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Abstract

The majority of bacterial genomes have high coding efficiencies, but there are some genomes of intracellular bacteria that have low gene density. The genome of the endosymbiont contains almost 50 % pseudogenes containing mutations that putatively silence them at the genomic level. We have applied multiple ‘omic’ strategies, combining Illumina and Pacific Biosciences Single-Molecule Real-Time DNA sequencing and annotation, stranded RNA sequencing and proteome analysis to better understand the transcriptional and translational landscape of pseudogenes, and potential mechanisms for their control. Between 53 and 74 % of the transcriptome remains active in cell-free culture. The mean sense transcription from coding domain sequences (CDSs) is four times greater than that from pseudogenes. Comparative genomic analysis of six Illumina-sequenced isolates from different host species shows pseudogenes make up ~40 % of the 2729 genes in the core genome, suggesting that they are stable and/or that is a recent introduction across the genus as a facultative symbiont. These data shed further light on the importance of transcriptional and translational control in deciphering host–microbe interactions. The combination of genomics, transcriptomics and proteomics gives a multidimensional perspective for studying prokaryotic genomes with a view to elucidating evolutionary adaptation to novel environmental niches.

Funding
This study was supported by the:
  • Wellcome Trust (Award 200690/Z/16/Z)
    • Principle Award Recipient: Ian Goodhead
  • Maihidol University (Award Studentship)
    • Principle Award Recipient: Pisut Pongchaikul
  • Biotechnology and Biological Sciences Research Council (Award BBJ017698/1)
    • Principle Award Recipient: Alistair C Darby
  • Biotechnology and Biological Sciences Research Council (Award BB/L014777/1)
    • Principle Award Recipient: Alistair C Darby
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2020-01-10
2024-04-26
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References

  1. Vizcaíno JA, Deutsch EW, Wang R, Csordas A, Reisinger F et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat Biotechnol 2014; 32:223–226 [View Article]
    [Google Scholar]
  2. McCutcheon JP, Moran NA. Extreme genome reduction in symbiotic bacteria. Nat Rev Microbiol 2012; 10:13–26 [View Article]
    [Google Scholar]
  3. Douglas AE. How multi-partner endosymbioses function. Nat Rev Microbiol 2016; 14:731–743 [View Article]
    [Google Scholar]
  4. Wernegreen JJ. Genome evolution in bacterial endosymbionts of insects. Nat Rev Genet 2002; 3:850–861 [View Article]
    [Google Scholar]
  5. Pérez-Brocal V, Gil R, Ramos S, Lamelas A, Postigo M et al. A small microbial genome: the end of a long symbiotic relationship?. Science 2006; 314:312–313 [View Article]
    [Google Scholar]
  6. Moran NA. Accelerated evolution and Muller's rachet in endosymbiotic bacteria. Proc Natl Acad Sci USA 1996; 93:2873–2878 [View Article]
    [Google Scholar]
  7. Mira A, Ochman H, Moran NA. Deletional bias and the evolution of bacterial genomes. Trends Genet 2001; 17:589–596 [View Article]
    [Google Scholar]
  8. Tamas I, Klasson L, Canbäck B, Näslund AK, Eriksson A-S et al. 50 million years of genomic stasis in endosymbiotic bacteria. Science 2002; 296:2376–2379 [View Article]
    [Google Scholar]
  9. Shigenobu S, Watanabe H, Hattori M, Sakaki Y, Ishikawa H. Genome sequence of the endocellular bacterial symbiont of aphids Buchnera sp. APS. Nature 2000; 407:81–86 [View Article]
    [Google Scholar]
  10. Akman L, Yamashita A, Watanabe H, Oshima K, Shiba T et al. Genome sequence of the endocellular obligate symbiont of tsetse flies, Wigglesworthia glossinidia. Nat Genet 2002; 32:402–407 [View Article]
    [Google Scholar]
  11. Batut B, Knibbe C, Marais G, Daubin V. Reductive genome evolution at both ends of the bacterial population size spectrum. Nat Rev Microbiol 2014; 12:841–850 [View Article]
    [Google Scholar]
  12. Kuo C-H, Ochman H. The extinction dynamics of bacterial pseudogenes. PLoS Genet 2010; 6:e1001050 [View Article]
    [Google Scholar]
