BIOMARKERS FOR VISCERAL LEISHMANIASIS

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 60/985,611, filed November 5, 2007, which is incoprorated by reference in its entirety,

FIELD

[0002] The invention relates generally to clinical diagnostics.

BACKGROUND

[0003] Leishmaniasis is a spectrum of diseases with important clinical and epidemiological diversity that exist in humans in three major forms, cutaneous (CL), mucocutaneous (MCL) and visceral leishmaniasis (VL). The diseases are caused by obligate intracellular protozoan parasites belonging to various species of the genus Lβishmania (Saha et a/., 2006). Leishmaniasis is endemic in 88 countries, 72 of which are developing countries, including 13 of the least developed. Ninety per cent of the reported CL cases occur in seven countries- Afghanistan, Algeria, Brazil, Iran, Peru, Saudi Arabia and Syria, while 90 per cent of VL occurs in five countries- Bangladesh, India, Nepal, Sudan and Brazil (Desjeux P., 2004). Leishmaniasis is of concern in many other countries too because of military activities (e.g. US missions in Iraq) and adventure tourism. An estimated 1.5-2 million children and adults develop symptomatic disease every year (cutaneous 1-1.5 million; visceral 0.5 million), The incidence of infection is substantial when subclinical infections are included (Desjeux P., 2004). VL is also a substantial problem in HIV-positive individuals [it is the fourth most common opportunistic parasitic disease in people with HTV in Spain (Paredes et ah, 2003), in

Ethiopia, Sudan and Southern Europe as many as 70 per cent of adults with VL are also HIV positive (Desjeυx, 2001)].

[0004] Cutaneous Leishmaniasis (CL) is caused by a wide range of species, including Leishmania major, L. aethiopica and L, tropica in the Old World, and L. mexicana, L. braziliensis, L, aniazonensis, L. pifanoi, L, garnhami, L, venezuelensis, L.guyanensis, L. peruviana, and L. panamensis in the New World, Mucocutaneous Leishmaniasis (MCL) is most commonly caused by the New World species, L. braziliensis (Renee et at,, 2005, Wilson et al, 2005), though L. aethiopica has also been reported to cause this syndrome. About 70 of around 1000 known sandfly species can transmit leishmaniasis (Murray et aL, 2005) including L. tutzomyia in the Americas and L. phlebotomous elsewhere (Mandell et al., 2005). Sandflies inoculate the skin with flagellated promastigotes, which invade or are phagocytosed by tissue macrophages or are immediately recruited by immune cells, including neutrophils. Within phagolysosomes of resident macrophages, surviving promastigotes transform and replicate as amastigotes, which can infect additional macrophages either locally or in distant tissues after dissemination (Murray et α/., 2005). Cutaneous leishmaniasis (CL) lesions can vary in severity (e.g., lesion size), clinical appearance (e.g., open ulcer versus flat plaques versus wart-like lesions), and duration [e.g., time of evolution, time to spontaneous cure (Reithinger and Dujardin, 2007)].

[0005] Visceral leishmaniasis (VL), commonly known as kala-azar, is caused by L. donovani and L. infantum in the Old World and L. chagasi in the New World. Desjeux (1996) report that visceral leishmaniasis has an estimated yearly incidence of 500 000 cases. VL is characterized by fever, cachexia, hepatosplenomegaly, and hematologic cytopaenias, and is usually fatal without specific chemotherapy (Saha et al, 2006). The name "kala azar" is thought to have originated from India, meaning "black fever", which refers to the hyperpϊgmentation of skin during the course of disease. Alternatively, the term might be derived from the word "kal" meaning "death", which signifies the fatality of the disease (Brahmachari UN, 1928). Rare patients, cured of VL in Sudan and India, can develop post- kala azar dermal leishmaniasis (PKDL), which appears as a dermatotropic form of L. donovani infection (Saha et σ/. _f 2006).

[0006] The broad clinical spectrum of the leishmaniasis makes the diagnosis of present and past cases difficult. However, differential diagnosis is important because other diseases that produce lesions with a clinical appearance similar to those of leishmaniases {e.g., leprosy, skin cancers, and tuberculosis for CL) are often present in areas of endemicity

(Reithinger and Dujardin, 2007). For example, KaIa Azar can be confused with other similar conditions such as tropical splenomegaly, malaria, schistosomiasis or cirrhosis with portal hypertension, military tuberculosis, brucellosis, African trypanosomiasis, typhoid fever, bacterial endocarditis, histoplasmosis, malnutrition, leukaemia and lymphoma (Singh and Sivakumar, 2005).

[0007] Because of its high specificity, microscopic visualization or culture remain the gold standard diagnostic tests in leishmaniasis (Herwaldt, 1999). Importantly, the sensitivity of microscopy and culture tends to be low and can be highly variable (Herwaldt, 1999), depending on the number and dispersion of parasites in biopsy samples, the sampling procedure, and the technical skills of the personnel. Polymerase chain reaction (PCR) applied to lesion material can be highly sensitive and specific (Singh, 2006) but usually requires a biopsy for optimal sensitivity and the expensive machinery and reagents are not feasible for most endemic areas. There is currently no serologic assay that can reliably diagnose subjects with cutaneous leishmaniasis.

[0008] In cutaneous leishmaniasis, serum aπtileishmanial antibodies can be detected in many subjects using sensitive reference assays (Romero et al, 2005). However, such tests are not widely available and, in practice, the diagnosis is made macroscopically by identification of amastigotes in biopsies, scrapings, or impression smears of lesions (Vega- Lopez, 2003). The highest yield is usually obtained from material from the ulcer base (Weina et al, 2004; Ramirez et a/., 2000). Diagnostic sensitivity can be increased to more than 85% when microscopy and culture are combined (Blum et al, 2004; Ramirez et ah, 2000). Tn addition culture (or DNA analysis) allows species identification (Vega-Lopez, 2003; Ramirez et al, 2000). Detection of parasite DNA in lesion material by PCR is generally considered to be the most sensitive test for the diagnosis of both cutaneous and mucosal leishmaniasis (Vega-Lopez, 2003; Weina etal, 2004; de OHveira et al _f 2003; Faber et al _t 2003; Oliveira et a!,, 2005), but is seldom me only test producing positive results (Weina et al, 2004; Oliveira et al., 2005). Culture and PCR testing are technically difficult laboratory techniques that are not currently practical in developing countries (Murray et al, 2005).

[0009] For visceral leishmaniasis direct visualization of amastigotes in clinical specimens is the diagnostic gold standard in regions where deep tissue aspiration is routinely performed and the microscopy and technical skills are available. In immunocompetent patients, the best samples for aspiration are the ones from the spleen with a sensitivity > 94 per cent (Singh, 2006). But this procedure is risky, and even in the most experienced hands,

the risk of fatal bleeding cannot be zeroed (Btycβson, 1987). Another option is the demonstration of the parasites in liver aspirates and biopsies, but the sensitivity can be as low as 40 per cent. Additionally liver aspiration has to be performed as well very carefully to minimize bleeding risks for the patient (Singh, 2006). A much safer but painful method is marrow obtained from iliac or sternal crest puncture (Singh, 2006). In visceral leishmaniasis the hallmark is hyperimmunoglobulinaemia. Tests to measure the antibody level were developed but lack sensitivity and/or specificity (Singh and Sivakumar _> 2003; Singh et al, 2005; Boelaert etal, 2004).

[0010] Diagnostic sensitivity for splenic, bone marrow, and lymph node aspirate smears is >95%, 55-97%, and 60%, respectively (Guerin et al, 2002; Sundar et al, 2002; Davidson, 1998; Sundar and Benjamin, 2003; Ziljstra and El-Hassan, 2001; Pagtiano et al. _t 2003). Elsewhere, including epidemic settings (Guerin et al, 2002; Marlet et al., 2003; Collin et al, 2004; Davidson, 1998), the diagnostic standard for VL is serum anti leishmanial immunoglobulin G in high titre, measured primarily with direct agglutination tests or other serological assays (Desjeux, 2004; Herwaldt, 1999; Guerin et al, 2002; Gama et al, 2004; Davidson, 1998; Sundar and Benjamin, 2003; Abdallah et al, 2004). Freeze-dried antigen, which don't need refrigeration (AbdaHah et al, 2004) and rapid detection of anti-K39 antibody with fmgerstick blood using an immunochromatographic strip test (Sundar et al. _t 2002) have advanced field serodiagnosis. In symptomatic patients, the anti-K.39 strip-test sensitivity is high (90-100%) (Sundar et al, 2002; Veeken et al, 2003; Boelaert et al, 2004), but specificity varies by region (Sundar et al, 2002; Davies et al, 2003; Weina et al, 2004; Boelaert ei al, 2004).

[0011 ] Accordingly, despite the worldwide prevalence and severity of Leishmaniasis, current diagnostic methods are lacking in sensitivity and/or specificity, and are often impractical in light of the available resources in affected areas. Thus, there is a present need for reliable and practical methods for diagnosing leishmaniasis. This invention is directed to this and other ends.

SUMMARY

[0012] The present invention provides polypeptide-based biomarkers that are differentially present in subjects with Leishmaniasis, and particularly that are differentially present in chronically infected subjects versus uninfected healthy individuals. In addition, the present invention provides methods of using the polypeptide-based biomarkers to qualify the

Leishmaniasis status of a subject, e.g., by measuring one or more biomaikers in a biological sample taken from a subject, such as a sample of serum, blood or other donated tissue.

[0013] In some aspects, the invention provides biomarkers that represent novel proteins or protein fragments expressed in infected individuals by a parasite of the genus Leishmania. In other aspects, biomarkers provided herein represent endogenous host proteins expressed, directly or indirectly, m response to infection by a parasite of the genus Leishmania,

[0014] In one aspect, the present invention provides a method for qualifying Leishmaniasis status in a subject, the method comprising: (a) measuring at least one biomarker in a biological sample from the subject, wherein the at least one biomarker is selected from the group consisting of the biomarkers set forth in Table 1, and the figures ; and (b) correlating the measurement with Leishmaniasis status. In some aspects, the biological sample is a serum sample.

[00151 In some aspects, a method is provided for qualifying Visceral Leishmaniasis status in a subject, the method comprising: (a) measuring at least one biomarker of Table 1 in a biological sample from the subject; and (b) correlating the measurement with Visceral Leishmaniasis status. In some aspects, the biological sample is a serum sample. In some aspects, the at least one biomarker is selected from the group consisting of biomarkers having molecular weights of: about 3.3 kDa, about 3.5 kDa, about 5 kDa, about 9.3 kDa, about 9.4 kDa, about 12.4 kDa, about 12.6 kDa, about 28.2 kDa, and about 51.3 kDa.

(0016) In some aspects, the at least one biomarker is selected from biomarkers having the following molecular weights: about 3.3 kDa, about 3.5 kDa, about 5 kDa, about 9.3 kDa, about 9.4 kDa, about 12.4 kDa, about 12.6 kDa, about 28.2 kDa, and about 51.3 kDa. In some aspects, the method comprises measuring biomarkers of each of the following molecular weights: about 3,3 kDa, about 3.5 kDa, about 5 kDa, about 9.3 kDa, about 9.4 kDa, about 12.4 kDa, about 12.6 kDa, about 28.2 kDa, and about 51.3 kDa. In some aspects, the method comprises measuring each of the biomarkers having the following molecular weights: about 5 kDa, about 13.9 kDa, about 28.2 kDa, about 47.7 kDa, about 3.3 kDa, about 12.4 kDa and about 51.3 kDa. In some aspects, the method further comprises measuring one or more of any of the biomarkers listed in Table 1 and/or in the figures.

[0017] Tn some aspects, the at least one biomarker is a protein or fragment thereof as provided in Table 1 and Table 5. In certain aspects, the at least one biomarker is represented by SEQ ID NOS: in Table 5,

[00181 In some aspects, the at least one biomarker is measured by capturing the biomarker on an adsorbent of a SELDI probe and detecting the captured biomarkers by laser desoiption-ionization mass spectrometry. In various aspects, the adsorbent is a cation exchange adsorbent or a metal chelation adsorbent In some aspects, the at least one biomarker is measured by immunoassay or another method known in the art,

(0019] In some aspects, the correlating is performed by a software classification algorithm. In some aspects, the Leishmaniasis status is selected from chronic symptomatic, chronic asymptomatic, acute, and uninfected. In some aspects, the Leishmaniasis status is selected from chronically infected versus uninfected. In some aspects, the Leishmaniasis status is selected from chronically infected status versus acutely infected disease status, chronically infected asymptomatic status versus chronically affected with symptoms, or acutely infected status versus healthy uninfected status. In some aspects, the Leishmaniasis status is selected from Leishmaniasis versus healthy. In some preferred aspects, the at least one biomarker is selected from the biomarkers of Table 1. In some aspects, the Leishmaniasis status is selected from Leishmaniasis versus non-Leishmaniasis. In some preferred aspects, the at least one biomarker is selected from biomarkers of molecular weight : about 3.3 kDa, about 3.5 kDa, about 5 kDa, about 9.3 kDa, about 9.4 kDa, about 12.4 kDa, about 12.6 kDa, about 28.2 kDa, and about 51.3 kDa.

[0020] In some aspects, one or more of the biomarkers measured according to methods described herein are differentially expressed in patients having a particular Leishmaniasis status relative to patients having one or more other Leishmaniasis statuses. In some aspects, measuring a differentially expressed biomarker in a subject according to a method herein qualifies the subject as having a Leishmaniasis status with a probability (P) of about 0.05 or lower, or about 0.01 or lower, or about 0.005 or lower. In some aspects, the measuring step comprises determining the level of expression of the at least one biomarker. In some aspects, the mean level of expression of the at least one biomarker in patients having a first Leishmaniasis status is about twice or more than the mean level of expression in patients having a second Leishmaniasis status.

