ABSTRACT

   Babesia divergens is an apicomplexan parasite that infects human red
   blood cells (RBCs), initiating cycles of invasion, replication, and
   egress, resulting in extensive metabolic modification of the host
   cells. Babesia is an auxotroph for most of the nutrients required to
   sustain these cycles. There are currently limited studies on the
   biochemical pathways that support these critical processes,
   necessitating the high-resolution global metabolomics approach
   described here to uncover the metabolic interactions between parasite
   and host RBC. Our results reveal an extensive parasite-mediated
   modulation of RBC metabolite levels of all classes, including lipids,
   amino acids, carbohydrates, and nucleotides, with numerous metabolic
   species varying in proportion to the level of infection. Many of these
   molecules are scavenged from the host RBCs. This is in accord with the
   needs of a rapidly proliferating parasite with limited biosynthetic
   capabilities. Probing these pathways in depth, we used growth
   inhibition assays to quantitate parasite susceptibility to drugs
   targeting these pathways and stimulated emission depletion (STED)
   microscopy to obtain high-resolution images of drug-treated parasites
   to correlate changes in morphology with specific metabolic blocks in
   order to validate the data generated by the untargeted metabolomics
   platform. Thus, interruption of cholesterol scavenging from the host
   cell led to premature parasite egress, while chemical targeting of the
   hydrolysis of acyl glycerides led to the buildup of malformed parasites
   that could not successfully egress. This is the first report detailing
   the global metabolomic profile of the B. divergens-infected RBC.
   Besides deciphering diverse aspects of the host-parasite relationship,
   our results can be exploited by others to uncover further drug targets
   in the host-parasite biochemical network.

   IMPORTANCE Human babesiosis is caused by apicomplexan parasites of the
   Babesia genus and is associated with transfusion-transmitted illness
   and relapsing disease in immunosuppressed populations. Through its
   continuous cycles of invasion, proliferation, and egress, B. divergens
   radically changes the metabolic environment of the host red blood cell,
   allowing us opportunities to study potential chemical vulnerabilities
   that can be targeted by drugs. This is the first global metabolomic
   profiling of Babesia-infected human red blood cells, and our analysis
   revealed perturbation in all biomolecular classes at levels
   proportional to the level of infection. In particular, lipids and
   energy flux pathways in the host cell were altered by infection. We
   validated the changes in key metabolic pathways by performing
   inhibition assays accompanied by high-resolution microscopy. Overall,
   this global metabolomics analysis of Babesia-infected red blood cells
   has helped to uncover novel aspects of parasite biology and identified
   potential biochemical pathways that can be targeted for
   chemotherapeutic intervention.

   KEYWORDS: Babesia, Babesia divergens, global metabolomics, RBC
   infection, babesiosis, lipids, Bd37, cholesterol, DAG, TAG, orlistat,
   invasion, egress, STED, infected RBC, lipid profile, Plasmodium

INTRODUCTION

   Intracellular erythrocytic parasites have established mechanisms to
   exploit their host red blood cells (RBCs) to ensure successful
   propagation. Babesia spp. are apicomplexan parasites that invade and
   infect the RBCs of various animal species, and a few of them have
   become zoonotic ([40]1, [41]2). Among these are Babesia divergens, a
   cattle parasite seen primarily in Europe, although B. divergens-like
   variants have been found in the United States, Babesia microti, a
   rodent parasite that is responsible for most of the infections in the
   United States, and Babesia duncani, a few cases of which have been
   identified in California ([42]3).

   The pathology of babesiosis is a consequence of parasitemia, which
   develops through the cyclical asexual replication of Babesia parasites
   in host RBCs ([43]4, [44]5). As is typical in such host-pathogen
   infections, the parasite must divert host nutrients to facilitate its
   continued proliferation, resulting in a metabolically impacted host red
   cell that struggles to maintain homeostasis. These infected host cells
   handle the impact of infection in the form of altered metabolic
   profiles that encompass by-products of parasite metabolism, including
   waste products and toxins ([45]6). Hemolysis, hemoglobinuria resulting
   in low hematocrit, low hemoglobin level, low haptoglobin level,
   elevated reticulocyte count, and elevated lactate dehydrogenase level
   are some of the measurable effects of host pathogenesis caused by the
   metabolic intertwining of Babesia and host ([46]7, [47]8). The
   metabolites required by Babesia to maintain successful parasitism can
   be scavenged whole from the host cell and/or environment or synthesized
   using building blocks available in the host cell.

   However, little is known of Babesia species metabolic pathways, and
   this report is the first to characterize the global metabolic
   modulation of the host RBC by Babesia using high-resolution, global,
   untargeted metabolomics. This area is critical to dissect because the
   pathology of human babesiosis is linked to dysregulation of the human
   host metabolism ([48]9, [49]10). Earlier reports have shown that
   intracellular parasites like Toxoplasma and Plasmodium have evolved to
   thrive with minimal metabolic pathways because of successful scavenging
   of their most essential nutrients from the host ([50]11[51]–[52]14).
   Interestingly, human Babesia spp. have much smaller genomes than other
   apicomplexans, further pointing toward adaptation through evolution
   ([53]15[54]–[55]20). Despite this, the parasite possesses crucial
   metabolic pathways that define its survival in the host. Therefore,
   global metabolomics studies have the power to unravel some of the more
   poorly understood aspects of Babesia biology, and these studies can
   have far-reaching clinical relevance in defining better therapeutic
   targets, as well as biomarkers of infection. Babesia’s metabolic
   network has so far been indirectly approached via inferences from
   genomic data or in vitro biochemical assays ([56]15,
   [57]21[58]–[59]23). However, metabolomic profiling provides one of the
   most integrated functional profiles of biologic status, as it
   accurately measures functional phenotypes that are the net result of
   genomic, transcriptomic, and proteomic changes ([60]24).

   In this study, we present results from globally targeted mass
   spectrometry that quantified metabolic changes in parasite-infected
   erythrocytes in comparison to uninfected erythrocytes during the
   asexual life cycle of B. divergens in the human RBC. As synchronized
   parasite populations exist only for a fleeting few hours after invasion
   ([61]25), analyses were conducted on asynchronous parasite cultures
   under both low and high infection conditions, comparing their metabolic
   profiles to that of uninfected host RBCs (uRBCs). To comprehensively
   analyze the parasite metabolism using metabolomic data collected during
   its asexual erythrocytic cycle, a series of computational and
   statistical methods were used and key global, pathway-level, and
   stage-specific metabolites were identified. Our study has revealed
   important changes in nucleotide, lipid, amino acid, and carbohydrate
   metabolites, most of which were associated with Babesia proliferation.
   We identified 650 metabolites, of which approximately 25% and 50% were
   differentially present in low and high infection, respectively,
   compared to their presence in uRBCs. To corroborate the metabolomics
   profiles, we investigated in detail specific pathways that the global
   metabolomic analysis identified as critical to the rapidly
   proliferating parasite, specifically those that yield building blocks
   of the parasite biomembrane, focusing on cholesterol and free fatty
   acids. Our growth inhibition assays (GIAs) with drugs targeting major
   enzymes in these pathways, supported by stimulated emission depletion
   (STED) microscopy of parasites, confirm our global metabolomics
   findings and emphasize the importance of global metabolite analysis as
   a tool to study Babesia-RBC interactions and parasite biology, as
   chemical interruption of these pathways resulted in severe consequences
   for the proliferating parasites.

   This paper provides the first overview of metabolic modulation of human
   red cells by B. divergens under both low and high infection conditions,
   highlighting the interplay between host and parasite metabolism.
   Importantly, these results also identify critical vulnerabilities of
   the parasite that can be exploited in the future for drug discovery
   efforts.

RESULTS

Global metabolic profiling of B. divergens-infected RBCs reveals major
changes in the RBC metabolome upon infection.

   Babesia spp. possess the smallest genomes sequenced among the
   apicomplexans. Genome-wide sequences of B. microti, B. divergens (Rouen
   1987), Babesia bovis, and Babesia canis reveal their genome sizes to be
   6.5 Mb, 10.7 Mb, 8.2 Mb, and 7.02 Mb ([62]17, [63]26). The highly
   reduced genomes of these parasites are reflected in minimal metabolic
   pathways and auxotrophic lifestyles with high dependence on hosts for
   nutrients required for growth. To investigate the metabolic pathways
   critical to the asexual growth of B. divergens in human RBCs, we
   performed global metabolomics on uninfected red blood cells (uRBCs) and
   infected red blood cells (iRBCs), at low and high parasitemia (n = 3
   for each set). Two infection levels were chosen to enable the
   identification of potential biomarkers for early and low infections,
   while data from the higher parasite load could potentially indicate
   vulnerabilities that can be targeted by therapeutic interruptions.

   Under low parasitemia conditions in infected cells collected on day 2
   postinvasion (D2; 10% parasitemia), a total of 657 metabolites were
   detected, among which differences in the levels of 145 metabolites
   between uRBCs and iRBCs were statistically significant (P < 0.05). Of
   these, 101 were upregulated and 44 were reduced in abundance. This is
   represented as a volcano plot in [64]Fig. 1a. A volcano plot is a
   scatterplot; the log[2] fold change (log[2]FC) values are on the x
   axis, and the −log[10] P values on the y axis. The volcano plots were
   constructed in RStudio using the package ggplot. The codes used to
   construct the plot have been provided in file 2 in the supplemental
   material. The plot characterizes metabolites based on differences
   between the two conditions, uninfected and infected, together with the
   associated statistical significance values. The log[2]FC threshold was
   set at more than or less than 1, and the P value threshold was set at a
   −log[10] P value of >1.3010. Thus, only metabolites with an absolute FC
   of >2 or <2 and an absolute P value of <0.05 were considered for
   further analysis. In [65]Fig. 1a and [66]b, gray dots represent
   metabolites that had an FC or P value that was less than the threshold.
   The pale red and green dots represent metabolites with log[2]FC values
   of >1 and <1, respectively, but with insignificant P values. The red
   and green dots represent metabolites with log[2]FC values of >1 and <1,
   respectively, and significant P values. The purple triangles and orange
   squares represent metabolites with log[2]FC values of >5 and <5,
   respectively. In terms of log conversion, a log[2]FC of 1 corresponds
   to an absolute FC of 2, while a log[2]FC of 5 corresponds to an
   absolute FC of 32. As is evident in [67]Fig. 1a, most metabolites
   detected were “gray,” indicating that at D2, the bulk of the
   metabolites detected did not differ substantially between uninfected
   and infected RBCs. However, some critical metabolites were
   differentially abundant at this time, including cytidine, pyruvate,
   CMP, pyridoxamine phosphate, and NaMN, each of which had an FC value of
   >10 between uRBCs and iRBCs. Thus, these molecules could be exploited
   as potential diagnostic tools to detect early infection.

