Malaria is a parasitic infection spread by mosquitoes that causes hundreds of millions of cases each year. People are most likely to die from malaria the first time they are infected – usually when they are young children. Among those who survive, however, few will develop severe symptoms again, even though they are often reinfected with as many (or even more) parasites. This indicates that people do not get better at eliminating the parasite. Instead, protection from severe malaria is a form of tolerance - the body learns to limit the damage the infection causes. But exactly which mechanisms have to be engaged to tolerate malaria is unclear.

One way to achieve tolerance may be to switch off damaging inflammation. Nahrendorf et al. explored this possibility by comparing the immune response of mice to their first and second infection with malaria parasites. During the first infection of life, immune cells release harmful inflammatory molecules that activate the lining of blood vessels, causing tissue damage and severe symptoms. During the second infection, these immune cells shut down inflammation and instead actively promote tissue health to reduce damage and improve outcome. This change in the immune response occurs despite the fact that the number of parasites is the same in both infections.

Nahrendorf et al. also found that the mouse’s immune cells ’remembered’ to tolerate subsequent infections, even after treatment with a drug that kills all malaria parasites. This was possible because malaria permanently altered the spleen, which reprogrammed the response of the immune cells. A single infection is therefore enough to induce long-lived mechanisms of tolerance that can prevent life-threatening disease.

These findings have the potential to change the understanding of immunity to malaria, which currently emphasises the importance of killing parasites. New ways to treat and vaccinate people - and to protect young children from severe malaria - may arise by treating tolerance as an equally important form of host defense.

Mechanisms of host resistance can eliminate pathogens, but it is disease tolerance that functions to preserve life. Tolerance mechanisms of host defense do not have a direct impact on pathogen load and instead act to minimise tissue damage caused by the pathogen and the immune response targeting it. They also function to protect vital homeostatic processes, such as energy metabolism, under conditions of infection-induced stress (Martins et al., 2019). We have taken tremendous steps to understand acquired resistance mechanisms that can eliminate pathogens and provide sterile immunity. On the other hand, it is unclear whether mechanisms of disease tolerance can persist after pathogen clearance to provide an alternative strategy of acquired immunity. And it is these mechanisms that are likely to be at the forefront of host defense when sterile immunity cannot be generated.

We propose that immunity to severe life-threatening malaria is underpinned by acquired mechanisms of disease tolerance. The majority of malaria-induced deaths occur in children under the age of 5 infected with Plasmodium falciparum (Weiss et al., 2019). A landmark prospective study in Tanzania followed 882 children from birth and showed that the risk of developing severe malaria is highest in the first few infections of life, and very few children (<1.8%) have more than one severe episode (Gonçalves et al., 2014). These data therefore support the longstanding view that immunity against severe malaria is acquired rapidly – often before 12 months of age (Gupta et al., 1999; Marsh and Snow, 1999). Crucially, this study further showed that children who survive severe malaria are frequently reinfected and experience episodes of febrile malaria with similar or even higher parasite densities (Gonçalves et al., 2014). Immunity to severe forms of malaria is therefore not due to improved parasite elimination (resistance) but instead underpinned by the improved ability of the host to limit the pathological consequences of infection (tolerance).

There is a growing body of evidence that shows mechanisms of disease tolerance are required to survive a first malaria episode: for example, acute infection causes hypoglycaemia and so the ability to maintain blood glucose levels within dynamic range – in crosstalk with iron metabolism (Weis et al., 2017) – can determine life or death (Cumnock et al., 2018). Furthermore, the induction of heme oxygenase-1 by nitric oxide (Jeney et al., 2014) leads to the detoxification of free heme, which is released from ruptured red cells (Seixas et al., 2009) – this protects renal proximal tubule epithelial cells and prevents acute kidney injury (Ramos et al., 2019). Nonetheless, whilst these metabolic adaptations are doubtless essential for the survival of naive hosts there is no evidence as yet that they can be recalled and applied more effectively in subsequent infections to provide clinical immunity.

Instead, the most effective way to induce persisting mechanisms of disease tolerance may be through host control of inflammation (Medzhitov et al., 2012), which can limit collateral tissue damage and avert fatal metabolic perturbations. In malaria, the ability to control systemic inflammation may also minimise the detrimental effects of parasite sequestration by reducing activation of the endothelium and restricting available binding sites in the microvasculature (Schofield and Grau, 2005). Field data support the idea that dampening the inflammatory response to P. falciparum affords protection; lower levels of circulating pro-inflammatory cytokines are found in children in Malawi who survive severe malaria compared to those who succumb (Mandala et al., 2017). And critically, inflammation can be reduced in the absence of improved parasite control; Ghanaian children in a high transmission area have higher parasite densities but less systemic inflammation and fewer febrile episodes than children in a lower transmission setting (Ademolue et al., 2017).

The blood cycle in malaria unleashes a plethora of parasite-derived as well as host tissue damage-associated signals, all of which can be sensed by innate immune cells. Additionally, pronounced changes in host physiology (such as hypoxia and acidaemia) are hallmarks of severe disease (von Seidlein et al., 2012). Together these diverse signals trigger monocytes and macrophages to produce pro-inflammatory molecules, many of which have been associated with a poor prognosis including the prototypical myeloid-derived cytokines TNF and IL-6 (Mandala et al., 2017). Macrophages can even directly cause severe anaemia through extensive bystander phagocytosis of healthy uninfected red cells in the spleen and impairment of erythropoiesis in the bone marrow (Jakeman et al., 1999; Pathak and Ghosh, 2016). Given these key roles in pathogenesis, it may be possible to quickly reduce the risk of severe malaria by modifying the myeloid response to blood-stage infection. Importantly, an emerging body of literature shows that the response of myeloid cells is not hardwired but can be reprogrammed by pathogens and their products. This was first demonstrated by stimulating bone marrow-derived macrophages with LPS in vitro, which reduces the production of pro-inflammatory cytokines and increases the release of antimicrobial effector molecules upon re-stimulation (Foster et al., 2007). The ability of monocytes and macrophages to adapt their response to repeated pathogen encounters through cell intrinsic modifications is termed innate memory (Netea et al., 2016). And although myeloid cells are usually short-lived and terminally differentiated, memory can nevertheless be imprinted through the epigenetic reprogramming of either long-lived tissue-resident macrophages (Wendeln et al., 2018) or progenitor cells in the bone marrow (Kaufmann et al., 2018).

Innate memory has been shown to operate independently of pathogen load (Dominguez-Andres and Netea, 2019; Seeley and Ghosh, 2017) and human monocytes produce less pro-inflammatory cytokines when stimulated after an episode of febrile malaria as compared to before (Portugal et al., 2014), suggesting that they are intrinsically modified by infection. We therefore hypothesised that innate memory – leading to the functional specialisation of monocytes and macrophages to limit inflammation and associated pathology – offers the most compelling explanation for how immunity to severe malaria can be acquired so quickly and without the need for enhanced parasite control.

To investigate acquired mechanisms of disease tolerance and the role of innate memory in vivo, we needed to examine monocyte progenitors in the bone marrow and long-lived tissue-resident macrophages in the spleen. And since these tissues are not readily accessible in human malaria, we needed a model that would recapitulate at least some key features of human infection. Given that a meta-analysis of malariotherapy data shows naive hosts quickly adapt to tolerate chronic parasitaemia (for example, by increasing their pyrogenic threshold [Gatton and Cheng, 2002]), we chose a rodent malaria parasite (Plasmodium chabaudi) that establishes chronic recrudescing infections in laboratory mice. Importantly, experimental infections were initiated with sporozoites, since we have previously shown that mosquito transmission resets expression of the large sub-telomeric multi-gene families that control parasite virulence (Spence et al., 2013). And furthermore, we used two parasite genotypes to try and uncouple the relative contribution of parasite-derived versus damage-associated signals in promoting mechanisms of tolerance. P. chabaudi AS causes a mild infection, characterised by a low pathogen load and few clinical symptoms (Figure 1A–C). In contrast, P. chabaudi AJ (which has more than 140,000 SNPs cf. P. chabaudi AS [Otto et al., 2014]) leads to acute hyperparasitaemia and severe anaemia (Figure 1B–C), accompanied by hypothermia and prostration (Figure 1—figure supplement 1A). AJ shares many key features with AS such as synchrony, chronicity and a persisting low-grade anaemia (Figure 1B–C, Figure 1—figure supplement 1B–C), and yet whilst AS sequesters in key immune sites such as bone marrow and spleen (Brugat et al., 2014) we find no evidence that AJ sequesters in host tissues (Figure 1—figure supplement 1D).

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Monocytes differentiate into inflammatory macrophages in naive hosts

We therefore carefully characterised the fate and function of inflammatory monocytes in the spleen by RNA sequencing (Figure 2A) and used clueGO to reveal the complexity of their response to a first encounter with malaria parasites (Bindea et al., 2009; Mlecnik et al., 2014). ClueGO assigns significant gene ontology (GO) terms based on differential gene expression and groups them into functional networks by relatedness. When we merged all linked nodes into supergroups (see Materials and methods) we found that more than one third of all GO terms were related to host defence (Figure 2B and C). Furthermore, clueGO identified interferon signaling as an upstream regulator of monocyte fate (Figure 2B); in agreement, interferon-inducible guanylate binding proteins were highly upregulated (Figure 2—figure supplement 1A).

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We next used core lineage signatures to predict the likely outcome of monocyte differentiation in the spleen, where they can be instructed to become either inflammatory macrophages or monocyte-derived dendritic cells (Menezes et al., 2016). This revealed that monocytes initiate a transcriptional programme that is typical of terminally differentiated inflammatory macrophages (Figure 2D). They upregulate their ability to sense parasite- and host-derived danger signals by increasing transcription of diverse pattern recognition receptors (Figure 2—figure supplement 1B), and upregulate expression of the hallmark cytokines and chemokines associated with a type I inflammatory response (Figure 2E). Furthermore, they enhance their capacity to engage T cell immunity by upregulating all major components of the antigen processing and presentation machinery (for class I and class II MHC), and attempt to fine-tune T cell activation by increasing their expression of co-stimulatory molecules and checkpoint inhibitors (Figure 2F–G). Notably, a clear Warburg effect – the metabolic switch from oxidative phosphorylation to glycolysis described when monocytes are stimulated with LPS in vitro (Cheng et al., 2014) – was not observed in vivo in response to malaria. Instead, the key enzymes involved in both metabolic pathways were transcriptionally induced (Figure 2—figure supplement 1C–D).

