# Systems analysis of clinical malaria reveals proteomic perturbation and innate-adaptive crosstalk linked to disease severity

Maximilian Julius Lautenbach, Katja Wyss, Victor Yman, Fariba Foroogh, Donya Satarvandi, Zaynab Mousavian, Klara Sondén, Jun Wang, María Bueno Álvez, Sofia Bergström, et al.

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## To cite this version:

Maximilian Julius Lautenbach, Katja Wyss, Victor Yman, Fariba Foroogh, Donya Satarvandi, et al. Systems analysis of clinical malaria reveals proteomic perturbation and innate-adaptive crosstalk linked to disease severity. Immunity, 2025, 58 (8), pp.2120-2136.e5. 10.1016/j.immuni.2025.06.014. pasteur-05266978

HAL Id: pasteur-05266978

https://pasteur.hal.science/pasteur-05266978v1

Submitted on 18 Sep 2025

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# Immunity

# Systems analysis of clinical malaria reveals proteomic perturbation and innate-adaptive crosstalk linked to disease severity

## Graphical abstract

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## Authors

Maximilian Julius Lautenbach,
Katja Wyss, Victor Yman, ...,
Mathias Uhlén, Christopher Sundling,
Anna Färnert

## Correspondence

julius.lautenbach@ki.se (M.J.L.),
christopher.sundling@ki.se (C.S.),
anna.farnert@ki.se (A.F.)

## In brief

While the immune response to *Plasmodium falciparum* malaria has been studied extensively, the circulating proteome and its cellular origins remain poorly characterized. Lautenbach et al. address this by integrating plasma proteomics and single-cell RNA-seq in 72 patients, revealing molecular patterns linked to disease severity, immune regulation, and tissue origin in malaria.

## Highlights

* Extensive plasma protein perturbations detected during natural *P. falciparum* malaria

* Protein profiles stratified patients and revealed a disease-severity signature

* Activated T cells emerge as key targets of innate immune-derived plasma proteins

* A systems-level resource for malaria pathogenesis and biomarker discovery

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Resource

Systems analysis of clinical malaria reveals
proteomic perturbation and innate-adaptive
crosstalk linked to disease severity

Maximilian Julius Lautenbach,<fnref n="1" />,<fnref n="2" />,<fnref n="3" />,* Katja Wyss,<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> Victor Yman,<fnref n="1" />,<fnref n="3" />,<fnref n="4" />,<fnref n="5" /> Fariba Foroogh,<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> Donya Satarvandi,<fnref n="1" />,<fnref n="3" />,<fnref n="6" />

Zaynab Mousavian,<fnref n="1" />,<fnref n="2" />,<fnref n="3" /> Klara Sondén,<fnref n="1" />,<fnref n="3" />,<fnref n="7" /> Jun Wang,<fnref n="8" /> María Bueno Álvez,<fnref n="9" /> Sofia Bergström,<fnref n="9" /> Peter Nilsson,<fnref n="9" />

Fredrik Edfors,<fnref n="9" /> Petter Brodin,<fnref n="8" /> Mathias Uhlén,<fnref n="9" /> Christopher Sundling,<fnref n="1" />,<fnref n="2" />,<fnref n="3" />,<fnref n="10" />,\* and Anna Färnert<fnref n="1" />,<fnref n="2" />,<fnref n="3" />,<fnref n="10" />,\*

SUMMARY

Malaria presents with varying degrees of severity. To improve clinical management and prevention, it is crucial to understand the pathogenesis and host response. We analyzed 1,463 plasma proteins during and after acute *Plasmodium falciparum* malaria in adult travelers and linked responses to peripheral immune cells by integrating with single-cell RNA sequencing (RNA-seq) data from a subset of donors. We identified extensive perturbations in over 250 proteins with diverse origins, including many not previously analyzed in malaria patients, such as hormones, circulating receptors, and intracellular or membrane-bound proteins from affected tissues. The protein profiles clustered participants according to disease severity, enabling the identification of a compressed 11-protein signature enriched in severe malaria. Conceptually, this study advances our understanding of malaria by linking systemic proteomic changes to immune cell communication and organ-specific responses. This resource, which includes an interactive platform to explore data, opens new avenues for hypothesis generation, biomarker discovery, and therapeutic target identification.

INTRODUCTION

Malaria, predominantly caused by *Plasmodium falciparum*,
remains a global health issue, with the World Health Organization
(WHO) estimating 263 million cases and 597,000 deaths in 2023,
primarily in Sub-Saharan Africa.<fnref n="1" /> Infections with *P. falciparum*
can result in a wide range of disease severities depending on
both parasite and host factors, including prior exposure and
immunity, as well as access to prompt health care and effective
treatment.<fnref n="2" /> Improved understanding of the pathogenesis and
host response in malaria is important to advance management
and treatment options, as well as preventive measures such as
vaccines.

Much of malaria pathogenesis is caused by the host response
to parasitized erythrocytes and parasite-derived molecules.<fnref n="3" />
Inflammation and systemic immune cell activation lead to clinical
manifestations such as fever, chills, body aches, and head-

aches.<fnref n="4" /> Moreover, specific features of *P. falciparum* parasites that are important for the pathogenesis include rapid parasite multiplication rates as well as sequestration and rosetting, whereby infected erythrocytes adhere to endothelial cells and uninfected erythrocytes.<fnref n="5" /> Sequestration in the microvasculature leads to reduced blood flow, resulting in organ dysfunction such as coma, renal failure, coagulopathy, acidosis, and eventually multi-organ failure. Both immune and endothelial cells respond to the parasite and inflammation by inducing the release of cytokines and other soluble mediators to activate and recruit immune cells.<fnref n="6" /> The bystander effects of extensive immune activation in the systemic circulation contribute to tissue and cellular damage.<fnref n="7" /> Just as for other infections, malaria often causes a sepsis-like syndrome.<fnref n="8" />,<fnref n="9" /> Some pathologies, such as fever and flu-like symptoms, are more general. By contrast, others, like cerebral malaria, respiratory distress, renal failure, and anemia, appear to be malaria-specific. Further understanding of the

<footnote n="1">¹Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden</footnote>

<footnote n="2">²Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden</footnote>

<footnote n="3">³Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden</footnote>

<footnote n="4">⁴Department of Infectious Diseases, Södersjukhuset, Stockholm, Sweden</footnote>

<footnote n="5">⁵Infectious Disease Epidemiology & Analytics Unit, Department of Global Health, Institut Pasteur, Paris, France</footnote>

<footnote n="6">⁶Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm, Sweden</footnote>

<footnote n="7">⁷Public Health Agency of Sweden, Department for Microbiology, Stockholm, Sweden</footnote>

<footnote n="8">⁸Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden</footnote>

<footnote n="9">⁹Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden</footnote>

<footnote n="10">¹⁰Senior author</footnote>

<footnote n="11">¹¹Lead contact</footnote>

<footnote>\*Correspondence: julius.lautenbach@ki.se (M.J.L.), christopher.sundling@ki.se (C.S.), anna.farnert@ki.se (A.F.)
https://doi.org/10.1016/j.immuni.2025.06.014</footnote>
host response in the pathogenesis is required for early identification of patients needing close monitoring or specific medical support and to guide optimized treatment strategies for severe malaria syndromes.

A unique opportunity to study how the host response contributes to pathogenesis in malaria is to study the circulating plasma proteins, as they are a combination of signaling molecules, such as cytokines and chemokines, as well as regulatory molecules and proteins released through damage of affected tissues. Several studies have reported associations between selected cytokines, or other protein biomarkers, and malaria disease severity or progression.¹⁰⁻¹⁷ Previously, most studies have used gene expression analysis to infer the role of gene expression products or select markers for protein analysis.¹⁸⁻²¹ Those studies have used a selection of the proteins known to be associated with the immune response. The most comprehensive studies on plasma protein profiles in malaria have examined an extended selection of proteins associated with disease severity in both pediatric²²,²³ and adult patients.²⁴,²⁵ These previous studies distill protein signatures to a reduced number of proteins that distinguish severe from non-severe cases and/or make associations of single markers with disease severity in predefined groups. Despite these studies targeting large numbers of proteins (115 or 1,015), the detection methods used in these studies were not sensitive enough to reveal the low-abundant proteins such as cytokines, providing insufficient data to investigate the interplay between immune signaling pathways. Technical advances over the last decade have now made it possible to measure more than thousands of proteins with high precision over a wide protein abundance range in a few microliters of a given sample.²⁶⁻²⁸ This development provides opportunities to comprehensively study the pathogenesis and immune responses during *P. falciparum* malaria.

Here, we have used a plasma proteomic profiling approach, combined with single-cell transcriptomics and clinical data from malaria patients, to explore the pathogenesis and host response during and after an acute *P. falciparum* malaria episode. We built an interactive open access platform (ShinyApp) to provide a resource to explore the comprehensive protein data during and after a malaria infection. Using the systems-level data on the host response to malaria, we describe and contextualize the circulating plasma proteome linked to the immune response and immune cell communication, as well as map plasma protein profiles to cell and tissue sources and in association with disease severity.

This study provides a comprehensive analysis of the plasma proteome in *P. falciparum* malaria, linking over 1,400 circulating proteins to immune cell activity, disease severity, and tissue-specific responses. By integrating proteomics, single-cell transcriptomics, and clinical data, it uncovers pathogenesis and immune communication pathways and offers a valuable resource for biomarker discovery and therapeutic development.

## RESULTS

### Extensive plasma profiling uncovers molecular signatures of clinical *P. falciparum* malaria

Using large-scale plasma protein profiling with Olink's Explore 1536 platform based on the proximity extension assay (PEA),

we analyzed 1,463 unique proteins in the plasma from 72 adults with symptomatic febrile *P. falciparum* malaria diagnosed at the Karolinska University Hospital in Stockholm, Sweden. Patients were sampled at the time of diagnosis (acute, *n* = 72, median 4 days after symptom onset, IQR 3.0–6.0) and at approximately 10 days (day 10, *n* = 27) after treatment, as well as 12 months later (month 12, *n* = 33) (Figure 1A). The 72 individuals with symptomatic malaria consisted of both European travelers (*n* = 24), infected for the first time, and individuals originating from malaria-endemic areas (mainly in Sub-Saharan Africa) with previous malaria exposure (*n* = 48), who had lived in Sweden for an average of 10 years (range 0–33 years) before traveling. The majority of the study participants were males (76%), and ages ranged from 20 to 63 years (average 39 years). The age distribution, the male-to-female ratio, and the proportion of previously exposed were consistent across time points among sampled individuals (Figures 1B–1D; Table S1).

To assess the overall perturbation of the circulating proteome associated with clinical malaria, we first used principal-component analysis (PCA) of all assayed proteins that passed quality control (*n* = 1,427) in all samples (*n* = 132). We observed that samples from the same time point clustered together, indicating similarity in protein profiles (Figure 1E).

To identify specific proteins that were altered during clinical malaria, we used linear mixed-effects models to determine differentially abundant proteins (DAPs) between the acute disease and healthy state (month 12) while accounting for prior malaria exposure and inter-individual variability (Table S2). We observed a total of 703 DAPs (false discovery rate [FDR] ≤ 0.01), of which 248 demonstrated a log fold change (log₂FC) > 1, being more abundant during clinical malaria, and 16 with a log₂FC < −1, being less abundant compared with the healthy state (Figure 1F). Among them, 671 proteins showed consistent changes in abundance over time across both exposure groups, whereas 32 proteins exhibited significantly different temporal changes between exposure groups (Figure S1). For most of these 32 proteins, abundance was significantly higher in primary infected individuals during acute malaria (Figure S1D). We also identified a limited number of measured proteins for which overall abundance was associated with other covariates, including parasitemia (% infected erythrocytes; *n* = 140), storage time (*n* = 27), sex (*n* = 6), and age (*n* = 1) (Figures S2A and S2B).

To obtain a general picture of the different host response pathways that were engaged during clinical malaria, we used pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database for all proteins with an absolute log₂FC > 1 (*n* = 264).²⁹ The enriched pathways were associated with pathogen sensing, antigen presentation, and cytokine and chemokine signaling during infection (Figure S3A). A closer examination of the cytokine-cytokine receptor interaction pathway revealed that most of those DAPs were ligands to receptors. This suggests some degree of functionality in blood during acute malaria, such as attracting and activating immune cells (Figure S3B).

We present data for the top 25 high and low DAPs at the acute time point (Figure 1G) as well as time-dependent changes in protein abundance for the top 10 differentially high-abundant and low-abundant proteins (Figures 1H and 1I). Consistent with a strong interferon (IFN) and cell-mediated immune response during malaria,³ IFN-gamma (IFN-γ) and the IFN-stimulated
<figure><img src="image_5.png" /><figcaption>Figure 1. Distinct protein expression patterns mark acute malaria and resolve during recovery</figcaption></figure>

(A) Study overview.

(B) Age and sex distribution of study participants.

(C) Female/male ratios of samples per time point.

(D) Primary infected/previously exposed ratios of samples per time point.

(E) PCA on 1,427 proteins in 132 samples (acute, pink; day 10, light pink; month 12, green).

