Abstract
The sandfly midgut and the human macrophage phagolysosome provide antagonistic metabolic niches for the endoparasite Leishmania to survive and populate. Although these environments fluctuate across developmental stages, the relative changes in both these environments across parasite generations might remain gradual. Such environmental restrictions might endow parasite metabolism with a choice of specific genotypic and phenotypic factors that can constrain enzyme evolution for successful adaptation to the host. With respect to the available cellular information for Leishmania species, for the first time, we measure the relative contribution of eight inter-correlated predictors related to codon usage, GC content, gene expression, gene length, multi-functionality, and flux-coupling potential of an enzyme on the evolutionary rates of singleton metabolic genes and further compare their effects across three Leishmania species. Our analysis reveals that codon adaptation, multi-functionality, and flux-coupling potential of an enzyme are independent contributors of enzyme evolutionary rates, which can together explain a large variation in enzyme evolutionary rates across species. We also hypothesize that a species-specific occurrence of duplicated genes in novel subcellular locations can create new flux routes through certain singleton flux-coupled enzymes, thereby constraining their evolution. A cross-species comparison revealed both common and species-specific genes whose evolutionary divergence was constrained by multiple independent factors. Out of these, previously known pharmacological targets and virulence factors in Leishmania were identified, suggesting their evolutionary reasons for being important survival factors to the parasite. All these results provide a fundamental understanding of the factors underlying adaptive strategies of the parasite, which can be further targeted.
Similar content being viewed by others
References
Alvarez-Ponce D, Fares MA (2012) Evolutionary rate and duplicability in the Arabidopsis thaliana protein-protein interaction network. Genome Biol Evol 4:1263–1274. https://doi.org/10.1093/gbe/evs101
Alvarez-Ponce D, Feyertag F, Chakraborty S (2017) Position matters: network centrality considerably impacts rates of protein evolution in the human protein–protein interaction network. Genome Biol Evol 9:1742–1756. https://doi.org/10.1093/gbe/evx117
Aslett M, Aurrecoechea C, Berriman M et al (2010) TriTrypDB: a functional genomic resource for the Trypanosomatidae. Nucleic Acids Res 38:D457–D462. https://doi.org/10.1093/nar/gkp851
Bello AM, Poduch E, Fujihashi M et al (2007) A potent, covalent inhibitor of orotidine 5‘-monophosphate decarboxylase with antimalarial activity. J Med Chem 50:915–921. https://doi.org/10.1021/jm060827p
Cantacessi C, Dantas-Torres F, Nolan MJ, Otranto D (2015) The past, present, and future of Leishmania genomics and transcriptomics. Trends Parasitol 31:100–108. https://doi.org/10.1016/j.pt.2014.12.012
Chavali AK, Whittemore JD, Eddy JA et al (2008) Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major. Mol Syst Biol 4:177. https://doi.org/10.1038/msb.2008.15
Chesmore KN, Bartlett J, Cheng C, Williams SM (2016) Complex patterns of association between pleiotropy and transcription factor evolution. Genome Biol Evol 8:3159–3170. https://doi.org/10.1093/gbe/evw228
Chu S, Wang J, Cheng H et al (2014) Evolutionary study of the isoflavonoid pathway based on multiple copies analysis in soybean. BMC Genet 15:1–12. https://doi.org/10.1186/1471-2156-15-76
Colombo M, Laayouni H, Invergo BM et al (2014) Metabolic flux is a determinant of the evolutionary rates of enzyme-encoding genes. Evolution 68:605–613. https://doi.org/10.1111/evo.12262
Drummond DA, Raval A, Wilke CO (2006) A single determinant dominates the rate of yeast protein evolution. Mol Biol Evol 23:327–337. https://doi.org/10.1093/molbev/msj038
Garami A, Ilg T (2001a) The role of phosphomannose isomerase in Leishmania mexicana glycoconjugate synthesis and virulence. J Biol Chem 276:6566–6575. https://doi.org/10.1074/jbc.M009226200
Garami A, Ilg T (2001b) Disruption of mannose activation in Leishmania mexicana: GDP-mannose pyrophosphorylase is required for virulence, but not for viability. EMBO J 20:3657–3666. https://doi.org/10.1093/emboj/20.14.3657
Ginger ML, McFadden GI, Michels PAM (2010) Rewiring and regulation of cross-compartmentalized metabolism in protists. Philos Trans R Soc B 365:831–845. https://doi.org/10.1098/rstb.2009.0259
Gladki A, Kaczanowski S, Szczesny P, Zielenkiewicz P (2013) The evolutionary rate of antibacterial drug targets. BMC Bioinform. https://doi.org/10.1186/1471-2105-14-36
Jeacock L, Faria J, Horn D (2018) Codon usage bias controls mRNA and protein abundance in trypanosomatids. Elife 7:e32496
