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• W2983607589 abstract "•1,342 barcoded P. berghei knockout (KO) mutants analyzed for stage-specific phenotypes•Life-stage-specific metabolic models reveal reprogramming of cellular function•High agreement between blood/liver stage metabolic models and genetic screening data•Essential metabolic pathways for parasite development and mechanistic origin revealed Plasmodium gene functions in mosquito and liver stages remain poorly characterized due to limitations in the throughput of phenotyping at these stages. To fill this gap, we followed more than 1,300 barcoded P. berghei mutants through the life cycle. We discover 461 genes required for efficient parasite transmission to mosquitoes through the liver stage and back into the bloodstream of mice. We analyze the screen in the context of genomic, transcriptomic, and metabolomic data by building a thermodynamic model of P. berghei liver-stage metabolism, which shows a major reprogramming of parasite metabolism to achieve rapid growth in the liver. We identify seven metabolic subsystems that become essential at the liver stages compared with asexual blood stages: type II fatty acid synthesis and elongation (FAE), tricarboxylic acid, amino sugar, heme, lipoate, and shikimate metabolism. Selected predictions from the model are individually validated in single mutants to provide future targets for drug development. Plasmodium gene functions in mosquito and liver stages remain poorly characterized due to limitations in the throughput of phenotyping at these stages. To fill this gap, we followed more than 1,300 barcoded P. berghei mutants through the life cycle. We discover 461 genes required for efficient parasite transmission to mosquitoes through the liver stage and back into the bloodstream of mice. We analyze the screen in the context of genomic, transcriptomic, and metabolomic data by building a thermodynamic model of P. berghei liver-stage metabolism, which shows a major reprogramming of parasite metabolism to achieve rapid growth in the liver. We identify seven metabolic subsystems that become essential at the liver stages compared with asexual blood stages: type II fatty acid synthesis and elongation (FAE), tricarboxylic acid, amino sugar, heme, lipoate, and shikimate metabolism. Selected predictions from the model are individually validated in single mutants to provide future targets for drug development. Malaria, caused by parasites of the genus Plasmodium, remains a disease of major significance to global public health. Despite increased attention and funding, malaria still kills about half a million people each year, and the combination of drug and insecticide resistance slows down progress against this deadly disease (World Health Organization, 2018World Health OrganizationWorld Malaria Report 2018. World Health Organization, 2018Crossref Google Scholar). Infection with Plasmodium parasites occurs through the bite of infected Anopheles mosquitoes, which inject motile sporozoites when feeding on blood. A proportion of them reaches and successfully invades hepatocytes. Over the course of two to five days, depending on the Plasmodium species, the parasite increases dramatically in size and eventually gives rise to thousands of daughter merozoites. With this immense and rapid expansion, parasites need to be highly metabolically active, despite their dependence on the host cell for nutrient acquisition. The merozoites are released into the bloodstream, where they invade red blood cells and undergo repeated rounds of asexual replication, each round culminating in the release of further invasive merozoites. It is the blood phase of the parasite life cycle that leads to the symptoms of malaria and, in the case of Plasmodium falciparum, can cause fatal disease (reviewed in Cowman et al., 2016Cowman A.F. Healer J. Marapana D. Marsh K. Malaria: biology and disease.Cell. 2016; 167: 610-624Abstract Full Text Full Text PDF PubMed Scopus (418) Google Scholar). Rather than undergo asexual replication, some merozoites will instead differentiate into sexual stages of the parasite, the male and female gametocytes. Upon uptake by susceptible mosquitoes, these gametocytes are activated to form gametes and following fertilization