1887

Abstract

spp. are important enteric pathogens in a wide range of vertebrates including humans. Previous comparative analysis revealed conservation in genome composition, gene content, and gene organization among spp., with a progressive reductive evolution in metabolic pathways and invasion-related proteins. In this study, we sequenced the genome of zoonotic pathogen and conducted a comparative genomic analysis. While most intestinal species have similar genomic characteristics and almost complete genome synteny, fewer protein-coding genes and some sequence inversions and translocations were found in the genome. The genome exhibits much higher GC content (39.6 %) than other species (24.3–32.9 %), especially at the third codon position (GC3) of protein-coding genes. Thus, has a different codon usage, which increases the use of less energy costly amino acids (Gly and Ala) encoded by GC-rich codons. While the tRNA usage is conserved among species, consistent with its higher GC content, uses a unique tRNA for GTG for valine instead of GTA in other species. Both mutational pressures and natural selection are associated with the evolution of the codon usage in spp., while natural selection seems to drive the codon usage in . Other unique features of the genome include the loss of the entire traditional and alternative electron transport systems and several invasion-related proteins. Thus, the preference for the use of some less energy costly amino acids in may lead to a more harmonious parasite–host interaction, and the strengthened host-adaptation is reflected by the further reductive evolution of metabolism and host invasion-related proteins.

Funding
This study was supported by the:
  • innovation team project of guangdong universities (Award 2019KCXTD001)
    • Principle Award Recipient: YaoyuFeng
  • 111 project (Award D20008)
    • Principle Award Recipient: LihuaXiao
  • national natural science foundation of china (Award 31820103014; U1901208)
    • Principle Award Recipient: LihuaXiao
  • guangdong major project of basic and applied basic research (Award 2020B0301030007)
    • Principle Award Recipient: LihuaXiao
  • This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License.
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2021-12-15
2022-01-28
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References

  1. Feng Y, Ryan UM, Xiao L. Genetic diversity and population structure of Cryptosporidium. Trends Parasitol 2018; 34:997–1011 [View Article] [PubMed]
    [Google Scholar]
  2. Xiao L, Feng Y. Zoonotic cryptosporidiosis. FEMS Immunol Med Microbiol 2008; 52:309–323 [View Article] [PubMed]
    [Google Scholar]
  3. Liu S, Roellig DM, Guo Y, Li N, Frace MA et al. Evolution of mitosome metabolism and invasion-related proteins in Cryptosporidium. BMC genomics 2016; 17:1006 [View Article] [PubMed]
    [Google Scholar]
  4. Ifeonu OO, Chibucos MC, Orvis J, Su Q, Elwin K et al. Annotated draft genome sequences of three species of Cryptosporidium: Cryptosporidium meleagridis isolate UKMEL1, C. baileyi isolate TAMU-09Q1 and C. hominis isolates TU502_2012 and UKH1. Pathog Dis 2016; 74:ftw080. [View Article] [PubMed]
    [Google Scholar]
  5. Sateriale A, Šlapeta J, Baptista R, Engiles JB, Gullicksrud JA et al. A genetically tractable, natural mouse model of cryptosporidiosis offers insights into host protective immunity. Cell Host Microbe 2019; 26:135–146 [View Article] [PubMed]
    [Google Scholar]
  6. Xu Z, Guo Y, Roellig DM, Feng Y, Xiao L. Comparative analysis reveals conservation in genome organization among intestinal Cryptosporidium species and sequence divergence in potential secreted pathogenesis determinants among major human-infecting species. BMC Genomics 2019; 20:406 [View Article]
