WO2012016333A1 - Biomarkers for malaria - Google Patents

Abstract

There is provided a method of identifying a subject having, or at risk of developing, severe or fatal malaria. Embodiments include determining the level of one or more biomarkers selected from angiopoietin-1 (ANG-1), angiopoietin-2 (ANG-2), von Willebrand factor (VWF), von Willebrand factor propeptide (VWFpp), soluble P-selectin, soluble ICAM-1 (sICAM-1), soluble endoglin, soluble FLT-1 (sFLT-1), soluble Tie-2 (sTie-2), C-Reactive Protein (CRP), procalicitonin (PCT), IP-10, chitinase-3-like-1 (CHI3L1), VEGF and Triggering Receptor Expressed on Myeloid cells-1 (TREM-1) in a test sample from a subject. Levels of the one or more biomarkers in the test sample are compared to a control sample, wherein a difference between the level of the one or more biomarkers in the test sample and the control sample indicates that the subject has, or is at risk of developing, severe or fatal malaria.

Description

Title: BIOMARKERS FOR MALARIA Related Applications

[0001] This is a Patent Cooperation Treaty Application which claims the benefit of 35 U.S.C. 1 19 based on the priority of corresponding U.S. Provisional Patent Application No. 61/371 ,401 filed August 6, 2010, which is incorporated herein in its entirety.

Field of the Invention

[0002] The present invention relates to methods for identifying subjects having, or at risk of developing, severe malaria and more specifically relates to biomarkers and associated methods for identifying subjects having, or at risk of developing, severe malaria.

Background of the Invention

[0003] Infectious diseases are an enormous health burden on the world's population. While some infectious diseases are relatively easy to diagnose and treat, others can progress rapidly to more complicated or severe forms or states that require serious attention or prove fatal.

[0004] Malaria remains the most important parasitic disease globally and is responsible for an estimated 500 million cases annually (Snow et al. 2005). Severe malarial complications occur primarily in Plasmodium falciparum infections and account for enormous morbidity and mortality in endemic regions. One of the more severe forms of malaria is cerebral malaria (CM), an encephalopathy associated with deep coma and a 15-40% mortality rate (World Health Organization, 2000; Newton, C.R., and S. Krishna, 1998); Kain et al. 1998). Unfortunately, there are limited diagnostic tools available to determine which patients infected with P. falciparum will go on to develop cerebral complications. Furthermore, even in patients felt to have CM, the diagnosis is challenging. Up to one third of those clinically diagnosed with CM will subsequently be shown to have alternative causes for their neurological syndrome (Taylor et al. 2004). [0005] Severe malaria causes almost 1 million pediatric deaths annually. At presentation, it is difficult to predict which children with severe malaria are at greatest risk of death. The most common manifestations of pediatric severe malaria are severe malarial anemia (SMA) and CM, syndromes with high case fatality rates (Murphy SC and Breman JG, 2001). It is challenging at clinical presentation to accurately determine which patients with severe malaria are at greatest risk of death. Simple and sensitive clinical scores have been developed to predict outcome, but they have low specificity (see Marsh et al. 1995, and Helbok et al. 2009). Differentiating severe and cerebral malaria from other causes of serious illness is also problematic, owing to the non-specific nature of clinical presentation and the high prevalence of incidental parasitaemia in both adults and children.

[0006] Accordingly, there is a need for biomarkers and associated methods for identifying subjects at risk of developing severe or fatal malaria.

Summary of the Invention

[0007] The applicants disclose biomarkers useful for identifying subjects having, or at risk of developing, severe malaria. These biomarkers are shown to reflect disease severity and predict outcome in subjects presenting with malaria. In one aspect, chitinase-3 like-1 (CHI3L1) is shown to be a biomarker for malaria and in particular for severe malaria. In another aspect, TREM1 and/or soluble TREM1 are shown to be biomarkers for malaria and in particular for severe malaria. In another aspect, sFLT-1 is shown to be a biomarker for malaria and in particular for severe malaria. In another aspect, sTie-2 is shown to be a biomarker for malaria and in particular for severe malaria. The applicants also disclose combinatorial and multivariate approaches to improve the accuracy of predicting disease severity or mortality using the biomarkers disclosed herein. Models based on combinations of biomarkers are shown to accurately predict death in subjects presenting with malaria. In addition, biomarkers and specific combinations of biomarkers are shown to distinguish subjects with severe malaria from those with uncomplicated malaria or from other CNS infections or to identify subjects having or at risk of developing fatal malaria. In a further aspect, there are provided biomarkers that are useful to differentiate between subjects with and without retinopathy.

[0008] Accordingly, in one aspect there is provided a method of identifying a subject having, or at risk of developing, severe malaria comprising:

(a) determining the level of one or more biomarkers in a test sample from the subject; and

(b) comparing the level of the one or more biomarkers in the test sample to a level of the one of more biomarkers in a control sample, wherein a difference between the level of the one or more biomarkers in the test sample and the control sample indicates that the subject has, or is at risk of developing, severe malaria.

[0009] In one embodiment, severe malaria comprises cerebral malaria and/or severe malarial anemia. In one embodiment the subject is a child. In one embodiment, the methods described herein are useful for identifying subjects with severe malaria from subjects with other disease states such as uncomplicated malaria or non-malarial central nervous system (CNS) infections.

[0010] In one aspect, the levels of individual biomarkers are useful for identifying a subject that has, or is at risk of developing, severe malaria. In another aspect, the levels of more than one biomarker or combinations of biomarkers are useful for identifying a subject that has, or is at risk of developing, severe or fatal malaria. In one embodiment, multivariate methods are used to compare and detect differences in the level of biomarkers in the test sample and the level of biomarkers in the control sample. In one embodiment, the methods described herein comprise determining the level of 3 or more biomarkers in a test sample and comparing the levels to the levels of 3 or more biomarkers in a control sample. In one embodiment, the methods described herein comprise determining the level of 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, or more than 12 biomarkers in a test sample and comparing the levels of the biomarkers to the levels of the biomarkers in a control sample. In one embodiment, the methods comprise determining the levels of a set of biomarkers listed in Table 4 or Table 13.

[0011] In one embodiment, the step of comparing the levels of the one or more biomarkers in the test sample to levels of the one of more biomarkers in a control sample comprises combining biomarker levels into a single composite variable. In one embodiment the step of comparing the levels of the one or more biomarkers in the test sample to levels of the one of more biomarkers in a control sample comprises classification and regression tree (CART) analysis or multivariate analysis. Optionally, other methods of statistical or mathematical analysis known to a person of skill in the art can be used to compare the levels of biomarkers in the test sample to the levels of the biomarkers in the control sample.

[0012] In one aspect, the level of the one of more biomarkers in a control sample is a predetermined or standardized control level such a numerical threshold.

[0013] In one aspect, the methods described herein are useful for identifying a subject that has a risk of developing severe malaria. In one embodiment, the methods described herein are useful for determining a prognosis for a subject with malaria. In one embodiment, the methods include determining the relative risk or magnitude of the subject developing severe or fatal malaria.

[0014] The methods described herein are useful for monitoring disease in a subject with malaria over time. In one embodiment, the control sample is determined from a test sample from the subject at an earlier time point.

[0015] In one aspect, the biomarkers are selected from angiopoietin-1 (ANG-1), angiopoietin-2 (ANG-2), von Willebrand factor (VWF), von Willebrand factor propeptide (VWFpp), soluble P-selectin, soluble ICAM- (slCAM-1 ), soluble endoglin, soluble FLT-1 (sFLT-1), soluble Tie-2 (sTie-2), C-Reactive Protein (CRP), procalicitonin (PCT), IP-10, chitinase-3-like-1 (CHI3L1), VEGF and soluble Triggering Receptor Expressed on Myeloid cells- 1 (sTREM-1 ).

[0016] In one embodiment, the one or more biomarkers are selected from ANG-2, VWF, VWFpp, slCAM-1 , sFLT-1 , sTie-2, CRP, PCT, IP-10, CHI3L1 and sTREM-1 an increase in the level of the biomarker in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, severe malaria. In one embodiment, an increase in the level of CHI3L1 in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, severe malaria. In one embodiment, an increase in the level of sTREM-1 in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, severe malaria. In one embodiment, an increase in the level of sFLT-1 in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, severe malaria. In one embodiment, an increase in the level of sTie-2 in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, severe malaria.

[0017] In one embodiment, an increase in the level of ANG-2, slCAM-1 , CHI3L1 , IP-10, sFLT-1 , sTREM-1 or PCT in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, fatal malaria. In one embodiment, the level of three or more biomarkers selected from ANG-2, slCAM-1 , CHI3L1, IP-10, sFLT-1 and PCT are determined in the test sample. In one embodiment, the biomarkers comprise ANG-2, IP-10 and CHI3L1. In another embodiment, the biomarkers comprise slCAM-1 and CHI3L1.

[0018] In one embodiment, the methods described herein are useful for identifying subjects that have, or are at risk of developing cerebral malaria with retinopathy from subjects with uncomplicated malaria. In one embodiment, a decrease in the level of ANG-1 or an increase in the level of ANG-2, ANG-2:ANG-1 , sTie-2, VWF, VWFpp, VEGF or slCAM-1 in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, cerebral malaria with retinopathy. In one embodiment, the biomarkers comprise ANG-1 , VWFpp, VWF and VEGF.

[0019] In one embodiment, the methods described herein are useful for identifying subjects that have, or are at risk of developing, cerebral malaria with retinopathy from subjects with CNS infections other than malaria such as In one embodiment, a decrease in the level of ANG-1 or an increase in the level of VWF or VWFpp in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, cerebral malaria with retinopathy. In one embodiment, the biomarkers comprise ANG-1 , VWFpp, VWF, VEGF and slCAM- .

[0020] In one aspect, the applicants have identified biomarkers useful for identifying subjects that have, or are at risk of developing, retinopathy. In one embodiment, there is provided a method for detecting subjects having, or at risk of developing, cerebral malaria with retinopathy comprising:

(a) determining the level of one or more biomarkers selected from ANG-2, ANG-2:ANG-1 , VWFpp, or slCAM-1 in a test sample from the subject; and

(b) comparing the level of the one or more biomarkers in the test sample to a level of the one of more biomarkers in a control sample, wherein the control sample represents subjects with cerebral malaria without retinopathy and wherein an increase in the level of the one or more biomarkers in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, cerebral malaria with retinopathy.

[0021] In one aspect, the methods described herein can be used to monitor disease severity in a subject with malaria. Accordingly, in one embodiment there is provided a method of monitoring severity of disease in a subject with malaria comprising: (a) determining the level of one or more biomarkers in a test sample from the subject;

(b) comparing the level of the one or more biomarkers in the test sample to a level of the one or more biomarkers in a control sample, wherein the levels of the one or more biomarkers in the control sample are determined from a sample from the subject at an earlier time point; and

(c) detecting an increase or decrease in the severity of disease in the subject with malaria by detecting a difference in the level of the one or more biomarkers in the test sample and the control sample.

[0022] In one embodiment, the biomarkers are selected from ANG-2, VWF, VWFpp, slCAM-1 , sFLT-1 , sTie-2, CRP, PCT, IP-10, sTREM-1 and CHI3L1 and an increase in the level of the one or more biomarkers in the test sample compared to the control sample indicates an increase in the severity of disease in the subject with malaria.

[0023] In another embodiment, the biomarkers are selected from ANG- 2, VWF, VWFpp, slCAM-1 , sFLT-1 , sTie-2, CRP, PCT, IP-10, sTREM-1 and CHI3L1 and a decrease in the expression level of the one or more biomarkers in the test sample compared to the control sample indicates a decrease in the severity of the disease in the subject with malaria.

[0024] Optionally, the methods are useful for monitoring the severity of disease in a subject with malaria in response to therapy.

[0025] In another aspect, there are provided kits useful for determining whether a subject has, or is at risk of developing, severe or fatal malaria. In one embodiment, the kit comprises one or more binding agents directed against a biomarker selected from angiopoietin-1 (ANG-1), angiopoietin-2 (ANG-2), von Willebrand factor (VWF), von Willebrand factor propeptide (VWFpp), soluble P-selectin, soluble ICAM-1 , soluble endoglin, soluble FLT-1 , soluble Tie-2 (sTie-2), C-Reactive Protein (CRP), procalicitonin, IP-10, chitinase-3-like-1 (CHI3L1 ), VEGF and soluble Triggering Receptor Expressed on Myeloid cells-1 (sTREM-1). In one embodiment, the kit comprises binding agents for a set of biomarkers shown to be useful for identifying subjects with severe malaria as described herein. In one embodiment, the kit comprises binding agents for a set of biomarkers listed in Table 4 or Table 13. Optionally, the binding agent is detectable labeled. In one embodiment, the binding agent is an antibody. In one embodiment, the kit further comprises a medium suitable for formation of an antigen-antibody complex, reagents for detection of the antigen-antibody complexes and/or instructions for the use thereof.

[0026] Other features and advantages of the present invention will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples while indicating preferred embodiments of the invention are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.

