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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Mol Biochem Parasitol. Author manuscript; available in PMC Feb 1, 2012.
Published in final edited form as:
PMCID: PMC3004989
NIHMSID: NIHMS247782

Metabolomics and malaria biology

Abstract

Metabolomics has ushered in a novel and multi-disciplinary realm in biological research. It has provided researchers with a platform to combine powerful biochemical, statistical, computational, and bioinformatics techniques to delve into the mysteries of biology and disease. The application of metabolomics to study malaria parasites represents a major advance in our approach towards gaining a more comprehensive perspective on parasite biology and disease etiology. This review attempts to highlight some of the important aspects of the field of metabolomics, and its ongoing and potential future applications to malaria research.

Keywords: Malaria, metabolomics

2. Introduction

Metabolomics in biology is the study of the entire repertoire of metabolites in a biological system [1]. This repertoire consists of many different classes of small molecules including sugars, amino acids and fatty acids. These small molecules perform critical functions such as energy storage, signal transduction, maintaining cellular structure, and feedback regulation of gene expression. Global as well as targeted metabolomic analyses have been widely and successfully used to study several pathogens that infect humans such as the opportunistic human fungal pathogen Aspergillus fumigatus [2], Streptococcus pneumoniae and Staphylococcus aureus that are common causes of pneumonia [3], hepatitis B virus [4], uropathogenic Escherichia coli strains [5], and Bacillus cereus [6] to list a few. Metabolomics of malaria is still in its infancy and only a handful of studies have been reported thus far [7-13]. Notwithstanding their recent application to malaria research, the remarkable versatility of the currently available metabolomic techniques and the extensive body of literature generated from their application in other fields, provide a strong platform to expand their application to the malaria field.

Widespread resistance to most anti-malarial drugs and the unavailability of an effective vaccine have fueled the emergence of malaria in recent years as a major global health and economic burden [14]. Despite these hurdles, the field of malaria research has witnessed some extremely notable developments in the recent past including sequencing of the malaria genome [15], the application of proteomics to studying malaria life cycle [16], the malaria transcriptome [17], and several web resources such as MalVac (database of malaria vaccine candidates), PlasmoDB (genome database of the Plasmodium genus), and VarDB (database for antigenic variation gene families) [18-20].

Metabolomics adds a unique and much-desired dimension in our endeavors to better understand malaria parasite biology and discover novel therapeutic and vaccine targets. Furthermore, metabolomics represents an important tool that complements genomics and proteomics in enabling investigators to perform the most exacting and thorough analysis of biological systems [1, 21]. This combinatorial systems biology approach of studying the multiple facets of a biological system would yield a detailed view of its intricate networks. In so doing, such an approach could inform many aspects of parasite biology including, but not limited to, basic biological models, drug development, biomarkers of infection, and models of human pathophysiology.

Mindful of the enormity of the field of metabolomics and the vast body of literature, this review attempts to highlight only some of its salient aspects. We provide an overview of the commonly used techniques and statistical approaches, and review the present state of malaria metabolomics research. We conclude with suggestive future applications of metabolomics in the field of malaria research, which could potentially lead us to an in-depth understanding of malaria parasites at both the basic and clinical levels.

3. Approaches to metabolomic analysis

The ultimate goal of metabolomics is to achieve a comprehensive and quantitative analysis of all the metabolites in a biological system, and to achieve the closest possible approximation of its physiology. Metabolites in a sample possess very diverse biochemical properties, and are present at varying abundance levels [22]. In addition, the characteristics of a molecule are influenced not just by its milieu but also its interactions with other molecules. A large-scale comprehensive analysis of the metabolites in a sample entails extracting and identifying thousands of compounds. Depending on the kinds of metabolites to be extracted, such analysis requires the development of tailor-made strategies for sample preparation, metabolite extraction and separation, and data analysis. Fortunately, numerous robust techniques have been developed to achieve an efficient and all-encompassing metabolomic analysis. However, it is important to note that a single extraction method cannot isolate/identify every molecule in the pool due to the diversity of the chemical structures. A prior knowledge of the types of molecules to be explored will dictate the protocols to be used, and the employment of multiple methods is essential to maximize the identification of the broadest range of molecules in a sample.

3.1 Extraction of metabolites

The different preparation methods that can be used to extract the most complete set of metabolites from a biological sample have been expertly reviewed by Dettmer et al. and Mashego et al. [23, 24]. For microbes, at least two different extraction procedures (sequential and simultaneous sample processing) are employed[24]. The former involves separate and sequential quenching and metabolite extraction steps, whereas in the latter both quenching and extraction are performed in a single step [24]. The efficiency of metabolite extraction varies depending on the microbe and procedure used, and the nature of the microbe [prokaryotic (e.g. bacteria) versus eukaryotic (e.g. yeasts)] seems to dictate the best method for extraction [24]. Extraction of compounds that are susceptible to hydrolysis (e.g. ATP and phosphorylated compounds) also presents challenges, but methods have been developed to overcome these barriers [25]. Malaria parasites undergo profound developmental and metabolic changes during the intraerythrocytic life cycle [10]. Extraction efficiency and the nature of metabolites extracted will thus depend on the extraction reagents used [11, 26]. Some investigators use parasites that have been freed from erythrocytes using saponin to specifically analyze parasite metabolites, yet others have used intact infected erythrocytes [9, 10].

Maintaining sample integrity to reflect the physiologic state is particularly challenging with small molecules as they can undergo rapid alteration in response to changes in environmental conditions. Sample quenching to achieve a rapid arrest of metabolic activity typically involves a rapid decrease (sometimes increase) in temperature to arrest enzymatic activity followed by extraction of metabolites with a combination of extracting solvents. The most popular quenching method involves the use of chilled methanol/water combination but several other reagent combinations have also been used [27]. Recently, a methodology for quenching and extracting metabolites was reported for the protozoan parasite Leishmania, which enabled the detection of ~20% of the parasite's predicted metabolome [28].

3.2 Chromatographic separation

Global metabolomic analysis begins with an extremely complex mixture of small molecules that first need to be separated. Chromatographic separation constitutes a critical initial step before the metabolites are subjected to further downstream analysis for identification. A separation step has several advantages, which include reducing isobaric interferences that can occur when compounds of the same mass appear as indistinguishable entities in downstream mass spectrometry (MS) analysis. Second, it minimizes ion suppression in which a more easily ionizable species masks the presence/detection of a less ionizable one (also known as matrix effect) [25]. Several different chromatographic techniques are currently used to separate the individual compounds in this complex mixture. These include gas chromatography (GC), liquid chromatography (LC) and capillary electrophoresis (CE) [22, 29]. Also, several solvent systems can be used for resolving the compounds, which will dictate the nature and efficiency of compound separation [29].