We next looked at all of these parameters of monocyte and macrophage biology in our mild model of malaria. Surprisingly, despite substantial differences in parasite density and patterns of sequestration (Figure 1B and Figure 1—figure supplement 1D) the response of myeloid and progenitor cells in bone marrow, blood, and spleen was remarkably similar between AS and AJ (Figure 2—figure supplement 2A–C). Moreover, the fate and function of spleen monocytes was essentially identical – a direct pairwise comparison identified only a single differentially expressed gene (Kelch34) between the two models. In turn, clueGO analysis revealed that the four largest supergroups in monocytes isolated from AJ-infected mice (Figure 2B–C) also dominated the response to AS (Figure 2—figure supplement 2D–E). It therefore appears that parasite genotype, pathogen load, and the sequestration of infected red cells in immune tissues does not fundamentally alter the myeloid response to acute infection. Instead, the haematopoietic switch that promotes myelopoiesis in the bone marrow (and relocates erythropoiesis to the spleen), the disappearance of tissue-resident macrophages and the differentiation of monocytes into inflammatory macrophages may all be part of an emergency response that is unavoidable in a naive host.

Tolerance persists to protect host tissues during reinfection

We next asked whether tolerance could persist in the absence of live replicating parasites to provide long-lived protection. To answer this question, we developed a novel reinfection model that allowed us to exactly match parasite densities between first and second infection. To this end, mice were first infected with the avirulent parasite genotype AS to induce chronic recrudescing parasitaemia and then drug-treated after 40 days of infection to clear circulating and sequestered parasites. One month later, mice were infected for a second time but now with the virulent genotype AJ (Figure 3A). In this model, parasite burden (Figure 3B) and the dynamics of red cell loss (Figure 3C) are both matched between first and second infection – this eliminates pathogen load as a confounding factor when analysing acquired mechanisms of disease tolerance.

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In contrast to a first malaria episode, the bone marrow does not prioritise the production of myeloid cells upon reinfection (Figure 3D) and preserves its cellularity and structural integrity (Figure 3—figure supplement 1A). Consequently, the number of inflammatory monocytes and neutrophils released into circulation does not increase and nor does their accumulation in the spleen (Figure 3E and Figure 3—figure supplement 1B). Furthermore, the spleen maintains its boundaries between red and white pulp and does not promote extramedullary erythropoiesis (Figure 3D and Figure 3—figure supplement 1C). And whilst first infection obliterates tissue-resident macrophages these cells are resistant to malaria-induced ablation second time around (Figure 3F); this allows the host to retain ferric iron stores (Figure 3—figure supplement 1D). A single malaria episode is therefore sufficient to disarm emergency myelopoiesis in the bone marrow and protect terminally differentiated macrophages in the spleen. What's more, tissue architecture is preserved and key homeostatic processes are maintained in tolerised hosts.

Monocytes minimise inflammation and stress in tolerised hosts

Nevertheless, we reasoned that the myeloid compartment cannot be entirely quiescent during reinfection since mice are able to control replication of the virulent parasite genotype AJ. We therefore isolated spleen monocytes and examined their transcriptional programme in the acute phase of second infection. We identified more than 3000 differentially expressed genes and found that most of these were unique to reinfection (Figure 4A); remarkably, monocytes did not differentiate into inflammatory macrophages and all functions associated with this fate were silenced (Figure 4B–C, Figure 4—figure supplement 1A–C). Host control of inflammation extended beyond the boundaries of the spleen with circulating levels of CXCL10 and IFNɣ also attenuated at the peak of second infection (Figure 4D).

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We therefore explored alternative monocyte fates, such as those associated with immune regulation, wound healing, and tissue repair. However, spleen monocytes were not polarised towards alternatively activated macrophages and nor did they induce signature genes associated with myeloid-derived suppressor cells (Gabrilovich, 2017) or reparative Ly6C^lo monocytes (Jung et al., 2017; Figure 4E). Furthermore, they did not upregulate anti-inflammatory cytokines or checkpoint inhibitors (Figure 4C and Figure 4—figure supplement 1C). To make sense of their complex transcriptional profile we therefore turned once more to clueGO. In contrast to first infection, we found minimal enrichment of GO terms linked to host defence; instead, all major supergroups in second infection related to regulation of cell cycle and nuclear division (Figure 5A–C). This localised proliferation of monocytes may support a critical spleen response without having to engage increased production and recruitment from the bone marrow.

Figure 5

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So what is the function of spleen monocytes in tolerised hosts? First off, they upregulate the expression of two alarmins (S100a8 and S100a9) (Figure 5D) that form the heterodimer calprotectin; this acts as an endogenous TLR4 ligand to silence the inflammatory response to pathogen-derived or damage-associated signals. Notably, increased expression of these alarmins in early life prevents hyperinflammation to protect neonates from sepsis (Ulas et al., 2017). Similarly, Chi3l1 (encoding the prototypical chitinase-like protein) is also upregulated in second infection and can attenuate inflammasome activation to minimise collateral tissue damage (Dela Cruz et al., 2012). In both cases, these mechanisms promote host control of inflammation without impairing pathogen clearance.

Monocytes also appear to minimise the effects of hypoxia and oxidative stress on their environment. For example, in first infection monocytes upregulate Sod2, which encodes a mitochondrial protein required to safeguard inflammatory macrophages from the reactive oxygen species that they produce. But in second infection they instead upregulate Sod3, which can be secreted to inactivate extracellular reactive oxygen species in the surrounding tissue (Yao et al., 2010; Figure 5D). In much the same way, monocytes minimise host stress by upregulating Ednra, which can scavenge endothelin-1 to prevent vasoconstriction and hypertension (Czopek et al., 2019), and they increase expression of Vegfa to promote angiogenesis. Evidently, the transcriptional profile of spleen monocytes suggests that they take on a tissue protective role in second infection.

In support of this argument, monocytes differentially regulate heme-iron metabolism to deal with the release of toxic free heme and reactive iron (Fe²⁺) from ruptured red cells (Figure 5E). In brief, they increase their transcription of a core set of genes required to sequester extracellular iron (Ltf encoding lactotransferrin), import sequestered iron for detoxification (Tfr1/Tfr2 encoding the transferrin receptors) and then export detoxified iron for use in the production of new red cells (Slc40a1 encoding ferroportin). Notably, monocytes do not appear to increase their iron storage capacity, which has been associated with tissue damage in malaria (Gozzelino et al., 2012). And nor do they upregulate expression of heme oxygenase-1 (Hmox1), which is required to detoxify free heme. These data therefore indicate that monocytes specifically enhance iron recycling to promote tolerance to hemolysis – a major source of stress in malaria. Crucially, this only occurs during reinfection, despite an identical red cell loss in naive and tolerised hosts (Figure 3C).

Taken together, these data clearly show that spleen monocytes initiate a transcriptional programme designed to promote tolerance to malaria parasites upon reinfection. This is achieved in two ways – first by minimising inflammation to reduce collateral tissue damage and second by engaging pathways that can impart stress tolerance on their environment. Significantly, mice were first infected with the avirulent parasite genotype AS, suggesting that parasite-derived signals may be sufficient to redirect monocyte fate.

Malaria does not induce epigenetic reprogramming of bone marrow monocytes

So how do tolerised hosts control inflammation independently of pathogen load? To begin to answer this question, we looked once again at the functional specialisation of monocytes in second infection. Our data are consistent with a model of innate memory, whereby myeloid progenitors in the bone marrow are epigenetically reprogrammed during first infection to intrinsically modify the response of monocytes to reinfection. To test this hypothesis, we asked if malaria induces heritable histone modifications that alter the epigenetic landscape of inflammatory monocytes before their release from the bone marrow. And since tolerance can persist in the absence of parasitaemia, we isolated monocytes from once-infected mice one month after drug cure (day 70, see Figure 1A). Crucially, this was exactly the same time-point at which we had performed all of our reinfection studies (Figure 3A). In this experiment, we interrogated the distribution of histone modifications genome-wide using ChIPseq and asked whether (i) transcription start sites were marked with H3K27ac to activate transcription (ii) enhancers or superenhancers were marked with H3K4me1 to promote gene expression or (iii) DNA was condensed into heterochromatin by H3K9me3 to silence gene expression.

We used the motif discovery software HOMER (Heinz et al., 2010) to identify peaks and visualised peaks with the genomics exploration tool Integrative Genomics Viewer (Thorvaldsdóttir et al., 2013). In the first instance, we looked at the histone modification profiles of genes that define monocyte function in first and second infection; for example, genes associated with inflammation versus proliferation. Subscribing to the notion that ChIPseq reveals qualitative (not quantitative) differences (Ma et al., 2018; Orlando et al., 2014), we simply asked whether these genes were marked or not marked. Our prediction was that genes that were upregulated during first infection but silenced during reinfection (tolerised genes) would lose marks associated with active transcription or be condensed into inactive heterochromatin. Conversely, specialised genes (those upregulated exclusively during second infection) would gain marks to promote transcription. Remarkably however, we found that in almost every case the histone modification profiles of tolerised and specialised genes were identical between monocytes isolated from once-infected mice and uninfected controls (Figure 6A–B and Figure 6—figure supplement 1).

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Even when we used HOMER to call differentially modified regions (DMR) and quantify differences between once-infected and control mice, we found little evidence to support epigenetic reprogramming of monocytes – of the 2848 tolerised/specialised genes identified by RNAseq 95% had no detectable histone modifications (Figure 6C). And in those rare cases where a DMR was called, HOMER assigned a low confidence peak score (Supplementary file 2). Innate memory can not therefore easily explain the widespread transcriptional changes that lead to the functional specialisation of monocytes in tolerised hosts.