Brief Description of the Drawings

[0027] Embodiments of the invention will now be described in relation to the drawings in which:

[0028] Figure 1 shows admission plasma biomarker levels in Ugandan children with uncomplicated malaria (UM) vs. cerebral malaria (CM) and severe malarial anemia (SMA). Biomarkers were measured by ELISA. Data are presented as dot plots with medians. A Mann Whitney U test was performed for each comparison, and p values were adjusted for multiple comparisons using Holm's correction. ^** p<0.01. The results of the analysis were not significantly changed after removing the CM+SMA patients from the CM group, and the SMA patients with decreased consciousness from the SMA group.

[0029] Figure 2 shows admission plasma biomarker levels in children with severe malaria who survived or subsequently died from infection. Presented are biomarkers that were significantly different for (A) CM patients only, (B) SMA patients only, and (C) all severe malaria patients combined. Biomarkers were measured by ELISA. Data are presented as dot plots with medians. A Mann Whitney U test was performed for each comparison, and p values were adjusted for multiple comparisons using Holm's correction. ^* p<0.05 and ^** p<0.01.

[0030] Figure 3 shows an assessment of biomarker utility in predicting outcome in children with severe malaria. Receiver operating characteristics (ROC) curves were generated for each biomarker. Area under the ROC curve is displayed with 95% confidence intervals in parentheses, p values were adjusted for multiple comparisons using Holm's correction. ^* p<0.05 and ^** p <0.01.

[0031] Figure 4 shows biomarker scores significantly associate with risk of fatality among children with severe malaria. The biomarker score for each patient was calculated as set out in Example 1. (A) Scores were plotted against observed probability of death. (B) An ROC curve was generated using the predicted probability of death for each patient as forecast by the logistic regression model. AUROCC is displayed with 95% confidence intervals in parentheses. ^*** p<0.001.

[0032] Figure 5 shows a classification tree useful to predict outcome of severe malaria infection with host biomarkers. Classification and regression tree (CRT) analysis was performed. All six biomarkers that discriminated survivors from fatalities were entered into the model. Prior probabilities of survival and death were specified (94.3% and 5.7%, respectively). The cost of misclassifying a true fatality was designated as 20 times the cost of misclassifying a true survivor. The cut-points selected by the analysis are indicated between parent and child nodes. Below each terminal node (i.e. no further branching), the predicted categorization of all patients in that node is indicated. This model yielded 100% sensitivity, 92.5% specificity, and a cross- validated misclassification rate of 20.6% (standard error 5.4%). The model was not altered by pruning. [0033] Figure 6 shows a modified classification tree useful to predict outcome of severe malaria infection with host biomarkers. The classification tree in Fig. 5 was modified to eliminate the final decision node based on IP- 10, so that only one IP-10 cut-point would be included in the model. The maximum depth of the tree was set at two levels and the cost of misclassifying a death as a survivor was increased to 25 times the cost of misclassifying a survivor. This model yielded 100% sensitivity, 83.8% specificity, and a misclassification rate of 19.1% (standard error 5.4%), and was not altered by pruning. Although this tree had lower specificity than the original tree, this may be outweighed by the simplification of the scheme.

[0034] Figure 7 shows the utility of combinations of biomarkers in predicting children with retinopathy positive CM. Multiple logistic regression models (255) were applied to determine how well combinations of biomarkers could predict the presence of retinopathy in children with a clinical diagnosis of CM. Every possible combination of the 8 biomarkers was included in the model and the area under the ROC curve was determined. The area under the ROC curve is plotted on the y-axis and the number of markers included in the model is shown on the x-axis. 8 biomarkers are able to predict retinopathy with an area under the ROC curve of 0.91 (95% CI 0.84-0.98).

[0035] Figure 8 shows that endothelial biomarkers differentiate between uncomplicated (UM) and cerebral malaria with retinopathy (CM-R). (A-D) Representative graphs showing the median and scatter of plasma biomarkers (A) Ang-1 (ng/mL), (B) VWF propeptide (VWFpp, nM), (C) VWF (nM), and (D) slCAM-1 (ng/mL) levels in children with uncomplicated malaria or cerebral malaria with retinopathy as measured by elisa (p<0.0009 for all markers by Mann-Whitney with Holms correction (9 pair-wise comparisons for all biomarkers)). Corresponding receiver operator characteristic curves (ROC) are plotted with the sensitivity or true positive rate on the y-axis and (1- specificity) or false positive rate on the x-axis (E-H) are shown for Ang-1 (E: area under the ROC (AUROC), 95% CI; 0.96, 0.93-1.0), VWFpp (F: AUROC, 95% CI; 0.93, 0.87-0.99), VWF (G: AUROC, 95% CI; 0.93, 0.88-0.99) and slCAM-1 (H: AUROC, 95% CI; 0.94, 0.87-1 .0). The 45 degree identity line represents the null hypothesis that the area under the ROC curve is 0.5.

[0036] Figure 9 shows the mathematical optimization of biomarker combinations in cerebral malaria (CM-R) vs. uncomplicated malaria (UM) or CNS controls. Multiple logistic regression models (255) were applied in (A) UM vs. CM-R, and (B) CNS vs. CM-R to determine the most parsimonious combinations of biomarkers. Every possible combination of the 8 biomarkers was included in the model and the area under the ROC curve was determined. The area under the ROC curve is plotted on the y-axis and the number of markers included in the model is shown on the x-axis. An area under the ROC curve of 1.0 (perfect discrimination) was possible with four markers for UM vs. CM-R and five markers for CNS vs. CM-R.

[0037] Figure 10 shows endothelial biomarkers differentiate between cerebral malaria (CM-R) and febrile children with impaired consciousness (CNS). (A-D) Representative graphs showing the median and scatter of plasma biomarkers (A) Ang-1 (ng/mL), (B) VWF propeptide (VWFpp, nM), (C) VWF (nM), and (D) slCAM-1 (ng/mL) levels in children with suspected CNS infections or cerebral malaria with retinopathy as measured by ELISA (p<0.0009 for Ang-1 , VWFpp and VWFs by Mann-Whitney with Holms correction (9 pair-wise comparisons for all biomarkers) and p>0.05 for sICAM- 1 ). The corresponding receiver operator characteristic (ROC) curves (E-H) are shown for Ang-1 (E: AUROC, 95% CI; 0.93, 0.88-0.99), VWFpp (F: AUROC, 95% CI; 0.90, 0.89-0.98), VWF (G: AUROC, 95% CI; 0.80, 0.66-0.92) and slCAM-1 (H: AUROC, 95% CI; 0.59, 0.41 -0.74).

[0038] Figure 1 1 shows biomarker levels at admission and 28 day follow up. Plasma levels of biomarkers were measured at admission and 28 days post-treatment in a cohort of retinopathy positive children with cerebral malaria. Wilcoxon signed rank test with Holms correction (9 pair-wise comparisons) was used to compare levels of (A) Ang-2 (ng/mL); sum of signed ranks (W), (W, p-value: 746, p<0.0009); (B) Ang-1 (ng/mL), (W, p- value: -741 , p<0.0009); (C) Ang-2: Ang-1 , (W, p-value: 741 , p<0.0009); (D) 2011/000894

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VWF propeptide (nM), (W, p-value: 768, p<0.0009); (E) VWF (nM), (W, p- value: 740, p<0.0009); (F) sTie-2 (ng/mL) (W, p-value: 754, p<0.0009); (G) slCAM-1 (ng/mL), (W, p-value: 732, p<0.0009); (H) VEGF (ng/mL), (W, p- value: -388, p=0.001); and (I) IP-10 (ng/mL), (W, p-value: 607, p<0.0009).

Detailed Description of the Invention

[0039] The present description provides biomarkers and combinations of biomarkers that are useful for identifying subjects having, or at risk of developing, severe malaria. Accordingly, in one embodiment, there is provided a method of identifying a subject having, or at risk of developing, severe malaria. In one embodiment, the method comprises:

(a) determining the level of one or more biomarkers in a test sample from the subject; and

(b) comparing the level of the one or more biomarkers in the test sample to a level of the one of more biomarkers in a control sample, wherein a difference between the level of the one or more biomarkers in the test sample and the control sample indicates that the subject has, or is at risk of developing, severe malaria.

[0040] As used herein a "biomarker" corresponds to a biomolecule such as a nucleic acid, protein or protein fragment present in a biological sample from a subject, wherein the quantity, concentration or activity of the biomarker in the biological sample provides information about whether the subject has, or is at risk of developing, a disease state. In one embodiment the disease state is severe malaria.

[0041] As used herein, "severe malaria" refers to a malarial infection characterized as cerebral malaria or severe malarial anemia (SMA). Optionally, severe malaria includes signs of organ dysfunction. Signs of organ dysfunction include, but are not limited to, respiratory distress, acute renal failure or hypotension. Optionally, subjects with severe malaria have retinopathy. As used herein, "cerebral malaria" refers to a neurological condition associated with severe malaria. Optionally, the neurological condition includes, but is not limited to, coma or seizures. Cerebral malaria may be optionally defined as subjects presenting with P. falciparum asexual parasitaemia; a Blantyre coma score ≤2 with no improvement following correction of hypoglycemia, within 30 minutes of cessation of seizure activity, or within 4 hours of admission; and no other identified cause. As used herein, "severe malarial anemia" or "SMA" refers to a subject presenting with P. falciparum asexual parasitaemia and a hemoglobin <5g/dL or hematocrit <15%. In one embodiment severe malaria optionally includes fatal malaria. As used herein, "fatal malaria" refers to severe malaria in a subject that progresses to a fatal outcome.

[0042] As used herein, "uncomplicated malaria" refers to subjects with a malaria infection and fever, but without the presence of the symptoms of severe malaria or cerebral malaria. Uncomplicated malaria is not considered to be within the meaning of "fatal malaria" although it is recognized that in some cases patients with uncomplicated malaria may progress to severe disease and die, especially if they have other complicating conditions that impair the ability to fight a malaria infection, such as congestive heart failure, diabetes, pneumonia or AIDS. Malaria infection is caused by members of the Plasmodium species. In one embodiment, the malaria infection is caused by P. falciparum, P. vivax, P. ovale, P. malariae or P. knowlesi A person skilled in the art will appreciate that malaria infection in a subject can be identified by methods known in the art, such as by positive identification of Plasmodium in a blood smear.

[0043] The term "identifying" as used herein refers to a process of determining a subject's likelihood of having, or risk of developing, severe malaria. Optionally, identifying a subject having, or at risk of developing, severe malaria includes determining the presence of malaria in a subject and/or determining a prognosis for a subject with respect to developing severe malaria. In one embodiment, the methods described herein are useful for identifying subjects who will progress to severe or fatal malaria. Subjects may be identified who present with symptoms of malaria, or who are pre- symptomatic.

[0044] In one embodiment, the methods described herein are useful to detect or monitor the appearance or severity of disease in a subject with malaria. In one embodiment, the methods are useful to monitor response to therapy in a subject with malaria. The methods described herein may also be used to improve clinical decision-making and case management of malaria. Optionally, the methods are useful for triage and cost effective management of malaria infections.

[0045] The term "subject" as used herein refers to any member of the animal kingdom. In one embodiment the subject is a mammal, such as a human. Optionally, the subject is a child.

[0046] In one embodiment, the methods described herein include comparing the level of one or more biomarkers in a test sample to a level of one of more biomarkers in a control sample.

[0047] The term "sample" as used herein refers to any fluid or other specimen from a subject that can be assayed for biomarker levels, for example, blood, serum, plasma, saliva, cerebrospinal fluid or urine. In one embodiment, the sample is whole blood, blood plasma or serum.

[0048] The term "level" as used herein refers to the quantity concentration, or activity of a biomarker in a sample from a subject. In one embodiment, the biomarker is a protein or protein fragment and the biomarker is detected using methods known in the art for detecting proteins such as ELISA or mass spectroscopy. In one embodiment, the biomarker is a protein or mRNA and the level is an expression level of the corresponding protein or mRNA. Optionally, the biomarker is an enzyme and enzyme activity levels are determined in a test sample from a subject to indicate a level of the biomarker in the subject. Optionally, biomarker mRNA levels or cDNA levels are determined in a test sample from a subject to indicate expression levels of the biomarker in the subject. [0049] As used herein, the term "control sample" refers to a sample representative of one or more subjects whose status with respect to malaria infection is known. In one embodiment, the control sample is representative of healthy subjects without malaria. In one embodiment, the control sample is representative of subjects with uncomplicated malaria. In another embodiment, the control sample is representative of subjects infected with malaria who do not develop severe or fatal malaria. In one embodiment, the control sample is representative of healthy subjects that are not suffering from malaria. Optionally, the control sample is age-matched or matched for ethnicity or genetic background with the subject who provides the test sample. In one embodiment, the one or more biomarker levels in the test sample are compared to levels of one or more biomarkers in a control sample. Optionally, the phrase "level of one or more biomarkers in a control sample" refers to a predetermined value or threshold of a biomarker or levels or more than one biomarker, such as a level or levels known to be useful for distinguishing between uncomplicated malaria and severe malaria as described herein. In one embodiment, the methods described herein are useful for identifying subjects with malaria from subjects with CNS infections other than malaria and the control samples are from subject with CNS infections other than malaria such as encephalitis, meningitis, toxic encephalopathy, or Reyes syndrome etc.

