Susceptibility to febrile malaria is associated with an inflammatory gut microbiome

Malaria is a major public health problem, but many of the factors underlying the pathogenesis of this disease are not well understood. Here, we demonstrate in Malian children that susceptibility to febrile malaria following infection with Plasmodium falciparum is associated with the composition of the gut microbiome prior to the malaria season. Gnotobiotic mice colonized with the fecal samples of malaria-susceptible children had a significantly higher parasite burden following Plasmodium infection compared to gnotobiotic mice colonized with the fecal samples of malaria-resistant children. The fecal microbiome of the susceptible children was enriched for bacteria associated with inflammation, mucin degradation, gut permeability and inflammatory bowel disorders (e.g., Ruminococcus gauvreauii, Ruminococcus torques, Dorea formicigenerans, Dorea longicatena, Lachnoclostridium phocaeense and Lachnoclostridium sp. YL32). However, the susceptible children also had a greater abundance of bacteria known to produce anti-inflammatory short-chain fatty acids and those associated with favorable prognosis and remission following dysbiotic intestinal events (e.g., Anaerobutyricum hallii, Blautia producta and Sellimonas intestinalis). Metabolomics analysis of the human fecal samples corroborated the existence of inflammatory and recovery-associated features within the gut microbiome of the susceptible children. There was an enrichment of nitric oxide-derived DNA adducts (deoxyinosine and deoxyuridine) and long-chain fatty acids, the absorption of which has been shown to be inhibited by inflamed intestinal epithelial cells, and a decrease in the abundance of mucus phospholipids. Nevertheless, there were also increased levels of pseudouridine and hypoxanthine, which have been shown to be regulated in response to cellular stress and to promote recovery following injury or hypoxia. Overall, these results indicate that the gut microbiome may contribute malaria pathogenesis and suggest that therapies targeting intestinal inflammation could decrease malaria susceptibility.


INTRODUCTION
Plasmodium falciparum infection remains a major cause of morbidity and mortality in tropical and subtropical regions throughout the world.There were 249 million cases and 608,000 deaths due to malaria in 2022, the majority of which occurred among children in the World Health Organization African region 1 .The clinical manifestation of P. falciparum infection can range from asymptomatic to severe and fatal symptoms, including respiratory distress and cerebral malaria.The proportion of P. falciparum infections that are asymptomatic is known to increase as the transmission intensity and the prevalence of malaria in the community increases 2 .Moreover, individuals residing in areas with moderate-to-high levels of transmission typically develop protection from severe symptoms in early childhood and protection from febrile symptoms by early adolescence [3][4][5] .However, the factors responsible for modulating malaria pathogenesis have not been fully de ned.
The gut microbiome is increasingly recognized as playing a role in the etiology of numerous diseases [6][7][8] , including those caused by intestinal and extraintestinal pathogens [9][10][11] .The susceptibility of mice to Plasmodium infection, as measured by their parasite burden, has been established to be highly dependent on the composition of their gut microbiome 10,12 , but the in uence of the gut microbiome on clinical malaria outcomes in humans is less well characterized.In a cross-sectional study, the composition of the fecal microbiome was shown to be different between Ugandan infants (aged 0.5-4 years) with severe malarial anemia and those with asymptomatic P. falciparum infection 12,13 , but whether the differences in the microbiome engendered the differences in malaria severity or were the result of the differential severity is unclear.Additionally, a longitudinal study in a cohort of Malian children and adults (aged 0.25-25 years) found that the fecal microbiome composition before the start of the malaria season correlated with the prospective risk of P. falciparum infection, but not with the development of febrile malaria 14 .Since the risk of developing febrile malaria decreases with age/malaria exposure in endemic areas, and the gut microbiome also varies with age 15 , it is possible that the wide age range of participants in this study left it underpowered to detect age-speci c correlations between the microbiome and the risk of febrile malaria.
Here, we demonstrate that the fecal microbiome of older Malian children (aged 10 years) before the start of the malaria transmission season correlates with the development of febrile malaria following P. falciparum infection during the ensuing malaria season.Gnotobiotic mice colonized with fecal samples collected from malaria-susceptible children had a signi cantly higher parasite burden following Plasmodium infection than those colonized with fecal samples from malaria-resistant children.Susceptibility to febrile malaria correlated with a greater abundance of bacteria associated with in ammatory bowel disease (IBD), intestinal mucus barrier degradation, and in ammation, but additionally with short-chain fatty acid (SCFA) production and remission following dysbiotic intestinal events.Metabolomics validated the potential metabolic activity indicated by the metagenomics analysis, with the fecal samples of the susceptible children having increased levels of metabolites associated with nitric oxide-induced in ammatory damage and impaired barrier function, but also those associated with recovery following intestinal dysbiosis.This longitudinal study provides the rst demonstration that the gut microbiome plays a role in the clinical outcome of an extraintestinal disease in humans.Further investigation into the intestinal environment of children who are differentially susceptible to febrile malaria and the mechanisms connecting the in ammatory and recovery processes with Plasmodium infection-induced pathogenesis has the potential to lead to interventions that limit the severity of malaria symptoms.