The advantages of GC include high resolution and reproducibility. But it separates compounds based on the metabolites' inherent volatility for which the compounds need to be thermally stable [22]. Alternatively, the metabolite may be rendered volatile and thermally stable through chemical derivatization (as in the case of sugars, nucleosides and amino acids). However, this can be time-consuming [22, 25]. Since derivatization requires prior knowledge of the chemistry of the metabolites in order to be able to select the best method to be used for this process, it limits the range of metabolites that can be analyzed by GC. Additionally, labile phosphorylated metabolites can easily degrade during derivatization and when exposed to the high temperature conditions involved in GC analysis. Metabolites having varying affinities for derivatizing agents could lead to potentially inaccurate quantification unless appropriate standards and normalization methods are used [22, 25]. Also, formation of byproducts from derivatization, possible biochemical conversion of metabolites to other species and/or degradation of the final product(s) could lead to skewing and misinterpretation of data [25]. To get around this, a two-step derivatization method that overcomes the drawbacks associated with derivatization can provide a better and wider coverage of analyzable metabolites [22, 25]. Some compounds cannot be volatilized, and therefore GC is not ideal for their analysis. Thus, a priori knowledge of the chemical properties of the compounds of interest becomes important. Despite the drawbacks, an important advantage of GC is a high separation efficiency of even isomeric compounds [25]. Furthermore, multidimensional GC and the use of two different columns, one for polar and another for non-polar compounds, can dramatically improve sensitivity [25].

In LC, the samples have to go through an extraction process prior to injection to the LC system to minimize matrix effects. But LC-based separation of compounds can be used without the need for chemical derivatization that is required for GC [22]. Liquid chromatography affords greater versatility by allowing the use of varying stationary and mobile phases during chromatographic separation enabling simultaneous qualitative/quantitative analysis of different classes of metabolites [25]. The nature of the column becomes critical for separation of metabolites by LC especially for biological fluids that contain both polar and non-polar compounds [30]. Normal phase LC columns are better at resolving polar compounds, whereas reverse phase (RP) columns work better for non-polar compounds. Hydrophilic interaction chromatography (HILIC) columns were developed to separate the more polar compounds, which offered an ideal complementary technique to the traditional RP chromatography used for the more non-polar compounds [29, 30].

Ultra performance liquid chromatography (UPLC) offers the best LC separation method allowing for the pumping and injection of metabolite extracts at a tremendously high pressure (>10 000 psi), yielding improved compound separation and resolution in a shorter time than regular high performance liquid chromatography (HPLC), sharper chromatographic peaks with increased peak capacity, lower ion suppression, and increased sensitivity [29]. Ultra performance liquid chromatography also results in at least a 20% increase in the number of components detected compared to traditional HPLC, as well as superior retention time reproducibility, and signal-to-noise ratios [31].

Capillary electrophoresis offers a powerful analytical tool for qualitative and quantitative “omics” level analysis of samples. Capillary electrophoresis offers several advantages such as the ability to analyze a variety of samples including inorganic ions, organic ions, nucleic acids, proteins, and peptides [32]. Also, CE requires a smaller sample volume, offers higher resolution potential, and is highly efficient compared to other separation techniques in that it is less labor intensive and requires a relatively shorter time-period for analysis. However, its limitations include relatively low sensitivity owing to small sample injection volumes and difficulty in the identification of metabolites. These shortcomings can be overcome by coupling CE with other techniques such as MS, which improves sensitivity and allows for the identification of metabolites [32].

3.3 Metabolite profiling

Nuclear magnetic resonance (NMR) and MS are the most commonly used tools for identification of metabolites [22]. Because of the complementary nature of their analytical features, NMR and MS can be used to achieve a highly comprehensive metabolic profiling [33]. Hybrid systems that combine NMR and MS approaches are rare [34], yet this combinatorial approach offers exciting metabolomics opportunities to be explored [33, 35]. Although using NMR typically does not require sample purification and only minimal sample preparation, a major limitation of NMR is that it requires large amounts of sample and lacks the sensitivity of MS [34]. Several different isotopes have been used to perform NMR-based studies. While the use of 1H-NMR achieves maximum sensitivity and broadest compound coverage, using other nuclei, particularly 13C, 31P and 15N, yields better biochemical/biological information (e.g. metabolite flux, biochemical reactions, etc.) [36].

Teng et al. used 1H-NMR to identify more than 50 metabolites from an extract of saponin-released P. falciparum trophozoites [11]. In addition to identifying and quantifying amino acids, nucleotides, carboxylates and other molecules, they found that perchloric acid was the optimal extraction reagent [11]. 1H-NMR metabolomics has been applied to other parasites to study, for example, the metabolites produced and released by Cryptococcus neoformans in culture media [37], the lipidome of Leishmania donovani [38], comparing intracellular metabolites produced by long-term in vitro cultured L. donovani axenic amastigotes with those produced by promastigotes and intracellular amastigotes [39], and the in vivo metabolism of Trypanosoma cruzi [40].

Basant et al. used 1H-NMR to analyze metabolite extracts of urine, serum and brain samples from mice infected with P. berghei to study metabolic alterations with disease progression [12]. Urine samples from female mice showed greater alterations in global metabolism during early stages of infection compared to samples from male mice. Global changes in serum samples were greater in male mice in the early stages, whereas changes in brain metabolism were minimal in either sex. An analysis of individual metabolites in serum and brain samples also revealed significant changes in female mice in the early stages of infection. These sexually dimorphic metabolic alterations were suggested to have a bearing on disease prognosis and treatment [12].

13C-based NMR studies have been used in the malaria field to study glucose metabolism in P. falciparum-infected erythrocytes [8], measuring glucose flux in P. falciparum and P. yoelii-infected erythrocytes as well as the suppression of glycolytic activity in uninfected erythrocytes by malaria-conditioned medium [9, 41]. 31P-NMR is particularly useful in tracking phosphate metabolism in Plasmodium spp. [42], Toxoplasma gondii, and Cryptosporidium parvum [43], as well as in the kinetoplastids [44].

Mass spectrometry is highly sensitive and selective because it can detect moieties such as sulfates that cannot be detected by NMR. However, MS alone may fail to discriminate between certain classes of compounds depending on the ionization method used, and thus needs coupling with metabolite separation techniques such as GC and LC [22]. Olszewski et al. used an LC-MS/MS approach to quantitatively measure the levels of ~200 known compounds of validated identity over the 48 hour blood-stage developmental of P. falciparum. An important discovery was the conversion of arginine to ornithine by parasite arginase, and the potential link between parasite-induced hypoargininemia and the development of cerebral malaria [10].

3.4 Statistical tools for data analysis

Metabolomic experiments produce large volumes of data. Efficient data handling is key to achieving accurate metabolite identification and quantification, and thus generating biologically meaningful interpretations. In the recent past, a wide variety of data processing software and other tools have been developed for data handling [45]. Chemometrics tools include mathematical, statistical, graphical, and symbolic methods used to extract the maximum amount of information possible from chemical and/or spectral data, and thus constitute an integral component of the data analysis [34].

The mostly commonly used statistical tools include principal component analysis (PCA), principal component discriminant analysis (PCDA) or partial least squares (PLS), and partial least squares discriminant analysis (PLSDA) [46]. Principal component analysis is a statistical technique that determines the most optimal linear transformation for a collection of data points in such a manner that the properties of that sample are most clearly displayed along the coordinate (i.e. principal) axes. In other words, PCA allows one to plot, visualize, and cluster multiple metabolomic data sets based on linear combinations of their shared features [34]. Partial least squares discriminant analysis similarly enables one to perform efficient classification and discrimination analysis of very large data sets containing a small number of samples. A variant of PLSDA, the orthogonal-PLSDA, classifies the data into two blocks, one representing the “between-class” variation, and the other representing the “within class” variation [47, 48]. It thus separates the more meaningful data (the former) from the less meaningful (the latter) [47]. Also, statistical techniques such as cross-validation and permutation testing enable one to assess the robustness of a statistical tool used [48]. Other statistical tools such as k-means clustering, hierarchical clustering, artificial neural networks, analysis of variance, and multivariate analysis of variance are also widely used [34, 48].

