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• W3146580645 abstract "Article6 April 2021Open Access Source DataTransparent process Non-canonical metabolic pathways in the malaria parasite detected by isotope-tracing metabolomics Simon A Cobbold Simon A Cobbold orcid.org/0000-0002-9927-8998 Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia Search for more papers by this author Madel V Tutor Madel V Tutor Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., AustraliaThese authors contributed equally to this work Search for more papers by this author Philip Frasse Philip Frasse orcid.org/0000-0002-5529-3280 Department of Medicine, Washington University School of Medicine, St. Louis, MO, USAThese authors contributed equally to this work Search for more papers by this author Emma McHugh Emma McHugh Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia Search for more papers by this author Markus Karnthaler Markus Karnthaler Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia Search for more papers by this author Darren J Creek Darren J Creek Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Vic., Australia Search for more papers by this author Audrey Odom John Audrey Odom John orcid.org/0000-0001-8395-8537 The Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA Search for more papers by this author Leann Tilley Leann Tilley orcid.org/0000-0001-9910-0199 Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia Search for more papers by this author Stuart A Ralph Stuart A Ralph orcid.org/0000-0003-0114-7808 Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia Search for more papers by this author Malcolm J McConville Corresponding Author Malcolm J McConville [email protected] orcid.org/0000-0002-7107-7887 Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia Search for more papers by this author Simon A Cobbold Simon A Cobbold orcid.org/0000-0002-9927-8998 Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia Search for more papers by this author Madel V Tutor Madel V Tutor Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., AustraliaThese authors contributed equally to this work Search for more papers by this author Philip Frasse Philip Frasse orcid.org/0000-0002-5529-3280 Department of Medicine, Washington University School of Medicine, St. Louis, MO, USAThese authors contributed equally to this work Search for more papers by this author Emma McHugh Emma McHugh Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia Search for more papers by this author Markus Karnthaler Markus Karnthaler Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia Search for more papers by this author Darren J Creek Darren J Creek Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Vic., Australia Search for more papers by this author Audrey Odom John Audrey Odom John orcid.org/0000-0001-8395-8537 The Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA Search for more papers by this author Leann Tilley Leann Tilley orcid.org/0000-0001-9910-0199 Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia Search for more papers by this author Stuart A Ralph Stuart A Ralph orcid.org/0000-0003-0114-7808 Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia Search for more papers by this author Malcolm J McConville Corresponding Author Malcolm J McConville [email protected] orcid.org/0000-0002-7107-7887 Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia Search for more papers by this author Author Information Simon A Cobbold1, Madel V Tutor1, Philip Frasse2, Emma McHugh1, Markus Karnthaler1, Darren J Creek3, Audrey Odom John4, Leann Tilley1, Stuart A Ralph1 and Malcolm J McConville *,1 1Department of Biochemistry and Molecular Biology, Bio21 Institute of Molecular Science and Biotechnology, University of Melbourne, Parkville, Vic., Australia 2Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA 3Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Vic., Australia 4The Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA *Corresponding author. Tel: +61 3 83442342; E-mail: [email protected] Molecular Systems Biology (2021)17:e10023https://doi.org/10.15252/msb.202010023 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The malaria parasite, Plasmodium falciparum, proliferates rapidly in human erythrocytes by actively scavenging multiple carbon sources and essential nutrients from its host cell. However, a global overview of the metabolic capacity of intraerythrocytic stages is missing. Using multiplex 13C-labelling coupled with untargeted mass spectrometry and unsupervised isotopologue grouping, we have generated a draft metabolome of P. falciparum and its host erythrocyte consisting of 911 and 577 