Monocytes are transcriptionally reprogrammed in the remodelled spleen

An alternative explanation is that monocyte fate is imprinted within the spleen; after all, tissue printing is a key route to organ-specific identity during monocyte to macrophage differentiation (Scott et al., 2016; van de Laar et al., 2016). We therefore looked for transcriptional evidence of long-lived changes in spleen monocytes that can persist after parasite clearance. We found 111 differentially expressed genes in monocytes isolated from once-infected mice compared to uninfected controls; remarkably, most of these genes were not differentially expressed during acute or chronic infection. Instead, this transcriptional signature was unique to the memory phase and was further enhanced upon reinfection (Figure 7A). This included the transcription factor Maf, which regulates macrophage programming in vivo (Kang et al., 2017; Liu et al., 2020), and Sirpa, which regulates recognition of self (Bian et al., 2016). The majority of genes, however, related to cell cycle and nuclear division – specialised functions of spleen monocytes in tolerised hosts. Indeed, we found remarkable overlap in the top GO terms identified in memory and second infection (Figure 7B). A critical part of the transcriptional programme designed to promote tolerance to malaria parasites is therefore already engaged in monocytes prior to reinfection. And this transcriptional signature does not appear to require epigenetic reprogramming in the bone marrow. These data thus provide compelling evidence that malaria may remodel the spleen to imprint tolerance.

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In this study, we show that mechanisms of disease tolerance can persist after pathogen clearance to minimise tissue damage, stress and pathology during subsequent infections. These inducible mechanisms of tolerance therefore provide memory and constitute an alternative strategy of acquired immunity. Crucially, these adaptations function to preserve key homeostatic processes and protect life, and are therefore likely to be the primary form of host defense when sterile immunity can not be generated. This is particularly relevant in malaria, where even partial control of parasite densities is not usually demonstrable until adolescence (Marsh and Kinyanjui, 2006). It seems likely that many complementary strategies of tolerance will need to cooperate to provide protection and we identify three mechanisms in the myeloid compartment alone – emergency myelopoiesis is disarmed; tissue-resident macrophages become resistant to malaria-induced ablation; and spleen monocytes eschew an inflammatory macrophage fate to take on a tissue protective role. These adaptations operate independently of pathogen load and coincide with a reduction in systemic inflammation; what's more, tissue integrity is preserved, ferric iron stores are maintained and endothelium activation/dysfunction is avoided during reinfection. Acquired mechanisms of disease tolerance can therefore avert hallmark features of severe malaria.

Host control of inflammation thus appears to provide an effective route to disease tolerance and monocyte activation is clearly not hardwired. However, their functional specialisation in tolerised hosts is not underpinned by epigenetic reprogramming of progenitor cells in the bone marrow but instead seems to be imprinted within the remodelled spleen. Tissue printing of immune cell function is well recognised, and allows monocytes to take on a remarkably diverse range of organ-specific roles. This has been best characterised during the differentiation of monocytes into long-lived tissue resident macrophages, in which local signals imprint specialised functions that are unique to every tissue (Scott et al., 2016; van de Laar et al., 2016). In this way, tissue printing maximises the plasticity of myeloid cells. One of many challenges when resolving the acute phase response to malaria is to refill the red pulp macrophage niche, which was obliterated by acute infection; this is achieved through the recruitment and differentiation of bone marrow-derived monocytes (Lai et al., 2018). We find that red pulp macrophages isolated from once-infected mice are transcriptionally identical to those from uninfected controls (Figure 7—figure supplement 1), which shows that tissue printing can precisely match the transcriptional programme of recruited monocytes with the functional requirements of the tissue – in this case, to re-establish ferric iron stores. It is clear then that the tissue niche imprints organ-specific identity onto monocytes that take residence to maintain homeostatic processes. But similar mechanisms must also operate to control the fate of monocytes that do not take residence and instead are recruited to deal with injury or infection (Guilliams et al., 2018). In this scenario, how might the tissue niche be remodelled by malaria to promote tolerance?

A niche is often considered to be a small self-contained tissue scaffold that provides the physical structure required to target specialised signals to resident cells or cells transiting the niche. Nevertheless, a niche can also be a dynamic and expansive space, such as the open circulatory system created by the pulp cords and sinuses of the spleen (Guilliams et al., 2020). Malaria causes pronounced splenomegaly and disruption of architecture, including a complete loss of boundaries between red and white pulp. During chronic infection, splenomegaly begins to resolve and these boundaries are re-established but the spleen may not be put back exactly as it was before – it may acquire new and altered features. For example, malaria pigment accumulates throughout the red pulp, and these persistent insoluble deposits may regulate the activation and differentiation of monocytes arriving in the remodelled spleen. In support of this idea, malaria pigment can suppress the oxidative burst (Schwarzer et al., 1992) and interferon-induced upregulation of class II MHC (Schwarzer et al., 1998) in human monocytes in vitro. Alternatively, re-assembly of the stromal cell networks that compartmentalise the spleen may alter cell patterning and thereby change the distribution and density of key signals that control monocyte fate (Bonnardel et al., 2019; Koliaraki et al., 2020). Or perhaps the tissue niche required to promote tolerance is already present in the spleen (even in a naive host) but is lost when the spleen becomes enlarged and disorganised in first infection. In this example, it is the preservation of spleen architecture that facilitates the functional specialisation of monocytes in tolerised hosts. In every scenario, monocytes arriving in the remodelled spleen receive a new combination of signals that can minimise inflammation and induce their tissue protective functions (see Figure 7C for a working model). Importantly, tissue remodelling and the accumulation of malaria pigment is observed in the human spleen (Buffet et al., 2011), and we therefore suggest that this is a conserved strategy through which malaria imprints tolerance. This will almost certainly benefit the parasite as well as the host; after all, if you can minimise host sickness behaviour you will likely maximise the probability of onward transmission.

Other changes to the niche include the appearance of malaria-specific memory B and T cells, which provide effective adaptive immunity in our reinfection model (peak AJ parasitaemia is reduced from 25% in naive hosts to 0.5% in tolerised hosts). Nonetheless, this relatively low pathogen load is more than sufficient to promote the differentiation of spleen monocytes into inflammatory macrophages, as demonstrated in naive hosts infected with P. chabaudi AS. So how might adaptive mechanisms of immunity influence the myeloid response independently of pathogen load? One route through which B cells may regulate monocyte fate is through the production of opsonising antibodies, which recognise merozoites and/or infected red cells. Indeed, it has been shown that phagocytosis of immune complexes in the context of Toll-like receptor signaling can induce M2 (or regulatory) macrophages (Guilliams et al., 2014). However, in these experiments M2 macrophages increased production of the pyrogenic cytokines IL-1β, IL-6 and TNF, and malaria-specific opsonising antibodies have been directly shown to promote inflammation in human macrophages (Osier et al., 2014; Zhou et al., 2012). Instead, the generation of memory T cells that reduce the production of IFNɣ may be a more simple explanation; in agreement, we find that circulating levels of IFNɣ are substantially reduced in second infection. Nevertheless, signaling through the IFNɣ receptor is not a prerequisite for the differentiation of inflammatory macrophages (Xue et al., 2014) and the early source of IFNɣ in naive hosts is not T cell-derived (Artavanis-Tsakonas and Riley, 2002). Changes in cytokine production may therefore need to be much more extensive if T cells are to push monocytes away from an inflammatory fate. In this context, it is notable that malaria-specific memory T cells have been shown to adopt new effector functions upon recall (Zander et al., 2017); as such, they may be well placed to modify the availability of diverse signals within the tissue niche.

Future studies should therefore aim to uncouple the relative contribution of T cell signals versus structural changes (inc the appearance of malaria pigment) in redirecting monocyte fate. This may be achieved by transplanting spleens from tolerised to naive hosts and then infecting with the avirulent parasite genotype AS. In this setting, it will be possible to a) provide definitive evidence that monocyte fate is imprinted within the spleen and b) assess the role of memory T cells by injecting donor mice with a depleting anti-CD3 antibody prior to transplantation. Interpretation of these experiments may be complicated however by the fact that recipient mice would still be expected to trigger emergency myelopoiesis and lose their tissue resident macrophages. Systemic inflammation (and the disruption of homeostatic processes) may override any local signals in the spleen that favour tolerance – reinfection is characterised by diverse adaptations in distinct tissues after all, and presumably these will need to work in unison to provide clinical immunity.

It is therefore important that we consider how other host adaptations might be established. Emergency myelopoiesis can be disarmed in the bone marrow; how is this avoided second time around despite an identical pathogen load? It could be that changes to the tissue niche promote tolerance, in much the same way as described in the spleen, as first infection leads to a loss of cellularity and tissue integrity in the bone marrow, which inevitably requires repair. But it is also possible that cell-intrinsic changes establish tolerance in this case. Current evidence indicates that systemic inflammation, which can push haematopoietic stem cells (HSC) down the myeloid path (Mitroulis et al., 2018), is triggered in the bone marrow by plasmacytoid dendritic cells (pDC), which produce type I interferon (Spaulding et al., 2016). Can you intrinsically modify pDC to attenuate their function (e.g. by increasing their activation threshold)? Or limit their access to parasites and their pyrogenic products? Or perhaps tolerance operates further upstream – the expression of pattern recognition receptors on HSC could be restricted to prevent their direct activation by malaria parasites. Whatever the mechanism, we believe that disarming emergency myelopoiesis in the bone marrow is just as important as imprinting monocytes with tissue protective functions in the spleen.

