CryoSeekV: Discovery of a novel virus AaTLV-IMCAS from Aedes albopictus cells by cryogenic electron microscopy
摘要
Discovering divergent RNA viruses remains challenging due to the limitations of reference-based sequencing and viral isolation. Here, we present the structure-guided discovery of Aedes albopictus-associated Tymoviridae-like virus strain IMCAS (AaTLV-IMCAS), isolated from C6/36 mosquito cell supernatant. Without prior genomic information, we determined its capsid structure at a resolution of 2.11 Å using cryogenic electron microscopy (cryo-EM). Deep-learning-based automated modeling via ModelAngelo generated position-specific residue profiles that directed the targeted capture and assembly of the complete 6,488-nucleotide positive-sense single-stranded RNA genome, establishing a structural discovery workflow named CryoSeekV. Phylogenetic analysis showed that AaTLV-IMCAS, along with the related mosquito-borne viruses, forms an independent monophyletic clade distinct from established plant-infecting genera, representing a novel genus; therefore, we proposed the genus Mosquivirus, within the family Tymoviridae. Structural analysis revealed a icosahedral capsid where a 9 Å main-chain shift within the N-terminus acts as a conformational switch governing quasi-equivalent capsomer assembly. Functional assays demonstrated a strict mosquito cell tropism and temperature-dependent replication optimized at 28 °C that establishes persistent infection without cytopathic effects. Time-resolved transcriptomics revealed that infection triggers dynamic host reprogramming: an early (24 h) surge in host translation, oxidative phosphorylation, and amino-acid metabolism coupled with selective repression of key melanization and recognition factors, which largely returned toward baseline by 48 h. Together, this study establishes CryoSeekV as an effective paradigm for structure-driven viral surveillance and provides foundational molecular and physiological insights into invertebrate-associated Tymovirales.
参考文献
1. A. A. Zayed et al., Cryptic and abundant marine viruses at the evolutionary origins of Earth’s RNA virome. Science 376, 156–162 (2022).
2. A. C. Gregory et al., Marine DNA Viral Macro- and Microdiversity from Pole to Pole. Cell 177, 1109–1123.e1114 (2019).
3. M. Shi et al., Redefining the invertebrate RNA virosphere. Nature 540, 539–543 (2016).
4. S. R. Krishnamurthy, D. Wang, Origins and challenges of viral dark matter. Virus Res 239, 136–142 (2017).
5. M. E. J. Woolhouse, L. Brierley, Epidemiological characteristics of human-infective RNA viruses. Scientific Data 5, 180017 (2018).
6. K. B. Scholthof et al., Top 10 plant viruses in molecular plant pathology. Mol Plant Pathol 12, 938–954 (2011).
7. A. Brun, Vaccines and Vaccination for Veterinary Viral Diseases: A General Overview. Methods Mol Biol 1349, 1–24 (2016).
8. Y. Z. Zhang, M. Shi, E. C. Holmes, Using Metagenomics to Characterize an Expanding Virosphere. Cell 172, 1168–1172 (2018).
9. S. Roux et al., Minimum Information about an Uncultivated Virus Genome (MIUViG). Nature Biotechnology 37, 29–37 (2019).
10. S. Nurk, D. Meleshko, A. Korobeynikov, P. A. Pevzner, metaSPAdes: a new versatile metagenomic assembler. Genome Res 27, 824–834 (2017).
11. N. Fierer, Embracing the unknown: disentangling the complexities of the soil microbiome. Nature Reviews Microbiology 15, 579–590 (2017).
12. W. Kühlbrandt, The Resolution Revolution. Science 343, 1443–1444 (2014).
13. Y. Cheng, Single-particle cryo-EM—How did it get here and where will it go. Science 361, 876–880 (2018).
14. T. Nakane et al., Single-particle cryo-EM at atomic resolution. Nature 587, 152–156 (2020).
15. K. M. Yip, N. Fischer, E. Paknia, A. Chari, H. Stark, Atomic-resolution protein structure determination by cryo-EM. Nature 587, 157–161 (2020).
16. S. H. Scheres, RELION: implementation of a Bayesian approach to cryo-EM structure determination. J Struct Biol 180, 519–530 (2012).
17. A. Punjani, J. L. Rubinstein, D. J. Fleet, M. A. Brubaker, cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nature Methods 14, 290–296 (2017).
18. P. V. Afonine et al., Real-space refinement in PHENIX for cryo-EM and crystallography. Acta Crystallogr D Struct Biol 74, 531–544 (2018).
19. C. M. Ho et al., Bottom-up structural proteomics: cryoEM of protein complexes enriched from the cellular milieu. Nat Methods 17, 79–85 (2020).