4. Metabolic highlights of Plasmodium falciparum

4.1 Malaria metabolomic databases

Metabolic pathways are typically constructed by integrating biochemical data from a diverse set of organisms. There are a number of databases describing malaria metabolic pathways including the Kyoto Encyclopedia of Genes and Genomes, Malaria Parasite Metabolic Pathways, PlasmoCyc, metaSHARK, and metaTIGER [49, 50]. These databases and others such as PlasmoDB and GeneDB are regularly updated based on new findings. These databases provide powerful resources for building metabolic pathways using gene annotation information. However, investigators face several hurdles while undertaking a bioinformatics-based reconstruction of metabolic pathways. These include the lack of proper gene annotation, absence of certain reactions in a pathway if the parasite utilizes host molecules, the demands imposed on the parasite by its novel niche to acquire pathways not previously seen in nature, and the availability of experimental confirmation for only a subset of pathways. These limitations have been eloquently described elsewhere [49]. We focus on polyamines, glycerol and lipids because they are illustrative of the different approaches that have been taken by investigators to unravel important biochemical attributes of the parasite. Astute use of these approaches also carries the potential for future discovery of novel parasite biology.

4.2 Polyamines

During the past three decades, polyamine metabolism has attracted much interest as a target for anti-proliferative therapy [51]. Polyamines (putrescine, spermine and spermidine) are low molecular-weight organic cations that are found in a wide range of organisms including bacteria, plants, animals and parasites (including Plasmodium), and mediate several important cellular processes including cellular development and proliferation, and stress responses [52, 53].

Polyamines are a major metabolite in Plasmodium and make up 14% of the total metabolome of isolated parasites [11]. Recent efforts have focused on chemically interfering with Plasmodium polyamine metabolism owing to the fact that rapid proliferation of Plasmodium is critically dependent on the availability of high amounts of polyamines [54]. Plasmodium has its own unique polyamine synthesis pathway in which arginine is converted to ornithine which then acts as the precursor for spermidine. The two rate-limiting enzymes in this pathway, ornithine decarboxylase and S-adenosylmethionine decarboxylase, differ significantly from the mammalian ones in their organization as a bi-functional protein complex [55].

The effect of polyamine depletion as an anti-malarial strategy was studied through co-inhibition of S-adenosylmethionine decarboxylase and ornithine decarboxylase in P. falciparum, and performing a comprehensive genomic, proteomic, and metabolomic analyses [7]. The latter involved an LC-MS/MS methodology to analyze ~100 metabolites including polyamines. Three perturbation-specific compensatory transcriptional responses were observed, two of which were confirmed at both the protein and metabolite levels. Specific effects on the parasite metabolome included a significant decrease in the levels of the polyamines putrescine and spermidine, and the downstream metabolite 5′-methylthioinosine. Ornithine aminotransferase was elevated at both the transcript and protein levels suggesting the occurrence of compensatory changes in the parasite. This study demonstrates the power of a combinatorial and correlative analysis at the transcription, protein and metabolomic levels [7]. More recently, Becker et al. showed that cyclohexylamine (a spermidine synthase inhibitor) treatment of P. falciparum resulted in transcriptional arrest of genes in the polyamine biosynthesis and associated metabolic pathways as well as differential expression of the corresponding proteins. Additionally, changes were observed in the levels of polyamine metabolites [56].

4.3 Glycerol and carbon metabolism

Glucose has long been shown to be a major carbon source for the parasite, and lactate is one of the major end-products of glucose metabolism. Drugs such as chloroquine and amodiaquine accumulate in P. falciparum in an energy-dependent manner [57, 58]. A recent study using 13C-glucose-supplemented media enabled the detection of all the by-products of glucose metabolism in P. falciparum [8]. 13C-NMR was used to confirm the generation of the expected end-products of glucose metabolism including lactate, pyruvate, and alanine [8]. Interestingly, the study also found that parasites generated glycerol and glycerol 3-phosphate from glucose, a metabolic conversion that had previously not been known to exist. These metabolites were thought to be produced due to the operation of a glycerol-3-phosphate shuttle in the parasite in response to growth under limited O2 and elevated CO2 [8].

Glycerol is produced during anaerobic catabolism of glucose in yeast, protozoan parasites (Trichomonas, Leishmania and Trypanosomes) and rumen ciliates, but not by humans [8, 40]. Incidentally, all of these studies that detected glycerol had used 13C-NMR spectroscopy [8]. In contrast, studies that had used HPLC or GC-MS/MS and LC-MS/MS could not detect glycerol because of its poor ionization potential [8]. Thus, a combinatorial approach is critical to achieving the most comprehensive analysis of small molecules in a biological sample.

Tracking the metabolism of 13C-labelled precursors using LC-MS led to the discovery that the malaria parasite employs the TCA cycle in an unconventional manner with important biological implications [59]. Glutamate and glutamine were the major metabolites that entered the parasites tricarboxylic acid cycle to generate acetyl-CoA moieties for histone acetylation, whereas glucose-derived acetyl-CoA was used to acetylate amino sugars [59]. For an in-depth account of carbon metabolism in malaria, we refer the reader to a recent comprehensive review [60].

4.4 Lipids

Cellular lipids as a class constitute a tremendous diversity of chemical structures and qualities. For example, phospholipids constitute a major class of lipids in P. falciparum and are important for membrane biogenesis during parasite proliferation [61]. These parasites use numerous pathways for phospholipid synthesis. Interestingly, rodent and non-rodent malaria parasites differ in their phospholipid synthesis pathways [61]. The parasite utilizes lipids for numerous functions including membrane formation, and as metabolic co-factors, and can affect host physiology through pro- or anti-inflammatory effects [62, 63]. Early metabolic labeling studies with fatty acids demonstrated that parasites can uptake host fatty acids, particularly palmitic and oleic acids [64, 65] and lipoproteins [66].

Interestingly, GC analysis demonstrated that parasite elongation of 14C-palmitic acid to oleic acid occurred only when the level of fatty acid supplementation of the culture media was lowered, suggesting that malaria parasites have the capacity to adapt to variations in exogenous nutrient levels [65]. Malaria parasites also have the ability to synthesize fatty acids through the type II fatty acid synthesis (FAS II) pathway. This pathway is absent from mammalian cells, and is thus an attractive drug target [67]. The FAS II pathway is required for liver stage development in rodent malaria model but is not essential for the blood stages [68, 69]. Parasites can incorporate of 14C acetate into fatty acids, preferentially resulting in the production of C10-C14 fatty acyl chains [70]. Although the FAS II pathway is inactive in the blood stages, transcriptional analysis of a subset of blood stage parasites collected from P. falciparum-infected patients found upregulation of FAS II genes and an overall pattern of parasite starvation [71]. However, further investigation is warranted to determine the mechanistic basis of this observation [72]. Thus, analyzing both parasite and host plasma metabolomes could address questions related to parasite adaptation in a dynamic host environment.