FIG 1.

   [68]FIG 1
   [69]Open in a new tab

   Global metabolic profiling of B. divergens-infected RBCs reveals major
   changes in the RBC metabolome on infection. (a and b) Volcano plots
   show the differential abundance of metabolites present in uRBCs and
   iRBCs on D2 (low parasitemia) and D4 (high parasitemia) postinvasion
   (n = 3 for each set at both time points). The x axis displays log[2]
   fold change (log[2]FC; threshold = 2-fold equivalent to log[2]FC = 1)
   values, and the y axis displays −log[10] P values (threshold
   significance cutoff, P < 0.05, equivalent to a −log[10] P value of
   1.301). Each dot represents a metabolite. Gray dots represent
   metabolites with log[2]FC values between −1 and 1 and nonsignificant P
   values. Black dots represent metabolites that passed the statistical
   threshold but not the log[2]FC threshold. Light green and light red
   circles show metabolites that passed the log[2]FC threshold but were
   not statistically significant and are labeled “UP_NOSIG” and
   “DOWN_NOSIG,” respectively. Red circles depict metabolites with
   2 < log[2]FC < 5 and statistical significance (UP_SIG), and green
   circles depict metabolites with log[2]FC values between −1 and −5
   (DOWN_SIG). Purple triangles show metabolites with log[2]FC values of
   >5 and statistical significance, which are identified by labels, while
   orange squares show metabolites with log[2]FC values of less than −5
   and statistical significance, also labeled. Approximately 23% of
   metabolites were differentially present on D2, and ~50% on D4. (c) Venn
   diagram shows the numbers of common and exclusive metabolites measured
   on D2 and D4. Approximately 75% of metabolites were common between the
   two time points. (d) Principal component analysis (PCA) plot shows
   analysis for both sets (uRBCs at D2 in light blue, iRBCs at D2 in light
   pink, uRBCs at D4 in dark blue, and iRBCs at D4 in dark pink),
   separating along PC1 and PC2. As is evident, in both sets, the uRBCs
   are separated from the iRBCs along PC1, with a greater separation for
   D4 sample sets than for D2 sample sets. (e) The heat map depicts the
   metabolites obtained in our analysis that are unique to uRBCs and to
   iRBCs on D4. Black indicates the absence of detection of the particular
   metabolite in our analysis, orange represents detection of the
   metabolite in 2/3 replicates in the set, and light yellow refers to
   detection of the metabolite in all samples of the data set. As shown,
   several metabolites were uniquely found in uRBCs (top left), while
   several metabolites were uniquely found in iRBCs (bottom right) at D4.

   In cells analyzed at the time of high parasitemia (50%) conditions on
   day 4 postinvasion (D4), a total of 673 metabolites were detected,
   among which the levels of expression of 325 metabolites were
   significantly different (P < 0.05) in uRBCs and iRBCs and an additional
   34 metabolites with differential expression had P values between 0.05
   and 0.1. Among these, totals of 233 metabolites were more abundant and
   126 metabolites were less abundant in iRBCs than in uRBCs. Thus, more
   than twice the number of metabolites differed significantly in quantity
   as the level of infection increased from 10% of cells parasitized to
   50% parasitemia. A volcano plot highlighting these differences was
   constructed ([70]Fig. 1b). As can be seen from the plot, the maximum
   numbers of metabolites that were upregulated and downregulated with
   log[2]FC values greater than 5 belonged to the lipid class of
   biomolecules, followed by those involved in nucleotide metabolism (UMP,
   cytidine, 7-methyguanine, 2-deoxycytidine, guanine, ribothymidine, NAD,
   and inosine) and energy metabolism (pyruvate, isocitrate, and malate).
   These molecules are critical for the rapidly proliferating parasite,
   providing both the DNA building blocks and the energy required for
   parasite multiplication, and hence, they can be expected to be found in
   significantly greater amounts than in uninfected cells or early
   infection events. Overall, several key metabolites were differentially
   abundant in Babesia-infected red cells on both D2 and D4, corresponding
   to low and high parasitemia, with elevated FC values at the later time
   point. As shown in the Venn diagram in [71]Fig. 1c, most of the
   metabolites detected were common between D2 and D4 (~75%); however, as
   described above, most of these metabolites were both at higher levels
   and showed a statistically significant FC at D4.

   We next wanted to analyze the relationship between our data sets
   (uninfected D2, uninfected D4, infected D2, and infected D4) to
   identify the major differences. We used Python programming language to
   construct principal component analysis (PCA) plots, and the code used
   for constructing these plots can be found in file 2 in the supplemental
   material. [72]Figure 1d shows a PCA plot with each point representing
   one data set. Light blue and dark blue dots are data sets from uRBCs at
   D2 and D4, respectively, while light red and dark red dots are data
   sets from iRBCs at D2 and D4, respectively. As is evident from the
   results in [73]Fig. 1d, uRBCs separate from iRBCs along principal
   component 1 (PC1) in the PCA plot. The uninfected and infected data
   sets (shown in blue and red dotted circles) show significant separation
   along PC1. Interestingly, even though the RBC is technically
   metabolically quiescent in vitro, uRBCs cultured for different lengths
   of time at 37°C yielded slightly different metabolic profiles. This has
   also been shown extensively by related work in malaria, where the
   metabolic profiles of uninfected red cells cluster based on time spent
   in culture ([74]12, [75]27).

   One of the key objectives of this study was to identify molecules that
   were present exclusively in uRBCs or exclusively in iRBCs at high
   parasitemia (D4). This analysis of unique metabolites could provide
   insights into metabolic pathways that are specifically impacted by high
   Babesia infection. [76]Figure 1e shows a heatmap with unique
   metabolites in uRBCs in the top of the graph and iRBCs in the bottom.
   Pale yellow indicates the presence of the metabolite in all samples of
   that group, orange indicates the presence of the metabolite in 66% of
   the samples in the group, and black indicates the complete absence of
   the metabolite in all samples of the group. As seen from the results in
   [77]Fig. 1f, several of the lysophospholipids were seen exclusively in
   uRBCs, while several diacylglycerols (DAGs) were present only in iRBCs.
   Therefore, there appears to be an extensive lipid remodeling of host
   cells upon Babesia infection, which provides a unique lipid signature
   specific to iRBCs as opposed to uRBCs. Other interesting metabolites
   uniquely present in iRBCs were those belonging to glutathione
   metabolism and redox metabolism (N-acetylcysteine and
   cysteine-glutathione disulfide), cAMP, folate, and derivatives of
   thiamine. Redox metabolism has been implicated as playing a major role
   in protozoan parasites like Plasmodium ([78]28[79]–[80]31) and, more
   recently by us, in Babesia ([81]32). cAMP has also been implicated as
   playing an essential role in signaling in other parasites ([82]33).

Pathway-specific comparison of uRBCs and B. divergens-infected RBCs
identifies specific metabolic pathways impacted by infection.

   As shown by our in-depth metabolomic analysis, several metabolites
   across all major classes were differentially abundant in iRBCs versus
   uRBCs at both low and high parasitemia ([83]Fig. 1 and [84]b). In this
   section, we analyze different aspects of the modification of
   metabolites in the infected sample set at high infection (D4) compared
   to uRBCs. We chose this data set for further analysis as it contains
   the maximal infection-related changes when compared to the set at low
   parasitemia, which provides a glimpse of early modifications in the
   metabolomics of host cell. To further discern the metabolic pathways
   with maximal impact, we constructed a pathway impact analysis plot with
   MetaboAnalyst 5.0 ([85]Fig. 2a) using the data from the high
   parasitemia culture set (D4), and the report generated is available in
   file 3 in the supplemental material. This pathway impact plot
   ([86]Fig. 2a) shows the pathway impacts on the x axis and −log[10] P
   values on the y axis. Each circle represents a metabolic pathway; the
   higher its value on the x axis, the higher is the impact of the
   pathways, and the impact is color coded along a gradient of white to
   yellow to orange to red in [87]Fig. 2a. The radius of the circle is
   determined by the number of metabolites identified in our analysis that
   belong to the specific metabolic pathway. Overall, a variety of
   metabolic pathways are impacted, prominent among which are
   phosphatidylcholine (PC) synthesis, phosphoinositide (PIP) metabolism,
   sphingolipid metabolism, aspartate metabolism, glycolysis and
   mitochondrial, etc.

FIG 2.

   [88]FIG 2
   [89]Open in a new tab

   Pathway-specific comparison of uRBCs and B. divergens-infected RBCs
   identifies specific metabolic pathways impacted by infection. (a) The
   Pathway Analysis module of MetaboAnalyst 5.0 was used to generate the
   plot, which combines results from pathway enrichment analysis and
   pathway topology to depict the most relevant pathways in the current
   set. The x axis defines the P value, while the y axis calculates the
   impact of the pathway in the metabolome analysis. The white to yellow
   to orange color gradation defines the significance of the pathway in
   the analysis, with red being the most significant. The radius of the
   circle defines the number of metabolites obtained in the pathway. (b)
   An overrepresentation analysis (ORA) plot was generated in
   MetaboAnalyst 5.0. The plot shows the top 25 metabolic pathways that
   were enriched in our analysis on D4 postinvasion (high parasitemia).
   (c) The PCA loading plot constructed from data from D4 postinvasion
   shows the different metabolites belonging to color-coded superpathways
   and labels the top 10 metabolites that contributed to differences
   between the uRBCs and iRBCs on D4. The farther away from the axis the
   metabolite is on the PC1 and PC2 axes, the higher its impact on the
   PCA. Lipids (pink) are the highest contributors to the differences
   observed between uRBCs and iRBCs. (d) The pie chart shows the
   percentage of metabolites belonging to each of the superpathways in the
   D4 metabolomic analysis. Lipids formed the largest class of metabolites
   observed in this study. (e) A z-score plot is shown where metabolites
   are segregated according to the superpathways. Each dot is a metabolite
   in each of the samples in the D4 sample set. Pink dots represent
   metabolites in the uRBCs, while blue dots represent metabolites in the
   iRBCs. The positions of the dots (z-scores) represent the distribution
   of the values for the metabolite, and the number of standard deviations
   is represented by the distance from the mean value. As is evident,
   metabolites belonging to the iRBCs (pink) deviate along both sides of
   the x axis, while uRBCs are concentrated in the center of the plot.