(F) Volcano plot of the log₂-fold difference in protein abundance between acute malaria and convalescence (month 12 samples). The *p* values were FDR corrected, significant (pink), and not significant (green) DAPs.

(legend continued on next page)
chemokines CXCL9 (MIG), CXCL10 (IP-10), and CXCL11 (I-Tac) and the cytotoxic molecules granzyme B (GZMB) and H (GZMH) were among the most differentially high-abundant proteins (Figure 1G). Both pro-inflammatory (interleukin [IL]-6) and anti-inflammatory (IL-10 and IL-1RN) proteins were also of higher abundance compared with the healthy state. Moreover, we observed proteins associated with different mechanisms of regulation of the host response, such as growth hormone 1 (GH1) modulating macrophage activation,³⁰ phospholipase A2 (PLA2G2A) implied in antimicrobial defense and tissue regeneration,³¹,³² heme oxygenase 1 (HMOX1) associated with stress responses, and calcitonin (CALCA) associated with vascular regulation (Figure 1G).³³ Proteins that were of low abundance compared with healthy samples included, for example, angiopoietin (ANGPT1) and the chemokines CCL17 and CCL5 (RANTES), some of which have been described previously.²³,³⁴

The abundance of most of the proteins decreased rapidly after treatment (e.g., IFN-γ, PLA2G2A, CXCL10, and IL-6), returning to baseline 10 days after treatment, while some proteins remained significantly elevated (e.g., IL-10 and HMOX1) (Figure 1H). For HMOX1, a stress-responsive heme-catabolizing enzyme heme oxygenase (HMOX1, also HO-1), the higher abundance at day 10 might be explained by the host reaction to erythrocyte breakdown/debris (hemolysis) with the release of reactive oxygen species (ROS) and free heme continuing after treatment.³⁵

In total, 43 proteins remained substantially elevated (log₂FC > 1) at the day 10 time point relative to the healthy state at 12 months, irrespective of the exposure group (Figures S2C and S2D). For several proteins, such as the killer cell immunoglobulin-like receptors (KIR3DL1 and KIR2DL3), CD22, FCRL3, and the erythropoiesis hormone erythropoietin (EPO), abundance had increased at day 10 compared with the acute time point (Figure S2E), potentially reflecting a change toward immune restoration and healing.³⁶

By contrast, proteins such as TNFSF11 (also known as RANKL) or cathepsin F (CTSF) were of lower abundance during acute malaria compared with healthy controls but were rapidly restored to normal after treatment (Figure 1I).

## Host-derived circulating proteins characterize immune and tissue stress responses in acute malaria

To investigate the potential tissue origin and function of the 703 DAPs, we used the Human Protein Atlas (HPA) resource to map the DAPs to secretome and tissue specificity categories.³⁷ Most of the DAPs were actively secreted into the blood (n = 186), while others were predicted to be of intracellular and membrane origin (n = 90) or secreted from other tissues (n = 35) (Figure 2A). Among the proteins that were not secreted, we identified their tissue specificity based on gene expression data³⁸ and referred to them as “not secreted-tissue enriched” (n = 37). The appearance of these proteins in circulation potentially indicated leakage from specific organs due to tissue damage. Examples include the liver-specific proteins alanine-glyoxylate aminotransferase (AGXT) and hydroxyacid oxidase 1 (HOA1), as well as hormonal involvement indicated by circulating growth-hormone-releasing

hormone receptor (GHRHR) with pituitary gland-specific expression (Figure S4C). The liver-specific proteins showed modest correlation with clinical liver biomarkers (Figure S4E).

To investigate functional patterns among the DAPs, we grouped them according to HPA categories and graphed the median log₂FC for up- or down-regulated proteins compared with 12-month healthy follow-up samples (Figure 2B). Additional functional annotations for the DAPs, such as chemokines, hormones, enzymes, receptors, and others, are annotated in Table S2. This included 27 “secreted to blood” receptors that were measured as circulating proteins in blood during acute malaria with a median log₂FC of 0.79. Their role and function in the host response to infection is unexplored and should be the focus of future investigations.

The top 25 most elevated proteins in acute malaria primarily belong to the category secreted to blood, containing several pro-inflammatory cytokines and chemokines. However, among the top 25, there were also soluble proteins without secretome or tissue-specific annotation, such as RRM2, HMOX1, or TNFRSF8 (Figure 2C). Proteins of the intercellular and membrane category, such as LAG3 or CDCP1, were also of high abundance, potentially indicating functional shedding or release in the form of cellular debris (Figure 2C). The top 25 low DAPs fell into more varied categories but were overall more enriched for proteins associated with either specific tissues or intracellular receptors, such as CA6, SSC4D, EPCAM, and EDAR or CTSV, CTSF, PLXNA4, and KITLG, respectively.

In summary, acute malaria resulted in an extensive perturbation of the circulating plasma proteome, increasing proteins from various tissue origins and functions. Notably, proteins secreted into the blood indicated roles in immune cell recruitment, communication, and activation.

## Innate immune cells shape the inflammatory plasma proteome and signal to CD4⁺ T cells during acute malaria

To identify the likely source of the most abundant proteins and underlying disease mechanism in the context of immune cell communication, we used targeted single-cell multi-omics, measuring 29 cell surface proteins and 399 genes to generate expression profiles from peripheral blood mononuclear cells (PBMCs) from four study participants. These individuals were selected as representatives of the whole cohort, comprising two primary infected and two previously exposed individuals (Figure 3; more detailed description in STAR Methods). We profiled >70,000 single cells and identified all major immune cell subsets (Figure 3A). The four individuals showed similar intra-individual distributions of immune cell subsets at each of the sample time points (Figure 3B). Detailed annotation of the clusters identified 27 immune cell subsets based on canonical markers and manual annotation (Figures 3C, S5D, and S5E). We calculated the average gene expression for each cell subset across all individuals at the acute disease state to get summarized gene expressions (Figure S5F). Mapping of malaria DAPs (log₂FC > 1) onto the average gene expression per cell type resulted in a comprehensive overview of potential immune cell

<footnote>(G) The top 25 high and low DAPs in normalized protein expression (NPX) during clinical malaria were hierarchically clustered based on similarity. (H and I) The top 10 high (H) and low (I) DAPs at the acute time point in comparison to 10 days and 12 months after treatment. See also Figures S1–S3 and Tables S1 and S2.</footnote>
<figure><img src="image_6.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure 2. Mapping of plasma proteins to cellular origins highlights functional enrichment and differential abundance in acute malaria</figcaption></figure>

(A) Mapping of plasma proteins (DAPs, *n* = 703) to source or origin according to the HPA.37

(B) Smarties plot indicating the median log<sub>2</sub>FC for proteins belonging to the same protein functional class, colored by source or protein origin shown in (A). The numbers represent the total number of proteins in the given category.

(C) Estimated difference in NPX (log<sub>2</sub>FC) at acute malaria compared with healthy state samples for the top 25 high- and low-abundant proteins, colored by source or protein origin shown in (A).

See also Figure S4 and Table S3.

contribution to the proteins measured in plasma (Figure 3D), with the caveat that only mononuclear cells were included in the analysis.

For example, CD4⁺ activated effector T cells showed the strongest gene expression for IFNG, while other pro-inflammatory proteins such as IL-18 and CXCL8/9/10/11 were highly expressed in myeloid dendritic cells (mDCs) and CD14⁺ monocytes. DAPs such as CD163, IL-1B, IL-1RN, ICAM1, S100A12, and LILRB4 were also expressed by both monocytes and DCs. Classical CD14⁺ monocytes expressed CCL2, CXCL11, CXCL9, and AZU1, while intermediate/non-classical CD16⁺ monocytes expressed VMO1, TNFRSF8, and chitinase-3-like-protein-1 (CHI3L1 or YYKL-40). Plasmacytoid dendritic cells (pDCs), together with plasma cells, might be responsible for the increased abundance of soluble MZB1.

Genes corresponding to circulating proteins CCL4, GZMA, GZMB, GZMH, and CST7 were primarily expressed by natural killer (NK) cells, NKT cells, γδ T cells, and effector CD4⁺ and CD8⁺ T cells. The intracellular and membrane protein encoded by TNFRSF9 (also known as CD137) was exclusively expressed by NKT cells and CD4⁺ regulatory T cells (Tregs). Treg cells also expressed IL-2RA, or CD25, encoding part of the high-affinity IL-2 receptor, potentially enabling these cells to receive IL-2 signaling³⁹ despite the receptor being highly elevated in blood during acute malaria (2.5 log₂FC). Activated CD4⁺ effector T cells showed specific expression of IL-1RL1 (also called ST2), which can act in its soluble form as an IL-33 decoy receptor.⁴⁰ In addition, they expressed the genes CD48, CD27, CD38, HAVCR2 (also known as TIM-3), IL-12RB1, IFNG, LAG3, LIF, PDCD1, and IL-4 of which the corresponding proteins were
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>Figure 3. Immune cell profiling reveals cellular sources and signaling networks underlying the inflammatory plasma proteome in malaria</figcaption></figure>

(A) Uniform manifold approximation and projection based on weighted nearest neighbor graph integration (wnnUMAP) plot of targeted multi-omics scRNA and Ab-seq from 73,121 single cells, 4 patients with 3 sample time points; low-level cell type annotation.

(B) Low-level cell type frequencies for all samples, grouped by sample time point.

(C) wnnUMAP with high-level cell type annotation.

(D) The average gene expression per high-level cell type (from C), corresponding to proteins highly abundant in acute malaria plasma as shown in the boxplots below as dNPX. Proteins are annotated by HPA origin (color top) and functional class (column labels), and immune cell subsets are color-annotated by their low-level annotation.

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elevated in plasma. CD4⁺ T cells showed the highest expression of LAG3, the gene encoding for the membrane-embedded protein involved in inhibiting T cell activation, which was also strongly elevated in plasma during acute disease.

In addition to assessing the potential contribution of immune cells to the plasma proteome, we also wanted to investigate potential interactions between cell subsets and the altered plasma proteome during acute malaria. Using CellPhoneDB,⁴¹ we built a ligand-receptor network to identify known receptors for the highly abundant proteins (log₂FC > 1) (Figure 3E). Further, we mapped the gene expression of those receptors onto the single-cell dataset at the acute time point to see cell type-specific receptor gene expression (Figure 3F).

The data indicated that innate myeloid cells such as monocytes were the most potential “active” contributors, while CD4⁺ T cells were among the most potential “receivers” of the proteins measured in circulation.

To validate the potential cellular origin of proteins identified in our immune cell data as well as to extend the analysis to proteins and ligands not included in our targeted scRNA-seq approach, we used a recently published whole transcriptome scRNA-seq dataset by Dooley et al.,⁴² where PBMCs had been profiled in adults (n = 3) and children (n = 2-3) during and after acute uncomplicated P. falciparum malaria in Malaysia (Figures S6A and S6B). Focusing only on the samples from the acute phase (day 0) (Figure S6C), we first compared the expression pattern of overlapping genes in both datasets for similarly binned immune cell subsets (Figure S6D). The high similarity of gene expression profiles enabled us to extrapolate the potential origin of a given plasma protein in our cohort, based on the gene expression in the Dooley et al. cohort.⁴² We observed that much of the gene expression that corresponded to soluble proteins measured in our cohort was present in innate cells, such as monocytes and DCs (Figures S6E and S6F).

In summary, immune cell transcriptomics from two malaria datasets linked cell subsets to circulating proteins, revealing their roles in the acute inflammatory response during clinical malaria.

## Plasma proteome signatures stratify malaria patients by disease severity and reflect systemic inflammation and organ involvement

Next, we assessed how the plasma proteome correlated with clinical chemistry variables that are used in routine clinical care. Within the cohort, the clinical chemistry markers that indicate inflammation and organ engagement (e.g., C-reactive protein [CRP], platelet counts, serum creatinine, aspartate transaminase [AST], alanine aminotransferase [ALT], and bilirubin levels) showed large inter-individual variation during acute symptomatic disease (Figure S7A). The magnitude of several of the blood chemistry tests was correlated with the abundance of DAPs at the acute malaria time point (absolute log₂FC > 1, n = 264) (Figure S7B).

We then identified plasma protein profiles linked to disease severity. Here, we deliberately choose not to use predefined

severity groups to capture the range of disease severity also among patients not fulfilling the rather strict and extreme WHO definition of severe malaria.⁴³ Instead, we evaluated the severity of the disease based on the sequential organ failure assessment (SOFA) score, a well-known scoring system used to predict mortality in critically ill patients with sepsis.⁴⁴ The overall SOFA score provides an estimate of how critically ill a patient is based on the function of six organ systems—the respiratory, cardiovascular, hepatic, renal, coagulation, and neurological systems.⁴⁴

Proteomic profiles were utilized to stratify individuals within the cohort based on relative profiles of 258 proteins. The clustering of six principal components, capturing 67% of the total variance in the data, resulted in three distinct patient clusters (Figures 4A and S7D-S7F). To assess the overall disease severity of the three patient clusters, we then compared their overall SOFA scores. Patient cluster 1 had significantly higher SOFA scores compared with the other clusters, indicating more severe disease (Figure 4B). Based on these observations, we denoted the clusters as representing severe (cluster 1), moderate (cluster 2), and mild (cluster 3) disease. Consistent with this stratification, the groups differed significantly in the amount of parasitemia (p value = 0.003), CRP (p value ≤ 0.002), and platelet counts (p value ≤ 0.001) (see Table S3). Most differences could be attributed to the comparison of the severely ill and the mildly ill patient groups (Figure 4C; Table S4). In addition, hepatocellular damage was more extensive among individuals in the severe group compared with the mild (p value<sup>AST</sup> = 0.0009; p value<sup>ALT</sup> = 0.0391) and moderate groups (p value<sup>AST</sup> = 0.015), indicating a greater impact on the liver in severe cases. The observed tissue leakage of liver-specific proteins AGXT and HOA1 further supported these findings (Figure S7G).