High performance liquid chromatography experiments of parasite extracts have identified large amounts of hydroxy derivatives of a diverse group of polyenoic fatty acids [73]. This was an unexpected finding because the parasite lacks the critical lipooxygenase enzyme involved in initiating the formation of these hydroxyl fatty acids. However, it was shown that these hydroxy fatty acids could be generated in a non-enzymatic fashion during heme catabolism. These hydroxy fatty acids were found to be inhibitory to monocyte function, potentially playing a role in parasite-mediated host immune modulation [73]. These studies suggest that parasite metabolic pathways could play a crucial role in host-parasite interactions [73].

Isoprenoids represent one of the largest groups of biologically active lipid-based metabolites. They serve as prosthetic groups for many enzymes, and precursors to molecules involved in electron transport, and the formation of glycoproteins [74]. The main difference between the isoprenoid pathways of humans and malaria parasites is that the parasites use the methylerythritol pathway whereas humans use the mevalonate pathway [75]. Couto et al. demonstrated using precursors of the isoprenoid pathway, 3H-farnesyl pyrophosphate and 3H-geranylgeranyl pyrophosphate, and 14C-acetate, that P. falciparum has an active isoprenoid pathway [76]. Parasite octaprenyl pyrophosphate synthase activity, which is involved in isoprenoid precursor biosynthesis, was demonstrated by the formation of polyisoprenoids with eight isoprenic units [77].

Given that P. falciparum contains an apicoplast, a relic of the plastid, this group also investigated whether P. falciparum is able to produce carotenoids akin to that produced by the plastids of algae and plants. Carotenoids are derived from the isoprenoid biosynthesis pathway, and the carotenoid biosynthetic pathway is not present in mammalian cells. Using metabolic labeling with 3H and 14C radioisotopes, P. falciparum parasites were shown to possess an active carotenoid biosynthesis [78]. Carotenoids in plants mainly function as antioxidants to protect the lipophilic compartments from oxidative damage, as well as scavenge and neutralize singlet molecular oxygen and peroxyl radicals that are generated during photooxidative processes [78]. The identification of a carotenoid biosynthesis pathway in P. falciparum adds to the growing list of features that are similar to those in plants, and that could serve as potential targets for developing novel antimalarials [74, 79, 80].

5. Potential future research

Metabolomics of malaria is still in its infancy, but by drawing on concepts and themes developed in other areas, it could be promptly adapted to malaria research. The application of metabolomic analysis to malaria research is bound to provide tremendous insights into the intricacies of the malaria parasite's life cycle, transmission and pathophysiology.

5.1 Parasite and vector biology

Nearly 60% of the P. falciparum genome is unannotated, and a combinatorial analysis can be useful in uncovering encoded enzymatic functions. Correlative analysis of gene expression, proteomic and biochemical metabolic profiling is a powerful way to identify genes that encode enzymatic functions [81]. This can be performed either in a global whole-genome manner or focused on specific mutation(s) or loss-of-function(s), and studying the metabolomes generated from different parasite genotypes can provide insight into the underlying genetic basis of distinctiveness. For example, quantitative trait loci mapping is an attractive way to identify genetic contributors to differing levels of particular metabolites between various P. falciparum strains [82]. The metabolite levels serve as the equivalent of phenotypic indicators of the existence of underlying genetic contributors [82]. Witola et al. showed that the disruption of P. falciparum phosphoethanolamine methyltrasferase results in a complete loss of phosphatidylcholine biosynthesis by the serine decarboxylase-phosphoethanolamine methyltransferase pathway [83]. Global metabolomic analysis on such parasite lines could uncover the effect of loss-of-function on other metabolic pathways, and help build a network of regulatory interactions between those pathways. In Arabidopsis, comparing four different genotypes showed that each genotype possesses a unique metabolic profile and informed the physiology related to each set of genetic mutations [84].

Another ingenious way of investigating whether a protein functions as an enzyme is to express and purify the protein(s) of interest, and incubate it with metabolite extracts prepared from the biological system being studied. Proteins of unknown function in E. coli were cloned, expressed, purified, and incubated with a complex metabolite mixture [85]. Capillary electrophoresis and MS were then used to identify enzyme activity by looking for signature substrate-product combinations, as well as compounds whose levels had changed following incubation with the purified protein. This allowed assignment of enzymatic function to proteins that had previously been uncharacterized [85]. It should be noted that heterologous expression of malaria parasite proteins has been a major hurdle in performing structure-function studies [86].

Performing a targeted analysis by investigating specific organelles in the malaria parasites can be highly probative of biochemical composition and function in those compartments. Mitochondria, which retain their structural and functional integrity, have been successfully purified from yeast cells, free of contamination by other organelles [87]. In a fascinating study on the mitochondria of P. falciparum, it was shown that erythrocytic stage parasites maintain an active mitochondrial electron transport chain exclusively to regenerate ubiquinone, which is required as the electron acceptor for dihydroorotate dehydrogenase, an essential enzyme for pyrimidine biosynthesis [88]. Transgenic P. falciparum parasites expressing Saccharomyces cerevisiae dihydroorotate dehydrogenase, which does not require ubiquinone as an electron acceptor, were completely resistant to inhibitors of mitochondrial electron transport [88]. Expressing proteins from another organism in P. falciparum might trigger functions that would otherwise be hard to elicit in this parasite. Organelle purification and metabolomics have also been used to study the peroixomes, organelles that play a key role in redox signaling and lipid homeostasis [89].

Metabolomics offers a unique and powerful tool to explore the biology of the mosquito vectors and developing strategies to interrupt malaria transmission [90]. It was shown that xanthurenic acid (a derivative of tryptophan) and another anonymous factor play a critical role in exflagellation and ookinete formation in Plasmodium [91]. Interrupting the formation of such metabolites can help reduce malaria transmission, which appears to be an insurmountable burden at the present time. Mosquito entomological inoculation rates (and hence the rate of transmission) vary depending on the geographical location [92], and it estimated that in some African communities as much as a 100-fold reduction in inoculation rate would be necessary to achieve reductions in parasite prevalence by 30-70% [14].

5.2 Parasite-host interaction studies

A review of the “omics” approach used in studying plant stress response provides an ideal benchmark from which to gather ideas [93]. Particularly interesting is malarial response to temperature stress. Malaria parasites are exposed to a wide range of temperatures between their invertebrate and vertebrate hosts. Subjecting malaria parasites to temperature stress in vitro was shown to cause marked transcriptional changes in genes involved in several cellular functions including basic metabolism, lipid/fatty acid/isoprenoid metabolism, and encoding known transporters [94]. Monitoring parasite metabolome under such stress conditions could provide insights into the association between gene expression changes and biochemical alterations.

Malaria presents an enormous global heath and economic burden. Plasmodium falciparum infects people in all age groups, and patients present with varying levels of disease severity. Mechanisms causing variations in infection outcomes remains poorly understood. Patients with severe disease can present with respiratory distress, coma, abnormalities in blood coagulation, and vital organ dysfunction. The molecular and biochemical bases of disease in each of these clinical manifestations may vary, and analysis of the small molecule repertoire in such patients may shed light on disease etiology. For example, our group recently used a microarray-based approach to study parasites collected directly from patients and identified novel transcriptional states of P. falciparum in the human host that had previously not been known to exist [71]. One could hypothesize that these novel parasite transcriptional states could produce characteristic metabolic outputs, which in a genetic-metabolic feedback loop could in turn trigger particular transcriptional profiles.

