https://orcid.org/0000-0001-9526-9309
Figures
Abstract
Malaria caused by Plasmodium falciparum (Pf) compromise innate immunity, yet the underlying mechanisms remain elusive. The immune dysregulation caused by the parasite may lead to bacterial superinfections and increase mortality. We reveal that Pf exploits extracellular vesicles (EVs) secreted by infected red blood cells (iRBC-EVs) to deliver host-derived miR451a to human neutrophils, impairing their antimicrobial defenses. Neutrophil phagocytosis of iRBC-EVs suppresses reactive oxygen species (ROS) production and compromised microbicidal activity against Salmonella typhimurium. Microfluidic assays show that miR451a transfer significantly disrupts neutrophil chemotaxis and swarming upon microbial challenge. Transcriptomic profiling indicates that EVs and miR451a reprogram neutrophil gene expression, notably upregulating ferroptosis-related genes, suggesting a role in further impairing immune responses. We have uncovered a novel mechanism of iRBC-EVs-induced neutrophil immune suppression and provide insights into increased susceptibility to bacterial superinfections in malaria. These findings have implications for therapeutic strategies aimed at mitigating bacterial superinfections and sepsis in malaria-endemic regions.
Author summary
Malaria caused by Plasmodium falciparum not only leads to severe disease on its own but also increases the risk of dangerous secondary infections with bacteria and fungi, particularly in children. The biological reasons for this heightened vulnerability have remained poorly understood. In this study, we uncover a mechanism by which the malaria parasite suppresses a key arm of the human immune system: neutrophils, the body’s first responders to infection. We show that red blood cells infected with P. falciparum release small membrane-bound particles, called extracellular vesicles, that are taken up by neutrophils. These vesicles deliver a host-derived regulatory RNA molecule, miR-451a, into neutrophils, profoundly impairing their ability to migrate, coordinate collective responses, produce antimicrobial molecules, and kill invading microbes. As a result, neutrophils become less effective at controlling bacterial and fungal pathogens. We further demonstrate that this process reprograms neutrophil gene expression, including pathways linked to oxidative stress and cell death. Our findings reveal how malaria parasites exploit host molecules to weaken immune defenses, providing a mechanistic explanation for the increased susceptibility to secondary infections during malaria and highlighting extracellular vesicles as potential therapeutic targets.
Citation: Babatunde KA, Ngara M, Martinez Murillo P, Subramanian B, Hopke A, Lucchesi S, et al. (2026) Plasmodium falciparum subverts neutrophil function via host miR-451a loaded extracellular vesicles driving bacterial and fungal superinfection susceptibility. PLoS Pathog 22(9): e1014533. https://doi.org/10.1371/journal.ppat.1014533
Editor: Ron Dzikowski, Hebrew University, ISRAEL
Received: January 26, 2026; Accepted: August 10, 2026; Published: September 1, 2026
Copyright: © 2026 Babatunde et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The RNA-seq data generated in this study have been deposited in the NCBI Gene Expression Omnibus (GEO) https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE296044.
Funding: This work is supported by the Swiss National Science Foundation (grant#31003A_182729 and grant# CRSK-3_190848 to PYM), the Novartis Foundation for Medical-Biological Research (to P.-Y.M.), the Kurt and Senta Herrmann Foundation, the Gottfried and Julia Bangerter-Rhyner-Stiftung (to PYM) and the Research Pool of the University of Fribourg. K.A.B. was supported by the Swiss Excellence Scholarships for Foreign Scholars (grant # 2016.0934), Swiss National Science Foundation mobility (grant # P1FRP3_181378 and P500PB_203002 to K.A.B) and Jubiläumsstiftung von Swiss Life (grant # 01-1242, 01-1309 and 01-1350 to K.A.B). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Malaria remains a significant global health challenge, with nearly half of the world’s population at risk of infection [1,2]. In 2022, there were an estimated 249 million cases of malaria, leading to 608,000 deaths worldwide. Alarmingly, the majority of these deaths was in children younger than the age of 5 [3]. Beyond causing direct mortality, malaria indirectly contributes to deaths by compromising the host immune response and heightening vulnerability to secondary infections from bacteria or fungi. Indeed, infection with Plasmodium falciparum, the most virulent malaria parasite species, has been linked to an elevated risk of invasive bacterial infections, which are associated with high mortality rates among children suffering from severe malaria in sub-Saharan Africa [4]. While the correlation between malaria and fungal infections is poorly documented, there is an increasing number of clinical reports on fungi associated malaria infection [5–9]. The underlying mechanism for the increased risk of co-infection is not fully understood.
Neutrophils are important in pathogen defense through processes such as inflammation, NETs formation, ROS production and swarming behavior around invading pathogens [10]. Neutrophil swarming is a coordinated, multistep response in which neutrophils migrate toward a focal stimulus, recruit additional cells and form an expanding cellular aggregate [11]. However, malaria infection has been linked to defective neutrophil responses. For instance, malaria patients often exhibit significantly reduced neutrophil counts [12], and neutrophils in these patients have been reported to exhibit reduced oxidative burst, reduced pathogen phagocytosis [13,14] and impaired chemotaxis [15]. This may partly explain the increased susceptibility to bacterial superinfection observed during malaria. While the exact cause of neutrophil impairment during malaria is ill defined, several hypotheses have been formulated such as the increase of free heme in circulating blood [13] and release of digestive vacuoles from the iRBCs [16,17].
During blood stage, the parasite releases membranous material in the form of extracellular vesicles (EVs), which are small vesicles ranging from 30 to 150 nm (exosomes) and 150 nm to 1–2 μm (microvesicles) in diameter. In severe malaria cases, the number of EVs derived from malaria-infected red blood cells (iRBCs) in patient serum is significantly elevated [18]. EVs are known to shuttle biological materials such as cytosolic proteins, lipids, DNA and RNA. Beside their role in immune regulation, EVs have been reported to aid cell-to-cell communication between P. falciparum parasites to synchronize the transmission stage [19] and between P. falciparum and host [20]. Importantly, Cunnington and colleagues have provided compelling clinical evidence that neutrophil dysfunction during malaria directly contributes to susceptibility to invasive bacterial infections. In a landmark study in Gambian children with severe malaria, they demonstrated that impaired neutrophil oxidative burst and phagocytic capacity were associated with an elevated risk of non-typhoidal Salmonella bacteremia and showed that heme suppresses granulocyte mobilization and oxidative burst via heme oxygenase-1 induction [13]. Their work established the clinical relevance of neutrophil impairment during malaria and highlighted the need to identify the upstream molecular mechanisms responsible. Here, we propose that iRBC-derived EVs and their miR451a cargo represent one such mechanism, operating in parallel with or upstream of heme-mediated effects. Our data show that iRBC-EVs transfer the host miRNA, miR451a to neutrophils resulting in several functional impairments. These results provide new insights into the molecular basis of neutrophils dysfunction in malaria and highlight the role of EVs as key mediator of host-parasite interactions.
Results
Neutrophils internalize iRBC derived EVs
iRBC-EVs are a heterogenous group of cell-derived membranous structures that have a size ranging from 50 nm to 1 μm in diameter [21]. We isolated EVs from in vitro cultured P. falciparum parasites. In line with previous studies, transmission electron microscopy demonstrated round nano-sized structures surrounded by a lipid bilayer (Fig 1A). To further confirm the size distribution of EVs, we used the nano tracking instrument that demonstrates that EVs have a size between 50 – 400 nm (Fig 1B). Next, to investigate the capacity of isolated human primary neutrophils to internalize iRBC-EVs, we cultured isolated primary human blood neutrophils in the presence of serum opsonized EVs (EVs pre-incubated with human serum to facilitate receptor-mediated uptake) for 1 hour and performed confocal fluorescence microscopy. EVs are taken up by neutrophils and are not only sticking to the cell membrane (Fig 1C). Furthermore, EV treatment did not affect viability of neutrophils (S7 Fig). To gain insight into the mechanisms of EV uptake, we pre-treated neutrophils with a series of known endocytosis inhibitors (Fig 1D). The endocytosis inhibitors tested included actin filament inhibitor: Latrunculin A, the dynamin inhibitor: dynasore, the microtubule inhibitors nocodazole and colchicine [22,23]. All tested endocytosis inhibitors beside nocodazole demonstrated significant inhibitory effects on iRBC-EV uptake. Notably, EV uptake was most strongly suppressed by inhibitors targeting actin polymerization and dynamin activity (Fig 1E).
(A) Transmission Electron Microscopy image of iRBC-EV. The bar represents a size of 1 μm, (B) Size distribution of iRBC EVs as determined by Nanoparticle Tracking Analysis showing the size distribution of iRBC-EV. (C) Confocal microscopy of EV uptake by neutrophils. Isolated neutrophils were incubated with 100μg of PKH-67 fluorescently labelled EVs for 1h at 37°C. Neutrophils were stained for actin (Phalloidin, red) and nuclei (Hoechst-blue). (D) Effect of endocytosis and cytoskeletal reorganization inhibitors on EV uptake. Cells were pretreated for 30 min, with the inhibitors before adding the EVs for 2h. i) Control, ii) Dynasore (80 μM), iii) Colchicine (31 nM), iv) Latrunculin A (5 μM) and iii) Nocodazole (20 μM). EVs are indicated with white arrows. One representative experiment of 3 independent experiments is shown. (E) effect of inhibitors measured as number of EVs per cell (median; n = 3 experiments). Comparisons versus the untreated control were performed using a one-way ANOVA with the Kruskal -Wallis test. Significance levels are indicated as *P < 0.05, **P < 0.01 and *** P < 0.001.
iRBC-EVs suppress immune response of human neutrophils
Neutrophils are known to produce reactive oxygen species (ROS) to kill a diverse array of pathogens. To assess how internalized EVs affect ROS production, we incubated freshly isolated human primary neutrophils with serum-opsonized EVs derived either from uninfected RBC EVs or infected RBC EVs for 1 hour, followed by stimulation with PMA (a protein kinase C activator used as a positive control for ROS and NETosis). Our results show a significant difference in extracellular ROS production between neutrophils stimulated with PMA alone and those pretreated with EVs prior to PMA stimulation. Specifically, pretreatment with iRBC EVs substantially reduced and delayed ROS generation compared to stimulation with PMA alone (Fig 2A). Whereas uninfected RBC derived EVs did not significantly affect ROS production. To evaluate the bactericidal capacity of EV-treated neutrophils, we incubated freshly isolated neutrophils with EVs for 1 hour at 37°C. Subsequently, these neutrophils were challenged with Salmonella typhimurium to assess the direct bactericidal activity of EV-treated neutrophils using a colony forming unit (CFU) assay. Neutrophils pre-treated with EVs derived from uninfected RBC EVs or iRBC-EVs were exposed to S. typhimurium at a multiplicity of infection (MOI) of 1:10 for 30 minutes. This assay allowed us to quantify the number of viable bacterial colonies following neutrophil-mediated killing, providing a measure of the neutrophil’s bactericidal efficiency. Our CFU assay revealed about 2-fold increase in viable bacteria colonies in iRBC-EV treated neutrophil compared to untreated, suggesting the reduced capacity of iRBC-EV treated neutrophils to kill the ingested bacteria (Fig 2B).