  13. Liu Y, Harrison PM, Kunin V, Gerstein M. Comprehensive analysis of pseudogenes in prokaryotes: widespread gene decay and failure of putative horizontally transferred genes. Genome Biol 2004; 5:R64 [View Article]
    [Google Scholar]
  14. Foley SL, Johnson TJ, Ricke SC, Nayak R, Danzeisen J. Salmonella pathogenicity and host adaptation in chicken-associated serovars. Microbiol Mol Biol Rev 2013; 77:582–607 [View Article]
    [Google Scholar]
  15. Toh H, Weiss BL, Perkin SAH, Yamashita A, Oshima K et al. Massive genome erosion and functional adaptations provide insights into the symbiotic lifestyle of Sodalis glossinidius in the tsetse host; 2006
  16. Cole ST, Eiglmeier K, Parkhill J, James KD, Thomson NR et al. Massive gene decay in the leprosy bacillus. Nature 2001; 409:1007–1011 [View Article]
    [Google Scholar]
  17. Gómez-Valero L, Latorre A, Silva FJ. The evolutionary fate of nonfunctional DNA in the bacterial endosymbiont Buchnera aphidicola. Mol Biol Evol 2004; 21:2172–2181 [View Article]
    [Google Scholar]
  18. Goodhead I, Darby AC. Taking the pseudo out of pseudogenes. Curr Opin Microbiol 2015; 23:102–109 [View Article]
    [Google Scholar]
  19. Ochman H, Davalos LM. The nature and dynamics of bacterial genomes. Science 2006; 311:1730–1733 [View Article]
    [Google Scholar]
  20. Nuccio S-P, Bäumler AJ. Comparative analysis of Salmonella genomes identifies a metabolic network for escalating growth in the inflamed gut. MBio 2014; 5:e00929-14 [View Article]
    [Google Scholar]
  21. Tamas I, Wernegreen JJ, Nystedt B, Kauppinen SN, Darby AC et al. Endosymbiont gene functions impaired and rescued by polymerase infidelity at poly(A) tracts. Proc Natl Acad Sci U S A 2008; 105:14934–14939 [View Article]
    [Google Scholar]
  22. Dennis JW, Durkin SM, Horsley Downie JE, Hamill LC, Anderson NE et al. Sodalis glossinidius prevalence and trypanosome presence in tsetse from luambe national park, zambia. Parasit Vectors 2014; 7:378 [View Article]
    [Google Scholar]
  23. Maudlin I. Inheritance of susceptibility to Trypanosoma congolense infection in Glossina morsitans . Ann Trop Med Parasitol 1982; 76:225–227 [View Article]
    [Google Scholar]
  24. Belda E, Silva FJ, Peretó J, Moya A. Metabolic networks of Sodalis glossinidius: a systems biology approach to reductive evolution. PLoS One 2012; 7:e30652 [View Article]
    [Google Scholar]
  25. Pontes MH, Babst M, Lochhead R, Oakeson K, Smith K et al. Quorum sensing primes the oxidative stress response in the insect endosymbiont, Sodalis glossinidius . PLoS One 2008; 3:e3541 [View Article]
    [Google Scholar]
  26. Hansen-Wester I, Hensel M. Salmonella pathogenicity islands encoding type III secretion systems. Microbes Infect 2001; 3:549–559 [View Article]
    [Google Scholar]
  27. Dale C, Young SA, Haydon DT, Welburn SC. The insect endosymbiont Sodalis glossinidius utilizes a type III secretion system for cell invasion. Proc Natl Acad Sci USA 2001; 98:1883–1888 [View Article]
    [Google Scholar]
  28. Breaker RR. Riboswitches and the RNA world. Cold Spring Harb Perspect Biol 2012; 4:a003566 [View Article]
    [Google Scholar]
  29. Storz G, Vogel J, Wassarman KM. Regulation by small RNAs in bacteria: expanding frontiers. Mol Cell 2011
    [Google Scholar]
  30. Sánchez-Romero MA, Cota I, Casadesús J. Dna methylation in bacteria: from the methyl group to the methylome. Curr Opin Microbiol 2015; 25:9–16 [View Article]
    [Google Scholar]
  31. Casadesús J, Low D. Epigenetic gene regulation in the bacterial world. Microbiol Mol Biol Rev 2006; 70:830–856 [View Article]
    [Google Scholar]
  32. Darby AC, Lagnel J, Matthew CZ, Bourtzis K, Maudlin I et al. Extrachromosomal DNA of the symbiont Sodalis glossinidius Extrachromosomal DNA of the Symbiont Sodalis glossinidius ; 2005; 187
  33. Otto TD, Dillon GP, Degrave WS, Berriman M. RATT: rapid annotation transfer tool. Nucleic Acids Res 2011; 39:e57 [View Article]
    [Google Scholar]
  34. Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 2010; 11:119 [View Article]
    [Google Scholar]
  35. Delcher AL, Salzberg SL, Phillippy AM. Using MUMmer to identify similar regions in large sequence sets. Curr Protoc Bioinformatics 2003; 00:Chapter 10:Unit 101031–10.310 [View Article]
    [Google Scholar]