[0021] In some aspects, the correlating step comprises comparing the level of expression of the at least one biomarker with a pre-determined level of expression. In various aspects, the pre-determined level of expression distinguishes between a first Leishmaniasis status and a second Leishmaniasis status with a probability (P) of about 0.05 or lower, or about 0.01 or lower, or about 0.005 or lower, and/or with a Receiver Operator Characteristic (ROC) of 0,7 or greater, 0.8 or greater, or 0.9 or greater. In some aspects, the pre-determined level of expression is less than the detection threshold, such that the measuring step comprises determining the presence or absence of the at least one biomarker.

[0022] In some aspects, the at least one biomarker is two or more biomarkers and the correlating step comprises comparing the level of expression of each biomarker with a pre-determined level of expression. In some aspects, the pre-determined level of expression of each biomarker distinguishes between a first Leishmaniasis status and a second Leishmaniasis status with an Receiver Operator Characteristic (ROC) of about 0.3 or less or about 0.7 or greater.

[0023] In various aspects, methods described herein distinguish between a first Leishmaniasis status and a second Leishmaniasis status with a probability (P) of about 0.05 or lower, or about 0.01 or lower, or about 0.005 or lower, and/or with a Receiver Operator Characteristic (ROC) of 0.7 or greater, 0.8 or greater, or 0-9 or greater wherein the at least one biomarker is a single biomarker.

[0024] In some aspects, the method further comprises managing subject treatment based on Leishmaniasis status. For example, if the measurement correlates with Leishmaniasis, then managing subject treatment can comprise administering sodium stibogluconate or another drug effective in treating Leishmaniasis.

[0025] In some aspects, the method further comprises measuring the at least one biomarker described herein after subject management.

[0026] In another aspect, the present invention provides a method comprising measuring at least one biomarker in a sample from a subject, wherein the at least one biomarker is selected from the group consisting of the biomarkers set forth in Table 1, and in the figures. In some aspects, the sample is a serum sample.

[0027] In some aspects, the present invention provides a kit comprising: (a) a solid support comprising at least one capture reagent attached thereto, wherein the capture reagent binds at least one biomarker from a first group consisting of the biomarkers set forth in Table

1 and in the figures; and (b) instructions for using the solid support to detect the at least one bioraarker set forth in Table 1 and in the figures.

[0028] In some aspects, the kit provides instructions for using the solid support to detect a biomarker selected from the group consisting of biomarkers having molecular weights of: about 3.3 kDa, about 3.5 kDa, about 5 kDa, about 9.3 KDa, about 9.4 kDa, about 12.4 kDa, about 12.6 kDa, about 28,2 kDa, and about 51.3 kDa. In some aspects, the kit provides instructions for using the solid support to detect each of the following biomarkers; biomarkers having molecular weights of about 5 kDa, about 13.9 kDa, about 28.2 kDa, about 47.7 kDa, about 3.3 kDa, about 12.4 kDa and about 51.3 kDa. In some aspects, the kit provides instructions for additionally measuring one or more of the biomarkers listed in Table 1 and in the figures.

[0029] In some aspects, the solid support comprising the capture reagent is a SELDI probe. In some aspects, the capture reagent is a cation exchange adsorbent. In other aspects, the kit additionally comprises (c) an anion exchange chromatography adsorbent. In other aspects, the kit additionally comprises (c) a container containing at least one of the biomarkers set forth in Table 1 or in the figures, preferably including one or more biomarkers of the following molecular masses: about : about 3.3 kDa, about 3.5 kDa, about 5 kDa, about 9.3 kDa, about 9.4 kDa, about 12.4 kDa, about 12.6 kDa, about 28-2 kDa, and about 51.3 kDa.

[0030] In a further aspect, the present invention provides a kit comprising: (a) a solid support comprising at least one capture reagent attached thereto, wherein the capture reagent binds at least one biomarker from a first group consisting of the biomarkers set forth in Table 1 and in the figures; and (b) a container comprising at least one of the biomarkers set forth in Table 1 and in the figures.

[0031] In some aspects, the solid support comprising the capture reagent is a SELDI probe. In some aspects, the capture reagent- is-a eatiooπβxchange adsorbent oi metal chelation adsorbent. In other aspects, the kit additionally comprises (c) an anion exchange chromatography adsorbent.

[0032] In yet a further aspect, the present invention provides a software product, the software product comprising: (a) code that accesses data attributed to a sample, the data comprising measurement of at least one biomarker in the sample, the biomarker selected from the group consisting of the biomarkers of Table 1 and in the figures; and (b) code that

executes a classification algorithm that classifies the Leishmaniasis status of the sample as a function of the measurement.

[0033] In some aspects, the classification algorithm classifies Visceral Leishmaniasis status of the sample as a function of the measurement of a biomarker selected from the biømarkers of Table 1. In some aspects, the classification algorithm classifies Visceral Leishmaniasis status of the sample as a function of the measurement of a biomarker selected from the group consisting of: biomarkers having molecular weights of: about 3.3 kDa, about 3.5 IdDa, about 5 kDa, about 9.3 KDa, about 9.4 kDa, about 12,4 kDa, about 12.6 kDa, about 28.2 kDa, and about 51.3 kDa, In some aspects, the classification algorithm classifies Visceral Leishmaniasis status of the sample as a function of the measurement of one of the following sets of biomarkers: biomarkers having molecular weights of about 5 kDa, about 13.9 kDa, about 28.2 kDa, about 47.7 kDa, about 3.3 kDa, about 12.4 kDa and about 51.3 kDa

[0034] In some aspects, the classification algorithm classifies the Leishmaniasis status of the sample as a function of the additional measurement one or more of any of the biomarkers listed in Table 1 and in the figures.

[00351 Tn other aspects, the present invention provides purified biomolecules selected from the biomarkers set forth in Table 1 and in the figures and, additionally, methods comprising detecting one or more biomarkers set forth in Table 1 and in the figures by mass spectrometry, immunoassay, or other method known in the art. In some preferred aspects of both of the foregoing aspects, the one or more biomarkers are selected from the following group: biomarkers having molecular weights of about : about 3.3 kDa, about 3.5 kDa, about 5 kDa, about 9.3 kDa, about 9.4 kDa, about 12.4 kDa, about 12.6 kDa, about 28.2 kDa, and about 51.3 kDa..

[0036] In some aspects, the method further comprises testing and qualifying stocks of blood based on the status of blood which has been tested according to the methods described herein.. For example, if the measurements taken from blood samples correlate with Leishmaniasis, then the management of blood stocks can comprise decontamination of the infected Wood by treatment of the infected blood with purification agents available to one skilled in the art including, but not limited to, agents such as sodium stibogluconate. Alternatively, the infected blood can be discarded or destroyed and only stocks of blood which have not tested positively for Leishmaniasis are retained.

[0037] In another aspect, the present invention provides a method of measuring at least two, three, four, or more biomarkers in a in a biological sample, wherein each of the at least two, three, four, or more biomarkers are selected from Table 1. In a preferred aspect, the at least two, three, four, or more biomarkers are selected from the following set of biomarkers: biomarkers having molecular weights of about 5 kDa, about 13.9 kDa, about 28.2 kDa, about 47.7 kDa, about 3.3 kDa, about 12,4 kDa and about 51.3 kDa.

|0038] In one aspect, the present invention provides a method for qualifying Leishmaniasis status in a subject in comparison to the status of a different parasitic disease (Le., a non-Leishmaniasis parasitic disease), the method comprising: (a) measuring at least one biomarker in a biological sample from the subject, wherein the at least one biomarker specifically indicates the presence of Leishmaniasis and does not indicate the presence of a different parasitic infection; and (b) correlating the measurement with Leishmaniasis status in comparison to the status of a different parasitic infection. In some aspects, the biological sample is a serum sample. In a preferred aspect of this method, the at lest one biomarker is selected from the biomarkers set forth inTable 1. In another preferred aspect, the at least one biomarker is selected from the following biomarkers: biomarkers having molecular weights of about 3.3 kDa, about 3-5 kDa, about 5 kDa, about 9.3 kDa, about 9.4 kDa, about 12.4 kDa, about 12.6 kDa, about 28.2 kDa, and about 51.3 kDa. In another preferred aspect, the parasitic infection comprises a kinetoplastidae infection. In still another preferred aspect, the parasitic infection includes, but is not limited to, African trypanosomiasis (sleeping sickness), toxoplasma, babesia, malaria and Chagas Disease.

[0039] In another aspect, the present invention provides a method for monitoring the course of progression of Leishmaniasis in a patient comprising: (a) measuring at least one biomarker in a first biological sample from the patient, wherein the at least one biomarker specifically indicates the presence of Leishmaniasis; (b) measuring the at least one biomarker in a second biological sample from the subject, wherein the second biological sample was obtained from the subject before or after the first biological sample; and (c) correlating the measurements with the progression or regression of Leishmaniasis in the subject. In some aspects, the at least one biomarker is selected from the group consisting of the biomarkers set forth in Table 1, and in the figures. In some preferred aspects, the at least one biomarker is selected from the group consisting of: biomarkers having molecular weights of about 3,3 kDa, about 3.5 kDa, about 5 kDa, about 9.3 kDa, about 9.4 kDa, about 12,4 kDa, about 12.6 kDa, about 28.2 kDa, and about 51.3 kDa,

[0040] Other features, objects and advantages of the invention and its preferred aspects will become apparent from the detailed description, examples and claims that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a m/z scatter plot showing the differential signal intensity of the 3,375 Da biomarker in subjects with Visceral Leishmaniasis (positive) compared to healthy subjects (negative).

[0042] Figure 2 shows a scanned image of an SDS-PAGE gel along with the band intensity of the 3,375 Da biomarker in 4 pretreatment sera from subjects with Visceral Leishmaniasis and 4 healthy control subjects.

[0043] Figure 3 is a m/z scatter plot showing the differential signal intensity of the 12,463 Da biomarker in subjects with Visceral Leishmaniasis (positive) compared to healthy subjects (negative).

[0044] Figure 4 is a decision tree illustrating the ability 3 biomarkers (with MW: 51,351 Da, 28,238 Da and 3,378 Da) found in F4CL to correctly classify sera from subjects with Visceral Leishmaniasis from healthy control subjects.

[0045] Figure 5 is a decision tree illustrating the ability of the 12.4 kDa biomarker found in F61H to correctly classify pretreatment sera from subjects with Visceral Leishmaniasis from healthy control subjects.

[0046] Figure 6 provides a scanned image of an SDS-PAGE gel showing biomarkers with MWs of 27,603 Da, 51,327 Da, 28,238 Da, 51,351 Da and 14,580 Da in total control versus total Visceral Leishmaniasis infected sera.

[0047J Figure 7 provides a scanned image of an SDS-PAGE gel showing biomarkers with MWs of 9,309 Da, 9,478 Da, 12,610 Da ₁ 51,111 Da and 12,463 Da in total control versus total Visceral Leishmaniasis infected sera.

DETAILED DESCRIPTION

1. INTRODUCTION

(004S] A biomarker is an organic biomolecule which is differentially present in a sample taken from a subject of one phenotypic status (e.g., having a disease) as compared with another phenotypic status (e.g., not having the disease). A biomarker is differentially

present between different phenotypic statuses if the expression level of the biomarker {e.g., as indicated by the mean, median, or other measure) in the different groups is calculated to be statistically significant. Common tests for statistical significance include, among others, t- test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann- Whitney and odds ratio. Biomarkers, alone or in combination, provide measures of relative risk that a subject belongs to one phenotypic status or another. Therefore, they are useful as markers for disease (diagnostics), therapeutic effectiveness of a drug (theranostics), drug toxicity, and the like.

2. BIOMARKERS FOR LEISHMANIASIS 2,1. Biomarkers

[00491 This invention provides, inter alia, polypeptide-based biomarkers that are differentially present in subjects having Leishmaniasis, in particular, and particularly those biomarkers that are differentially expressed in subjects infected with Leishmaniasis versus non uninfected individuals (e.g., control, healthy, benign condition or other disease state). The biomarkers are characterized by, e.g., mass-to-charge ratio as determined by mass spectrometry, by the shape of their spectral peak in time-of-flight mass spectrometry, by their binding characteristics to adsorbent surfaces, and/or by other distinguishing characteristics. These characteristics provide various means to determine whether a particular detected biomolecule is useful as a biomarker of Leishmaniasis. These characteristics preferably represent inherent characteristics of the biomolecules rather than process limitations in the manner in which the biomolecules are discriminated, In one aspect, the biomarkers described herein are provided in isolated and/or purified form.

[0050] The biomarkers of Table 1 and the figures were discovered using SELDI technology employing ProteinChip ^® arrays from Ciphergen Biosystems, Inc. (Fremont, CA) ("Ciphergen"). The biomarkers were discovered in serum samples collected from subjects diagnosed with Visceral Leishmaniasis (Table 1), both prior to treatment (pre-treatment) and following treatment with sodium stibogluconate for 14 days and 20 days. The samples were fractionated by anion exchange chromatography. Fractionated samples were applied to SELDI biochips and spectra of polypeptides in the samples were generated by time-of-flight mass spectrometry. The spectra thus obtained were analyzed by a Software algorithm and subjected to scatter plot analysis. A Mann- Whitney test analysis was employed to compare Leishmaniasis and control groups for each protein cluster in the scatter plot, and proteins were selected that differed significantly (pθ.05) between the two groups. This meihod is

described in more detail in the Examples Section, below. The identity of certain of the biomarkers of Table 1 of this invention has been determined and is indicated in Table 5 in the Examples section, for biomarkers whose identify has been determined, the presence of the biornarker can be determined by methods known in the art other than mass spectrometry.