Human populations carry genetic adaptations that have evolved in response to diverse climatic conditions, exposure to infectious diseases, and diets [95]. These factors are thus critical considerations in developing disease models using a combinatorial approach involving metabolomics, genomics, and proteomics. Metabolomics, for example, could be used to reveal the impact of dietary nutrients on the biochemistry of humans and their susceptibility/resistance to diseases [96, 97]. Moreover, current metabolomics techniques are highly sensitive requiring very small amounts of human plasma or tissue samples, thus making these kinds of analyses feasible.

Metabolomics has been implemented as a diagnostic tool in a number of human diseases and could also be applied to malaria [98]. For example, 1H-NMR spectra of human serum samples allowed >90% of subjects with significant coronary vessel blockages to be distinguished from subjects with angiographically-normal coronary arteries with a specificity of >90% [99]. In addition, metabolomics could potentially be used to detect organ-specific involvement in disease that could have prognostic or treatment implications in malaria. For example, cerebral malaria can be difficult to discern clinically in comatose malaria-infected children who have died of alternative causes [100]. Similarly, placental immunopathology, which causes anemia, low birth weight, and fetal loss due to infection at submicroscopic levels, can underestimate the incidence of placental malaria diagnosed using microscopic detection [101]. Metabolomics could potentially identify markers of such clinical conditions and improve clinical outcomes for these patients. Animal models for malaria afford us the opportunity to circumvent some of the problems associated with performing experiments on human subjects such as availability of tissue/organ samples and knockout animals [102]. Combining such metabolomic analysis with genomic and proteomic analyses can provide a comprehensive and correlative understanding of parasite biology.

Also, humans can be co-infected with multiple species of Plasmodium as well as other pathogens. In an interesting study in semi-immune children in Papua New Guinea infected with multiple species of Plasmodium, it was found that the total parasite density was maintained at a constant level [103]. This study also revealed that peaks of infection with each species occurred in a mutually exclusive manner. This was hypothesized to be due be a cross-species regulatory effect. It was proposed that the sequential episodes of infection were produced as a consequence of interactions between the density-dependent regulation of parasite levels and species-specific immune responses. However, the factor(s) though to be involved in this phenomenon has remained elusive till date [103, 104]. Metabolomics and proteomics, and the use of animal models, could prove useful in identifying the factor(s) that mediates this regulation, and assess whether or not immune regulatory mechanisms operate to bring about this effect [104-107].

6. Concluding remarks

Metabolomics has been a revelation in other fields. Several different approaches have been adopted, and a wealth of basic and clinically relevant information has been collected on other pathogens and clinical conditions. The successful application of metabolomics in other fields should provide us the impetus to adapt it to malaria research. This will enable us to unveil novel aspects of parasite and vector biology. It will also provide insights into the biochemistry of the host, and nature of host-parasite interactions during infection, and makes it feasible for us to discover species-specific biomarkers that might correlate with various clinical manifestations of malaria. Ultimately, it will foster our efforts towards designing improved antimalarials and better vector control measures, and discovering more effective vaccine candidates.

7. Acknowledgments

The authors thank Drs. Maria Belen Cassera (Department of Biochemistry, AECOM) and Claire Brown (Department of Medicine, AECOM) for insightful comments. We apologize to the authors whose work we were unable to cite due to space limitations. This work was funded by the National Institute of Allergy and Infectious Diseases (R01AI077623).