    [Google Scholar]
  7. Nader JL, Mathers TC, Ward BJ, Pachebat JA, Swain MT et al. Evolutionary genomics of anthroponosis in Cryptosporidium. Nat Microbiol 2019; 4:826–836 [View Article] [PubMed]
    [Google Scholar]
  8. Xu Z, Li N, Guo Y, Feng Y, Xiao L. Comparative genomic analysis of three intestinal species reveals reductions in secreted pathogenesis determinants in bovine-specific and non-pathogenic Cryptosporidium species. Microb Genom 2020; 6:e000379 [View Article] [PubMed]
    [Google Scholar]
  9. Zhu G, Guo F. Cryptosporidium metabolism. In Cacciò SM, Widmer G. eds Cryptosporidium: Parasite and Disease Vienna: Springer; 2014 pp 361–379
    [Google Scholar]
  10. Guo Y, Tang K, Rowe LA, Li N, Roellig DM et al. Comparative genomic analysis reveals occurrence of genetic recombination in virulent Cryptosporidium hominis subtypes and telomeric gene duplications in Cryptosporidium parvum. BMC Genomics 2015; 16:320 [View Article]
    [Google Scholar]
  11. Videvall E. Plasmodium parasites of birds have the most AT-rich genes of eukaryotes. Microb Genom 2018; 4:e000150 [View Article] [PubMed]
    [Google Scholar]
  12. Yadav MK, Swati D. Comparative genome analysis of six malarial parasites using codon usage bias based tools. Bioinformation 2012; 8:1230–1239 [View Article] [PubMed]
    [Google Scholar]
  13. Romiguier J, Ranwez V, Douzery EJP, Galtier N. Contrasting GC-content dynamics across 33 mammalian genomes: relationship with life-history traits and chromosome sizes. Genome Res 2010; 20:1001–1009 [View Article] [PubMed]
    [Google Scholar]
  14. Šmarda P, Bureš P, Horová L, Leitch IJ, Mucina L et al. Ecological and evolutionary significance of genomic GC content diversity in monocots. Proc Natl Acad Sci U S A 2014; 111:E4096–102 [View Article] [PubMed]
    [Google Scholar]
  15. Nikbakht H, Xia X, Hickey DA. The evolution of genomic GC content undergoes a rapid reversal within the genus Plasmodium. Genome 2014; 57:507–511 [View Article] [PubMed]
    [Google Scholar]
  16. Xiao L, Morgan UM, Limor J, Escalante A, Arrowood M et al. Genetic diversity within Cryptosporidium parvum and related Cryptosporidium species. Appl Environ Microbiol 1999; 65:3386–3391 [View Article] [PubMed]
    [Google Scholar]
  17. Guo Y, Li N, Lysén C, Frace M, Tang K et al. Isolation and enrichment of Cryptosporidium DNA and verification of DNA purity for whole-genome sequencing. J Clin Microbiol 2015; 53:641–647 [View Article] [PubMed]
    [Google Scholar]
  18. Darling AE, Mau B, Perna NT. ProgressiveMauve: multiple genome alignment with gene gain, loss and rearrangement. PLoS One 2010; 5:e11147 [View Article] [PubMed]
    [Google Scholar]
  19. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M et al. Versatile and open software for comparing large genomes. Genome Biol 2004; 5:R12 [View Article] [PubMed]
    [Google Scholar]
  20. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 2015; 31:3210–3212 [View Article] [PubMed]
    [Google Scholar]
  21. Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R et al. Circos: an information aesthetic for comparative genomics. Genome Res 2009; 19:1639–1645 [View Article] [PubMed]
    [Google Scholar]
  22. Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res 1997; 25:955–964 [View Article] [PubMed]
    [Google Scholar]
  23. Laslett D, Canback B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res 2004; 32:11–16 [View Article] [PubMed]
    [Google Scholar]
  24. Lagesen K, Hallin P, Rødland EA, Staerfeldt H-H, Rognes T et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res 2007; 35:3100–3108 [View Article] [PubMed]
    [Google Scholar]
  25. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol 1990; 215:403–410 [View Article] [PubMed]