To identify which proteins were abundant in each severity group, we compared each group's profiles against the healthy convalescent samples (month 12) (Figure 4D). Among the highly differentially expressed proteins (log₂FC > 1), 218 were significantly different in the severe group. Of these, 174 proteins were shared with the moderate group, while 74 proteins were shared among all 3 groups. The proteins demonstrated differences in protein abundance rather than specific patterns associated with each group (Figure 4E). Among those shared between all groups were IFN-pathway-related proteins (IFNG, CXCL8, CXCL9, CXCL10, and CXCL13), with individuals of the severe group having higher relative abundance relative to the other groups.

In summary, our findings indicate that proteomic profiles can effectively stratify patients into groups based on disease severity, supported by relevant clinical parameters.

## Co-abundant inflammatory proteins link monocyte-driven immune signaling to organ dysfunction in severe malaria

The WHO criteria for severe malaria and the SOFA score partly overlap with each other. However, the organ-specific SOFA score requires more variables and provides a more granular assessment of different organ functions by quantifying the extent

<footnote>(E) Ligand-receptor network based on CellPhoneDB, queried with plasma proteins (log₂FC > 1). Node color corresponds to log₂FC in plasma in acute malaria (red scale) and receptors that are not measured soluble in plasma (black).</footnote>

<footnote>(F) The average gene expression of receptor genes per cell type matched to interaction in CellPhoneDB and if the receptor was measured soluble in plasma. See also Figures S5 and S6.</footnote>
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>Figure 4. Proteomic clustering stratifies malaria patients by disease severity and reveals distinct inflammatory and clinical profiles</figcaption></figure>

(A) PCA, colored based on K-means clustering (k = 3) of the first six components of the proteomic data.

(B) Comparison of SOFA scores from individuals per cluster; between patient clusters: cluster 1 associates with more severe disease, cluster 2 associates with moderate disease, and cluster 3 associates with mild disease.

(C) Clinical variables at the time of acute sampling, separated by patient clusters.

(D) DAPs for each patient cluster against healthy follow-up samples (month 12) (LMEM, FDR < 0.01), showing proteins with the highest and lowest (top 8) abundance for each group.

(E) Overlap of high-abundant proteins (log₂FC > 1 from D) between the groups.
See also Figure S7 and Table S4.

of organ failure (Figure S7C, lower part). Whereas the WHO criteria for severe malaria are only based on threshold criteria, lacking different degrees of severity, and have been developed primarily for the evaluation of children in resource-limited settings. In this cohort of patients with mostly non-severe disease (62/72 non-severe cases), the coagulation system (coagulation SOFA), the liver (liver SOFA), and the kidney (renal SOFA) functions were mainly affected (Figure S7C).

To identify proteins and biological pathways potentially involved in driving higher SOFA scores and malaria pathogenicity, we used weighted correlation network analysis (WCNA).⁴⁵ This method identifies proteins based on their co-abundance, as it may indicate a co-regulated response to the infection. The correlation network was generated from all 703 DAPs rather than only the 264 DAPs with a log₂FC > 1 or the 218 proteins highly elevated in the severe group, as even
<figure><img src="image_13.png" /><figcaption>Figure 5. Protein correlation modules linked to disease severity reveal enriched immune signaling and inflammation-related pathways in malaria</figcaption></figure>

(A) Protein-protein correlation heatmap of all DAPs (n = 703) during acute malaria. Proteins are assigned to clusters (modules) using weighted correlation network analysis (WCNA).

(B) WCNA module association with SOFA scores based on Pearson correlation test with FDR correction for multiple testing. Only significant (FDR < 0.05) correlations are colored, but Pearson correlation coefficient r is shown for all comparisons.

(C) Reactome pathway over-representation analysis using protein members (n = 158) of the refined turquoise-WCNA module.

(D) Overrepresented proteins in Reactome pathways (v86), spider plot visualizes averaged NPX (mean ± 95% confidence interval [CI]) per patient severity group.

proteins with relatively small changes can describe biological effects (Figure 5A). Among the generated protein modules, the turquoise module, consisting of 297 proteins, was highly correlated (Pearson correlation coefficient r = 0.69) with disease severity, assessed via SOFA score (SOFA_total), with a large contribution from the coagulation system (SOFA_coag) (Figure 5B). We next refined the turquoise module by removing less important proteins based on module membership and submitted a final list of 158 proteins for pathway enrichment analysis using the Reactome database (Figure 5C).⁴⁶ The proteins of the turquoise module were enriched in several pathways, such as “immunomodulatory interactions between a lymphoid and a non-lymphoid cell,” “neutrophil degranulation,” and “TNFR2 non-canonical nuclear factor κB (NF-κB) pathway” (Figure 5D).

Further analysis of the proteins contributing to the pathways showed that patients with severe disease overall had the highest abundance, followed by those with moderate symptoms, and the least expression by those with mild symptoms (Figure 5D). Especially proteins involved in immunomodulatory interactions between a lymphoid and a non-lymphoid cell, such as SLAMF7, SIGLEC7, SIGLEC10, LILRB4, ICAM1, CD300C, and CD300E, had much higher average abundance in more severely ill patients compared with patients with moderate and mild

illness in our cohort. Several of these proteins could be linked to monocytes and dendritic cells, for example, LILRB4 and ICAM1 (Figure 3D) or SLAMF7 and CD300E/LF/C (Figure S6E). Among the proteins linked to neutrophil degranulation, the severely ill group had much higher abundance of the neutrophil-associated proteins PLAUR and CHI3L1 but also the monocyte-associated proteins TNFRSF1B and RETN. Indeed, CHI3L1 has been described to be elevated in different conditions with pronounced inflammation, such as sepsis, indicating its relevance in severe systemic inflammation.⁴⁷ Our scRNA-seq data indicated that CD16⁺ monocytes were one major cellular source of CHI3L1 in addition to neutrophils, thus corroborating studies describing monocytes as a major source of mediators for immune response activation, which contributes to malaria disease severity.⁴⁸ The TNFR2 non-canonical NF-κB pathways play an important role in immune function but are also involved in different inflammatory conditions.⁴⁹ Among others, we observed higher abundance of soluble TNFRSF1B (also TNFRII) to be linked to severity in our cohort. A potential role of soluble TNFRSF1B has not been reported in the context of malaria. However, a subset of Treg cells expressing TNFRII has been associated with suppressive activity in adults with severe malaria.⁵⁰
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /><figcaption>Figure 6. Discriminative plasma protein signature of malaria severity is conserved across infectious disease contexts</figcaption></figure>

(A) Sparse partial least square discriminant analysis (sPLS-DA) to identify proteins driving separation of severity groups (acute) and convalescence (12 months follow-up) based on NPX data (only 158 severity-associated proteins).

(B) Loading values from sPLS-DA, indicating proteins responsible for separation of the groups in (A).

(C) Top 9 proteins identified by sPLS-DA. Statistical significance was assessed using the Wilcoxon test without FDR correction.

(D and E) The signature, all proteins from sPLS-DA component 1, was further assessed in a plasma protein dataset of (D) COVID-19 patients, grouped by WHO-defined severity (1 = severe, 6 = not hospitalized)²⁸ and (E) patients with acute febrile illness and travel history diagnosed with other confirmed etiologies. The signature was evaluated using single-sample protein set variation analysis, which tests the over-representation of a specific signature in a sample and generates a summarized score.

See also Tables S5 and S6.

## Protein biomarker profiling identifies signatures elevated in severe malaria compared with other febrile infections

For further studies of disease severity and patient classification during malaria based on host response profiles, we condensed the 158 co-abundant proteins associated with higher SOFA scores to a low-number-protein signature using sparse partial least square discriminant analysis (sPLS-DA). This 11-protein signature enabled the separation of patients associated with progressively more severe disease (Figure 6A). However, the main effect in separation was attributed to 7 proteins, driving the separation in the first dimension of the sPLS-DA space and explaining 60.1% of the variation. Among these proteins with the strongest effect on distinguishing severe from moderate and mild cases were markers previously associated with malaria severity, such as enzymes (HMOX1⁵¹), soluble receptors (TNFRSF1B²³), markers of endothelial activation (ICAM-1⁵²),

and markers of T cell activation (IL-2RA⁵³). Other significant markers that were not found to be previously associated with malaria severity included other enzymes such as beta-1,4-galactosyltransferase 1 (B4GALT1) and the hormone and vasodilator CALCA³³ (Figure 6B).

Graphing the relative protein abundance (normalized protein expression [NPX]) for each group highlighted how higher abundance was associated with more severe disease. Despite the mild group having lower abundance, the proteins were still elevated compared with healthy month 12 convalescent samples (Figure 6C). This could indicate either a specific role in the response to the *P. falciparum* infection or a more general role associated with the systemic inflammatory immune responses.

To test this, we evaluated the condensed 11-protein signature (Table S5) in protein datasets from individuals with other types of infections but partly overlapping clinical presentations. We calculated a signature (single-sample enrichment score [ssES])
to evaluate if the protein signature was enriched in other datasets, also measured with the Olink Explore 1536 platform. For systemic inflammation, we re-used a COVID-19 dataset by Filbin and colleagues,²⁸ where 384 patients with a range of disease severity were profiled during acute symptomatic infection and grouped into severe to mild cases according to WHO criteria (ordinal outcomes scale). In individuals with severe COVID-19 disease (group 1), we observed a higher ssES compared with those with less severe or mild disease (groups 3–5) (Figure 6D).

We next evaluated the condensed protein signature in a set of samples from 97 individuals with other febrile illnesses after recent travel to a tropical or subtropical area.⁵⁴ These samples were collected to identify fever etiology and immune responses associated with different infection agents. Overall, they had relatively mild diseases compared with malaria based on hospitalization and SOFA score (range 0–3) (see cohort section and Table S6). Potential technical or batch effects were reduced as these samples were collected and processed at the same hospital and lab and analyzed in the same plasma protein profiling run as the malaria samples (protein profiles will be described in detail elsewhere). Using the ssES, we observed that samples from individuals with severe and moderately severe malaria had higher signature scores compared with mild malaria cases, malaria convalescence (healthy state, month 12) samples, and other febrile infections, including dengue fever, influenza A and B, bacterial gastroenteritis (Salmonella, Campylobacter, and Shigella), E. coli pyelonephritis, and Mycoplasma pneumonia (Figure 6E).

These results reveal that an 11-protein signature effectively stratifies malaria patients by disease severity and differentiates severe malaria from other febrile illnesses, highlighting its potential as a specific biomarker in P. falciparum infection.

## Integrative ligand-receptor analysis reveals the role of T cells in malaria pathogenesis

Finally, we aimed to investigate the mechanisms driving disease severity by integrating protein and transcriptomic data. Among the 703 DAPs in plasma, 158 were associated with SOFA score and disease severity (Figures 5A and 7A). Of these, 27% were actively secreted into the blood, while 46% were not known to be secreted and were either tissue-specific or of unclear origin (Figure 7B). Across the 158 proteins, patients in the severe group exhibited considerably higher relative abundance levels compared with those with moderate or mild disease or at the month 12 follow-up healthy state (Figure 7C).

Using immune-cell-specific gene expression data and ligand-receptor pair information presented earlier (Figure 3), we mapped the severity-associated proteins onto the interaction network and the average gene expression per immune cell subset (Figure 7D). Much of the interaction network was centered around four circulating proteins (LGALS9, CD274, CD70, and IL-4) and their corresponding receptors (Figure 7E). Soluble CD274 (PD-L1) was one of the few proteins that were affected by previous exposure. Primary infected individuals had 1.7-fold higher amounts in plasma during acute compared with previously exposed individuals ($log_2FC_{primary} = 2.44$ versus $log_2FC_{previously} = 1.71$; see Figure S1D). This aligned with previous observations in the same cohort, indicating that previously exposed individuals exhibited a disease tolerance response.²⁵

Innate immune cells, including mDCs, CD14, and CD16 monocytes, were the main source of LGALS9 (Galectin 9), based on gene expression, with severe patients having 2.18-fold higher circulating protein compared with patients with mild disease. The expression of its receptor HAVCR2 (TIM-3) in mDCs, NK cells, and activated effector CD4⁺T cells suggested these cells as potential signal recipients among PBMCs. Overall, activated effector CD4⁺T cells showed both source and recipient potential for these soluble ligands.²⁵

Consequently, we repurposed previously published cell phenotype data of activated CD38⁺HLADR⁺CD4⁺ and CD8⁺T cells from the same cohort (with 22 matching individuals)²⁵ to correlate cell numbers at acute malaria with the 158 severity-associated proteins (Figure 7F). Both activated CD4⁺ and CD8⁺T cells correlated with several of the same proteins, including PDCD1 (PD1), LAG3, TNFRSF8 (CD30), OSCAR, and LGALS9. Proteins exclusively associated with activated CD4⁺T cells included monocyte-associated CHI3L1 and soluble TIM-3 (HAVCR2) but also included two of the 11-protein severity signature (ICAM1 and B4GALT1). As these two proteins were most characteristic in stratifying severity, their association with activated CD4⁺T cells supports a pronounced role of these cells in disease severity.

