Urine and serum biomarkers associated with diabetic nephropathy

This application is a continuation of U.S. patent application Ser. No. 12/694,575, filed Jan. 27, 2010, which claims priority to U.S. Provisional Application No. 61/147,778, filed on Jan. 28, 2009. The contents of both applications are hereby incorporated by reference in their entirety.

Diabetic nephropathy (DN) is a progressive kidney disease associated with longstanding diabetes mellitus. It causes abnormal fluid filtration and increased urinary albumin excretion, eventually leading to kidney failure.

DN displays no symptoms in its early course. As such, it is difficult to detect the incipiency of this disease. In fact, present diagnosis of DN depends on development of microalbuminuria, which occurs when kidney damage is already in place. The lack of an early diagnostic test prevents effective treatment of early stage DN.

It is of great importance to identify reliable biomarkers useful in diagnosing early stage DN.

The present invention is based on unexpected discoveries that a number of urine and serum proteins and their fragments, either alone or in combination, are differentially presented in DN patients as compared to DN-free subjects. These protein molecules are therefore useful markers for diagnosing early stage DN.

Accordingly, one aspect of this invention features a method of diagnosing DN in a subject. This method includes at least two steps: (i) determining in a subject suspected of having DN a level of a biomarker, and (ii) assessing whether the subject has DN based on the level of the biomarker. An increase in the level of the biomarker, as compared to that in a DN-free subject, indicates that the subject has DN.

The biomarker used in this diagnostic method is one of the four protein molecules listed below:

(i) a first urine protein molecule that is precursor alpha-2-HS-glycoprotein or a fragment thereof having at least ten amino acid residues, such as, mature alpha-2-HS-glycoprotein, VVSLGSPSGEVSHPRKT (SEQ ID NO:1), or MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2);

(ii) a second urine protein molecule that is alpha-1 antitrypsin or a fragment thereof having at least ten amino acid residues, such as KGKWERPFEVKDTEEEDF (SEQ ID NO:3); MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4), EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAE (SEQ ID NO:5), or EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6);

(iii) a third urine protein molecule that is a fragment of alpha-1 acid glycoprotein having at least ten amino acid residues, such as GQEHFAHLLILRDTKTYMLAFDVNDEKNWGLS (SEQ ID NO:7); and

(iv) a serum protein molecule that is osteopontin or a fragment thereof having at least ten amino acid residues, such as YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8) or KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9).

The diagnostic method described above can further include, after the assessing step, a step of correlating the biomarker level with the DN status (i.e., whether it is at early or late stage). When the biomarker is protein molecules (i) or (iv), an increase in its level relative to that in a DN-free subject is indicative of late stage DN. For a biomarker that is protein molecules (ii) or (iii), its level indicates the DN status when compared with pre-determined reference biomarker levels representing early and late stage DN.

In another aspect, the present invention features a method for assessing efficacy of a DN treatment in a subject (e.g., a human patient or a laboratory animal). This method includes determining in the subject pre-treatment and post-treatment levels of protein molecules (i), (ii), (iii), or (iv), and assessing efficacy of the treatment based on a change in the level of the biomarker after the treatment. If the post-treatment level of the biomarker remains the same or decreases as compared to the pre-treatment level of the biomarker, it indicates that the treatment is effective.

In yet another aspect, this invention features a method for determining a DN stage, including at least four steps: (i) obtaining a urine sample and optionally, a serum sample from a subject suspected of having diabetic nephropathy, (ii) determining in the sample(s) a level of one of the biomarkers listed in the preceding paragraph, (iii) calculating a disease score based on the level of the biomarker, and (iv) assessing the subject's diabetic nephropathy stage based on the disease score as compared to pre-determined cutoff values indicating different diabetic nephropathy stages. In this method, the calculating step can be performed by ridge regression analysis, factor analysis, discriminant function analysis, and logistic regression analysis.

The biomarker used in the just-described DN staging method is composed of at least two of the following five protein molecules: protein molecules (i)-(iv) listed above and protein molecule (v) that is urine osteopontin or its fragment described above. In one example, the biomarker is composed of all of the five protein molecules. In another example, it is composed of at least two of protein molecules (i)-(iii) and (v).

Alternatively, the biomarker is composed of at least two of the five protein molecules listed above and additionally, one or more clinical factors, e.g., age, gender, HbA1c, albumin/creatinine ratio (ACR), and glomerular filtration rate (GFR).