(A) ROS-production by unstimulated, PMA, uninfected RBC EVs and iRBC EVs + PMA treated primary neutrophils stimulated with PMA. One representative experiment of 3 independent experiments performed in triplicates is shown. (B) To determine the anti-bacterial activity, isolated neutrophils were pre-exposed to uRBCs or iRBC EVs or not and then incubated with S. typhimurium before assessing bacterial load by colony forming unit (CFU) assay. Data are expressed as means ± SEM of 3 independent experiments performed in triplicates. Statistical significance was determined using the Mann-Whitney unpaired test, with *P < 0.05, **P < 0.01, and ***P < 0.001. (C) Representative images used for NET quantifications, showing staining for trapped extracellular DNA (green) in response to PMA (100nM), iRBC-EVs(100μg) and PMA/iRBC-EVs in whole blood from healthy donors (n = 4). Scale bars, 50 μm (D) Quantification of NETosis in whole blood from healthy donors (n = 4) in response to PMA (100nM), iRBC-EVs(100μg) and PMA/iRBC-EVs. Asterisks indicate significance: ***P < 0.001 by two-way analysis of variance (ANOVA). E) Representative immunofluorescence images of neutrophils across four experimental conditions (Unstimulated, EVs alone, PMA and PMA + EVs). The images show co-staining for DNA (blue) and citrullinated histone H3 (green) in response to PMA (100nM), iRBC-EVs (100 μg) and PMA/iRBC-EVs in PMNs isolated from healthy donors (n = 4) Scale bars, 20 μm.
To test whether malaria EVs induce NETs formation, we used a microfluidic device. We co-incubated freshly isolated human primary neutrophils from healthy adult donors with EVs derived from iRBC for an hour. PMA was used as positive control (Fig 2C). We found that PMA, EVs and PMA-EVs robustly induced NETs in whole blood. However, the mean NETs area of the trapped extracellular DNA was 2 folds larger for EVs compared to control, at 500’000 μm2 vs 240’000 μm2, respectively. The mean NETs were larger for PMA-EVs compared to PMA alone at 3.5 x 106 μm2 vs 2.8 x 106 μm2, respectively (Fig 2D). To further, confirm NETs stimulation by EVs, we stimulated purified neutrophils with serum opsonized iRBC-EVs for an hour and stained for extracellular DNA and citrullinated histone (cit-H3), a canonical marker of NET formation. Co-localization of extracellular DNA with cit-H3 signal identifies decondensed chromatin structures characteristic of NETs, distinguishing genuine NET release from intact or condensed nuclei. We found that iRBC-EV could also directly induce NETs (Fig 2E).
iRBC-EVs alter chemotaxis and swarming in isolated human primary neutrophils ex vivo
A previous study has shown that neutrophils isolated from individuals infected with P. vivax malaria exhibit reduced chemotactic activity, although the underlying mechanism for the impairment is unclear [15]. Neutrophil swarming is a coordinated collective response characterized by directed migration toward a localized stimulus, secondary recruitment of additional neutrophils and formation of an expanding cellular aggregate. We first determined whether EVs altered basic neutrophil chemotaxis toward an fMLP gradient. To assess whether iRBC-EVs impair neutrophil chemotaxis, we treated freshly isolated primary human neutrophils with serum-opsonized iRBC-EVs for 1 hour and quantified migration toward 100 nM formyl-methionyl-leucyl-phenylalanine (fMLP) using an egg-shaped microfluidic platform. This system enables real-time, single-cell resolution tracking of chemotaxis by directly measuring neutrophil migration toward chemokine gradients, a critical functional readout of chemotactic activity (Fig 3A, S1 Video). We found that both iRBC-EV treated and untreated neutrophils in the outer chamber migrate directionally into the central inner micro-chamber in response to the fMLP gradient. However, the mean percentage of migration was twice as much in untreated neutrophils compared to iRBC-EV treated neutrophils, at 30% vs 15%, respectively suggesting impairment of neutrophil chemotactic response following treatment with iRBC-EV (Fig 3B).
(A) Cartoon illustration of the chemotaxis egg shaped device (see method section for full description). (B) The percentage of migration of untreated and iRBC-EVs treated primary neutrophils toward fMLP gradients using the egg-shaped device in A. (C) Graph and images showing the uniformity in the size of each patterned C. albicans spot in the swarming assay device. (D) Sequential images showing accumulation of untreated neutrophils and iRBC-EVs treated neutrophils at the swarm site at 0, 60, 120, 240, 480 and 600 minutes around zymosan particles (scale bar 25 μm). (E) The dynamics of neutrophil swarm size in untreated neutrophils (blue line) and iRBC-EVs treated neutrophils (orange line). (Fi) Sequential images showing C. albicans growth in absence of neutrophils at 0mins, 120mins, 240mins, 480mins and 720 mins (scale bar 100 μm). C. albicans expresses a far-red fluorescent protein. The intensity of the red dye is used to assess growth. Transformed black and white images are shown. (ii) Sequential images showing the killing of C. albicans (far-red fluorescent protein) and swarm formation (blue, nuclei of neutrophils): untreated primary neutrophils. (iii) iRBC-EVs treated primary neutrophils around C. albicans at 0mins, 120mins, 240mins, 480mins and 720 mins (Scale bar 100 μm, n = 10 experiments). (Gi) The growth dynamics of C. albicans with untreated primary neutrophils (blue line) and iRBC-EVs treated primary neutrophils (orange line). (ii) The dynamics of primary neutrophils swarming. Untreated primary neutrophils (blue curve) and iRBC-EVs treated primary neutrophils (orange curve). N = 60 swarms across three donors for Fig 3E and 3G except for the control group. Error bars represent mean + /- standard error for these measurements.
For an effective microbe killing, neutrophils must exhibit a well-coordinated migration by swarming, thus we investigated the impact of iRBC-EV on swarming. We next assessed whether this reduction in individual migration was also reflected in collective neutrophil behavior. Swarm formation was quantified using changes in aggregate size, fluorescence intensity, diameter and roundness over time. The swarming assay device arranges microbes in clusters of 100-µm diameter, grouped in 8 × 8 arrays, separated inside individual wells, in a 16-well format (Fig 3C) [24]. The device allows monitoring of the interaction of primary neutrophils and live Candida albicans or zymosan particles in real-time [10].
First, we tested the effect of iRBC-EV on the swarming abilities of freshly isolated primary human neutrophils and we observed that both iRBC-EV treated and untreated neutrophils swarm efficiently toward zymosan particle (dead yeast particles) (Fig 3D and 3E). The swarming curve shows that the mean fluorescent intensity for both groups increased sharply the first 60 minutes (Fig 3D and 3E).
In the absence of a microbial target, EV-treated neutrophils formed smaller or less intense aggregates, suggesting that EVs impaired recruitment into the developing swarm rather than simply altering cell morphology. To determine whether this effect persisted in response to a biologically relevant target, neutrophil swarming was examined around C. albicans (Fig 3F). We first monitor the growth of C. albicans growth in the absence of primary neutrophils in the swarming assay (Fig 3Fi, S2 Video). We observed that C. albicans clusters growth increased over time. In the presence of neutrophils, we observed that both iRBC-EV treated and untreated neutrophils swarm efficiently toward the C. albicans target [Fig 3F (ii & iii)]. Surprisingly, iRBC-EVs treated neutrophils also demonstrated a similar ability to arrest the growth of C. albicans. The growth curve shows that the mean fluorescence intensity for fungal growth for both groups increased sharply from ~ 20 – 60 min. However, after ~420 minutes, untreated but not iRBC-EV treated neutrophils were able to stop the growth of C. albicans [Fig 3G (i)] (S3 Video). Notably, EV-treated and untreated neutrophils contained fungal growth similarly during the first 420 minutes of the assay. After this point, fungal proliferation resumed exclusively in the EV-treated condition (Fig 3G(i)). This transient initial containment followed by delayed loss of fungal control is consistent with the reduced swarm size and impaired ROS production observed in EV-treated neutrophils: neutrophils can initially swarm and physically restrain fungal growth, but their diminished antimicrobial capacity ultimately fails to sustain fungicidal activity over time. The mean fluorescent intensity for fungal growth of the iRBC-EVs treated neutrophils starts to peak after ~420 minutes, from ~60–100, suggesting reduced killing ability in neutrophil treated with iRBC-EVs [Fig 3G(i)] (S4 Video). The mean swarm fluorescent intensity around C. albicans target was larger for untreated neutrophils compared to iRBC-EVs treated neutrophils, suggesting a larger swarm size around the microbial target in untreated compared to iRBC-EVs treated neutrophils [Fig 3G(ii)].
