Pangolin Footprints: Infectivity, Virulence and Long COVID

Review Pangolin Footprints: Infectivity, Virulence and Long COVID Gerard Kian-Meng Goh 1,*, A. Keith Dunker 2, James A. Foster 3,4 and Vladimir N. Uversky 5,6 Citation: Lastname, F.; Lastname, F.; Lastname, F. Title. Biomolecules 2022, 12, x. https://doi.org/10.3390/xxxxx Academic Editor: Firstname Lastname Received: date Accepted: date Published: date Publisher’s Note: MDPI stay s neutral with regard to jurisdictional claims in published maps and institutional affliations. Copyright: © 2022 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licen ses/by/4.0/). 1Goh’s BioComputing, Singapore 548957. Republic of Singapore 2Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA; [email protected] 3Department of Biological Sciences, University of Idaho, Moscow, Idaho 83844, USA; [email protected] 4Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, Idaho 83844, USA 5Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, Florida 33612, USA; [email protected] 6Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, Moscow region, Russia Correspondence: [email protected]  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Abstract: This review focuses on three closely related models that use protein intrinsic disorder to link the N and M to infectivity, virulence and, potentially, long COVID. While the models, Shell Disorder Models (SDMs), were initially created using computational and empirical molecular techniques based on protein intrinsic disorder, experimental and clinical data were continuously used for refnement and checked for reproducibility and reliability of SDMs. Interestingly, SDMs are uniquely able to link the potentially major cause of infectivity, virulence and long COVID under one coherent concept of protein intrinsic disorder. An example of this is SDMs' ability to provide a novel and coherent explanation for the differences in the virulence and infectivity of Omicron, SARS-CoV-1 and non-Omicron SARS-CoV-2. SARS-CoV-2 has been clinically shown to induce much greater shedding of infectious particles in patients. Curiously, all known SARS-CoV-2-related viruses, excluding SARS-CoV-1, have an abnormally hard outer shell (low M disorder), which is associated with burrowing animals, such as rabbits and pangolins. Evidence of a unique molecular and evolutionary relationship (“pangolin footprints”) between pangolins and COVID-19 is examined. SDMs suggest that this hard outer shell is responsible for the high infectivity of COVID-19 and , potentially, long COVID, as the hard M provides resistance to the antimicrobial enzymes found in the immune and respiratory systems. The implications could provide clues towards further research involving long COVID and infectivity, including possible reservoirs among macrophages. This also explains clinical observations of the persistence of the virus throughout the body months after infection. In the case of virulence, greater disorder in N contributes to more rapid replication by providing more effcient protein-protein binding. As a results, N disorder correlates with viral titer and therefore virulence and, to some extent, infectivity. Keywords: coronavirus; COVID; intrinsic disorder; membrane; nucleocapsid; nucleoprotein; Omicron; pangolin; shell; virulence; long COVID; attenuation; variant; immune; perforin; complement system; macrophage; reservoir; Artifcial Intelligence; AI; hard shell; lysosome; unstructured;NL63;spike; Abbreviations: COVID: Coronavirus Disease; CoV: Coronavirus; SARS: Severe Acute Respiratory Syndrome; HCoV: Human coronavirus. SDM: Shell Disorder Model;, PONDR®:-VLXT: Predictor of Natural Disordered Regions using VLXT; PID: Percentage of Intrinsic Disorder (number of disordered residues divided by the total number of residues); Pang2017: SARS-CoV-2 related pangolin-CoV isolated in 2017; Pang2019: Pangolin-CoV isolated in 2019, N: Nucleocapsid protein, M Membrane protein; S: Spike protein; SARS-CoV-2, BANAL: SARS-CoV-2 related Bat-CoVs found in Laos, NL63: A common HCoV; RaTG13: A SARS-CoV-2 related bat-CoV discovered in Yunnan; AI: Artifcial intelligence; EBOV: Ebola virus; NiV: Nipah virus; DENV: Dengue virus; HIV: Human immunodefciency virus; YFV: Yellow fever virus; ZIKV: Zika virus; 1. Introduction 1.1 Overview This review focuses on the three closely related models as related to COVID-19 (Coronavirus Disease 2019) [1-3]. The models use protein intrinsic disorder to link the N and M to infectivity, virulence and, potentially, long COVID. While the three models, Shell Disorder Models (SDMs) [4], were computationally created using empirical 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 molecular techniques based on protein intrinsic disorder, experimental and clinical data will be examined for the reproducibility and reliability of SDMs. Interestingly, SDMs are