Jolliffe IT (1982) A note on the use of principal components in regression. Appl Stat. https://doi.org/10.2307/2348005
Jovelin R, Phillips PC (2009) Evolutionary rates and centrality in the yeast gene regulatory network. Genome Biol 10:R35. https://doi.org/10.1186/gb-2009-10-4-r35
Kawaguchi R, Bailey-Serres J (2005) mRNA sequence features that contribute to translational regulation in Arabidopsis. Nucleic Acids Res 33:955–965. https://doi.org/10.1093/nar/gki240
Lahav T, Sivam D, Volpin H et al (2011) Multiple levels of gene regulation mediate differentiation of the intracellular pathogen Leishmania. FASEB J 25:515–525. https://doi.org/10.1096/fj.10-157529
Lv W, Xu Y, Guo Y et al (2016) The drug target genes show higher evolutionary conservation than non-target genes. Oncotarget 7:4961–4971. https://doi.org/10.18632/oncotarget.6755
Mannaert A, Downing T, Imamura H, Dujardin J-C (2012) Adaptive mechanisms in pathogens: universal aneuploidy in Leishmania. Trends Parasitol 28:370–376. https://doi.org/10.1016/j.pt.2012.06.003
Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, Cambridge
Mantilla BS, Paes LS, Pral EMF et al (2015) Role of ∆1-pyrroline-5-carboxylate dehydrogenase supports mitochondrial metabolism and host-cell invasion of Trypanosoma cruzi. J Biol Chem 290:7767–7790. https://doi.org/10.1074/jbc.M114.574525
Martin WE, Bridgmon KD (2012) Quantitative and statistical research methods: from hypothesis to results. Wiley, Hoboken
Martin JL, Yates PA, Soysa R et al (2014) Metabolic reprogramming during purine stress in the protozoan pathogen Leishmania donovani. PLoS Pathog 10:e1003938. https://doi.org/10.1371/journal.ppat.1003938
McConville MJ, Naderer T (2011) Metabolic pathways required for the intracellular survival of Leishmania. Annu Rev Microbiol 6:543–561. https://doi.org/10.1146/annurev-micro-090110-102913
Moreno MA, Alonso A, Alcolea PJ et al (2014) Tyrosine aminotransferase from Leishmania infantum: a new drug target candidate. Int J Parasitol Drugs Drug Resist 4:347–354. https://doi.org/10.1016/j.ijpddr.2014.06.001
Mukherjee T, Ray M, Bhaduri A (1988) Aspartate transcarbamylase from Leishmania donovani. A discrete, nonregulatory enzyme as a potential chemotherapeutic site. J Biol Chem 263:708–713
Nirujogi RS, Pawar H, Renuse S et al (2014) Moving from unsequenced to sequenced genome: reanalysis of the proteome of Leishmania donovani. J Proteom 97:48–61. https://doi.org/10.1016/j.jprot.2013.04.021
Notebaart RA, Teusink B, Siezen RJ, Papp B (2008) Co-regulation of metabolic genes is better explained by flux coupling than by network distance. PLoS Comput Biol 4:e26. https://doi.org/10.1371/journal.pcbi.0040026
Pál C, Papp B, Lercher MJ (2006) An integrated view of protein evolution. Nat Rev Genet 7:337–348. https://doi.org/10.1038/nrg1838
Papp B, Notebaart RA, Pál C (2011) Systems-biology approaches for predicting genomic evolution. Nat Rev Genet 12:591–602. https://doi.org/10.1038/nrg3033
Rastrojo A, Carrasco-Ramiro F, Mart’\in D et al (2013) The transcriptome of Leishmania major in the axenic promastigote stage: transcript annotation and relative expression levels by RNA-sEq. BMC Genom 14:223. https://doi.org/10.1186/1471-2164-14-223
Rice P, Longden I, Bleasby A et al (2000) EMBOSS: the European molecular biology open software suite. Trends Genet 16:276–277. https://doi.org/10.1016/S0168-9525(00)02024-2
Salathé M, Ackermann M, Bonhoeffer S (2005) The effect of multifunctionality on the rate of evolution in yeast. Mol Biol Evol 23:721–722. https://doi.org/10.1093/molbev/msj086
Saunders EC, Ng WW, Kloehn J et al (2014) Induction of a stringent metabolic response in intracellular stages of Leishmania mexicana leads to increased dependence on mitochondrial metabolism. PLoS Pathog 10:e1003888
Scott DA, Hickerson SM, Vickers TJ, Beverley SM (2008) The role of the mitochondrial glycine cleavage complex in the metabolism and virulence of the protozoan parasite Leishmania major. J Biol Chem 283:155–165. https://doi.org/10.1074/jbc.M708014200
Searls DB (2003) Pharmacophylogenomics: genes, evolution and drug targets. Nat Rev Drug Discov 2:613. https://doi.org/10.1038/nrd1152
Sharma M, Shaikh N, Yadav S et al (2017) A systematic reconstruction and constraint-based analysis of Leishmania donovani metabolic network: identification of potential antileishmanial drug targets. Mol Biosyst 13:955–969. https://doi.org/10.1039/c6mb00823b
Subramanian A, Sarkar RR (2015) Comparison of codon usage bias across Leishmania and Trypanosomatids to understand mRNA secondary structure, relative protein abundance and pathway functions. Genomics 106:232–241. https://doi.org/10.1016/j.ygeno.2015.05.009