Transcriptional Regulation of Inflammatory Response in primary neutrophils via EV
Neutrophil function is driven by complex signaling pathways that involve generating and receiving diverse signals from a multiplicity of sources, including other host cells and bacterial pathogens. To gain insight into the molecular processes that are modulated by EVs in human primary neutrophils, we used bulk RNA-seq to analyze global changes in the neutrophil transcriptome after iRBC-EV and iRBC-EV + lipopolysaccharide (LPS) stimulation (Fig 4A). We used four different conditions: untreated, EVs for 6 hours, LPS for 5 hours and finally the combination of EVs + LPS for 6 and 5 hours respectively. The PCA revealed donor-driven batch effect in the neutrophil (3 donors) that was corrected using ComBat-Seq on the count data. The ComBat-Seq corrected-PCA of gene expression patterns revealed four main clusters according to the different treatments (Fig 4B).
(A) Experimental setup (n = 3 for each stimulation). Created in BioRender. Mantel, P. (2026) https://BioRender.com/ar9d416. (B) PCA of all samples using the full transcriptome. Shown in parentheses on the axes is the percentage of variance explained by each of the principal components. (C) Enrichment analysis of DEGs in neutrophils stimulated with P. falciparum EVs compared to unstimulated neutrophils. Enrichment dot plot of the top Gene Ontology (GO) terms enriched among DEGs. Gene ratio: proportion of DEGs associated with each GO term relative to the total number of genes in that term. Enriched GO terms in y-axis are ordered by adjusted p-value (p.adjust). Dots size: number of DEGs contributing to each term. Dot color: adjusted p-value, with darker colors indicating more statistically significant terms. (D) Volcano plot of DEGs in neutrophils co-treated with EV + LPS versus a pooled EV-only/LPS only reference (the union of the two single-stimulus sample sets), a comparison designed to isolate genes driven by EV-LPS interaction rather than by either stimulus alone. All dots correspond to 71 genes with 20% change in expression (LFC = log2(1.2)). Significantly upregulated genes (red dots) and significantly downregulated genes (blues dots). The dashed horizontal line indicates the threshold for statistical significance (−log10(0.05)), and the vertical dashed lines represent the log2 fold change thresholds of 1.2 and −1.2. The labeled genes in blue and the corresponding black dot are genes involve in ferroptosis. (E) Box plots display the expression levels of ferroptosis-related genes (HMOX1, KEAP1, NQO1, GPAT4, SLC7A11, and TGFB1) across different conditions (x-axis). The y-axis represents normalized gene expression values.
To identify genes specifically regulated by the combined EV + LPS co-treatment beyond the effect of either stimulus alone, we compared the EV + LPS co-treated samples (a single experimental condition receiving both stimuli, labeled “EV+LPS”) against a pooled reference built by combining the EV-alone and LPS-alone samples (the union of the two individual single-treatment groups, labeled “EV-only ∪ LPS-only pooled reference”). This comparison isolates transcriptional changes attributable to interaction between EV and LPS signaling, rather than a simple additive effect of the two stimuli individually. This revealed 511 differentially expressed genes (DEGs), including 71 with 20% change in expression (Fig 4C) (S1 Table)). Gene ontology enrichment showed that EV-treatment of neutrophil induces a molecular cascade of events that potentiate the expression of genes related to cytokine activity and regulation of both inflammatory responses and signaling receptor activity (Fig 4C). Interestingly, heme oxygenase 1 (HMOX1) was one the most up-regulated genes (Fig 4D) and given its role in ferroptosis, we analyzed if the DEGs in LPS-EV could be involved in ferroptosis and found five additional DEGs involved in ferroptosis (NQO1, SLC7AA11, KEAP1, GPAT4, TGFB1) (Fig 4D - 4E). Overall, our global analyses of the transcriptional landscape during iRBC-EV neutrophil interaction reveals a specific signature of the genes involved in ferroptosis in the presence of EVs.
Transfer of miR451a to neutrophils by EVs modulates neutrophil function
Previously, we have demonstrated that miRNAs, and tRNAs-derived fragments were the most enriched small RNAs in EVs, with miR451a being the most abundant miRNA [20,25]. Given the efficient uptake of EVs by primary neutrophils, we hypothesized that EVs may deliver regulatory miRNAs to neutrophils. We incubated freshly isolated neutrophils and then determined miR451a levels in primary neutrophils via qPCR. We observed a 30-fold increase of miR451a expression in neutrophils upon EV treatment (Fig 5A). To determine whether miR451a originates from EV transfer, rather than being produced by neutrophils after EV uptake. We pre-treated neutrophils with α-amanitin (an RNA polymerase II inhibitor used to block de novo miRNA transcription) before incubation with EVs [26]. Since miR451a expression remains high even with treatment with α-amanitin, it shows that it is an actual transfer of miR451a to neutrophils by EVs (Fig 5A). Next, using RNA fluorescent in situ hybridization (FISH), we were able to directly detect and quantify miR451a copy numbers in primary neutrophils upon EV uptake. The miR451a uptake was significantly increased upon EV treatment (Fig 5B). The imaging of individual miR451a molecules confirmed transfer of miR451a to neutrophils following iRBC-EV treatment. Absolute quantification of discrete fluorescent spots per cell (Fig 5B right panel), performed according to the single-molecule counting approach of Lu et al. [27], revealed a significant increase in miR451a copy number in EV-treated neutrophils compared to controls.
(A) EV uptake does not induce miRNA expression in neutrophils. Neutrophils, + /- pretreatment with α-amanitin for 30 minutes were incubated with EVs or left untreated. miR451a was quantified by qPCR in neutrophils upon EV incubation. qPCR results from all experiments are normalized by the 2-Ct method, using RNU6 as a reference and expressed as mean and fold induction over control (mean ± s.e.m.; n = 3 experiments), P versus control (Student’s t-test). (B) Detection of miRNA by RNA FISH. miRNA transfer to neutrophils by EVs is measured by RNA FISH. miR451a is only detected in neutrophils upon EV treatment, and only when a sequence-specific probe is used for detection (green spots: single-molecule RNA FISH; maximum intensity merges of Z-stacks). RNU6, positive control; scramble, negative control. Blue, nuclei (DAPI). P versus control (Student’s t-test). The adjacent graph shows absolute miR451a copy number per cell, quantified by counting discrete fluorescent spots in cells per condition. Data are shown as mean ± SEM. ****p < 0.0001 [27]. (C) Representative fluorescence microscopy images of dHL60 expressing miR451 or scramble RNA phagocytosis. Left panel: Staphylococcus aureus expressing GFP and DAPI nuclear counterstain. Right panel: overlay of all channels including target (green), DAPI (blue), and F-actin (phalloidin, red), showing internalization of bacteria. (D) To determine the antibacterial activity of dHL-60miR451a and dHL-60scramble, the cells were incubated with S. typhimurium (E) S. aureus in a CFU time course experiment. Viable bacteria colonies were counted after incubation (mean ± s.e.m.). N = 9 from 3 independent experiments. Comparisons between dHL-60miR451a and dHL-60scramble at each time point were performed using a two-way ANOVA with the Sídák’s multiple comparisons test. Significance levels are indicated as *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001.
To ascertain the role of iRBC-EV derived miR451a in inhibiting ROS and antimicrobial functions in neutrophils, we transduced Human leukemia cell (HL-60 cells) with a lentiviral expression vector containing miR451a (or scramble control, shRNA against GFP). Next, we differentiated HL-60 cells into neutrophil-like cells via dimethyl sulfoxide (DMSO) treatment [24]. We then compared the killing ability of HL-60 neutrophil-like cells expressing either miR451a (dHL-60miR451a) or a scramble dHL-60scramble upon challenge with either S. typhimurium (Gram-negative) or Staphylococcus aureus (Gram-positive bacteria). Microscopy revealed that the capacity of HL-60miR451a to subsequently ingest the bacteria remained unimpaired (Fig 5C). To test the killing ability of the HL-60 mutant cells, we challenged the cells with bacteria and monitored the killing for 60 minutes. The rate of killing in HL-60miR451a was significantly impaired and slowed down compared to HL-60scramble for both bacteria (Fig 5D and 5E). Importantly, we were able to recapitulate the observed effect of EVs on primary neutrophil functions in the HL-60miR451a neutrophil-like cells, further suggesting a crucial role of iRBC-EV derived miR451a in the inhibition of the antimicrobial function of primary human neutrophils.