[0050] In a further embodiment, the method includes comparing biomarker profiles in samples taken from a subject at different time points. For example, in one embodiment, the control sample is determined from a test sample taken from a subject at an earlier time point. Accordingly, the methods described herein may be used to monitor the progression of malaria or clinical response to therapy in a subject or group of subjects at different time points. In one embodiment, a test sample is taken from a subject and subsequent samples are taken at periodic intervals of between 1 hour and 14 days. In one embodiment, test samples are taken at periodic intervals of approximately 1 hour, 2 hours, 4 hours, 8 hours, 12 hours, 24 hours, 48 hours, 72 hours or greater than 72 hours. In one embodiment, the test samples are taken at periodic intervals of less than one hour or at any other suitable time interval for monitoring the subject.

[0051] In one embodiment, the methods described herein comprise comparing the level of one or more biomarkers in a test sample to a level of one of more biomarkers in a control sample. Optionally, the level of the biomarkers in the control sample is a predetermined or standardized level or threshold. For example, in one embodiment the level of the one or more biomarkers in the test sample is compared to one or more previously determined control levels. An increase or decrease in the observed levels of the biomarkers compared to the control level indicates the subject has, or is at risk of developing, severe malaria. In one embodiment the level of the one or more biomarkers in the test sample are compared to a threshold control level wherein an increased or decreased level in the test sample indicates the subject has, or is at risk of developing, severe or fatal malaria. In one embodiment, the magnitude of the difference between the level of the one or more biomarkers in the test sample from a subject and the one or more control levels is indicative of the severity of the disease in the subject. In one embodiment, the magnitude of the level of ANG-2 in a sample from a subject with malaria is indicative of the severity of the disease in the subject.

[0052] In one embodiment, a difference between the level of the biomarkers in the test sample and the control sample indicates that the subject has, or is at risk of developing, severe malaria. For example, in one embodiment an increase in the level of one or more biomarkers selected from ANG-2, VWF, VWFpp, slCAM-1 , sFLT-1 , sTie-2, CRP, PCT, IP-10 and CHI3L1 in the test sample compared to a control sample indicates that the subject has or is at risk of developing severe malaria. In another embodiment, a decrease in the level of ANG-1 or an increase in the level of ANG-2, ANG-2:ANG-1 , sTie-2, VWF, VWFpp, VEGF or slCAM-1 in a test sample compared to a control sample indicates that the subject has, or is at risk of developing, cerebral malaria with retinopathy. [0053] The step of comparing the level of one or more biomarkers in a test sample to a level of one of more biomarkers in a control sample can be performed by any one of a number of methods known in the art. For example, in one embodiment the levels of individual biomarkers, such as CHI3L1 , SFLT-1 or S-TREM1 are compared to determine if there is a difference indicative of the subject having, or at risk of developing, severe malaria. As set out in Example 1 , increased levels of CHI3L1 in a test sample compared to a control sample are indicative of severe or fatal malaria. In one embodiment, receiver operator characteristic (ROC) curves are generated for a biomarker and cut-off points generated using the Youden index (J = max[sensitivity+specificity-1]). In one embodiment, the cut-off points shown in Table 2 are used as levels of the biomarker in the control sample for identifying subjects having, or at risk of developing, severe or fatal malaria. In one embodiment, a level of CHI3L1 in the test sample greater than 177.5 ng/ml indicates that the subject has, or is at risk of developing, severe or fatal malaria.

[0054] In another embodiment, levels from more than one biomarker are compared to identify a subject having, or at risk of developing, severe or fatal malaria. For example, in one embodiment biomarker levels may be combined into a single composite variable as shown in Example 1 and Table 4. Methods that can be used to compare levels in test samples and control samples include, but are not limited to, analysis of variance (ANOVA), multivariate linear or quadratic discriminant analysis, multivariate canonical discriminant analysis, a receiver operator characteristics (ROC) analysis, and/or a statistical plots. In one embodiment, multivariate methods are useful to compare levels and identify differences for a plurality of biomarkers as shown in Example 2. For example, as shown in Figure 9 multivariate logistic regression models with a plurality of biomarkers selected from ANG-1 , ANG- 2, sTie-2, VWFpp, VWF, slCAM-1 , VEGF and IP-10 can be used to identify subjects with severe malaria (cerebral malaria with retinopathy) from subjects with uncomplicated malaria or other causes of central nervous system infections. Optionally, other combinations of markers described herein may be used to compare levels in test samples and control samples as set out above.

[0055] A person skilled in the art will appreciate that a number of different methods are useful to determine the level of the relevant biomarkers of the invention. In one embodiment, the level of the relevant biomarkers of the invention may be determined by real time PCR or other methods known in the art for determining gene expression. In one embodiment, the methods use mass spectroscopy for detecting biomarkers in a sample from a subject. In one embodiment, protocols for determining the level of biomarkers use agents that bind to the biomarker protein of interest. In one embodiment the agents are antibodies or antibody fragments. The term "antibody" as used herein is intended to include monoclonal antibodies, polyclonal antibodies, and chimeric antibodies. The antibody may be from recombinant sources and/or produced in transgenic animals. The term "antibody fragment" as used herein is intended to include Fab, Fab', F(ab')2, scFv, dsFv, ds-scFv, dimers, minibodies, diabodies, and multimers thereof and bispecific antibody fragments. Antibodies can be fragmented using conventional techniques. For example, F(ab')2 fragments can be generated by treating the antibody with pepsin. The resulting F(ab')2 fragment can be treated to reduce disulfide bridges to produce Fab' fragments. Papain digestion can lead to the formation of Fab fragments. Fab, Fab' and F(ab')2, scFv, dsFv, ds-scFv, dimers, minibodies, diabodies, bispecific antibody fragments and other fragments can also be synthesized by recombinant techniques.

[0056] Antibodies having specificity for biomarker proteins may be prepared by conventional methods. A mammal, (e.g. a mouse, hamster, or rabbit) can be immunized with an immunogenic form of the peptide which elicits an antibody response in the mammal. Techniques for conferring immunogenicity on a peptide include conjugation to carriers or other techniques well known in the art. For example, the peptide can be administered in the presence of adjuvant. The progress of immunization can be monitored by detection of antibody titers in plasma or serum. Standard ELISA or other immunoassay procedures can be used with the immunogen as antigen to assess the levels of antibodies. Following immunization, antisera can be obtained and, if desired, polyclonal antibodies isolated from the sera.

[0057] To produce monoclonal antibodies, antibody-producing cells (lymphocytes) can be harvested from an immunized animal and fused with myeloma cells by standard somatic cell fusion procedures thus immortalizing these cells and yielding hybridoma cells. Such techniques are well known in the art, (e.g. the hybridoma technique originally developed by Kohler and Milstein (Nature 256:495-497 (1975)) as well as other techniques such as the human B-cell hybridoma technique (Kozbor et al., Immunol.Today 4:72 (1983)), the EBV-hybridoma technique to produce human monoclonal antibodies (Cole et al., Methods Enzymol, 121 : 140-67 (1986)), and screening of combinatorial antibody libraries (Huse et al., Science 246: 1275 (1989)). Hybridoma cells can be screened immunochemically for production of antibodies specifically reactive with the peptide and the monoclonal antibodies can be isolated.

[0058] In one embodiment of the invention, the agents, such as antibodies or antibody fragments, that bind to the biomarker of interest are labeled with a detectable marker.

[0059] The label is preferably capable of producing, either directly or indirectly, a detectable signal. For example, the label may be radio-opaque or a radioisotope, such as ³H, ⁴C, ³²P, ³⁵S, ¹²³l, ²⁵l or ¹³¹l; a fluorescent (fluorophore) or chemiluminescent (chromophore) compound, such as fluorescein isothiocyanate, rhodamine or luciferin; an enzyme, such as alkaline phosphatase, beta-galactosidase or horseradish peroxidase; an imaging agent; or a metal ion.

[0060] In another embodiment, the detectable signal is detectable indirectly. For example, a labeled secondary antibody can be used to detect the protein of interest. [0061] A person skilled in the art will appreciate that a number of other methods are useful to determine the levels of biomarkers in a sample, including immunoassays such as Western blots, ELISA, and/or immunoprecipitation followed by SDS-PAGE immunocytochemistry etc. Other embodiments include the use of methods for determining levels of a biomarker in a sample such as lateral flow and related immunochromatic tests used in point-of-care tests. In addition, protein arrays (including microarrays) are useful. For nucleic acid biomarkers such as mRNA, RT-PCR or quantitative RT-PCR or other methods known in the art for detecting and/or quantifying nucleic acids are also useful for determining the level of a biomarker for use in the methods described herein.

[0062] Furthermore, in one embodiment of the invention, additional clinically relevant biomarkers are tested along with the biomarkers identified herein, such as specific malaria or pathogen-associated antigens.

[0063] Any of the described methods of the invention are useful in addition or in combination with diagnostic techniques for infectious disease known in the art.

[0064] In one embodiment of the invention, biomarker profiles and any additional markers of interest are determined using multiplex technology. This technology has the advantage of quantifying multiple proteins simultaneously in one sample. The advantages of this method include low sample volume, cost effectiveness and high throughput screening. Antibody-based multiplex kits are available from Linco (Millipore Corporation, MA), Bio-Rad Laboratories (Hercules, CA), Biosource (Montreal, Canada), and R&D Systems (Minneapolis, MN).

[0065] The invention also includes kits for identifying subjects at risk of developing severe malaria comprising a detection agent for biomarkers, typically with instructions for the use thereof. In one embodiment, the kit includes antibodies directed against one or more biomarkers selected from angiopoietin-1 (ANG-1), angiopoietin-2 (ANG-2), von Willebrand factor (VWF), von Willebrand factor propeptide (VWFpp), soluble P-selectin, soluble ICAM-1 (slCAM-1 ), soluble endoglin, soluble FLT- (sFLT-1 ), soluble Tie-2 (sTie-2), C-Reactive Protein (CRP), procalicitonin (PCT), IP-10, chitinase-3-like-1 (CHI3L1 ), VEGF and Triggering Receptor Expressed on Myeloid cells-1 (TREM-1 ). In one embodiment, the kit includes antibodies directed against CHI3L . In one embodiment the kit comprises antibodies directed against two or more or three or more of the biomarkers described herein.

[0066] In one embodiment, the kits optionally include one or more of a medium suitable for formation of an antigen-antibody complex, reagents for detection of the antigen-antibody complexes and instructions for the use thereof. In an additional embodiment, the invention relates to a composition comprising an, optionally provided together in a container.

[0067] The above disclosure generally describes the present invention. A more complete understanding can be obtained by reference to the following specific examples of certain embodiments of the invention.

EXAMPLE 1 : Identification and Validation of Biomarkers for Infectious Disease

[0068] Plasmodium falciparum malaria causes almost 1 million deaths annually, mostly among young children in sub-Saharan Africa (World Health Organization 2009). The most common manifestations of pediatric severe malaria are severe malarial anemia (SMA) and cerebral malaria (CM), syndromes with case fatality rates as high as 20% (Murphy, SC et al. 2001 ). It is challenging at clinical presentation to accurately determine which children with severe malaria are at greatest risk of death. Simple and sensitive clinical scores have been developed to predict outcome, but they have low specificity (Marsh, K et al. 1995; Helbok, R et al. 2009). A prognostic test that accurately identifies high-risk children would be useful for targeting limited health resources and for selecting patients to enroll in clinical trials of adjunctive therapies. [0069] Investigations into malaria pathogenesis have implicated host pathways in disease progression. Serum/plasma biomarkers of these pathways may have clinical utility as prognostic tools. In particular, dysregulated inflammatory responses and endothelial activation are considered central processes in severe malaria pathogenesis (van her Heyde, HC et al. 2006). Biomarkers of these pathways were selected and assessed for their utility as indicators of disease severity and outcome in Ugandan children with malaria.

[0070] Imbalanced pro-inflammatory responses are observed in both CM and SMA (Lyke, KE et al. 2004; Othoro, C et al. 1999). In this study, we measured plasma levels of acute-phase response components, C-reactive protein (CRP) and procalcitonin (PCT), and IP-10, a chemokine implicated in experimental CM (Campanella, GS et al. 2008) and reported to be elevated in fatal CM (Armah, HB et al. 2007). Levels of chitinase-3 like- (CHI3L1 ), which is associated with inflammatory conditions (Coffman, FD 2008) but has not been previously studied in malaria, were also measured.

[0071] Dysregulated inflammation is thought to promote CM in part via activation of brain endothelium. Pro-inflammatory cytokines upregulate cell adhesion receptors (e.g., intercellular adhesion molecule-1 [ICAM-1]) that mediate sequestration of parasitized erythrocytes in brain microvasculature, leading to vessel occlusion (Beare, NA et al. 2009) and blood-brain barrier dysfunction (Medana, IM et al 2006). Upon endothelial activation, soluble endothelial cell receptors are released via ectodomain shedding or alternative splicing. The soluble forms of ICAM-1 (slCAM-1) and the TGF-β receptor Endoglin (sEndoglin), previously shown to be increased in severe malaria (Dietmann, A et al. 2009; Turner, GD et al. 1998), and soluble FMS-like tyrosine kinase-1 (sFlt-1 ), which has been implicated in placental malaria (Muehlenbachs, A et al. 2008) were measured. Endothelial activation also causes Weibel-Palade body (WPB) exocytosis (Lowenstein et al. 2005). We assayed factors released from WPBs: angiopoietin-2 (Ang-2), von Willebrand factor (vWF), vWF propeptide, and soluble P-selectin (sP-selectin). Some of these factors are increased in CM (Hollestelle, MJ et al. 2006; Lovegrove, FE et al. 2009; Yeo, TW et al. 2008) and may contribute to pathology. Ang-2 promotes vascular activation by antagonizing the interaction of the Tie-2 receptor with angiopoietin-1 (Ang-1 ) (Parikh, SM et al. 2006), while vWF may help tether parasitized erythrocytes to endothelial cells via platelets (Bridges, DJ et al. 2010). Systemic endothelial activation has been shown to occur in adults with malaria (Turner, GD et al. 1998); however, few studies have characterized the extent and significance of this process in pediatric SMA.