Together, the integrated analytical approach highlights how soluble ligands and receptors in circulation impact peripheral immune cells, especially the T cell compartment, thereby contributing to disease severity in acute P. falciparum malaria.

## DISCUSSION

Understanding the host response to P. falciparum infection remains a critical challenge in malaria research, as disease manifestations can be heterogeneous and severe. By integrating high-sensitivity plasma proteomics with single-cell transcriptomics, our study provides a systems-level view of immune and tissue responses during acute malaria, revealing both established and previously underappreciated mechanisms of pathogenesis of the infection and the host immune response.

While prior studies have employed plasma cytokine profiling to identify disease severity signatures, particularly in pediatric cohorts,²²,²³ or mass spectrometry to assess high-abundance proteins in adult samples,²⁴,⁵⁵,⁵⁶ these approaches were limited in scope. Notably, they did not investigate the cellular origin of circulating proteins, nor did they capture the broader proteomic landscape.⁵⁷ Expanding on previous efforts, we utilized a high-sensitivity proteomic platform capable of detecting a wider array of proteins, revealing over 700 affected proteins of which 250 were highly altered, including low-abundance cytokines and immune mediators. By integrating these proteomic insights with cellular immune responses, we reveal the coordinated immune landscape during acute malaria, thereby addressing a key gap in our understanding of host-pathogen interactions.

Our study demonstrates that the plasma proteome could stratify patients along a spectrum of disease severity, independent of conventional clinical categorization. Rather than imposing predefined thresholds, we adopted an unsupervised, data-driven approach that allowed the proteomic architecture to delineate patient subgroups based on the extent of organ dysfunction. This strategy revealed a continuum of severity that is not captured by the binary classification of severe versus non-severe malaria as defined by
<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>E</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>F</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>**Figure 7.** Severity-associated plasma proteins originate from innate immune cells and signal to T cells in acute malaria</figcaption></figure>

(A) Overview of the proportion of severity-associated proteins (turquoise module from WCNA analysis) among all DAPs during acute malaria.

(B) Categorize by potential origin.

(C) Average protein abundance (mean NPX) for severe, moderate, mild, and convalescence (12-month follow-up samples). Error bars depict the 95% confidence interval.

(D) The circosplot represents a protein-centric view of potential immune cell sources and targets for circulating proteins. The outer track and the overall sections present the immune cell subsets, followed by their overall frequency among all CD45+ PBMCs (purple track). The third track (red heatmap) shows the average gene expression (GEX) for genes with GEX > 0.5 in the cell subset. The next track (green heatmap) describes the average abundance of the corresponding circulating proteins in plasma. Directed links depict the ligand-receptor pair based on CellPhoneDB knowledge.

(legend continued on next page)
WHO criteria. While these criteria are widely used, they rely on discrete clinical cutoffs and may obscure underlying biological heterogeneity. To address this, we employed the SOFA score, which integrates multi-organ dysfunction into a continuous metric with prior applications in the context of malaria.⁵⁸,⁵⁹ This enabled a more nuanced mapping of proteomic features to clinical severity and revealed that immune and inflammatory pathways are differentially modulated across the disease spectrum. Notably, this approach uncovered proteomic signatures indicative of systemic inflammation and organ-specific injury, such as hepatic involvement. The detection of liver-enriched proteins such as AGXT and HAO1 in circulation, even among patients not meeting clinical criteria for severe disease, suggests that subclinical tissue injury may be more prevalent than previously recognized. These findings are consistent with earlier reports of hepatic stress in malaria⁶⁰ and raise the possibility that such proteins could serve as early biomarkers of organ dysfunction, complementing traditional indicators such as AST and ALT. These findings underscore the potential of proteomics to refine clinical phenotyping and to uncover mechanistic correlates of pathophysiology that may be masked by traditional classification schemes.

Our data also highlight the immunological complexity of malaria severity. Co-abundant protein clusters associated with SOFA scores pointed to dysregulation in pathways involving neutrophil degranulation, TNFR2 signaling, and immune checkpoint regulation. The presence of soluble ligand-receptor pairs—CD70-CD27, PD-L1-PD1, and TIM-3-galectin-9—points to a substantial interplay between immune activation and regulatory feedback in malaria. These molecules, which modulate T cell function, may reflect both the intensity and exhaustion of the immune response.⁶¹⁻⁶⁴

Notably, the increased abundance of soluble PD1, in parallel with activated CD38⁺HLADR⁺CD4⁺ and CD8⁺T cells, suggests a link between checkpoint receptor shedding and T cell activation. A similar mechanism may underlie the elevated abundance of soluble TIM-3. As a checkpoint receptor,⁵⁵ the high abundance in circulation could come from increased shedding from T cells due to the infection, as has been described for HIV.⁶⁶ Consistent with such a mechanism, TIM-3 was primarily expressed by activated CD4⁺T cells. This suggests that checkpoint receptor shedding may serve as a surrogate marker of T cell activation and immune dysregulation in malaria, a concept that warrants further investigation.

The identification of LILRB4 as a severity-associated protein, highly expressed by monocytes, adds to the growing recognition of monocyte-driven pathology in malaria. Given the role of monocytes in shaping immune responses and clinical outcomes in malaria,⁴⁸,⁶⁷⁻⁶⁹ LILRB4 may represent a novel target for understanding or modulating monocyte function in this context. Monocytes are important drivers for severe forms of malaria,⁴⁸ and epigenetic and transcriptomic modulation of these cells has been proposed to be important mechanisms in the development of clinical immunity.²⁰,⁶⁸,⁷⁰ These findings suggest that markers such as LILRB4 require additional investigation to elucidate their role in monocyte-mediated immuno-

pathology and immune tolerance during malaria. Such insights may uncover novel therapeutic targets for modulating host immune responses.

Beyond mechanistic insights, our study identified a condensed protein signature for disease severity. An 11-protein signature, enriched in severe malaria and partially overlapping with severe COVID-19, distinguished malaria from other febrile illnesses, suggesting a degree of specificity in the host response to *P. falciparum*.

Among these 11 proteins, CALCA, of which the precursor peptide pro-calcitonin (PCT) is an established marker for systemic inflammation and sepsis, correlates with the severity of bacterial bloodstream infections.⁷¹,⁷² In malaria, elevated serum levels of PCT in *P. falciparum* infections have been described as indicators for disease severity and mortality.⁷³,⁷⁴ Given its role in both bacterial and parasitic infections, PCT may serve as a vital biomarker for assessing disease severity and guiding treatment decisions in diverse clinical scenarios.

HMOX, on the other hand, seems to be more specific for malaria as a reaction to the heme released during malaria infections⁷⁵ and potentially part of the protective response mechanism.⁷⁶

The prominence of soluble receptors such as IL-2RA and TNFRSF1B in the severity-associated signature suggests a dynamic regulation of immune signaling during acute infection. This challenges the traditional focus on cytokine levels alone and highlights the importance of receptor shedding as a potential biomarker of immune activation. Previous studies have identified individual markers associated with malaria severity, such as IL-2RA and TNFRSF1B.²³,⁷⁷,⁷⁸ This specificity suggests that the host response to *P. falciparum* involves unique immunological circuits, potentially exploitable for diagnostic or prognostic purposes.

The prominence of soluble receptors in this signature underscores the importance of receptor shedding as a biomarker of immune activation, challenging the traditional focus on cytokine levels alone. Validation of these signatures in other cohorts will be needed to further explore their use as biomarkers and potential drug targets.

While our findings offer a rich resource for the community, they also raise new questions. The functional consequences of soluble checkpoint molecules in malaria remain unclear—do they modulate immune responses systemically, or are they merely byproducts of cellular activation? Similarly, the role of monocyte-expressed proteins like LILRB4 in disease progression warrants deeper exploration, particularly in the context of immune memory and tolerance.

In summary, our integrative analysis provides a comprehensive map of the peripheral proteomic and immune landscape during acute *P. falciparum* infection. By linking molecular signatures to clinical severity and immune cell states, we offer both mechanistic insights and translational opportunities. The interactive platform accompanying this study enables further exploration of these data, supporting hypothesis generation and cross-disease comparisons. Ultimately, a deeper understanding of the host response

<footnote>(E) The relative abundance (NPX) of the main soluble mediators from (D) stratified into severity groups.</footnote>

<footnote>(F) Spearman correlation network of severity-associated proteins and activated CD4⁺ and CD8⁺ T cell counts of 22 individuals, using previously published immune cell phenotype data,²⁵ showing only significant correlations (FDR < 0.05).</footnote>
to malaria may inform the development of targeted interventions to mitigate severe disease outcomes.

## Limitations of the study

Our study has two main limitations: discrepancies in sample numbers due to the absence of follow-ups at the selected time points and single-cell analysis on only four participants. We used single-cell data qualitatively to support protein-centric analysis, which should be considered when interpreting results.

## RESOURCE AVAILABILITY

### Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Anna Färnert (anna.farnert@ki.se).

### Materials availability

This study did not generate new, unique reagents.

### Data and code availability

Data availability: due to patient consent and confidentiality agreements, datasets can be accessed for validation or reuse by contacting the lead contact. Data access will be evaluated according to the data access plan and Swedish legislation. Source data are provided in this paper. For individual exploration of the proteomic data, we created a ShinyApp with pre-computed aggregated data available online at https://malaria-proteomics-resource.serve.scilifelab.se. Additionally, measured proteins are visualized on the Human Disease Blood Atlas's individual protein summary pages at https://proteinatlas.org/humanproteome/blood.

Code availability: all code necessary for the data analysis and visualization is available at https://github.com/SundlingLab/Explore1536_Malaria. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

## ACKNOWLEDGMENTS

We thank all study participants, clinicians, nurses, laboratory staff, and students for their contribution. Special thanks to Annika van Vollenhoven for cell sorting. We acknowledge the Human Protein Atlas for their resources and the Färnert research group, the National Genomics Infrastructure, the Knut and Alice Wallenberg Foundation, the Swedish Research Council, and the SNIC/Uppsala Multidisciplinary Center for Advanced Computational Science for their support. Thanks to the SciLifeLab Data Centre for hosting the Shiny app. M.J.L. utilized BioRender to create the graphical abstract (https://BioRender.com/lubo5ku) and Figure 1A (https://BioRender.com/I72i116). M.J.L. was supported by PhD grants from Karolinska Institutet (2019-00992). The project was supported by grants to A.F. from the Swedish Research Council (521-2012-3311, 2018-02688, and 2021-03105), the Region Stockholm ALF Medicine (20150135 and 960104), and the Marianne and Marcus Wallenberg Foundation. V.Y. received support from Svenska Sällskapet för Medicinsk Forskning (PG-22-0346). C.S. supported the project with funding from the Karolinska Institutet (FS-2020:0007 and FS-2022:0010), the Åke Wiberg Foundation (M18-0076), and the Swedish Research Council (2019-01940 and 2023-01943).

## AUTHOR CONTRIBUTIONS

M.J.L., K.W., C.S., and A.F. conceptualized the study. M.J.L., F.F., J.W., F.E., and C.S. designed or performed experiments. M.J.L. analyzed data and generated figures and visualizations. C.S. and A.F. provided funding for the study. K.W., V.Y., K.S., F.F., D.S., and A.F. coordinated the clinical study and participant sampling. M.J.L., C.S., and A.F. wrote the original draft, and all authors contributed to the revision of the manuscript.

## DECLARATION OF INTERESTS

The authors declare no competing interests.

## DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS

During the preparation of this work, the authors used Microsoft's Copilot to improve wording and clarity. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

## STAR★METHODS

Detailed methods are provided in the online version of this paper and include the following:

* KEY RESOURCES TABLE

* EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

* Malaria immunology cohort

* Tropical fever cohort

* Ethical statement

* Clinical variables

* METHOD DETAILS

* Proteomics profiling and data pre-processing

* Targeted single-cell-multi-omics profiling and data pre-processing (BD Rhapsody's targeted scRNA seq + Ab)

* QUANTIFICATION AND STATISTICAL ANALYSIS

* Differential protein abundance analysis

* Protein annotation

* Potential tissue leakage

* Downstream single-cell multi-omics analysis

* CellPhoneDB

* Clinical chemistry - Protein abundance correlation

* Proteomic profile-based patient stratification

* Weighted Correlation Network Analysis

* Sparse Partial least square discriminant analysis

* Single sample enrichment score

## SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.immuni.2025.06.014.

Received: December 23, 2024

Revised: March 28, 2025

Accepted: June 13, 2025

Published: July 14, 2025

## REFERENCES

1. World Health Organization. (2024). Global Malaria Programme. World Malaria Report 2024. https://www.who.int/teams/global-malaria-programme/reports/world-malaria-report-2024.