(0051] The biomarkers of this invention can be further characterized by the shape of their spectral peak in time-of-flight mass spectrometry.

[0052] The biomarkers of this invention Eire further characterized by their binding properties on chromatographic surfaces.

[0053] The biomarkers thus discovered are presented in Table 1 which indicate the chromatographic fraction in which the biomarker was found (Fraction), the type of biochip to which the biotnarker binds (Array Surface), the beam intensity applied (Beam Intensity), molecular weight (M/Z (kDa)), and p-values and ROC values as measures of statistical significance and diagnostic potential. Each of these parameters is described in more detail below.

TABLE 1

Marker Array Beam

M/Z (kDa) P-Value ROC Fraction Surface Intensity

289a 0.0000139 0.901 Fraction 1 (pH 9+) CMlO Low

2960 0.000035θ 0.830 Fraction 1 (pH 9+) CMlO Low

3248 0.0000160 0.901 Fraction 1 (pH 9+) CMlO Low

3372 0.0001646 0.839 Fraction 1 (pH 9+) CMlO Low

351S 0.0001004 0.860 Fraction 1 (pH 9+) CMlO Low

3534 0.0000079 0.908 Fraction 1 (pH 9+) CMlO Low

3893 0.0000021 0.056 Fraction 1 (pH 9+) CMlO Low

4301 0.0008301 0.798 Fraction 1 (pH 9+) CMlO Low

43θ8 0.0004750 0.θ19 Fraction 1 (pH 9+) CMlO Low

4525 0.0000275 0-901 Fraction 1 (pH 9+) CMlO Low

4593 0.0009258 0.819 Fraction 1 (pH 9+) CMlO Low

4806 0.0000052 0.921 Fraction 1 (pH 9+) CMlO Low

4826 0.0000033 0.942 Fraction 1 (pH 9+) CMIO Low

4851 0.0000045 0.921 Fraction 1 (pH 9+) CMlO Low

5176 0.0004750 0.178 Fraction 1 (pH 9+) CMlO Low

5192 0.0000160 0.096 Fraction 1 (pH 9+) CMlO Low

5264 0.0002991 0.158 Fraction 1 (pH 9+) CMlO Low

6594 0.0001457 0.158 Fraction 1 (pH 9+) CMlO Low

15152 0.0002362 0.137 Fraction 1 (pH 9+) CMlO Low

15890 0.0001646 0.178 Fraction 1 (pH 9+) CMlO Low

Marker Array Beam

M/Z (kDa) P-Value ROC Fraction Surface Intensity

17762 0.0000531 0.137 Fraction 1 (pH 9+) CMlO Low

17894 0.0008301 0.199 Fraction 1 (pH 9+) CMlO Low

18584 0.0001138 0.137 Fraction 1 (pH 9+) CMlO Low

39926 0,0008301 0.171 Fraction 1 (pH 9+) CMlO Low

44285 0.0000069 0.056 Fraction 1 (pH 9+) CMlO Low

44306 0.0000079 0.076 Fraction 1 (pH 9+) CMlO Low

44308 0.0000060 0.076 Fraction 1 (pH 9+) CM10 Low

44309 0.0000052 0.056 Fraction 1 (pH 9+) CMlO Low

44309 0.0000033 0.056 Fraction 1 (pH 9+) CMIO Low

44311 0.0000045 0.056 Fraction 1 (pH 9+) CMlO Low

44312 0.0000060 0.076 Fraction 1 (pH 9+) CMlO Low

44312 0.0000060 0.076 Fraction 1 (pH 9+) CMlO Low

44316 0.0000060 0.076 Fraction I (pH 9+) CMlO Low

44318 0.0000069 0 076 Fraction 1 (pH 9+) CMlO Low

44331 0.0000060 0.076 Fraction 1 (pH 9+) CMlO Low

10406 0.0007736 0.828 Fraction 1 (pH 9+) CMlO High

10432 0.0003615 0.847 Fraction 1 (pH 9+) CMlO High

10456 0.0005G11 0.808 Fraction 1 (pH 9+) CMlO High

10488 0.0006250 0.828 Fraction 1 (pH 9+) CMlO High

10507 0.0009546 0.808 Fraction I (pH 9+) CMlO High

10536 0.0007736 0.808 Fraction 1 (pH 9+) CMlO High

11797 0.0004511 0.828 Fraction 1 (pH 9+) CMlO High

12324 0.0001282 0.847 Fraction 1 (pH 9+) CMlO High

12437 0.0002888 0.82θ Fraction 1 (pH 9+) CMlO High

12586 00004040 0.808 Fraction 1 (pH 9+) CMlO High

12793 0.0002299 0.828 Fraction 1 (pH 9+) CMlO High

15914 0.0000792 0.144 Fraction 1 (pH 9+) CMlO High

16022 0.0004511 0-197 Fraction 1 (pH 9+) CMlO High

16139 0.0003232 0.158 Fraction 1 (pH 9+) CMlO High

16626 0.0001624 0.144 Fraction 1 (pH 9+) CMlO High

16850 0 0001282 0.139 Fraction 1 (pH 9+) CMlO High

27603 0.0000029 0.944 Fraction 1 (pH 9+) CMlO High

29246 0.0000895 0.125 Fraction I (pH 9+) CMlO High

32326 0.0006250 0-183 Fraction 1 (pH 9+) CMlO High

33582 0.0000256 0-106 Fraction 1 (pH 9+) CMlO High

38039 0.0005033 0,183 Fraction 1 (pH 9+) CMlO High

39879 0.0001282 0-139 Fraction 1 (pH 9+) CMlO High

41774 0.0000426 0.144 Fraction 1 (pH 9+) CMlO High

44534 O.O000132 0.086 Fraction 1 (pH 9+) CMlO High

5S196 0.0000426 0.886 Fraction 1 (pH 9+) CMlO High s ^β ooα 0.0000173 0-106 Fraction 1 (pH 9+) CMlO High

