Understanding the emergence of novel viruses requires an accurate and comprehensive annotation of their genomes. Overlapping genes (OLGs) are common in viruses and have been associated with pandemics, but are still widely overlooked. We identify and characterize ORF3d, a novel OLG in SARS-CoV-2 that is also present in Guangxi pangolin-CoVs but not other closely related pangolin-CoVs or bat-CoVs. We then document evidence of ORF3d translation, characterize its protein sequence, and conduct an evolutionary analysis at three levels: between taxa (21 members of Severe acute respiratory syndrome-related coronavirus), between human hosts (3978 SARS-CoV-2 consensus sequences), and within human hosts (401 deeply sequenced SARS-CoV-2 samples). ORF3d has been independently identified and shown to elicit a strong antibody response in COVID-19 patients. However, it has been misclassified as the unrelated gene ORF3b, leading to confusion. Our results liken ORF3d to other accessory genes in emerging viruses and highlight the importance of OLGs.
All data generated or analyzed during this study are included in the manuscript and supplement. Scripts and source data for all analyses and figures are provided on GitHub at https://github.com/chasewnelson/SARS-CoV-2-ORF3d and Zenodo at https://zenodo.org/record/4052729.
EpiFluTMGISAID's EpiFluTM Database.
TARGETED PROTEOMICS FOR THE DETECTION OF SARS-COV-2 PROTEINSPRIDE database, PXD018760.
Proteome and Translatome of SARS-CoV-2 infected cellsPRIDE database, PXD017710.
Vero cells infected with SARS CoV 2 no quantitation slices 1-10 of 20; vero cells infected with SARS CoV2 slices 11-20 of 20 slicesZenodo, 10.5281/zenodo.3722590, 10.5281/zenodo.3722596.
Proteomics of SARS-CoV and SARS-CoV-2 infected cellsPRIDE database, PXD018581.
Decoding SARS-CoV-2 coding capacityGEO database, sample IDs: SRR11713366, SRR11713367, SRR11713368, SRR11713369 from GSE149973.
Assays and Merits of Proteomics for SARS-CoV-2 Research and TestingPRIDE database, PXD019645.
- Chase W Nelson
- Zachary Ardern
- Tony L Goldberg
- Sergios-Orestis Kolokotronis
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Antonis Rokas, Vanderbilt University, United States
© 2020, Nelson et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
A new mathematical model that can be applied to both single-cell and bulk DNA sequencing data sheds light on the processes governing population dynamics in stem cells.
Co-functional proteins tend to have rates of evolution that covary over time. This correlation between evolutionary rates can be measured over the branches of a phylogenetic tree through methods such as evolutionary rate covariation (ERC), and then used to construct gene networks by the identification of proteins with functional interactions. The cause of this correlation has been hypothesized to result from both compensatory coevolution at physical interfaces and nonphysical forces such as shared changes in selective pressure. This study explores whether coevolution due to compensatory mutations has a measurable effect on the ERC signal. We examined the difference in ERC signal between physically interacting protein domains within complexes compared to domains of the same proteins that do not physically interact. We found no generalizable relationship between physical interaction and high ERC, although a few complexes ranked physical interactions higher than nonphysical interactions. Therefore, we conclude that coevolution due to physical interaction is weak, but present in the signal captured by ERC, and we hypothesize that the stronger signal instead comes from selective pressures on the protein as a whole and maintenance of the general function.