We also show that long-lived prenatally seeded tissue-resident macrophages become resistant to malaria-induced ablation. Tissue-resident macrophages were often considered to be immune sentinels that provide a first line of defence against invading pathogens but actually they have been shown time and again to be sensitive to stress, and their disappearance in response to injury or infection is well recognised (Aegerter et al., 2020; Blériot et al., 2015; Machiels et al., 2017; Scott et al., 2016). Their functions instead mainly relate to development, tissue repair, and homeostasis. So how does malaria cause the death of tissue-resident macrophages in naive hosts and why are they protected in tolerised hosts? We suggest that the release and catabolism of free heme from ruptured red cells can explain this phenomenon. Free heme is toxic to tissue-resident macrophages (Cambos and Scorza, 2011; Ferreira et al., 2008; Haldar et al., 2014; Theurl et al., 2016) and the detoxification of free heme is known to confer disease tolerance (Seixas et al., 2009). In our model, heme accumulation is likely to be similar between first and second infection as parasite burden and red cell loss are comparable. The preservation of tissue-resident macrophages could therefore be a consequence of more efficiently detoxifying free heme to reduce its bioavailability and pathogenicity. Since we find no evidence that monocytes transcriptionally regulate their capacity to metabolise heme during reinfection (they exclusively upregulate iron recycling) other cell types would need to carry out this role, including hepatocytes and renal proximal tubule epithelial cells (Ramos et al., 2019; Theurl et al., 2016). In this scenario, metabolic adaptations that operate in non-lymphoid tissues must also be inducible, and would persist after pathogen clearance to provide memory and clinical immunity.

Acquired immunity to malaria is often considered to be slow and ineffective – but this is only true if we consider resistance (the ability to eliminate parasites) in isolation. Despite being an equally important part of host defense, the central role of disease tolerance in acquired immunity to malaria has not been fully appreciated. We demonstrate that disease tolerance can be attained after a single malaria episode and can persist in the absence of parasitaemia to allow the host to tolerate subsequent infections. We therefore argue that mechanisms of disease tolerance provide an alternative strategy of acquired immunity that functions not to kill parasites but to limit the damage that they can cause. Adaptations in the myeloid compartment minimise inflammation and promote stress tolerance, and it is likely that these will need to be complemented by adaptations in other immune compartments such as memory T cell reservoirs, which orchestrate innate and adaptive immunity. Furthermore, changes to the immune response will need to be complemented by long-lived metabolic adaptations that maintain homeostasis and preserve organ function. Together, all of these adaptations can work in concert to protect host tissues and minimise pathology. The mechanisms of disease tolerance uncovered in this study are therefore likely just the tip of the iceberg but they can begin to explain how children in endemic areas acquire immunity to severe malaria so quickly and without the need for improved pathogen control (Gonçalves et al., 2014; Gupta et al., 1999; Marsh and Snow, 1999).

All RNAseq, ChIPseq and microarray data have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO SuperSeries accession number GSE150479.

1. 1. Nahrendorf W
   2. Ivens A
   3. Spence PJ

   (2020) NCBI Gene Expression Omnibus

   ID GSE150047. A single malaria episode induces mechanisms that minimise inflammation and promote tolerance in spleen inflammatory monocytes.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150047
2. 1. Nahrendorf W
   2. Ivens A
   3. Spence PJ

   (2020) NCBI Gene Expression Omnibus

   ID GSE150478. Bone marrow monocytes from once-malaria infected mice have no epigenetic memory of the infection.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150478
3. 1. Nahrendorf W
   2. Ivens A
   3. Spence PJ

   (2020) NCBI Gene Expression Omnibus

   ID GSE149894. Tissue printing: splenic red pulp macrophages of once-malaria infected mice are transcriptionally identical to prenatally seeded red pulp macrophages from uninfected mice.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149894
4. 1. Nahrendorf W
   2. Ivens A
   3. Spence PJ

   (2020) NCBI Gene Expression Omnibus

   ID GSE150479. Inducible mechanisms of disease tolerance provide an alternative strategy of acquired immunity to malaria.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150479

1. 1. Ademolue TW
   2. Aniweh Y
   3. Kusi KA
   4. Awandare GA

   (2017) Patterns of inflammatory responses and parasite tolerance vary with malaria transmission intensity

   Malaria Journal 16:145.

   https://doi.org/10.1186/s12936-017-1796-x

   • PubMed
   • Google Scholar
2. 1. Aegerter H
   2. Kulikauskaite J
   3. Crotta S
   4. Patel H
   5. Kelly G
   6. Hessel EM
   7. Mack M
   8. Beinke S
   9. Wack A

   (2020) Influenza-induced monocyte-derived alveolar macrophages confer prolonged antibacterial protection

   Nature Immunology 21:145–157.

   https://doi.org/10.1038/s41590-019-0568-x

   • Google Scholar
3. 1. Akilesh HM
   2. Buechler MB
   3. Duggan JM
   4. Hahn WO
   5. Matta B
   6. Sun X
   7. Gessay G
   8. Whalen E
   9. Mason M
   10. Presnell SR
   11. Elkon KB
   12. Lacy-Hulbert A
   13. Barnes BJ
   14. Pepper M
   15. Hamerman JA

   (2019) Chronic TLR7 and TLR9 signaling drives anemia via differentiation of specialized hemophagocytes

   Science 363:eaao5213.

   https://doi.org/10.1126/science.aao5213

   • Google Scholar
4. 1. Artavanis-Tsakonas K
   2. Riley EM

   (2002) Innate immune response to malaria: rapid induction of IFN-gamma from human NK cells by live Plasmodium falciparum-infected erythrocytes

   The Journal of Immunology 169:2956–2963.

   https://doi.org/10.4049/jimmunol.169.6.2956

   • PubMed
   • Google Scholar
5. 1. Belyaev NN
   2. Brown DE
   3. Diaz AI
   4. Rae A
   5. Jarra W
   6. Thompson J
   7. Langhorne J
   8. Potocnik AJ

   (2010) Induction of an IL7-R(+)c-Kit(hi) myelolymphoid progenitor critically dependent on IFN-gamma signaling during acute malaria

   Nature Immunology 11:477–485.

   https://doi.org/10.1038/ni.1869

   • PubMed
   • Google Scholar
6. 1. Bian Z
   2. Shi L
   3. Guo YL
   4. Lv Z
   5. Tang C
   6. Niu S
   7. Tremblay A
   8. Venkataramani M
   9. Culpepper C
   10. Li L
   11. Zhou Z
   12. Mansour A
   13. Zhang Y
   14. Gewirtz A
   15. Kidder K
   16. Zen K
   17. Liu Y

   (2016) Cd47-Sirpα interaction and IL-10 constrain inflammation-induced macrophage phagocytosis of healthy self-cells

   PNAS 113:E5434–E5443.

   https://doi.org/10.1073/pnas.1521069113

   • PubMed
   • Google Scholar
7. 1. Bindea G
   2. Mlecnik B
   3. Hackl H
   4. Charoentong P
   5. Tosolini M
   6. Kirilovsky A
   7. Fridman W-H
   8. Pagès F
   9. Trajanoski Z
   10. Galon J

   (2009) ClueGO: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks

   Bioinformatics 25:1091–1093.

   https://doi.org/10.1093/bioinformatics/btp101

   • Google Scholar
8. 1. Blériot C
   2. Dupuis T
   3. Jouvion G
   4. Eberl G
   5. Disson O
   6. Lecuit M

   (2015) Liver-resident macrophage necroptosis orchestrates type 1 microbicidal inflammation and type-2-mediated tissue repair during bacterial infection

   Immunity 42:145–158.

   https://doi.org/10.1016/j.immuni.2014.12.020

   • PubMed
   • Google Scholar
9. 1. Bonnardel J
   2. T’Jonck W
   3. Gaublomme D
   4. Browaeys R
   5. Scott CL
   6. Martens L
   7. Vanneste B
   8. De Prijck S
   9. Nedospasov SA
   10. Kremer A
   11. Van Hamme E
   12. Borghgraef P
   13. Toussaint W
   14. De Bleser P
   15. Mannaerts I
   16. Beschin A
   17. van Grunsven LA
   18. Lambrecht BN
   19. Taghon T
   20. Lippens S
   21. Elewaut D
   22. Saeys Y
   23. Guilliams M

   (2019) Stellate cells, hepatocytes, and endothelial cells imprint the kupffer cell identity on monocytes colonizing the liver macrophage niche

   Immunity 51:638–654.

   https://doi.org/10.1016/j.immuni.2019.08.017

   • Google Scholar
10. 1. Brugat T
    2. Cunningham D
    3. Sodenkamp J
    4. Coomes S
    5. Wilson M
    6. Spence PJ
    7. Jarra W
    8. Thompson J
    9. Scudamore C
    10. Langhorne J

    (2014) Sequestration and histopathology in plasmodium chabaudi malaria are influenced by the immune response in an organ-specific manner

    Cellular Microbiology 16:687–700.

    https://doi.org/10.1111/cmi.12212

    • PubMed
    • Google Scholar
11. 1. Buffet PA
    2. Safeukui I
    3. Deplaine G
    4. Brousse V
    5. Prendki V
    6. Thellier M
    7. Turner GD
    8. Mercereau-Puijalon O

    (2011) The pathogenesis of Plasmodium falciparum malaria in humans: insights from splenic physiology

    Blood 117:381–392.

    https://doi.org/10.1182/blood-2010-04-202911

    • PubMed
    • Google Scholar
12. 1. Cambos M
    2. Scorza T

    (2011) Robust erythrophagocytosis leads to macrophage apoptosis via a hemin-mediated redox imbalance: role in hemolytic disorders

    Journal of Leukocyte Biology 89:159–171.

    https://doi.org/10.1189/jlb.0510249

    • PubMed
    • Google Scholar
13. 1. Carlin LM
    2. Stamatiades EG
    3. Auffray C
    4. Hanna RN
    5. Glover L
    6. Vizcay-Barrena G
    7. Hedrick CC
    8. Cook HT
    9. Diebold S
    10. Geissmann F

    (2013) Nr4a1-dependent Ly6C(low) monocytes monitor endothelial cells and orchestrate their disposal

    Cell 153:362–375.