and escape from the mosquito midgut, the parasite encysts between the epithelial midgut wall and the basal lamina. Within the oocyst, thousands of motile sporozoites are produced over the course of 7 to 10 days in a process known as sporogony. Motile sporozoites are liberated into the haemocoel of the mosquito and eventually accumulate in the salivary glands, where they await injection into a new mammalian host. For many years, the primary focus of malaria research has been the pathogenic blood stages, and all but two of the commercially available antimalarial drugs primarily target blood-stage infection. While this has been an effective strategy, P. falciparum has repeatedly and rapidly developed resistance to all available blood-stage drugs, including the current frontline antimalarial, artemisinin (Blasco et al., 2017Blasco B. Leroy D. Fidock D.A. Antimalarial drug resistance: linking Plasmodium falciparum parasite biology to the clinic.Nat. Med. 2017; 23: 917-928Crossref PubMed Scopus (280) Google Scholar). New drugs are urgently required. Targeting the pre-erythrocytic stage of the parasite has the considerable advantage that successful drug treatment would prevent any clinical disease symptoms and could also be used to clear dormant liver stages of the Plasmodium vivax parasite, which can re-activate to establish blood-stage infection many years after the original mosquito bite (Campo et al., 2015Campo B. Vandal O. Wesche D.L. Burrows J.N. Killing the hypnozoite--drug discovery approaches to prevent relapse in Plasmodium vivax.Pathog. Glob. Health. 2015; 109: 107-122Crossref PubMed Scopus (67) Google Scholar). A recent screen has begun to identify dozens of candidate compounds that target the liver stage, some with great specificity (Antonova-Koch et al., 2018Antonova-Koch Y. Meister S. Abraham M. Luth M.R. Ottilie S. Lukens A.K. Sakata-Kato T. Vanaerschot M. Owen E. Jado J.C. et al.Open-source discovery of chemical leads for next-generation chemoprotective antimalarials.Science. 2018; 362 (eaat9446)Crossref Scopus (62) Google Scholar). However, difficult experimental models and the limited nature of our understanding of liver stage metabolism now pose major challenges for identifying their modes of action. The high metabolic activity that enables parasites to expand rapidly from a single sporozoite to tens of thousands of daughter merozoites presents a major vulnerability. Metabolic differences between pre-erythrocytic Plasmodium and their human host cells are known to exist (Shears et al., 2015Shears M.J. Botté C.Y. McFadden G.I. Fatty acid metabolism in the Plasmodium apicoplast: Drugs, doubts and knockouts.Mol. Biochem. Parasitol. 2015; 199: 34-50Crossref PubMed Scopus (55) Google Scholar) and could in theory be exploited for drug development, but there are currently significant gaps in our understanding of liver-stage metabolism. Identifying genes with key roles in liver stage development that are potential drug targets requires the scaling up of experimental genetics and subsequent phenotyping during this poorly accessible stage. In P. berghei, a resource of >2,900 individually barcoded gene knockout vectors is now available (https://plasmogem.sanger.ac.uk/). These vectors integrate efficiently into the genome due to their long homology arms (Pfander et al., 2011Pfander C. Anar B. Schwach F. Otto T.D. Brochet M. Volkmann K. Quail M.A. Pain A. Rosen B. Skarnes W. Rayner J.C. Billker O. A scalable pipeline for highly effective genetic modification of a malaria parasite.Nat Methods. 2011; 8: 1078-1082Crossref PubMed Scopus (69) Google Scholar) and in our experience are not maintained episomally, such that detection of a barcode after drug selection is highly indicative of the presence of a specific knockout mutant in the selected parasite populations (Gomes et al., 2015Gomes A.R.R. Bushell E. Schwach F. Girling G. Anar B. Quail M.A.A. Herd C. Pfander C. Modrzynska K. Rayner J.C.C. Billker O. A genome-scale vector resource enables high-throughput reverse genetic screening in a malaria parasite.Cell Host Microbe. 