Classi cation of Malian children as resistant or susceptible to febrile malaria
The study was conducted in Kalifabougou, Mali, where P. falciparum transmission is seasonal; the majority of infections occur between July and December each year 16 .Children aged 6 to 10 years old (n = 181) were enrolled in a prospective cohort study from May 2014 to March 2015.Of the 181 children, 156 were included in the microbiome analysis after removing children who were missing fecal samples (n = 2) or who did not have a detected P. falciparum infection (by PCR or microscopy) during the study period (n = 23) (Table 1).
We were interested in determining if the gut microbiome of these children was associated with the clinical outcome of P. falciparum infection.Children were initially characterized as 'susceptible' if they experienced at least one febrile malaria episode during the study period and as 'resistant' if they had no febrile malaria episodes during the study period despite having at least one asymptomatic P. falciparum detected by PCR.Asymptomatic P. falciparum infections were detected at monthly scheduled visits, while febrile malaria episodes were detected at the same scheduled visits and during unscheduled sick visits.Almost all children under 10 years of age experienced at least one episode of febrile malaria during the study period (83 out of 86 children), whereas a quarter of children aged 10 years (16 out of 70 children) were resistant (infected without symptoms) (Table 1).This nding is consistent with the well-established phenomenon wherein individuals in high-transmission areas gradually develop partial protection from febrile malaria by early adolescence [3][4][5] ; therefore, to minimize the confounding effect of age, we focused the microbiome analysis on children 10 years of age.

Susceptibility to febrile malaria corresponds with the gut microbiome composition
Since transmission of P. falciparum in Mali is seasonal, the dry season (January to May) offers an approximate wash-out period between successive transmission seasons.Consequently, we analyzed the fecal samples collected in May to determine if the microbiome composition prior to the start of the transmission season prospectively correlated with the risk of febrile malaria during the ensuing season.16S rRNA sequencing was performed, and the microbiome composition of the pre-transmission season fecal samples was found to be signi cantly different between the malaria-resistant and -susceptible groups (P = 0.030) using Bray-Curtis dissimilarity (Fig. 1A).
Parasite burden of the humanized gnotobiotic mice corresponds with the susceptibility of the human donor to febrile malaria A humanized gnotobiotic mouse model was employed to determine if there was a causal link between the gut microbiome of the Malian children and malaria susceptibility.Fecal samples were chosen to cover the space spanned by the principal components (Fig. 1A).Four susceptible children (subject IDs 375, 400, 415, and 446) and four resistant children (subject IDs 404, 417, 450, and 452) were selected and the May fecal sample from each child was used to colonize four sex-matched germ-free mice.One week following the nal gavage, the mice were infected with Plasmodium yoelii and parasitemia was followed until the infection cleared.
Overall, the mice that were colonized with the fecal samples from the resistant children had signi cantly lower parasitemia (P = 0.0136) than the mice that were colonized with the fecal samples from the susceptible children (Fig. 1B and C).One mouse colonized with the fecal sample of resistant donor 404 and one mouse colonized with the fecal sample of resistant donor 452 had high parasitemia (Fig. 1D).
True germ-free mice (GF) typically have low levels of parasitemia following infection with P. yoelii, but it is relatively common to observe high parasitemia in a small subset of these mice (Supp Fig. 1A and B).
Additionally, this phenomenon has been observed in mice that are known to possess a microbiome associated with a low level of parasitemia (Tac) 10 (Supp Fig. 1 and B).
Mice colonized with the fecal sample of susceptible donor 415 exhibited low parasitemia.Given this outlier, we more closely examined the incidence of asymptomatic P. falciparum infections (detected by PCR) and febrile malaria episodes in the four susceptible donors.Over the study period, susceptible subjects 375, 400 and 446 each experienced 3 to 4 febrile malaria episodes while no asymptomatic infections were detected (Table 2), consistent with their malaria-susceptible phenotype.In contrast, subject 415 experienced two febrile malaria episodes and was asymptomatically infected at ve timepoints (Table 2), suggesting a higher degree of malaria resistance.
A similar analysis of the cumulative incidence of asymptomatic P. falciparum infections and febrile malaria episodes among all 156 children showed that 26 children initially classi ed as malariasusceptible had both asymptomatic and febrile infections, seven of which had 1 to 2 febrile malaria episodes and were asymptomatically infected at ve or more timepoints during the study period (Table 2).Re-analysis of the 16S rRNA sequencing after re-classifying these seven children as malariaresistant showed a similar trend to that which was observed using the initial de nition of malaria resistance and susceptibility (P = 0.055) (Fig. 1E).
16S rRNA sequencing was performed on the fecal samples collected from the mice on the day of infection and the engrafted microbiomes were determined to be signi cantly different (P < 0.001) between the groups of mice colonized with fecal samples from children reclassi ed as malaria-resistant versus malaria-susceptible using Bray-Curtis dissimilarity (Fig. 1F).
Bacteria increased in the high parasite burden mice are associated with impaired gut barrier function Differential abundance analysis was performed on the mouse 16S rRNA sequencing data to examine the relationship between the microbiome and parasite burden (Table 3).Eubacterium coprostanoligenes, Gemmiger formicilis, Anaerostipes hadrus and Roseburia faecis were signi cantly increased in the low parasitemia mouse groups (Fig. 2A-D).Clostridium citroniae, Dorea longicatena, Coprococcus comes, Blautia faecis, Bacteroides intestinalis, and Bacteroides ovatus, as well as two unclassi ed species, Clostridium sp.FS41, Ruminococcus sp.Marseille-P328, were signi cantly increased in the high parasitemia mouse groups (Fig. 2E-L).Interestingly, many of the bacteria that were signi cantly more abundant in the high parasite burden mice have previously been shown to be associated with impaired gut barrier function and to be enriched during IBD [17][18][19] .
Resistance to febrile malaria was associated with Streptococcus thermophilus and susceptibility with Eubacteriales Differential abundance analysis was performed on the human 16S rRNA sequencing data to further investigate the association of the microbiome with susceptibility (Table 3).Streptococcus thermophilus was signi cantly more abundant in the microbiome of the resistant children (Fig. 2M), and unclassi ed species of Ruminococcus, Dorea, and Blautia were signi cantly more abundant in the susceptible children (Fig. 2N-P).

