Visualization of community control (CC, non-parasitaemic) children <i>versus</i> other study groups.

<p>Each sphere represents an individual child proteome profile plotted in 3D space defined by the first three principal components. CM = Cerebral Malaria (red); SMA = Severe Malarial Anemia (purple); UM = Uncomplicated Malaria (yellow); DC = Disease Controls (blue); CC = Community Controls (green). (a.) CC vs. CM; (b.) CC vs. SMA; (c.) CC vs UM and (d.) CC vs. DC.</p

Discriminatory performance of proteome profiles across study groups in ROC space.

<p>CM = Cerebral Malaria; SMA = Severe Malarial Anemia; UM = Uncomplicated Malaria; DC = Disease Controls; CC = Community Controls. (a.) Discriminatory performance in ROC space of predictive models built with relevant m/z clusters using the discovery cohort data. Error bars indicate +/− standard deviations obtained by 100 train/test randomizations of the data. (b.) Discriminatory performance in ROC space of the best predictive model from (a.) when applied to the validation cohort data.</p

Visualization of (a-c) disease control (DC, non-parasitemic) children <i>versus</i> parasitemic children groups; (d-f) among CM, SMA and UM parasitaemic children groups.

<p>Each sphere represents an individual child proteome profile plotted in 3D space defined by the first three principal components. CM = Cerebral Malaria (red); SMA = Severe Malarial Anemia (purple); UM = Uncomplicated Malaria (yellow); DC = Disease Controls (blue). (a.) DC vs. CM; (b.) DC vs. SMA; (c.) DC vs. UM; (d.) CM vs. SMA; (e.) CM vs. UM and (f.) SMA vs. UM.</p

Discriminatory accuracy of proteome profiles across six anionic plasma fractions (f1 to f6) among CM, SMA and UM (all parasitemic) groups of children.

<p>CM = Cerebral Malaria; SMA = Severe Malarial Anemia; UM = Uncomplicated Malaria. f1 to f6 represent anionic plasma fractions at pH 9.0 (f1), pH 7.0 (f2), pH 5.0 (f3), pH 4.0 (f4), pH 3.0 (f5) and organic phase (f6). In brackets are shown the number of relevant m/z clusters that make up the discriminatory proteome profile. (a.) CM vs. SMA; (b.) CM vs. UM; (c.) SMA vs. UM.</p

Characteristics of discovery and validation study groups.

<p>CM = Cerebral Malaria; SMA = Severe Malarial Anemia; UM = Uncomplicated Malaria; DC = Disease Controls; CC = Community Controls. N = Number of Patients; IQR = Inter-quartile Range; sd = Standard Deviation; MP = Malaria Parasites. PCV = Packed Cell Volume.</p>*<p>All clinical groups PCV significantly different to SMA (p<0.05).</p>**<p>All clinical groups PCV significantly different to CC (p<0.05).</p

Discrimination of the three malaria disease sub-types with multi-protein signatures.

<p>L1-penalized logistic regression models were fitted for the three two-group comparisons. The plots show the resulting ROC curves when the model included all selected proteins (black line) and only the top ones (coloured line). The area under the ROC curve (AUC) for the optimal number of proteins and the combination with the smallest number of proteins after variable selection refinement is presented adjacent to the plots. (<b>A</b>) For classification of UM vs SMA a 3-protein signature provided an optimal result (AUC = 0.87) (black line). (<b>B</b>) For classification of UM vs CM a protein signature with 23 proteins showed the best result (black line). As comparison, the AUC of the top 4 proteins (blue line) and top 10 proteins (grey line) after step-by-step removal of selected proteins is shown. CA3 = HPA021775, CA3* = HPA026700. (<b>C</b>) For classification of SMA vs CM a protein signature with 9 proteins showed the best result (black line). As comparison, the AUC of the top 2 proteins after step-by-step removal of selected proteins is shown (green line). (<b>D</b>) ROC curves for the three subgroup comparisons using their respective best protein signatures in the verification cohort, UM vs SMA (red line), UM vs CM (blue line) and SMA vs CM (green line). SAA4 was excluded from verification due to technical failure.</p

Overview of single protein candidates with SOTA clusters (random array).

<p>Overview of single protein candidates with SOTA clusters (random array).</p

Discrimination of the three malaria disease sub-types with multi-protein signatures.

<p>L1-penalized logistic regression models were fitted for the three two-group comparisons. The plots show the resulting ROC curves when the model included all selected proteins (black line) and only the top ones (coloured line). The area under the ROC curve (AUC) for the optimal number of proteins and the combination with the smallest number of proteins after variable selection refinement is presented adjacent to the plots. (<b>A</b>) For classification of UM vs SMA a 3-protein signature provided an optimal result (AUC = 0.87) (black line). (<b>B</b>) For classification of UM vs CM a protein signature with 23 proteins showed the best result (black line). As comparison, the AUC of the top 4 proteins (blue line) and top 10 proteins (grey line) after step-by-step removal of selected proteins is shown. CA3 = HPA021775, CA3* = HPA026700. (<b>C</b>) For classification of SMA vs CM a protein signature with 9 proteins showed the best result (black line). As comparison, the AUC of the top 2 proteins after step-by-step removal of selected proteins is shown (green line). (<b>D</b>) ROC curves for the three subgroup comparisons using their respective best protein signatures in the verification cohort, UM vs SMA (red line), UM vs CM (blue line) and SMA vs CM (green line). SAA4 was excluded from verification due to technical failure.</p

Overview of affinity proteomics screening and study design.

<p>(<b>A</b>) Schematic overview of the affinity proteomics approach using antibody suspension bead arrays. Plasma samples were biotinylated, antibodies were coupled to color-coded magnetic beads, and both were combined for analysis. Bead identity and captured plasma proteins were then detected using a flow cytometric analyzer. (<b>B</b>) Experimental design of study. Initial screening with 1,132 antibodies from targeted and blinded selections was performed in the discovery cohort (n = 356). Data from the patient groups were compared using univariate tests and multivariate penalized regression models. Identified single proteins and multi-component protein panels discriminating the 3 disease groups were validated in the verification cohort (n = 363).</p

Overview of single protein candidates and SOTA clusters (targeted array).

<p>N/A: not applicable, no previous known link with malaria.</p