    [Google Scholar]
  26. Li L, Stoeckert CJ, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res 2003; 13:2178–2189 [View Article] [PubMed]
    [Google Scholar]
  27. Stanke M, Steinkamp R, Waack S, Morgenstern B. AUGUSTUS: a web server for gene finding in eukaryotes. Nucleic Acids Res 2004; 32:W309–12 [View Article] [PubMed]
    [Google Scholar]
  28. Lomsadze A, Ter-Hovhannisyan V, Chernoff YO, Borodovsky M. Gene identification in novel eukaryotic genomes by self-training algorithm. Nucleic Acids Res 2005; 33:6494–6506 [View Article] [PubMed]
    [Google Scholar]
  29. Korf I. Gene finding in novel genomes. BMC Bioinformatics 2004; 5:59 [View Article] [PubMed]
    [Google Scholar]
  30. Solovyev V, Kosarev P, Seledsov I, Vorobyev D. Automatic annotation of eukaryotic genes, pseudogenes and promoters. Genome Biol 2006; 7:S10 [View Article]
    [Google Scholar]
  31. Haas BJ, Salzberg SL, Zhu W, Pertea M, Allen JE et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the program to assemble spliced alignments. Genome Biol 2008; 9: [View Article] [PubMed]
    [Google Scholar]
  32. Conesa A, Götz S, García-Gómez JM, Terol J, Talón M et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 2005; 21:3674–3676 [View Article] [PubMed]
    [Google Scholar]
  33. Fankhauser N, Mäser P. Identification of GPI anchor attachment signals by a Kohonen self-organizing map. Bioinformatics 2005; 21:1846–1852 [View Article] [PubMed]
    [Google Scholar]
  34. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 2001; 305:567–580 [View Article] [PubMed]
    [Google Scholar]
  35. Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 2011; 8:785–786 [View Article] [PubMed]
    [Google Scholar]
  36. Moriya Y, Itoh M, Okuda S, Yoshizawa AC, Kanehisa M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res 2007; 35:W182–5 [View Article] [PubMed]
    [Google Scholar]
  37. Shanmugasundram A, Gonzalez-Galarza FF, Wastling JM, Vasieva O, Jones AR. Library of apicomplexan metabolic pathways: a manually curated database for metabolic pathways of apicomplexan parasites. Nucleic Acids Res 2013; 41:D706–13 [View Article] [PubMed]
    [Google Scholar]
  38. Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY et al. Pfam: the protein families database. Nucleic Acids Res 2014; 42:D222–30 [View Article] [PubMed]
    [Google Scholar]
  39. Xia X. DAMBE6: new tools for microbial genomics, phylogenetics, and molecular evolution. J Hered 2017; 108:431–437 [View Article] [PubMed]
    [Google Scholar]
  40. Wong EHM, Smith DK, Rabadan R, Peiris M, Poon LLM. Codon usage bias and the evolution of influenza A viruses. Codon usage biases of influenza virus. BMC Evol Biol 2010; 10:253. [View Article] [PubMed]
    [Google Scholar]
  41. Sharp PM, Li WH. An evolutionary perspective on synonymous codon usage in unicellular organisms. J Mol Evol 1986; 24:28–38 [View Article] [PubMed]
    [Google Scholar]
  42. Wright F. The “effective number of codons” used in a gene. Gene 1990; 87:23–29 [View Article] [PubMed]
    [Google Scholar]
  43. Comeron JM, Aguadé M. An evaluation of measures of synonymous codon usage bias. J Mol Evol 1998; 47:268–274 [View Article] [PubMed]
    [Google Scholar]
  44. Rapoport AE, Trifonov EN. Compensatory nature of Chargaff’s second parity rule. J Biomol Struct Dyn 2013; 31:1324–1336 [View Article] [PubMed]
    [Google Scholar]
  45. Jenkins GM, Holmes EC. The extent of codon usage bias in human RNA viruses and its evolutionary origin. Virus Res 2003; 92:1–7 [View Article] [PubMed]
    [Google Scholar]
  46. Anwar AM, Soudy M, Mohamed R. Vhcub: virus-host codon usage co-adaptation analysis. F1000Res 2019; 8:2137 [View Article] [PubMed]
    [Google Scholar]
