Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets

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Target-based screening of inhibitors is a major approach for drug discovery, but its application to malaria has been limited by the availability of few validated drug targets in Plasmodium. Here we utilize the recently available large-scale gene essentiality data in Plasmodium berghei and a related apicomplexan pathogen, Toxoplasma gondii, to identify potential anti-malarial drug targets. We find significant conservation of gene essentiality in the two apicomplexan parasites. The conservation of essentiality could be used to prioritize enzymes that are essential across the two parasites and show no or low sequence similarity to human proteins. Novel essential genes in Plasmodium could be predicted based on their essentiality in T. gondii. Essential genes in Plasmodium showed higher expression, evolutionary conservation and association with specific functional classes. We expect that the availability of a large number of novel potential drug targets would significantly accelerate anti-malarial drug discovery." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/6-23/v1", "name": "Conservation of gene essentiality in Apicomplexa and its application..." } } ] } Home Browse Conservation of gene essentiality in Apicomplexa and its application... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Singh GP. Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets [version 1; peer review: 2 approved with reservations] . F1000Research 2017, 6 :23 ( https://doi.org/10.12688/f1000research.10559.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Note Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets [version 1; peer review: 2 approved with reservations] Gajinder Pal Singh https://orcid.org/0000-0002-3972-3324 Gajinder Pal Singh https://orcid.org/0000-0002-3972-3324 PUBLISHED 09 Jan 2017 Author details Author details Molecular Medicine Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract New anti-malarial drugs are needed to address the challenge of artemisinin resistance and to achieve malaria elimination and eradication. Target-based screening of inhibitors is a major approach for drug discovery, but its application to malaria has been limited by the availability of few validated drug targets in Plasmodium . Here we utilize the recently available large-scale gene essentiality data in Plasmodium berghei and a related apicomplexan pathogen, Toxoplasma gondii, to identify potential anti-malarial drug targets. We find significant conservation of gene essentiality in the two apicomplexan parasites. The conservation of essentiality could be used to prioritize enzymes that are essential across the two parasites and show no or low sequence similarity to human proteins. Novel essential genes in Plasmodium could be predicted based on their essentiality in T. gondii . Essential genes in Plasmodium showed higher expression, evolutionary conservation and association with specific functional classes. We expect that the availability of a large number of novel potential drug targets would significantly accelerate anti-malarial drug discovery. READ ALL READ LESS Keywords Plasmodium falciparum, Toxoplasma gondii, drug targets, essential genes Corresponding Author(s) Gajinder Pal Singh ( [email protected] ) Close Corresponding author: Gajinder Pal Singh Competing interests: No competing interests were disclosed. Grant information: The work is supported by an Early Career Fellowship to G.P.S. by the Wellcome Trust/DBT India Alliance (IA/E/15/1/502297). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2017 Singh GP. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication). How to cite: Singh GP. Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets [version 1; peer review: 2 approved with reservations] . F1000Research 2017, 6 :23 ( https://doi.org/10.12688/f1000research.10559.1 ) First published: 09 Jan 2017, 6 :23 ( https://doi.org/10.12688/f1000research.10559.1 ) Latest published: 09 Jan 2017, 6 :23 ( https://doi.org/10.12688/f1000research.10559.1 ) Introduction Malaria killed an estimated half a million people in the year 2015, 70% of them were children under the age of five 1 . The emergence and spread of Plasmodium falciparum strains resistant to all currently used anti-malarial drugs 2 has created an urgent need to discover new drugs. New anti-malarial drugs are also needed for malaria elimination and global eradication, for which the currently available drugs are not adequate 3 . There are two main approaches for drug-discovery against pathogens: Phenotype screening and target-based approach 4 . In phenotype screening, compounds are identified that inhibit the cellular growth of the pathogen. Large-scale screening of millions of compounds against the erythrocytic stage of P. falciparum has identified thousands of such inhibitors 5 . Some of these inhibitors have progressed to clinical trials 6 . In the target-based approach, compounds are identified that inhibit the activity of a protein essential for the viability of the pathogen. Thus target-based approach requires previous knowledge about genes that are essential for the pathogen. Only a few essential genes have been identified in P. falciparum , hampering the target-based approach for anti-malarial drug discovery. Consequently, target-based approach has only identified a few anti-malarial candidates 6 . However, recent large-scale screening of about 2500 genes in a rodent malaria parasite P. berghei has identified about 1200 essential genes 7 , 8 . A recent genome-scale CRISPR screen in a related apicomplexan parasite Toxoplasma gondii has identified about 3000 essential genes 9 . Here we analyse this data and find significant conservation of gene essentiality in these two pathogens. From this, we identified potential anti-malarial drug targets that exhibit conserved essentiality in apicomplexan parasites; we predict novel essential genes in Plasmodium based on the essentiality of their orthologs in T. gondii . These targets could serve as starting points for target-based anti-malarial drug discovery. Methods Fitness data for knockout mutants The genome-wide CRISPR