20. G. Chojnowski et al., findMySequence: a neural-network-based approach for identification of unknown proteins in X-ray crystallography and cryo-EM. IUCrJ 9, 86–97 (2022).
21. K. Jamali et al., Automated model building and protein identification in cryo-EM maps. Nature 628, 450–457 (2024).
22. T. Wang et al., CryoSeek II: Cryo-EM analysis of glycofibrils from freshwater reveals well-structured glycans coating linear tetrapeptide repeats. Proc Natl Acad Sci U S A 122, e2423943122 (2025).
23. T. Wang et al., CryoSeek: A strategy for bioentity discovery using cryoelectron microscopy. Proc Natl Acad Sci U S A 121, e2417046121 (2024).
24. M. van Kempen et al., Fast and accurate protein structure search with Foldseek. Nature Biotechnology 42, 243–246 (2024).
25. M. Sadeghi et al., Virome of > 12 thousand Culex mosquitoes from throughout California. Virology 523, 74–88 (2018).
26. L. Wang et al., Genomic characterization of a novel virus of the family Tymoviridae isolated from mosquitoes. PLoS One 7, e39845 (2012).
27. J. Charles et al., Discovery of a novel Tymoviridae-like virus in mosquitoes from Mexico. Arch Virol 164, 649–652 (2019).
28. K. Laiton-Donato et al., Novel Putative Tymoviridae-like Virus Isolated from Culex Mosquitoes in Colombia. Viruses 15 (2023).
29. E. E. Matsumura et al., Citrus sudden death-associated virus as a new expression vector for rapid in planta production of heterologous proteins, chimeric virions, and virus-like particles. Biotechnol Rep (Amst) 35, e00739 (2022).
30. S. Gutiérrez, Y. Michalakis, M. Van Munster, S. Blanc, Plant feeding by insect vectors can affect life cycle, population genetics and evolution of plant viruses. Functional Ecology 27, 610–622 (2013).
31. S. Q. Zheng et al., MotionCor2: anisotropic correction of beam-induced motion for improved cryo-electron microscopy. Nat Methods 14, 331–332 (2017).
32. A. Rohou, N. Grigorieff, CTFFIND4: Fast and accurate defocus estimation from electron micrographs. J Struct Biol 192, 216–221 (2015).
33. A. Punjani, H. Zhang, D. J. Fleet, Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nat Methods 17, 1214–1221 (2020).
34. M. van Heel, M. Schatz, Fourier shell correlation threshold criteria. Journal of Structural Biology 151, 250–262 (2005).
35. R. Sanchez-Garcia et al., DeepEMhancer: a deep learning solution for cryo-EM volume post-processing. Commun Biol 4, 874 (2021).
36. E. F. Pettersen et al., UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem 25, 1605–1612 (2004).
37. P. Emsley, K. Cowtan, Coot: model-building tools for molecular graphics. Acta Crystallogr D Biol Crystallogr 60, 2126–2132 (2004).
38. S. F. Altschul, W. Gish, W. Miller, E. W. Myers, D. J. Lipman, Basic local alignment search tool. Journal of Molecular Biology 215, 403–410 (1990).
39. S. F. Altschul et al., Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25, 3389–3402 (1997).
40. D. Liebschner et al., Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix. Acta Crystallogr D Struct Biol 75, 861–877 (2019).
41. V. B. Chen et al., MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr D Biol Crystallogr 66, 12–21 (2010).
42. G. N. Ramachandran, C. Ramakrishnan, V. Sasisekharan, Stereochemistry of polypeptide chain configurations. J Mol Biol 7, 95–99 (1963).
43. M. Kearse et al., Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28, 1647–1649 (2012).
44. S. Kumar, G. Stecher, M. Li, C. Knyaz, K. Tamura, MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol Biol Evol 35, 1547–1549 (2018).
45. S. Kumar et al., MEGA12: Molecular Evolutionary Genetic Analysis Version 12 for Adaptive and Green Computing. Mol Biol Evol 41 (2024).
46. Z. Yang, Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. J Mol Evol 39, 306–314 (1994).
47. M. Delarue, An asymmetric underlying rule in the assignment of codons: possible clue to a quick early evolution of the genetic code via successive binary choices. Rna 13, 161–169 (2007).
48. S. Tavaré (1986) Some probabilistic and statistical problems in the analysis of DNA sequences.
49. M. J. Sanderson, CONFIDENCE LIMITS ON PHYLOGENIES: THE BOOTSTRAP REVISITED. Cladistics 5, 113–129 (1989).
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