miR451a alters chemotaxis and swarming in HL-60 neutrophil-like cells
To further investigate the role of EV derived miR451a in inhibiting chemotaxis and swarming, we tested the ability of HL-60miR451a and HL-60scramble to do chemotaxis and swarm using a tapered channel microfluidic device [24] and swarm assay respectively. The choice of a tapered channel device for this study was selected because it mimics the variation in blood vessel sizes and resistances encountered by neutrophils during migration. Using tapered-channel devices, we measured the fraction of HL-60miR451a and HL-60scramble cells migrating and quantified the frequency of various migration patterns. We found that and 40% of HL-60scramble and 10% of HL-60miR451a cells migrated in N-Formyl-L-methionyl-L-leucyl-phenylalanine (fMLP) gradients, respectively (Fig 6A). Next, to quantify the frequency of previously reported neutrophil migration patterns in both mutant HL-60 cells. We focused on four migration patterns identified in earlier studies, including persistent migration (P), arrest (A), oscillation (O), and retro taxis (R) [24,28]. Persistent migration indicates neutrophils that migrated through the channels without changing directions. Arrest describes neutrophils that are trapped in the channels. Oscillation indicates neutrophils that change migration direction more than two times. Retro-taxis describes neutrophils that migrated back to the cell-loading channel. We found that 70.9, 0.3, 20.9 and 7.9% of HL-60scramble neutrophils, and 18.9, 1.9, 77.3 and 1.9% of HL-60miR451a neutrophils like cells exhibited persistent, oscillation, arrest and retro taxis migratory patterns in fMLP gradients respectively (Fig 6B). To test the swarming ability of the HL-60 mutant strains, we use patterned zymosan clusters particles as microbial targets in the swarming assay. Three distinct phases of swarming have been previously reported [29]. We observed that both HL-60miR451a and HL-60scramble neutrophils like cells swarm toward zymosan targets (Fig 6C). For the HL-60scramble neutrophils like cells, swarming starts with random migration of the cells on the surface (scouting phase-5 min Fig 6C). After the first HL-60 neutrophil like cell interacts with the cluster, the number of migrating neutrophils towards the zymosan cluster increases rapidly (growing phase-Fig 6C). Swarms reach their peak size 220–240 min later, after which the size remains stable (stabilization phase-Fig 6C and 6D). In particular, the swarm area around the zymosan particle cluster for HL-60scramble increased sharply from 0 to ~ 35,000 μm2 in the first 240 min (scouting and growing phase-Fig 6C and 6D) and after 240 minutes, the swarm size remained constant throughout the imaging time (stabilization phase- Fig 6C and 6D). We observed similar aggregation dynamics in HL-60miR451a cells. Aggregation starts with the cells moving randomly on the surface of the zymosan particle clusters (scouting phase-5 min Fig 6C-second panel). Swarms in HL-60miR451a cells reach their peak size after 100–120 min, after which the swarm size remained small and constant throughout the imaging time (stabilization phase- Fig 6C and 6D). The HL-60miR451a swarm size increased from 0 to ~ 12,000 μm2 in the first 2 hours (growth phase-Fig 6C and D) after which the swarm size remained small and constant (stabilization phase-Fig 6D). We found that the final mean swarm size of HL-60scramble was 5 times larger as compared to HL-60miR451a, measuring 50,000 μm2 vs 10,000 μm2, respectively (Fig 6E).
(A) The percentage of migration of differentiated HL-60scramble and HL-60miR451a toward fMLP gradients (mean ± s.e.m.; n = 5 independent experiments). Comparisons were performed by unpaired, two-tailed t-test; ***P < 0.001. (B) Percentage of migratory patterns in i) DdHL-60scramble cells, ii) DdHL-60miR451a cells. Persistence (Blue color), Arrest (Red color), Oscillation (Orange color) and Retrotaxis (Green color). (C) Sequential images showing swarm formation in HL-60scramble and HL-60miR451a (blue) around patterned zymosan particle clusters at 5mins, 10mins, 30mins, 60mins, 120 mins and 240mins (scale bar: 25 µm). (D) Comparison of swarm area formed between dHL-60scramble and dHL-60miR451a. Comparisons were performed by unpaired, two-tailed t-test; ****P < 0.0001. (E) dHL-60scramble (blue curve) accumulation on targets is fast and form a bigger swarm and dHL-60miR451a (orange curve) aggregation proceeds fast and then continues slower over time. N = 30 swarms and Error bars represent standard error for these measurements. Comparisons were performed by unpaired, two-tailed t-test; ****P < 0.0001.
miR451a modulates HL-60 neutrophil like cell transcriptional response to bacterial-like challenge
Next, to assess the effect of miR451a on gene expression, we performed RNA sequencing analysis on HL-60 human promyelocyte cells engineered to constitutively express miR451a via bulk and single cell RNA seq. For bulk RNA seq, we compared HL-60scramble cells, HL-60scramble -LPS (90 mins), HL-60scramble –LPS (180mins), HL-60miR451a, HL-60miR451a-LPS (90 mins) and HL-60miR451a-LPS (180 mins). We stimulated the cells for 90 and 180 mins to monitor gene regulation over time [30]. Differential gene expression analysis revealed a significantly higher number of DEGs (FDR < 0.05) in HL-60miR451a cells compared to HL-60scramble cells without LPS stimulation (46%). However, notably fewer significant DEGs were identified in the LPS-stimulated cells (S2 Table). GO term enrichment analysis showed that miR451a induces a molecular cascade in HL-60 cells that involves 259 GO terms, and they are enriched with terms related to mitochondrial gene expression, translation and protein matrix in the absence of LPS stimulation (Fig 7A and 7B). Following 180 minutes of LPS stimulation, HL-60miR451a cells exhibited significant enrichment in 126 GO terms compared to HL-60scramble cells. Notably, the top 10 GO terms were linked to response to a molecule of bacterial origin such as LPS and cytokine activity and showed downregulation. These findings align with in vitro experiments, where genes associated with neutrophil functions like migration and chemotaxis, were downregulated in presence of EVs (Fig 7C and 7D). The PCA revealed distinct clustering patterns among the cell populations and treatment conditions. HL-60scramble and HL-60miR451a cells formed two separate clusters, indicating significant differences in their overall gene expression profiles. Furthermore, LPS activation for 90 and 180 minutes induced additional separation between these clusters. This time-dependent response to LPS stimulation was observed in both cell types, suggesting that the transcriptional changes elicited by LPS were substantial enough to be captured by PCA. The maintained separation between HL-60scramble and HL-60miR451a clusters following LPS treatment implies that the miR451a modification may alter the cellular response to inflammatory stimuli. These PCA results underscore the impact of miR451a on cellular phenotype and the dynamic nature of the cells’ response to LPS over time (Fig 7E). CNET analysis provided a comprehensive visualization of gene-pathway relationships, revealing that several enriched biological processes, including response to LPS, inflammatory response and positive regulation of ERK1 and ERK2 cascade, were driven by overlapping sets of genes (Fig 7F). The network structure identified TNF, RIPK2, IL-12B, as central nodes, linking multiple pathways. This suggests that miR451a genes may act as key regulators in coordinating the cellular response to bacterial infections.
(A, C) Enrichment dot plot of the top Gene Ontology (GO) terms enriched among DEGs in HL-60miR451a cells compared to HL-60scramble cell without LPS (A) and in HL-60miR451a cells compared to HL-60scramble cells after LPS stimulation (C). Gene ratio indicates the proportion of differentially expressed genes associated with each GO term relative to the total genes annotated to that term. The enriched GO terms displayed on the y-axis are ranked according to their adjusted p-values (p.adjust). Dots size: number of DEGs contributing to each term. Dot color: adjusted p-value, with darker colors indicating more statistically significant terms. (B, D) GSEA enrichment plot of the genes sets associated with three different GO terms in HL-60miR451a cells compared to HL-60scramble cell without LPS (B) and in HL-60miR451a cells compared to HL-60scramble cells after LPS stimulation (D). (E) Principal component analysis of all samples using the full transcriptome. (F) Cnet plot of enriched biological pathways and associated genes. Circular nodes represent enriched pathways, while circles nodes represent genes. (G) Figures highlighting pairwise PCA analysis of the main groups (HL-60mir451a and HL-60Scramble, HL60Scramble+Bacteria and HL-60Scramble, HL-60Scramble+Bacteria and HL-60miR451a+Bacteria, HL-60mir451a and HL-60miR451a+Bacteria. The projections are based on the top 1000 most variable genes and with the cells plotted on 1st and 2nd PCA components score. In each plot, a line distribution of the single cells within the PCA subspace is shown.
Next, we performed single cell RNA-seq to compare the control HL-60scramble with the HL-60miR451a cells and checked how they responded to bacterial exposure. We identified more than 1’000’000 read counts with 4’000 gene counts per cell types (S1A-C). We identified several neutrophil genes expressed in the top 30 most expressed genes in all the cell populations (S1D-H). The PCA of the differentially expressed genes (DEGs) clearly separated HL-60scramble from the HL-60miR451a population (Fig 7Gi). when HL-60scramble are activated with bacteria the population clearly separated (Fig 7Gii). In contrast, when HL-60miR451a were activated with LPS, they did not separate well from the unstimulated HL-60miR451a (Fig 7Giii). To further characterize the transcriptional differences between cell types and conditions, we performed pairwise comparisons of gene expression profiles (S2 Fig-5). Additionally, we examined the expression of published key markers associated with malaria and bacterial co-infection responses in HL-60scramble and HL-60miR451a cells exposed to LPS. This analysis revealed a significantly higher expression of pro-inflammatory genes in HL-60miR451a-LPS, including TNF, NCF1, NCF1B and NCF1C in HL-60miR451a-LPS (S2-7B). Gene set enrichment analysis (GSEA) of DEGs between the HL-60scramble-LPS and HL-60miR451a-LPS revealed that genes related to biological processes and immunological signature (neutrophil migration, defense to Gram- bacteria, inflammatory response and cell killing) (S6A and 6B).
Discussion
Our findings reveal that miR451a-containing EVs from iRBCs contribute to neutrophil dysfunction, highlighting a novel mechanism of immune evasion in malaria. The host’s survival during blood stage malaria hinges on two defense strategies: resistance, which aims to eliminate parasites, and tolerance, which prioritizes host fitness over pathogen clearance [31,32]. While tolerance may reduce self-inflicted damage, it potentially compromises resistance to secondary infections [33]. Thus, neutrophil dysregulation presents a double-edged sword: it benefits the parasite by reducing immune constraints yet potentially protecting the host from severe inflammatory damage. This delicate balance underscores the complex interplay between parasite virulence and host immune responses in determining malaria outcomes. Multiple factors released by iRBCs have been shown to promote tolerance by modulating the immune system, particularly through the suppression of neutrophil functions, such as heme [13] and food vacuoles [16,17]. Here, we show that EV treatment impaired several aspects of neutrophil functionality, including ROS production, chemotaxis, anti-bacterial activity and swarming. The neutrophil “dysregulation” appears to be part of the complex immune modulation induced by both P. falciparum and P. vivax infections. In malaria patients, neutrophils exhibit reduced chemotaxis and are a poor source of pro-inflammatory cytokines [15]. Our findings are directly relevant to the clinical observations of Cunnington and colleagues, who demonstrated that children with severe malaria in The Gambia exhibit profoundly impaired neutrophil oxidative burst and phagocytosis. These functional defects are associated with susceptibility to invasive Salmonella bacteremia [14]. While Cunnington et al. implicated free heme and HMOX1 induction as key drivers of neutrophil dysfunction, the upstream triggers of HMOX1 upregulation during malaria remained unclear [13]. Strikingly, our transcriptomic analysis identifies HMOX1 as one of the most significantly upregulated genes in EV-treated neutrophils, and our mechanistic data place iRBC-EV uptake and miR451a delivery upstream of this transcriptional response. This suggests that iRBC-EVs may act as an early trigger of the same HMOX1-driven immunosuppressive pathway characterized clinically by Cunnington and colleagues, providing a molecular bridge between parasite-derived EVs and the neutrophil dysfunction observed in malaria patients.