2015; 17: 404-413Abstract Full Text Full Text PDF PubMed Scopus (80) Google Scholar). Using barcode counting on a next-generation sequencer (barseq), we previously determined growth-rate phenotypes for the generated knockout mutants specifically during the asexual blood stages, identifying >1,360 non-essential genes from more than 2,500 screened genes (Bushell et al., 2017Bushell E. Gomes A.R. Sanderson T. Anar B. Girling G. Herd C. Metcalf T. Modrzynska K. Schwach F. Martin R.E. et al.Functional profiling of a Plasmodium genome reveals an abundance of essential genes.Cell. 2017; 170: 260-272.e8Abstract Full Text Full Text PDF PubMed Scopus (276) Google Scholar). In this study, we generated pools of these blood-stage-viable knockout mutants and analyzed their phenotypes throughout the entire parasite life cycle for the first time. Using barcode sequencing, we measured changes in the relative abundance of knockout mutants in midgut oocysts, salivary gland sporozoites, and in mice following injection of sporozoites, revealing stage-specific functions for 461 genes, including transcription factors, structural proteins, and enzymes. We combined the data of the genetic screen with a liver-stage transcriptome (Caldelari et al., 2019Caldelari R. Dogga S. Schmid M.W. Franke-Fayard B. Janse C.J. Soldati-Favre D. Heussler V. Transcriptome analysis of Plasmodium berghei during exo-erythrocytic development.Malar. J. 2019; 18: 330Crossref Scopus (20) Google Scholar) to generate a liver-stage metabolic model for P. berghei (iPbe-liver). We used this model to examine the reasons underlying the observed loss-of-function phenotypes and provide new insights into liver-stage physiology, systematically predicting thermodynamic bottlenecks, genetic interactions, and growth-limiting nutrients. To validate hypotheses generated from this model, we produced and analyzed individual knockout mutants for 20 genes and compared their phenotypes with their model-predicted essentiality. Only the asexual blood stages of Plasmodium parasites can be propagated continuously to drug-select for knockout mutants, meaning that only genes that are not required for blood-stage development can be investigated at later stages of the cycle using barseq. Extending barseq phenotyping beyond blood stages faces three potential obstacles: (1) population bottlenecks, (2) changes in ploidy following gamete fusion in the midgut, and (3) segregation of mutant alleles in the oocyst. In a pilot screen, we first tested whether barcoded alleles could be transmitted robustly through the population bottleneck posed by the only approximately 400 oocysts that in our hands form on average on each infected Anopheles stephensi midgut. Using knockout vectors targeting 15 genes with known functions at the liver stage and 19 control and test genes (shown in Table S1), a pool of mutant parasites was generated by transfection and used to infect three mice. Blood samples from each mouse were collected to establish the starting composition of mutants after drug selection (sample B1). 120–150 female mosquitoes were then allowed to feed on each mouse, and midguts (MG) from >30 mosquitoes were dissected 15 days post-infection, followed by salivary gland (SG) collection at day 22 post-infection from at least 60 mosquitoes. Half of these SGs were used to prepare a barseq library to establish the composition of the mutant pool in SG; the other half were used to collect sporozoites to infect another mouse. From this mouse, a blood sample (B2) was taken 5 days after intravenous injection of sporozoites to establish the composition of the mutant pool in B2, allowing assessment of parasite development in the liver (Figure 1A). The relative abundance of gene knockouts in the pilot dataset (Table S1) showed at least a 5-fold drop in relative abundance between SG and B2 specifically for genes known to have a critical role at the liver stage (more than 10-fold for PALM, UIS4, aLipDH, B9, P36, P36P, FabB/F, FabZ, and TRAP; more than 5-fold drop for SLARP, PLP1, and LISP1). An 11-fold drop in relative abundance was additionally seen for one of the individually selected genes in this pilot experiment, LipA, a gene not previously studied at the liver stage, revealing for the first time a potential liver-stage role for this enzyme (Figure 1B; Table S1). Having recapitulated published liver-stage phenotypes, we expanded the screen to cover all PlasmoGEM-targetable genes that are not essential