Discrimination of malaria-resistant and -susceptible children improved by increased sequencing depth
Since we demonstrated that the microbiome is causally linked to malaria susceptibility using a gnotobiotic mouse model, we performed shotgun metagenomics sequencing on the human samples to better understand the microbiome composition and the interactions underlying susceptibility to febrile malaria through the increased sequencing depth and the improved classi cation at the species level afforded by this method.The Bray-Curtis dissimilarity was signi cantly different (P < 0.001) between the resistant and susceptible groups using the metagenomic sequencing data (Supp Fig. 2A).
The malaria-susceptible network is more interconnected than the resistant network Network analysis was performed on the samples to determine if there were differential interactions between the bacteria in the resistant and susceptible children.Overall, there were three principal clusters shared by both networks; the rst predominated by Streptococcus and Veillonella species, the second by Prevotella and Bacteroides species, and the third by species in the order Eubacteriales (Fig. 3; Supp Fig. 3).Additionally, while the edges (connections between two taxa) were in general positive (indicative of positive correlation between the species) and similar between the two networks (71.97% in resistant versus 66.11% in susceptible), the majority of the inter-cluster interactions were negative (indicative of negative correlation between the species; Fig. 3).The edge density (proportion of total possible edges -a measure of network connectivity) of the susceptible network was nearly twice that of the resistant network (0.059 in resistant versus 0.112 in susceptible) (Supp Fig. 4A).This trend was also observed for the Streptococcus/Veillonella (0.071 versus 0.111), the Prevotella/Bacteroides (0.042 versus 0.149), and the Eubacteriales (0.096 versus 0.133) clusters individually (Supp Fig. 4B-D).
The Jaccard index was signi cantly different (Jacc = 0.029; P(J ≤ j) = 0.000019) for the two sets of hubs (the most connected taxa in each of the networks), indicating that the two sets of hubs were signi cantly more different than expected at random; the only shared hub was Sellimonas intestinalis in the Eubacteriales cluster (Table 5).Both the resistant and susceptible networks had hubs in the Eubacteriales cluster; the susceptible network also had hubs in the Prevotella/Bacteroides cluster, and neither network had hubs in the Streptococcus/Veillonella cluster (Table 5).Similar to what was observed for the full network, the edge density for the hubs was lower in the resistant network compared to the susceptible network (0.209 versus 0.277; Table 5).
Interestingly, the edges connected to the hubs in the resistant network had higher absolute median edge weights (a measure of the strength of the correlation between the taxa 20 ) compared to the edges connected to the hubs in the susceptible network, for both positive (0.659 versus 0.553) and negative edges (-0.616 versus − 0.464) (Supp Fig. 4E and F).Higher absolute median edge weights were also observed for the total resistant network compared to the total susceptible network (Supp Fig. 4G and H), for both the positive (0.656 versus 0.512) and negative edges (-0.611 versus − 0.443).
Eubacteriales cluster is associated with malaria-susceptible children and Prevotella/Bacteroides and Streptococcus/Veillonella clusters are associated with resistant children Sparse partial least squares discriminant analysis (sPLS-DA) was performed on the metagenomics sequencing samples in order to better understand the correlation of the bacteria with each other and with susceptibility to febrile malaria.The samples were projected onto the space spanned by the rst two components (Fig. 4A), which demonstrated moderate separation of the children by malaria outcome along the rst component.One group of bacteria was positively correlated with component one and one group of bacteria was negatively correlated with component one (Fig. 4B), approximately corresponding to the resistant and susceptible groups on the sample plot.
Closer examination of the correlation circle plot permitted a more granular characterization of the relationships between the different bacteria.The bacteria that correlated with the resistant group of children were those that were largely found within the Prevotella/Bacteroides and the Streptococcus/Veillonella clusters in the network analysis (Supp Fig. 5A).Conversely, the majority of the bacteria that correlated with the susceptible group were those that were detected in the Eubacteriales cluster (Supp Fig. 5B).In agreement with the correlation circle plot, the bacteria with the top loading weights (a measure of how much they contribute to the component) that had a higher median abundance in the resistant group were found primarily within the Prevotella/Bacteroides and the Streptococcus/Veillonella clusters, and the bacteria that had a higher median abundance in the susceptible group were found in the Eubacteriales cluster (Fig. 4C).
Bacteroides were recently demonstrated to be signi cantly increased in Ugandan infants with severe malaria anemia compared to infants with asymptomatic P. falciparum infection 12 .Bacteroides caccae, Bacteroides cellulosilyticus, Bacteroides fragilis and Bacteroides uniformis were signi cantly increased in the susceptible Malian children (Table 6), although this appears to be largely the result of outliers (Supp Fig. 7A-D).