[0072] Levels of these biomarkers of inflammation and endothelial activation were assessed at presentation to determine which markers discriminated between children who survived severe malaria infection and those who subsequently died. Furthermore, combinations of biomarkers that predicted mortality with high accuracy were identified.

Materials and Methods

[0073] Study site. This study was nested within a prospective case- control study conducted at Mulago Hospital in Kampala, Uganda (October 2007 to October 2009). Mulago Hospital is a national referral hospital that serves Kampala and surrounding districts. Malaria transmission in this region and the patient population at Mulago Hospital have been previously described (Opoka, RO et al. 2008).

[0074] Ethical approval and informed consent. Ethical approval for the study was obtained from the Mulago Hospital Research Ethics Committee, Makerere University Faculty of Medicine Research Ethics Committee, Uganda National Council for Science & Technology, and the University Health Network. Procedures followed were consistent with the Helsinki Declaration (1983). Written informed consent for participation in the study was obtained from parents/guardians before enrollment, and separate written consent was obtained for storage of a plasma sample for future analysis.

[0075] Study participants. Children (0.5-10.8 years old) presenting to hospital with P. falciparum infection were enrolled in the study. A convenience sample comprised of UM outpatients and CM and SMA inpatients (n=156) was used for biomarker analysis. CM and SMA were defined according to WHO criteria (World Health Organization 2000). Exclusion criteria were: negative blood film for malaria, sickle cell trait/disease, HIV co-infection, and severe malnutrition. Treatment was in accordance with national guidelines, including transfusions for all SMA patients. Parasitemia is reported as the arithmetic mean of two independent readings.

[0076] Sample collection and testing. Upon enrollment, venous blood samples were collected into sodium citrate. Plasma was stored at -20°C prior to testing. Ang-2, CRP, CHI3L1 , sEndoglin, IP-10, Flt-1 , slCAM-1 , sP-selectin, sTie-2 (R&D Systems), PCT (Ray BioTech), and vWF propeptide (Sanquin) were assayed by ELISA according to the manufacturers' instructions. For vWF, plates were coated with anti-human vWF antibody (Dako, 1 :600), incubated with samples and serial dilutions of vWF (American Diagnostica), then incubated with horseradish peroxidase-conjugated anti-human vWF (Dako, 1 :8000). Assays were developed with tetramethylbenzidine, and stopped with H₂S0₄.

[0077] Statistical analysis. GraphPad Prism v4, SPSS v18, and MedCalc software were used for analysis. Demographic/clinical characteristics were compared between groups using the Kruskal-Wallis test with Dunn's multiple comparison post-hoc tests for continuous variables or Chi-square test for categorical variables. Biomarker levels were compared using the Mann-Whitney U test, then corrected for multiple comparisons using Holm's correction. Receiver operating characteristic curves were generated, and cut-points were determined using the Youden index (J = max[sensitivity+specificity-1]). For logistic regression, linearity of an independent variable with the log odds of the dependent was confirmed by including a Box-Tidwell transformation into the model and ensuring that this term was not significant. Bootstrapping (1000 sample draws) was used to estimate 95% confidence intervals and standard errors. Model goodness-of-fit was assessed by Hosmer-Lemeshow test and calibration slope analysis. 94

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Positive/negative predictive values were calculated using the estimated case- fatality rate of 5.7% for microscopy-confirmed CM and SMA at Mulago Hospital (Opoka, RO et al. 2008). Classification and regression tree analysis was performed with the following settings: minimum 10 cases for parent nodes and 5 for child nodes; customized prior probabilities and misclassification costs (as indicated); and cross-validation with 10 sample folds to generate an estimate of the misclassification rate. Pruning was employed to avoid overfitting (maximum difference in risk: 1 standard error).

Results

[0078] Characteristics of study participants. Table 1 presents demographic and clinical characteristics of children with UM, CM, and SMA. Children with SMA were younger than children with UM and CM (p<0.001) and presented significantly later than the other groups (p<0.001 , approximately one day later). Children with severe malaria had lower hemoglobin levels and platelet counts than children with UM.

[0079] Biomarker levels in uncomplicated vs. severe malaria patients. Samples obtained at presentation were assayed for biomarkers of endothelial activation and inflammation (Fig. 1). slCAM-1 , sTie-2, and sFlt-1 were increased in CM and SMA compared to UM (p<0.01), while sEndoglin and sP-selectin did not differ between groups (p>0.05). WPB-associated proteins Ang-2, vWF, and vWF propeptide were elevated in children with severe malaria compared to UM (p<0.01), as were inflammatory biomarkers CRP, PCT, and CHI3L1 (p<0.01). IP- 0 trended towards an increase in CM compared to UM, but this failed to reach statistical significance (p=0.084). These data indicate markers of inflammation and endothelial activation are significantly elevated in severe malaria compared to UM.

[0080] Biomarkers as predictors of mortality in children with severe malaria. To evaluate the prognostic utility of these biomarkers, admission levels between children with severe malaria who survived infection and those who subsequently died were compared. A complete analysis is presented in Table 5. After correction for multiple comparisons, admission levels of Ang-2 and CHI3L1 (p<0.05) were significantly increased in CM fatalities compared to survivors (Fig. 2A), while Ang-2, CHI3L1 , slCAM-1 , IP- 10 (p<0.01), and sFlt-1 (p<0.05) were elevated in SMA fatalities compared to survivors (Fig. 2B).

[0081] The biomarkers that reached significance in the SMA group but not the CM group after correction for multiple comparisons (slCAM-1 , IP-10, sFlt-1) were significant or trending towards significance in the CM group before the correction was applied (Table 5). This suggests that apparent differences between syndromes may be due to low statistical power. All severe malaria patients were combined to improve power and to avoid the problem of classifying mixed clinical phenotypes. Characteristics of survivors and fatalities were similar (Table 6), although among fatalities there was a greater proportion of females (p=0.007) and increased parasitemia (p=0.023). Ang-2, slCAM-1 , sFlt-1 , IP-10, and CHI3L1 (p<0.01), as well as PCT (p<0.05), were elevated in fatal cases of severe malaria compared to survivors (Fig. 2C).

[0082] To assess how well these biomarkers discriminated between survivors and fatalities, receiver operating characteristic (ROC) curves were generated and the area under the curve (AUC) was determined (Fig. 3). Ang- 2, slCAM-1 , IP-10, and CHI3L1 had excellent predictive ability (AUC 0.8-0.9), and sFlt-1 and PCT had acceptable predictive ability (AUC 0.7-0.8) (Hosmer, DW and Lemeshow, S, 2000). The AUC for parasitemia, which is used in clinical practice as a prognostic factor (World Health Organization 2000), was 0.66.

[0083] The Youden index was used to obtain a cut-point for each biomarker, and evaluated clinical performance measures for these dichotomized biomarkers (Table 2). CHI3L1 achieved the highest sensitivity (93.1%) and IP-10 predicted death with the highest overall accuracy (82.6% sensitivity, 85% specificity). Preferably, however, the performance characteristics of an ideal prognostic test would exceed 90%. [0084] Predicting fatality with a "biomarker score". Biomarkers were combined in order to assess whether combinations of biomarkers would improve predictive accuracy. The modest number of deaths in the study precluded multivariate logistic regression analysis with more than 2 independent variables (Harrell, FE et al. 1996). Therefore, as performed for other conditions (Morrow, DA and Braunwald, E, 2003; Vinueza, CA et al. 2000), the biomarkers were combined into a score. For each marker, one point was assigned if the measured value was greater than the corresponding cut-point, and zero points if lower. A cumulative "biomarker score" was calculated for each patient by summing the points for all six markers. No two dichotomized biomarkers were highly correlated (Table 7), suggesting that each would contribute unique information to the score.

[0085] Risk of death increased with increasing score (Fig. 4A), and these two variables were positively correlated (Spearman's rho=0.96, p=0.003). In a univariate logistic regression model, the score was a significant predictor of death with an odds ratio of 6.5 (95% CI 4.0-31.0) (Table 3). The model fit was good (Table 3; calibration slope: 1.0). ROC curve analysis of the predicted probabilities of death yielded an AUROCC of 0.96 (95% CI 0.92- 0.99) (Fig. 4B), indicating that the score discriminated with high accuracy between survivors and fatalities. When the score was entered into a multivariate analysis along with parasitemia, it remained significant with a similar odds ratio (Table 8), indicating that the biomarker score is associated with the outcome of severe malaria infection independent of parasitemia.

[0086] To generate a simple model for predicting outcome, ROC curve analysis and cut-point determination were performed on the raw biomarker score. A score >4 was 95.7% sensitive and 88.8% specific for predicting death (Table 4). While the positive predictive value was low (33.9%) given a fatality rate of 5.7%, the negative predictive value (NPV) was 99.7%, indicating that a child with a score≤3 will likely respond well to standard treatment protocols. [0087] A score involving fewer biomarkers would improve practicality and reduce costs. Using the same scoring scheme, 2-marker combinations performed poorly (data not shown). However, specific 3-marker combinations yielded sensitivity > 90% and specificity >80% (Table 4). The 3-marker scores appeared to perform similarly to the 6-marker score. Notably, the Ang-2/IP- 10/CHI3L1 combination (cut-point >2) had moderate specificity (81.2%), but 100% sensitivity and an NPV of 100%.

[0088] To assess whether an accurate 2-marker combination could be achieved using alternative cut-points, combinations of the two biomarkers with the greatest AUCs (slCA - and CHI3L1) were evaluated using all possible cut-points with >90% sensitivity. Combining slCAM-1 (cut-point 605.4 ng/mL) and CHI3L1 (cut-point 116.3 ng/mL) yielded 91.3% sensitivity and 85.0% specificity (Table 4).

[0089] Predicting fatality using classification trees. To explore another combinatorial strategy, classification and regression tree (CART) modeling was used. CART analysis selects and organizes independent variables into a decision tree that optimally predicts the dependent measure. In this analysis the cost of misclassifying a death as a survivor was assigned as 20 times greater than the cost of misclassifying a survivor. A CART model based on IP-10, Ang-2, and slCAM-1 was generated (Fig. 5), with 100% sensitivity and 92.5% specificity. To reduce the complexity of having two distinct cut-points for IP-10, the final node of the tree (Fig. 6) was eliminated. The resulting model had 100% sensitivity and 83.8% specificity. Thus, combining dichotomized biomarkers using a simple scoring system or classification tree can predict fatality among subjects with severe malaria with high accuracy.

Discussion

[0090] Investigations into severe malaria often seek to characterize host pathways contributing to immunopathology. In addition to identifying potential targets for adjunctive therapies, this approach may be exploited to identify biomarkers of disease severity and outcome. It has been shown that combinations of markers, particularly if drawn from distinct pathobiological pathways, can improve prognostic power (Morrow, DA and Braunwald, E, 2003). The present Example provides data showing the measurement of a panel of biomarkers related to two central host-mediated pathological processes in severe malaria - inflammation and endothelial activation - in children with uncomplicated and severe malaria in Uganda. sTie-2, sFlt-1 , and CHI3L1 were identified as novel biomarkers of severe malaria in children and simple schemes combining as few as three dichotomized biomarkers were demonstrated to predict mortality with high accuracy.

[0091] Increased plasma levels of Ang-2, vWF, and vWF propeptide in CM vs. UM, were observed as previously described (Hollestelle, MJ et al. 2006; Lovegrove, FE et al. 2009; Yeo, TW et al. 2008). The present Example demonstrates that these factors are specifically elevated in SMA, suggesting that extensive WPB exocytosis occurs not only in CM but also in SMA. Few studies have directly addressed endothelial activation in SMA. WPB exocytosis can be induced by factors generated during malaria infection (e.g., cytokines, histamine, reactive oxygen species) that may be more elevated in SMA than UM (Othoro, C et al. 1999; Greve, B et al. 2000). It is biologically plausible that increased circulating levels of WPB contents could directly contribute to the pathogenesis of SMA. Ang-2 sensitization of endothelial cells to TNF (Fiedler, U et al. 2006) may amplify secretion of endothelial cytokines, such as IL-6, that can contribute to anemia (Raj, DS, 2009). Interestingly, Ang-2 can impair maintenance of long-term hematopoietic stem cells (LT- HSCs) in bone marrow by inhibiting the Tie-2/Ang-1 interaction (Gomei, Y et al. 2010). While the role of LT-HSCs in SMA requires clarification, dysregulated Ang-2 levels may contribute to anemia via LT-HSC depletion.