59261 0.0000224 0.106 Fraction I (pH 9+) CMlO High

66549 0.0000330 0.125 Fraction 1 (pH 9+) CMlO High

79207 0.0002299 0.158 Fraction 1 (pH 9+) CMlO High

124504 0.0004040 0.628 Fraction 1 (pH 9+) CMlO High

157018 0.0000291 0.906 Fraction 1 (pH 9+) CMlO High

2502 0.0001416 0-159 Fraction 2 (pH 7) ϊMAC30 Low

Marker Array Beam

M/Z (kDa) P-Value ROC Fraction Surface Intensity

2726 0.0000362 0.139 Fraction 2 (pH 7) IMAC30 Low

3538 0.0006762 0.821 Fraction 2 (pH 7) IMAC30 Low

3549 0.0001530 0.821 Fraction 2 (pH 7) IMAC30 Low

3562 0.0001416 0.841 Fraction 2 (pH 7) IMAC30 Low

3743 0.0003324 0.801 Fraction 2 (pH 7) IMAC30 Low

3749 0.0000725 0.861 Fraction 2 (pH 7) IMAC30 Low

4286 0-0002427 0-641 Fraction 2 (pH 7) IMAC30 Low

4303 0.0009073 0.801 Fraction 2 (pH 7) IMAC30 Low

4617 0.0004086 0.821 Fraction 2 (pH 7) IMAC30 Low

4852 0.0000812 0.841 Fraction 2 (pH 7) IMAC30 Low

5103 0.0000044 0.901 Fraction 2 (pH 7) 1MAC30 Low

3266 0.0001487 0.839 Fraction 3 (pH 5) CMlO Low

3539 0.0002082 0.839 Fraction 3 (pH 5) CMlO Low

3562 0.0000940 0.856 Fraction 3 (pH 5) CMlO Low

4088 0.0006743 0,803 Fraction 3 (pH 5) CMlO Low

4156 0.0007470 0.803 Fraction 3 (pH 5) CMlO Low

4302 0.0001665 0.858 Fraction 3 (pH 5) CMlO Low

4847 0.0000320 0.876 Fraction 3 (pH 5) CMlO Low

4870 0.0000837 0.876 Fraction 3 (pH 5) CMlO Low

55S5 0.0002325 0.839 Fraction 3 (pH 5) CMlO Low

5765 0.0002595 0.839 Fraction 3 <pH 5) CMlO Low

5912 0.0000744 0.858 Fraction 3 (pH 5) CMlO Low

15211 0.0000298 0.903 Fraction 3 (pH 5) CMlO High

23474 0.0002226 0.125 Fraction 3 (pH 5) CMlO High

51327 0.0000138 0.924 Fraction 3 (pH 5) CMlO High

3371 0.0000577 0.841 Fraction 4 (pH 4) CMlO Low

3379 0.0000322 0.861 Fraction 4 (pH 4) CMlO Low

4139 0.0000176 0.881 Fraction 4 (pH 4) CMlO Low

4531 0.0000577 0.861 Fraction 4 (pH 4) CMlO Low

6018 0.0003687 0.841 Fraction 4 (pH 4) CMlO Low

13901 0.0000199 0.119 Fraction 4 (pH 4) CMlO Low

16431 0-0000225 0.119 Fraction 4 (pH 4) CMlO Low

28238 0,0000176 0.119 Fraction 4 (pH 4) CMlO Low

51351 0.0000026 0.941 Fraction 4 (pH 4) CMlO Low

51774 0.0004526 0.781 Fraction 4 (pH 4) CMlO Low

52093 0.0001760 0.841 Fraction 4 (pM 4) CMlO Low

81850 0.0006762 0.199 Fraction 4 (pH 4) CMlO Low

14580 0.0000002 0.020 Fraction 4 (pH 4) CMlO High

14708 0.0000002 0.020 Fraction 4 (pH 4) CMlO High

14870 0 0000008 0.039 Fraction 4 (pH 4) CMlO High

16626 0.0005035 0.176 Fraction 4 (pH 4) CMlO High

16736 0.00028θ2 0.151 Fraction 4 (pH 4) CMlO High

16827 0.0001817 0.137 Fraction 4 (pH 4) CMlO High

17339 0.0000010 0.039 Fraction 4 (pH 4) CMlO High

17482 0.0000018 0.059 Fraction 4 (pH 4) CMlO High

17991 0.0000252 0.112 Fraction 4 (pH 4) CMlO High

19605 0.0005035 0.176 Fraction 4 (pH 4) CMlO High

Marker Array Beam

M/Z ζkDa) P-Value ROC Fraction Surface Intensity

26371 0.0001275 0.S46 Fraction 4 (pH 4) CMlO High

28535 0.0000012 0.059 Fraction 4 (pH 4) CMlO High

29387 0.0000057 0.07S Fraction 4 (pH 4) CMlO High

50020 0.0006968 0.826 Fraction 4 (pH 4) CMlO High

51367 0.0000003 0.983 Fraction 4 (pH 4) CMlO High

56942 0.0002882 0.846 Fraction 4 (pH 4) CMlO High

80097 0.0001436 0.165 Fraction 4 (pH 4) CMlO High

81θ45 0.0001131 0.118 Fraction 4 (pH 4) CMlO High

117781 00001436 0.866 Fraction 4 (pH 4) CMlO High

4746 0.0009994 0.821 Fraction 5 (pH 3) IMAC30 Low

9309 0.0001269 0.159 Fraction 5 (pH 3) 1MAC30 Low

9478 0,0000322 0.139 Fraction 5 (pH 3) IMAC30 Low

13917 0.0000725 0.139 Fraction 5 (pH 3) IMAC30 Low

33648 0.0000156 0.139 Fraction 5 (pH 3) IMAC30 Low

66992 0.0000199 0.119 Fraction 5 (pH 3) IMAC30 Low

71918 0.0001760 0.17S Fraction 5 (pH 3) IMAC30 Low

12299 0.0003362 0.870 Fraction 5 (pH 3) IMAC30 High

12333 0.0005954 0.808 Fraction 5 (pH 3) IMAC30 High

12610 0.0000531 0.663 Fraction 5 (pH 3) IMAC30 High

12678 0.0004750 0.822 Fraction 5 (pH 3) IMAC30 High

12735 0.0004237 0842 Fraction 5 (pH 3) IMAC30 High

14586 0.000185θ 0.140 Fraction 5 (pH 3) IMAC30 High

14703 0.0002991 0.140 Fraction 5 (pH 3) IMAC30 High

15675 0.0004750 0.808 Fraction 5 (pH 3) IMAC30 High

26333 0.0002659 0.822 Fraction 5 (pH 3) MAC30 High

49192 0.0004750 0.822 Fraction 5 (pH 3) IMAC30 High

49982 0.0000160 0.904 Fraction 5 (pH 3) IMAC30 High

50581 0.0000139 0.683 Fraction 5 (pH 3) IMAC30 High

51111 0.0000018 0.944 Fraction 5 (pH 3) IMAC30 High

51555 0.0001646 0.842 Fraction 5 (pH 3) IMAC30 High

52076 0.0001288 0.863 Fraction 5 (pH 3) IMAC30 High

52721 0.0000052 0.924 Fraction 5 (pH 3) IMAC30 High

54017 0.0000240 0.904 Fraction 5 (pH 3) IMAC30 High

66719 0.0008301 0.202 Fraction 5 (pH 3) IMAC30 High

108685 0.0009258 0.808 Fraction 5 (pH 3) 1MAC30 High

133227 0.0000105 0.099 Fraction 5 (pH 3) IMAC30 High

3168 0.00011383 0.828 Fraction 6 (Wash) IMAC30 Low

3375 0.00000017 0.983 Fraction 6 (Wash) IMAC3Q Low

3582 0.00000218 0.944 Fraction 6 (Wash) MAC30 Low

3599 0.00000031 0.964 Fraction 6 (Wash) IMAC30 Low

4261 0.00014436 0.847 Fraction 6 (Wash) 1MAC30 Low

4293 0.00036150 0847 Fraction 6 (Wash) IMAC30 Low

4363 0.00000058 0.933 Fraction 6 (Wash) IMAC30 Low

4474 0.00000017 0.983 Fraction 6 (Wash) IMAC30 Low

4544 0 00004259 0.906 Fraction 6 (Wash) IMAC30 Low

4631 0.00000078 0.964 Fraction 6 (Wash) IMAC30 Low

4745 0.00000218 0.944 Fraction 6 (Wash) IMAC30 Low

Marker Array Beam

M/Z φDa) F-Value ROC Fraction Surface Intensity

5073 0.00001158 0.906 Fraction 6 (Wash) IMAC30 Low

6200 0.00000586 0 911 Fraction 6 (Wash) IMAC30 Low

6405 0.00003304 0.911 Fraction 6 (Wash) IMAC30 Low

7295 0.00003304 0.886 Fraction 6 (Wash) IMAC30 Low

7629 0.00000443 0.0θ6 Fraction 6 (Wash) IMAC30 Low

7950 0.00010095 0.125 Fraction 6 (Wash) IMAC30 Low

11551 0.00000020 0.983 Fraction 6 (Wash) IMAC30 Low

11740 0.00000017 0.983 Fraction 6 (Wash) DV1AC30 Low

13765 0.00004259 0,125 Fraction 6 (Wash) IMAC30 Low

13907 0.00001513 0.0θ6 Fraction 6 (Wash) IMAC30 Low

14094 0.00000510 0.067 Fraction 6 (Wash) IMAC30 Low

15178 0.00002555 0.081 Fraction 6 (Wash) IMAC30 Low

15370 0.00010095 0.139 Fraction 6 (Wash) IMAC30 Low

15905 0.00002555 0.106 Fraction 6 (Wash) IMAC30 Low

16113 0.00001727 0.086 Fraction 6 (Wash) IMAC30 Low

17317 0.00032323 0.164 Fraction 6 (Wash) IMAC30 Low

28184 0.00001012 0.067 Fraction 6 (Wash) IMAC30 Low

29018 0.00000290 0.067 Fraction 6 (Wash) IMAC30 Low

50985 0.00000122 0.944 Fraction 6 (Wash) IMAC30 Low

51355 0.00001513 0.906 Fraction 6 (Wash) IMAC30 Low

53784 0.00004259 0-886 Fraction 6 (Wash) IMAC30 Low

60791 0.00003753 0.886 Fraction 6 (Wash) IMAC30 Low

62393 0.00000443 0.925 Fraction 6 (Wash) IMAC3Q Low

91819 0.00028877 0.847 Fraction 6 (Wash) IMAC30 Low

10959 0.00095457 0.806 Fraction 6 (Wash) IMAC30 High

11053 0.00085970 0.808 Fraction 6 (Wash) IMAC30 High

11074 O.OOO40398 o.aoa Fraction 6 (Wash) IMAC30 High

11128 0.00040398 0.828 Fraction 6 (Wash) ϊMAC30 High

11161 0.00085970 0.789 Fraction 6 (Wash) IMAC30 High

11730 0.00018246 0.828 Fraction 6 (Wash) IMAC30 High

12238 0.00000189 0.944 Fraction 6 (Wash) IMAC30 High

12307 0.00000290 0.925 Fraction 6 (Wash) IMAC30 High

12393 0.00000017 0.983 Fraction 6 (Wash) IMAC30 High

12463 0.00000014 0.983 Fraction 6 (Wash) IMAC30 High

12584 0.00000014 0-983 Fraction 6 (Wash) IMAC30 High

12625 0.00000014 0.983 Fraction 6 (Wash) IMAC30 High

12666 0.00000014 0,983 Fraction 6 (Wash) IMAC30 High

12786 0.00000058 0.964 Fraction 6 (Wash) IMAC30 High

12851 0.00000036 0.964 Fraction 6 (Wash) IMAC30 High

12931 0.00000027 0.983 Fraction 6 (Wash) IMAC30 High

13004 0.00000017 0.983 Fraction 6 (Wash) IMAC30 High

13076 0.00000078 0.964 Fraction 6 (Wash) IMAC30 High

13210 0.00018248 0.828 Fraction 6 (Wash) IMAC30 High

14711 0.00022992 0.158 Fraction 6 (Wash) IMAC30 High

14883 0.00003753 0.100 Fraction 6 (Wash) IMAC30 High

1535S 0.00001969 0.906 Fraction 6 (Wash) IMAC30 High

15558 0.00011383 0.847 Fraction 6 (Wash) IMAC30 High

Marker Array Beam

M/Z CkDa) P-Value ROC Fraction Surface Intensity

16626 0.00056108 0.197 Fraction 6 (Wash) IMAC30 High

16767 0.00014436 0.158 Fraction 6 (Wash) IMAC30 High

16825 0.00022992 0.158 Fraction 6 (Wash) IMAC30 High

23543 0.00002907 0.106 Fraction 6 (Wash) IMAC30 High

29326 0.00001369 0.106 Fraction 6 (Wash) IMAC30 High

50252 0.00000020 0.983 Fraction 6 (Wash) IMAC30 High

50627 0.00000017 0.983 Fraction 6 (Wash) IMAC30 High

5121B 000000017 0,983 Fraction 6 (Wash) IMAC30 High

5θ346 0.00001012 0.906 Fraction 6 (Wash) IMAC30 High

60195 0.00000163 0.964 Fraction 6 (Wash) IMAC30 High

61361 0.00000443 0.944 Fraction 6 (Wash) IMAC30 High

89621 0.00000031 0.983 Fraction 6 (Wash) IMAC30 High

94758 0.00001012 0.086 Fraction 6 (Wash) IMAC30 High

133020 0.00050329 0.164 Fraction 6 (Wash) IMAC30 High

2.2. Detection and Characterization of BIomarkers

[0054] The biomarkers of this invention can be detected by any suitable method. Detection paradigms that can be employed to this end include optical methods, electrochemical methods (voltametry and amperometry techniques), atomic force microscopy, and radio frequency methods, e,g,, multipolar resonance spectroscopy. Illustrative of optical methods, in addition to microscopy, both confocal and non-confocal, are detection of fluorescence, luminescence, chermhiminescence, absorbance, reflectance, transmittance, and birefringence or retractive index (eg., surface plasmon resonance, ellipsometry ₃ a resonant mirror method, a grating coupler waveguide method or interferometry).

[0055] In some aspects, a sample is analyzed by means of a biochip. Biochips generally comprise solid substrates and have a generally planar surface, to which a capture reagent (also called an adsorbent or affinity reagent) is attached. Frequently, the surface of a biochip comprises a plurality of addressable locations, each of which has the capture reagent bound there.

[0056] Protein biochips are biochips adapted for the capture of polypeptides. Many protein biochips are described in the art. These include, for example, protein biochips produced by Ciphergen Bjosystems, Inc. (Fremont, CA), Zyomyx (Hayward, CA), Invitrogen (Carlsbad, CA), Biacore (Uppsala, Sweden) and Procognia (Berkshire, UK) .

Examples of such protein biochips are described in the following patents or published patent applications: U.S. Patent No. 6,225,047 (Hutchβπs & Yip); U.S. Patent No. 6,537,749 (Kuimelis and Wagner); U.S. Patent No. 6,329,209 (Wagner si a/.); PCT International Publication No. WO 00/56934 (Englert et al)\ PCT International Publication No, WO 03/048768 (Boutell et al.) and U.S. Patent No. 5,242,828 (Bergstrom et al).

[0057J In a preferred aspect, the biomarkers of this invention are detected by mass spectrometry, a method that employs a mass spectrometer to detect gas phase ions. Examples of mass spectrometers are time-of-fligbx, magnetic sector, qiiadrupole filter, ion trap, ion cyclotron resonance, electrostatic sector analyzer and hybrids of these.

(005S] In a further preferred method, the mass spectrometer is a laser desorption/ionization mass spectrometer. In laser desorption/ionization mass spectrometry, the analytes are placed on the surface of a mass spectrometry probe, a device adapted to engage a probe interface of the mass spectrometer and to present an analyte to ionizing energy for ionization and introduction into a mass spectrometer. A laser desorption mass spectrometer employs laser energy, typically from an ultraviolet laser, but also _π ΌπI an infrared laser, to desorb analytes from a surface, to volatilize and ionize them and make them available to the ion optics of the mass spectrometer.

[0059] A preferred mass spectrometric technique for use in the invention is "Surface Enhanced Laser Desorption and Ionization" or "SELDI," as described, for example, in U.S. Patents No. 5,719,060 and No. 6,225,047, both to Hutchens and Yip. This refers to a method of desorption/ionization gas phase ion spectrometry (e.g., mass spectrometry) in which an analyte (here, one or more of the biomarkers) is captured on the surface of a SELDI mass spectrometry probe. There are several versions of SELDI, which are described in more detail below. f0060] One version of SELDI is called "affinity capture mass spectrometry." It also is called "Sur&ce-Enhanced Affinity Capture" or "SEAC". This version involves the use of probes that have a material on the probe surface that captures analytes through a non-covalent affinity interaction (adsorption) between the material and the analyte. The material is variously called an "adsorbent," a "capture reagent," an "affinity reagent" or a "binding moiety." Such probes can be referred to as "affinity capture probes" and as having an "adsorbent surface." The capture reagent can be any material capable of binding an analyte. The capture reagent is attached to the probe surface by physisoiption or chemisorption. In

certain aspects the probes have the capture reagent already attached to the surface. In other aspects, the probes are pre-activated and include a reactive moiety that is capable of binding the capture reagent, e.g., through a reaction forming a covalent or coordinate covalent bond. Epoxide and acyl-imidizole are useful reactive moieties to covalently bind polypeptide capture reagents such as antibodies or cellular receptors. Nitrilotriacetic acid and iminodiacetic acid are useful reactive moieties that function as chelating agents to bind metal ions that interact non-covalently with histidine containing peptides. Adsorbents are generally classified as chromatographic adsorbents and biospecific adsorbents.

[0061] "Chromatographic adsorbent" refers to an adsorbent material typically used in chromatography. Chromatographic adsorbents include, for example, ion exchange materials, metal chelators (e.g., nitrilotriacetic acid or iminodiacetic acid), immobilized metal chelates, hydrophobic interaction adsorbents, hydrophilic interaction adsorbents, dyes, simple biomolecules {e.g., nucleotides, amino acids, simple sugars and fatty acids) and mixed mode adsorbents (e.g. _t hydrophobic attraction/electrostatic repulsion adsorbents).

[0062] "Biospecific adsorbent" refers to an adsorbent comprising a biomolecule, e.g., a nucleic acid molecule (e.g., an aptamer), a polypeptide, a polysaccharide, a lipid, a steroid or a conjugate of these (e.g., a glycoprotein, a lipoprotein, a glycolipid, a nucleic acid (e.g., DNA)-protein conjugate). In certain instances, the biospecific adsorbent can be a macromolecular structure such as a multiprotein complex, a biological membrane or a virus. Examples of biospecific adsorbents are antibodies, receptor proteins and nucleic acids. Biospecific adsorbents typically have higher specificity for a target analyte than chromatographic adsorbents. Further examples of adsorbents for use in SELDI can be found in U.S. Patent No, 6,225,047. A "bioselective adsorbent" refers to an adsorbent that binds to an analyte with an affinity of at least 10 ^-8 M.

[0063] Protein biochips produced by Ciphergen Biosystems, Inc. comprise surfaces having chromatographic or biospecific adsorbents attached thereto at addressable locations. Ciphergen ProteinChip ^® arrays include NP20 (hydrophilic); H4 and H50 (hydrophobic); SAX-2, Q- 10 and LSAX-30 (anion exchange); WCX-2, CM-10 and LWCX-30 (cation exchange); lMAC-3, IMAC-30 and IMAC 40 (metal chelate); and PS-IO, PS-20 (reactive surface with acyl-imidizole, epoxide) and PG-20 (protein G coupled through acyl-imidizole). Hydrophobic ProteinChip arrays have isopropyl or nonylphenoxy-poly(ethylene glycol)methacrylate functionalities. Anion exchange ProteinChip arrays have quaternary ammonium functionalities. Cation exchange ProteinChip arrays have carboxylate

functionalities. Immobilized metal chelate ProteinChip arrays have nitiilotriacetic acid functionalities that adsorb transition metal ions, such as copper, nickel, zinc, and gallium, by chelation. Preactivated ProteinChip arrays have acyl-imidizole or epoxide functional groups that can react with groups on proteins for covalent binding.

[0064] Such biochips are further described in: U.S. Patent No. 6,579,719 (Hutchens and Yip, "Retentate Chromatography," June 17, 2003); U-S- Patent 6,897,072 (Rich et al, "Probes for a Gas Phase Ion Spectrometer," Can 24, 2005); U.S. Patent No. 6,555,813 (Beecher et al. , "Sample Holder with Hydrophobic Coating for Gas Phase Mass Spectrometer," April 29, 2003); U.S. Patent Application No. U.S. 2003 0032043 Al (Pohl and Papanu, "Latex Based Adsorbent Chip," July 16, 2002); and PCT International Publication No. WO 03/040700 (Um etal., hydrophobic Surface Chip," Can 15, 2003); U.S. Patent Application No. US 2003/0218130 Al (Boschβtti etal, "Biochips With Surfaces Coated With Polysaccharide-Based Hydrogels," April 14, 2003) and U-S- Patent Application No- 60/448,467, entitled "Photocrossliπked Hydrogel Surface Coatings" (Huang et al., filed February 21 , 2003).