screening data on the relative fitness of T. gondii genes during infection of human fibroblasts cells was obtained from Sidik et al. 9 . The authors defined log 2 fold change in abundance of single guide RNA (sgRNA) targeting a given gene as the “phenotype” score for that gene 9 . It was found that for a previously determined set of 81 essential and non-essential genes, a phenotype score of less than -2 identified most of the essential genes, but none of the non-essential genes 9 . We thus defined all genes with a phenotype score of less than -2 as essential (2870 genes). Genes with a phenotype score greater than 0 were defined as non-essential (3071 genes), while those with a phenotype score between 0 and -2 were not classified (2210 genes). The in vivo relative growth rate data for 2574 genes of P. berghei were obtained from the PlasmoGEM database 7 , 8 ( http://plasmogem.sanger.ac.uk/phenotypes ). The authors generated knockout mutants by transfection with large pools of barcoded gene knockout vectors. The in vivo growth rate in Balb/c mice was obtained by counting barcodes by next generation sequencing daily between days 4 and 8 post transfection 7 . Essential genes were defined as genes with a growth rate not significantly different from 0.1 (growth rate of the wild type taken as 1), while non-essential genes were defined as genes with growth rate not significantly different from 1 7 . Proteome data and sequence analyses Proteome sequences of P. falciparum 3D7, P. berghei ANKA, P. chabaudi chabaudi , P. cynomolgi B , P. knowlesi H, P. reichenowi CDC, P. vivax Sal1, P. yoelii 17X were downloaded from the PlasmoDB database 10 ( http://plasmodb.org/common/downloads/release-27/ ). The Proteome sequences for six apicomplexan species were obtained from EuPathDB 11 : Cryptosporidium hominis TU502 ( http://cryptodb.org/common/downloads/release-29/ChominisTU502/ ); T. gondii GT1 ( http://toxodb.org/common/downloads/release-29/TgondiiGT1/ ); Eimeria brunetti Houghton ( http://toxodb.org/common/downloads/release-29/EbrunettiHoughton/ ); Babesia bovis T2Bo ( http://piroplasmadb.org/common/downloads/release-29/BbovisT2Bo/ ); Theileria annulata Ankara ( http://piroplasmadb.org/common/downloads/release-29/TannulataAnkara/ ); and Gregarina niphandrodes ( http://cryptodb.org/common/downloads/release-29/GniphandrodesUnknown/ ). Proteome sequences for Homo sapiens were downloaded from EBI ( http://www.ebi.ac.uk/reference_proteomes ). Homologs of P. berghei genes in H. sapiens were identified with E-value cut-off of 1e-6, with soft mask set as true. Orthologous sequences were identified using best bidirectional hit algorithm 12 . Functional data RNA-seq data (FPKM values) for different stages of P. berghei was obtained from Otto et al. 13 . Proteomics data on different stages of P. berghei and dN, dN/S values were obtained from Hall et al. 14 . Gene Ontology information for P. falciparum was obtained from PlasmoDB 10 , and these functions were assigned to their orthologous proteins in P. berghei . Enzyme Commission (EC) numbers for P. berghei and P. falciparum were also obtained from PlasmoDB. Trans-membrane regions were identified using TMHMM 15 . All statistical analyses were performed in the R software version 3.3.1 ( https://www.r-project.org/ ). Results Conservation of gene essentiality in apicomplexan parasites The relative in vivo growth rate of knockout mutants for 2574 P. berghei genes (out of total 5076 genes in P. berghei ) has recently been measured, of which 1198 genes (46%) with very low growth rate were classified as essential 7 , 8 . Similarly, in vivo relative fitness of knockout mutants for 8151 T. gondii genes have been measured 9 , of which 2870 genes (35%) with very low relative fitness values were classified as essential (see Methods). Of the 2574 P. berghei genes with fitness data, 1617 genes have an ortholog in T. gondii . P. berghei genes with an ortholog in T. gondii were significantly more likely to be essential, compared to P. berghei genes without an ortholog in T. gondii (53% vs. 36%; Fisher test p = 7e-18; Figure 1A ). P. berghei genes with an essential ortholog in T. gondii were significantly more likely to be essential, compared to P. berghei genes with a non-essential ortholog in T. gondii (71% vs. 17%; Fisher test p = 6e-59; Figure 1A ). There was a significant correlation in relative fitness values of P. berghei and T. gondii (Spearman correlation coefficient 0.47; p = 3e-89; n =1617; Figure 1B ). The essentiality of 2502 P. berghei genes was not tested, but the essentiality information of T. gondii orthologs may be used to predict their essentiality in P. berghei . There were 687 genes in P. berghei with an essential ortholog in T. gondii, and thus may be predicted as essential in P. berghei ( Dataset 1 16 ). Figure 1. Conservation of essentiality between Plasmodium berghei and Toxoplasma gondii . ( A ) P. berghei genes with an ortholog in T. gondii were more likely to be essential, compared to P. berghei genes without an ortholog in T. gondii (Fisher test p = 7e-18). P. berghei genes with an essential ortholog in T. gondii were significantly more likely to be essential compared to P. berghei genes with a non-essential ortholog in T. gondii (Fisher test p = 6e-59). ( B ) There was a significant correlation in relative fitness values of P. berghei and T. gondii (Spearman correlation coefficient 0.47; p = 3e-89; n =1617). Genes classified as essential in both species are colored red. Genes classified as non-essential in both species are colored blue. Genes that are essential in only one of the species are colored green. Prioritization of anti-malarial drug targets We argue that genes identified as essential in both the apicomplexan parasites could be more useful drug targets for the following reasons: 1) Genome-scale fitness screens often involve significant false positives and false negatives 7 , thus genes identified as essential in independent experiments in different parasites could be more confidently assigned as essential; 2) the substantial conservation of gene essentiality between the two parasites demonstrates that essentiality information in T. gondii offers relevant information about gene essentiality