Susceptibility associated with metabolites indicative of in ammatory gut barrier damage
Since the susceptible children had signi cantly increased abundances of bacteria that have previously been associated with in ammation and mucin degradation, untargeted metabolomics was performed to determine if the metabolic activity of the microbiota within the Malian children supported the association of impaired gut barrier function with susceptibility to febrile malaria.Moderate separation of the resistant and susceptible children was observed along the rst component when the samples were projected onto the space spanned by the rst two components (Fig. 6A), and two groups of metabolites were observed to approximately correspond with the resistant and susceptible groups of children (Fig. 6B).The loading weights of the top thirty metabolites for component one were relatively evenly distributed (Fig. 6C), suggesting that the differences in metabolic activity may have been the result of comprehensive changes in pathways, rather than inordinate changes in a few select metabolites.Thus, the range of the metabolites of interest was extended to include all metabolites with an absolute loading weight of at least 0.05 (Table 7), which brought several metabolite categories into relief.
Consistent with the potential metabolic activity suggested by the metagenomics data, the metabolomics data indicated the presence of nitrosative stress and impaired gut barrier function (e.g., deoxyinosine and deoxyuridine) within the susceptible children [33][34][35][36] , but additionally supported the presence of a dynamically regulated anti-in ammatory recovery component (e.g., hypoxanthine and pseudouridine) 37- 39 .

