs [treated] [treated]

Corrected p- value FC regulation Retention Ionization

Compound : Normalized : Normalized : Normalized Time Freauencv mode

9-amino-nonanoic acid

C9 H19 N 02, db=80.38,

overall=80.38 0.002773 2.684494 down 173.1412 8.46025 12 + 2,4-Dichlorophenol

C6 H4 C12 O, db=73.02,

overall=73.02, HMP

ID=HMDB04811 0.028431 2.375815 down 161.963 1.006636 11

hydrocinnamic acid

C9 HIO 02, db=51.50,

overall=51.50, CAS ID=501-52-0 0 1.376084 down 150.0674 1.1339 10

5-Aminoimidazole

Ribonucleotide

C8 H14 N3 07 P, db=47.42,

overall=47.42, HMP

ID=HMDB01235 16 down 295.0567 2.407556

All of the compositions and/or methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of some embodiments, it will be apparent to those of skill in the art that variations may be applied to the compositions and methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the invention. More specifically, it will be apparent that certain agents which are both chemically and physiologically related may be substituted for the agents described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.

REFERENCES

The following references, to the extent that they provide exemplary procedural or other details supplementary to those set forth herein, are specifically incorporated herein by reference.

U.S. Patent 3,817,837

U.S. Patent 3,850,752

U.S. Patent 3,939,350

U.S. Patent 3,996,345

U.S. Patent 4,275,149

U.S. Patent 4,277,437

U.S. Patent 4,366,241

U.S. Patent 5,757,994

U.S. Patent 5,788,166

U.S. Patent 5,838,002

U.S. Patent 5,986,258

U.S. Patent RE 35,413

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