   Furthermore, we used MetaboAnalyst 5.0 to create a metabolite set
   enrichment analysis (MSEA), which provides a way to identify
   biologically meaningful patterns that are significantly enriched in the
   global metabolomic data. This analysis is analogous to gene set
   enrichment analysis (GSEA), typically performed for genomics data sets.
   It has the potential to identify subtle but consistent changes among a
   group of related compounds that may go undetected using conventional
   approaches. Within the MSEA, an overrepresentation analysis (ORA) was
   performed to identify pathways that may have been overrepresented in
   the data set, and the respective one-tailed P values are shown as a
   color gradient from yellow to red, with the latter being more
   significant. As seen in [90]Fig. 2b, the top 25 metabolic pathways are
   listed, and the metabolic enrichment ratio is plotted on the x axis.
   The major pathways identified in this analysis were those belonging to
   sphingolipid metabolism, PC and phosphatidylethanolamine (PE)
   metabolism, glycolysis, gluconeogenesis, amino acid metabolism, and
   glutathione metabolism. The results from this analysis are available in
   file 5 in the supplemental material.

   Next, we constructed a PCA loading plot in Python matplotlib to
   identify the key metabolite contributors to differences arising between
   uRBCs and iRBCs on D4 ([91]Fig. 2c). A PCA loading plot provides
   information about the variables that make the largest contributions to
   the two components: PC1 and PC2. The loading plot values range from −1
   to +1, with values close to 0 indicating a weak influence on the
   component. The greater the absolute value is, the greater is the
   contribution of the variable to the respective component. The sign of a
   loading mark (+ or −) indicates whether a variable is positively or
   negatively correlated to the PC. As shown by results in [92]Fig. 2d,
   the biomolecular class of lipids and then amino acids are strong
   contributors to variations seen between uRBCs and iRBCs on D4. The top
   10 absolute contributors to the variation along PC1 are labeled. Among
   them, three lysophospholipids are negatively correlated to PC1, while
   the remaining 7 metabolites are positively correlated to PC1. Among the
   latter, three metabolites are lipids (mead acid, linolenate, and
   dihomo-linolenate), one is an uncharacterized molecule, two are
   nucleotides (guanine and cytidine), and one is a key energy molecule,
   pyruvate. To obtain a strict quantification of significantly altered
   metabolites among major metabolic pathways, we constructed a pie chart
   for the metabolites obtained in uRBCs and iRBCs on D4 (high
   parasitemia). As evident from the chart, in both cases, lipids
   contributed >40% of the differentially present metabolites, followed by
   amino acids (~20%) and then nucleotides and carbohydrates
   ([93]Fig. 2d).

   To further analyze the pathways contributing to maximal changes upon
   infection of the RBCs, we constructed a z-score plot ([94]Fig. 2e)
   using the values of metabolites obtained in uRBCs and iRBCs on D4 and
   segregating them based on the superpathways (as defined in file 5 in
   the supplemental material). Each dot corresponds to a metabolite from
   each sample (n = 3) of each group (uRBCs in blue and iRBCs in pink). We
   used the z-score, which calculates the number of standard deviations by
   which the data point is distant from the average of the group and is
   thus an ideal way to standardize values, for easier interpretation of
   data ([95]Fig. 2e). If the z-score is positive, it means that the score
   is above the average value, whereas a negative z-score would indicate
   that the score is below the average value. As seen by the results in
   [96]Fig. 2e, most metabolites in the uRBCs (blue) are concentrated
   between −2 and +2 on the x axis, while metabolites from iRBCs for
   different pathways are scattered on both sides of the x axis, with the
   highest concentration of data points on the positive side of the x
   axis. Thus, this reiterates that there is a dynamic progression of
   metabolic activity upon Babesia infection, resulting in upregulation of
   several molecules in the host RBC.

Dissection of changes in the major metabolic pathways using biomolecular
classifiers reveals significant changes across major superpathways.

   We then constructed volcano plots for both infection groups, D2 and D4,
   based on the fold change values obtained for the top superpathways (as
   outlined in the Excel sheets in files 4 and 5 in the supplemental
   material), which were lipids, amino acids and peptides, nucleotides,
   and carbohydrates and energy ([97]Fig. 3a to [98]d for low parasitemia
   [D2], and [99]Fig. 3e to [100]h for high parasitemia [D4]).

FIG 3.

   [101]FIG 3
   [102]Open in a new tab

   Volcano plots of differential metabolites across different classes show
   dramatic changes at D2 and D4 postinvasion. The volcano plots are
   scatterplots with −log[10] P values (cutoff at P < 0.05) on the y axis
   and log[2] fold change (log[2]FC) values on the x axis. Black dots
   refer to metabolites with log[2]FC values between −1 and +1 and
   significant P values; gray dots represent metabolites with log[2]FC
   values between 1 and +1 but with nonsignificant P values; light green
   and light red dots represent metabolites with log[2]FC values between
   −1 and −2 or +1 and +2, respectively, and significant P values, except
   for lipids, where they refer to log[2]FC values between −1 and −5 or +1
   and +5, respectively, with significant P values; and orange squares and
   purple triangles represent metabolites with log[2]FC values of less
   than −2 or more than +2, respectively, and significant P values, except
   for lipids, where they represent log[2]FC values of less than −5 or
   more than +5, respectively, with significant P values. (a to d) Volcano
   plots were constructed from data from D2 postinvasion and represent
   metabolites belonging to amino acid (a), carbohydrate and energy (b),
   nucleotide (c), and lipid (d) pathways. (e to g) Plots were constructed
   from data from D4 postinvasion. As is shown, the numbers of metabolites
   observed with differential expression were drastically increased at the
   later time point (D4) that corresponded to higher parasitemia.

   (i) Lipids. In our analysis of the infected RBCs, different metabolic
   pathways of lipids were identified, namely, those associated with both
   monounsaturated fatty acids and polyunsaturated fatty acids (PUFA),
   phospholipids, phosphatidylcholine (PC), phosphatidylethanolamine (PE),
   phosphatidylserine (PS), phosphatidylinositol (PI), lysophospholipids,
   plasmalogens, monoacylglycerol (MAG), diacylglycerol (DAG),
   sphingolipids, ceramides, sterol, and bile acid metabolism. Membrane
   lipids are important modulators of the RBC rheology and are responsible
   for maintaining both membrane deformability and fluidity as the red
   cells pass through capillaries in the host. Across both infection
   levels, significant changes across all lipid classes were observed. As
   the parasite proliferates inside the RBCs, there is an enormous demand
   for lipids to form membranes of daughter cells (plasma membrane,
   membranes of organelles like mitochondria, and accumulation of fat in
   lipid bodies) ([103]34). Despite this high demand for lipids, it is
   most likely, based on its genome analysis, that Babesia does not
   synthesize most of its own lipids, much like Plasmodium and Toxoplasma
   ([104]17). Additionally, the host RBCs cannot synthesize phospholipids,
   cholesterol, or free fatty acids ([105]34). In the D2 (low parasitemia)
   group, the negative log fold change of iRBCs/uRBCs was maximal in the
   class of lysophospholipids (file 4 in the supplemental material), while
   the positive log fold change was maximal in the class of DAGs
   ([106]Fig. 3d). As shown by the results in [107]Fig. 3h, in the D4
   (high parasitemia) group, the top downregulated metabolites were also
   lysophospholipids, albeit with a much higher fold change (file 5 in the
   supplemental material). Lysophospholipids are present in cell membranes
   and are known to aid in cell signaling and protein folding, as well as
   mobilizing intracellular Ca2^+ stores; however, their role in the
   parasite has not been elucidated yet. The top 10 upregulated
   metabolites in the D4 set were diacylglycerols (DAGs) and long-chain
   polyunsaturated fatty acids (LC-PUFA), with the FC values obtained
   averaging more than 40-fold. Below, we describe some of the more
   striking differences observed in different lipid classes between uRBCs
   and iRBCs at both D2 and D4.

   (a) Fatty acids and acyl glycerides. The top upregulated metabolites in
   the high parasitemia cultures were LC-PUFA, followed by DAGs. DAGs and
   TAGs are the main glycerolipid species in the parasite and are used as
   a source of fatty acids that the parasite can use for membrane
   synthesis and beta oxidation. However, in our current study, TAGs were
   not detected on the global metabolomics platform due to their size and
   hydrophobicity. Therefore, early in infection, DAGs and
   lysophospholipids were the dominant differentially abundant lipids, and
   as infection progressed, these classes of lipids increased dramatically
   in amount; additionally, several lipids belonging to LC-PUFA were
   significantly upregulated. The differential expression of lipid
   metabolites on D2 and D4 is represented in [108]Fig. 3d and [109]3h,
   respectively, and as evident, with an increase in the percentage of
   infection, the differential regulation of lipid expression was more
   acute, with a higher number of lipids dysregulated, along with higher
   significant fold changes in the amounts.

   (b) Phospholipids and glycerophospholipids. The major classes of
   phospholipids include phosphatidylcholine (PC),
   phosphatidylethanolamine (PE), and phosphatidylserine (PS). Compared to
   uRBCs, on D2, several phospholipids were upregulated 2- to 5-fold.
   Interestingly, PE and PS were not perturbed significantly at this time
   point, but there were changes associated with PC and PI. PC plays an
   important role in the structure of membranes but can also modulate cell
   signaling, as the hydrolysis of PC forms DAGs, which were highly
   differentially abundant between uRBCs and iRBCs ([110]35).

   (c) Sphingolipids. Another lipid class that was differentially abundant
   between uninfected and infected RBCs was sphingolipids. These lipids
   possess a sphingosine backbone and can be acetylated to form ceramides,
   which in turn can be modified through the addition of phosphocholine or
   phosphoethanolamine to form sphingomyelins or glycosylated to form
   glycosphingolipids. Interestingly, while precursors to sphingolipids
   like sphingosine were enriched more than 10-fold in infected cells, the
   sphingomyelins and sphingosine were not. However,
   sphingosine-1-phosphate (S1P), which has been implicated as playing an
   important role in erythrocyte glycolysis and parasite survival, was
   also downregulated in this set by a log[2] fold change of >3.
   Sphingomyelins can also be used to synthesize ceramides, which have
   been shown to be potent second messengers regulating different cellular
   processes. We found that a few ceramides, including ceramide
   (d18:1/14:0, d16:1/16:0) and N-palmitoyl-sphingadienine (d18:2/16:0),
   were increased ~5- to 6-fold upon infection.