metabolites, respectively, corresponding to 41% of metabolites and over 70% of the metabolic reaction predicted from the parasite genome. An additional 89 metabolites and 92 reactions were identified that were not predicted from genomic reconstructions, with the largest group being associated with metabolite damage-repair systems. Validation of the draft metabolome revealed four previously uncharacterised enzymes which impact isoprenoid biosynthesis, lipid homeostasis and mitochondrial metabolism and are necessary for parasite development and proliferation. This study defines the metabolic fate of multiple carbon sources in P. falciparum, and highlights the activity of metabolite repair pathways in these rapidly growing parasite stages, opening new avenues for drug discovery. Synopsis Multiplex stable-isotope labelling defines the observable metabolome of red blood cell stages of the malaria parasite Plasmodium falciparum and indicates potential roles for metabolic enzymes of unknown function. A draft metabolome of P. falciparum and its host erythrocyte is presented, consisting of 911 and 577 metabolites respectively. The metabolome covers 70% of predicted metabolic reactions. 92 unpredicted reactions are identified, the largest group associated with metabolite damage repair. Mediators of isoprenoid biosynthesis, lipid homeostasis, and mitochondrial metabolism are identified. Introduction Considerable progress has been made in reducing the incidence of malaria over the last decade, although the decline in malaria cases has stalled in recent years and resistance to frontline antimalarials is on the rise (WHO, 2019). Identifying new antimalarials with novel targets therefore remains a priority, and significant investment has been made in expanding the drug development pipeline with novel classes of antimalarials (Antonova-Koch et al, 2018; Hooft van Huijsduijnen & Wells, 2018). Metabolic enzymes and metabolite transporters are direct or indirect targets of most of the existing antimalarials and current lead compounds (Cowell et al, 2018; Ross & Fidock, 2019). However, the total number of enzymes/transporters that have been rigorously validated as drug targets remains small. A detailed understanding of the metabolism of the different developmental stages of the malaria parasite, Plasmodium falciparum, and the host cells within which they live is therefore necessary for informing the development of new antimalarial therapies. Plasmodium falciparum progresses through a number of different developmental stages during its life cycle in the Anopheles mosquito and its human host. Infection in humans is initiated by infective sporozoites that develop asymptomatically in the liver, resulting in the release of thousands of merozoites that initiate repeated cycles of infection and asexual replication in erythrocytes (i.e. red blood cells (RBCs)) that cause the clinical symptoms associated with malaria. The P. falciparum intraerythrocytic developmental cycle (IDC) takes approximately 48 h and involves the development of the metabolically-active trophozoite and schizont stages, followed by cell division of individual parasites into 16–32 new merozoites. The massive expansion of parasite biomass during development is fuelled by the uptake and catabolism of glucose, as well as a number of other essential nutrients (e.g. amino acids, purines and vitamins) that are either directly scavenged from the RBC or derived from breakdown of RBC haemoglobin and other proteins (Roth, 1990; Atamna et al, 1994; Liu et al, 2006; Olszewski et al, 2009). Considerable progress has been made in delineating key salvage and metabolic pathways involved in P. falciparum asexual development, which has formed the basis for genome-scale models of parasite metabolism (Fatumo et al, 2009; Plata et al, 2010; Bazzani et al, 2012; Tymoshenko et al, 2013). Despite these advances, > 40% of the protein-encoding genome remains unannotated and a significant fraction of annotated metabolic genes have yet to be assigned to specific metabolic pathways or reactions. In the phylum Apicomplexa, many genes have also been repurposed to fulfil non-canonical functions, impeding genomic reconstructions and a systematic understanding of the total metabolic capacity of the pathogen (Oppenheim et al, 2014; Ke et al, 2015). Finally, enzyme promiscuity and side reactions can result in the production of unanticipated and novel metabolites that can have important roles in regulating cellular metabolism (Linster et al, 2013; Bommer et al, 2019; Dumont et al, 2019), further complicating predictions of enzyme function based on gene homology. Defining the observable metabolic capacity of key