2. Crompton, P.D., Moebius, J., Portugal, S., Waisberg, M., Hart, G., Garver, L.S., Miller, L.H., Barillas-Mury, C., and Pierce, S.K. (2014). Malaria Immunity in Man and Mosquito: Insights into Unsolved Mysteries of a Deadly Infectious Disease. Annu. Rev. Immunol. 32, 157–187. https://doi.org/10.1146/annurev-immunol-032713-120220.

3. Schofield, L., and Grau, G.E. (2005). Immunological processes in malaria pathogenesis. Nat. Rev. Immunol. 5, 722–735. https://doi.org/10.1038/nri1686.

4. Clark, I.A., Alleva, L.M., Mills, A.C., and Cowden, W.B. (2004). Pathogenesis of malaria and clinically similar conditions. Clin. Microbiol. Rev. 17, 509–539. https://doi.org/10.1128/CMR.17.3.509-539.2004.

5. Cowman, A.F., Healer, J., Marapana, D., and Marsh, K. (2016). Malaria: Biology and Disease. Cell 167, 610–624. https://doi.org/10.1016/j.cell.2016.07.055.

6. Stevenson, M.M., and Riley, E.M. (2004). Innate immunity to malaria. Nat. Rev. Immunol. 4, 169–180. https://doi.org/10.1038/nri1311.
7. Knackstedt, S.L., Georgiadou, A., Apel, F., Abu-Abed, U., Moxon, C.A., Cunningham, A.J., Raupach, B., Cunningham, D., Langhorne, J., Krüger, R., et al. (2019). Neutrophil extracellular traps drive inflammatory pathogenesis in malaria. Sci. Immunol. 4, eaaw0336. https://doi.org/10.1126/SCIIMMUNOL.AAW0336.

8. Maitland, K., and Newton, C.R.J.C. (2005). Acidosis of severe falciparum malaria: Heading for a shock? Trends Parasitol. 21, 11–16. https://doi.org/10.1016/j.pt.2004.10.010.

9. Trampuz, A., Jereb, M., Muzlovic, I., and Prabhu, R.M. (2003). Clinical review: Severe malaria. Crit. Care 7, 315–323. https://doi.org/10.1186/cc2183.

10. Dietmann, A., Helbok, R., Lackner, P., Issifou, S., Lell, B., Matsiegui, P.B., Reindl, M., Schmutzhard, E., and Kremsner, P.G. (2008). Matrix Metalloproteinases and Their Tissue Inhibitors (TIMPs) in Plasmodium falciparum Malaria: Serum Levels of TIMP-1 Are Associated with Disease Severity. J. Infect. Dis. 197, 1614–1620. https://doi.org/10.1086/587943.

11. Otterdal, K., Berg, A., Michelsen, A.E., Yndestad, A., Patel, S., Gregersen, I., Halvorsen, B., Ueland, T., Langeland, N., and Aukrust, P. (2021). IL-18 and IL-18 binding protein are related to disease severity and parasitemia during falciparum malaria. BMC Infect. Dis. 21, 1073. https://doi.org/10.1186/S12879-021-06751-Y.

12. Mandala, W.L., Msefula, C.L., Gondwe, E.N., Drayson, M.T., Molyneux, M.E., and MacLennan, C.A. (2017). Cytokine profiles in Malawian children presenting with uncomplicated malaria, severe malarial anemia, and cerebral malaria. Clin. Vaccine Immunol. 24, e0053316. https://doi.org/10.1128/CVI.00533-16.

13. O'Donnell, A.S., Fazavana, J., and O'Donnell, J.S. (2022). The von Willebrand factor - ADAMTS-13 axis in malaria. Res. Pract. Thromb. Haemost. 6, e12641. https://doi.org/10.1002/RTH2.12641.

14. Ademolue, T.W., Aniweh, Y., Kusi, K.A., and Awandare, G.A. (2017). Patterns of inflammatory responses and parasite tolerance vary with malaria transmission intensity. Malar. J. 16, 145. https://doi.org/10.1186/s12936-017-1796-x.

15. Aguilar, R., Campo, J.J., Chicuecue, S., Cisteró, P., Català, A., Luis, L., Ubillos, I., Galatas, B., Aide, P., Guinovart, C., et al. (2019). Changing plasma cytokine, chemokine and growth factor profiles upon differing malaria transmission intensities. Malar. J. 18, 406. https://doi.org/10.1186/s12936-019-3038-x.

16. Farrington, L., Vance, H., Rek, J., Prahl, M., Jagannathan, P., Katureebe, A., Arinaitwe, E., Kamya, M.R., Dorsey, G., and Feeney, M.E. (2017). Both inflammatory and regulatory cytokine responses to malaria are blunted with increasing age in highly exposed children. Malar. J. 16, 499. https://doi.org/10.1186/s12936-017-2148-6.

17. Mbengue, B., Niang, B., Niang, M.S., Varela, M.L., Fall, B., Fall, M.M., Diallo, R.N., Diatta, B., Gowda, D.C., Dieye, A., et al. (2016). Inflammatory cytokine and humoral responses to Plasmodium falciparum glycosylphosphatidylinositol correlates with malaria immunity and pathogenesis. Immun. Inflamm. Dis. 4, 24–34. https://doi.org/10.1002/IID3.89.

18. Tran, T.M., Samal, B., Kirkness, E., and Crompton, P.D. (2012). Systems immunology of human malaria. Trends Parasitol. 28, 248–257. https://doi.org/10.1016/j.pt.2012.03.006.

19. Tran, T.M., Jones, M.B., Ongoiba, A., Bijker, E.M., Schats, R., Venepally, P., Skinner, J., Doumbo, S., Quinten, E., Visser, L.G., et al. (2016). Transcriptomic evidence for modulation of host inflammatory responses during febrile Plasmodium falciparum malaria. Sci. Rep. 6, 31291. https://doi.org/10.1038/srep31291.

20. Tran, T.M., Guha, R., Portugal, S., Skinner, J., Ongoiba, A., Bhardwaj, J., Jones, M., Moebius, J., Venepally, P., Doumbo, S., et al. (2019). A Molecular Signature in Blood Reveals a Role for p53 in Regulating Malaria-Induced Inflammation. Immunity 51, 750–765.e10. https://doi.org/10.1016/J.IMMUNI.2019.08.009.

21. Portugal, S., Moebius, J., Skinner, J., Doumbo, S., Doumtabe, D., Kone, Y., Dia, S., Kanakabandi, K., Sturdevant, D.E., Virtaneva, K., et al. (2014). Exposure-Dependent Control of Malaria-Induced Inflammation in Children. PLoS Pathog. 10, e1004079. https://doi.org/10.1371/journal.ppat.1004079.

22. Bachmann, J., Burté, F., Pramana, S., Conte, I., Brown, B.J., Orimadegun, A.E., Ajetunmobi, W.A., Afolabi, N.K., Akinkunmi, F., Omokhodion, S., et al. (2014). Affinity Proteomics Reveals Elevated Muscle Proteins in Plasma of Children with Cerebral Malaria. PLoS Pathog. 10, e1004038. https://doi.org/10.1371/JOURNAL.PPAT.1004038.

23. Reuterswärd, P., Bergström, S., Orikiiriza, J., Lindquist, E., Bergström, S., Andersson Svahn, H., Ayoglu, B., Uhlén, M., Wahlgren, M., Normark, J., et al. (2018). Levels of human proteins in plasma associated with acute paediatric malaria. Malar. J. 17, 426. https://doi.org/10.1186/s12936-018-2576-y.

24. Kumar, V., Ray, S., Aggarwal, S., Biswas, D., Jadhav, M., Yadav, R., Sabnis, S.V., Banerjee, S., Talukdar, A., Kochar, S.K., et al. (2020). Multiplexed quantitative proteomics provides mechanistic cues for malaria severity and complexity. Commun. Biol. 3, 683. https://doi.org/10.1038/S42003-020-01384-4.

25. Lautenbach, M.J., Yman, V., Silva, C.S., Kadri, N., Broumou, I., Chan, S., Angenendt, S., Sondén, K., Plaza, D.F., Färnert, A., et al. (2022). Systems analysis shows a role of cytophilic antibodies in shaping innate tolerance to malaria. Cell Rep. 39, 110709. https://doi.org/10.1016/J.CELREP.2022.110709.

26. Zhong, W., Altay, O., Arif, M., Edfors, F., Doganay, L., Mardinoglu, A., Uhlen, M., and Fagerberg, L. (2021). Next generation plasma proteome profiling of COVID-19 patients with mild to moderate symptoms. EBioMedicine 74, 103723. https://doi.org/10.1016/j.ebiom.2021.103723.

27. Tebani, A., Gummesson, A., Zhong, W., Koistinen, I.S., Lakshmikanth, T., Olsson, L.M., Boulund, F., Neiman, M., Stenlund, H., Hellström, C., et al. (2020). Integration of molecular profiles in a longitudinal wellness profiling cohort. Nat. Commun. 11, 4487. https://doi.org/10.1038/s41467-020-18148-7.

28. Filbin, M.R., Mehta, A., Schneider, A.M., Kays, K.R., Guess, J.R., Gentili, M., Fenyves, B.G., Charland, N.C., Gonye, A.L.K., Gushterova, I., et al. (2021). Longitudinal proteomic analysis of severe COVID-19 reveals survival-associated signatures, tissue-specific cell death, and cell-cell interactions. Cell Rep. Med. 2, 100287. https://doi.org/10.1016/J.XCRM.2021.100287.

29. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M. (2016). KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462. https://doi.org/10.1093/nar/gkv1070.

30. Huang, Z., Xiao, L., Xiao, Y., and Chen, C. (2022). The Modulatory Role of Growth Hormone in Inflammation and Macrophage Activation. Endocrinology 163, bqac088. https://doi.org/10.1210/ENDOCR/BQAC088.

31. Dacheux, M., Chaouch, S., Joy, A., Labat, A., Payré, C., Petit-Paitel, A., Bihl, F., Lagrange, I., Grellier, P., Touqui, L., et al. (2021). Role of human group IIA secreted phospholipase A2 in malaria pathophysiology: Insights from a transgenic mouse model. Biochimie 189, 120–136. https://doi.org/10.1016/J.BIOCHI.2021.06.009.

32. Murakami, M., Sato, H., and Taketomi, Y. (2023). Modulation of immunity by the secreted phospholipase A2 family. Immunol. Rev. 317, 42–70. https://doi.org/10.1111/IMR.13205.

33. Russell, F.A., King, R., Smillie, S.J., Kodji, X., and Brain, S.D. (2014). Calcitonin gene-related peptide: physiology and pathophysiology. Physiol. Rev. 94, 1099–1142. https://doi.org/10.1152/physrev.00034.2013.

34. De Jong, G.M., Slager, J.J., Verbon, A., Van Hellemond, J.J., and Van Genderen, P.J.J. (2016). Systematic review of the role of angiopoietin-1 and angiopoietin-2 in Plasmodium species infections: biomarkers or therapeutic targets? Malar. J. 15, 581. https://doi.org/10.1186/S12936-016-1624-8/FIGURES/2.

35. Ferreira, A., Balla, J., Jeney, V., Balla, G., and Soares, M.P. (2008). A central role for free heme in the pathogenesis of severe malaria: The missing link? J. Mol. Med. (Berl) 86, 1097–1111. https://doi.org/10.1007/S00109-008-0368-5.

36. Nairz, M., Sonnweber, T., Schroll, A., Theurl, I., and Weiss, G. (2012). The pleiotropic effects of erythropoietin in infection and inflammation. Microbes Infect. 14, 238–246. https://doi.org/10.1016/J.MICINF.2011.10.005.
37. Uhlén, M., Karlsson, M.J., Hober, A., Svensson, A.S., Scheffel, J., Kotol, D., Zhong, W., Tebani, A., Strandberg, L., Edfors, F., et al. (2019). The human secretome. Sci. Signal. **12**, eaaz0274. https://doi.org/10.1126/scisig- nal.aaz0274.

38. Uhlén, M., Fagerberg, L., Hallström, B.M., Lindskog, C., Oksvold, P., Mardinoglu, A., Sivertsson, Å., Kampf, C., Sjöstedt, E., Asplund, A., et al. (2015). Proteomics. Tissue-based map of the human proteome. Science **347**, 1260419. https://doi.org/10.1126/science.1260419.

39. Malek, T.R., and Castro, I. (2010). Interleukin-2 Receptor Signaling: At the Interface between Tolerance and Immunity. Immunity **33**, 153–165. https://doi.org/10.1016/j.immuni.2010.08.004.