   (ii) Amino acids and peptides. As in other apicomplexans, in silico
   analysis of the genome of Babesia spp. suggests an absence of de novo
   amino acid synthesis pathways, implying that the parasite depends
   largely on the host cell and environment for acquiring these essential
   nutrients. It has been suggested that even in organisms like Toxoplasma
   gondii, pathways for the de novo biosynthesis of several amino acids
   were dispensable, especially in fast-replicating stages of the parasite
   ([111]36). While hemoglobin, the dominant RBC cytoplasmic protein, can
   potentially be used by the parasite for scavenging required amino
   acids, the actual pathway of hemoglobin degradation has not been
   elucidated in B. divergens ([112]37). In our analysis, at both
   experimental time points, amino acids were identified as differentially
   abundant in iRBCs compared to uRBCs. This has been represented using
   volcano plots of their abundances at D2 (low parasitemia)
   ([113]Fig. 3a) and D4 (high parasitemia) ([114]Fig. 3e).

   Strikingly, the key metabolites in the γ-glutamyl cycle and glutathione
   metabolism were downregulated at D2 (low parasitemia), but as infection
   progressed, several of these intermediate γ-glutamyl-amino acid
   metabolites, N-acetylcysteine, S-lactoyl-glutathione, cysteine,
   glutamate, and 5-oxoproline, were upregulated severalfold, with
   significant reductions in cysteinyl-glycine and GSH (reduced
   glutathione). This suggests that with increased proliferation of the
   parasite, there is an increased reliance on this redox regulatory
   pathway, as has also been implicated in a recent research study from
   our laboratory ([115]32). These findings also largely agree with
   studies on the metabolomics of Plasmodium falciparum-infected RBCs,
   where it has been shown that with the progress of infection, these
   redox pathway intermediates are elevated severalfold ([116]29,
   [117]38). Interestingly, pipecolic acid, which is a product of lysine
   degradation that increases severalfold in both P. falciparum and P.
   vivax infections ([118]29) and is projected to be a unique biomarker
   for malaria infection, was unaffected at low parasitemia and was
   significantly downregulated at higher parasitemia. Therefore, though
   these parasites are apicomplexans and evolutionarily close to each
   other, they seem to have unique metabolic pathways, which can be useful
   in both disease detection by biomarker analysis and developing new
   drugs targeting these pathways.

   (iii) Carbohydrate and energy pathways. The intracellular growth of
   Babesia requires considerable energy expenditure, as within a short
   window of time, parasite multiplication results in 4 to 8 Babesia
   progeny. Using global and targeted metabolomics, several studies have
   highlighted the importance of energy pathways in parasites ([119]12,
   [120]13, [121]39, [122]40). As shown by the results in [123]Fig. 3b and
   [124]f, multiple metabolites were differentially present in the energy
   pathways at both low and high parasitemia. Like Plasmodium, Babesia
   seems to depend largely on glycolysis for energy production and has all
   the enzymes required for glycolysis ([125]17). RBCs are rich in
   glucose/hexose transporters. Not much is known about glucose
   transporters in B. divergens; however, in B. bovis, iRBCs were reported
   to accumulate far more glucose than uRBCs ([126]41) and were not
   affected by human GLUT1 inhibitors at low concentrations ([127]42).
   Interestingly, in our study, at both D2 and D4, pyruvate was massively
   upregulated in iRBCs (20-fold and 500-fold, respectively), along with
   an upregulation of lactate (2.3-fold and 5.4-fold, respectively).
   Another striking difference from Plasmodium is the lack of pyruvate
   dehydrogenase (PDH) in Babesia spp. PDH is responsible for converting
   pyruvate to acetyl-coenzyme A (acetyl-CoA) in the mitochondria, which
   then feeds into the tricarboxylic acid (TCA) cycle and is converted
   into ATP via the electron transport chain (ETC) and oxidative
   phosphorylation. However, in Toxoplasma gondii and Plasmodium berghei,
   PDH is targeted to the apicoplast, and this function is fulfilled by
   branched-chain ketoacid dehydrogenase (BCKDH) ([128]43, [129]44). BCKDH
   is present in Babesia spp., but its functionality in converting
   pyruvate to acetyl-CoA has not yet been investigated.

   In silico analysis of the B. divergens genome shows that genes encoding
   the various enzymes mediating the TCA cycle are present, and this was
   confirmed by the metabolite analysis in our study, showing alteration
   in key TCA metabolite levels; for example, citrate and isocitrate were
   unaltered on D2 but were significantly reduced in iRBCs compared to
   their levels in uRBCs on D4. Other key metabolites, such as fumarate,
   malate, and α-ketoglutarate, were increased on D2 (~2-fold) and D4 (~3-
   to 4-fold). These metabolites were differentially abundant in iRBCs and
   uRBCs, and thus, the parasite may also use mitochondrial aerobic
   respiration to fulfill some of its energy needs. Our data show that
   carbon flux and energy pathways are altered in host red cells upon
   infection, and future work will allow detailed analysis of the
   contributions of specific subpathways.

   (iv) Nucleotides. Nucleotides are another important class of
   biomolecules that were differentially abundant in uRBCs and iRBCs.
   Intra-RBC proliferation of Babesia requires a constant supply of
   nucleotides for DNA replication to form daughter parasites. Each 1N
   parasite (“1N” refers to one genome copy) can proliferate to form 2N,
   4N, and >4N parasites, and each of these higher forms can egress and
   infect new host cells ([130]25). Not much is known about the synthesis
   and metabolic pathways used by the parasite. However, it has been shown
   that the parasite can incorporate tritiated hypoxanthine, adenine, and
   adenosine but not uridine, thymine, and thymidine, thus suggesting a
   differential regulation of purines versus pyrimidines in Babesia spp.
   ([131]45). As seen in our analysis, at both D2 and D4, guanine,
   cytidine, and uracil and their derivatives were enormously abundant in
   infected cells (as shown in the volcano plots in [132]Fig. 3).
   Therefore, the rapid growth of the parasite leads to an accumulation of
   nucleotides and their derivatives and precursors in iRBCs. Of specific
   interest, infected cells were found to have more than 500-fold
   enrichment in cytidine, which was not matched by the other nucleotides.
   However, the Kennedy pathway, which is the major pathway of
   phosphatidylcholine synthesis in eukaryotes, uses cytidine to convert
   the precursor phosphocholine to cytidine-5′-diphosphocholine. This step
   is catalyzed by CTP:phosphocholine cytidylyltransferase (CCT)
   ([133]46), whose homolog is present in B. divergens (Bdiv_002670).
   Cytidine-5′-diphosphocholine was also found to be present at more than
   a 6-fold-higher level in infected RBCs, which suggests that this
   pathway may be utilized in B. divergens.

   In the experiments reported in the next few sections, we analyzed some
   of these key metabolic pathways in more detail, using inhibitors of
   major enzymatic reactions in these pathways and studying the effects of
   their inhibition on various aspects of the parasite cycle. By unveiling
   the mechanism of action of these inhibitors, we direct attention to
   potential vulnerable spots in the parasite metabolic network and show
   how metabolomics analyses can lay the foundation for identifying
   promising therapeutics, as well as helping to uncover the mechanisms of
   their action.

High-resolution microscopy reveals distinct morphological lipid staining
patterns of B. divergens-infected cells and validates the lipidomic profile
of iRBCs.

   Lipids were one of the major classes of metabolites that was the most
   altered between uRBCs and iRBCs ([134]Fig. 4). To confirm the lipidomic
   profiles obtained in our global metabolomics analysis, we labeled
   uninfected and infected red cells with different lipid probes for
   neutral lipids, polar lipids, and cholesterol, making sure to image all
   fields at the same settings. The images in [135]Fig. 4b, d, and [136]f
   show the overall alterations in the lipid staining patterns of infected
   RBCs containing various pleotropic forms of the parasite compared to
   those of uRBCs. Of note, in uRBCs, all staining was predominantly
   localized to the cell plasma membrane ([137]Fig. 4a, c, and [138]e).

FIG 4.

   [139]FIG 4
   [140]Open in a new tab

   Confocal microscopy reveals distinct morphological patterns of B.
   divergens-infected cells and validates the lipidomic profile of iRBCs.
   Uninfected RBCs (uRBCs) and B. divergens-infected RBCs (iRBCs) were
   stained with lipid dyes and a DNA stain (silicone-rhodamine Hoechst
   stain [SiR-HO]) and viewed at ×100 magnification in an Abberior STED
   microscope. Each image is arranged in a panel to show the bright-field
   (BF) image, the respective dyes (DNA stain was used only in iRBCs), and
   the merged image. uRBCs are shown in panels a, c, and e, while iRBCs
   are shown in panels b, d, and f. (a and b) uRBCs and iRBCs were labeled
   with Bodipy TopFluor (Bod-TF; green), which stains for cholesterol, and
   SiR-HO (red), which stains for DNA. In uRBCs, the dye stains mainly the
   membrane of the cell. In iRBCs, the parasite membrane fluoresces
   brightly, along with some internal staining, while the signal along the
   periphery of the RBCs is dim. (c and d) uRBCs and iRBCs were stained
   with Nile red, which stains for polar lipids (red), and Hoechst stain,
   which stains for DNA (HO; blue). The uRBC membrane stains with Nile
   red, while iRBCs show stain only around the nucleus and along the
   membranes of the parasite. (e and f) uRBCs and iRBCs were stained with
   Bodipy 493/503 (Bod-493/503; green), which stains for neutral lipids,
   and SiR-HO (red), which stains for DNA. uRBCs and iRBCs have very dim
   signals, while the parasites fluoresce brightly. (g) The pie charts map
   the distributions of different lipids in uRBCs and iRBCs. Lipids were
   divided into subclasses and based on their average intensity in the
   sample set. The percentages of the lipids changed drastically upon
   infection, with the major changes reflected in percentages of neutral
   lipids, sphingolipids, and metabolites belonging to the fatty acid
   metabolism. (h) The z-score plot was constructed for the subclasses of
   lipids. Each metabolite from each of the uRBCs (blue) and iRBCs (D4;
   pink) was plotted. Across all subclasses, the lipid metabolites
   exhibited different levels in uRBCs and iRBCs (increased or decreased
   levels as shown by the distribution of pink dots across the positive
   and negative end of the x axis, respectively), suggesting that the
   parasite confers a distinct lipidomic signature on host RBCs.