[0092] There are some discrepancies between the data presented herein and previous reports in pediatric populations. sEndoglin was found to be increased in Gabonese children with severe malaria compared to UM (Dietmann, A et al. 2009), but we could not replicate these results. Similar levels of IP-10 in CM and SMA fatalities were observed, in contrast to a report that serum IP-10 was specifically elevated in Ghanaian children who died from CM (Armah, HB et al. 2007). However, these results may not be comparable since blood was obtained post-mortem in the Ghanaian study rather than at admission.

[0093] sTie-2 and sFlt-1 were observed to be significantly elevated in severe malaria. Stimuli such as vascular endothelial growth factor (VEGF) cause ectodomain shedding of Tie-2 in vitro, resulting in decreased Ang-1 signaling due to reduced membrane Tie-2 and competitive inhibition by sTie-2 (Findley, CM et al. 2007). Thus, high sTie-2 levels may exacerbate endothelial destabilization in malaria. However, the net effect of increased sTie-2 may depend on the Ang-1/Ang-2 balance present, since sTie-2 can also inhibit Ang-2 activity (Roviezzo, F et al. 2005). Ang-1 could not be measured due to poor detectability in citrated plasma. sFlt-1 is generated by alternative splicing of VEGF receptor-1 mRNA and antagonizes the pro-inflammatory and pro- angiogenic effects of VEGF. Increased sFlt-1 in severe malaria parallels findings in human sepsis (Shapiro, Nl et al. 2008). In a murine model of sepsis, sFlt-1 administration reduced VEGF-mediated vascular permeability and mortality (Yano, K. et al. 2006). In pediatric CM, VEGF levels positively correlated with neurological complications (Casals-Pascual, C et al. 2008); elevated sFlt-1 in severe malaria may represent a host response to counter the pathological effects of excess VEGF.

[0094] The present description identifies CHI3L1 , a 40 kDa chitin- binding protein, as a biomarker of severe and fatal malaria. The implication of CHI3L1 in angiogenesis (Shao, R et al. 2009) suggests that CHI3L1 may contribute to malaria pathology by promoting vascular permeability. However, elevated CHI3L1 in severe malaria may be an attempt by the host to regulate immunopathology, since CHI3L1 has been shown to have anti-inflammatory effects (Ling, H and Recklies, AD, 2004).

[0095] Combinations of biomarkers accurately predicted mortality among children with severe malaria. Notably, some biomarker combinations showed excellent (>95%) sensitivity, ensuring that the majority of children at high risk of death would be identified. While an effective adjunctive therapy for severe malaria remains elusive, prognostication would allow triage of patients for closer monitoring or intensive care resources, as available. Such a test may also assist in risk stratification and patient selection for clinical trials of adjunctive therapies, which are ongoing (Yeo, TW et al. 2007).

[0096] Previous studies have developed clinical scores to prognosticate outcome in pediatric severe malaria (Marsh, K et al. 1995; Helbok, R et al. 2009). The simplicity and low costs of these tests are attractive features. However, a prognostic test would ideally predict mortality with both high sensitivity and specificity based on a single criterion to avoid the uncertainty associated with non-extreme scores. The biomarker combinatorial strategies presented here appear to possess this attribute, and furthermore provide an objective measurement that is unaffected by between-clinician variability. Advances in point-of-care platforms (Lee, WG et al. 2009) may enable development of affordable tests that integrate malaria diagnostics with prognostic biomarkers.

[0097] The present description has identified novel biomarkers in African children, the population at greatest risk of malaria mortality, and specifically in SMA, for which few informative biomarkers have been described. Combining as few as 2-3 biomarkers using simple schemes can accurately predict outcome in severe malaria infection.

EXAMPLE 2: Identification and Diagnostic Accuracy of Biomarkers for Cerebral Malaria

[0098] Differentiating cerebral malaria (CM) from other conditions causing fever and altered consciousness is a clinical challenge, owing to the non-specific clinical presentations of CM (fever, coma, convulsions) and the high prevalence of incidental parasitaemia in malaria-endemic areas (Taylor, Fu et al. 2004). In a study of African children diagnosed with CM, approximately one quarter were shown to have alternative causes for their neurological syndrome at post-mortem examination (Taylor, Fu et al. 2004). These findings indicate that CM is over-diagnosed, a situation that is likely to have serious consequences for children in whom other treatable or life- threatening conditions are not identified (World Health Organization 2000; Taylor, Fu et al. 2004). There is a dear need for a diagnostic test that could distinguish CM from other conditions causing encephalopathy in malaria- endemic areas. In comatose African children, a distinctive retinopathy consisting of haemorrhage, patchy retinal whitening and vessel changes is strongly associated with malaria being the only identifiable cause of death (Taylor, Fu et al. 2004).

[0099] A feature of P. falciparum infection is the adhesion of mature parasitized erythrocytes to the microvasculature of vital organs and acute endothelial activation (reviewed in (Medana and Turner 2006); Jakobsen, Morris-Jones et al. 1994). Recent evidence has demonstrated exocytosis of Weibel-Palade bodies (WPBs) during endothelial activation and has identified the products of WPBs as biomarkers of disease severity (Hollestelle, Donkor et al. 2006; Yeo, Lampah et al. 2008; Larkin, de Laat et al. 2009; Lovegrove at el. 2009). WPBs release bioactive products, including von Willebrand factor (VWF), its propeptide (VWFpp), and angiopoietin-2 (Ang-2) into the systemic circulation. Together with vascular endothelial growth factor (VEGF), the angiogenic factors angiopoietin-1 (Ang-1) and Ang-2, are major regulators of vascular inflammatory response, endothelial activation and endothelial integrity (Fiedler et al 2006; Findley 2007). Ang-1 is constitutively released from perivascular cells including pericytes and smooth muscle cells and signals through the Tie-2 receptor to maintain vascular quiescence and stability. Ang-2 antagonizes Ang-1 function resulting in endothelial activation and increased vascular permeability. Ang-2 sensitizes the endothelium to subthreshold levels of tumour necrosis factor, resulting in increased expression of adhesion molecules such as ICAM-1 to which parasitized erythrocytes bind (Fiedler, Reiss et al. 2006). VEGF induces WPB exocytosis, mediates Tie-2 shedding and acts as a co-factor for Ang-1 and Ang-2 function (Findley, Cudmore et al. 2007). WPBs are also an important source of VWF, particularly ultralarge multiples (ULVWF) that are considered biologically hyperactive with respect to their enhanced binding avidity for collagen and platelets. Severe malaria has been associated with increased levels of VWF and ULVWF multimers and decreased levels of the regulatory VWF-specific cleaving protease ADAMTS13 (A disintegrin and metalloprotease with thrombospondin type-1 repeats) (Larkin, de Laat et al. 2009). ICAM-1 is a receptor for the cytoadherence of mature parasitized erythrocytes in the cerebral microvasculature and its soluble form (s-ICAM-1) has been used as a marker of endothelial activation and severe malaria (Jakobsen, Morris-Jones et al. 1994; Turner, Morrison et al. 1994; Tchinda, Tadem et al. 2007). In addition to the molecular markers and regulators of endothelial quiescence and activation, IP-10, an interferon-gamma inducible chemokine involved in recruitment of activated Th1 cells, has been reported as a biomarker in CM in studies from India and Ghana (Armah, Wilson et al. 2007; Jain, Armah et al. 2008).

[00100] Reliable diagnostic and prognostic biomarkers for CM and other forms of severe malaria may improve clinical management, resource allocation and outcome of serious childhood illness. This example evaluates the diagnostic accuracy of endothelial biomarkers to discriminate between different clinical disease states in malaria and other conditions associated with fever and altered consciousness in Malawian children. Endothelium-based proteins are shown to be informative biomarkers of disease severity and clinical response and a panel of biomarkers is shown to completely discriminate retinopathy positive CM from uncomplicated disease and other CNS infections. A distinctive pattern of endothelium-based proteins is demonstrated to be associated with retinopathy in a group of children with coma and parasitaemia.

Materials and Methods

[00101] Study Population. Children 6 months and 15 years of age presenting with fever to the Queen Elizabeth Central Hospital (QECH) in Blantyre, Malawi were prospectively enrolled between 1997 and 2009. Admission plasma samples were obtained from children after their parents or guardians had given their informed consent. The samples tested represented a subset of random samples collected from larger prospective case-control studies of the pathogenesis and management of CM and central nervous system infections (REF). Clinical and demographic data were collected from cases and controls at the time of blood collection, and all subsequent analyses were carried out blind to these details. All participants received standard treatment, including antimalarial and/or antibacterial therapy as indicated, according to Malawian National guidelines.

[00102] Ophthalmological Examination. After admission, study participant's pupils were dilated by application of drops (tropicamide and phenylephrine) and the fundi were examined by direct and indirect ophthalmoscopy. The findings of an ophthalmologist or experienced clinician were recorded on standardized forms. Retinopathy was defined by the presence of any one of the following retinal findings: haemorrhaging, whitening, or vessel changes with or without papilloedema as described (Beare, Taylor et al. 2006; Lewallen, Bronzan et al. 2008). Papilloedema alone did not constitute retinopathy.

[00103] Definitions of clinical syndromes.

[00104] Cerebral Malaria (CM). The clinical definition of CM for the purposes of this Example was P. falciparum asexual parasitaemia; a Blantyre coma score <2 with no improvement following correction of hypoglycemia, within 30 minutes of cessation of seizure activity, or within 4 hours of admission; and no evidence of meningitis on examination of cerebrospinal fluid. Children's fundi were examined and they were classified as retinopathy positive (CM-R) or retinopathy negative (CM-N) as above. CM-R children were considered to be confirmed CM and were used for all analysis comparing clinical groups. Paired admission and 28 day convalescence plasma samples were collected for each child.

[00105] Uncomplicated Malaria. For the purposes of this Example, children presenting to the outpatient clinic at Queen Elizabeth Central Hospital, Blantyre, Malawi with febrile illness and positive blood films without another explanation for fever, and no malarial complications were identified as having uncomplicated malaria.

[00106] CNS controls. Children (aged 1 month to 15 years) with fever and altered consciousness were included in the study. Samples were taken from a study looking at suspected central nervous system (CNS) infections. CNS infections were suspected in children with fever or history of fever, and at least one of the following: reduced level of consciousness, Blantyre coma score (BCS) <4 or altered mental status in general, neck stiffness, photophobia, Kernig's sign, tense fontanelle, focal neurological signs, convulsions, or irritability in infants. Children with a single generalized convulsion lasting less than 15 minutes, who recovered consciousness within 60 minutes were diagnosed as having a simple febrile convulsion, and were excluded.

Quantification of Biomarkers

[00107] Plasma concentrations of biomarkers Ang-1 , Ang-2, sTie-2, VEGF, IP-10 and slCAM- (DuoSets, R&D Systems, Minneapolis, MN), von Willebrand factor (VWF (capture REF P0226, detection REF A0082): DAKO, Denmark A/S) and von Willebrand factor propeptide (VWFpp (capture CLB- Pro35, detection CLB-Pro 14.3 HRP conjugated): Sanquin, Netherlands) were measured by ELISA as follows. Capture antibodies were diluted according to the manufacturer's instructions in PBS (Gibco) overnight at 4°C and were washed with PBS 0.05% Tween 20 (Sigma) five times and blocked for a minimum of 2 hours in PBS 1 % BSA (reagent diluent). The samples were then diluted as follows, Ang-1 : 1 in 5, Ang-2: 1 in 5, Tie-2: 1 in 25, VEGF: 1 in 5, IP-10: 1 in 5, slCAM-1 : 1 in 1000, VWF: 1 in 1000, and VWFpp: 1 in 100 in reagent diluent and standard curves were generated using recombinant proteins (R&D Systems). Normal plasma from a pool of 40 adult donors served as a standard for VWFpp and VWF. The plasma pool contained 5.5 nM of VWFpp and 49 nM of VWF. Samples were incubated overnight at 4°C, washed five times and detection antibodies were added according to manufacturer recommended dilutions for 2 hours at room temperature (RT). For Ang-1 and Ang-2, the detection antibodies were resuspended one hour prior to use with 2% heat inactivated goat or mouse serum respectively. Following wash steps (7x), VWF and VWFpp were developed using TMB (eBioscience) and the reaction was stopped using 2N H₂S0₄. The plate was read at 450nM (Dynex Technologies Opsys MR plate reader) and concentrations were extrapolated from the standard curve (4-PL) using revelation Quicklink software (v4.04). The ELISA assays from R&D systems were washed (7x) and Extravidin-Alkaline phosphatase (AP) (Sigma) was added 1 :1000 to each well for 1 hour at RT. The plates were then washed a final time (7x in PBS 0.05% Tween 20 and 2x in deionized water) before adding the substrate p-nitrophenyl phosphate tpNPP) (Sigma). The plates were read at 405nM and concentrations were extrapolated as above.