DISCUSSION
Malaria remains a major cause of morbidity and mortality in low-and middle-income countries.Individuals living in areas with moderate-to-high P. falciparum transmission typically develop protection from febrile symptoms by early adolescence, but the factors underlying this transition are not well understood.We observed in a cohort of children aged 6 to 10 years old, that children younger than 10 years of age were generally susceptible to febrile malaria following P. falciparum infection, while a quarter of the 10-year-old children were resistant.The microbiome of the 10-year-old children in May (before the start of the transmission season) was signi cantly different between children that were malaria-resistant or -susceptible during the subsequent transmission season.Furthermore, gnotobiotic mice colonized with the pre-transmission season fecal sample of susceptible children had a signi cantly higher parasite burden following infection with P. yoelii compared to gnotobiotic mice colonized with the fecal sample of resistant children.
Network analysis of the human fecal metagenomics samples revealed three principal clusters shared by both the resistant and the susceptible microbiomes: the rst predominated by Streptococcus and Veillonella species, the second by Prevotella and Bacteroides species, and the third by species in the order Eubacteriales.The species within the susceptible microbiome were more interconnected than those within the resistant microbiome (higher edge density); however, the average strength of the individual interactions was greater in the resistant network (higher edge weights).The lower number of relatively stronger correlations between the bacteria in the resistant network is potentially indicative of metabolic interactions that are more speci c and tightly controlled, while the high number of low strength correlations in the susceptible network may be suggestive of the bacteria responding in an overall similar manner to the environment within the colon.The sPLS-DA and the differential abundance analysis of the whole genome shotgun metagenomics data found that the majority of the bacteria that were associated with/signi cantly more abundant in the resistant children were from the genera Prevotella, Streptococcus and Veillonella, while the majority of the bacteria that were associated with/signi cantly more abundant in the susceptible children were from the order Eubacteriales.P. copri, which was more abundant in the resistant children using shotgun metagenomics, has been associated with contradictory impacts on human health, likely due to the high genetic diversity present in this species [40][41][42] .The different strains within the P. copri complex have been linked to country of origin and ber intake, and are associated with substantial functional diversity 41,42 .Higher ber levels correlated with increased potential for the degradation of complex carbohydrates by this species, whereas omnivore diets were linked to an increased incidence of metabolic syndrome in individuals with higher levels of P. copri 43,44 .Given the high ber content in the diet of these children 45 , it seems likely that the increased abundance of P. copri in the resistant children is associated with improved degradation of complex carbohydrates.Several Prevotella-associated operational taxonomic units (OTUs) were also found to be differentially abundant in the human 16S rRNA sequencing data, but it is di cult to make direct comparisons with the shotgun metagenomics, as the human 16S rRNA data was classi ed using the SILVA database, which divides Prevotellaceae into multiple non-monophyletic groups, each of which are associated with several individual OTUs.S. thermophilus, which was found to be signi cantly increased in the resistant children by both shotgun metagenomics and 16S rRNA sequencing, has been shown to be capable of reducing in ammation in a murine model of sepsis 46 .Moreover, V. parvula, which was increased in the resistant children, has been shown to modulate the immune response in in vitro co-stimulation experiments with different strains of Streptococcus 47 .Thus, the increased abundance of S. thermophilus and V. parvula in the resistant children may have contributed to a more anti-in ammatory intestinal environment (Fig. 7).
The species associated with low parasite burden in the mice were different than those associated with resistance to febrile malaria in children.G. formicilis, A. hadrus and R. faecis, which were increased in the low parasite burden mice, are known producers of SCFA, which promote gut barrier integrity and reduce in ammation [48][49][50] .Furthermore, G. formicilis, A. hadrus, and R. faecis have been shown to be increased in healthy controls compared to ulcerative colitis (UC) and Crohn's disease (CD), irritable bowel syndrome (IBS), and CD, respectively [49][50][51] .Moreover, E. coprostanoligenes, which was also signi cantly more abundant in the low parasite burden mice, was shown to be enriched by oroxylin A treatment in conjunction with improved protection of the colonic mucus barrier and alleviation of colitis in mice 52 .
Many of the bacteria that were more abundant in the susceptible children were associated with in ammation, impaired gut barrier function, and IBD (Fig. 7).R. torques, R. gauvreauii, D. formicigenerans and D. longicatena are mucolytic bacteria; they possess glycoside hydrolases that allow them to initiate mucin degradation by releasing the sialic acids from the non-reducing ends of glycans, impairing gut barrier function 17,18 .Accordingly, increased abundance of these bacteria has been associated with IBD 19,21-23 .C. comes, while not in possession of glycoside hydrolases, has also been shown to grow with mucin as the main carbon source 53 and to be enriched during IBD 19 .L. sp.YL32 was positively correlated with gut permeability and in ammation 54 and L. phocaeense was shown to be enriched in patients with active IBD 55 .Finally, E. clostridioformis and E. bolteae were both shown to be signi cantly enriched in CD patients 51,56 .
Interestingly, the susceptible children also had increased abundance of several bacteria that have been shown to correspond with favorable prognosis and remission following intestinal dysbiosis (Fig. 7). A. hallii cannot degrade complex oligo-and polysaccharides, but participates downstream in mucin crossfeeding, and produces SCFA from mucin-derived monosaccharides 24,25 .While A. hallii has been shown to be enriched in IBD groups compared to control groups 19 , patients who achieved remission after fecal microbiota transplantation (FMT) treatment of UC had increased abundance of A. hallii and SCFA 26 , potentially suggesting that the presence of A. hallii during IBD is indicative of a favorable prognosis.An increase in SCFA was also associated with the amelioration of dextran sulfate sodium (DSS)-induced colitis in mice following oral administration of B. producta 27 , and S. intestinalis has been shown to be increased in patients during homeostasis recovery following dysbiosis events [28][29][30][31] .
Achievement of remission after FMT treatment of UC has also been associated with increased levels of secondary bile acids 26 , and treatment with secondary bile acids has been shown to decrease intestinal in ammation in three models of murine colitis 57 .C. sp.M62/1 was shown to play a signi cant role in bile acid metabolism using in silico metabolic modelling 58 , and C. scindens, which possesses 7αdehydroxylases that transform primary bile acids into secondary bile acids 59 , was shown to enhance resistance to Clostridum di cile infection in a secondary bile acid-dependent manner 60 .D. longicatena and C. comes were the only species associated with susceptibility in the Malian children that were also associated with high parasite burden in the colonized mice.However, despite the taxonomical differences, the bacteria that were more abundant in the high parasite burden mice were also associated with impaired barrier function, suggesting that the high parasite burden phenotype engendered in the colonized mice may have been the product of similar metabolic activities to that which was associated with susceptibility in the children, but with a mouse speci c microbiome composition.
Gavage with B. intestinalis following antibiotic depletion of the gut microbiota has been shown to increase ileal damage compared to mice allowed to naturally repopulate their gut microbiota 61 , and C. citroniae possesses D-cysteine desulfhydrases, which increase the concentration of colonic sul des, potentially inhibiting the utilization of butyrate by intestinal epithelial cells 62 .Additionally, similar to the susceptible children, the high parasite burden mice were also associated with an anti-in ammatory component: B. ovatus has been shown to reduce mucosal in ammation during DSS-induced colitis 63,64 and B. faecis has been shown to be reduced in patients with CD and to produce SCFA 50 .
Congruent with the potential metabolic activity indicated by the metagenomics data, many of the metabolites associated with the susceptible children were indicative of an in ammatory environment and gut barrier damage (Fig. 7).Deoxyinosine and deoxyuridine are DNA adducts formed through nitrosative deamination that occurs due to the generation of nitric oxide during chronic in ammation 33 and are thus potential biomarkers of in ammatory processes.m6A is a post-transcriptional RNA modi cation that is in uenced by the microbiota 65 and has been shown to be involved in the initiation and pathogenesis of IBD 66 .Moreover, the increased levels of LCFAs and sterols in the susceptible children further support an in ammatory intestinal environment and impaired gut barrier function, as cats with chronic in ammatory digestive disorders were shown to have higher levels of LCFAs and animal-derived sterols in their feces compared to healthy cats 34 , and LPS-induced in ammation was shown to inhibit the absorption of LCFA by intestinal epithelial cells, concomitant with an increase in m6A modi cation levels 35 .
Nevertheless, similar to what was suggested by the metagenomics data, the metabolomics data also supported the presence of a dysbiosis recovery component in the susceptible children (Fig. 7).
Pseudouridine modi cation has been shown to be dynamically regulated in response to cellular stress and arti cial pseudouridylation was shown to reduce immune stimulation in vitro 37 .Additionally, hypoxanthine was shown to be lower in the fecal samples of IBD patients compared to healthy controls 38 and to promote intestinal barrier function and recovery following injury or hypoxia 39 .These results suggest that while the intestinal environment in the susceptible children may be in a more in ammatory state than that of the resistant children, there are supplementary mechanisms in place that are potentially preventing excessive damage and/or allowing recovery.
Furthermore, the metabolites associated with the resistant children also agreed with the potential role of impaired gut barrier function in susceptibility to febrile malaria (Fig. 7).Mucus phospholipids play a role in maintaining the intestinal mucus barrier 36 , thus the increased amounts of phospholipids in the resistant children may indicate a more impregnable mucosal barrier.GPC and LPE were shown to be decreased in UC and CD patients 21,67 and LPC was decreased in UC patients 36,68 .Furthermore, betaine treatment has been shown to attenuate in ammation and upregulate tight junction proteins during DSSinduced colitis 69 , and 9-PAHSA was shown to attenuate the immune response and prevent mucosal damage during DSS-induced colitis 70 .
Interestingly, elevated levels of intestinal damage biomarkers were observed in young children with severe malaria compared to healthy community controls in a study in Uganda 71 , suggesting that in ammatory gut barrier damage may play a general role in worsening the immune response to Plasmodium infection and consequently increasing the severity of the infection.However, as was mentioned, the microbiome is known to vary greatly with age 15 , as is protection from malaria symptoms.Thus, if the microbiome is playing a role in the increased intestinal damage observed in the Ugandan infants with severe malaria, it is possible that the speci c bacteria and metabolic activity involved are not the same.
Overall, this study demonstrated that the microbiome plays a role in the susceptibility to febrile malaria.
Bacteria and metabolites associated with increased in ammation and gut barrier impairment were enriched within the gut microbiome of the susceptible children; however, the metagenomics and metabolomic data also indicated that the microbiome of the susceptible children also possessed features associated with recovery from dysbiosis.It is possible that the in ammatory intestinal environment within the susceptible children is priming the immune response in a manner that renders them more susceptible to the development of febrile symptoms following P. falciparum infection.Further research into the dynamics of the differential bacteria and metabolites during febrile Plasmodium infection and recovery, including targeted approaches to examine gut barrier function, and into the mechanisms through which these differences in uence the pathogenesis of Plasmodium infection, has the potential to lead to treatments capable of mitigating malaria severity.