   Cholesterol staining was visualized using Bodipy TopFluor (Bod-TF),
   which has been shown to be an excellent tool for visualizing sterol
   distribution in various cells ([141]47). The plasma membrane of uRBCs
   is known to contain high levels of free cholesterol, which facilitates
   the fluidity of the membranes while carefully controlling its
   permeability. This was corroborated in our microscopy analysis
   ([142]Fig. 4a), where it was seen that the uRBC membrane was highly
   fluorescent. However, upon infection, the intensity of the cholesterol
   staining decreased in the RBC membrane, with the concomitant appearance
   of the stain in the parasite membranes, and this depletion was
   proportional to the parasite load ([143]Fig. 4b). While the parasite
   plasma membrane was fluorescent, accumulation of sterol was also
   detected within the parasite ([144]Fig. 4b), which may be parasite
   organellar structures.

   Nile red was next used to probe the polar lipid localization in both
   uRBCs and iRBCs ([145]4c and [146]d). The uRBC membrane showed
   fluorescence that was weaker than the Bod-TF staining of cholesterol
   but stronger than the neutral lipid staining ([147]Fig. 4e and [148]f).
   However, iRBCs showed strong staining with Nile red that was
   distributed throughout the parasite structure but was stronger in
   specific areas, which again could be parasite organelles
   ([149]Fig. 4d). Organellar membranes are known to be rich in
   cholesterol and phospholipids ([150]48), and further work is needed to
   ascertain whether the intracellular lipid staining is localized to
   these membranes.

   The neutral lipids, including the acyl glycerides, were revealed to be
   the most enriched lipids in the infected cells from our metabolomics
   data (files 4 and 5 in the supplemental material). While our
   metabolomics platform does not detect triacylglycerides (TAGs) due to
   their size and hydrophobicity, all the DAGs were changed at high
   parasitemia, with an FC of 5 or more in iRBCs with respect to their
   levels in uRBCs. This is consistent with our confocal microscopy
   analysis using Bodipy-493/50, which due to its nonpolar structure and
   long-wavelength absorption and fluorescence can be used as a stain for
   neutral lipids. While uRBC membranes stained only weakly with this dye
   ([151]Fig. 4e), iRBCs exhibited strong fluorescence within the parasite
   cytoplasm, specifically staining structures that may be lipid bodies,
   based on their appearance ([152]Fig. 4f).

   Overall, the microscopic analysis confirmed the elevated levels of the
   major classes of lipids in the Babesia-infected cells and allowed
   dissection of these quantitative differences into morphological
   distributions within the iRBCs and the parasites.

   To analyze the major changes in lipid classes during infection, we
   constructed a pie chart based on the percentage intensity of each class
   of lipids in uRBCs and iRBCs. For each metabolite within the 7 lipid
   classes, the metabolic intensity obtained from the Metabolon platform
   was averaged across replicates, and its proportion compared to those of
   all classes was defined and plotted in the pie chart. Therefore, this
   gives a semiquantitative contribution of metabolites in each of the
   lipid classes in both uRBCs and iRBCs. As seen in [153]Fig. 4g, the
   major changes in the metabolic profile upon infection were from the
   medium-chain, long-chain, and fatty acids (6.11% in uRBCs versus 31.50%
   in iRBCs), neutral lipids (2.65% in uRBCs versus 11.86% in iRBCs), and
   phospholipid metabolism (52.62% in uRBCs versus 30.54% in iRBCs).
   Therefore, infection not only modulated the levels of lipid metabolites
   but also caused alterations in the absolute composition of lipids, as
   has also been shown in Plasmodium ([154]34). The changes in the
   physical properties of the red cell membrane due to alteration in
   important membrane lipids by the parasite remain to be investigated in
   Babesia-infected red cells.

   Finally, we constructed a z-score plot for the lipid classes
   ([155]Fig. 4h). The seven classes of lipids are labeled and separated
   by horizontal lines. Blue dots represent metabolites from each uRBC’s
   D4 sample, while pink dots represent metabolites from each iRBC’s D4
   sample. The extent of deviation of the pink dots away from the blue
   dots (centrally located along the x axis) is representative of the
   number of standard deviations by which the absolute value of that
   metabolite is separated from the mean value. Therefore, the greater the
   value of the metabolite along the negative or positive x axis, the more
   downregulated or upregulated it is, respectively. Therefore, our
   results suggest that parasites depend heavily upon host/host-derived
   lipids and that alteration in lipids is integral to parasite growth and
   proliferation. In the experiments described in the next couple of
   sections, we investigated possible drug targets for disruption of
   specific lipid homeostasis and the effects on parasite survival.

Loss of cholesterol in the RBC membrane by methyl-β-cyclodextrin (MβCD)
treatment does not impact parasite invasion but results in the premature
release of the intracellular parasite.

   While B. divergens possesses some pathways for both the synthesis and
   modification of lipids, we found that, like Plasmodium and Toxoplasma,
   B. divergens lacks the machinery to synthesize cholesterol de novo
   ([156]49, [157]50). An in silico BLASTp analysis for the key enzymes of
   the cholesterol synthesis pathway against several varied model
   organisms (Homo sapiens, Arabidopsis thaliana, Mycobacterium
   tuberculosis, and Schizosaccharomyces pombe) showed that no homologs of
   this pathway are present in B. divergens. In Fig. S1 in the
   supplemental material, a simple illustration of the cholesterol
   biosynthetic pathway is shown, with key enzymes in green. The red
   cross-marks on these enzymes (HMG CoA synthase, HMG CoA reductase, and
   lanosterol-14α-demethylase) point out the absence of these enzymes in
   B. divergens. Thus, these results suggest that, like their related
   apicomplexans, it is probable that Babesia spp. also scavenge
   cholesterol from their host cells.

   Cholesterol is the most abundant lipid present in the RBC membrane, and
   thus, this membrane could serve as the source for the parasite’s
   escalating needs. The metabolomics profile of the infected RBCs
   revealed an almost 7-fold increase of 7-hydroxycholesterol, which is an
   oxysterol and, apart from being a biomarker of lipid peroxidation, is
   the first step in the conversion of cholesterol to bile acids. This
   allows us to hypothesize that the parasite scavenges cholesterol from
   its host cell to fulfill this sterol requirement.

   To prove that the parasite in fact scavenges cholesterol from its host,
   we prelabeled RBCs with Bod-TF ([158]Fig. 5a, green, 0 hpi) and
   performed an invasion assay by adding freshly purified merozoites. The
   parasite DNA was stained with silicone-rhodamine Hoechst stain (SiR-HO)
   (red) and imaged on an STED microscope at 0 hpi, 1 hpi, and 24 hpi. As
   can be seen in the sequence of events imaged in [159]Fig. 5a, at 1 h
   postinvasion, the earliest time point assessed in this experiment,
   there was fluorescence associated with the newly invaded parasite,
   Thus, the invading merozoite must acquire this cholesterol from the RBC
   membrane. As the parasite proliferates within the RBC, its requirement
   for cholesterol is constant, and thus, we followed this sequence of
   development within the labeled RBC at 1 and 48 h postinvasion
   ([160]Fig. 5a). Interestingly, even upon binary fission, we could
   detect the fluorescent cholesterol in the newly formed parasites at all
   subsequent divisions, albeit at lower intensities. At 24 hpi, while the
   parasite fluoresced bright green, the fluorescence in the RBC membrane
   was heavily depleted compared to the fluorescence at the zero-hour time
   point. These experiments suggest that B. divergens is continuously
   scavenging cholesterol from the host RBC membrane for its expanding
   requirements as the intracellular parasite load increases.

FIG 5.

   [161]FIG 5
   [162]Open in a new tab

   Cholesterol plays an important role in B. divergens infection of RBCs.
   (a) Uninfected RBCs (uRBCs) were stained with Bodipy TopFluor (Bod-TF)
   at 0 hpi, and merozoites were allowed to invade these stained cells.
   Cells were imaged at 1 hpi and 24 hpi. As is evident, the parasites
   scavenged the labeled cholesterol from their host RBCs at 1 hpi, with a
   significant signal also present along the membrane of the red blood
   cell. However, as parasites grew (24 hpi), the signal on the RBC
   surface decreased drastically, while the parasites had a strong green
   fluorescent signal around the red DNA dye, along the periphery of the
   parasite. (b) Cholesterol was measured fluorometrically in control RBCs
   and in the presence of RBCs treated with the cholesterol inhibitor
   methyl-β-cyclodextrin (MβCD) (5 mM for 30 min). The cholesterol content
   in MβCD-treated red cells was significantly depleted, to ~50% of the
   cholesterol content of control red cells (P = 0.0042, n = 4). Error
   bars show standard errors of the means. (c) Purified merozoites were
   added to control and MβCD (5 mM for 30 min)-treated RBCs, and invasion
   was monitored using flow cytometry. As is shown, invasion parasitemia
   was similar under both conditions (P = 0.976, n = 4). Thus, invasion of
   B. divergens seems to be unaffected by a reduction in cholesterol
   content in host RBCs. Error bars show standard errors of the means. ns,
   not significant. (d and e) Growth inhibition assays (GIAs) were
   performed with asynchronous ~2% parasitemia cultures mock treated or
   treated with different concentrations of cholesterol inhibitor, and
   parasitemia was measured and plotted every 24 h (n = 3) (d), along with
   the percentages of inhibition (e). On day 1 of the assay, parasitemia
   was 4.6% ± 0.5% (mean value ± SEM) for the control, while the cells
   treated with MβCD at 1 mM, 2.5 mM, 5 mM, and 10 mM had parasitemias of
   3.2% ± 0.4%, 3.2% ± 0.4%, 3.4% ± 0.5%, and 1.1% ± 0.2%, respectively.
   On day 3, the parasitemia was 24.457% ± 21.191% for the control, while
   for the increasing drug concentrations, the levels were 15.7% ± 1.4%,
   17.183% ± 1.3%, 5.0% ± 0.6%, and 0.5% ± 0.1%, corresponding to
   percentages of inhibition of ~30% for 1 mM and 2.5 mM and >85% for 5 mM
   and 10 mM drug concentrations (P < 0.0001). (f and g) Control
   mock-treated or MβCD (5 mM)-treated asynchronous parasites at ~25%
   parasitemia were incubated for 2 h and 4 h, and at both time points,
   cells were labeled with DNA dye SiR-HO (red), RBC membrane marker GPA
   (violet), and cholesterol-staining dye Bod-TF (green). Bright-field
   images and each of the fluorescent signals are shown, along with a
   merged image and a merged and zoomed region of interest (ROI) showing
   the BF image and all the dyes. The ROI from each image is marked in a
   white square. As is shown, at 2 h and 4 h, RBCs and parasites in
   control cultures were morphologically preserved, while the cultures
   treated with 5 mM MβCD had extracellular parasites ejected out of red
   cells, which resemble ghost cells.