at the asexual blood stage in what is hereafter referred to as the mosquito-stage liver-stage (M-L) screen. To minimize random losses of barcodes through non-representative sampling, the pool size was limited to 60 mutants, and each pool was studied in three independent transmission experiments (Figure 1A). In the absence of suitable control genes known to lack knockout phenotypes at all developmental stages, we normalized the stage-specific conversion efficiency of each mutant to the quartile of most effectively converting mutants in each set. We additionally corrected SG-B2 conversion rates using the known blood-stage growth rate of each mutant (Bushell et al., 2017Bushell E. Gomes A.R. Sanderson T. Anar B. Girling G. Herd C. Metcalf T. Modrzynska K. Schwach F. Martin R.E. et al.Functional profiling of a Plasmodium genome reveals an abundance of essential genes.Cell. 2017; 170: 260-272.e8Abstract Full Text Full Text PDF PubMed Scopus (276) Google Scholar) to detect pre-erythrocytic phenotypes more specifically. In total, the screen involved 1,379 vectors, transfected in 27 pools (Table S2). It required dissection of >7,000 mosquitoes and barseq of more than 600 PCR amplicons. To interpret the data from a transmission screen, we considered how changes in ploidy following gamete fusion in the midgut and segregation of mutant alleles in the oocyst (Figure 2A) would affect how knockout alleles are transmitted. Mutants of the transcriptional regulators AP2-G and AP2-G2, are known to lack fertile gametocytes of both sexes (Sinha et al., 2014Sinha A. Hughes K.R. Modrzynska K.K. Otto T.D. Pfander C. Dickens N.J. Religa A.A. Bushell E. Graham A.L. Cameron R. et al.A cascade of DNA-binding proteins for sexual commitment and development in Plasmodium.Nature. 2014; 507: 253-257Crossref PubMed Scopus (263) Google Scholar) and in the screen were therefore only poorly transmitted to oocysts (Figure 2B). The same was true for GEST, the gametocyte egress and sporozoite traversal gene (Talman et al., 2011Talman A.M. Lacroix C. Marques S.R. Blagborough A.M. Carzaniga R. Ménard R. Sinden R.E. PbGEST mediates malaria transmission to both mosquito and vertebrate host.Mol. Microbiol. 2011; 82: 462-474Crossref PubMed Scopus (67) Google Scholar), which showed both a B1-MG and a SG-B2 phenotype, consistent with its published functions (Figure 2B). In contrast, cross-fertilization between different mutants in the bloodmeal limited the power of the screen to reveal gene functions during the subsequent diploid and polyploid stages (i.e., zygotes, ookinetes, and oocysts). For instance, knockout mutants in which only one sex is sterile (Ning et al., 2013Ning J. Otto T.D. Pfander C. Schwach F. Brochet M. Bushell E. Goulding D. Sanders M. Lefebvre P.A. Pei J. et al.Comparative genomics in Chlamydomonas and Plasmodium identifies an ancient nuclear envelope protein family essential for sexual reproduction in protists, fungi, plants, and vertebrates.Genes Dev. 2013; 27: 1198-1215Crossref PubMed Scopus (63) Google Scholar, Bennink et al., 2016Bennink S. Kiesow M.J. Pradel G. The development of malaria parasites in the mosquito midgut.Cell. Microbiol. 2016; 18: 905-918Crossref PubMed Scopus (83) Google Scholar) can transmit their barcodes to the oocyst by inheritance through the fertile sex (Figure 2C). As a result, reductions in barcode abundance for these sex-specific knockout mutants often did not reach significance at the B1-MG conversion. Known gene functions in the polyploid ookinete were also generally not recapitulated in the screen, presumably due to heterozygous rescue (Figure 2D). While these observations highlight the need for future screens to be designed specifically to reveal sexual and mosquito-stage phenotypes, they also rationalize how knockout alleles of genes functioning in fertility or ookinete and oocyst development can be transmitted to salivary gland sporozoites to reveal additional gene functions after sporozoite transmission to the vertebrate host. This is illustrated by AP2-O4, a putative transcriptional regulator of oocyst maturation (Modrzynska et al., 2017Modrzynska K. Pfander C. Chappell L. Yu L. Suarez C. Dundas K. Gomes A.R. Goulding D. Rayner J.C. Choudhary J. Billker O. A knockout screen of ApiAP2 genes reveals networks of interacting transcriptional regulators controlling the Plasmodium life cycle.Cell Host Microbe. 