40. Griesenauer, B., and Paczesny, S. (2017). The ST2/IL-33 axis in immune cells during inflammatory diseases. Front. Immunol. **8**, 475. https://doi.org/10.3389/fimmu.2017.00475.

41. Efremova, M., Vento-Tormo, M., Teichmann, S.A., and Vento-Tormo, R. (2020). CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. **15**, 1484–1506. https://doi.org/10.1038/s41596-020-0292-x.

42. Dooley, N.L., Chabikwa, T.G., Pava, Z., Loughland, J.R., Hamelink, J., Berry, K., Andrew, D., Soon, M.S.F., SheelaNair, A., Piera, K.A., et al. (2023). Single cell transcriptomics shows that malaria promotes unique regulatory responses across multiple immune cell subsets. Nat. Commun. **14**, 7387. https://doi.org/10.1038/s41467-023-43181-7.

43. World Health Organization (WHO) (2015). Guidelines for the Treatment of Malaria, Third Edition (WHO, Regional Office for Africa), pp. 1–316. https://www.afr WHO.int/publications/guidelines-treatment-malaria-third-edition.

44. Lambden, S., Laterre, P.F., Levy, M.M., and Francois, B. (2019). The SOFA score – Development, utility and challenges of accurate assessment in clinical trials. Crit. Care **23**, 374. https://doi.org/10.1186/S13054-019- 2663-7/TABLES/4.

45. Langfelder, P., and Horvath, S. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics **9**, 559. https://doi.org/10.1186/1471-2105-9-559.

46. Gillespie, M., Jassal, B., Stephan, R., Milacic, M., Rothfels, K., Senff-Ribeiro, A., Griss, J., Sevilla, C., Matthews, L., Gong, C., et al. (2022). The reactome pathway knowledgebase 2022. Nucleic Acids Res. **50**, D687–D692. https://doi.org/10.1093/NAR/GKAB1028.

47. Hattori, N., Oda, S., Sadahiro, T., Nakamura, M., Abe, R., Shinozaki, K., Nomura, F., Tomonaga, T., Matsushita, K., Kodera, Y., et al. (2009). YKL-40 identified by proteomic analysis as a biomarker of sepsis. Shock **32**, 393–400. https://doi.org/10.1097/SHK.0b013e31819e2c0c.

48. Stanisic, D.I., Cutts, J., Eriksson, E., Fowkes, F.J.I., Rosanas-Urgell, A., Siba, P., Laman, M., Davis, T.M.E., Manning, L., Mueller, I., et al. (2014). γδ T cells and CD14+ Monocytes Are Predominant Cellular Sources of Cytokines and Chemokines Associated With Severe Malaria. J. Infect. Dis. **210**, 295–305. https://doi.org/10.1093/infdis/jju083.

49. Sun, S.C. (2017). The non-canonical NF-κB pathway in immunity and inflammation. Nat. Rev. Immunol. **17**, 545–558. https://doi.org/10.1038/ nri.2017.52.

50. Minigo, G., Woodberry, T., Piera, K.A., Salwati, E., Tjitra, E., Kenangalem, E., Price, R.N., Engwerda, C.R., Anstey, N.M., and Plebanski, M. (2009). Parasite-Dependent Expansion of TNF Receptor II–Positive Regulatory T Cells with Enhanced Suppressive Activity in Adults with Severe Malaria. PLoS Pathog. **5**, e1000402. https://doi.org/10.1371/JOURNAL.PPAT. 1000402.

51. Walther, M., de Caul, A., Aka, P., Njie, M., Amambua-Ngwa, A., Walther, B., Predazzi, I.M., Cunnington, A., Deininger, S., Takem, E.N., et al. (2012). HMOX1 gene promoter alleles and high HO-1 levels are associated with severe malaria in Gambian children. PLoS Pathog. **8**, e1002579. https://doi.org/10.1371/journal.ppat.1002579.

52. Videm, V., and Albrigtsen, M. (2008). Soluble ICAM-1 and VCAM-1 as markers of endothelial activation. Scand. J. Immunol. **67**, 523–531. https://doi.org/10.1111/j.1365-3083.2008.02029.x.

53. Otterdal, K., Berg, A., Michelsen, A.E., Patel, S., Tellevik, M.G., Haanshuus, C.G., Fevang, B., Aukrust, P., Langeland, N., and Ueland, T. (2018). Soluble markers of neutrophil, T-cell and monocyte activation are associated with disease severity and parasitemia in falciparum malaria. BMC Infect. Dis. **18**, 670. https://doi.org/10.1186/S12879-018- 3593-8.

54. Sundling, C., Yman, V., Mousavian, Z., Angenendt, S., Foroogh, F., von Horn, E., Lautenbach, M.J., Grunewald, J., Färnert, A., and Sondén, K. (2024). Disease-specific plasma protein profiles in patients with fever after traveling to tropical areas. Eur. J. Immunol. **54**, e2350784. https://doi.org/ 10.1002/EJI.202350784.

55. Fraering, J., Salnot, V., Gautier, E.F., Ezinmegnon, S., Argy, N., Peoc'h, K., Manceau, H., Alao, J., Guillonneau, F., Migot-Nabias, F., et al. (2024). Infected erythrocytes and plasma proteomics reveal a specific protein signature of severe malaria. EMBO Mol. Med. **16**, 319–333. https://doi. org/10.1038/s44321-023-00010-0.

56. Sobota, R.S., Stucke, E.M., Coulibaly, D., Lawton, J.G., Cummings, B.E., Sebastian, S., Dara, A., Munro, J.B., Ouattara, A., Kone, A.K., et al. (2025). A shared inflammatory signature across severe malaria syndromes manifested by transcriptomic, proteomic and metabolomic analyses. Nat. Commun. **16**, 4620. https://doi.org/10.1038/s41467-025-59281-5.

57. Anderson, N.L., and Anderson, N.G. (2002). The Human Plasma Proteome: History, Character, and Diagnostic Prospects. Mol. Cell. Proteomics **1**, 845–867. https://doi.org/10.1074/MCP.R200007-MCP200.

58. Bhutani, A., Kaushik, R.M., and Kaushik, R. (2020). A study on multi-organ dysfunction syndrome in malaria using sequential organ failure assessment score. Trop. Parasitol. **10**, 86–94. https://doi.org/10.4103/tp. TP_12_19.

59. Gupta, B.K., Gupta, A., Nehra, H.R., Balotia, H.R., Meena, S.L., and Kumar, S. (2015). Clinical Profile and Prognostic Indicators in Adults Hospitalized with Severe Malaria Caused by Different Plasmodium Species. Infect. Dis. (Auckl) **8**, 45–50. https://doi.org/10.4137/IDRT. S34039.

60. Reuling, I.J., de Jong, G.M., Yap, X.Z., Asghar, M., Walk, J., van de Schans, L.A., Koelewijn, R., Färnert, A., de Mast, Q., van der Ven, A.J., et al. (2018). Liver Injury in Uncomplicated Malaria is an Overlooked Phenomenon: An Observational Study. EBiomedicine **3**, 131–139. https://doi.org/10.1016/J.EBIOM.2018.09.018.

61. Levine, S.J. (2004). Mechanisms of Soluble Cytokine Receptor Generation. J. Immunol. **173**, 5343–5348. https://doi.org/10.4049/jimmunol.173.9.5343.

62. Bailly, C. (2023). Contribution of the TIM-3/Gal-9 immune checkpoint to tropical parasitic diseases. Acta Trop. **238**, 106792. https://doi.org/10. 1016/j.actatropica.2022.106792.

63. Nolte, M.A., Van Olffen, R.W., Van Gisbergen, K.P.J.M., and Van Lier, R.A.W. (2009). Timing and tuning of CD27-CD70 interactions: The impact of signal strength in setting the balance between adaptive responses and immunopathology. Immunol. Rev. **229**, 216–231. https://doi.org/10.1111/j.1600-065X. 2009.00774.x.

64. Denoeud, J., and Moser, M. (2011). Role of CD27/CD70 pathway of activation in immunity and tolerance. J. Leukoc. Biol. **89**, 195–203. https:// doi.org/10.1189/jlb.0610351.

65. Wolf, Y., Anderson, A.C., and Kuchroo, V.K. (2020). TIM3 comes of age as an inhibitory receptor. Nat. Rev. Immunol. **20**, 173–185. https://doi.org/10. 1038/s41577-019-0224-6.

66. Clayton, K.L., Douglas-Vail, M.B., Nur-ur Rahman, A.K.M.N., Medcalf, K.E., Xie, I.Y., Chew, G.M., Tandon, R., Lanteri, M.C., Norris, P.J., Deeks, S.G., et al. (2015). Soluble T Cell Immunoglobulin Mucin Domain 3 Is Shed from CD8+ T Cells by the Sheddase ADAM10, Is Increased in Plasma during Untreated HIV Infection, and Correlates with HIV Disease Progression. J. Virol. **89**, 3723–3736. https://doi.org/10.1128/JVI.00006-15.

67. Dobbs, K.R., Embury, P., Vulule, J., Odada, P.S., Rosa, B.A., Mitreva, M., Kazura, J.W., and Dent, A.E. (2017). Monocyte dysregulation and systemic inflammation during pediatric falciparum malaria. JCI Insight **2**, e95352. https://doi.org/10.1172/JCI.INSIGHT.95352.
68. Guha, R., Mathioudaki, A., Doumbo, S., Doumtabe, D., Skinner, J., Arora, G., Siddiqui, S., Li, S., Kayentao, K., Ongoiba, A., et al. (2021). Plasmodium falciparum malaria drives epigenetic reprogramming of human monocytes toward a regulatory phenotype. PLoS Pathog. 17, e1009430. https://doi.org/10.1371/journal.ppat.1009430.

69. Ioannidis, L.J., Eriksson, E., and Hansen, D.S. (2020). CD14⁺ monocytes are the main leucocytic sources of CXCL10 in response to Plasmodium falciparum. Parasitology 147, 465–470. https://doi.org/10.1017/S0031182019001744.

70. Nideffer, J., Ty, M., Donato, M., John, R., Kajubi, R., Ji, X., Nankya, F., Musinguzi, K., Press, K.D., Yang, N., et al. (2024). Clinical immunity to malaria involves epigenetic reprogramming of innate immune cells. PNAS Nexus 3, pgae325. https://doi.org/10.1093/PNASNEXUS/PGAE325.

71. Meisner, M. (2010). Procalcitonin - Biochemie und klinische Diagnostik (UNI-Med Verlag AG).

72. Wacker, C., Prkno, A., Brunkhorst, F.M., and Schlattmann, P. (2013). Procalcitonin as a diagnostic marker for sepsis: A systematic review and meta-analysis. Lancet Infect. Dis. 13, 426–435. https://doi.org/10.1016/S1473-3099(12)70323-7.

73. Chiwakata, C.B., Manegold, C., Bönicke, L., Waase, I., Jülch, C., and Dietrich, M. (2001). Procalcitonin as a Parameter of Disease Severity and Risk of Mortality in Patients with Plasmodium falciparum Malaria. J. Infect. Dis. 183, 1161–1164. https://doi.org/10.1086/319283.

74. Hesselink, D.A., Burgerhart, J.S., Bosmans-Timmerarends, H., Petit, P., and Van Genderen, P.J.J. (2009). Procalcitonin as a biomarker for severe Plasmodium falciparum disease: A critical appraisal of a semi-quantitative point-of-care test in a cohort of travellers with imported malaria. Malar. J. 8, 206. https://doi.org/10.1186/1475-2875-8-206/TABLES/3.

75. Hamilton, F., Mitchell, R., Cunnington, A., Ghazal, P., and Timpson, N.J. (2022). HMOX1 STR polymorphism and malaria: an analysis of a large clinical dataset. Malar. J. 21, 342. https://doi.org/10.1186/s12936-022-04352-x.

76. Seixas, E., Gozzelino, R., Chora, A., Ferreira, A., Silva, G., Larsen, R., Rebelo, S., Penido, C., Smith, N.R., Coutinho, A., et al. (2009). Heme oxygenase-1 affords protection against noncerebral forms of severe malaria. Proc. Natl. Acad. Sci. USA 106, 15837–15842. https://doi.org/10.1073/pnas.0903419106.

77. Jakobsen, P.H., Morris-Jones, S., Theander, T.G., Hviid, L., Hansen, M.B., Bendtzen, K., Ridley, R.G., and Greenwood, B.M. (1994). Increased plasma levels of soluble IL-2R are associated with severe Plasmodium falciparum malaria. Clin. Exp. Immunol. 96, 98–103. https://doi.org/10.1111/J.1365-2249.1994.TB06237.X.

78. Deloron, P., Lepers, J.P., and Coulanges, P. (1989). Evolution of the Levels of Soluble Interleukin-2 Receptors during Plasmodium falciparum and P. vivax Malaria. J. Clin. Microbiol. 27, 1887–1889. https://doi.org/10.1128/JCM.27.8.1887-1889.1989.