In still another aspect, the present invention provides a method for monitoring DN progress based on the level of any of the above-mentioned biomarkers. This method includes obtaining two urine samples and optionally, two serum samples, within a time span of 2 weeks to 12 months (e.g., 2-24 weeks or 3-12 months) from a subject suspected of having DN, determining in the samples a level of one of the biomarkers, calculating disease scores based on the biomarker levels, and assessing DN progress in the subject based on the disease scores. The disease score for the later-obtained samples being greater than that for the earlier-obtained samples is indicative of DN exacerbation.

The biomarkers mentioned above can also be used to assess efficacy of a DN treatment. The treatment is effective if the post-treatment level of one of the biomarkers remains unchanged or decreases as compared to the pre-treatment level of the same biomarker.

The present invention further provides a kit for use in any of the methods described above. This kit includes two, three, or four antibodies with different antigen specificities. Each of these antibodies is capable of binding to one of (i) alpha-2-HS-glycoprotein, (ii) alpha-1 antitrypsin, (iii) alpha-1 acid glycoprotein, and (iv) osteopontin. In one example, this kit contains only antibodies specific to antigens to be detected (e.g., biomarkers associated with DN) for practice one of the methods disclosed herein. Namely, it consists essentially of such antibodies.

Also within the scope of this invention is an isolated antibody specifically binding one of the following peptide:

The terms “an isolated antibody” used herein refers to an antibody substantially free from naturally associated molecules. More specifically, a preparation containing the antibody is deemed as “an isolated antibody” when the naturally associated molecules in the preparation constitute at most 20% by dry weight. Purity can be measured by any appropriate method, e.g., column chromatography, polyacrylamide gel electrophoresis, and HPLC.

Any of the antibodies described above can be used in manufacturing a kit useful in practicing any of the methods of this invention.

The details of one or more embodiments of the invention are set forth in the description below. Other features or advantages of the present invention will be apparent from the following drawings and detailed description of several embodiments, and also from the appended claims.

The drawing is first described.

FIG. 1 is a diagram showing boxplots for urine alpha-2-HS-glycoprotein (uDN2; see panel A), urine alpha-1 antitrypsin (uDN5; see panel B), urine alpha-1 acid glycoprotein (uGR3; see panel C), and serum osteopontin (sDNO; see panel D) in various groups of DN patients. The upper and lower limits of the boxes mark the 25% and 75% values with the medians as the lines across the boxes. The upper whisker marks the largest value below the upper fence, which is the 75% value plus 1.5 interquartile range and the lower whisker marks the smallest value above the lower fence, which is the 25% value minus 1.5 interquartile range.

FIG. 2 is a graph showing MALDI-TOF-MS peak intensities from urine samples of control subjects and diabetic nephropathy (DN) patients. There was a significant increase in the peak intensity of Peak 4 (representing a unique peptide) in urine samples of DN patients as compared to healthy subjects, patients with diabetic mellitus without nephropathy (DM), and patients with diabetes and uremia.

FIG. 3 includes a table and a graph showing that the level of an osteopontin (DNO) peptide in DN patients was decreased using isobaric tags for relative and absolute quantification (iTRAQ).

FIG. 4 is a set of Western blots obtained using antibodies generated against DN peptide biomarkers. H: healthy subject; DN: diabetic nephropathy patients; DM: diabetic patients without nephropathy.

DN is a kidney disorder associated with diabetes. It has five progression phases:

Stage 1: characterized by diabetic mellitus with normal GFR and normal albuminuria (ACR<30 mg/g);

Stage 2: characterized by glomerular hyperfiltration (greater than 120 mL/minute/1.73 m 2 ) and renal enlargement accompanying with normal GFR and normal albuminuria (ACR<30 mg/g);

Stage 3: characterized by microalbuminuria;

Stage 4: characterized by overt albuminuria and a progressive decline in GFR; and

Stage 5: characterized by a GFR of less than 15 mL/minute/1.73 m 2 .

Commonly, stages 1-3 are deemed as early stage and stages 4 and 5 are deemed as late stage.

We have identified a number of biomarkers associated with DN, especially DN in different stages. These biomarkers are composed of one or more of the following four proteins and their fragments, either in urine or in serum: (a) alpha-2-HS-glycoprotein (GenBank accession no. NP — 001613; 10 Jan. 2010); (b) alpha-1-antitrypsin (GenBank accession no. AAB59495; 10 Jan. 2010); (c) alpha-1 acid glycoprotein (GenBank accession no. EAW87416; 10 Jan. 2010); and

(d) Osteopontin, which includes two isoforms known as secreted phosphoprotein 1a (GenBank accession no. NP — 001035147; 17 Jan. 2010) and secreted phosphoprotein 1b (GenBank accession no. NP — 000573; 10 Jan. 2010).