uniquely able to link the potentially major cause of infectivity, virulence and long COVID under one coherent concept of protein intrinsic disorder. An example of this is SDMs For instance, it is able to provide a novel and coherent explanation for the differences in virulence and infectivity between SARS-CoV-1 and SARS-CoV-2, which has been clinically shown to induce much greater shedding of infectious particles in patients. Curiously, all known SARS-CoV-2-related viruses, excluding SARS-CoV-1, have an abnormally hard outer shell (low M disorder), which is associated with burrowing animals, such as rabbits and pangolins. Evidence of a unique molecular and evolutionary relationship (“pangolin footprints”) between pangolins and COVID-19 is examined. SDMs suggests that this hard outer shell is responsible for the high infectivity of COVID-19 and, potentially, long COVID, as the hard M provides resistance to the antimicrobial enzymes found in the immune and respiratory systems. The implications could provide clues towards further research involving long COVID and infectivity, including the search for possible reservoirs among macrophages. This also explains clinical observations of the persistence of the virus throughout the body months after infection. In the case of virulence, greater disorder in N contributes to more rapid replication by providing more effcient protein-protein binding. As a results, N disorder correlates with viral titer and therefore virulence and, to a limited extent, infectivity. As S (spike protein) is currently yet to be part of SDMs, S is mentioned sparingly as part of a discussion involving the potentials and limitations of SDMs. It should also be reminded that while there are current review and research papers that attempt to link various viral proteins to virulence or infectivity [5-8], we will attempt to show that the approach using protein intrinsic disorder provides a more coherent concept that links virulence and infectivity via M and N. S could also be later added to the framework at some point. 1.2 Pangolin Footprints: Enigmas of COVID-19 Related Viruses It has been more than 4 years since the frst outbreak of COVID-19 [1-3]. Even as we are just beginning to understand what was previously unclear about it, there is still much to be uncovered and resolved. For instance, why is SARSCoV-2 highly contagious in contrast to 2003 SARS-CoV (SARS-CoV-1, SARS: Severe Acute Respiratory Syndrome, CoV: Coronavirus)? The total number of people infected thus far is more than 700 million [3], whereas there were only about 10,000 cases in the 2002-3 SARS outbreak [4]. Why is SARS-Co-2 less virulent and much more infectious than SARS-CoV-1? What are the structural differences responsible for the differences? What is the cause of long COVID? Early in the outbreak it was believed that the reason for the extraordinary contagiousness of COVID-19 lies solely on the Spike (S) protein [9-12]. As more computational, clinical, and experimental data became available, the reproducibility of such a hypothesis can be increasingly questioned even though there are few current papers that examine this issue especially pertaining links between infectivity and virulence in a coherent manner, which we believed is best approached using the concept of protein intrinsic disorder. This paper re-examines the roles of two highly important, though lessresearched, proteins: M and N, while keeping the functions of S in mind. The framework of many of the studies were established using an AI tool to study the sequence of the M and N proteins. One important but peculiar discovery using the protein disorder AI tool, PONDR®-VLXT [13-17], is that all SARS-CoV-2-related viruses, not just SARS-CoV-2, have among the hardest outer shells (low M disorder) known within the CoV family [18-21]. It is believed that it is this anomaly pertaining to M that is primarily responsible for the high contagiousness of COVID-19, since a harder M provides greater resistance for the virus against the large array of antimicrobial enzymes present in the saliva and mucus [21-28] and, thus, making it more likely for the host to shed much more infectious particles [29]. N, on the other hand, could help modulate the infectivity and virulence as greater N disorder allows for more 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 effcient protein-protein/RNA/lipid binding [4,16,30-33] that could lead to more rapid replication of the virus, especially in vital organs [34-40]. A retrospective search after the initial COVID-19 outbreak yielded a batCoV sample (RaTG13) obtained from a Yunnan cave in 2013 that had a 96.4% genetic identity with SARS-CoV-2 [4-43]. Furthermore, two sets of pangolinCoVs that were obtained from pangolins confscated by customs in Guangxi (GX) and Guangdong (GD) provinces during 2017-18 (Pang2017, Pang2018) and 2019 (Pang2019) periods