    https://doi.org/10.1016/j.cell.2013.03.010

    • PubMed
    • Google Scholar
14. 1. Cheng SC
    2. Quintin J
    3. Cramer RA
    4. Shepardson KM
    5. Saeed S
    6. Kumar V
    7. Giamarellos-Bourboulis EJ
    8. Martens JH
    9. Rao NA
    10. Aghajanirefah A
    11. Manjeri GR
    12. Li Y
    13. Ifrim DC
    14. Arts RJ
    15. van der Veer BM
    16. van der Meer BM
    17. Deen PM
    18. Logie C
    19. O'Neill LA
    20. Willems P
    21. van de Veerdonk FL
    22. van der Meer JW
    23. Ng A
    24. Joosten LA
    25. Wijmenga C
    26. Stunnenberg HG
    27. Xavier RJ
    28. Netea MG

    (2014) mTOR- and HIF-1α-mediated aerobic glycolysis as metabolic basis for trained immunity

    Science 345:1250684.

    https://doi.org/10.1126/science.1250684

    • PubMed
    • Google Scholar
15. 1. Chomczynski P
    2. Sacchi N

    (2006) The single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction: twenty-something years on

    Nature Protocols 1:581–585.

    https://doi.org/10.1038/nprot.2006.83

    • PubMed
    • Google Scholar
16. 1. Colvin GA
    2. Lambert JF
    3. Abedi M
    4. Hsieh CC
    5. Carlson JE
    6. Stewart FM
    7. Quesenberry PJ

    (2004) Murine marrow cellularity and the concept of stem cell competition: geographic and quantitative determinants in stem cell biology

    Leukemia 18:575–583.

    https://doi.org/10.1038/sj.leu.2403268

    • PubMed
    • Google Scholar
17. 1. Cumnock K
    2. Gupta AS
    3. Lissner M
    4. Chevee V
    5. Davis NM
    6. Schneider DS

    (2018) Host energy source is important for disease tolerance to malaria

    Current Biology 28:1635–1642.

    https://doi.org/10.1016/j.cub.2018.04.009

    • PubMed
    • Google Scholar
18. 1. Czopek A
    2. Moorhouse R
    3. Guyonnet L
    4. Farrah T
    5. Lenoir O
    6. Owen E
    7. van Bragt J
    8. Costello HM
    9. Menolascina F
    10. Baudrie V
    11. Webb DJ
    12. Kluth DC
    13. Bailey MA
    14. Tharaux PL
    15. Dhaun N

    (2019) A novel role for myeloid endothelin-B receptors in hypertension

    European Heart Journal 40:768–784.

    https://doi.org/10.1093/eurheartj/ehy881

    • PubMed
    • Google Scholar
19. 1. Dela Cruz CS
    2. Liu W
    3. He CH
    4. Jacoby A
    5. Gornitzky A
    6. Ma B
    7. Flavell R
    8. Lee CG
    9. Elias JA

    (2012) Chitinase 3-like-1 promotes Streptococcus pneumoniae killing and augments host tolerance to lung antibacterial responses

    Cell Host & Microbe 12:34–46.

    https://doi.org/10.1016/j.chom.2012.05.017

    • PubMed
    • Google Scholar
20. 1. Dominguez-Andres J
    2. Netea MG

    (2019) Long-term reprogramming of the innate immune system

    Journal of Leukocyte Biology 105:329–338.

    https://doi.org/10.1002/JLB.MR0318-104R

    • PubMed
    • Google Scholar
21. 1. Edgar R
    2. Domrachev M
    3. Lash AE

    (2002) Gene expression omnibus: ncbi gene expression and hybridization array data repository

    Nucleic Acids Research 30:207–210.

    https://doi.org/10.1093/nar/30.1.207

    • PubMed
    • Google Scholar
22. 1. Felger I
    2. Maire M
    3. Bretscher MT
    4. Falk N
    5. Tiaden A
    6. Sama W
    7. Beck HP
    8. Owusu-Agyei S
    9. Smith TA

    (2012) The dynamics of natural Plasmodium falciparum infections

    PLOS ONE 7:e45542.

    https://doi.org/10.1371/journal.pone.0045542

    • PubMed
    • Google Scholar
23. 1. Ferreira A
    2. Balla J
    3. Jeney V
    4. Balla G
    5. Soares MP

    (2008) A central role for free heme in the pathogenesis of severe malaria: the missing link?

    Journal of Molecular Medicine 86:1097–1111.

    https://doi.org/10.1007/s00109-008-0368-5

    • PubMed
    • Google Scholar
24. 1. Foster SL
    2. Hargreaves DC
    3. Medzhitov R

    (2007) Gene-specific control of inflammation by TLR-induced chromatin modifications

    Nature 447:972–978.

    https://doi.org/10.1038/nature05836

    • PubMed
    • Google Scholar
25. 1. Gabrilovich DI

    (2017) Myeloid-Derived suppressor cells

    Cancer Immunology Research 5:3–8.

    https://doi.org/10.1158/2326-6066.CIR-16-0297

    • PubMed
    • Google Scholar
26. 1. Gatton ML
    2. Cheng Q

    (2002) Evaluation of the pyrogenic threshold for plasmodium falciparum malaria in naive individuals

    The American Journal of Tropical Medicine and Hygiene 66:467–473.

    https://doi.org/10.4269/ajtmh.2002.66.467

    • PubMed
    • Google Scholar
27. 1. Gautier EL
    2. Shay T
    3. Miller J
    4. Greter M
    5. Jakubzick C
    6. Ivanov S
    7. Helft J
    8. Chow A
    9. Elpek KG
    10. Gordonov S
    11. Mazloom AR
    12. Ma'ayan A
    13. Chua WJ
    14. Hansen TH
    15. Turley SJ
    16. Merad M
    17. Randolph GJ
    18. Immunological Genome Consortium

    (2012) Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages

    Nature Immunology 13:1118–1128.

    https://doi.org/10.1038/ni.2419

    • PubMed
    • Google Scholar
28. 1. Gonçalves BP
    2. Huang C-Y
    3. Morrison R
    4. Holte S
    5. Kabyemela E
    6. Prevots DR
    7. Fried M
    8. Duffy PE

    (2014) Parasite burden and severity of malaria in tanzanian children

    New England Journal of Medicine 370:1799–1808.

    https://doi.org/10.1056/NEJMoa1303944

    • Google Scholar
29. 1. Gozzelino R
    2. Andrade BB
    3. Larsen R
    4. Luz NF
    5. Vanoaica L
    6. Seixas E
    7. Coutinho A
    8. Cardoso S
    9. Rebelo S
    10. Poli M
    11. Barral-Netto M
    12. Darshan D
    13. Kühn LC
    14. Soares MP

    (2012) Metabolic adaptation to tissue iron overload confers tolerance to malaria

    Cell Host & Microbe 12:693–704.

    https://doi.org/10.1016/j.chom.2012.10.011

    • PubMed
    • Google Scholar
30. 1. Guilliams M
    2. Bruhns P
    3. Saeys Y
    4. Hammad H
    5. Lambrecht BN

    (2014) The function of fcγ receptors in dendritic cells and macrophages

    Nature Reviews Immunology 14:94–108.

    https://doi.org/10.1038/nri3582

    • PubMed
    • Google Scholar
31. 1. Guilliams M
    2. Mildner A
    3. Yona S

    (2018) Developmental and functional heterogeneity of monocytes

    Immunity 49:595–613.

    https://doi.org/10.1016/j.immuni.2018.10.005

    • PubMed
    • Google Scholar
32. 1. Guilliams M
    2. Thierry GR
    3. Bonnardel J
    4. Bajenoff M

    (2020) Establishment and maintenance of the macrophage niche

    Immunity 52:434–451.

    https://doi.org/10.1016/j.immuni.2020.02.015

    • PubMed
    • Google Scholar
33. 1. Gupta S
    2. Snow RW
    3. Donnelly CA
    4. Marsh K
    5. Newbold C

    (1999) Immunity to non-cerebral severe malaria is acquired after one or two infections

    Nature Medicine 5:340–343.

    https://doi.org/10.1038/6560

    • PubMed
    • Google Scholar
34. 1. Haldar M
    2. Kohyama M
    3. So AY
    4. Kc W
    5. Wu X
    6. Briseño CG
    7. Satpathy AT
    8. Kretzer NM
    9. Arase H
    10. Rajasekaran NS
    11. Wang L
    12. Egawa T
    13. Igarashi K
    14. Baltimore D
    15. Murphy TL
    16. Murphy KM

    (2014) Heme-mediated SPI-C induction promotes monocyte differentiation into iron-recycling macrophages

    Cell 156:1223–1234.

    https://doi.org/10.1016/j.cell.2014.01.069

    • PubMed
    • Google Scholar
35. 1. Hashimoto D
    2. Chow A
    3. Noizat C
    4. Teo P
    5. Beasley MB
    6. Leboeuf M
    7. Becker CD
    8. See P
    9. Price J
    10. Lucas D
    11. Greter M
    12. Mortha A
    13. Boyer SW
    14. Forsberg EC
    15. Tanaka M
    16. van Rooijen N
    17. García-Sastre A
    18. Stanley ER
    19. Ginhoux F
    20. Frenette PS
    21. Merad M

    (2013) Tissue-resident macrophages self-maintain locally throughout adult life with minimal contribution from circulating monocytes

    Immunity 38:792–804.

    https://doi.org/10.1016/j.immuni.2013.04.004

    • PubMed
    • Google Scholar
36. 1. Heinz S
    2. Benner C
    3. Spann N
    4. Bertolino E
    5. Lin YC
    6. Laslo P
    7. Cheng JX
    8. Murre C
    9. Singh H
    10. Glass CK

    (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities

    Molecular Cell 38:576–589.

    https://doi.org/10.1016/j.molcel.2010.05.004

    • PubMed
    • Google Scholar
37. 1. Jakeman GN
    2. Saul A
    3. Hogarth WL
    4. Collins WE

    (1999) Anaemia of acute malaria infections in non-immune patients primarily results from destruction of uninfected erythrocytes

    Parasitology 119:127–133.

    https://doi.org/10.1017/S0031182099004564

    • PubMed
    • Google Scholar
38. 1. Jeney V
    2. Ramos S
    3. Bergman ML
    4. Bechmann I
    5. Tischer J
    6. Ferreira A
    7. Oliveira-Marques V
    8. Janse CJ
    9. Rebelo S
    10. Cardoso S
    11. Soares MP