  47. Anwar A. BCAWT: automated tool for codon usage bias analysis for molecular evolution. JOSS 2019; 4:1500 [View Article]
    [Google Scholar]
  48. Bailey TL, Boden M, Buske FA, Frith M, Grant CE et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 2009; 37:W202–8 [View Article] [PubMed]
    [Google Scholar]
  49. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article] [PubMed]
    [Google Scholar]
  50. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9:357–359 [View Article] [PubMed]
    [Google Scholar]
  51. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009; 25:2078–2079 [View Article] [PubMed]
    [Google Scholar]
  52. Narasimhan V, Danecek P, Scally A, Xue Y, Tyler-Smith C et al. BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data. Bioinformatics 2016; 32:1749–1751 [View Article] [PubMed]
    [Google Scholar]
  53. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014; 30:1312–1313 [View Article] [PubMed]
    [Google Scholar]
  54. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol 2013; 30:2725–2729 [View Article] [PubMed]
    [Google Scholar]
  55. DeBarry JD, Kissinger JC. Jumbled genomes: missing apicomplexan synteny. Mol Biol Evol 2011; 28:2855–2871 [View Article] [PubMed]
    [Google Scholar]
  56. Birdsell JA. Integrating genomics, bioinformatics, and classical genetics to study the effects of recombination on genome evolution. Mol Biol Evol 2002; 19:1181–1197 [View Article] [PubMed]
    [Google Scholar]
  57. Duret L, Galtier N. Biased gene conversion and the evolution of mammalian genomic landscapes. Annu Rev Genomics Hum Genet 2009; 10:285–311 [View Article] [PubMed]
    [Google Scholar]
  58. Castillo AI, Nelson ADL, Lyons E. Tail wags the dog? Functional gene classes driving genome-wide GC content in Plasmodium spp. Genome Biol Evol 2019; 11:497–507 [View Article] [PubMed]
    [Google Scholar]
  59. Hershberg R, Petrov DA. General rules for optimal codon choice. PLoS Genet 2009; 5:e1000556 [View Article] [PubMed]
    [Google Scholar]
  60. Bulmer M. The selection-mutation-drift theory of synonymous codon usage. Genetics 1991; 129:897–907 [View Article] [PubMed]
    [Google Scholar]
  61. Bunnik EM, Chung D-W, Hamilton M, Ponts N, Saraf A et al. Polysome profiling reveals translational control of gene expression in the human malaria parasite Plasmodium falciparum. Genome Biol 2013; 14: [View Article] [PubMed]
    [Google Scholar]
  62. Gajbhiye S, Patra PK, Yadav MK. New insights into the factors affecting synonymous codon usage in human infecting Plasmodium species. Acta Trop 2017; 176:29–33 [View Article] [PubMed]
    [Google Scholar]
  63. Chanda I, Pan A, Dutta C. Proteome composition in Plasmodium falciparum: higher usage of GC-rich nonsynonymous codons in highly expressed genes. J Mol Evol 2005; 61:513–523 [View Article] [PubMed]
    [Google Scholar]
  64. Akashi H, Gojobori T. Metabolic efficiency and amino acid composition in the proteomes of Escherichia coli and Bacillus subtilis. Proc Natl Acad Sci U S A 2002; 99:3695–3700 [View Article] [PubMed]
    [Google Scholar]
  65. Rider SD, Zhu G. Cryptosporidium: genomic and biochemical features. Exp Parasitol 2010; 124:2–9 [View Article] [PubMed]
    [Google Scholar]
  66. Song C, Chiasson MA, Nursimulu N, Hung SS, Wasmuth J et al. Metabolic reconstruction identifies strain-specific regulation of virulence in Toxoplasma gondii. Mol Syst Biol 2013; 9:708. [View Article] [PubMed]
    [Google Scholar]
  67. Lorenzi H, Khan A, Behnke MS, Namasivayam S, Swapna LS et al. Local admixture of amplified and diversified secreted pathogenesis determinants shapes mosaic Toxoplasma gondii genomes. Nat Commun 2016; 7:10147. [View Article] [PubMed]
    [Google Scholar]
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