Statistics

[00108] Data were analyzed in GraphPad Pris v5.0 and SPSS v16.0. All analyses were non-parametric with Spearman's correlation for two-way correlations between biomarkers, Mann-Whitney U tests to compare biomarkers between groups with Holms correction for multiple testing, and receiver operator characteristic (ROC) curves to assess the diagnostic accuracy of the tests. Wilcoxon matched pairs test was used to compare biomarker levels measured at admission and convalescence. For the combinatorial analysis of biomarkers, two complementary statistical methods were used which converged on similar results. First, linear discriminant analysis was used to determine the linear combination of biomarker levels or log-transformed values (to obtain Gaussian distributions) which best discriminated between CM-R and control subjects. Second, multiple logistic regression was applied to all possible combinations of 1 to 8 biomarkers (255 models), to obtain the maximum likelihood estimates of the linear combination of biomarkers that best discriminated between clinical groups (UM vs. CM-R and CNS vs. CM-R). Models comprised of 0-8 variables were compared using the c statistic, a measure of the discriminative power of the logistic equation. Results

Patient Characteristics

[00109] A total of 123 febrile children with either UM (n=32), suspected CNS infections (n=24), or CM (n=67) were included in the study. Of the children that met study criteria for CM, 38 were retinopathy positive and were classified as retinopathy-validated CM (CM-R); whereas the other 29 had normal ocular fundi (CM-N) (Lewallen, Bronzan et al. 2008). Demographic and clinical data for these children are shown in Table 9.

Endothelial biomarkers differentiate retinopathy positive CM cases from those without retinopathy

[00110] Since retinopathy has been established as a discriminant tool in the diagnosis of CM, biomarker levels in children with CM and malaria retinopathy (CM-R) were compared to children with clinical CM without retinopathy (CM-N). Individually, Ang-2, Ang-2:Ang-1 , sTie-2, VWFpp and slCAM-1 were significantly associated with retinopathy (Table 10). In order to determine whether combinations of biomarkers may be useful in discriminating between those with and without retinopathy, multivariate logistic regression models were applied including 1 to 8 biomarkers. The combination of all 8 markers were able to predict retinopathy with an area under the ROC curve of 0.91 (95% CI 0.84-0.98 (Figure 7)).

Endothelial biomarkers differ between CM from UM

[00111] Admission levels of plasma biomarkers in children with CM-R (n=38) were compared to children with UM (n=32). Median concentrations of Ang-1 were significantly lower, and median levels of Ang-2:Ang-1 , sTie-2, VWFpp, VWF, sICAM, VEGF were significantly higher in patients with CM-R compared to patients with UM (Table 1 1 ).

[00112] For each analyte tested, a receiver operator characteristic (ROC) curve was generated to assess the diagnostic accuracy of the biomarker to discriminate between UM and CM-R. The area under the ROC (AUROC) curve was computed and the sensitivity, specificity and positive and negative likelihood ratios were calculated at the optimal biomarker cut-off (Table 12). Ang-1 , sTie-2, VWFpp, WVF, ICAM, VEGF were each able to differentiate between UM and CM-R whereas IP-10 was not. Although Ang-2 on its own was no longer significant after correcting for multiple comparisons, the Ang-2: Ang-1 ratio had an AUROC as good as Ang-1 and resulted in an improved positive likelihood ratio versus Ang-1 alone (Ang-1 (LR(+))=7.1 compared to Ang-2: Ang-1 (LR(+))=18). Representative scatter plots and ROC curves are shown for Ang-1 , VWFpp, VWF and slCAM-1 (Figure 8).

[00113] Endothelial biomarkers were then tested in combination to examine whether the use of combinations improves diagnostic accuracy. Linear discriminant analysis was applied to determine the linear combination of biomarker levels (discriminant function) that best classified patients according to clinical status (UM vs. CM-R). The combination of 8 biomarkers provided a c statistic of 1.0 (perfect discrimination; sensitivity=100%, specificity=100%). Biomarkers with the highest standardized coefficients (greatest contribution to the discriminant functions) were Ang-1 , VEGF, and VWFpp. Multivariate logistic regression models including 1 to 8 biomarkers also accurately discriminated between clinical groups, with c ranging from 0.96 (95% CI 0.93-1.0) for Ang-1 alone to 1 .0 (perfect discrimination) with all 8 biomarkers. Perfect discrimination between UM and CM-R (100% sensitivity and 100% specificity) was possible with more parsimonious biomarker combinations, including a 4 variable model (Ang-1 , VWFpp, VWF and VEGF). Of note, Ang-1 was a significant predictor in all logistic models of 1-4 variables with the highest c statistics (Table 13, Figure 9).

Distinct biomarker profiles in CM differ between retinopathy validated CM and other causes of fever and altered mental status

[00114] Distinguishing CM-R from other causes of fever and altered level of consciousness is clinically challenging yet critical for instituting timely, specific, and potentially life-saving treatment. It was hypothesized that CM-R may be associated with a characteristic pattern of endothelial biomarker abnormalities, which may be clinically informative in distinguishing CM-R from other causes of fever and CNS dysfunction. Comparing children with CM-R (n=38) to a control group of children admitted with fever and altered level of consciousness but without CM (n=24), median Ang-1 levels were lower, and median Ang-2, Ang-2:Ang-1 , VWFpp and VWF higher in children with CM-R compared to CNS controls (Table 1 1 ).

[00115] Using ROC curve analysis, three biomarkers, Ang-1 , VWFpp, and VWF, discriminated CM-R from non-malarial CNS disease (Table 14). Median Ang-2 was elevated in CM-R compared to the CNS controls, and the Ang-2: Ang-1 ratio had an AUROC equal to that of Ang-1 alone but with a two-fold increase in the positive likelihood ratio (Table 14). slCAM-1 , sTie-2, and VEGF, while useful biomarkers for UM vs. CM-R, did not discriminate between CM-R and CNS controls. Sensitivity, specificity and likelihood ratios were calculated and are shown in Table 14. Selected biomarkers Ang-1 , VWFpp and VWF are shown graphically in Figure 10, with slCAM-1 (not a statistically useful discriminator) provided for comparison.

[00116] Applying a combinatorial biomarker approach, linear discriminant analysis using a combination of 8 biomarkers provided a discriminant function with c=0.999 [95%CI 0.995-1.0] for CM-R vs. CNS controls. Multivariate logistic regression models using 1 to 8 biomarkers also accurately discriminated between clinical groups (Figure 9). Surprisingly, perfect discrimination between CM-R and CNS controls (sensitivity=100%, specificity= 00%) could be achieved with a 5 variable model that included endothelial markers (Ang-1 , VWFpp, VWF, VEGF and slCAM-1 ) (Figure 9, Table 13).

Correlation between endothelial biomarkers

[00117] Given the complex interplay of molecular regulators of endothelial function, it was hypothesized that significant correlations would exist between biomarkers. Previous studies have demonstrated an association between VWFpp, VWF, ICAM, VEGF, and IP-10 and malaria seventy (Jakobsen, Morris-Jones et al. 1994; Turner, Morrison et al. 1994; Hollestelle, Donkor et al. 2006; Armah, Wilson et al. 2007; Tchinda, Tadem et al. 2007; Jain, Armah et al. 2008; Yeo, Lampah et al. 2008; Larkin, de Laat et al. 2009). After applying two-way rank correlations of Ang-1 , Ang-2 and sTie- 2 with these established biomarkers, significant correlations were found between Ang-2 and sTie-2, VWFpp, VWF, slCAM-1 , and IP-10 and inverse correlations between Ang-1 , VWF, and slCAM-1 (Table 15).

Endothelial biomarker abnormalities resolve with clinical recovery

[00118] Previous studies examining the dynamics of VWF, VWFpp and Ang-2 levels in malaria infection demonstrated a marked decrease in these proteins three days after the initiation of treatment; by three days VWFpp returned to the normal levels found in the local population, and Ang-2 levels declined by 3-4 days in those who were successfully treated and showed no further decrease in levels by two weeks post-infection (Hollestelle, Donkor et al. 2006; Yeo, Lampah et al. 2008). Based on these findings, it was hypothesized that following endothelial biomarker levels could provide objective and quantitative evidence of clinical recovery and disease resolution. To test this hypothesis, paired measurements of endothelial biomarkers were obtained at admission and at day 28, following treatment and recovery, from 38 survivors of CM-R. Levels of Ang-1 displayed a uniform and consistent increase in all participants, whereas levels of Ang-2, Tie-2, VWFpp, VWF, slCAM-1 , and IP-10 decreased with convalescence (Figure 11 , Table 16). Overall, VEGF showed an increase in levels at convalescence. Notably, the Ang-2; Ang-1 ratio showed the most dynamic range between levels at admission and follow-up and there was a universal decrease in Ang- 2: Ang-1 levels at convalescence.

Discussion

[00119] The diagnosis of cerebral malaria in children is clinically challenging since it may be confused with other causes of fever and altered consciousness. Diagnostic tools that can accurately identify children with "true" CM enable improved triage and management of these life-threatening infections. Currently retinopathy is the best tool to predict which children have true CM but it has operational constraints. Alternative methods of discriminating between these groups have not been identified.

[00120] In the present Example, a defined panel of endothelial and angiogenic biomarkers is shown to able to discriminate, with a high degree of accuracy, children with retinopathy-confirmed CM (CM-R) from those without retinopathy (CM-N) as well as from children with uncomplicated infection UM) or with fever and altered consciousness due to other causes (CNS). These biomarkers can be incorporated into combinatorial models to improve their diagnostic accuracy, achieving perfect discrimination between both UM and CM-R and between CNS controls and CM-R with a panel of 4 or 5 biomarkers respectively. A marked and uniform decrease in Ang-2: Ang-1 levels was observed at follow-up, indicating that the ratio between these two proteins can be used to monitor clinical response.

[00121] The endothelium is a dynamic organ system representing the interface between the vascular space and vital organs. The regulation of the endothelial barrier is critical, particularly in the face of infection-related injury. Endothelial adhesion of parasitized red cells and endothelial activation are prominent features in the pathology of fatal malaria. Parasitized erythrocytes bind to the endothelium directly through endothelial receptors and may indirectly bind through VWF and platelet complexes (Bridges, Bunn et al. 2010). There is evidence to suggest that blood-brain-barrier dysfunction and breakdown occurs in paediatric CM (Brown, Hien et al. 1999; Brown, Rogerson et al. 2001) (van der Heyde, Nolan et al. 2006). However, the molecular basis of CM pathophysiology is incompletely understood.

[00122] Research demonstrates that ICAM-1 is upregulated in the cerebral endothelium during malaria infection and is associated with parasite sequestration within the cerebral vasculature (Turner, Morrison et al. 1994), a pathological hallmark of CM in both paediatric and adult populations. slCAM-1 is released by activated endothelium during malaria and has been reported as a biomarker of disease severity (Turner, Ly et al. 1998) (Tchinda, Tadem et al. 2007). Similarly, in the present Example slCAM-1 was able to discriminate between UM and CM, but it was also elevated in the CNS control group. Recent reports have described increased circulating concentrations of VWF and activation of the coagulation system in severe malaria, with possible implications for pathogenesis (Hollestelle, Donkor et al. 2006; Larkin, de Laat et al. 2009; Moxon, Heyderman et al. 2009). It has been postulated that an increase in ultra large VWF strings and a decrease in its cleavage protein ADAMTS13 can result in platelet accumulation and contribute to sequestration of parasitized erythrocytes (Bridges, Bunn et al. 2010). In the current Example, both VWF and VWFpp were elevated in CM-R compared to UM or CNS controls and these proteins represent good candidate biomarkers. These proteins are also of interest because they are exocytosed from Weibel- Palade bodies together with Ang-2.

[00123] Elevated Ang-2 levels have previously been associated with severe malaria in a paediatric population from Uganda and independent adult populations in South East Asia (Yeo, Lampah et al. 2008; Lovegrove, Tangpukdee et al. 2009) (Conroy, Lafferty et al. 2009). In the present Example, Ang-2 was elevated in CM-R compared to UM and CNS controls but became of borderline significance after correcting for multiple comparisons. However, inclusion of Ang-2 as a component of the Ang2:Ang-1 ratio, markedly improved the specificity and positive likelihood ratio compared to Ang-1 alone. The greater degree of elevation of Ang-2 levels reported in Ugandan children (Lovegrove et al 2009) than in the present study may be attributable to the exclusion of fatal cases in which Ang-2 levels are highest. Ang-2 has been associated with increased disease severity (Yeo, Lampah et al. 2008; Conroy, Lafferty et al. 2009) and increased risk of death (Yeo, Lampah et al. 2008) in Asian adults with severe malaria. Together, these data suggest that changes in Ang-2 are reflective of overall disease severity and mortality and may thus be a good surrogate endpoint for trials investigating mortality or evaluating adjunctive therapies. In the context of endothelial biology, the balance between Ang-1 and Ang-2 regulates the functional responsiveness of the endothelium. Ang-1 is synthesized by periendothelial cells to promote vascular quiescence under normal physiologic conditions; however, the release of Ang-2 from WP bodies can inhibit Ang-1 signalling in a dose-dependent manner, resulting in local destabilization of the endothelium (Yuan, Khankin et al. 2009). In the present Example, there were markedly lower Ang-1 levels at presentation in children with CM-R compared to those with UM or the CNS controls. The observed decreases in Ang-1 levels combined with increases in Ang-2 may contribute to the endothelial dysfunction observed in CM.