  36. Priam P, Krasteva V, Rousseau P, D’Angelo G, Gaboury L et al. SMARCD2 subunit of SWI/SNF chromatin-remodeling complexes mediates granulopoiesis through a CEBP[epsiv] dependent mechanism. Nat Genet advance on 2017
    [Google Scholar]
  37. Kröger C, Dillon SC, Cameron ADS, Papenfort K, Sivasankaran SK et al. The transcriptional landscape and small RNAs of Salmonella enterica serovar typhimurium. Proc Natl Acad Sci U S A 2012; 109:E1277–E1286 [View Article]
    [Google Scholar]
  38. Ghali F, Krishna R, Lukasse P, Martinez-Bartolome S, Reisinger F et al. A toolkit for the mzIdentML standard: the ProteoIDViewer, the mzidLibrary and the mzidValidator. Mol Cell Proteomics 20133026–3035
    [Google Scholar]
  39. Davis BM, Chao MC, Waldor MK. Entering the era of bacterial epigenomics with single molecule real time DNA sequencing. Curr Opin Microbiol 2013
    [Google Scholar]
  40. Moran NA, Mira A. The process of genome shrinkage in the obligate symbiont buchnera aphidicola. Genome Biol 2001; 2:research0054.1–005412 [View Article]
    [Google Scholar]
  41. WS L, Huang YY, Kuo CH. Winding paths to simplicity: genome evolution in facultative insect symbionts. FEMS Microbiol Rev 2016
    [Google Scholar]
  42. Aken BL, Achuthan P, Akanni W, Amode MR, Bernsdorff F et al. Ensembl 2017. Nucleic Acids Res 2017; 45:D635–D642 [View Article]
    [Google Scholar]
  43. McCutcheon JP, McDonald BR, Moran NA. Convergent evolution of metabolic roles in bacterial co-symbionts of insects. Proc Natl Acad Sci USA 2009; 106:15394–15399 [View Article]
    [Google Scholar]
  44. Heddi A, Vallier A, Anselme C, Xin H, Rahbe Y et al. Molecular and cellular profiles of insect bacteriocytes: mutualism and harm at the initial evolutionary step of symbiogenesis. Cell Microbiol 2005; 7:293–305 [View Article]
    [Google Scholar]
  45. Clayton AL, Enomoto S, Su Y, Dale C. The regulation of antimicrobial peptide resistance in the transition to insect symbiosis. Mol Microbiol 2017; 103:958–972 [View Article]
    [Google Scholar]
  46. Taniguchi Y, Choi PJ, Li G-W, Chen H, Babu M et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 2010; 329:533–538 [View Article]
    [Google Scholar]
  47. Venkataramanan KP, Min L, Hou S, Jones SW, Ralston MT et al. Complex and extensive post-transcriptional regulation revealed by integrative proteomic and transcriptomic analysis of metabolite stress response in Clostridium acetobutylicum. Biotechnol Biofuels 2015; 8:81 [View Article]
    [Google Scholar]
  48. Kröger C, Dillon SC, Cameron ADS, Papenfort K, Sivasankaran SK et al. The transcriptional landscape and small RNAs of Salmonella enterica serovar typhimurium. Proc Natl Acad Sci U S A 2012; 109:E1277–E1286 [View Article]
    [Google Scholar]
  49. Rabilloud T. Membrane proteins and proteomics: love is possible, but so difficult. Electrophoresis 2009; 30 Suppl 1:S174–S180 [View Article]
    [Google Scholar]
  50. Marinus MG, Casadesus J. Roles of DNA adenine methylation in host–pathogen interactions: mismatch repair, transcriptional regulation, and more. FEMS Microbiol Rev 2009; 33:488–503 [View Article]
    [Google Scholar]
  51. Heithoff DM, Sinsheimer RL, Low DA, Mahan MJ. An essential role for DNA adenine methylation in bacterial virulence. Science 1999; 284:967–970 [View Article]
    [Google Scholar]
  52. Reingold V, Luria N, Robichon A, Dombrovsky A. Adenine methylation may contribute to endosymbiont selection in a clonal aphid population. BMC Genomics 2014; 15:999 [View Article]
    [Google Scholar]
  53. Andreani NA, Hesse E, Vos M. Prokaryote genome fluidity is dependent on effective population size. ISME J 2017; 11:1719–1721 [View Article]
    [Google Scholar]
  54. Chen X, Li S, Aksoy S. Concordant evolution of a symbiont with its host insect species: molecular phylogeny of genus Glossina and its bacteriome-associated endosymbiont, Wigglesworthia glossinidia. J Mol Evol 1999; 48:49–58 [View Article]
    [Google Scholar]
  55. Lawrence JG, Hendrix RW, Casjens S. Where are the pseudogenes in bacterial genomes?. Trends Microbiol 2001; 9:535–540 [View Article]
    [Google Scholar]
  56. De Vooght L, Caljon G, Van Hees J, Van Den Abbeele J. Paternal transmission of a secondary symbiont during mating in the viviparous tsetse fly. Mol Biol Evol 2015; 32:1977–1980 [View Article]