Footnotes

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8. References

1. Vinayavekhin N, Homan EA, Saghatelian A. Exploring disease through metabolomics. ACS Chem Biol. 2010;5:91–103. [PubMed]
2. Willger SD, Grahl N, Cramer RA., Jr. Aspergillus fumigatus metabolism: clues to mechanisms of in vivo fungal growth and virulence. Med Mycol. 2009;47(Suppl 1):S72–9. [PMC free article] [PubMed]
3. Slupsky CM, Cheypesh A, Chao DV, Fu H, Rankin KN, Marrie TJ, et al. Streptococcus pneumoniae and Staphylococcus aureus pneumonia induce distinct metabolic responses. J Proteome Res. 2009;8:3029–36. [PubMed]
4. Rodgers MA, Saghatelian A, Yang PL. Identification of an overabundant cholesterol precursor in hepatitis B virus replicating cells by untargeted lipid metabolite profiling. J Am Chem Soc. 2009;131:5030–1. [PMC free article] [PubMed]
5. Henderson JP, Crowley JR, Pinkner JS, Walker JN, Tsukayama P, Stamm WE, et al. Quantitative metabolomics reveals an epigenetic blueprint for iron acquisition in uropathogenic Escherichia coli. PLoS Pathog. 2009;5:e1000305. [PMC free article] [PubMed]
6. Bundy JG, Willey TL, Castell RS, Ellar DJ, Brindle KM. Discrimination of pathogenic clinical isolates and laboratory strains of Bacillus cereus by NMR-based metabolomic profiling. FEMS Microbiol Lett. 2005;242:127–36. [PubMed]
7. van Brummelen AC, Olszewski KL, Wilinski D, Llinas M, Louw AI, Birkholtz LM. Co-inhibition of Plasmodium falciparum S-adenosylmethionine decarboxylase/ornithine decarboxylase reveals perturbation-specific compensatory mechanisms by transcriptome, proteome, and metabolome analyses. J Biol Chem. 2009;284:4635–46. [PMC free article] [PubMed]
8. Lian LY, Al-Helal M, Roslaini AM, Fisher N, Bray PG, Ward SA, et al. Glycerol: an unexpected major metabolite of energy metabolism by the human malaria parasite. Malar J. 2009;8:38. [PMC free article] [PubMed]
9. Mehta M, Sonawat HM, Sharma S. Glycolysis in Plasmodium falciparum results in modulation of host enzyme activities. J Vector Borne Dis. 2006;43:95–103. [PubMed]
10. Olszewski KL, Morrisey JM, Wilinski D, Burns JM, Vaidya AB, Rabinowitz JD, et al. Host-parasite interactions revealed by Plasmodium falciparum metabolomics. Cell Host Microbe. 2009;5:191–9. [PMC free article] [PubMed]
11. Teng R, Junankar PR, Bubb WA, Rae C, Mercier P, Kirk K. Metabolite profiling of the intraerythrocytic malaria parasite Plasmodium falciparum by (1)H NMR spectroscopy. NMR Biomed. 2009;22:292–302. [PubMed]
12. Basant A, Rege M, Sharma S, Sonawat HM. Alterations in urine, serum and brain metabolomic profiles exhibit sexual dimorphism during malaria disease progression. Malar J. 2010;9:110. [PMC free article] [PubMed]
13. Kafsack BF, Llinas M. Eating at the table of another: metabolomics of host-parasite interactions. Cell Host Microbe. 2010;7:90–9. [PMC free article] [PubMed]
14. Wellems TE, Hayton K, Fairhurst RM. The impact of malaria parasitism: from corpuscles to communities. J Clin Invest. 2009;119:2496–505. [PMC free article] [PubMed]
15. Gardner MJ, Hall N, Fung E, White O, Berriman M, Hyman RW, et al. Genome sequence of the human malaria parasite Plasmodium falciparum. Nature. 2002;419:498–511. [PMC free article] [PubMed]
16. Florens L, Washburn MP, Raine JD, Anthony RM, Grainger M, Haynes JD, et al. A proteomic view of the Plasmodium falciparum life cycle. Nature. 2002;419:520–6. [PubMed]
17. Bozdech Z, Llinas M, Pulliam BL, Wong ED, Zhu J, DeRisi JL. The transcriptome of the intraerythrocytic developmental cycle of Plasmodium falciparum. PLoS Biol. 2003;1:E5. [PMC free article] [PubMed]
18. Chaudhuri R, Ahmed S, Ansari FA, Singh HV, Ramachandran S. MalVac: database of malarial vaccine candidates. Malar J. 2008;7:184. [PMC free article] [PubMed]
19. Hayes CN, Diez D, Joannin N, Honda W, Kanehisa M, Wahlgren M, et al. varDB: a pathogen-specific sequence database of protein families involved in antigenic variation. Bioinformatics. 2008;24:2564–5. [PMC free article] [PubMed]
20. Aurrecoechea C, Brestelli J, Brunk BP, Dommer J, Fischer S, Gajria B, et al. PlasmoDB: a functional genomic database for malaria parasites. Nucleic Acids Res. 2009;37:D539–43. [PMC free article] [PubMed]
21. Tan KC, Ipcho SV, Trengove RD, Oliver RP, Solomon PS. Assessing the impact of transcriptomics, proteomics and metabolomics on fungal phytopathology. Mol Plant Pathol. 2009;10:703–15. [PubMed]
22. Xiayan L, Legido-Quigley C. Advances in separation science applied to metabonomics. Electrophoresis. 2008;29:3724–36. [PubMed]
23. Dettmer K, Aronov PA, Hammock BD. Mass spectrometry-based metabolomics. Mass Spectrom Rev. 2007;26:51–78. [PMC free article] [PubMed]
24. Mashego MR, Rumbold K, De Mey M, Vandamme E, Soetaert W, Heijnen JJ. Microbial metabolomics: past, present and future methodologies. Biotechnol Lett. 2007;29:1–16. [PubMed]
25. Garcia DE, Baidoo EE, Benke PI, Pingitore F, Tang YJ, Villa S, et al. Separation and mass spectrometry in microbial metabolomics. Curr Opin Microbiol. 2008;11:233–9. [PubMed]
26. Zhang Y, A J, Wang G, Huang Q, Yan B, Zha W, et al. Organic solvent extraction and metabonomic profiling of the metabolites in erythrocytes. J Chromatogr B Analyt Technol Biomed Life Sci. 2009;877:1751–7. [PubMed]
27. van Gulik WM. Fast sampling for quantitative microbial metabolomics. Curr Opin Biotechnol. 2010;21:27–34. [PubMed]
28. T'Kindt R, Jankevics A, Scheltema RA, Zheng L, Watson DG, Dujardin JC, et al. Towards an unbiased metabolic profiling of protozoan parasites: optimisation of a Leishmania sampling protocol for HILIC-orbitrap analysis. Anal Bioanal Chem. 2010 [PubMed]
29. Want EJ, Nordstrom A, Morita H, Siuzdak G. From exogenous to endogenous: the inevitable imprint of mass spectrometry in metabolomics. J Proteome Res. 2007;6:459–68. [PubMed]
30. Idborg H, Zamani L, Edlund PO, Schuppe-Koistinen I, Jacobsson SP. Metabolic fingerprinting of rat urine by LC/MS Part 1. Analysis by hydrophilic interaction liquid chromatography-electrospray ionization mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 2005;828:9–13. [PubMed]
31. Nordstrom A, O'Maille G, Qin C, Siuzdak G. Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: quantitative analysis of endogenous and exogenous metabolites in human serum. Anal Chem. 2006;78:3289–95. [PMC free article] [PubMed]
32. Oh E, Hasan MN, Jamshed M, Park SH, Hong HM, Song EJ, et al. Growing trend of CE at the omics level: the frontier of systems biology. Electrophoresis. 2010;31:74–92. [PubMed]
33. Pan Z, Raftery D. Comparing and combining NMR spectroscopy and mass spectrometry in metabolomics. Anal Bioanal Chem. 2007;387:525–7. [PubMed]
34. Wishart DS. Computational approaches to metabolomics. Methods Mol Biol. 2010;593:283–313. [PubMed]
35. Crockford DJ, Holmes E, Lindon JC, Plumb RS, Zirah S, Bruce SJ, et al. Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: application in metabonomic toxicology studies. Anal Chem. 2006;78:363–71. [PubMed]
36. Ward JL, Baker JM, Beale MH. Recent applications of NMR spectroscopy in plant metabolomics. FEBS J. 2007;274:1126–31. [PubMed]
37. Bubb WA, Wright LC, Cagney M, Santangelo RT, Sorrell TC, Kuchel PW. Heteronuclear NMR studies of metabolites produced by Cryptococcus neoformans in culture media: identification of possible virulence factors. Magn Reson Med. 1999;42:442–53. [PubMed]