   To analyze the importance of cholesterol in the biological processes of
   the parasite, we then used methyl-β-cyclodextrin (MβCD) to deplete the
   erythrocyte plasma membrane of cholesterol. While MβCD does not fully
   extract all the cholesterol from the membrane, it has been reported to
   significantly reduce the content of the sterol in the host RBC
   membranes ([163]51[164]–[165]55). We used RBCs from four different
   donors, as cholesterol content is known to vary among individuals
   ([166]Fig. 5b). To ensure that MβCD treatment (5 mM for 30 min at 37°C)
   did indeed diminish membrane cholesterol content, we quantified
   cholesterol using a cholesterol fluorometric kit and solutions with
   varying cholesterol standards. The results presented in [167]Fig. 5b
   show that after 5 mM MβCD treatment, all 4 RBC samples exhibited an
   ~50% decrease in cholesterol content, which was statistically
   significant (P = 0.0042, n = 4). Thus, MβCD treatment yielded host cell
   membranes significantly depleted of cholesterol and suitable to assess
   the impact of cholesterol loss on parasite processes.

   We first examined whether B. divergens could efficiently invade these
   MβCD-treated RBCs. Previous studies with P. falciparum have shown that
   the RBC cannot support parasite invasion upon MβCD treatment ([168]51,
   [169]53). However, the efficiency of B. divergens invasion using RBCs
   pretreated with 5 mM MβCD (30 min at 37°C) remained comparable to the
   efficiency in untreated RBCs using 4 different human blood samples
   ([170]Fig. 5c). Erythrocyte raft domains are thought to be the areas of
   the membrane that are cholesterol rich and, thus, targets of MβCD
   ([171]52). Furthermore, these raft domains are also the points of
   invasion of Plasmodium ([172]56). This leads us to believe that B.
   divergens invasion must differ from that of malaria parasites in either
   the specific areas of the RBC membrane that the parasite requires for
   successful invasion or the cholesterol richness of those same
   membranes.

   Although invasion was not impacted by MβCD treatment, we examined
   whether parasites cultured in the presence of the drug would show
   impaired growth and proliferation. Growth inhibition assays (GIAs) were
   set up using 0 to 10 mM concentrations of MβCD. As seen by the results
   in [173]Fig. 5d and [174]e, there was a significant inhibition of
   parasitemia with increase in drug concentration, and the inhibition was
   time dependent. Both 5 and 10 mM MβCD exhibited dramatic inhibition of
   parasite growth, but treatment of cells with 10 mM MβCD also resulted
   in host RBC lysis. We therefore did not use the 10 mM concentration of
   MβCD further in our experiments. We confirmed that concentrations of
   5 mM MβCD and lower had no impact on RBC integrity and did not cause
   lysis but showed >75% inhibition by day 4. Concentrations of 1 and 2 mM
   MβCD were more moderate in their inhibition, and these parasites
   presented continued culture progression, although at significantly
   lower parasitemia than the growth rate of the control cultures, and
   they exhibited a constant 25% inhibition of parasitemia through all
   4 days of the assay ([175]Fig. 5d).

   To explore the mechanism of this inhibition, we examined Giemsa-stained
   smears of these MβCD-treated parasite cultures. The drug-treated
   parasites present a contrasting picture to control cultures. While
   control cultures contained intra-RBC parasites with only a few
   extracellular merozoites, drug-treated cultures showed bunches of
   parasites outside the RBCs. This expulsion of parasites from host RBCs
   was dose dependent and increased as the time of drug exposure
   increased. To investigate this inhibition more carefully, a time series
   of imaging was performed where asynchronous parasite cultures at 25%
   parasitemia were exposed to 5 mM MβCD and stained with Bod-TF (green)
   as a marker of cholesterol, SiR-HO (red) as a marker of DNA, and
   anti-glycophorin A (GPA) antibody (violet) as a marker of the RBC
   membrane. The images reveal that soon after the introduction of MβCD,
   cultures began to reveal extracellular parasites, and the host red cell
   membrane appeared ghost-like, with a disordered GPA stain. In fact,
   microscopy revealed that the treated red cells seemed to spit out the
   parasites, leading to the decreased parasitemia in these cultures.
   These cells grew in number, and at 4 h postexposure, most of the
   parasites appeared to be extracellular, in contrast to control
   parasites, which continued their regular intracellular development. Our
   results echo those obtained in P. falciparum, where a dramatic
   expulsion of mature-stage parasites occurred upon exposure to MβCD
   ([176]51). Therefore, our results indicate that, although cholesterol
   is scavenged by the parasite from the RBC membrane, selective depletion
   of cholesterol by MβCD did not interfere with parasite invasion.
   However, the viability of the intracellular parasite was compromised
   upon treatment of infected cultures with MβCD. Additionally, the
   mechanism of inhibition by MβCD appears to be like that seen in
   Plasmodium, where the parasite is forcefully ejected from the infected
   red cells ([177]51).

Orlistat is a potent inhibitor of the lipase that degrades acylglycerides and
disrupts B. divergens merozoite membrane formation.

   The neutral glycerolipids, including several forms of diacyl glycerols
   (DAGs), were one of the major classes of lipids that were highly
   expressed in the erythrocytic B. divergens stages. To understand the
   potential role these lipids play in the asexual erythrocytic cycle of
   the parasite, we used the inhibitor orlistat, which is an irreversible
   inhibitor of the lipase that hydrolyzes TAGs and DAGs to a
   monoglyceride molecule and free fatty acids ([178]57, [179]58).
   Orlistat is known to target the human enzyme monoacylglycerol lipase
   (MGLL) ([180]59), which has the characteristic GXSXG motif, a catalytic
   triad (serine, histidine, and aspartate), and a cysteine residue
   adjacent to the active site. In P. falciparum, several serine
   hydrolases were identified, but it was conclusively shown that
   PF3D7_1038900 (also called PfMAGLLP) was the target of orlistat
   ([181]60). An in silico search of the B. divergens genome revealed a
   homolog of PfMAGLLP, namely, Bdiv_023350c. As shown in Fig. S2, the
   lipase homolog in B. divergens has all the conserved domains (the GXSXG
   motif, critical cysteine residue, and catalytic triad). Thus, we
   hypothesize that this enzyme is the target of orlistat, although there
   could be additional targets in B. divergens.

   Upon orlistat treatment (7.5 μM for 24 h), we could see an increase in
   the intensity of the signal of the nonpolar lipid-staining dye (Bodipy
   493/503), suggesting that our treatment was targeting TAG hydrolysis.
   Growth inhibition assays (GIAs) were performed with asynchronous ~2%
   parasitemia cultures treated with different concentrations of orlistat
   (2.5, 5. 10, 20, and 50 μM), and parasitemia was measured and plotted
   every 24 h, along with the percentages of inhibition, as shown in
   [182]Fig. 6b. As a control, cultures with dimethyl sulfoxide (DMSO)
   diluent were used for monitoring differential parasite proliferation.
   Inhibition assays were run for 4 days, with medium changes with the
   drug carried out every 24 h. As can be seen from the results in
   [183]Fig. 6a and [184]b, all concentrations of orlistat inhibited
   parasite growth, and this inhibition was both concentration and time
   dependent. Significant changes in parasitemia were noted as early as 24
   h posttreatment, with the highest concentrations (50 and 20 μM) of
   orlistat yielding 25 to 35% inhibition and 10 μM orlistat showing ~10%
   inhibition. The parasitemia on day 3 for the control was 24.5% ± 2.2%
   (mean value ± standard error of the mean [SEM]), while for
   orlistat-treated cultures, the values were 16.7% ± 2.0% (2.5 μM),
   12.9% ± 1.7% (5 μM), 5.7% ± 1.3% (10 μM). 2.3% ± 0.5% (20 μM), and
   1.4% ± 0.2% (50 μM), with statistically significant percentages of
   growth inhibition compared to the growth of the control (P < 0.0001,
   n = 3). As shown, at 10 μM drug treatment for 2 days, parasites were
   inhibited by ~50%, while higher concentrations of the drug led to an
   inhibition of >75% on D2. Thus, orlistat was potent in inhibiting
   parasite growth, implying the importance of these lipases for parasite
   survival.

FIG 6.

   [185]FIG 6
   [186]Open in a new tab

   Orlistat potently inhibits the growth of the parasite by disrupting its
   plasma membrane organization. (a and b) Growth inhibition assays (GIAs)
   were performed with asynchronous ~2% parasitemia cultures mock treated
   or treated with different concentrations of orlistat, and parasitemia
   was measured and plotted every 24 h (n = 3) (a), along with percentages
   of inhibition (b). Parasitemia on day 3 for the control was
   24.5% ± 2.2% (mean value ± SEM), while for orlistat-treated cultures,
   the levels were 16.7% ± 2.0% (2.5 μM), 13.0% ± 1.7% (5 μM), 5.7% ± 1.3%
   (10 μM), 2.4% ± 0.5% (20 μM), and 1.4% ± 0.15% (50 μM), with
   statistically significant parasitemia and percentages of inhibition
   compared to the control (P < 0.0001, n = 3). (c) The subpopulation
   structure of parasites (D2 of the GIA) growing under each of these
   conditions was examined and plotted as percentages of parasites that
   were 1N, 2N, 4N, and >4N. With increasing drug concentration, the
   proportion of >4N parasites was significantly increased. While >4N
   contributed 7.77% ± 0.659% of the subpopulation structure of control
   parasites, treatment with orlistat resulted in proportions of >4N
   parasites of 11.5% ± 0.07% (2.5 μM), 12.1% ± 0.6% (5 μM), 15.0% ± 0.2%
   (10 μM), 28.4% ± 3.1% (20 μM), and 54.6% ± 1.2% (50 μM). (d)
   Giemsa-stained iRBCs corroborate our findings from the experiment whose
   results are shown in panel c. At 24 h and 48 h, several iRBCs in
   orlistat-treated cultures harbored multiple >4N parasites (marked with
   red arrows), in contrast to the few seen in control iRBCs. (e) To
   assess the viability of parasites in orlistat-treated cultures,
   merozoites were purified from control or orlistat (7.5 μM for
   24 h)-treated cultures, and an invasion assay was set up with RBCs.
   While merozoites purified from control cultures invaded normally, the
   invasion of merozoites purified from the drug-treated culture was ~0.2,
   and it continued to remain <1% until day 3 postinvasion, while control
   parasites grew to ~60% (n = 2). Error bars show standard errors of the
   means. (f) Control parasites from the GIA were imaged in the Abberior
   STED microscope at ×100 magnification and stained with antibody against
   parasite membrane protein Bd37, as well as an FITC-labeled secondary
   antibody (green) and the DNA dye SiR-HO (red). As seen, parasites
   appear morphologically healthy and Bd37 was seen labeling the periphery
   of the parasite. (g and h) However, for parasites treated with orlistat
   (7.5 μM) for 24 h (g) and 48 h (h) and stained with Bd37-FITC and
   SiR-HO, it was clearly evident that the parasites were morphologically
   defective and the Bd37-FITC was not uniform across the periphery of the
   parasites, with the effect being time dependent (also shown in Movies
   S1, S2, and S3 in the supplemental material).