respectively have about 90% genetic proximity to SARS-CoV-2 [44-48]. Later, pangolin samples obtained in Vietnam showed similar results [49]. Likewise, a series of COVID-19 related bat-CoVs (BANAL) were found in Laos [50-51]. In fact, one of the samples, BANAL-52, had an even greater genetic proximity (96.8%) to SARS-CoV-2 than that of RaTG13 to SARSCoV-2. A search for similarly hard M yielded CoVs associated with burrowing animals such rabbits. This enigmatic association explains the true intimate relationship between all COVID-19 related viruses and pangolin-CoVs since pangolins are also burrowing animals [4,18-20,52-54]. The hard outer shell (M) is necessary since the virus has to be able to survive longer in buried feces before further transmission. While most scientists believe that COVID-19 is the result of a zoonotic spillover, a better understanding of the evolution helps. The knowledge of N and M proteins can make the understanding of this evolution more complete as it bridges the relationship between SARS-CoV-2 and pangolin-CoV. The relationship as evidenced by the pangolin molecular “footprints” can account for many of the behaviors of COVID-19. The implications of this relationship will be re-visited in greater detail later. . One odd characteristic found in all SARS-CoV-2-related viruses and their variants found thus far is the unusually hard outer shell (low M disorder) that is typically found only in CoV associated with a burrowing animal. This hallmark is one of the “pangolin footprints” found in all COVID-19 related viruses discovered so far. The other set of footprints involve attenuations arising of lesser disorder in N found in Pang2017 and, later, in the variant Omicron [18-20,52-56]. The harder inner shell (N), especially in Pang2017, may be also a refection of N protecting the viral RNA in buried feces, just like M [52,55]. Evidence of pangolin footprints offers clues that are beginning to uncover many of the mysteries of COVID-19 that are still haunting us. These include questions such as: why is SARS-CoV-2 much more infectious than SARS-CoV1? Why is COVID-19 is highly infectious to this day? Why is SARS-CoV-2 less virulent than SARS-CoV-1? Why is Wuhan-Hu-1 much more virulent than Omicron? What is the actual structural cause of long COVID? Many of these questions have remained unanswered, but the study of the M and N proteins is now beginning to provide some answers from one integrated concept, namely the pangolin footprints. We should not be at all surprised by the explanatory prowess of the N and M proteins, as they are the most abundant proteins found in the cell and virion, respectively. 1.3 Long COVID Yet another major mystery in COVID-19 is the presence of long COVID among many patients, i.e. the persistence of symptoms long after recovery [57-58] . It should also be noted that S, unlike M, is unable to account for the persistence of the virus even months after the initial infection as observed by clinical studies. Even as progress has been made in research to explain the mechanism and cause of long COVID, long COVID has remained by and large a mystery [59-64]. In this paper, we will review an alternative explanation that has already been previously mentioned. This explanation involves the unusually hard M found in SARS-CoV-2 that allows the virus to resist immune enzymes 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 and enables the virus to hide in macrophages. The abnormally hard outer shell, M, that protects the virion from the onslaught of the antimicrobial enzymes found in saliva and mucus [19-28,40,52], is also likely to reduce the chances of the elimination of viral particles via actions of the immune system. We will examine literature pertaining to current knowledge of immunology that could provide a potential framework for the mechanism of long COVID caused by hard M . SDMs will be used to gain further insight to the potential mechanisms involved. The abnormally hard outer shell, M, that protects the virion from the onslaught of the antimicrobial enzymes found in saliva and mucus [1928,40,52], is also likely to reduce the chances of the elimination of viral particles via actions of the immune system. Upon examination of immunological principles, Shell-Disorder Models (SDMs) even go on to pinpoint the exact immunological mechanisms that are likely to be hindered. 