developmental stages of P. falciparum and its host cell is required to verify the accuracy of genomic reconstructions and to identify unexpected metabolic pathways and gene functions. A number of approaches have been used to undertake a global analysis of the metabolic capacity of other organisms. For example, a system-wide reverse genetics approach was used to identify the metabolic function or indirect metabolic impact of each gene within E. coli (Fuhrer et al, 2017). The emergence of genome-wide disruption libraries in P. falciparum and P. berghei makes this approach theoretically possible (Bushell et al, 2017; Zhang et al, 2018). However, these studies have highlighted the essentiality of many metabolic enzymes in Plasmodium spp., limiting the effectiveness of this approach. Even viable but slow-growing mutants generated through such approaches are likely to be difficult to compare directly to parental parasites at a metabolic level. The converse approach – acquiring untargeted mass spectrometry data and verifying the “observed” metabolome – has not yet been fully exploited because of the lack of well-established pipelines for data filtering and metabolite identification. Current liquid chromatography–mass spectrometry platforms allow detection of > 10,000 mass-to-charge (m/z) features, yet a significant majority (> 90%) of these features correspond to background noise or degeneracy (Creek et al, 2011; Mahieu & Patti, 2017; Wang et al, 2019). The absence of autonomous methods for controlling the false discovery rate has hampered the compilation of an accurate metabolome for most organisms to date. Here we use stable-isotope resolved metabolomics to prioritise m/z features corresponding to metabolites actively synthesised by P. falciparum or the host RBC (Huang et al, 2014; Sevin et al, 2017). Previous work has demonstrated the ability of this approach to define the extent of active 13C-glucose metabolism in RBCs (Srivastava et al, 2017), and here we expand this approach to ten biologically relevant 13C-substrates in P. falciparum-infected RBCs. Filtering for actively-labelled metabolites enabled > 95% of m/z features to be removed, and the remaining m/z features were then identified and the active metabolome defined. This approach led to the identification of 577 metabolites in uninfected human RBCs and 911 metabolites in P. falciparum-infected RBCs corresponding to 41% coverage across the predicted metabolome of P. falciparum (the summation of all expected metabolites from all known pathways inferred from a genomic reconstruction irrespective of enzyme gaps). The pattern of stable-isotope labelling for each metabolite allowed us to further infer metabolic reactions corresponding to 70.5% coverage across predicted reactions in P. falciparum, with the mis-match between metabolite and reaction coverage largely due to a subset of metabolites participating in many reactions. Defining the “observed” metabolome without constraining the results to the expected composition inferred from genomic reconstructions revealed 89 metabolites and 92 reactions not predicted from genomic reconstructions. These studies have highlighted unanticipated complexity in P. falciparum metabolism, including the presence of active metabolite damage and repair systems in rapidly dividing parasite stages. Results Global stable-isotope labelling filters for metabolites actively synthesised in uninfected and P. falciparum-infected human erythrocytes Plasmodium falciparum trophozoite-infected red blood cells (iRBCs) or uninfected RBCs (uRBCs) were metabolically labelled for 5 h in parallel cultures containing different 13C-labelled compounds. Metabolites were extracted and analysed in parallel by GC-MS, LC-MS polar and LC-MS apolar analytical platforms to maximise coverage of different metabolites classes. All mass-to-charge (m/z) features were extracted and untargeted isotopologue grouping performed to identify m/z features that correspond to metabolites that were differentially labelled between iRBCs and uRBCs (Fig 1A). Metabolites were provisionally identified based on METLIN database matching and their identities subsequently confirmed based on comparison with authentic standards, MS/MS matching, stable-isotope incorporation pattern and exact mass. The resulting list of metabolites and their respective labelling patterns were compiled into the “observed” metabolome of P. falciparum at the trophozoite stage (Dataset EV1). Figure 1. Strategy for defining the metabolic capacity of P. falciparum-infected red blood cells (iRBCs) and uninfected red blood cells (uRBCs) using untargeted stable-isotope labelling Purified trophozoite-stage iRBCs and matched uRBCs were labelled with one of ten 13C-substrates (listed in purple). All mass-to-charge (m/z) features identified by LC-MS were extracted and isotopologues grouped. 