The susceptibility to secondary infections during P. falciparum malaria is not uniform across pathogen classes. The strongest and most consistent clinical association is with invasive non-typhoidal Salmonella (iNTS) bacteremia, driven by a convergence of gut barrier disruption, heme-mediated macrophage dysfunction, and, as we propose here, neutrophil impairment [14,34]. Gram-positive co-infections, principally S. aureus and S. pneumoniae, do occur and can worsen outcomes, but the evidence that malaria specifically predisposes to gram-positive bacteremia is inconsistent across settings, possibly reflecting the greater dependence of gram-positive clearance on antibody-mediated opsonization rather than neutrophil oxidative killing [35,36]. Fungal co-infections complicating malaria are rarely reported in the clinical literature [9]; however, neutrophils are the principal effectors against Candida and Aspergillus [37] and severe malaria shares several host-risk features: critical illness, hemolysis, immune dysregulation, and intensive care exposure, associated with invasive fungal disease in other severe infectious contexts [38,39]. The apparent rarity of fungal co-infections may therefore reflect under-recognition rather than true absence, particularly given limited diagnostic capacity in malaria-endemic settings. Our demonstration of impaired neutrophil killing of C. albicans in vitro identifies a mechanistic vulnerability consistent with this risk, even in the absence of robust epidemiological confirmation.
We confirmed that iRBC-EVs are efficiently internalized by neutrophils in an endocytic manner similar to endothelial cells [20], in a process that requires actin polymerization [40]. Coordinated neutrophil migration by swarming is crucial for arresting the growth and spread of various pathogens [41]. We demonstrate that neutrophils-iRBC-EVs interaction alters the chemotactic response of human neutrophils towards fMLP, reduces their ability to swarm and to arrest the growth and progression of C. albicans. Additionally, these interactions trigger NETosis in both whole blood and isolated neutrophils in vitro. While EVs might contribute to NET release during malaria [42], the ability of neutrophils to form large swarm is reduced, resulting in diminished antimicrobial activity at infection sites and increased pathogen proliferation.
Furthermore, we showed that EVs diminished the capacity of neutrophils to produce ROS upon PMA stimulation, consistent with observations of reduced ROS production in children with P. falciparum infection [14]. Neutrophils treated with EVs also exhibit impaired killing of Gram-negative bacteria such as Salmonella typhimurium. These findings suggest that EVs contribute to the neutrophil dysfunction observed in malaria patients, potentially increasing the risk of death from bacteremia [43–46]. An apparent paradox in our findings is that EVs reduced ROS production yet enhanced NETosis. This seeming contradiction is reconciled by the existence of ROS-independent NETosis pathways. The canonical pathway, exemplified by PMA stimulation, relies on NADPH oxidase-derived ROS to drive nuclear decondensation and NET extrusion [47]. However, an alternative “vital NETosis” pathway operates independently of oxidative burst, instead engaging calcium flux, peptidylarginine deiminase 4 (PAD4) and mitochondrial ROS as distinct triggers [48,49]. Our observation that EVs combined with PMA produced significantly larger NET areas than PMA alone suggests that EVs activate or potentiate these ROS-independent mechanisms rather than amplifying canonical NADPH oxidase-dependent signaling. Collectively, these data indicate that EVs differentially modulate distinct neutrophil effector functions: suppressing the oxidative burst while simultaneously priming or facilitating alternative NET-release pathways. This functional dissociation has important implications, as it suggests EVs do not globally suppress neutrophil activity but instead reshape the effector response in ways that may selectively amplify tissue-damaging or immunomodulatory outcomes.
The temporal dynamics of C. albicans swarming assay are particularly informative. The delayed, rather than immediate, loss of fungal containment in EV-treated neutrophils suggests that iRBC-EVs do not abolish neutrophil responses outright but progressively undermine their antimicrobial capacity. This has potential clinical relevance: neutrophils in malaria patients may mount an initial response that fades over time, which could explain why secondary infections in malaria are often characterized by uncontrolled pathogen growth rather than failure to respond at all.
MiR451a is a microRNA critical for erythropoiesis [50–54], is the most abundant miRNA in erythrocytes and regulates erythroblasts differentiation and oxidative stress [55]. Given that EVs can shuttle functional RNA between cells [56,57], we investigated whether miRNAs contained in EVs modulate neutrophil function. We focused on miR451a, due to its abundance in EVs [25]. We demonstrated that EVs transferred miR451a to neutrophils. To further explore the molecular mechanisms, we generated a cell line overexpressing miR451a. HL-60miR451a differentiated toward a neutrophil-like phenotype displayed the “dysfunctional neutrophil” features, implicating miR451a directly in the suppression of chemotaxis, swarming and antibacterial activity. Previous studies have shown that miR451a has immunoregulatory properties, with reduced expression of miR451a observed in patients with rheumatoid arthritis and elevation of miR451a expression markedly reduces neutrophil chemotaxis [52]. In addition to miRNAs, EVs from iRBCs can transfer parasitic genomic DNA to immune cells, activating STING-dependent DNA sensing and immune activation [58]. While genomic DNA in EVs may activate monocytes, EVs also suppress CXCL10 secretion by disrupting ribosome association with CXCL10 transcripts, highlighting the complex nature of EV-mediated immune modulation [59]. Several important caveats apply to the interpretation of the HL-60 neutrophil-like cell data. DMSO-differentiated HL-60 cells are a widely used reductionist model for neutrophil biology, but they differ from primary human neutrophils in several fundamental respects. Unlike mature neutrophils, HL-60 cells retain proliferative capacity, have a lobulated but not fully segmented nucleus and exhibit a less developed granule compartment with quantitatively reduced myeloperoxidase and defensin content. Their oxidative burst response to stimulation, while present is generally weaker and more variable than that of primary neutrophils and their lifespan in culture is not constrained by the characteristic short half-life of circulating neutrophils in vivo. Furthermore, constitutive lentiviral overexpression of miR451a in HL-60 cells differs mechanistically from the transient, EV-mediated delivery of miR451a that occurs in primary neutrophils: the former results in sustained, high-level miR451a expression throughout differentiation, which may engage target pathways differently from the acute post-transcriptional regulation triggered by EV uptake. These limitations mean that the HL60 miR451a experiments should be interpreted as a reductionist genetic tool to isolate and test the functional contribution of miR451a, rather than as direct substitute for primary neutrophil studies. Critically, the key phenotypes observed in primary human neutrophils following iRBC-EV treatment, were recapitualed in the HL-60 model, lending validity to the model within these specific functional readouts. We therefore present the HL-60 data as complementary genetic evidence supporting the role of miR451a, interpreted in the context of and not in place of the primary neutrophil experiments [60,61].
We next examined the effect of EVs on neutrophil transcriptional activity, particularly in response to bacterial stimulation. The transcriptomics data revealed that HO-1 is the most upregulated gene following EV treatment, whereas it is slightly downregulated by LPS alone. HO-1, encoded by HMOX1, plays a key role in malaria [14,62], with promoter polymorphisms and high HO-1 levels associated with severe malaria [63]. Evidence from the rodent model showed that HO-1 plays a protective role by inducing tolerance and reducing damage to the host, since mice deficient in HO-1 are more susceptible to cerebral malaria [64–66].
HO-1 plays a crucial role for heme catabolism and provides anti-inflammatory, antioxidant, cytoprotective and vascular protection. Moreover, HMOX1 plays a complex and significant role in ferroptosis a form of programmed cell death characterized by iron-dependent lipid peroxidation [67]. We found that several genes involved in ferroptosis were upregulated, neutrophils appear particularly sensitive to ferroptosis, which can promote tolerance and tumor growth by limiting antitumor activity [68].
Neutrophils are crucial players in the immune response to malaria, but their activation must be carefully regulated. While they contribute to parasite clearance, excessive neutrophil activation and NET formation can lead to tissue damage and exacerbate disease severity. Future research and therapeutic strategies may focus on harnessing the protective functions of neutrophils while mitigating their potential harmful effects in severe malaria.
While our in vitro and ex vivo data demonstrate that iRBC-derived EVs impair neutrophil function through miR451a transfer, establishing direct clinical relevance will require additional approaches. Quantification of miR451a in neutrophils from P. falciparum-infected patients compared to healthy controls and non-malaria febrile illness, would determine whether miR451a enrichment is recapitulated during natural infection. Correlating plasma EV levels with neutrophil functional readouts and susceptibility to bacterial co-infection would provide epidemiological support for the link we describe. Most directly, testing the ex vivo effect of patient plasma EVs on healthy donor neutrophils would address whether patient-derived EVs are sufficient to reproduce the functional defects observed with culture-derived iRBC-EVs.