[0065] In general, a probe with an adsorbent surface is contacted with the sample for a period of time sufficient to allow the biomarker or biomarkers that can be present in the sample to bind to the adsorbent. After an incubation period, the substrate is washed to remove unbound material. Any suitable washing solutions can be used; preferably, aqueous solutions are employed. The extent to which molecules remain bound can be manipulated by adjusting the stringency of the wash. The elution characteristics of a wash solution can depend, for example, on pH, ionic strength, bydrophobicity, degree of chaotropism _s detergent strength, and temperature. Unless the probe has bofli SEAC and SEND properties (as described herein), an energy absorbing molecule then is applied to the substrate with the bound biomarkers.

[0066] The biomarkers bound to the substrates are detected in a gas phase ion spectrometer such as a time-of-flight mass spectrometer. The biomarkers are ionized by an ionization source such as a laser, the generated ions are collected by an ion optic assembly, and then a mass analyzer disperses and analyzes the passing ions. The detector then translates information of the detected ions into mass-to-charge ratios. Detection of a biomarker typically will involve detection of signal intensity. Thus, both the quantity and mass of the biomarker can be determined.

[00671 Another version of SELDI is Surface-Enhanced Neat Desorption (SEND), which involves the use of probes comprising energy absorbing molecules that are chemically bound to the probe surface ("SEND probe**). The phrase "energy absorbing molecules" (EAM) denotes molecules that are capable of absorbing energy from a laser desoφtion/ionization source and, thereafter, contribute to desorption and ionization of analyte molecules in contact therewith. The EAM category includes molecules used in MALDI, frequently referred to as "matrix," and is exemplified by cinnamic acid derivatives, sinapinic acid (SPA), cyano-hydroxy-cinnamic acid (CHCA) and dihydroxybenzoic acid, ferulic acid, and hydroxyaceto-phenone derivatives. In certain aspects, the energy absorbing molecule is incorporated into a linear or cross-Iiύked polymer, e.g., a polymethacrylate. For example, the composition can be a co-polymer of α-cyano-4-methacryloyloxycinnamic acid and acrylate. In some aspects, the composition is a co-polymer of α-cyano-4-methacryloyloxycinnainic acid, acrylate and 3-(tri-ethαxy)silyl propyl methacrylate. In some aspects, the composition is a co-polymer of α-cyano-4-methacryloyIoxycinnamic acid and octadecylmethacrylate ("CIS SEND"). SEND is further described in U.S. Patent No. 6,124,137 and PCT International Publication No. WO 03/64594 (Kitagawa, "Monomers And Polymers Having Energy Absorbing Moieties Of Use Ih Desorption/Ionization Of Analytes," August 7, 2003).

[0068] SEAC/SEND is a version of SELDI in which both a capture reagent and an energy absorbing molecule are attached to the sample presenting surface. SEAC/SEND probes therefore allow the capture of analytes through affinity capture and ionizatioπ/desorption without the need to apply external matrix. The CI8 SEND biochip is a version of SEAC/SEND, comprising a Cl 8 moiety which functions as a capture reagent, and a CHCA moiety which functions as an energy absorbing moiety.

[0069] Another version of SELDI, called Surface-Enhanced Photolabile Attachment and Release (SEPAR), involves the use of probes having moieties attached to the surface that can covalently bind an analyte, and then release the analyte through breaking a photolabile bond in the moiety after exposure to light, e.g., to laser light (see, U.S. Patent No, 5,719,060). SEPAR and other forms of SELDI are readily adapted to detecting a biomarker or biomarker profile, pursuant to the present invention.

(0070J In another mass spectrometry method, the biomarkers are first captured on a chromatographic resin having chromatographic properties that bind Hie biomarkers. In the present example, this could include a variety of methods. For example, one could capture the

biomarkers on a cation exchange resin, such as CM Ceramic HyperD F resin, wash the resin, elute the biomarkers and detect by MALDI. Alternatively, this method could be preceded by fractionating the sample on an anion exchange resin before application to the cation exchange resin. In another alternative, one could fractionate on an anion exchange resin and detect by MALDI directly. In yet another method, one could capture the biomarkers on an immuno- chromatographic resin that comprises antibodies that bind the biomarkers, wash the resin to remove unbound material, elute the biomarkers from the resin and detect the eluted biomarkers by MALDI or by SELDI. In yet another method, one could isolate the biomarkers using gel electrophoresis and detect the biomarkers by MALDI OR SELDϊ.

[0071] Analysis of analytes by time-of-flight mass spectrometry generates a time- of-flight spectrum. The time-of-flight spectrum ultimately analyzed typically does not represent the signal from a single pulse of ionizing energy against a sample, but rather the sum of signals from a number of pulses. This reduces noise and increases dynamic range. This time-of-flight data is then subject to data processing. In Ciphergen's PrøteiπChip® software, data processing typically includes TOF-to-M/Z transformation to generate a mass spectrum, baseline subtraction to eliminate instrument offsets and high frequency noise filtering to reduce high frequency noise.

[0072] Data generated by desorptiøn and detection of biomarkers can be analyzed with the use of a programmable digital computer. The computer program analyzes the data to indicate the number of biomarkers detected, and optionally the strength of the signal and the determined molecular mass for each biomarker detected. Data analysis can include steps of determiϋing signal strength of a biomarker and removing data deviating from a predetermined statistical distribution. For example, the observed peaks can be normalized, by calculating the height of each peak relative to some reference.

[0073] The computer can transform the resulting data into various formats for display. The standard spectrum can be displayed, but in one useful format only the peak height and mass information are retained from the spectrum view, yielding a cleaner image and enabling biomarkers with nearly identical molecular weights to be more easily seen. In another useful format, two or more spectra are compared, conveniently highlighting unique biomarkers and biomarkers that are up- or down-regulated between samples. Using any of these formats, one can readily determine whether a particular biomarker is present in a sample.

[0074] Analysis generally involves the identification of peaks in the spectrum that represent signal from an analyte. Peak selection can be done visually, but software is available, as part of Ciphergen's ProteinChip® software package, that can automate the detection of peaks. Tn general, this software functions by identifying signals having a signal- to-noise ratio above a selected threshold and labeling the mass of the peak at the centroid of the peak signal. In one useful application, many spectra are compared to identify identical peaks present in some selected percentage of the mass spectra. One version of this software clusters all peaks appearing in the various spectra within a denned mass range, and assigns a mass (M/Z) to all the peaks that are near the mid-point of the mass (M/Z) cluster.

[0075] Software used to analyze the data can include code that applies an algorithm to the analysis of the signal to determine whether the signal represents a peak in a signal that corresponds to a biomarker according to the present invention. The software also can subject the data regarding observed biomarker peaks to classification tree or ANN analysis, to determine whether a biomarker peak or combination of biomarker peaks is present that indicates the status of the particular clinical parameter under examination. Analysis of the data can be "keyed" to a variety of parameters that are obtained, either directly or indirectly, from the mass spectrometric analysis of the sample. These parameters include, but are not limited to, the presence or absence of one or more peaks, the shape of a peak or group of peaks, the height of one or more peaks, the log of the height of one or more peaks, and other arithmetic manipulations of peak height data.

[0076] A preferred protocol for the detection of the biomarkers of this invention is as follows. The biological sample to be tested, e.g., serum, preferably is subject to pre- fractionation before SELDI analysis. This simplifies the sample and improves sensitivity. A preferred method of pre-fractionation involves contacting the sample with an anion exchange chromatographic material, such as Q HyperD (BioSepra, SA). The bound materials are then subject to stepwise pH elution using buffers at pH 9, pH 7, pH 5 and pH 4. (The fractions in which the biomarkers are eluted also are indicated in Table 1.) Various fractions containing the biomarker are collected.

[0077] The sample to be tested (preferably pre-fractionated) is then contacted with an affinity capture probe comprising an cation exchange adsorbent (preferably a WCX ProteinChip array (Ciphergen Biosystems, Inc.)) or an IMAC adsorbent (preferably an IMAC3 ProteinChip array (Ciphergen Biosystems, Inc.)), again as indicated in Table 1. The probe is washed with a buffer that will retain the biomarker while washing away unbound

molecules. A suitable wash for each biomarker is the buffer identified in Table 1. The biomarkers are detected by laser desorption/ionization mass spectrometiy.

[0078] Alternatively, if antibodies that recognize the biomarker are available, these can be attached to the surface of a probe, such as a pre-activated PSlO or PS20 ProteinChip array (Ciphergen Biosystems, Inc.). These antibodies can capture the biomarkers from a sample onto the probe surface. Then the biomarkers can be detected by, e,g., laser desorption/ionization mass spectrometry,

2.3. Detection by Immunoassay

[0079] In some aspects of the invention, the biomarkers of the invention are measured by a method other than mass spectrometry or other than methods that rely on a measurement of the mass of the biomarker. In one such aspect that does not rely on mass, the biomarkers of this invention are measured by immunoassay. Immunoassay requires biospecific capture reagents, such as antibodies, to capture the biomarkers. Antibodies can be produced by methods well known in the art, e.g., by immunizing animals with the biomarkers. Biomarkers can be isolated from samples based on their binding characteristics. Alternatively, if the amino acid sequence of a polypeptide biomarker is known, the polypeptide can be synthesized and used to generate antibodies by methods well known in the art.

[0080] This invention contemplates traditional immunoassays including, for example, sandwich immunoassays including ELISA or fluorescence-based immunoassays, as well as other enzyme immunoassays. Nephelometry is an assay done in liquid phase which measures the binding of an antigen to an antibody in solution by measuring changes in absorbance upon antibody-antigen binding. In the SELDI-based immunoassay, a biospecific capture reagent for the biomarker is attached to the surface of an MS probe, such as a pre- activated ProteinChip array. The biomarker is then specifically captured on the biochip through this reagent, and the captured biomarker is detected by mass spectrometry.

3. DETERMINATION OF SUBJECT LEISHMANIASIS STATUS 3,1, Single Markers

[0081] The biomarkers of the invention can be used in diagnostic tests to assess Leishmaniasis status in a subject, e.g., to diagnose Leishmaniasis. The phrase "Leishmaniasis status" includes any distinguishable manifestation of the disease, including non-disease. For example, disease status includes, without limitation, the presence or absence of disease (e.g.,

Leishmaniasis v. non Leishmaniasis or Leishmaniasis v. other parasitic disease {e.g., African sleeping sickness, malaria)), the risk of developing disease, the stage of the disease, the progress of disease (e.g., progress of disease or remission of disease over time) and the effectiveness or response to treatment of disease. The status of the subject can inform the practitioner about what status set is being distinguished. For example, a subject that presents with signs of a parasitic disease could be classed into Leishmaniasis v. non-Leishmaniasis parasitic disease, while a person exposed to a situation in which Leishmaniasis infection is possible and who is presenting with signs of Leishmaniasis infection could be classified into Leishmaniasis v. non-Leishmaniasis. Based on this status, further procedures can be indicated, including additional diagnostic tests or therapeutic procedures or regimens-

[0082] The power of a diagnostic test to correctly predict status is commonly measured as the sensitivity of the assay, the specificity of the assay or the area under a receiver operated characteristic ("ROC") curve. Sensitivity is the percentage of true positives that are predicted by a test to be positive, while specificity is the percentage of true negatives that are predicted by a test to be negative. An ROC curve provides the sensitivity of a test as a function of 1 -specificity. The greater the area under the ROC curve, the more powerful the predictive value of the test. Other useful measures of the utility of a test are positive predictive value and negative predictive value. Positive predictive value is the percentage of people who test positive that are actually positive. Negative predictive value is the percentage of people who test negative that are actually negative.

[0083] The biomarkers of this invention show a statistical difference in different Leishmaniasis statuses of at least p ≤ 0.05, p ≤ 10 ^-z , p ≤ 10 ^~3 , p ≤ 10^ or p ≤ 10 ^-5 . Diagnostic tests that use these biomarkers alone or in combination show a sensitivity and specificity of at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98% and about 100%.

[0084] Certain biomarkers listed in Table lare differentially present in Leishmaniasis, and, therefore, each is individually useful in aiding in the determination of Leishmaniasis status. The method involves, first, measuring the selected biomarker in a subject sample using the methods described herein, e.g., capture on a SELDI biochip followed by detection by mass spectrometry and, second, comparing the measurement with a diagnostic amount or cut-off that distinguishes a positive Leishmaniasis status from a negative Leishmaniasis status. The diagnostic amount represents a measured amount of a biomarker above which or below which a subject is classified as having a particular

Leishmaniasis status. For example, if the biomarker is up-regulated compared to normal during Leishmaniasis, then a measured amount above the diagnostic cutoff provides a diagnosis of Leishmaniasis . Alternatively, if the biomarker is down-regulated during Leishmaniasis, then a measured amount below the diagnostic cutoff provides a diagnosis of Leishmaniasis. As is well understood in the art, by adjusting the particular diagnostic cut-off used in an assay, one can increase sensitivity or specificity of the diagnostic assay depending on the preference of the diagnostician. The particular diagnostic cut-off can be determined, for example, by measuring the amount of the biomarker in a statistically significant number of samples from subjects with the different Leishmaniasis statuses, as was done here, and drawing the cut-off to suit the diagnostician's desired levels of specificity and sensitivity.

3.2. Combinations of Markers

[0085] While individual biomarkers are useful diagnostic biomarkers, it has been found that a combination of biomarkers can provide greater predictive value of a particular status than single biomarkers alone. Specifically, the detection of a plurality of biomarkers in a sample can increase the sensitivity and/or specificity of the test. A combination of at least two biomarkers is sometimes referred to as a "biomarker profile" or "biomarker fingerprint."