   To understand the mechanism of this inhibition, we first turned to
   Giemsa-stained smears of the control and orlistat-treated cultures. As
   seen in [187]Fig. 6d, while the control cultures exhibited morphologies
   consistent with a healthy mix of the various pleomorphic parasite
   forms, (mix of rings, paired-figures and Maltese crosses), the
   orlistat-treated cultures, even at low drug concentrations (5 and
   10 μM) had predominantly infected cells hosting multiple parasites
   ([188]Fig. 6d). Flow cytometric analysis confirmed this finding, where
   it was found that orlistat-treated cultures had significantly higher 4N
   and >4N subpopulations of parasites ([189]Fig. 6c). With an increasing
   drug concentration, the proportion of >4N parasites was significantly
   increased. While >4N parasites contribute 7% ± 0.7% of the
   subpopulation structure of control parasites, their proportions in
   cultures treated with orlistat were 11.5% ± 0.1% at 2.5 μM,
   12.1% ± 0.6% at 5 μM, 15.0% ± 0.3% at 10 μM, 28.4% ± 3.2% at 20 μM, and
   54.6% ± 1.2% at 50 μM orlistat. This led us to conclude that while
   intracellular proliferation continued, inhibition was occurring at the
   level of merozoite release or merozoite maturation. A rational
   explanation for this conclusion could be that the formation of free
   fatty acids from DAGs that help in merozoite membrane formation is
   inhibited by orlistat. Conversely, the free fatty acids may also play a
   role in RBC membrane lysis due to their detergent-like properties, and
   this is impaired by orlistat treatment ([190]61).

   To assess the viability and functionality of merozoites from
   orlistat-treated cultures, we purified merozoites from both control and
   7.5 μM orlistat-treated cultures (24h) harvested at the same
   parasitemia (~25%). Following invasion, parasitemias were assessed at
   1h and 24 h postinvasion. As can be seen from the results in
   [191]Fig. 6e, merozoites from orlistat-treated cultures were unable to
   successfully invade new host RBCs, and thus, new cycles of invasion
   could not be initiated. Therefore, these results suggest that
   ineffective RBC membrane lysis was not responsible for the low
   parasitemia in drug-treated cultures. Instead, defective merozoite
   maturation may have been the responsible factor. To assess whether the
   loss of free fatty acids resulted in defective merozoite membrane
   genesis, we used Bd37 as a marker to examine the merozoite membranes.
   Bd37 is the major surface antigen of B. divergens and has been shown to
   be glycosylphosphatidylinositol (GPI) anchored at the surface of the
   merozoite ([192]62[193]–[194]64).

   Parasites were treated with 7.5 μM orlistat at 20% parasitemia and
   imaged for Bd37 (green) localization after 24 h of incubation with
   Orlistat. Parasites were stained with SiR-HO (red) to stain the DNA of
   the parasite. The images in [195]Fig. 6f to [196]h show the results of
   STED-based microscopic analysis of orlistat-treated parasite cultures
   compared to DMSO-treated cultures. While the DMSO-treated parasites
   exhibited normal parasite membrane structures, as evidenced by the
   uniform localization of Bd37 around the merozoites ([197]Fig. 6f),
   orlistat-treated merozoites lacked defined membrane structures, as seen
   in the discontinuity in Bd37 localization around the daughter
   merozoites at 24h ([198]Fig. 6g) and even more severely at 48h
   ([199]Fig. 6h). In the control cultures, the plasma membrane of the
   merozoites completely enveloped the merozoites, while incomplete
   ingression of the plasma membrane in daughter merozoites characterized
   the orlistat-treated parasites. Our results suggest that the drug
   inhibited merozoite maturation before egress and that TAG and DAG
   hydrolysis is essential for proper merozoite maturation. To corroborate
   our two-dimensional (2-D) images from the STED microscopy, Z-stack
   images were acquired to create a nearly isotropic three-dimensional
   (3-D) image in Imaris. Parasites stained with Bd37-fluorescein
   isothiocyanate (FITC; green) and SiR-HO (red) in samples from the DMSO
   control for 24 h (Movie S1) and 7.5 μM orlistat for 24 h (Movie S2) and
   48 h (Movie S3) are shown in the supplemental material.

DISCUSSION

   Metabolomic analyses provide a chemical fingerprint of thousands of
   metabolites present in cells, allowing us to probe the mechanisms
   behind both biological processes and disease states. Here, we have
   shown that the development of Babesia divergens within the human host
   RBCs modulates the levels of hundreds of compounds from every major
   metabolic process, including lipid, amino acid, and nucleotide
   metabolism, energy generation, and cellular redox potential. Our data
   suggest an intricate interplay between the host RBCs and the parasite
   for nutrient acquisition and turnover, as well as waste removal. This
   is the first global metabolomics study of host RBCs infected with B.
   divergens in an in vitro culture, performed at both low and high
   parasitemia using the metabolomics platform Metabolon. Our analysis
   revealed that in infection, there are major changes across all
   biomolecules and major metabolic pathways. More than 600 metabolites
   were detected, with significant changes in ~150 and ~350 metabolites in
   low parasitemia (D2) and high parasitemia (D4). As highlighted by the
   results in [200]Fig. 1 and [201]Fig. 4, lipids were the major class of
   molecules impacted by parasite growth. The profiles of amino acids and
   carbohydrates were also shown to be massively altered by parasite
   growth in human RBCs. As parasite growth continued, there was an
   increased reliance on energy pathways like glycolysis and the TCA cycle
   to fuel the rapid proliferation, and these changes were reflected in
   the metabolome of the infected host cells. Additionally, as expected
   and as previously shown by us using redox dyes ([202]32), there were
   significant changes in the redox regulatory pathways in the infected
   red cells ([203]Fig. 3f).

   We further corroborated changes in specific major subclasses of lipids
   that were modified by the parasite using both growth inhibition assays
   and high-resolution STED microscopy ([204]Fig. 4, [205]5, and [206]6).
   We investigated two subclasses of lipids: cholesterol, which is the
   major lipid in the plasma membrane of cells, and acylglycerols. Our
   experiments revealed that the parasite scavenges all its cholesterol
   requirements from the host cell ([207]Fig. 5a) and that a disruption in
   cholesterol scavenging (as seen by treatment with MβCD) is lethal for
   the parasite, as it is prematurely ejected out of the RBC ([208]Fig. 5d
   to [209]g). Furthermore, we found that orlistat, which is a lipase
   inhibitor and prevents the conversion of TAGs to DAGs, stalls the
   egress of the parasite from the host, leading to a buildup of 4N and
   >4N parasite population ([210]Fig. 6c and [211]d) and disrupted
   parasite membrane structure ([212]Fig. 6f to [213]h and Movies S2 and
   S3). Our findings are in broad agreement with those reported for the
   related apicomplexan parasite P. falciparum and, expectedly, as they
   both share a host cell, the human RBC. However, subtle differences were
   also recorded, as seen when MβCD did not impact B. divergens invasion,
   in contrast to Plasmodium. Further work is needed to obtain a
   comprehensive atlas of the similarities and differences in the
   metabolic networks of these intraerythrocytic parasites.

   A limitation of metabolomics of uninfected and infected red cells is
   the fact that the host RBC also undergoes metabolic modulation when
   kept in culture. This has been extensively studied in malaria ([214]12,
   [215]27), and it has been shown that the RBC metabolism of the donor
   contributes to time-dependent changes in RBC metabolic activities
   during in vitro culture. In our system, we also observed these changes
   in uninfected RBCs kept in culture on D2 versus D4. However, it must be
   noted that the changes due to infection are far greater than the
   relatively minor changes in uninfected red cells.

   In conclusion, high-resolution metabolomic data were generated and
   analyzed from uninfected RBCs and B. divergens-infected RBCs at two
   quantitative infection levels. A global snapshot of changes brought
   about by B. divergens infection of human RBCs is presented in
   [216]Fig. 7, with spatial separation by superpathways as described in
   file 5 in the supplemental material. Those obtained in this study are
   marked as nodes in green and red, where these colors represent
   downregulation and upregulation of metabolite abundance, respectively,
   in iRBCs compared to uRBCs. The metabolites that were unaltered between
   uRBCs and iRBCs are shown as white nodes. The data in [217]Fig. 7
   further emphasize that lipids were the dominant class of biomolecules
   identified in our study as differentially abundant in iRBCs, as evident
   from the spread of the lipid web in the figure. Perturbations in amino
   acid pathways and carbohydrate and nucleotide metabolites are also
   evident from the overview. Overall, parasite infection of RBCs
   completely changes the metabolic landscape of the host cells, and the
   data reported here will deepen our understanding of Babesia biology and
   its auxotrophic dependence on the host RBC. Furthermore, our study
   provides a blueprint to further investigate clinically important
   biochemical pathways in Babesia, as well as related apicomplexans, in
   greater detail to identify promising chemotherapeutic targets for
   future investigation.

FIG 7.