2017; 21: 11-22Abstract Full Text Full Text PDF PubMed Scopus (110) Google Scholar) whose phenotype is rescued in the polyploid oocyst until the SG stage, but then, the haploid knockout sporozoites show an ∼3,000-fold loss during transmission back to mice, revealing a new function for AP2-O4, possibly at the liver stage (Figure 2D; Table S2). Since in Plasmodium all products of meiosis are propagated into the oocyst, which remains functionally heterozygous until alleles segregate at the point of sporogony, it is likely that sporozoites lacking an essential gene can inherit sufficient protein from the oocyst to survive. TRAP (thrombospondin-related adhesive protein), which is required for sporozoite gliding, entry into salivary glands and hepatocyte invasion (Sultan et al., 1997Sultan A.A. Thathy V. Frevert U. Robson K.J. Crisanti A. Nussenzweig V. Nussenzweig R.S. Ménard R. TRAP is necessary for gliding motility and infectivity of plasmodium sporozoites.Cell. 1997; 90: 511-522Abstract Full Text Full Text PDF PubMed Scopus (508) Google Scholar) might be an example of protein inheritance from heterozygous oocysts to sporozoites. An ∼4-fold reduction in SG sporozoites in our screen (Figure 1B; Table S1) contrasts with a 34-fold reduction in sporozoite numbers of the TRAP gene knockout clone in the previous study, possibly because TRAP protein obtained by the sporozoite from heterozygous oocysts alleviates the phenotype of the knockout. The same phenomenon is unlikely to extend to all sporozoite expressed genes, because once inside the salivary glands, sporozoites reprogram transcription from their now once more haploid genome in preparation for transmission back to the vertebrate host (Mikolajczak et al., 2008Mikolajczak S.A. Silva-Rivera H. Peng X. Tarun A.S. Camargo N. Jacobs-Lorena V. Daly T.M. Bergman L.W. de la Vega P. Williams J. et al.Distinct malaria parasite sporozoites reveal transcriptional changes that cause differential tissue infection competence in the mosquito vector and mammalian host.Mol. Cell. Biol. 2008; 28: 6196-6207Crossref PubMed Scopus (69) Google Scholar). At this phase of the life cycle, the ability of the screen to reveal phenotypes was therefore predicted to increase, which is confirmed by a comparison of ranked effect sizes, which are much greater for the SG-B2 transition as compared to the MG-SG conversion (Figure 3A). By first approximation, we will assume losses of mutants at the SG-B2 transition to reflect gene functions at the liver stage in the broadest sense, i.e., starting with sporozoite transmigration and invasion of hepatocytes and culminating in the release of merozoites into the bloodstream. A more precise elucidation of gene functions will require analysis of single knockout mutants (see below). Taking a conservative approach to calling phenotypes that takes into account both the effect size and the variance across biological triplicates as illustrated in Figure S1, we find that at each transition, only a small proportion of mutants (9%–18%) are significantly depleted, while for the majority of genes, we can either be confident that they are “not reduced” or the statistical power is considered insufficient to make a clear call (Figure 3B). Of the 1,359 mutants for which data was obtained, 898 showed no significant reduction at any transition. At the B1-MG, MG-SG, and SG-B2 transitions, 251, 129, and 185 mutants, respectively, showed reduced stage conversion (Figure 3B). Statistically robust transmission phenotypes were revealed, regardless of whether mutants were previously found to have normal or slow growth at the asexual blood stage (Figure 3C). The latter does not, therefore, appear to be a major confounder of our ability to detect phenotypes during the rest of the life cycle. Mutants that were reduced strongly (>100-fold) at the SG-B2 transition showed a remarkable enrichment (p < 0.01) for metabolic genes (15 of 31 genes in this category encoded enzymes versus only 4 expected; Table S2). Some of the pathways represented by lost mutants are consistent with the known importance of heme and fatty acid biosynthesis at the liver stage (Shears et al., 2015Shears M.J. Botté C.Y. McFadden G.I. Fatty acid metabolism in the Plasmodium apicoplast: Drugs, doubts and knockouts.Mol. Biochem. Parasitol. 