The fragments of these four proteins have a minimum length of ten amino acids and preferably, a maximum length of 190 to 410 amino acids. For example, fragments of proteins (a), (b), (c), and (d) can contain up to 357, 408, 191, and 290 amino acid residues, respectively.

We have also found that biomarkers composed of one or more of the above mentioned proteins/fragments, and one or more clinical factors (e.g., age, gender, HbA1c, ACR, and GFR) are also associated with DN in different stages.

Accordingly, one aspect of the present invention relates to a DN diagnostic method using any of the biomarkers described above. To practice this method, a urine sample and, when necessary, a serum sample, is collected from a subject suspected of having DN and the urine and serum levels of one or more of the four proteins listed above or their fragments can be determined via routine methods, e.g., mass spectrometry and immune analysis. If applicable, the clinical factors are determined by route methods.

When a biomarker contains a single protein molecule, its level in a subject can be compared with a reference point to determine whether that subject has DN. The reference point, representing the level of the same biomarker in a DN-free subject, can be determined based on the representative levels of the biomarker in groups of DN patients and DN-free subjects. For example, it can be the middle point between the mean levels of these two groups. A biomarker level higher than the reference point is indicative of DN.

When a biomarker contains at least two protein molecules and optionally, at least one clinical factor, the levels of the protein molecules and the value(s) of the clinical factor(s) can be subjected to a suitable analysis to generate a disease score (e.g., represented by a numeric number) that characterizes the level of the biomarkers. Examples of the analysis include, but are not limited to, discriminate function analysis, logistic regression analysis, ridge regression analysis, principal component analysis, factor analysis, and generalized linear model. The disease score is then compared with a reference point representing the level of the same biomarker in DN-free subjects. The reference point can be determined by conventional methods. For example, it can be a score obtained by analyzing the mean levels of the protein molecules and when necessary, the mean value(s) of the clinical factor(s) in DN-free subjects with the same analysis. The disease score being higher than the reference point is indicative of DN presence.

Another aspect of this invention relates to a method for determining a DN stage based on any of the biomarkers described above. To practice this method, a biomarker level of a DN patient, preferably represented by a disease score, is compared with a set of pre-determined cutoff values that distinguish different DN stages to determine the subject's DN stage. The cutoff values can be determined by analyzing the representative levels of the same biomarker in different-staged DN patients via the same analysis.

Described below is an exemplary procedure for determining the aforementioned cutoff values based on a biomarker associated with DN in different stages:

(1) assigning DN patients to different groups according to their disease conditions (e.g., DN stages and risk factors);

(2) determining in each patient group the levels/values of the protein molecules and clinical factors constituting the biomarker;

(4) subjecting the protein levels and clinical factor values to a suitable analysis to establish a model (e.g., formula) for calculating a disease score, and

(6) determining a cutoff value for each disease stage based on a disease score (e.g., mean value) representing each patient group, as well as other relevant factors, such as sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

Any of the models thus generated can be assessed for its diagnosis value by a receiver-operating characteristic (ROC) analysis to create a ROC curve. An optimal multivariable model provides a large Area under Curve (AUC) in the ROC analysis. See the models described in Examples 1-3 below.

In still another aspect, this invention relates to a method of monitoring nephropathy progress in a subject based on any of the biomarkers described above. More specifically, two urine samples and/or serum samples from a subject can be obtained within a suitable time span (e.g., 2 weeks to 12 months) and examined to determine the levels of one of the biomarkers. Disease scores are then determined as described above. If the disease score representing the biomarker level in the later obtained sample(s) is lower than that in the earlier-obtained sample(s), it indicates DN exacerbation in the subject.

The monitoring method can be applied to a human subject suffering from or at risk for DN. When the human subject is at risk for or in early stage DN, the level of the biomarker can be examined once every 6 to 12 months to monitor DN progress. When the human subject is already in late stage DN, it is preferred that the biomarker level be examined once every 3 to 6 months.

The monitoring method described above is also applicable to laboratory animals, following routine procedures, to study DN. The term “a laboratory animal” used herein refers to a vertebrate animal commonly used in animal testing, e.g., mouse, rat, rabbit, cat, dog, pig, and non-human primate. Preferably, a laboratory animal is examined to determine the biomarker level once every 2 to 24 weeks.

Any of the biomarkers can also be used to assess efficacy of a DN treatment in a subject in need (i.e., a human DN patient or a laboratory animal bearing DN). In this method, disease scores representing levels of one of the biomarkers described above are determined before, during, and after the treatment. If the disease scores remain the same or decline over the course of the treatment, it indicates that the treatment is effective.