in P. berghei ; 3) genes that are essential in both P. berghei and T. gondii should be more likely to be essential in human malarial species, such as P. falciparum and P. vivax ; 4) genes that are essential in both P. berghei and T. gondii should be more likely to be essential across different developmental stages of Plasmodium , which is a highly desirable property of Plasmodium drug targets 17 . We thus identified 710 genes that were essential in both species. A total of 289 of these 710 genes encode enzymes, which are typically used as drug targets against pathogens. Of these 289 genes, 245 had an ortholog in all Plasmodium species and did not have more than one trans-membrane segment. We removed proteins with more than one trans-membrane segments, as these are often difficult to purify for in vitro assays. Of the 245 proteins, 30 showed no significant sequence similarity to any human proteins (listed in Table 1 ), and 83 showed less than 30% identity and 151 showed less than 40% identity to any human protein ( Dataset 1 16 ). Figure 2 shows the flow chart of the selection process. Table 1. Essential Plasmodium enzymes with no significant similarity to human proteins. P. berghei ID P. falciparum ID Gene name Description PBANKA_0306300 PF3D7_0209200 3’ exoribonuclease PBANKA_0507000 PF3D7_1022800 GcpE 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase PBANKA_0615800 PF3D7_0718100 EST exported serine/threonine protein kinase PBANKA_0802700 PF3D7_0705000 mRNA cap guanine-N7 methyltransferase PBANKA_0823300 PF3D7_0922400 pBAS para-aminobenzoic acid synthetase PBANKA_0828600 PF3D7_0927800 COX5B cytochrome c oxidase subunit 5B PBANKA_0828800 PF3D7_0928000 COX6B cytochrome c oxidase subunit 6B PBANKA_0927800 PF3D7_1120500 tRNA nucleotidyltransferase PBANKA_1006500 PF3D7_0408900 KAE1 tRNA N6-adenosine threonylcarbamoyltransferase PBANKA_1017700 PF3D7_1426900 QCR6 cytochrome b-c1 complex subunit 6 PBANKA_1033400 PF3D7_1409100 aldo-keto reductase PBANKA_1035300 PF3D7_1406900 radical SAM protein PBANKA_1104700 PF3D7_0505100 TRS85 trafficking protein particle complex subunit 8 PBANKA_1121500 PF3D7_0622600 QCR9 cytochrome b-c1 complex subunit 9 PBANKA_1122100 PF3D7_0623200 FNR ferredoxin--NADP reductase PBANKA_1138500 PF3D7_1362500 exonuclease PBANKA_1139700 PF3D7_1363700 conserved Plasmodium protein, unknown function PBANKA_1210700 PF3D7_1012300 QCR7 cytochrome b-c1 complex subunit 7 PBANKA_1228500 PF3D7_0801700 SENP2 sentrin-specific protease 2 PBANKA_1304200 PF3D7_1440200 SPP stromal-processing peptidase PBANKA_1310600 PF3D7_1446800 HDP heme detoxification protein PBANKA_1322800 PF3D7_1459100 GTP-binding protein PBANKA_1330600 PF3D7_1467300 DXR 1-deoxy-D-xylulose 5-phosphate reductoisomerase PBANKA_1338400 PF3D7_1323200 V-type proton ATPase subunit G PBANKA_1406100 PF3D7_1307600 DNA-directed RNA polymerase alpha chain PBANKA_1409500 PF3D7_1311000 ISD11 protein ISD11 PBANKA_1418400 PF3D7_1320100 ClpS ATP-dependent Clp protease adapter protein ClpS PBANKA_1426700 PF3D7_0810800 PPPK-DHPS hydroxymethyldihydropterin pyrophosphokinase- dihydropteroate synthase PBANKA_1442600 PF3D7_1227900 RNA pseudouridylate synthase PBANKA_1443200 PF3D7_1228500 RNA pseudouridylate synthase Figure 2. Selection of potential drug targets in Plasmodium . Among the P. berghei enzymes that were not tested for essentiality, 186 had an essential ortholog in T. gondii and thus may be predicted as essential in P. berghei . To increase the confidence of these genes to be essential in Plasmodium , we considered 53 genes that were conserved across Plasmodium and apicomplexan species. Among the enzymes tested for essentiality, such a criteria led to a set with 77% enzymes as essential, suggesting high enrichment for essentiality among predicted essential enzymes. In total, 28 of these enzymes had low sequence similarity (<40% identity) with human proteins and thus may also be considered as potential drug targets ( Dataset 1 16 ). Properties of essential P. berghei genes Essential genes show different expression, evolutionary and functional properties 9 . We thus tested whether similar patterns would be observed for P. berghei . Essential P. berghei genes showed higher mRNA expression levels in asexual stages, but lower expression levels in sexual stages compared to non-essential genes ( Figure 3A ). Proteins encoded by essential genes were more likely to be detected by mass-spectrometry in different developmental stages compared to non-essential genes ( Figure 3B ). Essential genes showed a lower evolutionary rate (dN and dN/dS) and higher conservation in apicomplexan species ( Figure 3C ). Essential genes were significantly enriched in functional classes, such as “Translation”, “Ribosome”, “DNA replication”, “Intracellular protein transport”, “Cytoplasm”, and “Nucleus” ( Figure 4 ). Figure 3. Properties of essential Plasmodium berghei genes. ( A ) Essential P. berghei genes showed higher mRNA expression levels in asexual stages, but lower mRNA expression levels in sexual stages. The mean FPKM values for the essential and non-essential genes were calculated for different development stages and their log 2 ratio was taken. All stages except ‘ookinete 24h’ showed a statistically significant difference between essential and non-essential genes (t-test; p < 0.05). The RNA-seq data was taken from Otto et al. 13 . ( B ) Proteins encoded by essential genes were more likely to be detected by mass-spectrometry in different stages compared to non-essential genes. All stages except ‘sporozoites’ showed a significant difference between essential and non-essential genes (Chi-square test; p < 0.05). Overall 47% of the tested genes were essential. The proteomics data was obtained from Hall et al. 14 ( C ) Essential genes showed a lower evolutionary rate and higher conservation across apicomplexan species. The mean dN and dN/dS values for essential and non-essential genes was calculated and their log 2 ratio was taken. This data was taken from Hall et al. 14 . The mean number of apicomplexan species (out of six), in which an ortholog was identified, was calculated for essential and non-essential genes and their log 2 ratio was taken. dN and conservation in apicomplexan species showed