13C-labelled and 12C-labelled isotopologue groups were compared to identify m/z features in each cell type and that were differentially labelled between iRBC and uRBC. Putative metabolite identities were then confirmed with authentic standards, MS/MS spectral matching, 13C-labelling profile and exact mass. The observed metabolic network of each cell type was then constructed using the draft metabolome and 13C-labelling information and compared to the predicted P. falciparum metabolome reported by Huthmacher et al, (2010). All m/z features detected from iRBC extracts plotted as the log2 ratio of their abundance detected in 13C-glucose versus 12C-glucose conditions. 859 m/z features (from a total 33,691 m/z features identified) were significantly altered in 13C versus 12C samples of iRBC. Highlighted in black is a single m/z feature (m/z 275.0167). The 859 significant m/z features (x-axis; putative M0 species) were ranked by their fractional enrichment (y-axis) and the observed mass queried against the METLIN metabolite database. m/z features that returned a hit were classified as presumptive features (blue) and retained for validation. Presumptive m/z features displayed a wide range of fractional enrichments, indicating no systematic bias. Changes in the isotopologue group distribution in 12C-glucose and 13C-glucose labelled samples of m/z 275.0167. Data are presented as the mean 13C-fractional enrichment ± SEM from six biological replicates. MS/MS fragmentation of m/z 275.0167 matched the reference spectrum of 6-phosphogluconate, consistent with the exact mass and labelling pattern, confirming the metabolite identity. This approach was repeated for all presumptive m/z features. Download figure Download PowerPoint As an example of the workflow, polar LC-MS analysis of 13C-glucose labelled iRBC extracts revealed that 859 of the original 33,691 m/z features detected in unlabelled iRBCs exhibited decreased intensity following 13C-glucose labelling (Fig 1B), indicating that they likely correspond to mono-isotopic masses (i.e. the unlabelled species) that decrease as metabolites become enriched for 13C atoms. These m/z features were then ranked by fractional enrichment and the exact mass of the unlabelled feature queried against the METLIN metabolite database (Fig 1C). Of the 859 m/z features, 410 returned putative matches within a 10 ppm tolerance for M-H precursors and are annotated as “presumptive” features. Presumptive features exhibited a broad range of fractional enrichments, ranging from 0.01 to 0.999, indicating no significant bias in annotating putatively labelled metabolites via stable-isotope enrichment. Highlighted is the presumptive feature m/z 275.0167 and its isotopologue group (Fig 1D), matching to three possible metabolites (2-carboxyarabinitol 5-phosphate, 2-carboxyarabinitol 1-phosphate and 6-phosphogluconate; all Δ2 ppm). MS/MS spectral matching, together with the predominance of +6 mass isotopomer in 13C-glucose labelled cells, confirmed the identity of this metabolite to be 6-phosphogluconate (Fig 1E). This procedure resulted in the identification of 232 polar metabolites that were significantly labelled with 13C-glucose and was repeated for all labelled substrates and MS platforms. The observed metabolome of uninfected and P. falciparum-infected human erythrocytes The consolidated list of all 13C-labelled metabolites detected on the three MS platforms was then incorporated into the observable metabolome for uRBC and iRBC (Fig 2A). To capture additional metabolites that were not labelled with any of the 13C-substrates tested, all m/z features from unlabelled uRBC and iRBC extracts were compared with the expected exact mass of all metabolites in the predicted P. falciparum and RBC metabolomes (Huthmacher et al, 2010). Putative matches were then verified as described above. The metabolic network reconstruction reported by Huthmacher and colleagues contains predictions of both host and parasite metabolic activity, incorporates literature and manual curation for higher accuracy predictions, and is structured for matching enzyme classification reactions and PlasmoDB IDs. The reconstruction contains 566 metabolites and 349 reactions in human erythrocytes and 1,622 metabolites and 998 reactions in P. falciparum (Fig 2A). Figure 2. The observable metabolome of human uninfected RBCs and RBCs infected with P. falciparum The total observed and predicted metabolome of uRBC and iRBC. The total number of metabolites and those with a unique