79. Yman, V., White, M.T., Asghar, M., Sundling, C., Sondén, K., Draper, S.J., Osier, F.H.A.A., and Färnert, A. (2019). Antibody responses to merozoite antigens after natural Plasmodium falciparum infection: Kinetics and longevity in absence of re-exposure. BMC Med. 17, 22. https://doi.org/10.1186/s12916-019-1255-3.

80. Yman, V., Tuju, J., White, M.T., Kamuyu, G., Mwai, K., Kibinge, N., Asghar, M., Sundling, C., Sondén, K., Murungi, L., et al. (2022). Distinct kinetics of antibodies to 111 Plasmodium falciparum proteins identifies markers of recent malaria exposure. Nat. Commun. 13, 331. https://doi.org/10.1038/s41467-021-27863-8.

81. Sundling, C., Rönnberg, C., Yman, V., Asghar, M., Jahmatz, P., Lakshmikanth, T., Chen, Y., Mikes, J., Forsell, M.N., Sondén, K., et al. (2019). B cell profiling in malaria reveals expansion and remodelling of CD11c+ B cell subsets. JCI Insight 5, e126492. https://doi.org/10.1172/jci.insight.126492.

82. Sondén, K., Satarvandi, D., Hildenwall, H., Falck-Jones, S., Yman, V., Johansson, N., Wyss, K., Folkesson, E., Asgeirsson, H., Nordling, I., et al. (2025). Fever in travelers returning from tropical and subtropical areas: a hospital-based study of factors affecting investigations and diag-

noses in children and adults. Travel Med. Infect. Dis. 65, 102861. https://doi.org/10.1016/j.tmaid.2025.102861.

83. Valik, J.K., Mellhammar, L., Sundén-Cullberg, J., Ward, L., Unge, C., Dalianis, H., Henriksson, A., Strålin, K., Linder, A., and Naumlér, P. (2022). Peripheral Oxygen Saturation Facilitates Assessment of Respiratory Dysfunction in the Sequential Organ Failure Assessment Score with Implications for the Sepsis-3 Criteria. Crit. Care Med. 50, e272–e283. https://doi.org/10.1097/CCM.00000000000005318.

84. Wik, L., Nordberg, N., Broberg, J., Björkesten, J., Assarsson, E., Henriksson, S., Grundberg, I., Pettersson, E., Westerberg, C., Liljeroth, E., et al. (2021). Proximity Extension Assay in Combination with Next-Generation Sequencing for High-throughput Proteome-wide Analysis. Mol. Cell. Proteomics 20, 100168. https://doi.org/10.1016/j.MCPRO.2021.100168.

85. Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W.M., Zheng, S., Butler, A., Lee, M.J., Wilk, A.J., Darby, C., Zager, M., et al. (2021). Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29. https://doi.org/10.1016/J CELL.2021.04.048.

86. Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., et al. (2019). Welcome to the Tidyverse. J. Open Source Softw. 4, 1686. https://doi.org/10.21105/joss.01686.

87. Gu, Z., Eils, R., and Schlesner, M. (2016). Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849. https://doi.org/10.1093/bioinformatics/btw313.

88. Gu, Z., Gu, L., Eils, R., Schlesner, M., and Brors, B. (2014). Genome analysis cirlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812. https://doi.org/10.1093/bioinformatics/btu393.

89. Chang, W., Cheng, J., Allaire, J.J., Sievert, C., Schloerke, B., Xie, Y., Allen, J., McPherson, J., Dipert, A., and Borges, B. (2023). shiny: Web Application Framework for R. https://cran.r-project.org/web/packages/shiny/index.html.

90. Kuznetsova, A., Brockhoff, P.B., and Christensen, R.H.B. (2017). ImerTest Package: Tests in Linear Mixed Effects Models. J. Stat. Softw. 82, 1–26. https://doi.org/10.18637/jss.v082.i13.

91. Lenth, R.V. (2024). emmeans: Estimated Marginal Means, aka Least-Squares Means. https://cran.r-project.org/web/packages/emmeans/emmeans.pdf.

92. Benjamini, Y., and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

93. Wu, T., Hu, E., Xu, S., Chen, M., Guo, P., Dai, Z., Feng, T., Zhou, L., Tang, W., Zhan, L., et al. (2021). clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb) 2, 100141. https://doi.org/10.1016/j.xinn.2021.100141.

94. Mair, F., Erickson, J.R., Voillet, V., Simoni, Y., Bi, T., Tyznik, A.J., Martin, J., Gottardo, R., Newell, E.W., and Prlic, M. (2020). A Targeted Multi-omic Analysis Approach Measures Protein Expression and Low-Abundance Transcripts on the Single-Cell Level. Cell Rep. 31, 107499. https://doi.org/10.1016/j.celrep.2020.03.063.

95. Troulè, K., Petryszak, R., Prete, M., Cranley, J., Harasty, A., Tuong, Z.K., Teichmann, S.A., Garcia-Alonso, L., and Vento-Tormo, R. (2025). CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data. Published online March 25, 2025. Nat. Protoc.

96. Lê Cao, K.A., Boitard, S., and Besse, P. (2011). Sparse PLS discriminant analysis: Biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics 12, 253. https://doi.org/10.1186/1471-2105-12-253/TABLES/3.

97. Hänzelmann, S., Castelo, R., and Guinney, J. (2013). GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14, 7. https://doi.org/10.1186/1471-2105-14-7/FIGURES/7.

98. Lee, E., Chuang, H.-Y., Kim, J.-W., Ideker, T., and Lee, D. (2008). Inferring Pathway Activity toward Precise Disease Classification. PLoS Comput. Biol. 4, e1000217. https://doi.org/10.1371/journal.pcbi.1000217.
## STAR★METHODS

### KEY RESOURCES TABLE

<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td colspan="3"><strong>BD Rhapsody antibodies</strong></td></tr><tr><td>BD AbSeq Human CD11c</td><td>3.9</td><td>RRID:AB_2876233</td></tr><tr><td>BD AbSeq Human CD19</td><td>HIB19</td><td>RRID:AB_2876128</td></tr><tr><td>BD AbSeq Human CD20</td><td>2H7</td><td>RRID:AB_2875907</td></tr><tr><td>BD AbSeq Human CD27</td><td>M-T271</td><td>RRID:AB_2875909</td></tr><tr><td>BD AbSeq Human FcRL5</td><td>509F6</td><td>Cat # 460120 (Quote)</td></tr><tr><td>BD AbSeq Human IgD</td><td>IA6-2</td><td>RRID:AB_2875917</td></tr><tr><td>BD AbSeq Human IgG</td><td>G18-145</td><td>RRID:AB_2875918</td></tr><tr><td>BD AbSeq Human IgM</td><td>G20-127</td><td>RRID:AB_2876154</td></tr><tr><td>BD AbSeq Human CD21/CR2</td><td>B-ly4</td><td>RRID:AB_2875939</td></tr><tr><td>BD AbSeq Human BAFF-R</td><td>11C1</td><td>RRID:AB_2876161</td></tr><tr><td>BD AbSeq Human CD3</td><td>SK7</td><td>RRID:AB_2875891</td></tr><tr><td>BD AbSeq Human CD4</td><td>SK3</td><td>RRID:AB_2875892</td></tr><tr><td>BD AbSeq Human CD8</td><td>RPA-T8</td><td>RRID:AB_2875894</td></tr><tr><td>BD AbSeq Human CD45RA</td><td>HI100</td><td>RRID:AB_2875902</td></tr><tr><td>BD AbSeq Human CD38</td><td>HIT2</td><td>RRID:AB_2875904</td></tr><tr><td>BD AbSeq Human HLA-DR</td><td>G46-6</td><td>RRID:AB_2875901</td></tr><tr><td>BD AbSeq Human CCR7</td><td>3D12</td><td>RRID:AB_2875905</td></tr><tr><td>BD AbSeq Human CD127</td><td>HIL-7R-M21</td><td>RRID:AB_2875903</td></tr><tr><td>BD AbSeq Human CD25</td><td>2A3</td><td>RRID:AB_2875900</td></tr><tr><td>BD AbSeq Human CD56</td><td>NCAM16.2</td><td>RRID:AB_2875898</td></tr><tr><td>BD AbSeq Human CD16</td><td>3G8</td><td>RRID:AB_2875897</td></tr><tr><td>BD AbSeq Human TCR γδ</td><td>11F2</td><td>RRID:AB_2876235</td></tr><tr><td>BD AbSeq Human Vd2 TCR</td><td>b6</td><td>RRID:AB_2876174</td></tr><tr><td>BD AbSeq Human CD14</td><td>MφP9</td><td>RRID:AB_2875896</td></tr><tr><td>BD AbSeq Human CD123</td><td>7G3</td><td>RRID:AB_2875911</td></tr><tr><td>BD AbSeq Human CD33</td><td>HIM3-4</td><td>RRID:AB_2876191</td></tr><tr><td>BD AbSeq Human CD39</td><td>TU66</td><td>RRID:AB_2875964</td></tr><tr><td>BD AbSeq Human CD80</td><td>L307.4</td><td>RRID:AB_2875927</td></tr><tr><td>BD AbSeq Human CXCR5</td><td>RF8B2</td><td>RRID:AB_2875933</td></tr><tr><td colspan="3"><strong>Biological samples</strong></td></tr><tr><td>PBMCs and plasma from patients with P. falciparum malaria and follow up samples</td><td>Karolinska University Hospital</td><td>N/A</td></tr><tr><td>Plasma from patients with febrile infections after return from travel</td><td>Karolinska University Hospital</td><td>N/A</td></tr><tr><td colspan="3"><strong>Critical commercial assays</strong></td></tr><tr><td>Explore 1536 panel</td><td>Olink</td><td>N/A</td></tr><tr><td>BD single cell multiplexing kit</td><td>BD</td><td>RRID:AB_2870299</td></tr><tr><td>BD Rhapsody cartridge reagent kit</td><td>BD</td><td>Cat # 633731</td></tr><tr><td>BD Rhapsody cDNA kit</td><td>BD</td><td>Cat # 633773</td></tr><tr><td>BD Rhapsody targeted mRNA and AbSeq amplification kit</td><td>BD</td><td>Cat # 633774</td></tr><tr><td>BD Rhapsody Human Immune Response Panel</td><td>BD</td><td>RRID:AB_2870293</td></tr></tbody></table>

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<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td colspan="3"><strong>Deposited data</strong></td></tr><tr><td>Olink Explore 1536 data malaria</td><td>This study</td><td>N/A</td></tr><tr><td>Olink Explore 1536 data febrile infections</td><td>This study</td><td>N/A</td></tr><tr><td>Consensus transcript expression level data of 50 tissues (HPA &amp; GTEx)</td><td>HPA/Uhlén et al.<sup>38</sup></td><td>https://v23.proteinatlas.org/download/rna_tissue_gtex.tsv.zip</td></tr><tr><td>scRNA-sequencing data malaria</td><td>Dooley et al.<sup>42</sup></td><td>https://doi.org/10.5281/zenodo.6973241</td></tr><tr><td>Olink Explore 1536 data covid19</td><td>Filbin et al.<sup>28</sup></td><td>https://data.mendeley.com/datasets/nf853r8xsj/1</td></tr><tr><td colspan="3"><strong>Software and algorithms</strong></td></tr><tr><td>R version 4.3.2</td><td>The R Foundation</td><td>https://www.r-project.org</td></tr><tr><td>Seven Bridges Platform</td><td>https://www.sevenbridges.com/platform/</td><td>N/A</td></tr><tr><td>Custom analysis scripts</td><td>This study</td><td>https://github.com/SundlingLab/Explore1536_Malaria</td></tr><tr><td>Shiny App</td><td>This study</td><td>https://malaria-proteomics-resource.serve.scilifelab.se</td></tr><tr><td colspan="3"><strong>Other</strong></td></tr><tr><td>Human Protein Atlas – The Blood Resource</td><td></td><td>https://www.proteinatlas.org/humanproteome/blood</td></tr></tbody></table>

## EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

### Malaria immunology cohort

Adults diagnosed with acute *P. falciparum* malaria at Karolinska University Hospital in Stockholm, Sweden between 2011 and 2021 were included in a prospective cohort study and followed with repeated sampling for one year. The cohort has been previously described in studies focusing on antibody and B cell responses, and tolerance.⁷⁹,⁸⁰ Here, study participants sampled during the acute episode of malaria (Acute, n=72) were included and a subset also approximately 10 days after treatment (day 10, n=27), and 12 months later (month 12, n=33) (see Figure 1A). On each sampling occasion, venous blood was collected in EDTA for plasma isolation, peripheral blood mononuclear cells (PBMC) preparation, parasite diagnostics, and blood chemistry. Peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll-Paque density gradient separation, resuspended in 90% fetal calf serum supplemented with 10% DMSO, and stored at -150°C, as described previously.⁸¹

### Tropical fever cohort

A selection of 97 samples from an additional cohort of approximately 500 patients with self-reported fever after travel to tropical or subtropical areas was used for validation purposes. The patients were enrolled at the Emergency Unit at Karolinska University Hospital in Stockholm, Sweden, and venous blood samples were collected in K2EDTA vacutainer tubes using the same procedures as for the malaria cohort. Parts of this cohort have been recently described in more detail.⁵⁴,⁸² Here, we included adults diagnosed with infections caused by different microbiologically confirmed etiologies: Dengue virus (n=19), *E. coli* pyelonephritis (n=9), Influenza A (n=17), Influenza B (n=18), *Mycoplasma* pneumonia (n=5), and *Campylobacter* (n=17), *Salmonella* (n=12) and *Shigella* gastroenteritis (n=3).