EVs are produced constitutively by both infected and uninfected erythrocytes; however, EV release is markedly amplified during P. falciparum infection. Infected RBCs produce approximately 15-fold more EVs than their uninfected counterparts and preparations derived from infected cultures contain more than 80% iRBC-derived vesicles as confirmed by plasmodium RNA transfer [20]. Consistent with this, circulating RBC-derived EV levels are significantly elevated in malaria patients compared to healthy controls, with the highest concentrations observed in severe disease including cerebral malaria [18,69]. This disease-severity correlation is mechanistically relevant to our findings: if iRBC-EVs drive neutrophil dysfunction through miR451a transfer, patients with the highest EV burdens, those with severe or hyperparasitemia malaria, would be predicted to exhibit the greatest degree of neutrophil impairment and consequently, the highest susceptibility to bacterial and fungal superinfections. Whether plasma EV concentration directly correlates with secondary infection incidence in clinical cohorts remains to be established and represents a key translational question arising from our study.
Collectively, our in vitro data suggest that iRBC-EVs released during malaria contribute to the host immune suppression and increased susceptibility to opportunistic infections by inhibiting neutrophil functions such as swarming and antimicrobial activity. Previous observations of elevated iRBC-EVs in severe malaria patients [18] strongly support a role for these vesicles in malaria pathology. The combination of mechanistic in vitro data, genetic mouse models and patient-based evidence will be required to fully establish iRBC-EV-mediated miR451a transfer as a clinically relevant determinant of secondary infection susceptibility in P. falciparum malaria.
In summary, our study identifies miR451a-containing EVs from iRBCs as key mediators of neutrophil dysfunction, providing new insights into how malaria parasites subvert host immunity. By impairing critical neutrophil functions such as chemotaxis, swarming, and antimicrobial activity, these EVs contribute to both immune evasion and increased susceptibility to secondary infections. This dual effect underscores the complex evolutionary balance between parasite survival strategies and host defense mechanisms, where immune modulation may simultaneously limit immunopathology and compromise resistance [70]. Our findings highlight the importance of targeting EV-mediated pathways in future therapeutic strategies aimed at restoring neutrophil function and improving outcomes in malaria.
Materials and methods
Ethics statement
Experiments performed in Boston (USA) used peripheral blood from healthy adult donors (≥21 years of age) obtained commercially from Research Blood Components, LLC (Allston, MA, USA). Blood was collected into 10-mL sodium heparin Vacutainer tubes (Becton Dickinson), and immune cells were isolated according to the manufacturer’s procedures and applicable regulations.
Experiments performed at the University of Fribourg (Switzerland) used fresh blood from healthy volunteer blood donors obtained as anonymized material. Samples were irreversibly anonymized before being provided to the investigators, and no personal or clinical data were available to the research team. According to the determination of the Cantonal Research Ethics Committee of Vaud (CER-VD; Request No. Req-2018–01049), this research falls outside the scope of the Swiss Human Research Act (HRA, Art. 2 para. 2b) and therefore ethics committee approval was not required. Blood donors provided written informed consent for blood collection and anonymized use of residual material in research in accordance with institutional procedures.
Fungi
Candida albicans SC5314 constitutively expressing a far-red fluorescent protein was cultured overnight at 30°C with shaking in YPD broth media [71].
Plasmodium falciparum in vitro culture
The 3D7 P. falciparum strain was kept in fresh type O+ human erythrocytes suspended at 2% hematocrit in HEPES-buffered RPMI 1640 (Sigma-Aldrich), containing 10% (w/v) heat inactivated human serum, 0.5 mL gentamicin (Sigma-Aldrich), 2.01 g sodium bicarbonate, and 0.05 g hypoxanthine at pH 6.74. Prior to culture, the complete medium was depleted of EVs and debris by ultracentrifugation at 100,000 × g for 1 h before the culture. The parasite cultures were maintained in a controlled environment at 37 °C in a gassed chamber at 5% CO2 and 1% O2.
Purification of EVs Derived from Malaria-Infected Red Blood Cells (iRBC-EVs)
EVs from iRBCs (mixed stages, at a maximum parasitemia of 5%) were isolated from cell culture supernatants as previously described [20] and RBC-EVs were isolated from uninfected RBCs stimulated with the calcium ionophore A23187 (10 μM, 2 hours, 37°C) to trigger vesiculation. First, 15 ml of cell culture supernatants of P. falciparum-infected RBCs were collected. Cells and cellular debris were removed from the supernatant by centrifugation at 600 × g, 1600 × g, 3600 × g, and, finally, 10,000 × g for 15 min. To further concentrate the EVs, the supernatant was filtered through a Vivacell 100 filter (100 kDa molecular weight cut off; Sartorius). Then, the concentrated supernatant was pelleted at 100,000 × g, the pellet resuspended in PBS and layered on top of a 60% sucrose cushion and spun at 100,000 × g for 16 h. The interphase was collected and washed with PBS twice at 100,000 × g for 1 h to yield EVs.
Neutrophil isolation from whole blood
Human blood samples from healthy donors (aged 18 years and older) were purchased from Research Blood Components, LLC. Primary neutrophils were isolated within 1h after draw using the human neutrophil direct isolation kit (STEMcell Technologies) following the manufacturer’s instructions. The purity was shown to be higher than 96% by flow cytometry. For microfluidic experiments, primary neutrophils were stained with Hoechst 33342 trihydrochloride dye (Thermo Fisher Scientific). Stained neutrophils were then suspended in RPMI 1640 media containing 20% FBS (Thermo Fisher Scientific) at a concentration of 2 × 107 cells/mL in the chemotaxis device or 2.5 x 106 cells/mL in the swarming assay.
Neutrophil stimulation
Where indicated, neutrophils were stimulated with lipopolysaccharide from Escherichia coli O111:B4 (Sigma-Aldrich) at 100 ng/mL for 5 hours. E. coli O111:B4 is a smooth-form LPS and a potent TLR4/MD-2 agonist widely used as a standard activator of primary human neutrophils.
EV treatment of neutrophils
The EV-to-neutrophil ratio in our assays (about 3000 EVs per neutrophil) was calculated to fall within the physiological range estimated from circulating EV concentrations in plasma (1010/mL) [72] and normal blood neutrophil counts (1,500–7,500 per μl), which predict a theoretical maximum of approximately 1,300–6,700 EVs per neutrophil in vivo. The percentage of EVs derived from RBCs is around 45% [73].
Confocal analysis for internalized EVs
Neutrophils (2 x 106 cells/mL) were seeded onto coverslips treated with 0.01% poly-L-lysin for 30 min at room temperature (RT) in a 24-well tissue culture plates, incubated at 37°C in a humidified 5% CO2 atmosphere. Cells were then washed three times before inhibitor pre-treatment for 30 min, before adding 100μg of PKH67 labeled EVs to the neutrophils and incubated for 2 hours. The following inhibitors were used: Dynasore (80 μM) [74,75], colchicine (31 nM) [76,77], latrunculin A (5 μM) [78,79] and nocodazole (20 μM) [80]. Next, the excess and unbound EVs were removed by 3 washes with PBS and the cells were fixed with 3% paraformaldehyde in PBS for 15 minutes at RT. After fixation, cells were permeabilized for 5 minutes in PBS containing 0.1% Triton X-100, washed with PBS, and stained with phalloidin dye (Invitrogen) for 20 min at RT then washed off gently 3 times with PBS afterward to remove excess phalloidin dye. The DNA was counterstained for 5 minutes with Hoechst 33342. Cover- slips were mounted on a glass slide with mounting media. Images were captured using a Leica TCS SP5 confocal microscope with a 63X oil immersion objective. Images were collected, and confocal z-sections at 1024 X 1024 magnification were acquired at 0.3-mm intervals. Images were further analyzed and processed using Imaris software, Image J and affinity designer software.
Killing experiment in neutrophils and HL-60
Ex vivo killing of bacteria by blood neutrophils was assessed in a gentamicin protection assay and quantified by bacterial culture. Salmonella typhimurium strain SL1344 or Staphylococcus aureus were cultured overnight in 10 mL LB at 37 °C with shaking at 175 rpm. The next day, the bacteria were diluted 1:20 and let grown to the exponential phase (OD = 600). The bacteria were opsonized for 10 min in human serum (EVs pre-incubated with human serum to facilitate receptor-mediated uptake). Cells were resuspended at a density of 1x106/mL in RPMI without antibiotics and seeded at 5x105 cells per well in a 24-well. Plates were incubated at 37°C and 5% CO2 for 20 minutes prior to the addition of S. typhimurium or S. aureus at a multiplicity of infection 10:1 (MOI, bacterium: eukaryotic cell ratio). After the incubation time, the medium was removed and replaced with medium containing gentamicin (10 μg/mL in RPMI) for 30 minutes to kill extracellular bacteria. The cells were washed and lysed in 500 μl of ice-cold distilled water containing 1% Triton for 1 hour for lysis of neutrophils and 10-fold dilutions were plated on LB agar, incubated for 18 h at 37°C and viable bacteria colonies were quantified using Image J. Growth experiments were repeated at least three times.
Generation of HL60miR451a and HL60scramble lines
For the construction of the miR451a lentiviral vector, fragments containing the human miR451a genomic clusters were amplified by PCR from human genomic DNA and directionally cloned into the AgeI and EcoRI sites of the pLKO-1 vector (Addgene#8453, Cambridge, MA) using the primers: forward 5′- AAAAACCGGTCTAGTCCGGGCACCCCCAG -3′ and reverse 5′- AAAAGAATTCCCTACCCCCAATCCCACGC -3′. Lentiviral particles were produced by transfecting HEK 293F cells using the LV-MAX Lentiviral Production System with packaging plasmid psPAX2 and envelope plasmid pMD2.G (both Addgene#12260, #12259) and harvesting supernatants after 48 h, as described by manufacturer’s instructions. Viral supernatant was harvested 48 h post transfection, filtered (0.45 μm pore size), and transduced into HL-60 cells in the presence of 8 μg/mL of polybrene (Sigma-Aldrich). After 24 h, cells were maintained in medium containing 0.6 μg ml−1 puromycin (Sigma-Aldrich) for selection. As a negative control we used the plasmid pLKO.1 GFP shRNA Addgene#30323) with the target sequence 5’GCAAGCTGACCCTGAAGTTCAT3’. Stable lentiviral integration and miR451a overexpression were confirmed by quantitative RT-PCR (S8).