    (2014) Control of disease tolerance to malaria by nitric oxide and carbon monoxide

    Cell Reports 8:126–136.

    https://doi.org/10.1016/j.celrep.2014.05.054

    • PubMed
    • Google Scholar
39. 1. Jung S
    2. Aliberti J
    3. Graemmel P
    4. Sunshine MJ
    5. Kreutzberg GW
    6. Sher A
    7. Littman DR

    (2000) Analysis of fractalkine receptor CX(3)CR1 function by targeted deletion and green fluorescent protein reporter gene insertion

    Molecular and Cellular Biology 20:4106–4114.

    https://doi.org/10.1128/MCB.20.11.4106-4114.2000

    • PubMed
    • Google Scholar
40. 1. Jung K
    2. Heishi T
    3. Khan OF
    4. Kowalski PS
    5. Incio J
    6. Rahbari NN
    7. Chung E
    8. Clark JW
    9. Willett CG
    10. Luster AD
    11. Yun SH
    12. Langer R
    13. Anderson DG
    14. Padera TP
    15. Jain RK
    16. Fukumura D

    (2017) Ly6Clo monocytes drive immunosuppression and confer resistance to anti-VEGFR2 Cancer therapy

    Journal of Clinical Investigation 127:3039–3051.

    https://doi.org/10.1172/JCI93182

    • Google Scholar
41. 1. Kang K
    2. Park SH
    3. Chen J
    4. Qiao Y
    5. Giannopoulou E
    6. Berg K
    7. Hanidu A
    8. Li J
    9. Nabozny G
    10. Kang K
    11. Park-Min K-H
    12. Ivashkiv LB

    (2017) Interferon-γ represses M2 gene expression in human macrophages by disassembling enhancers bound by the transcription factor MAF

    Immunity 47:235–250.

    https://doi.org/10.1016/j.immuni.2017.07.017

    • Google Scholar
42. 1. Kauffmann A
    2. Huber W

    (2010) Microarray data quality control improves the detection of differentially expressed genes

    Genomics 95:138–142.

    https://doi.org/10.1016/j.ygeno.2010.01.003

    • PubMed
    • Google Scholar
43. 1. Kaufmann E
    2. Sanz J
    3. Dunn JL
    4. Khan N
    5. Mendonça LE
    6. Pacis A
    7. Tzelepis F
    8. Pernet E
    9. Dumaine A
    10. Grenier J-C
    11. Mailhot-Léonard F
    12. Ahmed E
    13. Belle J
    14. Besla R
    15. Mazer B
    16. King IL
    17. Nijnik A
    18. Robbins CS
    19. Barreiro LB
    20. Divangahi M

    (2018) BCG educates hematopoietic stem cells to generate protective innate immunity against tuberculosis

    Cell 172:176–190.

    https://doi.org/10.1016/j.cell.2017.12.031

    • Google Scholar
44. 1. Kohyama M
    2. Ise W
    3. Edelson BT
    4. Wilker PR
    5. Hildner K
    6. Mejia C
    7. Frazier WA
    8. Murphy TL
    9. Murphy KM

    (2009) Role for Spi-C in the development of red pulp macrophages and splenic iron homeostasis

    Nature 457:318–321.

    https://doi.org/10.1038/nature07472

    • PubMed
    • Google Scholar
45. 1. Koliaraki V
    2. Prados A
    3. Armaka M
    4. Kollias G

    (2020) The mesenchymal context in inflammation, immunity and Cancer

    Nature Immunology 21:974–982.

    https://doi.org/10.1038/s41590-020-0741-2

    • PubMed
    • Google Scholar
46. 1. Lai SM
    2. Sheng J
    3. Gupta P
    4. Renia L
    5. Duan K
    6. Zolezzi F
    7. Karjalainen K
    8. Newell EW
    9. Ruedl C

    (2018) Organ-Specific fate, recruitment, and refilling dynamics of Tissue-Resident macrophages during Blood-Stage malaria

    Cell Reports 25:3099–3109.

    https://doi.org/10.1016/j.celrep.2018.11.059

    • Google Scholar
47. 1. Langmead B
    2. Salzberg SL

    (2012) Fast gapped-read alignment with bowtie 2

    Nature Methods 9:357–359.

    https://doi.org/10.1038/nmeth.1923

    • PubMed
    • Google Scholar
48. 1. Liu M
    2. Tong Z
    3. Ding C
    4. Luo F
    5. Wu S
    6. Wu C
    7. Albeituni S
    8. He L
    9. Hu X
    10. Tieri D
    11. Rouchka EC
    12. Hamada M
    13. Takahashi S
    14. Gibb AA
    15. Kloecker G
    16. Zhang H-ge
    17. Bousamra M
    18. Hill BG
    19. Zhang X
    20. Yan J

    (2020) Transcription factor c-Maf is a checkpoint that programs macrophages in lung Cancer

    Journal of Clinical Investigation 130:2081–2096.

    https://doi.org/10.1172/JCI131335

    • Google Scholar
49. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
50. 1. Ma Z
    2. Wang H
    3. Cai Y
    4. Wang H
    5. Niu K
    6. Wu X
    7. Ma H
    8. Yang Y
    9. Tong W
    10. Liu F
    11. Liu Z
    12. Zhang Y
    13. Liu R
    14. Zhu ZJ
    15. Liu N

    (2018) Epigenetic drift of H3K27me3 in aging links glycolysis to healthy longevity in Drosophila

    eLife 7:e35368.

    https://doi.org/10.7554/eLife.35368

    • PubMed
    • Google Scholar
51. 1. Machiels B
    2. Dourcy M
    3. Xiao X
    4. Javaux J
    5. Mesnil C
    6. Sabatel C
    7. Desmecht D
    8. Lallemand F
    9. Martinive P
    10. Hammad H
    11. Guilliams M
    12. Dewals B
    13. Vanderplasschen A
    14. Lambrecht BN
    15. Bureau F
    16. Gillet L

    (2017) A gammaherpesvirus provides protection against allergic asthma by inducing the replacement of resident alveolar macrophages with regulatory monocytes

    Nature Immunology 18:1310–1320.

    https://doi.org/10.1038/ni.3857

    • PubMed
    • Google Scholar
52. 1. Mandala WL
    2. Msefula CL
    3. Gondwe EN
    4. Drayson MT
    5. Molyneux ME
    6. MacLennan CA

    (2017) Cytokine profiles in malawian children presenting with uncomplicated malaria, severe malarial Anemia, and cerebral malaria

    Clinical and Vaccine Immunology 24:00533-16.

    https://doi.org/10.1128/CVI.00533-16

    • Google Scholar
53. 1. Marsh K
    2. Kinyanjui S

    (2006) Immune effector mechanisms in malaria

    Parasite Immunology 28:51–60.

    https://doi.org/10.1111/j.1365-3024.2006.00808.x

    • PubMed
    • Google Scholar
54. 1. Marsh K
    2. Snow RW

    (1999)

    Malaria transmission and morbidity

    Parassitologia 41:241–246.

    • PubMed
    • Google Scholar
55. 1. Martins R
    2. Carlos AR
    3. Braza F
    4. Thompson JA
    5. Bastos-Amador P
    6. Ramos S
    7. Soares MP

    (2019) Disease tolerance as an inherent component of immunity

    Annual Review of Immunology 37:405–437.

    https://doi.org/10.1146/annurev-immunol-042718-041739

    • PubMed
    • Google Scholar
56. 1. Medzhitov R
    2. Schneider DS
    3. Soares MP

    (2012) Disease tolerance as a defense strategy

    Science 335:936–941.

    https://doi.org/10.1126/science.1214935

    • PubMed
    • Google Scholar
57. 1. Menezes S
    2. Melandri D
    3. Anselmi G
    4. Perchet T
    5. Loschko J
    6. Dubrot J
    7. Patel R
    8. Gautier EL
    9. Hugues S
    10. Longhi MP
    11. Henry JY
    12. Quezada SA
    13. Lauvau G
    14. Lennon-Duménil AM
    15. Gutiérrez-Martínez E
    16. Bessis A
    17. Gomez-Perdiguero E
    18. Jacome-Galarza CE
    19. Garner H
    20. Geissmann F
    21. Golub R
    22. Nussenzweig MC
    23. Guermonprez P

    (2016) The heterogeneity of Ly6Chimonocytes controls their differentiation into iNOS⁺ Macrophages or Monocyte-Derived Dendritic Cells

    Immunity 45:1205–1218.

    https://doi.org/10.1016/j.immuni.2016.12.001

    • PubMed
    • Google Scholar
58. 1. Mildner A
    2. Schönheit J
    3. Giladi A
    4. David E
    5. Lara-Astiaso D
    6. Lorenzo-Vivas E
    7. Paul F
    8. Chappell-Maor L
    9. Priller J
    10. Leutz A
    11. Amit I
    12. Jung S

    (2017) Genomic characterization of murine monocytes reveals C/EBPβ transcription factor dependence of Ly6C − cells

    Immunity 46:849–862.

    https://doi.org/10.1016/j.immuni.2017.04.018

    • Google Scholar
59. 1. Miller JC
    2. Brown BD
    3. Shay T
    4. Gautier EL
    5. Jojic V
    6. Cohain A
    7. Pandey G
    8. Leboeuf M
    9. Elpek KG
    10. Helft J
    11. Hashimoto D
    12. Chow A
    13. Price J
    14. Greter M
    15. Bogunovic M
    16. Bellemare-Pelletier A
    17. Frenette PS
    18. Randolph GJ
    19. Turley SJ
    20. Merad M
    21. Immunological Genome Consortium

    (2012) Deciphering the transcriptional network of the dendritic cell lineage

    Nature Immunology 13:888–899.

    https://doi.org/10.1038/ni.2370

    • PubMed
    • Google Scholar
60. 1. Mitroulis I
    2. Kalafati L
    3. Hajishengallis G
    4. Chavakis T