a statistically significant difference between essential and non-essential genes (t-test; p < 0.05), but not dN/dS. Figure 4. Prevalence of essential genes in different functional classes. The Gene Ontology information for Plasmodium falciparum genes was obtained from PlasmoDB 10 and assigned to their P. berghei orthologs. Classes with a significant difference (Chi-square test; p < 0.05) in essential genes are marked with *. Dataset 1. Fitness, expression, functionality, conservation and evolutionary information of Plasmodium berghei genes. Discussion The recent availability of gene essentiality data from P. berghei and the related apicomplexan T. gondii provides an unprecedented opportunity to identify potential drug targets to accelerate anti-malarial drug discovery. We find a significant correlation of gene essentiality between P. berghei and T. gondii ( Figure 1 ). Thus, the information about gene essentiality in T. gondii provides independent experimental support for gene essentiality in P. berghei , which not only increases the confidence of gene essentiality in P. berghei, but also increases the likelihood that these genes would be essential in other Plasmodium species that cause human malaria, and probably in different Plasmodium developmental stages. Drug targets that are essential in multiple species and stages of Plasmodium are particularly desirable 17 . Novel essential genes in Plasmodium could also be predicted based on the essentiality of their orthologs in T. gondii . Further prioritization of these genes could be made based on their conservation across Plasmodium and apicomplexan species, low sequence similarity to human proteins, as well as practical information, such as previous availability of clones, assays, protein structure and inhibitors 18 , 19 . The high conservation of essentiality between P. berghei and T. gondii may allow prediction of essential genes in other apicomplexan pathogens, such as Cryptosporidium . We found gene and protein properties significantly associated with essentiality in P. berghei . At the mRNA level, essential genes, compared to non-essential genes, were expressed at higher levels in asexual stages, but at lower levels in sexual stages ( Figure 3A ). Since gene essentiality was measured at the asexual stage, this might explain the positive correlation between essentiality and mRNA expression in asexual stages. Proteins encoded by essential genes were more likely to be detected by mass-spectrometry in different development stages ( Figure 3B ). Essential genes showed lower evolutionary rates and higher conservation across apicomplexan species ( Figure 3C ). The higher evolutionary conservation of essential genes is well-documented 20 . We find Gene Ontology classes “Translation”, “Ribosome”, “DNA replication”, “Intracellular protein transport”, “Cytoplasm”, and “Nucleus” to be significantly enriched in essential genes ( Figure 4 ). “Translation” class was also enriched in essential genes after excluding “Ribosome” genes (69% essential; Chi-square test; p = 0.0001), suggesting that enrichment of essential genes in the “Translation” category is not only due to ribosomal genes. Thus enzymes involved in protein translation may be important targets for anti-malarial drug discovery. Data availability The in vivo relative growth rate data for 2574 genes of P. berghei genes was obtained from PlasmoGEM database ( http://plasmogem.sanger.ac.uk/phenotypes ) 8 . The genome-wide CRISPR screening data for the relative fitness of 8151 T. gondii genes during infection of human fibroblasts cells was obtained from Sidik et al. 9 . Dataset 1: Fitness, expression, functionality, conservation and evolutionary information of Plasmodium berghei genes. doi, 10.5256/f1000research.10559.d148698 16 Author contributions G.P.S. conceived and designed the study, performed the research and wrote the manuscript. Competing interests No competing interests were disclosed. Grant information The work is supported by an Early Career Fellowship to G.P.S. by the Wellcome Trust/DBT India Alliance (IA/E/15/1/502297). Acknowledgements The author would like to acknowledge suggestions and criticism on the manuscript by Ms. Preeti Goel. Faculty Opinions recommended References 1. World Health Organization: The World Malaria Report 2015. 2015. Reference Source 2. Duru V, Witkowski B, Ménard D: Plasmodium falciparum Resistance to Artemisinin Derivatives and Piperaquine: A Major Challenge for Malaria Elimination in Cambodia. Am J Trop Med Hyg. 2016; 95 (6): 1228–1238. PubMed Abstract | Publisher Full Text | Free Full Text 3. Alonso PL, Brown G, Arevalo-Herrera M, et al. : A research agenda to underpin malaria eradication. PLoS Med. 2011; 8 (1): e1000406. PubMed Abstract | Publisher Full Text | Free Full Text 4. Gilbert IH: Drug discovery for neglected diseases: molecular target-based and phenotypic approaches. J Med Chem. 2013; 56 (20): 7719–7726. PubMed Abstract | Publisher Full Text | Free Full Text 5. Spangenberg T, Burrows JN, Kowalczyk P, et al. : The open access malaria box: a drug discovery catalyst for neglected diseases. PLoS One. 2013; 8 (6): e62906. PubMed Abstract | Publisher Full Text | Free Full Text 6. Wells TN, Hooft van Huijsduijnen R, Van Voorhis WC: Malaria medicines: a glass half full? Nat Rev Drug Discov. 2015; 14 (6): 424–442. PubMed Abstract | Publisher Full Text 7. Gomes AR, Bushell E, Schwach F, et al. : A genome-scale vector resource enables high-throughput reverse genetic screening in a malaria parasite. Cell Host Microbe. 