RNA isolation and sequencing
Total RNA was extracted from 5x106 neutrophils or 1x106 HL-60 cells using the RNeasy Mini Kit including a DNase digest according to the manufacturer’s instructions (QIAGEN). The quality and quantity of the extracted RNA were assessed using a NanoDrop spectrophotometer and a Qubit fluorometer (Thermo Fisher Scientific). RNA integrity was further evaluated using the Agilent 2100 Bioanalyzer (Agilent Technologies), ensuring that all samples had an RNA integrity number (RIN) >7.0 before proceeding to library preparation.
For library preparation, the mRNA present in the total RNA sample was isolated with magnetic beads of oligos d(T). Subsequently, mRNA was randomly fragmented, and cDNA synthesis was performed using random hexamers and reverse transcriptase enzyme. The library was ready after end repair, A-tailing, adapter ligation, size selection, amplification, and purification. The library preparation was performed using Novogene’s property RNA Library Prep Kit (PT042) based on NEB Next Ultra RNA Library Prep Kit and NEB Next.
The library was checked with Qubit 2.0 and real-time PCR for quantification and bioanalyzer Agilent 2100 for size distribution detection. Quantified libraries were pooled and sequenced on Illumina NovaSeq X Plus. The NovaSeq X Plus sequencing system was used to sequence the libraries in a paired end 150bp (PE150) with 6 GB raw of data output per sample.
Single-end cDNA libraries were prepared for each sample using TruSeq RNA Sample Preparation Kit and sequenced on Illumina’s HiSeq 2000 platform. The data for RNA-Seq were uploaded to GEO (GEO accession number: GSE296044).
Microfluidic device fabrication
To prepare the glass swarming assay, ultraclean glass slides (ThermoFisher UltraClean Microarray Slides, Fisher Scientific) were micro-patterned with a solution containing poly-L-lysin and FITC-ZETAG (1.6mg/mL) using a Polypico micro-dispensing machine (PolyPico Technologies). Live Candida albicans were used as targets for neutrophils swarms. Candida albicans were cultured overnight and suspended in sterile water prior to micro-patterning. C. albicans was seeded onto each glass slide and was allowed to adhere for 5–10 mins on a rocker. Excess C. albicans was then washed twice with PBS. Prior to experiment, glass slides were placed in a commercially available open well chamber [10].
The microfluidic devices for NETs assay were fabricated using standard microfabrication technologies. One layer (35 µm thick) of SU8 photoresist (Microchem) was patterned on a silicon wafer using photolithography masks, following standard processing recommendations from the manufacturer. The wafer with patterned photoresist was used as a mold to produce pieces of polydimethylsiloxane (PDMS, Fisher Scientific), which were subsequently bonded irreversibly to standard glass slides (1 × 3 inches, Fisher).
Prior to use, microfluidic devices were primed with phosphate buffered saline (1X PBS pH 7.4, Life technologies). Blood samples (20 µL), within 1 h after collection, were diluted in 480 µL of PBS and 0.50 µL of 1 mM Sytox-Green (Thermo Fisher Scientific). The diluted blood was loaded into the large inlet reservoir while the outlet of the device is connected to a 1 mL syringe via a tube mounted on an electric pump. A volume of 100 µL diluted blood was pipetted into the outlet and pumping was done at a flow rate of 10 µL/min. The final volume processed by the device is 50 µL.
The microfluidic device used for chemotaxis assays consists of a large egg-shaped chamber with a single entrance channel connected to the inner central reservoir (Fig 3A) [81]. A chemoattractant gradient is established in the device from the large egg-shaped chamber through the connecting channel to the inner chemoattractant chambers (S1 Video). This device enables us to monitor and track chemotaxis in primary neutrophil towards chemoattractant gradient. To estimate number of chemotactic neutrophils using the egg-shaped device, we calculated the percentage of cells migrating into the inner chemoattractant chamber.
Visualization and quantification of NET formation in whole blood
The quantification of NETosis in whole blood using the NET microfluidic device was carried out by measuring the Sytox-green fluorescence area on the device. Sytox-green fluorescence area was recorded by taking stitched images of the entire device using the FITC channel on a time lapse microscope at RT. The areas of chromatin trapped in the device was quantified after automatic thresholding using Image J (triangle threshold filtration, U.S. National Institutes of Health, Bethesda, MD, USA) by using the analyze particle plugin in Image J.
Visualization of NET formation in isolated primary neutrophils
The visualization of NETosis was carried out as previously described [82]. Briefly, isolated neutrophils were fixed for 30 min at RT in 3% paraformaldehyde, permeabilized with 0.5% Triton X-100, and blocked for 30 min in blocking buffer. The samples were then incubated for 60 min with the primary antibody as follow: anti-human histone H3 mouse monoclonal antibody (diluted 1:100) and anti-human Cit-H3 rabbit polyclonal antibody (1:100) (ab5103; Abcam, Cambridge, UK). After washing in PBS, each primary antibody was visualized using secondary antibodies coupled to 1:500 Alexa Fluor 546 goat anti- mouse IgG (Thermo Fisher Scientific) and 1:500 Alexa Fluor 488 goat anti- rabbit IgG (Thermo Fisher Scientific). After incubation for 60 minutes with the secondary antibodies, the specimens were washed with PBS, and the DNA stained with 49,6-diamidino-2-phenylindole (Thermo Fisher Scientific) in PBS for 5 min. All procedures were performed at RT. The specimens were analyzed using a confocal laser-scanning microscope and processed using ImageJ/Fiji software.
Analysis of primary neutrophil migration
We used Track-mate module in Fiji ImageJ (ImageJ, NIH) to track and analyze primary neutrophils migrating from the large egg-shaped chamber through the entrance channel connected to the chemo-attractant inner central reservoir. Primary neutrophils with fluorescently stained iRBC-EVs were identified by using the track mate plugin in Image-J before and after migration. Percentage of migration was calculated as thus: (NTIC)/(NTC)*100. Where NTIC is total number of migrated cells in the chemo-attractant inner central reservoir after migration and NTC is total number of cells in the device before migration.
Swarming experiment
All imaging experiments were conducted using a fully automated Nikon TiE microscope. Time-lapse imaging was conducted using a × 10 Plan Fluor Ph1 DLL (NA = 0.3) lens. Swarming targets to be observed during time lapse were selected and saved using the multipoint function in NIS elements prior to loading of neutrophils. 200–300 hundred thousand neutrophils were added to each well unless otherwise noted. All selected points were optimized using the Nikon Perfect Focus system before starting the experiment. Changes in swarm size over time were estimated using track mate plugin in Image J. The cell-occupied area was measured from the Hoechst labeled primary neutrophil using filter and suitable threshold on image J.
Single cell RNA-seq
A total of 192 single cells were sorted in 96 well plates containing lysis buffer using FACS Aria and libraries generated according to Smart-seq2 protocol [83]. These included 48 single cells from each of the four experimental groups namely: HL60miR451aBact, HL60mir451a, HL60Scramblebact and HL60Scramble. Libraries were sequenced on Illumina’s NextSeq 550 platform and the raw reads mapped towards the human reference genome (hg19) using STAR mapper [84]. Gene expression was quantified based on uniquely mapped reads as guided by Ensembl annotations. To filter low quality cells different metrics were assessed including, number of detected genes (at least 50 detected genes per cell), ratio of exon-intron mapped reads (greater than 50%), fraction of mitochondrial gene (less than 20%), fraction of reads attributable to the highly expressed genes among others. Only cells that passed quality control analysis and without detectable batch effects were used for downstream analysis. Here individual cell’s transcriptome was normalized to counts per 100K and log transformed. Using variable genes, individual cells were transformed into low dimensional space using Principal Component Analysis (PCA) analysis and projected onto UMAP/tSNE subspace. Clusters were detected using Louvain algorithm for each subpopulation. The quality control analysis, subpopulation discovery and exploration were carried out using the methods as implemented in scanpy (version 1.8.1) [85]. We used nonparametric Wilcoxon rank-sum test for differential gene expression analysis between subpopulations.
Reactive oxygen species (ROS) assay
ROS production was assessed by measuring extracellular hydrogen peroxide release using Amplex UltraRed (Thermo Fisher) in the presence of 10 U/mL horseradish peroxidase. Neutrophils were resuspended in HBSS at a concentration of 1 × 10⁶ cells/mL. In a black 96-well flat-bottom microplate (Costar), 100 µL of cell suspension was added per well. Amplex UltraRed was used at a final concentration of 50 µM, and HRP was added at 0.1 U/mL. The cells were pre-treated or not with EVs for 1 hour. For stimulation, PMA (100 nM final concentration) was added to the wells. Control wells received vehicle alone. The plate was incubated at 37°C in the dark, and fluorescence was measured at 530 nm excitation and 590 nm emission using a microplate reader (Synergy, H1, Biokek).
RNA Fluorescence in situ hybridization
We performed RNA fluorescent in situ hybridization staining essentially as described [27]. Briefly, we used 10 nM of DIG-labelled miRNA LNA probes (Exiqon). A scrambled miRNA LNA probe was used as a negative control. After a series of post-hybridization washes, the LNA signal was amplified using the Tyramide Signal Amplification PLUS Fluorescein Kit (Perkin-Elmer, Waltham MA) according to the manufacturer’s instructions.
