A Comparative Experimental and Computational Study on the Nature of the Pangolin-CoV and COVID-19 Omicron

Abstract

The relationship between pangolin-CoV and SARS-CoV-2 has been a subject of debate. Further evidence of a special relationship between the two viruses can be found by the fact that all known COVID-19 viruses have an abnormally hard outer shell (low M disorder, i.e., low content of intrinsically disordered residues in the membrane (M) protein) that so far has been found in CoVs associated with burrowing animals, such as rabbits and pangolins, in which transmission involves virus remaining in buried feces for a long time. While a hard outer shell is necessary for viral survival, a harder inner shell could also help. For this reason, the N disorder range of pangolin-CoVs, not bat-CoVs, more closely matches that of SARS-CoV-2, especially when Omicron is included. The low N disorder (i.e., low content of intrinsically disordered residues in the nucleocapsid (N) protein), first observed in pangolin-CoV-2017 and later in Omicron, is associated with attenuation according to the Shell-Disorder Model. Our experimental study revealed that pangolin-CoV-2017 and SARS-CoV-2 Omicron (XBB.1.16 subvariant) show similar attenuations with respect to viral growth and plaque formation. Subtle differences have been observed that are consistent with disorder-centric computational analysis.

Keywords: AI; COVID; XBB; artificial intelligence; attenuation; coronavirus; delta; intrinsic disorder; long COVID; membrane; nucleocapsid; nucleoprotein; omicron; pangolin; shell; vaccine; variant; virulence.

Figure 1

CoV Transmission SDM. Groups A–C are heavily dependent on PID_N, whereas CoVs in group D have abnormally low M disorder (PID_M), which includes all COVID-19-related viruses. (r = 0.78, p < 0.05).

Figure 2

SDMs: Effects of Evolutionary Mutations on the Shell Proteins. (A) A harder M protects the virion from damage incurred from antimicrobial enzymes in the saliva and mucus, as in the case of SARS-CoV-2, even if a more disordered N allows replication of more infectious particles, especially in vital organs, which is the case in SARS-CoV-1. (B) Attenuation is likely to occur when the virus enters animal hosts like pigs or pangolins, where fecal-oral transmission is an important route. It is believed that SARS-CoV-2 entered and re-entered pangolins multiple times before it appeared as Wuhan-Hu-1 or Omicron.

Figure 3

CoV Phylogenetic Trees Using M protein. Omicron (XBB.1.16 and BA.1) is clustered alongside pangolin-CoVs and bat-CoVs, not SARS-CoV-2. (A) Tree that includes SARS-CoV-1 (SARS1) and COVID-19-related viruses. (B) Tree that includes COVID-19-related viruses and SARS-CoV-2 variants. Both trees were generated using CLUSTAL OMEGA [57]. A similar tree using M but utilizing CLUSTALW [58] with distance optimization placed Omicron even closer to pangolin-CoV [19,32].

Figure 4

Virulence-Inner Shell Disorder Model as Applied to COVID-19-Related Viruses. Using mutivariate analysis, a strong correlation (r = 0.8) between SARS-CoV-2, Omicron PID_N, and CFR has been found. CFRs of SARS-CoV-2 (SARS2, non-Omicron) have been estimated to be 2.0–0.5%. CFRs of various SARS-CoV-2 viruses and SARS-CoV-1 viruses were collected from various published sources [14,24,25], whereas PID_M and PID_N were calculated from PONDR^®-VLXT using sequences as seen in Table 2.

Figure 5

PONDR^®-VLXT Plots and Sequence Comparison. (A) PONDR^® VLXT plots for the comparison N of SARS-CoV-1 (SARS1). (B) Sequence and disorder analysis of crucial N regions of Wuhan-Hu-1 and Pang2017. (C) Comparison of crucial N regions of Pang2017 and Omicron XBB (XBB) using sequence and disorder analysis. Residues predicted to be disordered are denoted by “D” in (A,B).

Figure 6

Comparative Analysis of Pangolin Coronavirus Pang2017 and SARS-CoV-2 XBB.1.16 Growth Characteristics in Vero Cells. (A) Microscopic evaluation of cytopathic effects in Vero cells post infection with Pang2017 and XBB.1.16 reveals analogous cellular morphological changes, including cell degeneration and rounding. By 48 h, XBB.1.16-infected cells display pronounced monolayer disruption; (B) one-step growth kinetics of Pang2017 and XBB.1.16 in Vero cells over time. Pang2017’s viral genome exhibited an initial increase between 8–10 h post-infection, with a consistently higher concentration relative to XBB.1.16 from 10–36 h. Following 48 h post-infection, Pang2017’s proliferation plateaued, lagging behind XBB.1.16. (C) At the 120-h mark, plaque assays demonstrated that Pang2017 (149.4 ± 11.85 µm, n = 30) forms significantly smaller plaques than XBB.1.16 (242.7 ± 11.14 µm, n = 30) in Vero cells. Data are presented as means ± SD. **** p < 0.0001, determined using a two-tailed Student’s t-test.