KEGG identification number are reported. Numbers in parentheses refer to unmatched metabolites that were predicted from the genomic reconstruction but not observed or observed metabolites that were not predicted. The total number of metabolites common to both predicted and observed lists (percentage in parentheses) is referred to as “matched”. An UpSet plot summarising the overlap of metabolites present in the predicted and observable metabolomes across uRBCs and iRBCs, with the inset depicting the metabolite size of each cell type. Metabolic pathway enrichment analysis for the observed metabolome of uRBCs and iRBCs. Unique KEGG ID metabolites were queried against the Small Molecular Pathway Database with −log10 (Holm P) reported for the number of metabolite hits reported for each pathway. Extracted ion chromatogram of pyridoxine-P, one of five metabolites detected exclusively in uRBCs. X-axis corresponds to retention time (RT), and y-axis corresponds to arbitrary ion intensity of the extracted peak. The inset indicates the integrated peak area for each biological replicate. ND indicates “not detected”. Extracted ion chromatogram of glycero-P-glycerol, an observed metabolite that was not predicted from genomic reconstructions and is significantly elevated in iRBC compared to uRBC. X-axis corresponds to retention time (RT), and y-axis corresponds to arbitrary ion intensity of the extracted peak. Inset indicates the integrated peak area across each cell type and biological replicate. The abundance of all detected polar metabolites was compared between iRBCs and uRBCs and presented as the log2 fold change (iRBC/uRBC) with respect to the −log10P. Significance cut-off was set with −log10 (0.05) + c/(x − x0). Each data point represents a single metabolite, and the insets depict the extracted ion chromatograms for two metabolites that were either elevated (acetyl-CoA) or decreased (fructose-1,6-bisphosphate) in iRBC relative to uRBC. Download figure Download PowerPoint Following manual curation and verification, we compiled an “observable” metabolome of iRBCs and uRBCs, which comprised 911 and 577 metabolites, respectively. All metabolites were collapsed into unique KEGG IDs and compared to the predicted metabolome of each cell type (396 and 299 metabolites for iRBC and uRBC, respectively). 255 observed metabolites (with unique KEGG IDs) matched to the predicted metabolome of iRBCs, corresponding to 41% coverage of the predicted metabolome, whereas 152 observed metabolites matched the predicted metabolome of uRBCs (36.3% coverage). Core metabolic pathways were statistically over-represented in the observable metabolomes of both iRBC and uRBC (Fig 2B), with consistently higher metabolite coverage across each pathway for iRBC (Dataset EV2). Metabolites predicted from genome annotations but not observed in iRBC samples could reflect: (i) incorrect annotation of the genome (e.g. the annotated branched-chain amino acid degradation pathway is likely missing and the sole annotated enzyme in the pathway, BCKDH, is known to fulfil an alternative function; Oppenheim et al, 2014), (ii) down-regulation of the metabolic pathway during trophozoite development (e.g. de novo fatty acid biosynthesis is known to be down-regulated in the presence of exogenous fatty acids in the intraerythrocytic stages; Yu et al, 2008) or (iii) technical issues such as low metabolite abundance, sequestration by host/parasite proteins or incompatibility with the applied extraction or MS methods for detection (e.g. haem and ubiquinone biosynthesis). Pathway enrichment analysis of predicted metabolites that were not observed yielded no statistically-enriched pathways (Dataset EV2). We were interested in defining which metabolites were uniquely detected in iRBCs or uRBCs. iRBCs contained 102 unique KEGG IDs corresponding to 339 metabolites that were not observed in uRBCs, with phospholipid and CoA biosynthetic pathways most enriched (unadjusted P = 0.017 and 0.033, respectively). These pathways are not active in human RBCs, but are required for the rapid growth of P. falciparum asexual stages (Fig 2B). Interestingly, five metabolites were only detected in uRBCs. These included N-acetylmannosamine, sucrose, 5-formyl-tetrahydrofolate, glucosamine and pyridoxine-5-P. Pyridoxine-5-P is an essential vitamin/cofactor required for the activity of multiple enzymes in both cell types. The absence of detectable pyridoxine-5-P in iRBCs may reflect the sequestration of this cofactor by parasite enzymes (Fig 2C). A significant number of observed metabolites did not match to the predicted metabolomes of iRBCs and uRBCs (141 and 147, respectively). These unpredicted metabolites