Statistical analysis
Statistical significance of the differences between multiple groups were tested using two-way Analysis of Variance (ANOVA) in GraphPad Prism (GraphPad Software, version 8.3.0). Within ANOVA, significance between two sets of data was further analyzed using two-tailed t-tests. Differential gene expression analysis was performed with the R package edgeR (v4.0.16) [86]. GO term enrichment analysis was performed using the ClusterProfiler R package (v3.14.3) [87]. Since most genes did not show a normal distribution, differences in gene expression were assessed with the Wilcoxon test, corrected for multiple testing with the Benjamini-Hochberg method, on vst (variance stabilizing transformation) normalized data from edgeR.
Supporting information
S1 Video. PMN migrating in the egg-shaped chip device.
PMN migrate towards fMLP gradient from the large egg-shaped chamber into the inner micro-chamber via the connecting channel. The inner central reservoir is about 200 µm wide while the large egg-shaped chamber is 300 µm wide. The connecting entrance channel is about 125 µm long and 10 µm wide. The circled primary neutrophils are the cells with iRBC-EVs identified by the tracking plugin on image J. The time interval between frames is 4 minutes. Scale bar is 50 μm. The nucleus of the PMN is stained with Hoechst dye (blue).
https://doi.org/10.1371/journal.ppat.1014533.s001
(AVI)
S2 Video. C. albicans target growth.
Micro-patterned C. albicans (expressing a far-red fluorescent protein) spot growth in the microfluidic device assay was monitored for 10hours. The time interval between frames is 4 minutes. Scale bar is 50 μm.
https://doi.org/10.1371/journal.ppat.1014533.s002
(MOV)
S3 Video. Untreated PMN swarming on C. albicans target.
PMN (blue - nucleus stained with Hoechst dye) swarm around C. albicans target. The C. albicans stained in violet dye. Isolated neutrophils are loaded on the swarming assay device at a concentration of 2.5 x 106 cells/mL. The time interval between frames is 5 minutes. Scale bar is 50 μm.
https://doi.org/10.1371/journal.ppat.1014533.s003
(MOV)
S4 Video. iRBC-EVs treated PMN swarming on C. albicans target.
PMN (blue - nucleus stained with Hoechst dye) swarm around C. albicans target. The C. albicans stained in violet dye. Isolated neutrophils are loaded on the swarming assay device at a concentration of 2.5 x 106 cells/mL. The time interval between frames is 5 minutes. Scale bar is 50 μm.
https://doi.org/10.1371/journal.ppat.1014533.s004
(AVI)
S1 Fig. Datasets overview.
A. Total reads for per cell in each treatment category (herein also referred to as cell type) HL60scramble. B. Number of genes detected per cell grouped according to the treatment category. C. Scatter plot of total counts vs. number of genes per cell colored according to cell treatment category. D. Top 30 highly expressed genes across all cells independent of treatment. E. Top 30 highly expressed genes across all cells in HL60scramble group. F. Top 30 highly expressed genes across all cells in HL60mir451a group. G. Top 30 highly expressed genes across all cells in HL60scramble Bact group. H. Top 30 highly expressed genes across all cells in HL60mir451aBact group.
https://doi.org/10.1371/journal.ppat.1014533.s005
(TIF)
S2 Fig. HL60scramble versus HL60mir451a.
These figures highlight the results from pairwise analysis of the two groups (HL60scramble and HL60mir451a. A. PCA projection of all HL60scramble and HL60mir451a cells based on components 1 and 2. Projection based on other components and using varying parameters failed to generate clear separation between the two cell populations. B. Dotplot of expression of some of the published key markers involved in malaria and bacterial co-infection response in the two subpopulations. C. The loading scores of top 10 genes that may be responsible for the separation of HL60scramble and HL60mir451a based on PC1 and PC2. D. The expression of top 3 genes that may be responsible for the separation of HL60mir451a from HL60scramble in PCA subspace based on the first two components 1 and 2. E. The expression of top 3 genes that may be responsible for the separation of HL60mir454a from HL60scramble in PCA subspace. F. The projection of both cell types onto UMAP based on PCA components 4 and 5. G. The 4 subpopulations (SPs) discovered based on unsupervised approach (Louvain method as implemented in Scanpy) and projected unto UMAP space. H. Some of ten genes that may be involved in defining the four SPs described above (6G).
https://doi.org/10.1371/journal.ppat.1014533.s006
(TIF)
S3 Fig. HL60Scramble versus HL60Scramble Bact.
These figures highlight the results from pairwise analysis of the two groups (HL60scramble and HL60scramble Bact). A. PCA projection of all HL60scramble and HL60scrambleBact cells based on components 3 and 4. This was the best separation attained according to treatment of these cell types after several trials with different approaches and parameters. B. Dotplot of expression of some of the published key markers/genes involved in malaria and bacterial co-infection response. C. The loading scores of top 10 genes that may be responsible for the separation of HL60Scramble from HL60ScrambleBact when projected on PC3 and PC4. D.The expression of top 3 genes that may be responsible for the separation of HL60Scramble from HL60Scramble Bact in PCA subspace based on the first two components 1 and 2. E. The expression of top 3 genes that may be responsible for the separation of HL60scrambleBact from HL60scramble in PCA subspace based on the first two components 1 and 2. F. The projection of both cell types onto UMAP based on PCA components 3 and 4. G. The 5 subpopulations (SPs) discovered based on unsupervised approach (Louvain method as implemented in Scanpy) and projected unto UMAP space. H. Some of ten genes that may be involved in defining the five SPs described above (3G).
https://doi.org/10.1371/journal.ppat.1014533.s007
(TIF)
S4 Fig. HL60mir451a versus HL60mir451aBact.
These figures highlight the results from pairwise analysis of the two groups (HL60mir451a and HL60mir451aBact). A. PCA projection of all HL60mir451a and HL60mir451aBact cells based on components 1 and 2. Projection based on other components and using varying parameters failed to generate clear separation between the two cell populations. B. Dotplot of expression of some of the published key markers involved in malaria and bacterial co-infection response in the two subpopulations. C. The loading scores of top 10 genes that may be responsible for the separation of HL60mir451 and HL60mir451aBact based on PC1 and PC2. D. The expression of top 3 genes that may be responsible for the separation of HL60mir451a from HL60mir451aBact in PCA subspace based on the first two components 1 and 2. E. The expression of top 3 genes that may be responsible for the separation of HL60mir451aBact from HL60mir451a in PCA subspace. F. The projection of both cell types onto UMAP based on PCA components 2 and 3. G. The 4 subpopulations (SPs) discovered based on unsupervised approach (Louvain method as implemented in Scanpy) and projected unto UMAP space. H. Some of ten genes that may be involved in defining the four SPs described above (8G).
https://doi.org/10.1371/journal.ppat.1014533.s008
(TIF)
S5 Fig. HL60scramble Bact versus HL60mir451aBact.
These figures highlight the results from pairwise analysis of the two groups (HL60Scramble Bact and HL60mir451a Bact). A. PCA projection of all HL60scramble Bact and HL60mir451a Bact cells based on components 1 and 2. Projection based on other components and using varying parameters failed to generate clear separation between the two cell populations. B. Dotplot of expression of some of the published key markers involved in malaria and bacterial co-infection response in the two subpopulations. C. The loading scores of top 10 genes that may be responsible for the separation of HL60scrambleBact from HL60mir451Bact based on PC1 and PC2. D. The expression of top 3 genes that may be responsible for the separation of HL60scrambleBact from HL60mir451Bact in PCA subspace based on the first two components 1 and 2. E. The expression of top 3 genes that may be responsible for the separation of HL60mir451Bact from HL60scrambleBact in PCA subspace. F. The projection of both cell types onto UMAP based on PCA components 2 and 3. G. The 5 subpopulations (SPs) discovered based on unsupervised approach (Louvain method as implemented in Scanpy) and projected unto UMAP space. H. Some of ten genes that may be involved in defining the five SPs described above (4G).
https://doi.org/10.1371/journal.ppat.1014533.s009
(TIF)
S6 Fig. Differential gene expression analysis between HL60ScrambleBact and HL60mir451aBact A.
The mean-log fold change (MA) plot of the two cell types with the significant differential expressed genes colored in orange, significant genes overlapping published ones from Kobayashi et al (Kobayashi et al. 2003; Kobayashi et al. 2002) in red and non-significant ones in black. B. Four of the significant gene ontology (GO) biological processes based on gene set enrichment analysis of log-fold change ranked genes between the two populations.
https://doi.org/10.1371/journal.ppat.1014533.s010
(TIF)
S7 Fig. iRBC-EVs do not induce neutrophil apoptosis or death after 6 h treatment.
Representative flow cytometry plots of Annexin V-FITC/7-AAD staining in primary human neutrophils left untreated (PBS) or treated for 6 hours with EVs derived from uninfected RBCs (RBC EVs) or P. falciparum-infected RBCs (iRBC EVs). The percentage of cells in each quadrant is comparable across all three conditions, indicating that neither RBC EVs nor iRBC EVs significantly affect neutrophil viability at this time point. Values represent mean ± SD (n = 3 independent donors).
https://doi.org/10.1371/journal.ppat.1014533.s011
(TIF)
S8 Fig. Validation of miR451a overexpression in the HL-60miR451a line by RT-qPCR.
Relative expression of mature miR451a, measured by quantitative RT-PCR and normalized to U6 snRNA, in HL-60miR451a compared to HL-60scramble control line. HL-60miR451a cells show robust overexpression of miR451a relative to the scramble control, confirming successful transduction and expression of the miR451a construct. Bars represent mean ± SD.
https://doi.org/10.1371/journal.ppat.1014533.s012
(TIF)
S1 Table. Number of differentially expressed genes in human neutrophils after different in-vitro stimulations.
https://doi.org/10.1371/journal.ppat.1014533.s013
(DOCX)
S2 Table. Number of DEG in HL60miR451a after in-vitro stimulation with LPS at different time points.
https://doi.org/10.1371/journal.ppat.1014533.s014
(DOCX)
Acknowledgments
The authors thank Isabelle Fellay, Oriana Coquoz and Marianne Blanchard for their technical support.
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