2015; 17 (3): 404–413. PubMed Abstract | Publisher Full Text | Free Full Text 8. Schwach F, Bushell E, Gomes AR, et al. : Plasmo GEM, a database supporting a community resource for large-scale experimental genetics in malaria parasites. Nucleic Acids Res. 2015; 43 (Database issue): D1176–D1182. PubMed Abstract | Publisher Full Text | Free Full Text 9. Sidik SM, Huet D, Ganesan SM, et al. : A Genome-wide CRISPR Screen in Toxoplasma Identifies Essential Apicomplexan Genes. Cell. 2016; 166 (6): 1423–1435.e12. PubMed Abstract | Publisher Full Text | Free Full Text 10. Aurrecoechea C, Brestelli J, Brunk BP, et al. : PlasmoDB: a functional genomic database for malaria parasites. Nucleic Acids Res. 2009; 37 (Database issue): D539–D543. PubMed Abstract | Publisher Full Text | Free Full Text 11. Aurrecoechea C, Barreto A, Basenko EY, et al. : EuPathDB: the eukaryotic pathogen genomics database resource. Nucleic Acids Res. 2016; 45 (D1): D581–D591, pii: gkw1105. PubMed Abstract | Publisher Full Text 12. Wolf YI, Koonin EV: A tight link between orthologs and bidirectional best hits in bacterial and archaeal genomes. Genome Biol Evol. 2012; 4 (12): 1286–1294. PubMed Abstract | Publisher Full Text | Free Full Text 13. Otto TD, Böhme U, Jackson AP, et al. : A comprehensive evaluation of rodent malaria parasite genomes and gene expression. BMC Biol. 2014; 12 : 86. PubMed Abstract | Publisher Full Text | Free Full Text 14. Hall N, Karras M, Raine JD, et al. : A comprehensive survey of the Plasmodium life cycle by genomic, transcriptomic, and proteomic analyses. Science. 2005; 307 (5706): 82–86. PubMed Abstract | Publisher Full Text 15. Krogh A, Larsson B, von Heijne G, et al. : Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001; 305 (3): 567–580. PubMed Abstract | Publisher Full Text 16. Singh G: Dataset 1 in: Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets. F1000Research. 2017. Data Source 17. Burrows JN, van Huijsduijnen RH, Möhrle JJ, et al. : Designing the next generation of medicines for malaria control and eradication. Malar J. 2013; 12 : 187. PubMed Abstract | Publisher Full Text | Free Full Text 18. Magariños MP, Carmona SJ, Crowther GJ, et al. : TDR Targets: a chemogenomics resource for neglected diseases. Nucleic Acids Res. 2012; 40 (Database issue): D1118–D1127. PubMed Abstract | Publisher Full Text | Free Full Text 19. Crowther GJ, Shanmugam D, Carmona SJ, et al. : Identification of attractive drug targets in neglected-disease pathogens using an in silico approach. PLoS Negl Trop Dis. 2010; 4 (8): e804. PubMed Abstract | Publisher Full Text | Free Full Text 20. Doyle MA, Gasser RB, Woodcroft BJ, et al. : Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes. BMC Genomics. 2010; 11 : 222. PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 09 Jan 2017 ADD YOUR COMMENT Comment Author details Author details Molecular Medicine Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India Competing interests No competing interests were disclosed. Grant information The work is supported by an Early Career Fellowship to G.P.S. by the Wellcome Trust/DBT India Alliance (IA/E/15/1/502297). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 09 Jan 2017, 6:23 https://doi.org/10.12688/f1000research.10559.1 Copyright © 2017 Singh GP. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication). Download Export To Sciwheel Bibtex EndNote ProCite Ref. 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Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 09 Jan 2017 Views 0 Cite How to cite this report: Picard D. Reviewer Report For: Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets [version 1; peer review: 2 approved with reservations] . F1000Research 2017, 6 :23 ( https://doi.org/10.5256/f1000research.11378.r19792 ) The direct URL for this report is: https://f1000research.com/articles/6-23/v1#referee-response-19792 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 13 Feb 2017 Didier Picard , Department of Cell Biology, University of Geneva, Geneva, Switzerland Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.11378.r19792 This Research Note reports on an interesting and potentially useful exercise to identify and to prioritize candidates for target-based drug development in Plasmodium. The whole approach is relatively straightforward and provides a list of candidates to think about, not more, ... Continue reading READ ALL This Research Note reports on an interesting and potentially useful exercise to identify and to prioritize candidates for target-based drug development in Plasmodium. The whole approach is relatively straightforward and provides a list of candidates to think about, not more, not less. Additional considerations could subsequently be applied by others to home in on reasonable targets to focus on. Overall, this short report was worth publishing, but would benefit from some revisions outlined below. Specific comments: How many genes are experimentally essential in both species is mentioned in the text at a relatively late stage of the presentation. It would be helpful to mention it earlier, e.g. in the legend to Figure 1 (the number of red dots). At some point, the author focuses on enzymes as targets. I do not think that enzymes are the only druggable targets. But if that's what the author wants to focus on, the term "enzyme" should be defined. Is it just based on the GO term associated with these genes/proteins? 40% sequence identity is still a lot, and may be too much if active sites are even more highly conserved. Moreover, in this conext I also agree with point 2 of the referee report by Gregory Crowther 1 . While I agree with Gregory Crowther's comment 3 1 about the relevance to drug discovery of the data in Figures 3 and 4, I still find this analysis interesting and not superfluous in the context of the overall story presented here. Figure 2: I share the confusion with Gregory Crowther 1 with respect to the math here. The text at the bottom of page 3 clearly suggests that 245 = 30+83+151, which of course cannot be. This needs to be fixed/clarified. References 1. Crowther GJ: Referee Report For: Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets [version 1; referees: 1 approved with reservations]. F1000Research . 2017; 6 (23). Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Picard D. Reviewer Report For: Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets [version 1; peer review: 2 approved with reservations] . F1000Research 2017, 6 :23 ( https://doi.org/10.5256/f1000research.11378.r19792 ) The direct URL for this report is: https://f1000research.com/articles/6-23/v1#referee-response-19792 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Crowther GJ. Reviewer Report For: Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets [version 1; peer review: 2 approved with reservations] . F1000Research 2017, 6 :23 ( https://doi.org/10.5256/f1000research.11378.r19085 ) The direct URL for this report is: https://f1000research.com/articles/6-23/v1#referee-response-19085 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 16 Jan 2017 Gregory J. Crowther , Division of Biological Sciences, School of STEM, University of Washington, Bothell, WA, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.11378.r19085 This paper analyzes genome-wide data on gene essentiality from two apicomplexan parasites: Plasmodium berghei (the cause of malaria in rodents) and Toxoplasma gondii (the cause of toxoplasmosis). The paper is a new analysis of previously reported data (rather than a ... Continue reading READ ALL This paper analyzes genome-wide data on gene essentiality from two apicomplexan parasites: Plasmodium berghei (the cause of malaria in rodents) and Toxoplasma gondii (the cause of toxoplasmosis). The paper is a new analysis of previously reported data (rather than a presentation of new wet-lab results), which is fine. Those whole-genome datasets are so rich that the papers with the original data cannot possibly cover every interesting angle, so I am happy to see interesting follow-up papers such as this one, which offers additional insight into the datasets. The following comments go from broad to specific. Broad While the analysis is interesting, I’m not fully convinced that it advances malaria drug discovery in important ways; it might actually be most useful as an investigation of basic apicomplexan parasite biology. Target-based drug discovery researchers are certainly glad to know whether particular genes of interest (corresponding to specific enzymes or pathways in which they have expertise) are essential or not. However, the figures present genome-wide trends that, while interesting, don’t seem that helpful in prioritizing possible drug targets. Figure 1 is probably the most relevant to drug discovery. It shows that genes found to be essential in one species (P. berghei or T. gondii) are more likely to also be essential in the other; thus, P. berghei genes not covered by the Gomes et al. (2015) screen 1 are fairly likely to be essential if their T. gondii orthologs are essential. Figure 2 shows a prioritization exercise which is not incorrect, but I don’t think sequence similarity to human proteins is an especially useful criterion. (This is also a limitation of Table 1, in my view). The hope is that we can avoid toxicity by targeting parasite proteins that are dissimilar to human proteins; however, overall sequence similarities tell us very little about whether a parasite protein will have any binding pockets (each of which represents a small part of the total amino acid sequence) that, in three dimensions, closely resemble any binding pockets of human proteins. Figure 3 shows gene expression data at the level of transcripts and proteins; I don’t think this information really applies to drug discovery. (For example, I don’t think anyone should say of a particular target, “Well, this isn’t highly expressed; maybe it isn’t a good/essential target after all.”. If I recall correctly, some excellent targets such as DHFR and PfATP4 are not expressed that highly) Figure 4 shows that some functional classes of proteins have a higher percentage of essential proteins than others – but I don’t think this helps us choose possible drug targets either. Even the right-most categories have plenty of essential genes, which is why, for example, there is interest in targeting fatty acid metabolism, the second-lowest category in terms of percent essentiality (see, for example, Shears et al. 2 ). Likewise, the unimpressive-looking “transport” category (~52% essential) includes PfATP4, a red-hot target of current Plasmodium research (see Wells et al. 3 ). Drug discovery researchers do not usually think in terms of the big broad categories shown in Figure 4, so knowing percent essentiality by category won’t help them much with target selection. The above observations lead me to the overall recommendation to revise the paper in one of two ways. Option 1 is to emphasize the drug-discovery stuff less and the basic biology more. Option 2 is to enhance the drug-discovery theme by addressing my concerns about the figures (i.e. explaining why they are more relevant to drug discovery than I’m giving them credit for) and/ or adding analyses that have clearer, stronger relevance to drug discovery. The paper does not currently try to combine the essentiality data with genome-wide predictions of “druggability” (which are hard!), but perhaps a collaborator could be enlisted to help with that. In general, most proteins (including most essential proteins) are not that druggable, so essentiality information in the absence of druggability information does not get us that far down the drug-discovery road. Specific Figure 1B: The legend says that green dots represent “non-conserved” proteins. I think that only conserved proteins are shown in this panel, and the green dots are proteins that are neither essential in both species nor nonessential in both species. Please check. Figure 2: Aside from my above-mentioned concern about homology to human proteins, it might make sense to show the arrows as follows: 710 => 289 => 245 => 151 => 83 => 30, thus showing the winnowing of the targets with additional criteria. In its current form, the figure initially led me to think, incorrectly, that the 245 genes could be split into subgroups of 30, 83, and 151. Figure 3: For 3A and 3B, the transcriptome data (relative abundance) don’t seem to correlate that closely with the proteome data (detectable or not). For example, essential gene expression in the sexual stages looks low at the level of RNA in 3A but average-to-high at the protein level in 3B. Are such discrepancies surprising/interesting? Discuss in the Discussion! Also, briefly define dN and dS (nonsynonymous and synonymous substitutions; 3C) somewhere in the paper. Also, to improve clarity, consider using one color for the bars corresponding to the asexual stages and another color for the bars corresponding to the sexual stages. Figure 4: Others must have done analyses like this for other (non-apicomplexan) species, e.g., of bacteria. Please compare the Figure 4 data to previous work in the Discussion. Also, why did the “cytoplasm” category come out as statistically significant? Are there a huge number of genes in that category? Can a paragraph be added to the Discussion on what sort of specific work might follow naturally from the present analysis? That would help readers appreciate the significance of the present work. References 1. Gomes AR, Bushell E, Schwach F, Girling G, et al.: A genome-scale vector resource enables high-throughput reverse genetic screening in a malaria parasite. Cell Host Microbe . 