did not statistically over-represent any conventional metabolic pathways (Dataset EV2). For example, 13C-glucose incorporation was observed into the non-canonical glycolytic metabolites, glycero-P-glycerol and acetyl-P in iRBC, along with incorporation into canonical glycolytic and pentose phosphate pathway (PPP) intermediates (Appendix Fig S1). The identification of 13C-incorporation into unpredicted metabolites highlighted the presence of unanticipated enzyme activities. Strikingly, many of the observed metabolites not predicted from genomic reconstructions corresponded to non-canonical metabolites generated by enzyme side reactions or “damaged” metabolites generated by non-enzymatic processes (i.e. oxidation of methionine to methionine sulphoxide; Dataset EV2). Examples of the former include P-lactate and 4-P-erythronate that are formed when enzymes consume their non-preferred substrate (Dumont et al, 2019). Glycero-P-glycerol is another non-canonical metabolite that is formed during lipid biosynthesis (Fig 2D) and correlates with high glycolytic flux (Hutschenreuther et al, 2013). We sought to explore in more detail how parasite infection leads to changes in host cell metabolism by comparing the metabolite pool sizes between each cell type (Fig 2E). Metabolites associated with nucleotide biosynthesis, arginine metabolism and phospholipid production were all significantly elevated in iRBCs compared to uRBCs (Dataset EV3), consistent with high rates of synthesis of these metabolites and the need to accumulate biomass during parasite development. However, amino acids and purines are maintained at comparable levels in both cell types, while intermediates in both glycolysis and PPP intermediates were significantly reduced in iRBC (Appendix Fig S3). Glycolytic and PPP flux is increased up to 100-fold and 78-fold, respectively, in trophozoite stage-infected RBCs (Roth, 1990; Atamna et al, 1994), highlighting the lack of correlation between metabolite abundance and corresponding metabolic fluxes. The reduced pool size of glycolytic/PPP intermediates in iRBC may increase the sensitivity of parasite pathways to subtle changes in exogenous glucose levels by aligning substrate levels more closely to the respective km’s of rate-controlling enzymes (Bennett et al, 2009; Park et al, 2019). The metabolic activity network of P. falciparum reveals a plethora of metabolic damage-repair systems To further define the unpredicted metabolic activity of P. falciparum and the host RBC, all metabolites labelled with the different 13C-substrates were mapped to pathways to identify all observable reactions from both iRBC and uRBC. In all cases, the number of metabolites labelled with each tracer was higher in iRBCs compared to uRBCs, as was the complexity of the corresponding sub-networks (Fig 3A). Strikingly, the majority of the detectable metabolome of uRBCs was unlabelled, consistent with the loss of many enzymes and metabolic pathways in mature erythrocytes (Srivastava et al, 2017). In contrast, most metabolites detected in iRBC were labelled with one or more 13C-substrates (Dataset EV1), indicating a high level of redundancy and metabolic complexity in these intracellular parasite stages. Metabolites in iRBC that did not label with any 13C-substrate tested (183 compounds) mainly consisted of vitamins (e.g. pyridoxine and riboflavin), purines and specific lipid classes (e.g. sphingomyelins) for which the parasite is known to be auxotrophic and dependent on salvage from the host cell or media in the case of ex vivo culture. Figure 3. Reconstruction of the active metabolic networks in P. falciparum trophozoite stages The sum of metabolites labelled with each 13C-substrate in iRBCs and uRBCs. Dark grey indicates metabolites that were 13C-labelled in both cell types, whereas light grey represents metabolites 13C-labelled in either iRBCs or uRBCs. The number of predicted metabolic reactions in each cell type, the number of reactions with an observed substrate, an observed product, and the number of predicted reactions with no observed substrate or product (unobserved). The same analysis is reproduced following removal of the top ten cofactors present in the observed metabolome. Predicted reactions were deconstructed into individual metabolites and were ranked according to their frequency across all predicted reactions. A small subset of metabolites participate in a large number of reactions" @default.
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• W3146580645 title "Non‐canonical metabolic pathways in the malaria parasite detected by isotope‐tracing metabolomics" @default.
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