38. Adosraku RK, Anderson MM, Anderson GJ, Choi G, Croft SL, Yardley V, et al. Proton NMR lipid profile of Leishmania donovani promastigotes. Mol Biochem Parasitol. 1993;62:251–62. [PubMed]
39. Gupta N, Goyal N, Singha UK, Bhakuni V, Roy R, Rastogi AK. Characterization of intracellular metabolites of axenic amastigotes of Leishmania donovani by 1H NMR spectroscopy. Acta Trop. 1999;73:121–33. [PubMed]
40. Penin P, Sanchez-Moreno M, de Diego JA. Proton nuclear magnetic resonance analysis of metabolic end products of the Bolivia strain of Trypanosoma cruzi and three of its clones. Comp Biochem Physiol A Mol Integr Physiol. 1998;120:571–4. [PubMed]
41. Mehta M, Sonawat HM, Sharma S. Malaria parasite-infected erythrocytes inhibit glucose utilization in uninfected red cells. FEBS Lett. 2005;579:6151–8. [PubMed]
42. Deslauriers R, Ekiel I, Kroft T, Smith IC. NMR studies of malaria. 31P nuclear magnetic resonance of blood from mice infected with Plasmodium berghei. Biochim Biophys Acta. 1982;721:449–57. [PubMed]
43. Moreno B, Bailey BN, Luo S, Martin MB, Kuhlenschmidt M, Moreno SN, et al. (31)P NMR of apicomplexans and the effects of risedronate on Cryptosporidium parvum growth. Biochem Biophys Res Commun. 2001;284:632–7. [PubMed]
44. Mendoza M, Mijares A, Rojas H, Rodriguez JP, Urbina JA, DiPolo R. Physiological and morphological evidences for the presence acidocalcisomes in Trypanosoma evansi: single cell fluorescence and 31P NMR studies. Mol Biochem Parasitol. 2002;125:23–33. [PubMed]
45. Katajamaa M, Oresic M. Data processing for mass spectrometry-based metabolomics. J Chromatogr A. 2007;1158:318–28. [PubMed]
46. Novotny MV, Soini HA, Mechref Y. Biochemical individuality reflected in chromatographic, electrophoretic and mass-spectrometric profiles. J Chromatogr B Analyt Technol Biomed Life Sci. 2008;866:26–47. [PMC free article] [PubMed]
47. Wiklund S, Johansson E, Sjostrom L, Mellerowicz EJ, Edlund U, Shockcor JP, et al. Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models. Anal Chem. 2008;80:115–22. [PubMed]
48. Westerhuis JA, van Velzen EJ, Hoefsloot HC, Smilde AK. Multivariate paired data analysis: multilevel PLSDA versus OPLSDA. Metabolomics. 2010;6:119–28. [PMC free article] [PubMed]
49. Ginsburg H. Caveat emptor: limitations of the automated reconstruction of metabolic pathways in Plasmodium. Trends Parasitol. 2009;25:37–43. [PubMed]
50. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. [PMC free article] [PubMed]
51. Muller IB, Das Gupta R, Luersen K, Wrenger C, Walter RD. Assessing the polyamine metabolism of Plasmodium falciparum as chemotherapeutic target. Mol Biochem Parasitol. 2008;160:1–7. [PubMed]
52. Muller S, Coombs GH, Walter RD. Targeting polyamines of parasitic protozoa in chemotherapy. Trends Parasitol. 2001;17:242–9. [PubMed]
53. Kusano T, Berberich T, Tateda C, Takahashi Y. Polyamines: essential factors for growth and survival. Planta. 2008;228:367–81. [PubMed]
54. Clark K, Niemand J, Reeksting S, Smit S, van Brummelen AC, Williams M, et al. Functional consequences of perturbing polyamine metabolism in the malaria parasite, Plasmodium falciparum. Amino Acids. 2010;38:633–44. [PubMed]
55. Muller S, Da'dara A, Luersen K, Wrenger C, Das Gupta R, Madhubala R, et al. In the human malaria parasite Plasmodium falciparum, polyamines are synthesized by a bifunctional ornithine decarboxylase, S-adenosylmethionine decarboxylase. J Biol Chem. 2000;275:8097–102. [PubMed]
56. Becker JV, Mtwisha L, Crampton BG, Stoychev S, van Brummelen AC, Reeksting S, et al. Plasmodium falciparum spermidine synthase inhibition results in unique perturbation-specific effects observed on transcript, protein and metabolite levels. BMC Genomics. 2010;11:235. [PMC free article] [PubMed]
57. Krogstad DJ, Gluzman IY, Herwaldt BL, Schlesinger PH, Wellems TE. Energy dependence of chloroquine accumulation and chloroquine efflux in Plasmodium falciparum. Biochem Pharmacol. 1992;43:57–62. [PubMed]
58. Hawley SR, Bray PG, Park BK, Ward SA. Amodiaquine accumulation in Plasmodium falciparum as a possible explanation for its superior antimalarial activity over chloroquine. Mol Biochem Parasitol. 1996;80:15–25. [PubMed]
59. Olszewski KL, Mather MW, Morrisey JM, Garcia BA, Vaidya AB, Rabinowitz JD, et al. Branched tricarboxylic acid metabolism in Plasmodium falciparum. Nature. 2010;466:774–8. [PMC free article] [PubMed]
60. Olszewski KL, Llinas M. Central carbon metabolism of Plasmodium parasites. Mol Biochem Parasitol. 2010 [PMC free article] [PubMed]
61. Besteiro S, Vo Duy S, Perigaud C, Lefebvre-Tournier I, Vial HJ. Exploring metabolomic approaches to analyse phospholipid biosynthetic pathways in Plasmodium. Parasitology. 2010:1–14. [PubMed]
62. Allary M, Lu JZ, Zhu L, Prigge ST. Scavenging of the cofactor lipoate is essential for the survival of the malaria parasite Plasmodium falciparum. Mol Microbiol. 2007;63:1331–44. [PMC free article] [PubMed]
63. Debierre-Grockiego F, Rabi K, Schmidt J, Geyer H, Geyer R, Schwarz RT. Fatty acids isolated from Toxoplasma gondii reduce glycosylphosphatidylinositol-induced tumor necrosis factor alpha production through inhibition of the NF-kappaB signaling pathway. Infect Immun. 2007;75:2886–93. [PMC free article] [PubMed]
64. Krishnegowda G, Gowda DC. Intraerythrocytic Plasmodium falciparum incorporates extraneous fatty acids to its lipids without any structural modification. Mol Biochem Parasitol. 2003;132:55–8. [PubMed]
65. Mi-Ichi F, Kita K, Mitamura T. Intraerythrocytic Plasmodium falciparum utilize a broad range of serum-derived fatty acids with limited modification for their growth. Parasitology. 2006;133:399–410. [PubMed]
66. Grellier P, Rigomier D, Clavey V, Fruchart JC, Schrevel J. Lipid traffic between high density lipoproteins and Plasmodium falciparum-infected red blood cells. J Cell Biol. 1991;112:267–77. [PMC free article] [PubMed]
67. Lee PJ, Bhonsle JB, Gaona HW, Huddler DP, Heady TN, Kreishman-Deitrick M, et al. Targeting the fatty acid biosynthesis enzyme, beta-ketoacyl-acyl carrier protein synthase III (PfKASIII), in the identification of novel antimalarial agents. J Med Chem. 2009;52:952–63. [PMC free article] [PubMed]
68. Yu M, Kumar TR, Nkrumah LJ, Coppi A, Retzlaff S, Li CD, et al. The fatty acid biosynthesis enzyme FabI plays a key role in the development of liver-stage malarial parasites. Cell Host Microbe. 2008;4:567–78. [PMC free article] [PubMed]
69. Vaughan AM, O'Neill MT, Tarun AS, Camargo N, Phuong TM, Aly AS, et al. Type II fatty acid synthesis is essential only for malaria parasite late liver stage development. Cell Microbiol. 2009;11:506–20. [PMC free article] [PubMed]
70. Surolia N, Surolia A. Triclosan offers protection against blood stages of malaria by inhibiting enoyl-ACP reductase of Plasmodium falciparum. Nat Med. 2001;7:167–73. [PubMed]
71. Daily JP, Scanfeld D, Pochet N, Le Roch K, Plouffe D, Kamal M, et al. Distinct physiological states of Plasmodium falciparum in malaria-infected patients. Nature. 2007;450:1091–5. [PubMed]