    [Google Scholar]
  57. Andersson JO, Andersson SG. Insights into the evolutionary process of genome degradation. Curr Opin Genet Dev 1999; 9:664–671 [View Article]
    [Google Scholar]
  58. Bentkowski P, van Oosterhout C, Ashby B, Mock T. The effect of extrinsic mortality on genome size evolution in prokaryotes. ISME J 2017; 11:1011–1018 [View Article]
    [Google Scholar]
  59. Lee M-C, Marx CJ. Repeated, selection-driven genome reduction of accessory genes in experimental populations. PLoS Genet 2012; 8:e1002651 [View Article]
    [Google Scholar]
  60. Le Rhun A, Beer YY, Reimegård J, Chylinski K, Charpentier E. RNA sequencing uncovers antisense RNAs and novel small RNAs in Streptococcus pyogenes . RNA Biol 2016; 13:177–195 [View Article]
    [Google Scholar]
  61. Arnold WK, Savage CR, Brissette CA, Seshu J, Livny J et al. Rna-Seq of Borrelia burgdorferi in multiple phases of growth reveals insights into the dynamics of gene expression, transcriptome architecture, and noncoding RNAs. PLoS One 2016; 11:e0164165 [View Article]
    [Google Scholar]
  62. Hansen AK, Degnan PH. Widespread expression of conserved small RNAs in small symbiont genomes. ISME J 2014; 8:2490–2502 [View Article]
    [Google Scholar]
  63. Güell M, Yus E, Lluch-Senar M, Serrano L. Bacterial transcriptomics: what is beyond the RNA horiz-ome?. Nat Rev Microbiol 2011; 9:658–669 [View Article]
    [Google Scholar]
  64. Guo X, Zheng D. Regulatory roles of novel small RNAs from pseudogenes, p. 193–208. In Non Coding RNAs in Plants. Springer 2011
    [Google Scholar]
  65. Matthew CZ, Darby AC, Young SA, Hume LH, Welburn SC. The rapid isolation and growth dynamics of the tsetse symbiont Sodalis glossinidius . FEMS Microbiol Lett 2005; 248:69–74 [View Article]
    [Google Scholar]
  66. Delcher AL, Salzberg SL, Phillippy AM. Using MUMmer to identify similar regions in large sequence sets. Curr Protoc Bioinformatics 2003; 00:Chapter 10:Unit 1010.3.1–10.310 [View Article]
    [Google Scholar]
  67. Carver TJ, Rutherford KM, Berriman M, Rajandream M-A, Barrell BG et al. Act: the ARTEMIS comparison tool. Bioinformatics 2005; 21:3422–3423 [View Article]
    [Google Scholar]
  68. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:btu153-2068–2069 [View Article]
    [Google Scholar]
  69. Havill JT, Bhatiya C, Johnson SM, Sheets JD, Thompson JS. A new approach for detecting riboswitches in DNA sequences. Bioinformatics 2014; 30:3012–3019 [View Article]
    [Google Scholar]
  70. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. Journal 2011; 17:10–12
    [Google Scholar]
  71. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 2015; 31:3691–3693 [View Article]
    [Google Scholar]
  72. Rohland N, Reich D. Cost-Effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res 2012; 22:939–946 [View Article]
    [Google Scholar]
  73. Joshi N, Fass J. 2011. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software]. Available at 2011 https://github.com/najoshi/sickle
    [Google Scholar]
  74. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012; 9:357–359 [View Article]
    [Google Scholar]
  75. 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]
    [Google Scholar]
  76. Anders S, Pyl PT, Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 2015; 31:166–169 [View Article]
    [Google Scholar]
  77. Wagner GP, Kin K, Lynch VJ. Measurement of mRNA abundance using RNA-Seq data: RPKM measure is inconsistent among samples. Theory Biosci 2012; 131:281–285 [View Article]
    [Google Scholar]
  78. Tjaden B. De novo assembly of bacterial transcriptomes from RNA-Seq data. Genome Biol 2015; 16:1 [View Article]
    [Google Scholar]
  79. Ghali F, Krishna R, Perkins S, Collins A, Xia D et al. ProteoAnnotator- open source proteogenomics annotation software supporting PSI standards. Proteomics 2014; 14:2731–2741 [View Article]
    [Google Scholar]
  80. Medina-Aunon JA, Krishna R, Ghali F, Albar JP, Jones AJ. A guide for integration of proteomic data standards into laboratory workflows. Proteomics 2013; 13:480–492 [View Article]
    [Google Scholar]
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