Evolution of transient RNA structure–RNA polymerase interactions in respiratory RNA virus genomes
RNA viruses are important human pathogens that cause seasonal epidemics and occasional pandemics. Examples are influenza A viruses (IAV) and coronaviruses (CoV). When emerging IAV and CoV spill over to humans, they adapt to evade immune responses and optimize their replication and spread in human cells. In IAV, adaptation occurs in all viral proteins, including the viral ribonucleoprotein (RNP) complex. RNPs consist of a copy of the viral RNA polymerase, a double-helical coil of nucleoprotein, and one of the eight segments of the IAV RNA genome. The RNA segments and their transcripts are partially structured to coordinate the packaging of the viral genome and modulate viral mRNA translation. In addition, RNA structures can affect the efficiency of viral RNA synthesis and the activation of host innate immune response. Here, we investigated if RNA structures that modulate IAV replication processivity, so-called template loops (t-loops), vary during the adaptation of pandemic and emerging IAV to humans. Using cell culture-based replication assays and in silico sequence analyses, we find that the sensitivity of the IAV H3N2 RNA polymerase to t-loops increased between isolates from 1968 and 2017, whereas the total free energy of t-loops in the IAV H3N2 genome was reduced. This reduction is particularly prominent in the PB1 gene. In H1N1 IAV, we find two separate reductions in t-loop free energy, one following the 1918 pandemic and one following the 2009 pandemic. No destabilization of t-loops is observed in the influenza B virus genome, whereas analysis of SARS-CoV-2 isolates reveals destabilization of viral RNA structures. Overall, we propose that a loss of free energy in the RNA genome of emerging respiratory RNA viruses may contribute to the adaption of these viruses to the human population.
Acknowledgements: The authors would like to thank Dr Michael Oade, Rene Vigeveno, and Sarah van Leeuwen for discussions and reagents. Portions of the work reported in this paper were performed using the Princeton Research Computing resources at Princeton University, which is a consortium of groups led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology’s Research Computing. CVR was supported by a studentship from Public Health England. K.R.S. was supported by NIH Grant Nos. R01 GM140032 and R01 AI170520. K.B. was supported by NIH Grant No. DP2 AI175474. A.T.V. was supported by NIH Grant Nos. R21 AI147172, DP2 AI175474, and R01 AI170520, and Wellcome Trust and Royal Society Grant No. 206579/17/Z.
Funder: Public Health England; DOI: https://doi.org/10.13039/501100002141
Wellcome Trust (206579/17/Z)
NIH Office of the Director (DP2 AI175474)