Also disclosed herein is a kit useful in practicing any of the above-described methods. This kit contains two, three, or four antibodies with different antigen specificities. Each of these antibodies is capable of binding to one of (i) alpha-2-HS-glycoprotein, (ii) alpha-1 antitrypsin, (iii) alpha-1 acid glycoprotein, or (iv) osteopontin. The antibodies specific to proteins (i), (ii), (iii), and (iv) can bind to their fragments MGVVSLGSPSGEVSHPRKT (SEQ ID NO:2), KGKWERPFEVKDTEEEDF (SEQ ID NO:3), MIEQNTKSPLFMGKVVNPTQK (SEQ ID NO:4), EDPQGDAAQKTDTSHHDQDHPTFNKITPNLAEFA (SEQ ID NO:6), GQEHFAHLLILRDTKTYMLADVNDEKNWGLS (SEQ ID NO:7), YPDAVATWLNPDPSQKQNLLAPQNAVSSEETNDFKQETLPSK (SEQ ID NO:8), and KYPDAVATWLNPDPSQKQNLLAPQTLPSK (SEQ ID NO:9), i.e., specific to any antibody epitopes contained in these fragments. In one example, this kit contains only antibodies specific to antigens to be detected (e.g., protein molecules associated with DN) for practice one of the methods disclosed herein. Namely, the kit consists essentially of such antibodies.

The kit described above can include two different antibodies (i.e., a coating antibody and a detecting antibody) that bind to the same antigen. Typically, the detecting antibody is conjugated with a molecule which emits a detectable signal either on its own or via binding to another agent. The term “antibody” used herein refers to a whole immunoglobulin or a fragment thereof, such as Fab or F(ab′) 2 that retains antigen-binding activity. It can be naturally occurring or genetically engineered (e.g., single-chain antibody, chimeric antibody, or humanized antibody).

The antibodies included in the kit of this invention can be obtained from commercial vendors. Alternatively, they can be prepared by conventional methods. See, for example, Harlow and Lane, (1988) Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, New York. To produce antibodies against a particular biomarker as listed above, the marker, optionally coupled to a carrier protein (e.g., KLH), can be mixed with an adjuvant, and injected into a host animal. Antibodies produced in the animal can then be purified by affinity chromatography. Commonly employed host animals include rabbits, mice, guinea pigs, and rats. Various adjuvants that can be used to increase the immunological response depend on the host species and include Freund's adjuvant (complete and incomplete), mineral gels such as aluminum hydroxide, CpG, surface-active substances such as lysolecithin, pluronic polyols, polyanions, peptides, oil emulsions, keyhole limpet hemocyanin, and dinitrophenol. Useful human adjuvants include BCG (bacille Calmette-Guerin) and Corynebacterium parvum . Polyclonal antibodies, i.e., heterogeneous populations of antibody molecules, are present in the sera of the immunized animal.

Monoclonal antibodies, i.e., homogeneous populations of antibody molecules, can be prepared using standard hybridoma technology (see, for example, Kohler et al. (1975) Nature 256, 495; Kohler et al. (1976) Eur. J. Immunol. 6, 511; Kohler et al. (1976) Eur J Immunol 6, 292; and Hammerling et al. (1981) Monoclonal Antibodies and T Cell Hybridomas, Elsevier, N.Y.). In particular, monoclonal antibodies can be obtained by any technique that provides for the production of antibody molecules by continuous cell lines in culture such as described in Kohler et al. (1975) Nature 256, 495 and U.S. Pat. No. 4,376,110; the human B-cell hybridoma technique (Kosbor et al. (1983) Immunol Today 4, 72; Cole et al. (1983) Proc. Natl. Acad. Sci. USA 80, 2026, and the EBV-hybridoma technique (Cole et al. (1983) Monoclonal Antibodies and Cancer Therapy, Alan R. Liss, Inc., pp. 77-96). Such antibodies can be of any immunoglobulin class including IgG, IgM, IgE, IgA, IgD, and any subclass thereof. The hybridoma producing the monoclonal antibodies of the invention may be cultivated in vitro or in vivo. The ability to produce high titers of monoclonal antibodies in vivo makes it a particularly useful method of production.

Moreover, antibody fragments can be generated by known techniques. For example, such fragments include, but are not limited to, F(ab′) 2 fragments that can be produced by pepsin digestion of an antibody molecule, and Fab fragments that can be generated by reducing the disulfide bridges of F(ab′) 2 fragments.

Without further elaboration, it is believed that one skilled in the art can, based on the above description, utilize the present invention to its fullest extent. The following specific embodiments are, therefore, to be construed as merely illustrative, and not limitative of the remainder of the disclosure in any way whatsoever. All publications cited herein are incorporated by reference.