   [218]FIG 7
   [219]Open in a new tab

   Global metabolomic view of B. divergens-infected RBCs. The image shows
   the different metabolic pathways uncovered by our analysis classified
   according to the major biomolecules: lipids, amino acids,
   carbohydrates, energy production, nucleotides, cofactors and vitamins,
   xenobiotics, and partially characterized molecules. Under each of these
   categories, different superpathways are labeled, and metabolites are
   shown by different colored circular nodes. The color of the circle
   refers to the fold change of metabolites in iRBCs compared to uRBCs, as
   follows: white, not significantly affected in the study; light red,
   increased, with a P value between 0.01 and 0.05; dark red, increased,
   with a P value of <0.05; light green, decreased, with a significance
   value between 0.1 and 0.5; dark green, decreased, with a P value of
   <0.05. Therefore, this study provides the first metabolome of B.
   divergens-infected RBCs and comprehensively covers modifications of the
   different metabolic pathways in the infected host cell.

MATERIALS AND METHODS

Ethics statement.

   Human blood from healthy volunteer donors was used to culture B.
   divergens in vitro. The blood samples were deidentified and approved
   for use by the NYBC IRB. All blood donors gave informed written consent
   for use of their blood for research purposes.

B. divergens in vitro culture.

   B. divergens parasites (Bd Rouen 1986 strain) were maintained in human
   RBCs at 5% hematocrit in complete medium (RPMI 1640 supplemented with
   50 μg/mL hypoxanthine, 0.24% [vol/vol] sodium bicarbonate, and 10%
   human serum) under a low-oxygen atmosphere (5% O[2], 5% CO[2], 90%
   N[2]) at 37°C, as previously described ([220]65). A+ RBCs were
   collected in 10% citrate-phosphate-dextrose (CPD) and washed thrice
   with RPMI 1640 medium for the complete removal of plasma and white
   blood cells.

Free-merozoite isolation.

   A high concentration of viable free merozoites was isolated from
   unsynchronized cultures at high parasitemia (~35% to 40%) as described
   previously ([221]25). Briefly, ~12 × 10^9 infected RBCs were
   resuspended in 6 mL of fresh complete medium at room temperature and
   filtered once with a 5-μm filter and twice with 2-μm filters (Versapor
   membranes) using a 1-mL syringe. Centrifugation (670 × g for 5 min) was
   applied only after the last filtration. The supernatant (suspension of
   free merozoites) was further spun at 10,000 rpm for 5 min at room
   temperature. The merozoite pellet was resuspended in 2 mL of complete
   medium and used for invasion of an equal number of host RBCs. All
   inoculum preparations were checked for potential contamination with
   carryover RBCs by light microscopy and flow cytometry.

Assessment of invasion, development, and egress in various RBCs.

   Fresh cultures were seeded with purified merozoite suspension at 20%
   (vol/vol) of culture volume. Host RBC counts were made to ensure that
   equal numbers of host cells were taken during the invasion. After 60
   min of invasion at 37°C, cultures were centrifuged (670 × g for 2 min
   at room temperature) and the medium replaced with fresh complete medium
   to remove the lysed RBC components introduced into the culture during
   the inoculation. At additional time points (24 to 72 h), samples were
   collected to assess the culture progression and subpopulation dynamics
   from the perspective of parasite development and egress, as previously
   described ([222]25). The culture size (parasitemia) and parasite
   proliferation analysis were carried out by flow cytometry, as described
   in detail below. Characterization of parasite morphology and
   development was performed on Giemsa-stained slides using light
   microscopy.

Flow cytometry.

   Samples were analyzed in 1 mL of cell suspension at a concentration of
   1 × 10^7 infected cells/mL in fresh complete medium without previous
   fixation or washing procedures, as previously described ([223]66).
   Samples were analyzed on an LSR Fortessa SORP analyzer (BD Biosciences,
   San Jose, CA) equipped with a 488-nm blue laser for Vybrant DyeCycle
   green (1:500) (catalog number V350041; Thermo Fisher) detection
   (bandpass [BP] 530/30 nm) and a 405-nm violet laser for BV421 mouse
   anti-human CD235a antibody (1:500; BD-562938) and anti-GPA antibody (BP
   450/50 nm) detection. Totals of 50,000 singlets were collected at a low
   flow rate to achieve the maximum resolution among subpopulations of
   iRBC and further analyzed with FCS Express Software version 7.12.
   Appropriate compensations were applied to all experiments. All
   parameters were processed using log scaling.

Light microscopy.

   Blood smears were fixed with methanol and stained with 20% Giemsa stain
   (Sigma-Aldrich, St. Louis, MO) for the morphological analysis of
   parasites. A minimum of 2,000 cells were scanned for assessment of
   changes in morphology using a Nikon Eclipse E 600 microscope.

Metabolomics of red cell pellets from uRBCs and iRBCs.

   Invasion assays were performed in fresh wild-type cells at 5%
   hematocrit, parasitemia was measured postinvasion, and cultures were
   incubated at 37°C in an incubator with 90% N[2], 5% O[2], and 5% CO[2].
   An equal number of uninfected cells was incubated under identical
   conditions. At the required time point, 150 μL amounts of the cell
   pellets were washed thoroughly with 1× phosphate-buffered saline (PBS),
   flash frozen in a methanol-dry ice bath, and stored in bar-coded tubes
   at −80°C. Samples were shipped to Metabolon, Inc., and details of the
   workflow and data analysis, along with statistical parameters used in
   the study, are provided in file 1 in the supplemental material.

Membrane cholesterol estimation assays.

   Membrane cholesterol was quantified using a fluorometric cholesterol
   quantitation kit (Sigma-Aldrich) in accordance with the manufacturer’s
   instructions. Membranes were extracted with a
   chloroform-isopropanol-IGEPAL CA-630 solution and spun at 13,000 × g
   for 10 min, and the organic phase was transferred to a new tube.
   Samples were speed vacuum treated for 30 min to remove any residual
   organic solvents before the lipid pellets were dissolved in cholesterol
   assay buffer and vortexed until the mixture was homogenous.
   Fluorescence intensity was measured using a fluorescence plate reader
   (excitation, 535 nm; emission, 587 nm). A standard curve was graphed,
   and for the test samples, the concentrations of cholesterol correlating
   to their respective fluorescence intensities were determined.

Immunofluorescence assays.

   For immunofluorescence assays, the following dilutions of
   antibodies/dyes were used: Bodipy 493/503 (catalog number D3922; Life
   Technology) was used at 10 μg/mL (1 mg/mL stock in ethanol), LipidTOX
   green (catalog number [224]H34475; Life Technology) was used at the
   recommended 1:200 dilution, Bodipy TopFluor (SKU 810255P; Avanti Polar
   Lipids) was used at 7.5 μM (stock 1.73 mM in ethanol),
   silicone-rhodamine Hoechst stain (SiR-HO) (catalog number SC007;
   Spirochrome) was used at 10 μM (10 mM stock in DMSO), Hoechst 33342
   (catalog number PI62249; Life Technology) was used at 0.5 μM, and Nile
   red (product number 72485; Sigma) was used at 1 μg/mL (1 mg/mL stock in
   acetone). Glass slides were coated with 0.01% poly-l-lysine overnight
   and washed thrice with PBS. Parasite culture (200 μL at 5% hematocrit)
   was added to the slides, and RBCs were allowed to adhere for 30 min.
   Similar cell concentrations of uRBCs were used for attachment.
   Following this, RBCs were fixed with 4% paraformaldehyde and 0.0075%
   glutaraldehyde for 20 min at room temperature, followed by three washes
   with PBS. Following this, cells were stained with the respective dyes
   in accordance with manufacturer’s instructions. For the Bd37
   experiment, cells were permeabilized with 0.1% Triton X-100 for 5 min
   postfixation and washed thrice with PBS. Cells were incubated with
   primary antibody anti-Bd37 antibody ([225]15) at 1:1,000 for 1 h at
   room temperature. This was followed by 3 washes and incubation with
   Alexa Fluor 488 donkey anti-rabbit IgG (catalog number 406416;
   BioLegend) at 1:200.

Drug treatments.

   The following drugs and stock concentrations were used for the
   experiments described in this manuscript: orlistat (38 mM stock in
   DMSO) (product number O4139; Sigma) and methyl β-cyclodextrin (MβCD;
   320 mM stock in 1× PBS) (product number C4555; Sigma). For drug assays
   in B. divergens in vitro cultures (growth inhibition assays [GIA]),
   drugs were added to the culture at the required concentration and at
   ~2% parasitemia. Every 24 h, fresh medium with drug was added. For
   invasion experiments with MβCD ([226]Fig. 5c), 1 mL of 50% uRBCs was
   treated with 5 mM MβCD, as previously described ([227]51), for 30 min
   in incomplete RPMI and washed thrice with PBS. Invasion was performed
   as described above, and parasitemia was measured using flow cytometry
   at 1 h postinvasion.

STED microscopy and image analysis.

   Images were acquired in an Abberior Facility Line stimulated emission
   depletion (STED) microscope at Rockefeller University Bio-Imaging
   Resource Center, New York, NY, USA. A 100× objective lens was used. The
   respective lasers were used for FITC (green) and SiR-HO (red; marker
   for DNA). For each experiment, laser power and accumulation were
   adjusted. For 2-D images, the pixel size was kept at 15 nm, while for
   z-stacks, the pixel size was 35 nm and z-steps were set at 50 nm to
   achieve a nearly isotropic 3-D image. Dwell time was always kept at 10
   μs. The .obf images obtained were converted to .ims format and further
   analyzed in the Imaris software. Imaris 9.9.1 software by Oxford
   Instruments was used to analyze and process images and videos. A 3-D
   model was processed using surface rendering, with a surface grain size
   of 0.025 μm. Animation of the 3-D model was recorded at 20 frames per
   second with a total of 550 frames and 1,600 by 1,200 Ultra eXtended
   Graphics array (UXGA) (4:3) resolution. The video clips were saved in
   the .mp4 format.

Statistics.

   Parasitemia was defined as the total number of infected RBCs (iRBCs) in
   every 100 RBCs, not taking into consideration the number of parasites
   present in each cell when measured by flow cytometry. As RBCs do not
   contain a nucleus, iRBCs and their subpopulations were identified as a
   function of the presence and number of intraerythrocytic
   parasites/genome (intraerythrocytic parasite load), where “1N” refers
   to one genome copy, based on the method previously described ([228]66).
   Inhibition of culture growth was calculated as the percentage of
   parasitemia of control cultures. Data reproducibility was ensured by
   running independent experiments, each in triplicate. Results are
   represented as the mean values ± standard errors of the means (SEM).
   Student’s t test, one-way analysis of variance (ANOVA), and two-way
   ANOVA were calculated using GraphPad Prism 9, as applicable. The
   significance level was set at a P value of <0.05.

ACKNOWLEDGMENTS