Subramanian A, Sarkar RR (2016) Network structure and enzymatic evolution in Leishmania metabolism: a computational study. In: BIOMAT 2015: Proceedings of the international symposium on mathematical and computational biology, p 1. https://doi.org/10.1142/9789813141919_0001
Subramanian A, Sarkar RR (2017) Revealing the mystery of metabolic adaptations using a genome scale model of Leishmania infantum. Sci Rep 7:10262. https://doi.org/10.1038/s41598-017-10743-x
Szappanos B, Fritzemeier J, Csörg\Ho B et al (2016) Adaptive evolution of complex innovations through stepwise metabolic niche expansion. Nat Commun 7:11607. https://doi.org/10.1038/ncomms11607
Tabachnick BG, Fidell LS (2007) Using multivariate statistics, 5th edn. Allyn and Bacon, New York
Titus RG, Gueiros-Filho FJ, de Freitas LA, Beverley SM (1995) Development of a safe live Leishmania vaccine line by gene replacement. Proc Natl Acad Sci 92:10267–10271. https://doi.org/10.1073/pnas.92.22.10267
Tovar J, Wilkinson S, Mottram JC, Fairlamb AH (1998) Evidence that trypanothione reductase is an essential enzyme in Leishmania by targeted replacement of the tryA gene locus. Mol Microbiol 29:653–660
van der Voet H (1994) Comparing the predictive accuracy of models using a simple randomization test. Chemom Intell Lab Syst 25:313–323. https://doi.org/10.1016/0169-7439(94)85050-X
Vitkup D, Kharchenko P, Wagner A (2006) Influence of metabolic network structure and function on enzyme evolution. Genome Biol 7:R39. https://doi.org/10.1186/gb-2006-7-5-r39
Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73:5261–5267
Warringer J, Blomberg A (2006) Evolutionary constraints on yeast protein size. BMC Evol Biol 6:61. https://doi.org/10.1186/1471-2148-6-61
Yamada T, Bork P (2009) Evolution of biomolecular networks—lessons from metabolic and protein interactions. Nat Rev Mol Cell Biol 10:791–803. https://doi.org/10.1038/nrm2787
Yang Z (1998) Synonymous and nonsynonymous rate variation in nuclear genes of mammals. J Mol Evol 46:409–418. https://doi.org/10.1007/PL00006320
Yang Z (2007) PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol 24:1586–1591. https://doi.org/10.1093/molbev/msm088
Yang L, Gaut BS (2011) Factors that contribute to variation in evolutionary rate among Arabidopsis genes. Mol Biol Evol 28:2359–2369. https://doi.org/10.1093/molbev/msr058
Zhang J, Yang J-R (2015) Determinants of the rate of protein sequence evolution. Nat Rev Genet 16:409. https://doi.org/10.1038/nrg3950
Zhang WW, Ramasamy G, McCall L-I et al (2014) Genetic analysis of Leishmania donovani tropism using a naturally attenuated cutaneous strain. PLoS Pathog 10:e1004244. https://doi.org/10.1371/journal.ppat.1004244
Zilberstein D, Shapira M (1994) The role of pH and temperature in the development of Leishmania parasites. Annu Rev Microbiol 48:449–470
Acknowledgements
This work was supported by a Grant from the Department of Biotechnology, Government of India [BT/PR14958/BID/7/537/2015] provided to RRS. AS also acknowledges the Senior Research Fellowship from DBT-BINC. The authors are thankful to the anonymous reviewers for their critical comments and suggestion to improve the quality of the paper.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
239_2018_9857_MOESM1_ESM.doc
Supplementary Text S1: This file contains results supporting the reported observations and further details of methodology provided in the main article (DOC 1023 KB)
239_2018_9857_MOESM2_ESM.xls
Supplementary File S1: The final shortlisted set of singleton orthologous genes and their features considered for the regression analyses for the three Leishmania species provided in separate sheets within the file (XLS 228 KB)
239_2018_9857_MOESM3_ESM.xls
Supplementary File S2: The principal components for the response dN and dS rates in the three Leishmania species (provided in separate sheets) identified after performing principal component regression (XLS 309 KB)
239_2018_9857_MOESM4_ESM.xls
Supplementary File S3: Orthologous groups of singleton and duplicated genes that occur in different subcellular locations across species and average number of flux-couplings associated with them. The singleton and duplicated genes are provided in separate sheets (XLS 79 KB)
239_2018_9857_MOESM5_ESM.xls
Supplementary File S4: File containing gene clusters as identified by K-means performed with respect to the coordinates of the genes in the selected principal component space for the dN and dS rates and the centroid of each cluster within the n-dimensional feature space. The gene clusters and the centroids for the three species are provided in separate sheets (XLS 239 KB)
Rights and permissions
About this article
Cite this article
Subramanian, A., Sarkar, R.R. Evolutionary Perspectives of Genotype–Phenotype Factors in Leishmania Metabolism. J Mol Evol 86, 443–456 (2018). https://doi.org/10.1007/s00239-018-9857-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00239-018-9857-5