2015; 17 (3): 404-13 PubMed Abstract | Publisher Full Text 2. Shears MJ, Botté CY, McFadden GI: Fatty acid metabolism in the Plasmodium apicoplast: Drugs, doubts and knockouts. Mol Biochem Parasitol . 199 (1-2): 34-50 PubMed Abstract | Publisher Full Text 3. Wells TN, Hooft van Huijsduijnen R, Van Voorhis WC: Malaria medicines: a glass half full?. Nat Rev Drug Discov . 2015; 14 (6): 424-42 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Crowther GJ. Reviewer Report For: Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets [version 1; peer review: 2 approved with reservations] . F1000Research 2017, 6 :23 ( https://doi.org/10.5256/f1000research.11378.r19085 ) The direct URL for this report is: https://f1000research.com/articles/6-23/v1#referee-response-19085 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 09 Jan 2017 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 1 09 Jan 17 read read Gregory J. Crowther , University of Washington, Bothell, USA Didier Picard , University of Geneva, Geneva, Switzerland Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2017 Picard D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 13 Feb 2017 | for Version 1 Didier Picard , Department of Cell Biology, University of Geneva, Geneva, Switzerland 0 Views copyright © 2017 Picard D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This Research Note reports on an interesting and potentially useful exercise to identify and to prioritize candidates for target-based drug development in Plasmodium. The whole approach is relatively straightforward and provides a list of candidates to think about, not more, not less. Additional considerations could subsequently be applied by others to home in on reasonable targets to focus on. Overall, this short report was worth publishing, but would benefit from some revisions outlined below. Specific comments: How many genes are experimentally essential in both species is mentioned in the text at a relatively late stage of the presentation. It would be helpful to mention it earlier, e.g. in the legend to Figure 1 (the number of red dots). At some point, the author focuses on enzymes as targets. I do not think that enzymes are the only druggable targets. But if that's what the author wants to focus on, the term "enzyme" should be defined. Is it just based on the GO term associated with these genes/proteins? 40% sequence identity is still a lot, and may be too much if active sites are even more highly conserved. Moreover, in this conext I also agree with point 2 of the referee report by Gregory Crowther 1 . While I agree with Gregory Crowther's comment 3 1 about the relevance to drug discovery of the data in Figures 3 and 4, I still find this analysis interesting and not superfluous in the context of the overall story presented here. Figure 2: I share the confusion with Gregory Crowther 1 with respect to the math here. The text at the bottom of page 3 clearly suggests that 245 = 30+83+151, which of course cannot be. This needs to be fixed/clarified. References 1. Crowther GJ: Referee Report For: Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets [version 1; referees: 1 approved with reservations]. F1000Research . 2017; 6 (23). Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Picard D. Peer Review Report For: Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets [version 1; peer review: 2 approved with reservations] . F1000Research 2017, 6 :23 ( https://doi.org/10.5256/f1000research.11378.r19792) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/6-23/v1#referee-response-19792 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2017 Crowther G. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 Jan 2017 | for Version 1 Gregory J. Crowther , Division of Biological Sciences, School of STEM, University of Washington, Bothell, WA, USA 0 Views copyright © 2017 Crowther G. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This paper analyzes genome-wide data on gene essentiality from two apicomplexan parasites: Plasmodium berghei (the cause of malaria in rodents) and Toxoplasma gondii (the cause of toxoplasmosis). The paper is a new analysis of previously reported data (rather than a presentation of new wet-lab results), which is fine. Those whole-genome datasets are so rich that the papers with the original data cannot possibly cover every interesting angle, so I am happy to see interesting follow-up papers such as this one, which offers additional insight into the datasets. The following comments go from broad to specific. Broad While the analysis is interesting, I’m not fully convinced that it advances malaria drug discovery in important ways; it might actually be most useful as an investigation of basic apicomplexan parasite biology. Target-based drug discovery researchers are certainly glad to know whether particular genes of interest (corresponding to specific enzymes or pathways in which they have expertise) are essential or not. However, the figures present genome-wide trends that, while interesting, don’t seem that helpful in prioritizing possible drug targets. Figure 1 is probably the most relevant to drug discovery. It shows that genes found to be essential in one species (P. berghei or T. gondii) are more likely to also be essential in the other; thus, P. berghei genes not covered by the Gomes et al. (2015) screen 1 are fairly likely to be essential if their T. gondii orthologs are essential. Figure 2 shows a prioritization exercise which is not incorrect, but I don’t think sequence similarity to human proteins is an especially useful criterion. (This is also a limitation of Table 1, in my view). The hope is that we can avoid toxicity by targeting parasite proteins that are dissimilar to human proteins; however, overall sequence similarities tell us very little about whether a parasite protein will have any binding pockets (each of which represents a small part of the total amino acid sequence) that, in three dimensions, closely resemble any binding pockets of human proteins. Figure 3 shows gene expression data at the level of transcripts and proteins; I don’t think this information really applies to drug discovery. (For example, I don’t think anyone should say of a particular target, “Well, this isn’t highly expressed; maybe it isn’t a good/essential target after all.”