2015; 199: 34-50Crossref PubMed Scopus (55) Google Scholar, Goldberg and Sigala, 2017Goldberg D.E. Sigala P.A. Plasmodium heme biosynthesis: to be or not to be essential?.PLoS Pathog. 2017; 13: e1006511Crossref PubMed Scopus (17) Google Scholar); others implicate more unexpected roles for fatty acid elongation, amino sugar metabolism, and the electron transport chain (Table S2; Figure 3D). By comparison, we did not see liver-stage-specific essentiality for genes with functions in DNA repair, DNA replication, or proteolysis (Figure 3D). With metabolism emerging as a defining feature of the SG-B2 transition, we decided to construct a genome-scale model of P. berghei metabolism to evaluate the screen results systematically in the context of current knowledge. As with our previous general P. falciparum model (iPfa) (Chiappino-Pepe et al., 2017aChiappino-Pepe A. Tymoshenko S. Ataman M. Soldati-Favre D. Hatzimanikatis V. Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks.PLoS Comput. Biol. 2017; 13: e1005397Crossref PubMed Scopus (35) Google Scholar), we based the in silico P. berghei (iPbe) model on a set of metabolic tasks (Table S3) and on annotated metabolic gene functions (Table S4). We build upon this computational framework through a process we call PhenoMapping (STAR Methods). In a unique decomposition approach, we consider separately different layers of information, such as nutrient availability, gene expression, and gene knockout phenotypes in order to refine the model, for instance, by adding missing enzymatic or transport capabilities (Figure S2A). We initially used asexual blood-stage growth rates of Bushell et al., 2017Bushell E. Gomes A.R. Sanderson T. Anar B. Girling G. Herd C. Metcalf T. Modrzynska K. Schwach F. Martin R.E. et al.Functional profiling of a Plasmodium genome reveals an abundance of essential genes.Cell. 2017; 170: 260-272.e8Abstract Full Text Full Text PDF PubMed Scopus (276) Google Scholar and subsequently incorporated the phenotypes from the SG-B2 transition of the current screen (STAR Methods). The iPbe model integrates 428 genes and 1,318 reactions (transport and enzymatic reactions; Figures S2B and S2C) that reflect available knowledge and new postulates on the metabolism of the parasite based on our PhenoMapping analysis. We used the iPbe model to analyze essential metabolic capabilities in a stage-specific manner (Figure 4A), working under the assumption that most metabolic phenotypes at the SG-B2 transition reflect gene functions during liver-stage development, a prediction we will validate experimentally below.Figure 4The PhenoMapping Workflow and Degree of Agreement for Metabolic Subsystems in iPbe-Liver with the M-L ScreenShow full caption(A) Illustration of the PhenoMapping workflow for the integration of organism- and context-specific information into the genome-scale iPbe metabolic models. Context-specific information denotes life-cycle stage-specific processes, such as gene expression, as well as environmentally specific factors, such as substrate availability. Metabolic tasks are at the interface between organism- and context-specific information. The production of molecules, such as amino acids, fatty acids, nucleotides, etc., is required for growth independent of the context, but the ratios in which they are required might change with the growing conditions or life stage. See STAR Methods and Table S4 for a detailed description of iPbe.(B) Degree of agreement (DoA) between the gene essentiality predictions in iPbe-liver and the experimental phenotypes at the SG-B2 transition. Metabolic subsystems are ranked by level of agreement. Numbers show genes with screen data per subsystem (needs to be >1 for inclusion).View Large Image Figure ViewerDownload Hi-res image Download (PPT) (A) Illustration of the PhenoMapping workflow for the integration of organism- and context-specific information into the genome-scale iPbe metabolic models. Context-specific information denotes life-cycle stage-specific processes, such as gene expression, as well as environmentally specific factors, such as substrate availability. Metabolic tasks are at the interface