METHODS
Study design, participants, and detection of P. falciparum infection 181 children aged 6 to 10 years old were enrolled in a prospective cohort study from May 2014 to March 2015 conducted in Kalifabougou, Mali.A detailed description of this cohort has been previously published 72 .The Ethics Committee of the Faculty of Medicine, Pharmacy and Dentistry at the University of Sciences, Techniques and Technology of Bamako and the Institutional Review Board of the National Institute of Allergy and Infectious Disease, National Institutes of Health approved this study (ClinicalTrials.govidenti er: NCT01322581).Written, informed consent was obtained from the parents and/or guardians of participating children.Two children were removed from this study due to missing fecal samples.To detect asymptomatic P. falciparum infections, ngerprick blood samples were collected at monthly scheduled visits and PCR was performed on the dried blood spots as described previously 73 .Positive P. falciparum PCRs were considered to represent an asymptomatic infection if there were no reported febrile symptoms for at least 3 weeks following the PCR.Children were considered to have had a febrile malaria infection if they presented to the clinic with fever and P. falciparum parasites were detected by microscopic examination of blood smears.Twenty-three children were excluded from further analysis because they had no P. falciparum infections detected during the study period by microscopy or by PCR, leaving a total of 156 children for microbiome analysis (Table 1).Aliquots of stool collected during the prospective cohort study were cryopreserved at -80°C in Mali and shipped to the U.S. on dry ice for analysis.