[00124] The functions of Ang-1 and Ang-2 are also modulated by interactions with VEGF. VEGF can induce Ang-2 mRNA in endothelial cells under stress but maintains endothelial cells in an anti-apoptotic state when Ang-2 is present (Oh, Takagi et al. 1999; Lobov, Brooks et al. 2002). VEGF can also increase permeability of endothelial cells in vitro, whereas Ang-1 can stabilize the endothelium and inhibit angiogenesis (Satchell, Anderson et al. 2004). Ang-1 can suppress the expression of tissue factor and ICAM-1 induced by VEGF and TNF (Kim, Moon et al. 2001 ; Kim, Oh et al. 2002). This may be particularly important in the context of paediatric CM, where coagulopathy and increased tissue factor expression may occur (Francischetti, Seydel et al. 2007; Moxon, Heyderman et al. 2009). In a systematic and un-biased approach to determine the most parsimonious combination of biomarkers, VEGF was not informative on its own but was informative together with Ang-1 , Ang-2 and VWF in distinguishing UM from CM and VEGF was also included alongside Ang-1 , VWF, VWFpp, and slCAM-1 in differentiating between CNS controls and CM, indicating that levels of VEGF may be an important factor in the regulation of the angiopoietin-Tie-2 system. In this way, the combinatorial approach described in the present Example may identify a critical network of proteins, which may be useful in clinical diagnosis of true CM, disease progression, and recovery.

[00125] Identifying retinopathy in a comatose child greatly increases the confidence with which the illness can be attributed to malaria. Observing retinal changes ideally requires indirect ophthalmoscopy, a procedure that is usually not available where resources are limited. In the present Example, several plasma biomarkers (Ang-2, sTie-2, VWFpp and slCAM-1) and the combination of 8 biomarkers were strongly associated with retinopathy.

[00126] The endothelial proteins measured in the present Example return to a normal-range at convalescence, suggesting that the alterations in biomarker levels at presentation were mediated by the infection status of the child rather than a natural host-mediated susceptibility as a result of genetic or epigenetic changes in the biomarkers examined. Based on previous studies, VWFpp was shown to return to baseline three days after the initiation of antimalarial therapy (Hollestelle, Donkor et al. 2006), and Ang-2 and the RH-PAT index, a measure of peripheral endothelial dysfunction, returned to normal four days post treatment (Yeo, Lampah et al. 2008).

[00127] The present Example reports measurement of a panel of endothelial-based biomarkers in a well characterized patient population and the use of a combinatorial approach to improve the diagnostic accuracy. Endothelial biomarkers are shown to be useful in differentiating between coma of severe malaria and comas of other causes.

EXAMPLE 3: Modulation of membrane and soluble TREM-1 in malaria infection

[00128] Excessive or dysregulated host pro-inflammatory responses to malaria infection have been implicated in the pathogenesis of severe disease. A number of innate immune components have been shown to contribute to these responses, including Toll-like receptors (TLR), although the full complement of host inflammatory pathways remains to be characterized. Triggering receptor expressed on myeloid cells-1 (TREM-1) is a germline receptor on monocytes and neutrophils that is upregulated upon TLR stimulation. TREM-1 synergizes with TLRs to induce inflammation, and has been found to play a role in sepsis pathophysiology. It was hypothesized that TREM-1 expression is modulated during malaria infection and that TREM-1 may contribute to disease severity. Human peripheral blood mononuclear cells (PBMCs) were exposed to Plasmodium Aa/c/ arum-infected red blood cells (RBCs) or uninfected RBCs in vitro. Incubation of PBMCs with malaria- infected RBCs for 24 hours resulted in a significant decrease in TREM-1 surface levels on monocytes (p=0.018), and induced release of soluble TREM-1 (sTREM-1 ), which is thought to be generated by cleavage of membrane TREM-1. TREM-1 expression was then examined in the Plasmodium berghei ANKA model of experimental cerebral malaria. TREM-1 mRNA expression in the brain was elevated in mice on Day 6 of infection compared to uninfected mice (p<0.05). Finally, sTREM-1 was measured in the plasma of pediatric malaria patients in a case-control study in Uganda. Plasma sTREM-1 levels at admission were significantly elevated in severe malaria patients compared to uncomplicated cases (median (range) in pg/mL: uncomplicated 154.9 (44, 1519) vs. severe 371.5 (72.7, 2428); pO.0001 ), and were higher in fatal cases of severe malaria compared to survivors (survivors 324.6 (72.7, 1321 ) vs. fatal 528.7 (244.3, 2428); p=0.0021 ). TREM- 1 is therefore modulated during malaria infection and sTREM-1 and/or cell surface TREM-1 are biomarkers for malaria and in particular for severe and fatal malaria.

[00129] While the present disclosure has been described with reference to what are presently considered to be the preferred examples, it is to be understood that the disclosure is not limited to the disclosed examples. To the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

[00130] All publications, patents and patent applications are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety. References

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Table 1. Demographic and clinical characteristics of study participants presenting with uncomplicated and severe malaria.³

aAII variables except gender are presented as median (interquartile range). Groups were compared using the Kruskal-Wallis test with Dunn's multiple comparison post- hoc tests (continuous variables) or Chi-square test (categorical variables).

b UM, uncomplicated malaria; CM, cerebral malaria; SMA, severe malaria anemia. ^cSix children with concurrent CM and SMA were included in the CM group. Five children with SMA exhibited decreased consciousness but did not meet WHO criteria for CM.

^* p<0.05, ^*" p<0.001 vs. UM.

# p<0.05, ^ pO.001 vs. CM.

Table 2. Clinical performance measures of biomarkers that predict fatality among children with severe malaria.⁸

aAII parameters are presented with 95% Cls in parentheses.

Cut-points were determined using the Youden Index (J = maxfsensitivity + specificity - 1]).

^CPLR, positive likelihood ratio; NLR, negative likelihood ratio; PPV, positive predictive value; NPV, negative predictive value.

dPPVs and NPVs were based on estimates that 5.7% of CM and SMA patients at Mulago hospital die of the malaria infection [22].

Table 3. Association of biomarker score with outcome among children with severe malaria: univariate lo istic re ression.⁹

aThe reference category was "survival".

bPseudo-R² (Cox& Snell): 0.476.

Table 4. Clinical performance measures for biomarker combinations that predict fatality amon children with severe malaria.³

aAII parameters are presented with 95% Cls in parentheses.

"Cut-points were determined based on the Youden Index (J = max[sensitivity + specificity - 1 ]).

^CPLR, positive likelihood ratio; NLR, negative likelihood ratio; PPV, positive predictive value; NPV, negative predictive value.

dPPVs and NPVs were based on estimates that 5.7% of CM and SMA patients at Mulago hospital die of the malaria infection[22],

eCut-points were 605.4 ng/mL for slCAM-1 (sensitivity 91.3%, specificity 67.5%) and 1 16.3 ng/mL for CHI3L1 (sensitivity 100%, specificity 53.8%). Table 5. Plasma biomarkers in survivors versus fatalities in children with CM or SMA.^a

Raw p Corrected p

CM Survivors Fatalities

value value

Ang-2 3.4 ng/mL (1.7, 5.5) 6.6 ng/mL (4.2, 10.7) 0.0032 0.038 vWF 24.6 ng/mL (18.7, 31.9) 23.1 ng/mL (17.0, 29.5) 0.57 NS vWFpp 6.2 ng/mL (4.2, 13.8) 9.4 ng/mL (5.1 , 14.4) 0.37 NS sP-

27.6 ng/mL (19.1 , 36.9) 29.4 ng/mL (23.2, 43.5) 0.31 NS selectin

544.0 ng/mL (450.6, 727.1 ng/mL (636.6,

slCA -1 0.0063 NS

678.1) 946.5)

892.8 pg/mL (296.5, 1476 pg/mL (897.1 ,

sFlt-1 0.071 NS

2643) 3603)

sEndoglin 5.4 ng/mL (4.3, 6.4) 6.5 ng/mL (5.3, 7.8) 0.1 1 NS sTie-2 33.6 ng/mL (26.4, 39.5) 38.8 ng/mL (27.6, 46.5) 0.27 NS CRP 15.1 ug/mL (8.2, 20.0) 15.4 ug/mL (5.0, 23.6) 0.89 NS PCT 25.5 ng/mL (14.2, 47.5) 55.1 ng/mL (33.7, 78.6) 0.023 NS

1324 pg/mL (573.9,

IP-10 357.8 pg/mL (180, 1380) 0.043 NS

1928)

149.4 ng/mL (65.1 , 423.9 ng/mL (235.4,

CHI3L1 0.0033 0.036

399.6) 791.9)

Raw p Corrected p

SMA Survivors Fatalities

value value

Ang-2 3.3 ng/mL (2.5, 5.1 ) 1 1.2 ng/mL (7.6, 15.6) 0.0001 0.0012 vWF 18.0 ng/mL (15.0, 28.0) 22.3 ng/mL (14.0, 41 .6) 0.38 NS vWFpp 5.0 ng/mL (3.0, 8.9) 10.7 ng/mL (4.4, 13.8) 0.038 NS sP-

25.6 ng/mL (22.2, 34.8) 31.4 ng/mL (24.0, 89.6) 0.21 NS selectin

525.4 ng/mL (425.8, 758.3 ng/mL (710.4,

si CAM- 1 0.0001 0.0012

642.4) 969.0)

901.0 pg/mL (264.1 ,

sFlt-1 3091 pg/mL (1261 , 7119) 0.0019 0.015

1478)

sEndoglin 5.9 ng/mL (4.9, 6.3) 7.4 ng/mL (5.2, 7.9) 0.055 NS sTie-2 36.2 ng/mL (29.7, 41 .6) 39.0 ng/mL (25.5, 45.7) 0.79 NS CRP 10.2 ug/mL (7.0, 13.7) 15.8 ug/mL (9.2, 22.6) 0.046 NS PCT 13.9 ng/mL (8.1 , 29.6) 25.4 ng/mL (18.7, 45.8) 0.1 1 NS

245.8 pg/mL (98.6,

IP-10 1502 pg/mL (1011 , 1577) 0.0004 0.004

471.3)

363.9 ng/mL (237.2,

CHI3L1 98.8 ng/mL (61.2, 204.2) 0.0004 0.004

1 108)

a Biomarker values are presented as median (interquartile range). Groups were compared using a Mann-Whitney test. Raw p values and p values adjusted using Holm's correction are listed. Significant comparisons are indicated in bold font.

Table 6. Demographic and clinical characteristics of severe malaria patients.³

Survivors (n=80) Fatalities (n=23)

Gender (% female) 46.3 65.2

Age (years) 1.6 (1.0, 3.1) 1.9 (1.2, 3.3)

Weight (kg) 10.0 (8.0, 13.0) 11.0 (10.0, 13.0)

Days reported sick before , _n „ _{n 7}.

presentation

Parasitemia (parasites/uL) 3.7

Hemoglobin (g/dL)^b

Platelet count (x10⁹/L) 10 ^aAII variables except gender are presented as median (interquartile range). Groups were compared using the Kruskal-Wallis test with Dunn's multiple comparison post- hoc tests (continuous variables) or Chi-square test (categorical variables).

The seemingly paradoxical increase in hemoglobin among fatalities was due to the higher CM:SMA ratio in this group.

*p<0.05, ^** p<0.01.

Table 7. Correlations between biomarkers predicting fatality among children with severe malaria.³

Ang-2 slCAM-1 sFlt-1 PCT IP-10 CHI3L1

Ang-2 0.28^" 0.45^" 0.17 0.45^" 0.35^" slCAM-1 — 0.26^" 0.15 0.33^" 0.33^" sFlt-1 — 0.26^" 0.46^" 0.25^*

PCT — 0.24^* 0.43^"

IP-10 0.29"

CHI3L1 ^aSpearman's rho is displayed for each pair-wise comparison. Although many of the correlations between biomarkers were moderate and statistically significant, none was considered high (i.e. >0.7).

*p<0.05, ^" p<0.01. Table 8. Multivariate logistic regression model for predicting death with biomarker

aThe reference category was "survival".

bPseudo-R² (Cox & Snell): 0.480.

cBiomarker score and log-parasitemia had a significant but low correlation (Spearman's rho 0.245, p=0.013).

Parasitemia was log-transformed in order to achieve linearity with the log-odds of the dependent

Table 9: Demo ra hic and clinical characteristics of stud o ulation

Median (range) unless otherwise indicated

N/A- not applicable (exclusion criteria)

* Clinical information unavailable as children seen on an outpatient basis **p<0.01 , ***p<0.001 (R vs. N)

11 000894

- 59 -

Table 10: The ability of biomarkers to predict retinopathy in a cohort of children with clinical cerebral malaria CM .

Median (range)

Mann-Whitney U test

* Holms correction for 9 pair-wise comparisons

CM-R: Cerebral malaria, retinopathy positive

CM-N: Coma and parasitemia children, retinopathy negative

AUROC: Area under the receiver operator characteristic curve

Table 11. Biomarker levels in Malawian children with uncomplicated malaria (UM), cerebral malaria with retinopathy (CM-R) and fever with altered consciousness CNS

Median (range)

*p<0.05, **p<0.01 for difference compared to CM-R (corrected for multiple testing at 18 pair-wise comparisons) Table 12: Receiver operating characteristic curves of endothelial biomarkers in children with uncomplicated malaria (UM) and cerebral malaria with retinopathy (CM-R)

^* Holms correcttion for multiple comparisons (9 pair-wise comparisons)

CI: Confidence Interval; LR: likelihood ratio, AUROC: Area under the operator characteristic curve

Table 13: The most parsimonious combinations of endothelial biomarkers between cerebral malaria (CM) and controls

Multivariate logistic regression models to determine the most parsimonious combination of biomarkers as determined by a non-biased mathematical approach.