between organism- and context-specific information. The production of molecules, such as amino acids, fatty acids, nucleotides, etc., is required for growth independent of the context, but the ratios in which they are required might change with the growing conditions or life stage. See STAR Methods and Table S4 for a detailed description of iPbe. (B) Degree of agreement (DoA) between the gene essentiality predictions in iPbe-liver and the experimental phenotypes at the SG-B2 transition. Metabolic subsystems are ranked by level of agreement. Numbers show genes with screen data per subsystem (needs to be >1 for inclusion). To evaluate computationally the essentiality of the 428 genes in iPbe by PhenoMapping, we initially assumed unlimited transport capabilities, but we worked with the known range of metabolite concentrations and gene expression levels (Caldelari et al., 2019Caldelari R. Dogga S. Schmid M.W. Franke-Fayard B. Janse C.J. Soldati-Favre D. Heussler V. Transcriptome analysis of Plasmodium berghei during exo-erythrocytic development.Malar. J. 2019; 18: 330Crossref Scopus (20) Google Scholar, Otto et al., 2010Otto T.D. Wilinski D. Assefa S. Keane T.M. Sarry L.R. Böhme U. Lemieux J. Barrell B. Pain A. Berriman M. et al.New insights into the blood-stage transcriptome of Plasmodium falciparum using RNA-Seq.Mol. Microbiol. 2010; 76: 12-24Crossref PubMed Scopus (280) Google Scholar, Teng et al., 2009Teng R. Junankar P.R. Bubb W.A. Rae C. Mercier P. Kirk K. Metabolite profiling of the intraerythrocytic malaria parasite Plasmodium falciparum by (1)H NMR spectroscopy.NMR Biomed. 2009; 22: 292-302Crossref PubMed Scopus (90) Google Scholar, Teng et al., 2014Teng R. Lehane A.M. Winterberg M. Shafik S.H. Summers R.L. Martin R.E. van Schalkwyk D.A. Junankar P.R. Kirk K. 1H-NMR metabolite profiles of different strains of Plasmodium falciparum.Biosci. Rep. 2014; 34: e00150Crossref Scopus (20) Google Scholar, Vo Duy et al., 2012Vo Duy S. Besteiro S. Berry L. Perigaud C. Bressolle F. Vial H.J. Lefebvre-Tournier I. A quantitative liquid chromatography tandem mass spectrometry method for metabolomic analysis of Plasmodium falciparum lipid related metabolites.Anal. Chim. Acta. 2012; 739: 47-55Crossref PubMed Scopus (24) Google Scholar), and we considered the potential for dynamic regulation of gene expression between isoenzymes (Figure 4A; STAR Methods). A stage agnostic model initially predicted 155 of the 428 genes as essential in at least one condition (Table S3). To create blood- and liver-stage-specific models, we used existing knowledge of host metabolite availability as constraints to identify the combinations of nutrients the parasite would need to access to maximize agreement with the phenotypes of the respective knockout screens. We allowed iPbe to uptake 90 metabolites from the surroundings (i.e., the hepatocyte), and we integrated thermodynamic data (pH of intracellular compartments and membrane potential), as well as liver stage transcriptome data (Caldelari et al., 2019Caldelari R. Dogga S. Schmid M.W. Franke-Fayard B. Janse C.J. Soldati-Favre D. Heussler V. Transcriptome analysis of Plasmodium berghei during exo-erythrocytic development.Malar. J. 2019; 18: 330Crossref Scopus (20) Google Scholar), to generate a liver-stage-specific metabolic model, iPbe-liver. Analogously, an optimized thermodynamic blood-stage model, iPbe-blood, assumes uptake of 94 metabolites from the reticulocyte and integrates blood-stage metabolomic and transcriptomic data (Otto et al., 2014Otto T.D. Böhme U. Jackson A.P. Hunt M. Franke-Fayard B. Hoeijmakers W.A.M. Religa A.A. Robertson L. Sanders M. Ogun S.A. et al.A comprehensive evaluation of rodent malaria parasite genomes and gene expression.BMC Biol. 2014; 12: 86Crossref PubMed Scopus (181) Google Scholar, Teng et al., 2009Teng R. Junankar P.R. Bubb W.A. Rae C. Mercier P. Kirk K. Metabolite profiling of the intraerythrocytic malaria parasite Plasmodium falciparum by (1)H NMR spectroscopy.NMR Biomed. 2009; 22: 292-302Crossref PubMed Scopus" @default.
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• W2983607589 title "Genome-Scale Identification of Essential Metabolic Processes for Targeting the Plasmodium Liver Stage" @default.
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