3.3. Presence of Leishmaniasis

[0086] In some aspects, this invention provides methods for determining the presence or absence of Leishmaniasis in a subject (status: Leishmaniasis v. non- Leishπianiasis), The presence or absence of Leishmaniasis is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular risk level.

3.4. Determining Risk of Developing Disease

[0087] In some aspects, this invention provides methods for determining the risk of developing disease in a subject. Biomarker amounts or patterns are characteristic of various risk states, e.g., high, medium or low. The risk of developing a disease is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular risk level

3.5. Determining Stage of Disease

[008S) In some aspects, this invention provides methods for determining the stage of disease in a subject. Each stage of the disease has a characteristic amount of a biomarker or relative amounts of a set of bioowkers (a pattern). The stage of a disease is determined by measuring the relevant biomarker or biomarkers and then either submitting them to a classification algorithm or comparing them with a reference amount and/or pattern of biomarkers that is associated with the particular stage.

3.6. Determining Course (Progression/Remission) of Disease

[0089] In some aspects, this invention provides methods for determining the course of disease in a subject. Disease course refers to changes in disease status over time, including disease progression (worsening) and disease regression (improvement). Over time, the amounts or relative amounts (e.g., the pattern) of the biomarkers changes. Therefore, the trend of these markers, either increased or decreased over time toward diseased or non- diseased indicates the course of the disease. Accordingly, this method involves measuring one or more biomarkers in a subject at least two different time points, e.g., a first time and a second time, and comparing the change in amounts, if any. The course of disease is determined based on these comparisons.

3.7. Subject Management

[0090] In certain aspects of the methods of qualifying Leishmaniasis status, the methods further comprise managing subject treatment based on the status. Such management includes the actions of the physician or clinician subsequent to determining Leishmaniasis status. For example, if a physician makes a diagnosis of Leishmaniasis, then a certain regime of treatment, such as prescription or administration of sodium stibogluconate might follow. Alternatively, a diagnosis of non- Leishmaniasis might be followed with further testing to determine a specific disease that might the patient might be suffering from. Also, if the diagnostic test gives an inconclusive result on Leishmaniasis status, further tests can be called for.

[0091] Additional aspects of the invention relate to the communication of assay results or diagnoses or both to technicians, physicians or patients, for example. In certain aspects, computers will be used to communicate assay results or diagnoses or both to interested parties, e.g., physicians and their patients. In some aspects, the assays will be

performed or the assay results analyzed in a country or jurisdiction which differs from the country or jurisdiction to which the results or diagnoses are communicated.

[0092] In a preferred aspect of the invention, a diagnosis based on the presence or absence in a test subject of any the biomarkers of Table 1 is communicated to the subject as soon as possible after the diagnosis is obtained. The diagnosis can be communicated to the subject by the subject's treating physician. Alternatively, the diagnosis can be sent to a test subject by email or communicated to the subject by phone. A computer can be used to communicate the diagnosis by email or phone. In certain aspects, the message containing results of a diagnostic test can be generated and delivered automatically to the subject using a combination of computer hardware and software which will be familiar to artisans skilled in telecommunications. One example of a healthcare-oriented communications system is described in U.S. Patent Number 6,283,761; however, the present invention is not limited to methods which utilize this particular communications system. In certain aspects of the methods of the invention, all or some of the method steps, including the assaying of samples, diagnosing of diseases, and communicating of assay results or diagnoses, can be carried out in diverse (e.g., foreign) jurisdictions.

3.8. Determining Therapeutic Efficacy of Pharmaceutical Drug

[0093] In some aspects, this invention provides methods for determining the therapeutic efficacy of a pharmaceutical drug. These methods are useful in performing clinical trials of the drug, as well as monitoring the progress of a patient on the drug. Therapy or clinical trials involve administering the drug in a particular regimen. The regimen can involve a single dose of the drug or multiple doses of the drug over time. The doctor or clinical researcher monitors the effect of the drug on the patient or subject over the course of administration. If the drug has a. pharmacological impact on the condition, the amounts or relative amounts (e.g., the pattern or profile) of the biomarkers of this invention changes toward a non-disease profile. One can follow the course of the amounts of these biomarkers in the subject during the course of treatment. Accordingly, this method involves measuring one or more biomarkers in a subject receiving drug therapy, and correlating the amounts of the biomarkers with the disease status of the subject. Some aspects of this method involves determining the levels of the biomarkers at least two different time points during a course of drug therapy, e g-, a first time and a second time, and comparing the change in amounts of the biomarkers, if any. For example, the biomarkers can be measured before and after drug administration or at two different time points during drug administration. The effect of

therapy is determined based on these comparisons. If a treatment is effective, then the biomarkers will trend toward normal, while if treatment is ineffective, the biomarkers will trend toward disease indications. If a treatment is effective, then the biomarkers will trend toward normal, while if treatment is ineffective, the biomarkers will trend toward disease indications,

3.9. Biomarkers and Modified Forms of a Protein

[0094] Proteins frequently exist in a sample in a plurality of different forms. These forms can result from either, or both, of pre- and post-translatioπal modification. Pre- translational modified forms include allelic variants, slice variants and RNA editing forms. Post-translationally modified forms include forms resulting from proteolytic cleavage (e.g., fragments of a parent protein), glycosylation, phosphorylation, lipidation, oxidation, methylation, cysteinylation, sulphonation and acetylation. When detecting or measuring a protein in a sample, the ability to differentiate between different forms of a protein depends upon the nature of the difference and the method used to detect or measure. For example, immunological methods of detection typically cannot distinguish between different forms of a protein that contain the same epitope or epitopes to which the antibody or antibodies are directed. In diagnostic assays, the inability to distinguish different forms of a protein has little impact when the forms detected by the particular method used are equally good biomarkers as any particular form. However, when a particular form (or a subset of particular forms) of a protein is a better biomarker than the collection of modified forms detected together by a particular method, the power of the assay can suffer. In this case, it is useful to employ an assay method that distinguishes between forms of a protein and that specifically detects and measures a desired modified form or forms of the protein. Distinguishing different forms of an analyte or specifically detecting a particular form of an analyte is referred to as "resolving" the analyte.

[0095] The collection of analytes detected in an assay and the ability to resolve modified forms of a protein of course depends on the methodology used. For example, an immunoassay using a monoclonal antibody will detect all forms of a protein containing the eptiope and will not distinguish between them. However, a sandwich immunoassay that uses two antibodies directed against different epitopes on a protein will detect all forms of the protein that contain both epitope and will not detect those forms that contain only one of the epitopes. Accordingly this method can be useful when the modified forms differ in a terminal amino acid and one of the antibodies is directed to the terminus of one of these forms.

[0096] Preferably, the biospecific capture reagent is bound to a solid phase, such as a bead, a plate, a membrane or a chip, Methods of coupling biomolecules, such as antibodies, to a solid phase are well known in the art. They can employ, for example, bifimctional linking agents, or the solid phase can be derivatized with a reactive group, such as an epoxide or an imidizole, that will bind the molecule on contact. Biospecific capture reagents against different target proteins can be mixed in the same place, or they can be attached to solid phases in different physical or addressable locations. For example, one can load multiple columns with derivatized beads, each column able to capture a single protein cluster. Alternatively, one can pack a single column with different beads derivatized with capture reagents against a variety of protein clusters, thereby capturing all the analytes in a single place. Accordingly, antibody-derivatized bead-based technologies, such as xMAP technology of Luminex (Austin, TX) can be used to detect the protein clusters. However, the biospecific capture reagents must be specifically directed toward the members of a cluster in order to differentiate them.

(0097) Mass spectrometry is a particularly powerful resolving methodology because different forms of a protein typically have different masses and can be differentiated by mass spectrometry. One useful methodology combines mass spectrometry with immunoassay. First _; , a biosepcific capture reagent {e.g., an antibody, aptamer or Affibody that recognizes the biomarker and modified forms of it) is used to capture the biomarker of interest. Preferably, the biospecific capture reagent is bound to a solid phase, such as a bead, a plate, a membrane or a chip, After unbound materials are washed away, the captured analytes are detected and/or measured by mass spectrometry. (This method also will also result in the capture of protein interactors that are bound to the proteins or that are otherwise recognized by antibodies and that, themselves, can be biomarkers.) Then, the captured proteins can be detected by SELDI mass spectrometry or by eluting the proteins from the capture reagent and detecting the eluted proteins by traditional MALDI, SELDI or any other ionization method for mass spectrometry {e.g., electrospray).

[0098] Thus, when reference is made herein to detecting a particular protein or to measuring the amount of a particular protein, it means detecting and measuring the protein with or without resolving modified forms of protein. For example, the step of "measuring Apolipoprotein A-IV precursor" includes measuring Apolipoprotein A-IV precursor by means that do not differentiate between various forms of the protein (e.g., certain immunoassays) as well as by means that differentiate some forms from other forms or that

measure a specific form of the protein. In contrast, when it is desired to measure a particular form or forms of a protein, the particular form (or forms) is specified. For example, "measuring M7.065159" means measuring M7.065159 in a way that distinguishes it from forms of the protein that do not have the characteristic properties identified in Table 1.

4. GENERATION OF CLASSI FICATION ALGORITHMS FOR QUALIFYING LEISHMANIASIS STATUS

[0099] In some aspects, data derived from the spectra (e.g., mass spectra or time-of- flight spectra) that are generated using samples such as "known samples" can then he used to "train" a classification model. A "known sample" is a sample that has been pre-classified. The data that are derived from the spectra and are used to form the classification model can be referred to as a "training data set." Once trained, the classification model can recognize patterns in data derived from spectra generated using unknown samples. The classification model can then be used to classify the unknown samples into classes. This can be useful, for example, in predicting whether or not a particular biological sample is associated with a certain biological condition (e.g., diseased versus non-diseased).

[0100] The training data set that is used to form the classification model can comprise raw data or pre-processed data. In some aspects, raw data can be obtained directly from time-of-flight spectra or mass spectra, and then can be optionally "pre-processed" as described above.

[0101] Classification models can be formed using any suitable statistical classification (or "learning") method that attempts to segregate bodies of data into classes based on objective parameters present in the data. Classification methods can be either supervised or unsupervised. Examples of supervised and unsupervised classification processes are described in Jain, "Statistical Pattern Recognition: A Review", IEEE Transactions on Pattern Analysis and Machine Intelligence, VoL 22, No, I ₁ January 2000, the teachings of which are incorporated by reference.

[0102] In supervised classification, training data containing examples of known categories are presented to a learning mechanism, which learns one or more sets of relationships that define each of the known classes. New data can then be applied to the learning mechanism, which then classifies the new data using the learned relationships. Examples of supervised classification processes include linear regression processes (e.g., multiple linear regression (MLR), partial least squares (PLS) regression and principal

components regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as CART - classification and regression trees), artificial neural networks such as back propagation networks, discriminant analyses {e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (support vector machines).

[0103] A preferred supervised classification method is a recursive partitioning process. Recursive partitioning processes use recursive partitioning trees to classify spectra derived from unknown samples. Further details about recursive partitioning processes are provided in U.S. Patent Application No. 20020138208 Al to Paulse et ai, "Method for analyzing mass spectra."

[0104] Tn other aspects, the classification models that are created can be formed using unsupervised learning methods. Unsupervised classification attempts to learn classifications based on similarities in the training data set, without pre-classifying the spectra from which the training data set was derived. Unsupervised learning methods include cluster analyses. A cluster analysis attempts to divide the data into "clusters" or groups that ideally should have members that are very similar to each other, and very dissimilar to members of other clusters. Similarity is then measured using some distance metric, which measures the distance between data items, and clusters together data items that are closer to each other. Clustering techniques include the MacQueen's K-means algorithm and the Kohonen's Self- Organizing Map algorithm.

[0105] Learning algorithms asserted for use in classifying biological information are described, for example, in PCT International Publication No. WO 01/31580 (Bamhill et ai, "Methods and devices for identifying patterns in biological systems and methods of use thereof), U.S. Patent Application No. 2002 0193950 Al (Gavin et at. _f "Method or analyzing mass spectra"), U.S. Patent Application No. 2003 0004402 Al (Hitt et al, "Process for discriminating between biological states based on hidden patterns from biological data"), and U.S. Patent Application No.2003 0055615 Al (Zhang and Zhang, "Systems and methods for processing biological expression data").

[0106] The classification models can be formed on and used on any suitable digital computer. Suitable digital computers include micro, mini, or large computers using any standard or specialized operating system, such as a Unix, Windows™ or Linux™ based operating system. The digital computer that is used can be physically separate from the mass

spectrometer that is used to create the spectra of interest, or it can be coupled to the mass spectrometer.

[01071 The training data set and the classification models according to aspects of the invention can be embodied by computer code that is executed or used by a digital computer. The computer code can be stored on any suitable computer readable media including optical or magnetic disks, sticks, tapes, and the like, and can be written in any suitable computer programming language including C, C++, visual basic, and the like

[0108] The learning algorithms described above are useful both for developing classification algorithms for the biomarkers already discovered, and for finding new biomarkers for Leishmaniasis, The classification algorithms, in turn, form the base for diagnostic tests by providing diagnostic values (e.g., cut-off points) for biomarkers used singly or in combination.

5. COMPOSITIONS OF MATTER

[0109] In another aspect, this invention provides compositions of matter based on the biomarkers of this invention.