2. The Shell Disorder Models (SDMs): Three Closely Related Models 2.1 Three Closely Interrelated Models Three closely related models were developed using the concept of protein intrinsic disorder via AI tools. Protein intrinsic disorder refers to the lack of structure in an entire or part of a protein. Disorder is known to have various i m p o r t a n t f u n c t i o n s e s p e c i a l l y i n p r o t e i n - protein/RNA/DNA/lipid/carbohydrate binding, and various tools have been developed to recognize disorder regions and disordered proteins. Among the frst developed is PONDR®-VLXT, which involves the use of neural network AI to recognize disordered residues given the sequence input [13-17]. PONDR®- VLXT has been shown to be a highly appropriate tool particularly when it involves viral proteins of a large variety of viruses including Ebola virus (EBOV), Dengue virus (DENV), Nipah virus, and HIV [4,18-20,34-40,52,-56,6567]. PONDR®-VLXT is especially suited for the study of viral structural proteins as it is highly sensitive in the detection of disorder in structured proteins [16]. The frst SDM was initiated before 2008 when PONDR®-VLXT was used to examine the shell proteins of a variety of viruses. A useful number used is percentage of intrinsic disorder (PID), which is defned as the number of disordered residues predicted divided by the total number of residues in a protein chain [4,65]. A disordered residue is predicted to be disordered if its VLXT score is 0.5 or above. Upon comparison of the shell disorder of a fairly large number of viruses, it became obvious that the outer shell of HIV, especially HIV-1, has an abnormally high average disorder in its outer shells. It was also discovered that HSV and HCV share this similar characteristic, even though it is not as pronounced as in HIV-1 [418-20,34-40,65-67]. Since very few other viruses, if any, have this characteristic, it seems to have to do with the ability of the viruses to evade the immune system, and results in the absence of effective vaccines for the mentioned viruses. This SDM was labeled “Viral Shapeshifting” [4] and became the parent model for two other closely related SDM, as seen in Figure 1. Given the behaviors of HIV, HCV, and HSV, higher disorder at the outer shell can also be associated with the ability to penetrate hard-to-reach places such as the brain and placenta, as in the case of the Zika virus [34,35]. Protein disorder provides for greater effciency in proteinprotein/DNA/RNA/lipid/glycoprotein binding [16.30-33,55,58] . Table 1. The Three Shell Disorder Models (SDMs). SDMs were developed using principles of protein intrinsic disorder applied to viral shell proteins. Year of First Shell Disorder Details 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 Publication Model 2008 Viral Shapeshifter Model (Parent) Disorder of shell proteins was measured for a wide variety of viruses. Only very few viruses have been found to have unusually high disorder at the outer shell (HIV-1, HCV, and HSV). There is no effective vaccine yet found for the three viruses. 2012 CoV Transmission SDM Links between modes of transmission (fecal-oral and respiratory) and N-M disorder were found. 2015 VirulenceInner Shell Disorder Model Strong correlation between inner shell disorder and virulence of a wide variety of viruses including DENV, EBOV, NiV, and SARS-CoV-1/2. Another model was developed and published in 2015 when it was discovered that there is a strong correlation between DENV virulence and disorder at the inner shell protein [34,35]. Similar correlations were found in a large variety of other viruses including NIV, CoVs, and EBOV [4,34-40,52-56,65-67]. The presence of such correlations has to do with the fact that the inner shells are often associated with replication in many viruses, and because disorder p r o v i d e s f o r g r e a t e r e f f c i e n c y i n p r o t e i n - protein/RNA/DNA/lipid/glycoprotein binding [16,30-33]. These form the basis of the Virulence-Inner Shell-Disorder Model (Table 1). 257 258 259 260 261 262 263 264 265 266 268 Figure 1. Virulence-Inner SDM (Shell Disorder Model). A. Shells PID of DENV and SARS-CoV-2. Inner shell of DENV has been found to be correlated to virulence (r=0.95) [34,35]. B. Correlation between the SARS-related viruses and N PID. The correlation is based on estimated CFRs of SARS-CoV-1/2 and Omicron. Note: DENV and SARS-CoV-1/2 shells are not correlated. They are just placed together for illustrative purpose only. The third SDM was frst published in 2012 [53] before MERS-CoV was discovered in 2013 [55]. The model divided CoVs into three groups, labeled AC, that can be seen in Figure 2, with group D added during the COVID-19 pandemic [18-20,40,53,55,56]. Group D was not recognized in the initial model because there were very few CoVs that involved burrowing animals such as rabbits and pangolins at that time [4,18-16,52,53]. Before the COVID-19 pandemic, nearly all CoVs in available our database have M PIDs of at least 8% (See Figure 2 and Supplementary Table). The exceptions that have M PIDs lower than 8% are CoVs associated with burrowing animals. It was during the pandemic that it was discovered that all SARS-CoV-2-related viruses have M PIDs lower than 7%(4-6.3%) (See Figure 2 and Table 2). While the statistical differences seem small, in reality, it is actually not