Mouse husbandry and gnotobiotic experiment
Germ-free, female and male C57BL/6N mice (5-8 weeks old) were purchased from Charles River Laboratories.Mice were housed in autoclaved Tecniplast IsoCage P cages (Tecniplast Group) with ALPHA-dri bedding (Shepherd Specialty Papers, Inc) and Bed-r'Nest nesting material (The Andersons Plant Nutrient Group) under a strict 12 hr light cycle.Cages were changed once every two weeks.The mice were provided ad libitum with autoclaved reverse osmosis water and autoclaved 7013 (NIH-31 Modi ed Open Formula Mouse/Rat Sterilizable Diet) purchased from Inotiv/Envigo.Cages were submerged in a tank of Exspor for 5 min before being opened within an Exspor-sterilized biosafety cabinet (40 min contact time).Additionally, all items used with the mice (e.g., gavage needles) were autoclaved and their packaging wiped down with Exspor before being transferred into the biosafety cabinet.Fecal samples were collected upon arrival and from the sentinels at the end of the experiment and sterility veri ed by IDEXX BioAnalytics through generic bacteria 16S rRNA gene PCR, and fungal, and aerobic and anaerobic bacteria culture (case numbers 120118-2022 and 125378-2022).All animal experiments were carried out at Indiana University adhering to the local and national regulation of laboratory animal welfare, and all procedures were reviewed and approved by the Indiana University Institutional Animal Care and Use Committees (protocol numbers 19024 and 22010).
May stool samples for the participants with the subject identi cation numbers 375, 400, 404, 415, 417, 446, 450 and 452 were selected to colonize the gnotobiotic mice.A portion of each fecal sample was scraped off while on dry ice, and diluted in sterile saline at 1:10 (w/v).The fecal suspension was vortexed, and the larger particles allowed to settle on ice, before the supernatant was collected.200 µL of each fecal suspension was gavaged into four mice each at weekly intervals for four weeks, as was previously described 45 .Fecal samples were collected from the colonized mice and were ash frozen in liquid nitrogen and stored at -80°C.
Samples were analyzed with FlowJo (Tree Star), and parasitized red blood cells were de ned as CD45.2 − TER-119 + dihydroethidium + Hoechst + cells.The statistical signi cance of the difference in the area under the curve (AUC) for the parasitemia was determined using the Mann Whitney U test in GraphPad Prism Version 9.4.1.