[0110] In some aspects, this invention provides biomarkers of this invention in purified form. Purified biomarkers have utility as antigens to raise antibodies. Purified biomarkers also have utility as standards in assay procedures. As used herein, a "purified biomarker" is a biomarker that has been isolated from other proteins and peptides, and/or other material from the biological sample in which the biomarker is found. Biomarkers can be purified using any method known in the art, including, but not limited to, mechanical separation (e.g., centrifugation), ammonium sulphate precipitation, dialysis (including size- exclusion dialysis), size-exclusion chromatography, affinity chromatography, anion-exchange chromatography, cation-exchange chromatography, and metal-chelate chromatography. Such methods can be performed at any appropriate scale, for example, in a chromatography column, or on a biochip.

[0111] In some aspects, this invention provides a biospecifϊc capture reagent, optionally in purified form, that specifically binds a biomarker of this invention. In some aspects, the biospecific capture reagent is an antibody- Such compositions are useful for detecting the biomarker in a detection assay, e.g., for diagnostics.

[0112] In some aspects, this invention provides an article comprising a biospecific capture reagent that binds a biomarker of this invention, wherein the reagent is bound to a

solid phase. For example, this invention contemplates a device comprising bead, chip, membrane, monolith or microtiter plate derivatized with the biospecific capture reagent. Such articles are useful in biomarker detection assays.

[0113] In another aspect this invention provides a composition comprising a biospecific capture reagent, such as an antibody, bound to a biomarker of this invention, the composition optionally being in purified form. Such compositions are useful for purifying the biomarker or in assays for detecting the biomarker.

[0114] In some aspects, this invention provides an article comprising a solid substrate to which is attached an adsorbent, e.g., a. chromatographic adsorbent or a biospecific capture reagent, to which is further bound a biomarker of this invention. In some aspects, the article is a biochip or a probe for mass spectrometry, e.g., a SELDϊ probe. Such articles are useful for purifying the biomarker or detecting the biomarker.

6. KITS FOR DETECTION OF BIOMARKERS FOR LEISHMANIASIS

[0115] In another aspect, the present invention provides kits for qualifying Leishmaniasis status, which kits are used to detect biomarkers according to the invention. In some aspects, the kit comprises a solid support, such as a chip, a microtiter plate or a bead or resin having a capture reagent attached thereon, wherein the capture reagent binds a biomarker of the invention. Thus, for example, the kits of the present invention can comprise mass spectrometry probes for SELDI, such as ProteinChip ^® arrays. In the case of biospecfic capture reagents, the kit can comprise a solid support with a reactive surface, and a container comprising the biospecific capture reagent.

[0116] The kit can also comprise a washing solution or instructions for making a washing solution, in which the combination of the capture reagent and the washing solution allows capture of the biomarker or biomarkers on the solid support for subsequent detection by, e.g., mass spectrometry. The kit can include more than type of adsorbent, each present on a different solid support,

[0117] In a. further aspect, such a kit can comprise instructions for suitable operational parameters in the form of a label or separate insert. For example, the instructions can inform a consumer about how to collect the sample, how to wash the probe or the particular biomarkers to be detected.

[0118] In some aspects, the kit can comprise one or more containers with biomarker samples, to be used as standard(s) for calibration.

7. USE OF BIOMARKERS FOR LEISHMANIASIS IN SCREENING ASSAYS AND METHODS OF TREATING LEISHMANIASIS

[0119] The methods of the present invention have other applications as well. For example, the biomarfcers can be used to screen for compounds that modulate the expression of the biomarkers in vitro or in vivo, which compounds in turn can be useful in treating or preventing Leishmaniasis in patients. In another example, the biomarkers can be used to monitor the response to treatments for Leishmaniasis. In yet another example, the biomarkers can be used in heredity studies to determine if the subject is at risk for developing Leishmaniasis.

[01201 Thus, for example, the kits of this invention could include a solid substrate having a hydrophobic function, such as a protein biochip (e g-, a Ciphergen H50 ProteinChip array, e.g., ProteinChip array) and a sodium acetate buffer for washing the substrate, as well as instructions providing a protocol to measure the biomarkers of this invention on the chip and to use these measurements to diagnose Leishmaniasis.

[0121] Compounds suitable for therapeutic testing can be screened initially by identifying compounds which interact with one or more biomarkers listed in Table 1 and 5. By way of example, screening might include recombiπantly expressing a biomarker listed in Table 1 and 5, purifying the biomarker, and affixing the biomarker to a substrate. Test compounds would then be contacted with the substrate, typically in aqueous conditions, and interactions between the test compound and the biomarker are measured, for example, by measuring elution rates as a function of salt concentration. Certain proteins can recognize and cleave one or more biomarkers of Table 1 and 5, in which case the proteins can be detected by monitoring the digestion of one or more biomarkers in a standard assay, e.g., by gel electrophoresis of the proteins.

[0122] In a related aspect, the ability of a test compound to inhibit the activity of one or more of the biomarkers of Table 1 and 5 can be measured, One of skill in the art will recognize that the techniques used to measure the activity of a particular biomarker will vary depending on the function and properties of the biomarker. For example, an enzymatic activity of a biomarker can be assayed provided that an appropriate substrate is available and provided that the concentration of the substrate or the appearance of the reaction product is readily measurable. The ability of potentially therapeutic test compounds to inhibit or enhance the activity of a given biomarker can be determined by measuring the rates of

catalysis in the presence or absence of the test compounds. The ability of a test compound to interfere with a non-enzymatic (e.g., structural) function or activity of one of the biomarkers of Table 1 and 5 can also be measured. For example, the self-assembly of a multi-protein complex which includes one of the biomarkers of Table I can be monitored by spectroscopy in the presence or absence of a test compound. Alternatively, if the biomarker is β non- enzymatic enhancer of transcription, test compounds which interfere with the ability of the biomarker to enhance transcription can be identified by measuring the levels of biomarker- dependent transcription in vivo or in vitro in the presence and absence of the test compound. f 0123] Test compounds capable of modulating the activity of any of the biomarkers of Table 1 and 5 can be administered to patients who are suffering from or are at risk of developing Leishmaniasis. For example, the administration of a test compound which increases the activity of a particular biomarker can decrease the risk of Leishmaniasis in a patient if the activity of the particular biomarker in vivo prevents the accumulation of proteins for Leishmaniasis. Conversely, the administration of a test compound which decreases the activity of a particular biomarker can decrease the risk of Leishmaniasis in a patient if the increased activity of the biomarker is responsible, at least in part, for the onset of Leishmaniasis.

[0124] In an additional aspect, the invention provides a method for identifying compounds useful for the treatment of disorders such as Leishmaniasis which are associated with increased levels of modified forms of the biomarkers in Table 1 and 5. For example, in some aspects, cell extracts or expression libraries can be screened for compounds which catalyze the cleavage of a full-length biomarker to form truncated forms of the biomarker. In some aspects of such a screening assay, cleavage of the biomarker can be detected by attaching a fluorophore to the biomarker which remains quenched when the biomarker is uncleaved but which fluoresces when the protein is cleaved. Alternatively, a version of full- length biomarker modified so as to render the amide bond between amino acids x and y uncleavable can be used to selectively bind or "trap" the cellular protease which cleaves full- length biomarker at that site in vivo. Methods for screening and identifying proteases and their targets are well-documented in the scientific literature, e.g., in Lopez-Ottin et aϊ. (Nature Reviews, 3:509-519 (2002)).

[0125] In some aspects, the invention provides a method for treating or reducing the progression or likelihood of a disease, e.g., Leishmaniasis, which is associated with the increased levels of a truncated biomarker. For example, after one or more proteins have been

identified which cleave the fiill-leπgth biomarker, combinatorial libraries can be screened for compounds which inhibit the cleavage activity of the identified proteins. Methods of screening chemical libraries for such compounds are well-known in art. See, e.g>, Lopez-Otin et al (2002). Alternatively, inhibitory compounds can be intelligently designed based on the structure of the biomarker.

[Ql 26] At the clinical level, screening a test compound includes obtaining samples from test subjects before and after the subjects have been exposed to a test compound. The levels in the samples of one or more of the biomarkers listed in Tables 1 or Table 5 can be measured and analyzed to determine whether the levels of the biomarkers change after exposure to a test compound. The samples can be analyzed by mass spectrometry, as described herein, or the samples can be analyzed by any appropriate means known to one of skill in the art. For example, the levels of one or more of the biomarkers listed in Tables 1, 2, or 3 can be measured directly by Western blot using radio- or fluorescently-labeled antibodies which specifically bind to the biomarkers. Alternatively, changes in the levels of mRNA encoding the one or more biomarkers can be measured and correlated with the administration of a given test compound to a subject. In a further aspect, the changes in the level of expression of one or more of the biomarkers can be measured using in vitro methods and materials. For example, human tissue cultured cells which express, or are capable of expressing, one or more of the biomarkers of Table 1 or Table 5 can be contacted with test compounds. Subjects who have been treated with test compounds will be routinely examined for any physiological effects which can result from the treatment. In particular, the test compounds will be evaluated for their ability to decrease disease likelihood in a subject. Alternatively, if the test compounds are administered to subjects who have previously been diagnosed with Leishmaniasis, test compounds will be screened for their ability to slow or stop the progression of the disease.

8. EXEMPLARY ASPECTS

[0127] It is understood that the exemplary aspects described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims.

Example 1 - Biomarkers for Visceral Leishmaniasis Methods

S era

[0128] A total of 64 sera were available for this study which were split into set 1 (consisting of 21 pretreatment VL sera and 19 healthy controls from the same geographic region) used to find preliminary biomarkers and set 2 (24 pretreatment sera) as a validation set. The material was obtained from Dr Nirmal Baral, Koirala Institute, Nepal.

[0129] Samples were aliquoted (30 μl/tube) and stored at -2O°C until further use. All fractionated serum samples had been thawed a maximum of 3 times between collection and analysis.

[0130] The following clinical information was available from the positive samples: duration of fever and cough, hepatomegaly (size), splenomegaly (size), BM aspirate, albumin level, bilirubin level and Hb.

Fractionation and Binding of Sera

[0131] For fractionation, Cyphergen's Expression Difference Mapping ™ Kit- Serum Fractionation was used allowing high throughput processing on anion exchange beads in a 96- well microplate format. To increase the number of proteins visualized, sera were separated into six different fractions (pH 9+ flow through, pH7, pH5, pH4, pH3, and organic wash). This fractionation procedure significantly increases the number of peaks detectable from each sample (Fung and Enderwick, 2002). For the anion exchange fractionation, we used 20 μl of serum and 30 μl of U9 buffer as starting material according to the Ciphergen protocols.

[0132] Pretreatment sera and controls were bound on 2 different array surfaces (ProteinChip™: Ciphergen Biosystems Inc.), the weak cation exchange chip (CMlO) and immobilized metal affinity capture coupled with copper (IMAC-Cu ²⁺ ) chip arrays.

[0133] To find the optimal fractions for each array type (CM 10 and IMAC30), all 6 fractions from 8 randomly chosen sera (pretreatment and controls) were loaded on both array surfaces (ProteinChipTM: Ciphergen Biosystems Inc.). The best 3 fractions (that produced the highest number of peaks with good intensity) for each array type were chosen to be bound from all samples (on CMlO fraction 1, 3 and 4; on IMAC30 fraction 2, 5 and 6).

[0134] CMlO ProteinChip Arrays incorporate a negatively charged carboxylate chemistry that makes them act as a weak cationic exchanger. Their surface binds proteins that are positively charged at a given pH (from the product folder CMlO ProteinChip ^E Arτay, Ciphergen Biosystems, Inc.). DVLA.C30 ProteinChip Arrays reversibly bind proteins to the surface through a coordinated metal interaction. These ProteinChip Arrays incorporate Nitrilotriacetic Acid (NTA) groups and form stable octahedral complexes with polyvalent metal ions including Cu ²⁺ , Ni ²⁺ , Fe ³⁺ , Ga ³⁺ , and the like After applying the desired metal ion to the array surface, two free sites from the formed octahedral complex can interact with specific amino acid residues (such as histidine) or post-traπslatioπal modifications such as phosphate groups (from the product folder IMAC30 ProteinChip ^® Array, Ciphergen Biosystems Inc.).

[0135] For ProteinChip Array binding, the spots on the IMAC arrays were preloaded with 50 μl CuSO4 (0.1 M). The CMlO and IMAC arrays were equilibrated with 150 μl binding buffer. 10 μl of each fraction was diluted in 90 μl of specific binding buffer, added to each well of the bioprocessor and mixed for 30 minutes with vigorous shaking on a MicroMix. After removing the remaining sample, the arrays were washed and mixed three times with 150 μl binding buffer for 5 min followed by two short washes with 150 μl de- ionized water. All steps were performed at room temperature.

[0136] An energy absorbing matrix containing sinapinic (SPA) was prepared according to the recommendations of the manufacturer [5 rog/vial dissolved in 400 μl solution (200 μl Acetonitrile; 200 μl of 1% Triftøoroacetic Acid in HPLC grade H ₂ O)] and, after the arrays were dry, 1 μl SPA solution applied on each spot. This was repeated once and the ProteinChip arrays were stored at room temperature in the dark until used.

Reading of the Arrays

[0137] The arrays were read on a Protein Biosystem He (PBS Hc) instrument, which was calibrated externally using the Protein MW standards kit ElOO-0001 (Ciphergen Biosystems) loaded on an NP-20 array. Each array was run twice with two different laser intensities to achieve better resolution for low- and high-molecular-mass proteins.

Data Analysis

[013S] Serum protein profiles of visceral leishmaniasis patients and healthy controls were compared with each other to detect biomarkers for visceral leishmaniasis. Peaks were first auto detected using CiphergenExpress Data manager 2.1 [l ^sι pass analysis calculates p-

values (p<0.05) and ROC (Receiver Operator Characteristic: calculation used to indicate how good the peak is to distinguish between infected and non-infected samples) from autodetected peaks] and each peak visually inspected for the 2 ^nd pass analysis (p-values<0.001 , recalculates p- values and ROC from the peaks manually relabeled).