Table 14: Receiver operating characteristic curves of endothelial biomarkers in children with fever and altered consciousness (CNS) and cerebral malaria with retino ath CM-R

* Holms correction for multiple comparisons (9 pair-wise comparisons)

Table 15. Two-way rank correlations (Spearman's rho) between biomarkers

Table 16. Admission and Convalescent levels of plasma biomarkers from

Median (range)

* Wilcoxon signed rank test with Holms correction (9 pair-wise comparisons)

Claims

Claims:

1. A method of identifying a subject having, or at risk of developing, severe malaria comprising:

(a) determining the level of one or more biomarkers in a test sample from the subject; and

(b) comparing the level of the one or more biomarkers in the test sample to a level of one or more biomarkers in a control sample, wherein one of the biomarkers is CHI3L1 and an increase in the level of CHI3L1 in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, severe malaria.

2. The method of claim 1 , further comprising determining the level of one or more biomarkers in the test sample selected from angiopoietin-1 (ANG-1), angiopoietin-2 (ANG-2), von Willebrand factor (VWF), von Willebrand factor propeptide (VWFpp), soluble P-selectin, soluble ICAM-1 (slCAM-1 ), soluble endoglin, soluble FLT-1 (sFLT-1), soluble Tie-2 (sTie-2), C-Reactive Protein (CRP), procalicitonin (PCT), IP-10, VEGF, Triggering Receptor Expressed on Myeloid cells-1 (TREM-1 ) and soluble Triggering Receptor Expressed on Myeloid cells-1 (sTREM-1).

3. The method of claim 2, wherein the one or more biomarkers are selected from ANG-2, VWF, VWFpp, slCAM-1 , sFLT-1 , sTie-2, CRP, PCT, and IP-10 and an increase in the level of the biomarkers in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, severe malaria.

4. The method of claim 2, wherein the biomarkers include ANG-2 and IP- 10 and an increase in the level of ANG-2 and IP-10 in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, severe malaria.

5. The method of any one of claims 1 to 4, wherein the control sample represents subjects with uncomplicated malaria.

6. The method of any one of claims 1 to 5, wherein severe malaria is cerebral malaria and/or severe malarial anemia.

7. The method of any one of claims 1 to 6 wherein the level of one or more biomarkers in the control sample is a predetermined or standardized control level.

8. The method of any one of claims 1 to 6, wherein the levels of the one or more biomarkers in the control sample are determined from a sample from the subject at an earlier time point and an increase in the level of CHI3L1 in the test sample compared to the control sample indicates an increase in severity of disease in the subject and a decrease in the level of CHI3L1 in the test sample compared to the control sample indicates a decrease in severity of disease in the subject.

9. The method of claim 8, wherein the biomarkers are selected from ANG-2, VWF, VWFpp, slCAM-1 , sFLT-1 , sTie-2, CRP, PCT, IP-10, and sTREM-1 and an increase in the level of the one or more biomarkers in the test sample compared to the control sample indicates an increase in the severity of disease in the subject with malaria.

10. The method of claim 8, wherein the biomarkers are selected from ANG-2, VWF, VWFpp, slCAM-1 , sFLT-1 , sTie-2, CRP, PCT, IP-10, and sTREM-1 and a decrease in the level of the one or more biomarkers in the test sample compared to the control sample indicates a decrease in the severity of the disease in the subject with malaria.

11. The method of any one of claims 8 to 10, wherein a difference in the level of the one or more biomarkers in the test sample and the control sample over time is used to monitor the response of the subject to therapy.

12. The method of any one of claims 1 to 11 , wherein the test sample is a blood sample, serum sample or plasma sample.

13. The method of any one of claims 1 to 12, wherein step (a) comprises determining the levels of two or more biomarkers and step (b) comprises multivariate analysis.

14. A kit for determining whether a subject has, or is at risk of developing, severe or fatal malaria comprising a binding agent selective for chitinase-3- like-1 (CHI3L1).

15. The kit of claim 14 further comprising one or more binding agents selective for angiopoietin-1 (ANG-1), angiopoietin-2 (ANG-2), von Willebrand factor (VWF), von Willebrand factor propeptide (VWFpp), soluble P-selectin, soluble ICAM-1 (slCAM-1), soluble endoglin, soluble FLT-1 (sFLT-1), soluble Tie-2 (sTie-2), C-Reactive Protein (CRP), procalicitonin (PCT), IP-10, VEGF and soluble Triggering Receptor Expressed on Myeloid cells-1 (sTREM-1).

16. The kit of claim 14 or 15, wherein the one or more binding agents are detectable labeled.

17. The kit of any one of claims 14 to 16, wherein the one or more binding agents are antibodies.

18. The kit of any one of claims 14 to 17, further comprising a medium suitable for formation of an antigen-antibody complex, reagents for detection of the antigen-antibody complexes and/or instructions for the use thereof.

19. A method of identifying a subject having, or at risk of developing, severe malaria comprising:

(a) determining the level of one or more biomarkers in a test sample from the subject; and

(b) comparing the level of the one or more biomarkers in the test sample to a level of the one of more biomarkers in a control sample, wherein a difference between the level of the one or more biomarkers in the test sample and the control sample indicates that the subject has, or is at risk of developing, severe malaria.

20. The method of claim 19, wherein the one or more biomarkers comprises sTREM-1 and an increase in the level of sTREM-1 in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, severe or fatal malaria.

21. The method of claim 19, wherein the one or more biomarkers comprises sFLT-1 and an increase in the level of sFLT-1 in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, severe or fatal malaria.

22. The method of claim 19, wherein the one or more biomarkers comprises sTie-2 and an increase in the level of sTie-2 in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, severe or fatal malaria.

23. The method of claim 19, wherein the one or more biomarkers are selected from angiopoietin-1 (ANG-1), angiopoietin-2 (ANG-2), von Willebrand factor (WVF), von Willebrand factor propeptide (VWFpp), soluble P-selectin, soluble ICAM-1 (slCAM-1 ), soluble endoglin, soluble FLT-1 (sFLT-1), soluble Tie-2 (sTie-2), C-Reactive Protein (CRP), procalicitonin (PCT), IP-10, chitinase-3-like-1 (CHI3L1), VEGF and soluble Triggering Receptor Expressed on Myeloid cells-1 (sTREM-1 ).

24. The method of claim 23, wherein the one or more biomarkers are selected from ANG-2, WVF, VWFpp, slCAM-1 , sFLT-1 , sTie-2, CRP, PCT, IP- 10 and CHI3L1 and wherein an increase in the level of one or more biomarkers in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, severe malaria.

25. The method of any one of claims 19 to 24, wherein the control sample represents subjects with uncomplicated malaria.

26. The method of any one of claims 19 to 25, wherein severe malaria comprises cerebral malaria and/or severe malarial anemia.

27. The method of any one of claims 19 to 26 wherein the level of one or more biomarkers in the control sample is a predetermined or standardized control level.

28. The method of any one of claims 19 to 26, wherein the levels of the one or more biomarkers in the control sample are determined from a sample from the subject at an earlier time point.

29. The method of claim 28, wherein differences in the levels of the one of more biomarkers over time are used to monitor the severity of disease in the subject.

30. The method of claim 23, wherein an increase in the level of ANG-2, slCAM-1 , CHI3L1 , IP-10, sFLT-1 , sTREM-1 or PCT in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, fatal malaria.

31. The method of claim 30, wherein the control sample represents subjects who survived infection with cerebral malaria or severe malarial anemia.

32. The method of claim 23, wherein three or more biomarkers are selected from ANG-2, slCAM-1 , CHI3L1 , IP-10, sFLT-1 and PCT.

33. The method of claim 32, wherein the three or more biomarkers comprise ANG-2, IP-10 and slCAM-1.

34. The method of claim 32, wherein the biomarkers comprise slCAM-1 and CHI3L1.

35. The method of claim 23, wherein the one or more biomarkers are selected from ANG-1 , ANG-2, VWF, VWFpp, slCAM-1 , sTie-2, IP-10, and VEGF.

36. The method of claim 35, wherein the control sample represents subjects with uncomplicated malaria and a decrease in the level of ANG-1 or an increase in the level of ANG-2, ANG-2:ANG-1 , sTie-2, VWF, VWPpp, VEGF or slCAM-1 in the test sample compared to the control sample indicates that the subject has or is at risk of developing cerebral malaria with retinopathy.

37. The method of claim 36, wherein the biomarkers comprise ANG-1 , VWFpp, VWF and VEGF.

38. The method of claim 36, wherein the biomarkers consist of ANG-1 , VWFpp, VWF and VEGF.

39. The method of claim 35, wherein the control sample represents subjects with CNS infections other than malaria and a decrease in the level of

ANG-1 or an increase in the level of VWF or VWFpp in the test sample compared to the control sample indicates that the subject has, or is at risk of developing, cerebral malaria with retinopathy.

40. The method of claim 35, wherein the control sample represents subject with cerebral malaria without retinopathy and an increase in the level of one or more biomarkers selected from ANG2, ANG2:ANG1 , VWFpp, and slCAM-1 in the test sample compared to the control sample indicates that the subject has or is at risk of developing, cerebral malaria with retinopathy.

41. The method of claim 35, wherein the biomarkers comprise ANG-1 , VWFpp, VWF, VEGF and slCAM-1.

42. The method of claim 35, wherein the biomarkers consist of ANG-1 , VWFpp, VWF, VEGF and slCAM-1.

43. The method of claim 35, wherein the one or more biomarkers comprises at least 4 biomarkers.

44. The method of claim 35, wherein the one or more biomarkers comprises at least 5 biomarkers.

45. The method of any one of claims 19 to 44, wherein step (a) comprises determining the levels for two or more biomarkers and step (b) comprises combining biomarker levels into a single composite variable.

46. The method of any one of claims 19 to 44, wherein step (a) comprises determining the levels for two or more biomarkers and step (b) comprises classification and regression tree (CART) analysis.

47. The method of any one of claims 19 to 44, wherein step (a) comprises determining the levels for two or more biomarkers and step (b) comprises multivariate analysis.

48. A method of monitoring severity of disease in a subject with malaria comprising:

(a) determining the level of one or more biomarkers in a test sample from the subject;

(b) comparing the level of the one or more biomarkers in the test sample to a level of the one or more biomarkers in a control sample, wherein the levels of the one or more biomarkers in the control sample are determined from a sample from the subject at an earlier time point; and

(c) detecting an increase or decrease in the severity of disease in the subject with malaria by detecting a difference in the level of the one or more biomarkers in the test sample and the control sample.

49. The method of claim 48, wherein the one or more biomarkers are selected from ANG-2, VWF, WFpp, slCAM-1 , sFLT-1 , sTie-2, CRP, PCT, IP- 10, sTREM-1 and CHI3L1 and an increase in the level of the one or more biomarkers in the test sample compared to the control sample indicates an increase in the severity of disease in the subject with malaria.

50. The method of claim 48, wherein the one or more biomarkers are selected from ANG-2, VWF, VWFpp, slCAM-1 , sFLT-1 , sTie-2, CRP, PCT, IP-

10, sTREM-1 and CHI3L1 and a decrease in the level of the one or more biomarkers in the test sample compared to the control sample indicates a decrease in the severity of the disease in the subject with malaria.

51. The method of any one of claims 48 to 50, wherein detecting a difference in the level of the one or more biomarkers in the test sample and the control sample is used to monitor the response of the subject to therapy.

52. The method of any one of claims 48 to 51 , wherein step (a) comprises determining the levels for two or more biomarkers and step (b) comprises combining biomarker levels into a single composite variable.

53. The method of any one of claims 48 to 5 , wherein step (a) comprises determining the levels for two or more biomarkers and step (b) comprises multivariate analysis.

54. The method of claim 19, wherein the one or more biomarkers comprises a set of biomarkers listed in Table 4 or Table 13.

55. The method of any one of claims 1 to 54, wherein the subject is a child.

56. The method of any one of claims 1 to 55, wherein the test sample is a blood sample, serum sample or plasma sample.

57. A kit for determining whether a subject has, or is at risk of developing, severe or fatal malaria comprising one or more binding agents directed against a biomarker selected from angiopoietin-1 (ANG- ), angiopoietin-2 (ANG-2), von Willebrand factor (VWF), von Willebrand factor propeptide (VWFpp), soluble P-selectin, soluble ICAM-1 , soluble endoglin, soluble FLT-1 , soluble Tie-2 (sTie-2), C-Reactive Protein (CRP), procalicitonin, IP-10, chitinase-3-like-1 (CHI3L1), VEGF and soluble Triggering Receptor Expressed on Myeloid cells-1 (sTREM-1 ).

58. The kit of claim 57, wherein the binding agent is detectable labeled.

59. The kit of claim 57, wherein the binding agent is an antibody.

60. The kit of any one of claims 57 to 59, wherein the kit comprises binding agents directed against a set of biomarkers listed in Table 4 or Table 13.

61. The kit of any one of claims 57 to 60, further comprising a medium suitable for formation of an antigen-antibody complex, reagents for detection of the antigen-antibody complexes and instructions for the use thereof.