. If I recall correctly, some excellent targets such as DHFR and PfATP4 are not expressed that highly) Figure 4 shows that some functional classes of proteins have a higher percentage of essential proteins than others – but I don’t think this helps us choose possible drug targets either. Even the right-most categories have plenty of essential genes, which is why, for example, there is interest in targeting fatty acid metabolism, the second-lowest category in terms of percent essentiality (see, for example, Shears et al. 2 ). Likewise, the unimpressive-looking “transport” category (~52% essential) includes PfATP4, a red-hot target of current Plasmodium research (see Wells et al. 3 ). Drug discovery researchers do not usually think in terms of the big broad categories shown in Figure 4, so knowing percent essentiality by category won’t help them much with target selection. The above observations lead me to the overall recommendation to revise the paper in one of two ways. Option 1 is to emphasize the drug-discovery stuff less and the basic biology more. Option 2 is to enhance the drug-discovery theme by addressing my concerns about the figures (i.e. explaining why they are more relevant to drug discovery than I’m giving them credit for) and/ or adding analyses that have clearer, stronger relevance to drug discovery. The paper does not currently try to combine the essentiality data with genome-wide predictions of “druggability” (which are hard!), but perhaps a collaborator could be enlisted to help with that. In general, most proteins (including most essential proteins) are not that druggable, so essentiality information in the absence of druggability information does not get us that far down the drug-discovery road. Specific Figure 1B: The legend says that green dots represent “non-conserved” proteins. I think that only conserved proteins are shown in this panel, and the green dots are proteins that are neither essential in both species nor nonessential in both species. Please check. Figure 2: Aside from my above-mentioned concern about homology to human proteins, it might make sense to show the arrows as follows: 710 => 289 => 245 => 151 => 83 => 30, thus showing the winnowing of the targets with additional criteria. In its current form, the figure initially led me to think, incorrectly, that the 245 genes could be split into subgroups of 30, 83, and 151. Figure 3: For 3A and 3B, the transcriptome data (relative abundance) don’t seem to correlate that closely with the proteome data (detectable or not). For example, essential gene expression in the sexual stages looks low at the level of RNA in 3A but average-to-high at the protein level in 3B. Are such discrepancies surprising/interesting? Discuss in the Discussion! Also, briefly define dN and dS (nonsynonymous and synonymous substitutions; 3C) somewhere in the paper. Also, to improve clarity, consider using one color for the bars corresponding to the asexual stages and another color for the bars corresponding to the sexual stages. Figure 4: Others must have done analyses like this for other (non-apicomplexan) species, e.g., of bacteria. Please compare the Figure 4 data to previous work in the Discussion. Also, why did the “cytoplasm” category come out as statistically significant? Are there a huge number of genes in that category? Can a paragraph be added to the Discussion on what sort of specific work might follow naturally from the present analysis? That would help readers appreciate the significance of the present work. References 1. Gomes AR, Bushell E, Schwach F, Girling G, et al.: A genome-scale vector resource enables high-throughput reverse genetic screening in a malaria parasite. Cell Host Microbe . 2015; 17 (3): 404-13 PubMed Abstract | Publisher Full Text 2. Shears MJ, Botté CY, McFadden GI: Fatty acid metabolism in the Plasmodium apicoplast: Drugs, doubts and knockouts. Mol Biochem Parasitol . 199 (1-2): 34-50 PubMed Abstract | Publisher Full Text 3. Wells TN, Hooft van Huijsduijnen R, Van Voorhis WC: Malaria medicines: a glass half full?. Nat Rev Drug Discov . 2015; 14 (6): 424-42 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Crowther GJ. Peer Review Report For: Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets [version 1; peer review: 2 approved with reservations] . F1000Research 2017, 6 :23 ( https://doi.org/10.5256/f1000research.11378.r19085) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/6-23/v1#referee-response-19085 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Click here to access the data. The problem Spreadsheet data files may not format correctly if your computer is using different default delimiters (symbols used to separate values into separate cells) - a spreadsheet created in one region is sometimes misinterpreted by computers in other regions. You can change the regional settings on your computer so that the spreadsheet can be interpreted correctly. How to fix it Save downloaded CSV file Open spreadsheet program (e.g. Excel) Click the ‘Data’ tab at the top Click the ‘From text’ icon (top left) Browse for downloaded CSV file, click ‘Import’ Ensure ‘Delimited’ radio button is selected, click ‘Next’ Check one of the appropriate delimiter checkboxes (you can visualize the formatting by looking at the data preview below these options) Click ‘Finish’ Downloaded data do not display as expected? Download the data Dataset citation: Singh GP. Dataset 1 in: Conservation of gene essentiality in Apicomplexa and its application for prioritization of anti-malarial drug targets. 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