    (2018) Myelopoiesis in the context of innate immunity

    Journal of Innate Immunity 10:365–372.

    https://doi.org/10.1159/000489406

    • PubMed
    • Google Scholar
61. 1. Mlecnik B
    2. Bindea G
    3. Angell HK
    4. Sasso MS
    5. Obenauf AC
    6. Fredriksen T
    7. Lafontaine L
    8. Bilocq AM
    9. Kirilovsky A
    10. Tosolini M
    11. Waldner M
    12. Berger A
    13. Fridman WH
    14. Rafii A
    15. Valge-Archer V
    16. Pagès F
    17. Speicher MR
    18. Galon J

    (2014) Functional network pipeline reveals genetic determinants associated with in situ lymphocyte proliferation and survival of Cancer patients

    Science Translational Medicine 6:228ra37.

    https://doi.org/10.1126/scitranslmed.3007240

    • PubMed
    • Google Scholar
62. 1. Nahrendorf W
    2. Spence PJ
    3. Tumwine I
    4. Lévy P
    5. Jarra W
    6. Sauerwein RW
    7. Langhorne J

    (2015) Blood-stage immunity to plasmodium chabaudi malaria following chemoprophylaxis and sporozoite immunization

    eLife 4:e05165.

    https://doi.org/10.7554/eLife.05165

    • Google Scholar
63. 1. Netea MG
    2. Joosten LA
    3. Latz E
    4. Mills KH
    5. Natoli G
    6. Stunnenberg HG
    7. O'Neill LA
    8. Xavier RJ

    (2016) Trained immunity: a program of innate immune memory in health and disease

    Science 352:aaf1098.

    https://doi.org/10.1126/science.aaf1098

    • PubMed
    • Google Scholar
64. 1. O'Neill LA
    2. Kishton RJ
    3. Rathmell J

    (2016) A guide to immunometabolism for immunologists

    Nature Reviews Immunology 16:553–565.

    https://doi.org/10.1038/nri.2016.70

    • PubMed
    • Google Scholar
65. 1. Okabe Y
    2. Medzhitov R

    (2014) Tissue-specific signals control reversible program of localization and functional polarization of macrophages

    Cell 157:832–844.

    https://doi.org/10.1016/j.cell.2014.04.016

    • PubMed
    • Google Scholar
66. 1. Orlando DA
    2. Chen MW
    3. Brown VE
    4. Solanki S
    5. Choi YJ
    6. Olson ER
    7. Fritz CC
    8. Bradner JE
    9. Guenther MG

    (2014) Quantitative ChIP-Seq normalization reveals global modulation of the epigenome

    Cell Reports 9:1163–1170.

    https://doi.org/10.1016/j.celrep.2014.10.018

    • PubMed
    • Google Scholar
67. 1. Osier FH
    2. Feng G
    3. Boyle MJ
    4. Langer C
    5. Zhou J
    6. Richards JS
    7. McCallum FJ
    8. Reiling L
    9. Jaworowski A
    10. Anders RF
    11. Marsh K
    12. Beeson JG

    (2014) Opsonic phagocytosis of Plasmodium falciparum merozoites: mechanism in human immunity and a correlate of protection against malaria

    BMC Medicine 12:108.

    https://doi.org/10.1186/1741-7015-12-108

    • PubMed
    • Google Scholar
68. 1. Otto TD
    2. Böhme U
    3. Jackson AP
    4. Hunt M
    5. Franke-Fayard B
    6. Hoeijmakers WA
    7. Religa AA
    8. Robertson L
    9. Sanders M
    10. Ogun SA
    11. Cunningham D
    12. Erhart A
    13. Billker O
    14. Khan SM
    15. Stunnenberg HG
    16. Langhorne J
    17. Holder AA
    18. Waters AP
    19. Newbold CI
    20. Pain A
    21. Berriman M
    22. Janse CJ

    (2014) A comprehensive evaluation of rodent malaria parasite genomes and gene expression

    BMC Biology 12:86.

    https://doi.org/10.1186/s12915-014-0086-0

    • PubMed
    • Google Scholar
69. 1. Palmieri F

    (2013) The mitochondrial transporter family SLC25: identification, properties and physiopathology

    Molecular Aspects of Medicine 34:465–484.

    https://doi.org/10.1016/j.mam.2012.05.005

    • PubMed
    • Google Scholar
70. 1. Pathak VA
    2. Ghosh K

    (2016) Erythropoiesis in malaria infections and factors modifying the erythropoietic response

    Anemia 2016:1–8.

    https://doi.org/10.1155/2016/9310905

    • Google Scholar
71. 1. Portugal S
    2. Moebius J
    3. Skinner J
    4. Doumbo S
    5. Doumtabe D
    6. Kone Y
    7. Dia S
    8. Kanakabandi K
    9. Sturdevant DE
    10. Virtaneva K
    11. Porcella SF
    12. Li S
    13. Doumbo OK
    14. Kayentao K
    15. Ongoiba A
    16. Traore B
    17. Crompton PD

    (2014) Exposure-dependent control of malaria-induced inflammation in children

    PLOS Pathogens 10:e1004079.

    https://doi.org/10.1371/journal.ppat.1004079

    • PubMed
    • Google Scholar
72. 1. Ramos S
    2. Carlos AR
    3. Sundaram B
    4. Jeney V
    5. Ribeiro A
    6. Gozzelino R
    7. Bank C
    8. Gjini E
    9. Braza F
    10. Martins R
    11. Ademolue TW
    12. Blankenhaus B
    13. Gouveia Z
    14. Faísca P
    15. Trujillo D
    16. Cardoso S
    17. Rebelo S
    18. Del Barrio L
    19. Zarjou A
    20. Bolisetty S
    21. Agarwal A
    22. Soares MP

    (2019) Renal control of disease tolerance to malaria

    PNAS 116:5681–5686.

    https://doi.org/10.1073/pnas.1822024116

    • PubMed
    • Google Scholar
73. 1. Schofield L
    2. Grau GE

    (2005) Immunological processes in malaria pathogenesis

    Nature Reviews Immunology 5:722–735.

    https://doi.org/10.1038/nri1686

    • PubMed
    • Google Scholar
74. 1. Schwarzer E
    2. Turrini F
    3. Ulliers D
    4. Giribaldi G
    5. Ginsburg H
    6. Arese P

    (1992) Impairment of macrophage functions after ingestion of Plasmodium falciparum-infected erythrocytes or isolated malarial pigment

    Journal of Experimental Medicine 176:1033–1041.

    https://doi.org/10.1084/jem.176.4.1033

    • Google Scholar
75. 1. Schwarzer E
    2. Alessio M
    3. Ulliers D
    4. Arese P

    (1998) Phagocytosis of the malarial pigment, hemozoin, impairs expression of major histocompatibility complex class II antigen, CD54, and CD11c in human monocytes

    Infection and Immunity 66:1601–1606.

    https://doi.org/10.1128/IAI.66.4.1601-1606.1998

    • PubMed
    • Google Scholar
76. 1. Scott CL
    2. Zheng F
    3. De Baetselier P
    4. Martens L
    5. Saeys Y
    6. De Prijck S
    7. Lippens S
    8. Abels C
    9. Schoonooghe S
    10. Raes G
    11. Devoogdt N
    12. Lambrecht BN
    13. Beschin A
    14. Guilliams M

    (2016) Bone marrow-derived monocytes give rise to self-renewing and fully differentiated kupffer cells

    Nature Communications 7:10321.

    https://doi.org/10.1038/ncomms10321

    • PubMed
    • Google Scholar
77. 1. Seeley JJ
    2. Ghosh S

    (2017) Molecular mechanisms of innate memory and tolerance to LPS

    Journal of Leukocyte Biology 101:107–119.

    https://doi.org/10.1189/jlb.3MR0316-118RR

    • PubMed
    • Google Scholar
78. 1. Seixas E
    2. Gozzelino R
    3. Chora A
    4. Ferreira A
    5. Silva G
    6. Larsen R
    7. Rebelo S
    8. Penido C
    9. Smith NR
    10. Coutinho A
    11. Soares MP

    (2009) Heme oxygenase-1 affords protection against noncerebral forms of severe malaria

    PNAS 106:15837–15842.

    https://doi.org/10.1073/pnas.0903419106

    • PubMed
    • Google Scholar
79. 1. Shannon P
    2. Markiel A
    3. Ozier O
    4. Baliga NS
    5. Wang JT
    6. Ramage D
    7. Amin N
    8. Schwikowski B
    9. Ideker T

    (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks

    Genome Research 13:2498–2504.

    https://doi.org/10.1101/gr.1239303

    • PubMed
    • Google Scholar
80. 1. Simpson IA
    2. Dwyer D
    3. Malide D
    4. Moley KH
    5. Travis A
    6. Vannucci SJ

    (2008) The facilitative glucose transporter GLUT3: 20 years of distinction

    American Journal of Physiology-Endocrinology and Metabolism 295:E242–E253.

    https://doi.org/10.1152/ajpendo.90388.2008

    • PubMed
    • Google Scholar
81. 1. Spaulding E
    2. Fooksman D
    3. Moore JM
    4. Saidi A
    5. Feintuch CM
    6. Reizis B
    7. Chorro L
    8. Daily J
    9. Lauvau G

    (2016) STING-Licensed macrophages prime type I IFN production by plasmacytoid dendritic cells in the bone marrow during severe plasmodium yoelii malaria

    PLOS Pathogens 12:e1005975.

    https://doi.org/10.1371/journal.ppat.1005975

    • PubMed
    • Google Scholar
82. 1. Spence PJ
    2. Jarra W
    3. Lévy P
    4. Nahrendorf W
    5. Langhorne J

    (2012) Mosquito transmission of the rodent malaria parasite plasmodium chabaudi

    Malaria Journal 11:407.

    https://doi.org/10.1186/1475-2875-11-407

    • PubMed
    • Google Scholar
83. 1. Spence PJ
    2. Jarra W
    3. Lévy P
    4. Reid AJ
    5. Chappell L
    6. Brugat T
    7. Sanders M
    8. Berriman M
    9. Langhorne J