### Ethical statement

Both prospective studies were conducted at the Karolinska University Hospital in Stockholm, Sweden, with ethical approval from the Ethical Review Board in Stockholm and the Swedish Ethical Review Authority (Malaria immunology cohort: Dnr 2006/893-31/4, 2013/550-32, 2015/2200-32, 2016/1940/32, 2018/2354-32, 2019-03436, 2020-00859, 2020-04147, 2024-00374-02; Tropical fever cohort: Dnr 2016/2512-31/2, 2018/2302-32, 2019-06548, 2021-04087, 2022-01050-02, 2024-01535-02). All participants gave written informed consent.

### Clinical variables

Demographic and clinical data such as vital signs and blood chemistry, were extracted from the electronic medical records. All malaria cases were diagnosed by conventional microscopy at the Unit of Parasitology at the Department of Clinical Microbiology at Karolinska University Hospital, which also assessed parasitemia (% infected erythrocytes). Additional information, such as health status, travel history, previous traveling, and malaria exposure, was assessed through questionnaires when enrolling the patients in the study.
## Assessment of severe malaria

Severe malaria was defined according to the World Health Organization (WHO) criteria from 2015⁴³ and based on data on clinical signs and chemistry collected from electronic medical records.

## SOFA score calculation

Sequential Organ Failure Assessment (SOFA) score was calculated according to recent definitions⁴⁴ with a minor modification. In short, the SOFA score is used to evaluate the type and extent of organ failure in severely ill patients. It is the sum of six sub-SOFA scores (0-24), assessing the function central nervous system (SOFA_cns), cardiovascular system (SOFAabl), coagulation (SOFA_coag), liver (SOFA_liver), renal function (SOFA_renal) and the respiratory system (SOFA resp). The respiratory SOFA was calculated using saturation values instead of PaO₂/FiO₂, a modification previously suggested in the context of sepsis.⁸³

## METHOD DETAILS

### Proteomics profiling and data pre-processing

The samples were analyzed using the Olink Explore 1536 platform (Olink Proteomics, Sweden) which uses a Proximity Extension Assay (PEA) technology with an NGS readout⁸⁴ at the SciLifeLab Affinity Proteomics infrastructure unit in Uppsala. Three proteins (CXCL8, IL6, TNF) are measured on each panel to ensure the high quality of the data and assess correlation. The protein concentrations are given as Olink's relative protein quantification unit on log₂ scale: Normalized Protein eXpression (NPX).

The data was generated in collaboration with the Human Protein Atlas as part of the Blood resource. Within that effort, 10,000 samples from over 6,000 patients with different diseases were run in 4 batches, and the data was bridge normalized over all runs.

The data was further pre-processed by removing proteins with NPX below the LOD in more than 70% of the samples (n<sub>Proteins</sub>=36), resulting in 1427 proteins in the plasma samples. For the remaining proteins with NPX values below the LOD in some samples, the reported NPX values were kept unchanged. For the three proteins that were measured on several panels (CXCL8, IL6, TNF), median NPX values were calculated. We removed two samples from one individual (Acute, D10) due to clear separation from the majority of patients in a PCA using all proteins, indicating outlier behavior.

### Targeted single-cell-multi-omics profiling and data pre-processing (BD Rhapsody's targeted scRNA seq + Ab)

PBMCs from four of the study participants in the malaria cohort (two primary infected, two previously exposed) at three time points (Acute, D10, M12) were labeled using BD Single-Cell Multiplexing Kit and BD AbSeq Ab-Oligos reagents strictly following the manufacturer's protocol (BD Biosciences). Briefly, cells were labeled with sample tags, pooled per patient, and incubated with pooled 29 AbSeq Ab-Oligos. To prevent batch effects, longitudinal samples from the same patient were pooled together after sample tagging. Each sample pool was then washed twice, and FACS sorted for CD45⁺ cells and approximately 20,000 cells resuspended in 620µL cold BD Sample Buffer for each cartridge (one patient, three sample time points for each cartridge). Cartridge and library preparation was performed according to the manufacturer's protocol. Final libraries were pooled and diluted to 8.6nM in 80µL for a 60-8-0-42 read setup on a NovaSeq6000 sequencer (Illumina) with 20% PhiX. FASTQ files were processed using the Targeted Analysis Pipeline v1.1 (BD Biosciences) on Seven Bridges (https://sevenbridges.com/) with the default setting, except for the cell calling step that was based on AbSeq data instead of RNAseq data.

The resulting expression matrices containing DBEC-adjusted molecule counts after sample tag assignment were used for downstream analysis in R (v.4.2.1) and the r package Seurat (v.4.0).⁸⁵

## QUANTIFICATION AND STATISTICAL ANALYSIS

All data wrangling and analysis were performed in R (v.4.3.2) using packages from the tidyverse (v.2.0.0).⁸⁶ If not stated otherwise, data was visualized using r packages ggplot2 (v.3.4.2), ComplexHeatmap (v.2.16.0), Circlerize (v.0.4.15), ggprah (v.2.1.0) and tidycgraph (v.1.2.3).⁸⁷,⁸⁸ For the heatmap visualization, data was rescaled to a 0-1 scale and hierarchical clustering was performed using the "ward.D2" method. The figures were assembled using patchwork (v.1.1.2). The interactive plasma protein data exploration resource was written using the r package Shiny (v.1.7.4.1).⁸⁹

## Differential protein abundance analysis

To identify differentially abundant proteins (DAPs), a linear mixed-effects model with a random intercept was used to assess changes in normalized protein levels (NPX) over time while accounting for prior malaria exposure.⁹⁰ For each protein, we compared two models: (i) a model with fixed effects for time (acute vs. 12 months) and exposure status (primary infected vs. previously exposed) and (ii) a model additionally including the time-by-exposure interaction. Model selection was based on the Bayesian Information Criterion (BIC), with a difference of >6 considered substantial evidence in favor of the more complex model. All proteins, except for 14, favored the simple model, reinforcing our decision to calculate DAPs using this model over the complex one for the main analysis. Linear combinations of coefficients were used to estimate the difference in marginal mean protein levels between the acute and 12-month time points within each exposure group.⁹¹ P-values were adjusted for multiple testing using the Benjamini-Hochberg (BH) procedure,⁹² with a significance threshold of 0.01. Enrichment analysis of DAPs (log₂FC>1) was performed using the clusterProfiler package (v. 3.18.1).⁹³ The enrichKEGG() function in clusterProfiler was used to perform over-representation analysis against the
database of KEGG pathways,²⁹ a background universe of all measured proteins (after QC, n=1,427) and subsequent *p*-value adjustment using BH and using adjusted *p*-value < 0.01 as threshold for significance.

## Protein annotation

Proteins were annotated using the Human Protein Atlas, version 24.0, retrieved from https://proteinatlas.org. DAPs in acute malaria were mapped based on “Secretome location” and “Secretome function” annotation, and for non-secreted proteins, tissue specificity was used based on “RNA Tissue Specificity”.

## Potential tissue leakage

To find potential tissue leakage, we utilized a consensus transcript expression level data set, summarizing expression per gene in 50 tissues based on transcriptomics data from HPA and GTEx.³⁸ The data is given in normalized expression ("nTPM") and is based on The Human Protein Atlas version 23.0 and Ensembl version 109. After retrieving the data set from https://v23.proteinatlas.org/download/rna_tissue_gtex.tsv.zip, we filtered for genes, corresponding to DAPs (log₂FC>0), and HPA gene metadata information for not being secreted (Secretome location), not detected in immune cells (RNA Blood Cell Specificity) and with high tissue specificity/enrichment (RNA Tissue Specificity).

## Downstream single-cell multi-omics analysis

DBEC-adjusted molecule counts matrices were parsed into R and converted into Seurat objects using Seurat v.4,⁸⁵ merged and split for each modality -RNA expression and surface antibody staining (Ab), similar to described previously.⁹⁴ Doublets and undetermined cells, determined by the BD SevenBridges pipeline, were removed. Seurat objects were created, including features that were detected in at least 3 cells and cells with at least 10 features of the RNA assay. The result of this QC step was 374 genes across 73,121 cells. The total number of cells per sample is shown in Figure S6A. For initial cell type identification, we removed the effect of sample time point and a study subject (Figures S6B and S6C) using CCA (horizontal data integration, 12-sample integration) on a per sample split for each modality. On the RNA assay, a natural log normalization was performed with a scale factor of 10,000 while a centered log-ratio normalization was performed on the Ab assay. For the RNA modality 30 PCs were used, while the Ab modality retained 18 PCs.

To make the best use of the multi-modality of the data, weighted nearest neighbor (WNN) integration (vertical integration, 2 layer - RNA - ADT - integration) was performed retaining 30 PCs for RNA and 18 PCs for Ab modality. The integrated WNN graph was subsequently clustered using Louvain clustering (resolution 0.8) into 32 clusters. Cell types were manually assigned into low-level annotation (L1 - 8 cell subsets) and high-level annotation (L2 - 27 cell subsets) based on RNA and protein markers (Figure S3).

## CellPhoneDB

Ligand-Receptor interactions were mapped using the curated database CellPhoneDB, version 5.0.0, retrieved from https://cellphonedb.org.⁹⁵ The protein information file ("v5_protein_input.csv ") was used to determine receptors by filtering for receptor and transmembrane proteins, while the interaction information file ("v5_intersection_input.csv") was used to map the ligand-receptor interactions. All mapping was done using UniprotIDs. In case the receptor part consisted of more than one protein, the information was split to generate interaction pairs for further mapping and visualization.

## Clinical chemistry - Protein abundance correlation

Clinical chemistry variables, routinely used to assess malaria patients were retrieved from the medical records and correlated to all highly abundant DAPs during acute malaria (absolute log2FC >1, n protein=264). Spearman correlation with subsequent correction for multiple testing using FDR was applied and only correlations with adjusted *p*-value < 0.05, more than 37 observations, and absolute Spearman rho > 0.45. Absolute association was visualized in a circosplot using circlerize.⁸⁸

## Proteomic profile-based patient stratification

Patients were stratified based on their relative protein profiles, using the actual change of the 264 proteins (absolute log₂FC>1) in abundance in acute relative to the healthy sample time point by using delta NPX values (ΔNPX = NPX<sub>Acute</sub> - median NPX<sub>M12</sub>). The ΔNPX data from 72 individuals was subjected to dimensionality reduction using principal component analysis, followed by k-means clustering (k=3) of the first six components capturing the most variance (66% of total variance), resulting in 3 patient clusters (Figures 4A and S8D-S8F). To assess the overall disease severity of the three patient clusters, we then compared their overall SOFA scores.

## Weighted Correlation Network Analysis

Weighted (gene) correlation network analysis (WGCNA)⁴⁵ was performed on all acute malaria samples (n = 72) using only the 703 DAPs to summarize proteins with similar abundance profiles and to identify their association to clinical traits. Protein modules were defined using the WGCNA (v.1.6). Hyperparameters were estimated as recommended by the authors of the original article.⁴⁵ The minimum module size was set to 6 and the co-correlation algorithm defined modules of proteins. Association to clinical traits (SOFA score as proxy for disease severity) and protein module eigengenes were determined by Pearson correlation using the eigen-gene of each module. *P*-values were corrected for multiple testing using and FDR-adjusted *p*-values ≤ 0.05 were considered
significant. The turquoise protein module (n=297 proteins) was subsequently reduced by the module membership score (>0.6) and a p-value <0.05. The reduced turquoise module, associated with disease severity, contained 158 proteins with high module membership. We performed pathway enrichment using the online tool ReactomeDB, an over-representation analysis (hypergeometric distribution test) to determine if a given list (UniprotIDs) of proteins is overrepresented in a certain Reactome pathway.⁴⁶

## Sparse Partial least square discriminant analysis

Sparse partial least square discriminant analysis (sPLSDA), a supervised machine learning method based on multivariate dimensionality-reduction, which identifies proteins that maximize the separation of the groups in multidimensional space while simultaneously minimizing the within-group variance. sPLSDA from mixOmics⁹⁶ was used to identify a protein signature separating severe, moderate, and mild diseased patients, as well as healthy control samples. Only proteins associated with severity (restricted turquoise-WGCNA-module) were included. The model parameters were tuned using the function “tune.spllda” with ncomp=3, centroid distance (centroids.dist), a 5-fold validation (Mfold), 10 repeats (nrepeat) and a maximum number of features to keep of 10 (test.keepX). Parameter tuning suggested to keep only two components.

## Single sample enrichment score

Single sample enrichment was performed using the package GSVA.⁹⁷ The “zscore” method was used, because it first standardizes the protein abundance matrix into z-scores over the samples, before combining them as their sum divided by the square root of the size of the gene set.⁹⁸