10139] Spectra were baseline subtracted and normalized to the total ion current from 2000 Da to 100000 Da (low energy settings) and 10000 Da to 200 000 Da (high energy settings). Spectra that were not within the "twice the average" QC rule were deleted. For peak cluster detection EDM (expression difference mapping) analysis was performed. Peaks were autodetected in a 0.3% cluster mass window for low energy settings and 2% cluster mass window for high energy settings. Biomarker Patterns Software (BPS; Ciphergen Biosystems) was used to generate diagnostic classification trees (algorithms) for patients and controls.

Identification

[0140] 80 μl of the fractioned sample that showed the highest concentration of targeted candidate biomarkers as well as SO μl of the fractionated control sample that showed the lowest concentration of the same particular biomarker were desalted and 10 μl of each sample loaded on a 12% SDS-PAGE Bis-Tris gel. Each positive sample was compared to its control. Gels were fixed with fixer solution for 2 hours and afterwards stained with Coomassie overnight to visualize the bands. De-staining with distilled water was performed until the bands were clearly visible.

Sequencing of the Proteins

[0141] Bands were cut from the gels and sequenced using LC-MS-MS using procedures known in the art, followed by a Mascot database search.

Results

[0142] The tested patients consisted of 25 men and 20 women with an age between 10 and 50 years. Their liver measured between 2 and 10 cm, the spleen between 3 and 25 cm. f 01431 Table 8 shows the molecular weights of all 212 biomarkers found in pretreatment samples of visceral leishmaniasis patients after the 2 ^nd pass analysis. Highlighted molecular weights indicate proteins that were additionally detected in the BPS analysis (for decision tree building).

Table 2: 212 biomarkers detected in pretreatment samples of visceral leishmaniasis patients after 2nd pass analysis. Highlighted MW' s indicate markers also found in BPS.

F - fraction

C = CMlO

I = IMAC30

L = low energy settings

H = high energy settings

[0144] Altogether 212 preliminary biomarkers (p-value < 0.001) were detected after the 2 ^nd pass analysis using Ciphergen Express. Molecular weights ranged between 2.5 kDa and 157 kDa. The greatest number of biomarkers was found in fraction 6 bound on an IMAC30 array read under high energy settings (see table 8). 15 of the 212 biomarkers were also detected using BPS (indicated with highlighted MW in table 8).

Cluster Plots

[0145] The cluster plots in Figures 7-8 illustrate the ability of candidate biomarkers to distinguish between the positive and negative samples. Figure 1 shows a m/z scatter plot of the 3.3 kDa biomarker found in F6IL of pretreatment sera from VL cases, where each data point represents one serum sample. The scatter plot distinguishes successfully between the positive (pretreatment) and negative (control) samples (ROC value of 0.98). The p-value is very low, and the intensity of the protein is relatively high (the 3.3 kDa biomarker had the highest overall intensity). Fig. 8 shows levels of the 3.3 kDa biomarker as measured by SDS- PAGE, and illustrates the substantial difference in expression levels between the pretreatment and control groups.

[0146] Fig. 9 shows an m/z scatter plot of the biomarker with a MW of 12463 Da found in F61H, which successfully distinguishes between the positive (pretreatment) and negative (control) samples (ROC value of 0.98) with a very low p-value and a high signal intensity.

Classification Trees (Biomarker Pattern Software BPS)

[0147J Biomarker Patterns™ Software is a Windows-based package for supervised classification of SELDI mass spectral data sets derived from the Ciphergen ProteinChip* platform. It identifies proteins that are relevant to a particular disease and summarizes and displays the results in a clear way. With this program it is possible to discover protein multi- markers and to translate them into assays with high predictive accuracy.

[0148] Figure 4 shows a classification tree that combines three biomarkers to correctly classify all 21 positive sera from the 19 controls. The first biomarker (MW of 51,351 Da) correctly classifies 17 out of 21 positive samples, the second biomarker (MW of 28,238 Da) correctly classifies an additional 3 positive samples (out of the remaining 4), and finally the third biomarker (MW of 3379 Da) correctly classifies the remaining positive sample. Thus, by combining the 3 biomarkers it is possible to successfully distinguish between all 21 positive and 19 negative samples.

[0149] Figure 5 shows a decision tree for the 12.4 kDa biomarker. If the intensity of the 12.4 kDa biomarker is <= 0-155, all 18 control sera are correctly classified as negative, and if the protein intensity is > 0.155 all 20 sera from visceral leishmaniasis patients are correctly classified positive. Thus, it is possible using only the 12-4 JdDa biomarker to correctly classify all positive samples from the negative controls.

Correlation between Peak Intensity and Clinical Data

[0150] No correlation between the clinical data (duration of fever, cough, hepatomegaly, splenomegaly, BM aspirate, albumin, bilirubin, Hb) and the peak intensities of any biomarker was detectable.

Identification

[0151] The 15 potential biomarkers detected in the 2 ^nd pass analysis (using CiphergenExpress) and BPS were isolated and purified using SDS-PAGE and identified with LC-MS-MS. Four biomarkers are visible on the gels shown in Figures 6 and 7 (2 down- regulated proteins: 28.2 kDa and 14.5 kDa, and 2 up-regulated proteins: 51.1 kDa and 12.4 kDa, see Table 2). The gel of Fig. 6 shows 5 targeted biomarkers (MW: 27,603 Da, 51,327 Da, 28,238 Da, 51,351 Da and 14,580 Da) with their associated controls (C). 2 biomarkers (indicated by boxes on the gel) could be visualized. The gel of Fig, 7 shows 5 targeted biomarkers (9,309 Da, 9,478 Da, 12,610 Da, 51,111 Da and 12,463 Da) with their associated controls (C). As in Fig. 6, 2 biomarkers (indicated with boxes and upregulated in the positive sera) were detected on the gel. The band in the 12.4 kDa range was faint but clearly visible on the original gel. Sequencing of the 51.1 kDa candidate biomarker revealed that it likely represents kininogen 1 precursor. The paired samples used to visualize biomarkers in Figure 6 and Figure 7 also show several additional differences in expression levels.

Table 3: Targeted and visible biomarkers on gels (the numbers represent the MWs of the proteins; highlighted/boldface numbers, indicate the visible markers).

Validation of the biomarkers

[0152] To validate the 15 candidate biomarkers (found in sample set 1 using 2 ^nd pass analysis/BPS) we tested the remaining 24 pretreatment samples (under the same settings as set 1) and obtained substantially identical MW measurements across all fractions (Dalton shift max +/- 3% when the second set was run on a different day). 14 from 15 preliminary biomarkers in set 1 were also detected in sample set 2. Only one (down-regulated) protein

with a MW of 9478 Da (found in set 1 in F5IL) was undetectable in the 2 ^nd sample set,. To validate the 15 candidate biomarkers (found in sample set 1 using 2 ⁿ pass analysis/BPS) we tested the remaining 24 pretreatment samples (under the same settings as set 1) and got across all fractions a maximum Dalton shift of ± 1% when the second set was run on a different day (see Table 4 below).

1: MW of biomarkers found in sample set 1

2: MW of biomarkers found in sample set 2 (validation) n.d.: not detected

Discussion

[0153] In this study we show that SELDI-TOF-MS can be successfully used to detect candidate biomarkers in visceral VL patients. Using CipbergenExpress on its own, the largest number of biomarkers was detected in fraction 6 bound on IMAC30 arrays, whereas using both software programs the largest number was found in fraction 4 on CMlO arrays. The ROCs in the m/z scatter plots show that the biomarkers were able to distinguish successfully between positive and negative serum samples.

[0154] In the past concerns about the reproducibility of SELDI-data were mentioned by several groups. Generation of reproducible SELDI-TOF spectra requires prompt separation of serum and storage at -20°C or below, and because repeated freeze-thaw cycles result in degradation of spectra (Papadopoulos, M.C. et al , 2004, Lancet 363:1358-1363), it is necessary to use a robust cold chain (AgranorY et al, 2005).

[0155] Ransohoff (2005) discusses that lessons can be learned from how the question of reproducibility has been effectively addressed in other "-omics" research Ransohoff, D, , 2005, Journal of the National Cancer Institute 97, Rosenwald et al (2002) showed in a genomics study that by splitting the samples in a training set (to derive a pattern- recognition model) and an independent validation set, chance or over-fitting does not explain the results. Rosenwald,, A. et al, 2002, N EnglJ Med, 346:1937-47. They demonstrated reproducibility in different patients in a setting in which technical features were held constant.

[0156] In our study we targeted the reproducibility problem in the same way and successfully validated the biomarkers in a second sample set (consisting of pretreatrπent sera from VL patients which were compared to the same controls as used for set 1) under the same settings. The intensities of the biomarkers found in the validation set were very similar to the ones in the original sample set. One biomarker with 9478 Da found in sample set 1 (fraction 5 bound on an IMAC30 array) was down-regulated in the positive samples. In the validation set, this protein was not detected as a biomarker at all (positive samples compared to controls). Therefore, its value as a marker can be questioned.

[0157] The small molecular weight differences are most likely caused by the fact that we processed the validation set with the sensitive SELDI technology on a different day using different arrays (even with the same settings on the same SELDI machine in the same

laboratory using the same array type small molecular weight differences can be visible). This problem is discussed by Gretzer et al, 2003, Reviews in Urology, 5: 81-89. They mention that one limitation of SELDI is the lot-to-lot reproducibility of the chip surface chemistry. To maintain the reproducibility of mass-produced chips they ask for improved manufacturing methods. Agranoff et al, Trends in Parasitology, 21 : 154-157 (2005) report that the biological complexity of most disease states means that single biomarkers, with a few notable exceptions such as Troponin I or Troponin T as indicators of myocardial injury, have extremely limited diagnostic sensitivities and specificities. They mention that analyzing combinations of multiple biomarkers offers the possibility of greatly enhanced diagnostic accuracy. But in fact there are already existing examples of highly sensitive and specific single protein 'biomarkers' (e.g., hepatitis B surface antigen, circulating malaria antigens).

[0158] In this study single biomarkers which distinguished correctly between infected patients and controls were detected, Each candidate biomarker shown in Figures 7 and 9, fulfilling that task very well. It could be that because of the high sensitivity of SELDI- TOF-MS it is easier to find single biomarkers which distinguish between positive and negative samples.

[0159] It was possible to successfully visualize at least four biomarkers on the gel. Sequencing of the 51 kDa band detected in pretreatrnent sera using LC-MS-MS followed by a Mascot search revealed a number of potential proteins. The 51 kDa band can represent kinmogen 1 precursor. High and low molecular weight kininogen are strong inhibitors of cysteine proteinases. Three related domains on its heavy chain are responsible for this activity (Higashiyama et al., 1986, Biochemistry 25: 1669-75).

[0160] Since kininogen- 1 precursor was up-regulated in pretreatment samples and Leishmania parasites have cysteine proteases, an infection with Leishmania could up-regulate cysteine protease inhibitors in the host. Kininogen is not in its active form because the parasite blocks its activation, which would be in favor of Leishmania.

[0161] High molecular weight kininogen plays a role in blood coagulation. It is also a precursor of bradykiπin; this vasodilator substance is released through positive feedback by kallikrein.

[0162] Kininogen is not in its active form because in that way blood does not coagulate, which could be in favor of the sandfly trying to get a bloodmeal. In this way the chance of the parasite to get transmitted into other people is enhanced as well. There are

reports about coagulation disorders in leishmaniasis patients. According to Font et al (Font, A. et al. , 1993 , Journal of Small Animal Practice, 34: 466-470; Font, A. et al.. 1994, Journal of the American Veterinary Medical Association, 204: 1043-44) and Moreno (Moreno, P., 1999, Veterinary Record, 144: 169-71) report that one of the characteristics of established VL are hematological disorders. Dubey et ai address about low hemoglobin and a deranged coagulation due to thrombocytopenia (Dubey P, K. et al, 2001, General Anesthesia, 6/26: 529-531). Sipahi et a/.describe a VL case with abnormal clotting time and reduced fibrinogen which can develop due to ongoing disseminated intravascular coagulation and hepatic synthetic dysfunction Sipahi et al , 2005, The Turkish Journal of Pediatrics, 47: 191 - 194.).

[0163] As Agranoff et al (2005) discussed, identification of the individual biomarkers will enable the design of immunologically based antigen-detection tests that could be implemented in dipstick or cassette formats. Furthermore, insights into disease pathophysiology afforded by knowledge of changes in circulating proteins could, ultimately, help to direct the search for novel drug targets. Because immunodetection of proteins is quantitatively more precise than MS, diagnostic performance of these downstream applications might surpass that of the original high-tech discovery-phase 'test'.

[0164] From the foregoing description, various modifications and changes in the compositions and methods will occur to those skilled in the art. AH such modifications coming within the scope of the appended claims are intended to be included therein. Each recited range includes all combinations and sub-combinations of ranges, as well as specific numerals contained therein.

[0165] Each recited range includes all combinations and sub-combinations of ranges, as well as specific numerals contained therein.

[0166] All publications and patent documents cited above are hereby incorporated by reference in their entirety for all purposes to the same extent as if each were so individually denoted.

[0167] Although the foregoing invention has been described in detail by way of example for purposes of clarity of understanding, it will be apparent to the artisan that certain changes and modifications are comprehended by the disclosure and can be practiced without undue experimentation within the scope of the appended claims, which are presented by way of illustration not limitation.