72. Lemieux JE, Gomez-Escobar N, Feller A, Carret C, Amambua-Ngwa A, Pinches R, et al. Statistical estimation of cell-cycle progression and lineage commitment in Plasmodium falciparum reveals a homogeneous pattern of transcription in ex vivo culture. Proc Natl Acad Sci U S A. 2009;106:7559–64. [PMC free article] [PubMed]
73. Schwarzer E, Kuhn H, Valente E, Arese P. Malaria-parasitized erythrocytes and hemozoin nonenzymatically generate large amounts of hydroxy fatty acids that inhibit monocyte functions. Blood. 2003;101:722–8. [PubMed]
74. Lim L, McFadden GI. The evolution, metabolism and functions of the apicoplast. Philos Trans R Soc Lond B Biol Sci. 2010;365:749–63. [PMC free article] [PubMed]
75. Cassera MB, Gozzo FC, D'Alexandri FL, Merino EF, del Portillo HA, Peres VJ, et al. The methylerythritol phosphate pathway is functionally active in all intraerythrocytic stages of Plasmodium falciparum. J Biol Chem. 2004;279:51749–59. [PubMed]
76. Couto AS, Kimura EA, Peres VJ, Uhrig ML, Katzin AM. Active isoprenoid pathway in the intra-erythrocytic stages of Plasmodium falciparum: presence of dolichols of 11 and 12 isoprene units. Biochem J. 1999;341(Pt 3):629–37. [PMC free article] [PubMed]
77. Tonhosolo R, D'Alexandri FL, Genta FA, Wunderlich G, Gozzo FC, Eberlin MN, et al. Identification, molecular cloning and functional characterization of an octaprenyl pyrophosphate synthase in intra-erythrocytic stages of Plasmodium falciparum. Biochem J. 2005;392:117–26. [PMC free article] [PubMed]
78. Tonhosolo R, D'Alexandri FL, de Rosso VV, Gazarini ML, Matsumura MY, Peres VJ, et al. Carotenoid biosynthesis in intraerythrocytic stages of Plasmodium falciparum. J Biol Chem. 2009;284:9974–85. [PMC free article] [PubMed]
79. Balaji S, Babu MM, Iyer LM, Aravind L. Discovery of the principal specific transcription factors of Apicomplexa and their implication for the evolution of the AP2-integrase DNA binding domains. Nucleic Acids Res. 2005;33:3994–4006. [PMC free article] [PubMed]
80. Maughan SC, Pasternak M, Cairns N, Kiddle G, Brach T, Jarvis R, et al. Plant homologs of the Plasmodium falciparum chloroquine-resistance transporter, PfCRT, are required for glutathione homeostasis and stress responses. Proc Natl Acad Sci U S A. 2010;107:2331–6. [PMC free article] [PubMed]
81. Fridman E, Pichersky E. Metabolomics, genomics, proteomics, and the identification of enzymes and their substrates and products. Curr Opin Plant Biol. 2005;8:242–8. [PubMed]
82. Huang Y, Wuchty S, Ferdig MT, Przytycka TM. Graph theoretical approach to study eQTL: a case study of Plasmodium falciparum. Bioinformatics. 2009;25:i15–20. [PMC free article] [PubMed]
83. Witola WH, El Bissati K, Pessi G, Xie C, Roepe PD, Mamoun CB. Disruption of the Plasmodium falciparum PfPMT gene results in a complete loss of phosphatidylcholine biosynthesis via the serine-decarboxylase-phosphoethanolamine-methyltransferase pathway and severe growth and survival defects. J Biol Chem. 2008;283:27636–43. [PMC free article] [PubMed]
84. Fiehn O, Kopka J, Dormann P, Altmann T, Trethewey RN, Willmitzer L. Metabolite profiling for plant functional genomics. Nat Biotechnol. 2000;18:1157–61. [PubMed]
85. Saito N, Robert M, Kitamura S, Baran R, Soga T, Mori H, et al. Metabolomics approach for enzyme discovery. J Proteome Res. 2006;5:1979–87. [PubMed]
86. Birkholtz LM, Blatch G, Coetzer TL, Hoppe HC, Human E, Morris EJ, et al. Heterologous expression of plasmodial proteins for structural studies and functional annotation. Malar J. 2008;7:197. [PMC free article] [PubMed]
87. Gregg C, Kyryakov P, Titorenko VI. Purification of mitochondria from yeast cells. J Vis Exp. 2009 [PMC free article] [PubMed]
88. Painter HJ, Morrisey JM, Mather MW, Vaidya AB. Specific role of mitochondrial electron transport in blood-stage Plasmodium falciparum. Nature. 2007;446:88–91. [PubMed]
89. Schluter A, Real-Chicharro A, Gabaldon T, Sanchez-Jimenez F, Pujol A. PeroxisomeDB 2.0: an integrative view of the global peroxisomal metabolome. Nucleic Acids Res. 2010;38:D800–5. [PMC free article] [PubMed]
90. Tittiger C. Functional genomics and insect chemical ecology. J Chem Ecol. 2004;30:2335–58. [PubMed]
91. Arai M, Billker O, Morris HR, Panico M, Delcroix M, Dixon D, et al. Both mosquito-derived xanthurenic acid and a host blood-derived factor regulate gametogenesis of Plasmodium in the midgut of the mosquito. Mol Biochem Parasitol. 2001;116:17–24. [PubMed]
92. Greenwood B, Marsh K, Snow R. Why do some African children develop severe malaria? Parasitol Today. 1991;7:277–81. [PubMed]
93. Shulaev V, Cortes D, Miller G, Mittler R. Metabolomics for plant stress response. Physiol Plant. 2008;132:199–208. [PubMed]
94. Oakley MS, Kumar S, Anantharaman V, Zheng H, Mahajan B, Haynes JD, et al. Molecular factors and biochemical pathways induced by febrile temperature in intraerythrocytic Plasmodium falciparum parasites. Infect Immun. 2007;75:2012–25. [PMC free article] [PubMed]
95. Campbell MC, Tishkoff SA. African genetic diversity: implications for human demographic history, modern human origins, and complex disease mapping. Annu Rev Genomics Hum Genet. 2008;9:403–33. [PMC free article] [PubMed]
96. Whitfield PD, German AJ, Noble PJ. Metabolomics: an emerging post-genomic tool for nutrition. Br J Nutr. 2004;92:549–55. [PubMed]
97. Gibney MJ, Walsh M, Brennan L, Roche HM, German B, van Ommen B. Metabolomics in human nutrition: opportunities and challenges. Am J Clin Nutr. 2005;82:497–503. [PubMed]
98. Mendrick DL, Schnackenberg L. Genomic and metabolomic advances in the identification of disease and adverse event biomarkers. Biomark Med. 2009;3:605–15. [PubMed]
99. Brindle JT, Antti H, Holmes E, Tranter G, Nicholson JK, Bethell HW, et al. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat Med. 2002;8:1439–44. [PubMed]
100. Taylor TE, Fu WJ, Carr RA, Whitten RO, Mueller JS, Fosiko NG, et al. Differentiating the pathologies of cerebral malaria by postmortem parasite counts. Nat Med. 2004;10:143–5. [PubMed]
101. Mockenhaupt FP, Ulmen U, von Gaertner C, Bedu-Addo G, Bienzle U. Diagnosis of placental malaria. J Clin Microbiol. 2002;40:306–8. [PMC free article] [PubMed]
102. Griffin JL. Understanding mouse models of disease through metabolomics. Curr Opin Chem Biol. 2006;10:309–15. [PubMed]
103. Bruce MC, Donnelly CA, Alpers MP, Galinski MR, Barnwell JW, Walliker D, et al. Cross-species interactions between malaria parasites in humans. Science. 2000;287:845–8. [PubMed]
104. Bruce MC, Day KP. Cross-species regulation of Plasmodium parasitemia in semi-immune children from Papua New Guinea. Trends Parasitol. 2003;19:271–7. [PubMed]
105. Ellis DI, Dunn WB, Griffin JL, Allwood JW, Goodacre R. Metabolic fingerprinting as a diagnostic tool. Pharmacogenomics. 2007;8:1243–66. [PubMed]
106. Hu C, van der Heijden R, Wang M, van der Greef J, Hankemeier T, Xu G. Analytical strategies in lipidomics and applications in disease biomarker discovery. J Chromatogr B Analyt Technol Biomed Life Sci. 2009;877:2836–46. [PubMed]
107. Kimura T, Noguchi Y, Shikata N, Takahashi M. Plasma amino acid analysis for diagnosis and amino acid-based metabolic networks. Curr Opin Clin Nutr Metab Care. 2009;12:49–53. [PubMed]
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