DNA sequencing and feature table construction
DNA isolation and sequencing for the human 16S rRNA sequence data was performed by the J. Craig Venter Institute (JCVI).The V4 region of the 16S rRNA gene was ampli ed using the 515F (GTGCCAGCMGCCGCGGTAA) and 806R (GGACTACHVGGGTWTCTAAT) primer pair and sequenced using an Illumina MiSeq.An in-house pipeline was used by JCVI to construct the feature table, using the SILVA SSU Ref NR99 database (v123) for taxonomic classi cation.For the mouse 16S rRNA sequencing and the human whole genome shotgun metagenomics sequencing, DNA was extracted from the feces using the QIAamp PowerFecal DNA kit (QIAGEN, Germantown, MD) according to the manufacturer's instructions.For the mouse 16S rRNA sequencing, the DNA samples were shipped overnight on ice packs to the Genome Technology Access Center (GTAC; Washington University, St. Louis, MO) for 16S rRNA gene sequencing using Multiple 16S Variable Region Species-level Identi cation (MVRSION), an approach that sequences all 9 hypervariable regions of the 16S rRNA gene with 14 primer pairs 74 .The OTU table was constructed by GTAC and imported into QIIME2 75 .For the human whole genome shotgun metagenomics sequencing, the DNA library preparation and sequencing was performed by the Center for Medical Genomics (CMG) at the Indiana University School of Medicine using the Nextera XT DNA Library Preparation Kit (Illumina) and the NovaSeq 6000 with 150bp paired-end sequencing.Quality control and host sequence removal was performed using KneadData (v0.12.0) 76 .Brie y, FastQC (v0.11.9) 77 removed overrepresented sequences (> 0.1% frequency), Trimmomatic (v0.33) 78 removed low quality reads and adapters (SLIDINGWINDOW:4:20 MINLEN: 50), TRF (v4.09.1) 79 removed tandem repeats, and bowtie2 (v2.5.1) 80 mapped the samples to the human genome assembly GRCh37 (hg37) to remove possible human read contamination.The feature table was created using Kraken2 (v2.1.2) 81 and Bracken (v2.8.0) 82 with the pre-built standard Kraken2 database (version k2_standard_20230314).Minimum hit groups was increased to 4 and the con dence score was increased to 0.10.

Metabolomics
The fecal samples were shipped overnight to Metabolon on dry ice and maintained at -80°C at Metabolon until processing.Brie y, samples were prepared using the automated MicroLab STAR® system from Hamilton Company.Proteins were precipitated with methanol and vigorous shaking for 2 min (Glen Mills GenoGrinder 2000) followed be centrifugation.The samples were then divided into multiple fractions: two fractions for analysis by two separate reverse phase/UPLC-MS/MS methods with positive ion mode electrospray ionization, one fraction for analysis by reverse phase/UPLC-MS/MS with negative ion mode electrospray ionization, and one fraction for analysis by HILIC/UPLC-MS/MS with negative ion mode ESI.Organic solvent was removed using a TurboVap® (Zymark) and the samples stored under nitrogen until analysis.The dried samples were reconstituted in different solvents according to the four methods.Each of the four methods used a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scienti c Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution.Two aliquots were analyzed using acidic positive ion conditions, one chromatographically optimized for more hydrophilic compounds (PosEarly) and the other chromatographically optimized for more hydrophobic compounds (PosLate).The third aliquot was analyzed using basic negative ion optimized conditions (Neg), and the fourth aliquot was analyzed via negative ionization following elution from a HILIC column (HILIC).The MS analysis alternated between MS and data-dependent MS n scans using dynamic exclusion.The scan range varied slightly between methods but covered 70-1000 m/z.Raw data was extracted, peak-identi ed and quality control processed using a combination of Metabolon developed software services.

Beta diversity analysis
Cumulative sum scaling 83 was used to normalize the mouse and human 16S rRNA sequencing feature tables, and relative log expression 84 was used to normalize the human whole genome metagenomics sequencing feature table.Bray-Curtis diversity was calculated using the default settings in QIIME2 (v2022.11.1) 75 without rarefaction.

Figures
Figures

Microbiome
composition correlates with susceptibility to febrile malaria in children and high parasite burden in mice.A) Principal coordinate analysis (PCoA) plot of the Bray-Curtis dissimilarity of the human fecal samples.Gavage samples are highlighted by increased point size.B) Parasitemia and C) AUC of the gavaged gnotobiotic mice by resistant and susceptible outcome groups, and D) parasitemia by individual gavage groups.PCoA plots of the Bray-Curtis dissimilarity for the E) human and the F) murine fecal samples using the updated resistant de nition.The P-value for the Bray-Curtis distance was determined using PERMANOVA and the P-value for parasitemia was determined using the Mann Whitney U test.

Figure 4 Species
Figure 4