    (2013) Vector transmission regulates immune control of plasmodium virulence

    Nature 498:228–231.

    https://doi.org/10.1038/nature12231

    • PubMed
    • Google Scholar
84. 1. Theurl I
    2. Hilgendorf I
    3. Nairz M
    4. Tymoszuk P
    5. Haschka D
    6. Asshoff M
    7. He S
    8. Gerhardt LM
    9. Holderried TA
    10. Seifert M
    11. Sopper S
    12. Fenn AM
    13. Anzai A
    14. Rattik S
    15. McAlpine C
    16. Theurl M
    17. Wieghofer P
    18. Iwamoto Y
    19. Weber GF
    20. Harder NK
    21. Chousterman BG
    22. Arvedson TL
    23. McKee M
    24. Wang F
    25. Lutz OM
    26. Rezoagli E
    27. Babitt JL
    28. Berra L
    29. Prinz M
    30. Nahrendorf M
    31. Weiss G
    32. Weissleder R
    33. Lin HY
    34. Swirski FK

    (2016) On-demand erythrocyte disposal and iron recycling requires transient macrophages in the liver

    Nature Medicine 22:945–951.

    https://doi.org/10.1038/nm.4146

    • PubMed
    • Google Scholar
85. 1. Thorvaldsdóttir H
    2. Robinson JT
    3. Mesirov JP

    (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration

    Briefings in Bioinformatics 14:178–192.

    https://doi.org/10.1093/bib/bbs017

    • PubMed
    • Google Scholar
86. 1. Ulas T
    2. Pirr S
    3. Fehlhaber B
    4. Bickes MS
    5. Loof TG
    6. Vogl T
    7. Mellinger L
    8. Heinemann AS
    9. Burgmann J
    10. Schöning J
    11. Schreek S
    12. Pfeifer S
    13. Reuner F
    14. Völlger L
    15. Stanulla M
    16. von Köckritz-Blickwede M
    17. Glander S
    18. Barczyk-Kahlert K
    19. von Kaisenberg CS
    20. Friesenhagen J
    21. Fischer-Riepe L
    22. Zenker S
    23. Schultze JL
    24. Roth J
    25. Viemann D

    (2017) S100-alarmin-induced innate immune programming protects newborn infants from Sepsis

    Nature Immunology 18:622–632.

    https://doi.org/10.1038/ni.3745

    • PubMed
    • Google Scholar
87. 1. van de Laar L
    2. Saelens W
    3. De Prijck S
    4. Martens L
    5. Scott CL
    6. Van Isterdael G
    7. Hoffmann E
    8. Beyaert R
    9. Saeys Y
    10. Lambrecht BN
    11. Guilliams M

    (2016) Yolk sac macrophages, fetal liver, and adult monocytes can colonize an empty niche and develop into functional Tissue-Resident macrophages

    Immunity 44:755–768.

    https://doi.org/10.1016/j.immuni.2016.02.017

    • PubMed
    • Google Scholar
88. 1. von Seidlein L
    2. Olaosebikan R
    3. Hendriksen IC
    4. Lee SJ
    5. Adedoyin OT
    6. Agbenyega T
    7. Nguah SB
    8. Bojang K
    9. Deen JL
    10. Evans J
    11. Fanello CI
    12. Gomes E
    13. Pedro AJ
    14. Kahabuka C
    15. Karema C
    16. Kivaya E
    17. Maitland K
    18. Mokuolu OA
    19. Mtove G
    20. Mwanga-Amumpaire J
    21. Nadjm B
    22. Nansumba M
    23. Ngum WP
    24. Onyamboko MA
    25. Reyburn H
    26. Sakulthaew T
    27. Silamut K
    28. Tshefu AK
    29. Umulisa N
    30. Gesase S
    31. Day NP
    32. White NJ
    33. Dondorp AM

    (2012) Predicting the clinical outcome of severe falciparum malaria in african children: findings from a large randomized trial

    Clinical Infectious Diseases 54:1080–1090.

    https://doi.org/10.1093/cid/cis034

    • PubMed
    • Google Scholar
89. 1. Weis S
    2. Carlos AR
    3. Moita MR
    4. Singh S
    5. Blankenhaus B
    6. Cardoso S
    7. Larsen R
    8. Rebelo S
    9. Schäuble S
    10. Del Barrio L
    11. Mithieux G
    12. Rajas F
    13. Lindig S
    14. Bauer M
    15. Soares MP

    (2017) Metabolic adaptation establishes disease tolerance to Sepsis

    Cell 169:1263–1275.

    https://doi.org/10.1016/j.cell.2017.05.031

    • Google Scholar
90. 1. Weiss DJ
    2. Lucas TCD
    3. Nguyen M
    4. Nandi AK
    5. Bisanzio D
    6. Battle KE
    7. Cameron E
    8. Twohig KA
    9. Pfeffer DA
    10. Rozier JA
    11. Gibson HS
    12. Rao PC
    13. Casey D
    14. Bertozzi-Villa A
    15. Collins EL
    16. Dalrymple U
    17. Gray N
    18. Harris JR
    19. Howes RE
    20. Kang SY
    21. Keddie SH
    22. May D
    23. Rumisha S
    24. Thorn MP
    25. Barber R
    26. Fullman N
    27. Huynh CK
    28. Kulikoff X
    29. Kutz MJ
    30. Lopez AD
    31. Mokdad AH
    32. Naghavi M
    33. Nguyen G
    34. Shackelford KA
    35. Vos T
    36. Wang H
    37. Smith DL
    38. Lim SS
    39. Murray CJL
    40. Bhatt S
    41. Hay SI
    42. Gething PW

    (2019) Mapping the global prevalence, incidence, and mortality of Plasmodium falciparum, 2000-17: a spatial and temporal modelling study

    The Lancet 394:322–331.

    https://doi.org/10.1016/S0140-6736(19)31097-9

    • PubMed
    • Google Scholar
91. 1. Wendeln AC
    2. Degenhardt K
    3. Kaurani L
    4. Gertig M
    5. Ulas T
    6. Jain G
    7. Wagner J
    8. Häsler LM
    9. Wild K
    10. Skodras A
    11. Blank T
    12. Staszewski O
    13. Datta M
    14. Centeno TP
    15. Capece V
    16. Islam MR
    17. Kerimoglu C
    18. Staufenbiel M
    19. Schultze JL
    20. Beyer M
    21. Prinz M
    22. Jucker M
    23. Fischer A
    24. Neher JJ

    (2018) Innate immune memory in the brain shapes neurological disease hallmarks

    Nature 556:332–338.

    https://doi.org/10.1038/s41586-018-0023-4

    • PubMed
    • Google Scholar
92. Book

    1. Wickham H

    (2016) Ggplot2: Elegant Graphics for Data Analysis

    Springer-Verlag.

    https://doi.org/10.1007/978-0-387-98141-3

    • Google Scholar
93. 1. Xue J
    2. Schmidt SV
    3. Sander J
    4. Draffehn A
    5. Krebs W
    6. Quester I
    7. De Nardo D
    8. Gohel TD
    9. Emde M
    10. Schmidleithner L
    11. Ganesan H
    12. Nino-Castro A
    13. Mallmann MR
    14. Labzin L
    15. Theis H
    16. Kraut M
    17. Beyer M
    18. Latz E
    19. Freeman TC
    20. Ulas T
    21. Schultze JL

    (2014) Transcriptome-based network analysis reveals a spectrum model of human macrophage activation

    Immunity 40:274–288.

    https://doi.org/10.1016/j.immuni.2014.01.006

    • PubMed
    • Google Scholar
94. 1. Yao H
    2. Arunachalam G
    3. Hwang JW
    4. Chung S
    5. Sundar IK
    6. Kinnula VL
    7. Crapo JD
    8. Rahman I

    (2010) Extracellular superoxide dismutase protects against pulmonary emphysema by attenuating oxidative fragmentation of ECM

    PNAS 107:15571–15576.

    https://doi.org/10.1073/pnas.1007625107

    • PubMed
    • Google Scholar
95. 1. Yeo TW
    2. Lampah DA
    3. Gitawati R
    4. Tjitra E
    5. Kenangalem E
    6. Piera K
    7. Price RN
    8. Duffull SB
    9. Celermajer DS
    10. Anstey NM

    (2008) Angiopoietin-2 is associated with decreased endothelial nitric oxide and poor clinical outcome in severe falciparum malaria

    PNAS 105:17097–17102.

    https://doi.org/10.1073/pnas.0805782105

    • PubMed
    • Google Scholar
96. 1. Zander RA
    2. Vijay R
    3. Pack AD
    4. Guthmiller JJ
    5. Graham AC
    6. Lindner SE
    7. Vaughan AM
    8. Kappe SHI
    9. Butler NS

    (2017) Th1-like Plasmodium-Specific memory CD4⁺ T cells support humoral immunity

    Cell Reports 21:1839–1852.

    https://doi.org/10.1016/j.celrep.2017.10.077

    • PubMed
    • Google Scholar
97. 1. Zhou J
    2. Ludlow LE
    3. Hasang W
    4. Rogerson SJ
    5. Jaworowski A

    (2012) Opsonization of malaria-infected erythrocytes activates the inflammasome and enhances inflammatory cytokine secretion by human macrophages

    Malaria Journal 11:343.

    https://doi.org/10.1186/1475-2875-11-343

    • PubMed
    • Google Scholar

Author details

1. Wiebke Nahrendorf

   Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   Wiebke.Nahrendorf@ed.ac.uk

   "This ORCID iD identifies the author of this article:" 0000-0002-4503-8761

2. Alasdair Ivens

   1. Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
   2. Centre for Immunity, Infection and Evolution, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Data curation, Software, Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

3. Philip J Spence

   1. Institute of Immunology and Infection Research, University of Edinburgh, Edinburgh, United Kingdom
   2. Centre for Immunity, Infection and Evolution, University of Edinburgh, Edinburgh, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   Philip.Spence@ed.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5506-2773

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