anything small or trivial as we will see, later, that M is the most abundant protein that encases the entire virion. Even small changes will affect the rigidity of the entire shell. This CoVTransmission SDM invokes the same molecular principle that the VirulenceInner SDM uses, which involves greater disorder at the inner shell that provides greater effciency in viral replication. However, the CoVTransmission SDM extends the principle to the levels of fecal-oral and respiratory transmission potentials, by showing that respiratory transmission is viable only when suffcient copies of the virus are shed nasally. This results in N being adequately disordered. Multivariate analysis have found strong correlation between modes of transmission and levels of M/N PIDs with statistical signifcance (Multivariate analysis: p < 0.001,r~0,8, N = 32, see Suppl. Table for further information). 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 Figure 2. CoV Transmission Shell Disorder Model (SDM) CoVs. In groups A-C, the levels of respiratory/fecal-oral transmission are heavily dependent on N PID, whereas those in group D have unusually low M PIDs (M disorder) that are usually associated with burrowing animals such as pangolins. Group D includes all COVID-19-related viruses. (Multivariate analysis: p < 0.001,r~0,8, N = 32, see Suppl. Table for further information) The CoV-Transmission SDM categories SARS-CoV-1 in group B consist of CoVs with intermediate fecal-oral and respiratory transmission potentials. Upon the publication of the original paper, the MERS-CoV outbreak took place in 2012-13 when the SDM had to place MERS-CoV in group C, in which the CoVs have higher and lower fecal-oral and respiratory transmission potentials respectively [54]. This prediction has been reproduced clinically and experimentally [4]. It is also known that MERS-CoV has long been entrenched among camels, especially farmed camels, where it spreads easily by fecal-oral means. 3. Pangolin Footprints: Applying SDMs to SARS-CoV-2 3.1.. The First Sign of a Pangolin Footprint: Abnormally Hard Shell in All SARSCoV-2-Related Viruses When the CoV-Transmission SDM was frst developed and implemented in 2012-13, strong correlation was seen between N disorder (N PID) and the modes of transmission [4,53,54]. However, statistical calculations detected a small correlation between M disorder (M PID) and modes of transmission. PID 309 310 311 312 313 314 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 is defned as the number of disordered residues divided by the total number of residues in the protein. At that time, it was not understood why such a correlation existed. It was not until the arrival of the COVID-19 pandemic that came with a torrent of data that things began to fall in place. The CoVTransmission SDM was applied to the Wuhan-Hu-1 as soon as the M and N proteins became available, with Wuhan-Hu-1 having N and M PIDs of 48.2% and 5.9% respectively [14,40,52-55]. Using the original model, SDM placed SARS-CoV-2 in group B, which is the same group that SARS-CoV-1 is in. SDM did detect something highly unusual about this virus that was seldom seen in our curated database of known CoVs: the outer shell of SARSCoV-2 is abnormally hard, i.e. has a low M PID [18-20]. As more data became available, it became clear that the odd hard outer shell was not just something that pertains only to SARS-CoV-2 but to all COVID-19-related viruses, as seen in Table 2 and Figure 2. We can see that the hard M (low M PID < 7%, Table 2) extends to all SARS-CoV-2-related viruses, including pangolin-CoVs and batCoVs such as RaTG13 and the Laotian bat-CoV (BANAL). Details found in the tables can also be found in previous papers [52,55-56] and the protein sequences were obtained either from UniProt [69] or NCBI-Protein [70], and the PONDR®-VLXT scores were obtained by inputting the sequences into PONDR®-VLXT [13-17]. The PIDs (percentages of intrinsic disorder) were calculated as the number of disordered residues divided by the total number of protein in a protein chain [4,65-67]. Table 2. N/M PIDs and Sequence Similarities of COVID-19 Related Viruses CoVs with SARS-CoV-1 and Non-SARS-CoV-2-related Bat-CoVs as references. It should be noted that BA1 was the initial Omicron. At least two Delta subvariants have been detected by SDMs. Coronavirus Sequence Similarity M (%) M PID (%) Accession: UniProt (U); GenBank (G) Sequence Similarity N (%) N PID (%) Accession UniProt (U); GenBank(G) SARS-CoV-1 Civet-SARS-CoV 90.5 90.1 8.6 8.6 P59596(U) Q3ZTE9(U) 90.5 90.01 50.2 49.1 P59595(U) Q3ZTE4(U) Laotian Bat-CoV [Banal-52] [Banal-103] [Banal-236] - 98,7 98.7 99.1 6.0+0.2 6.3 5.9 4.1 - UAY13220.1 UAY13232.1 UAY13256.1 - 99.3 99.1 99.3 48.3+0.2 48.2 48.5 48.5 - UAY13225.1 UAY13257.1 UAY1326.1 Pangolin-CoV 2019 2018 2017 - 98.2 97.7 98.2 5.6+0.9 6.3 4.5 5.9 - QIG55948(G) QIQ54051(G) QIA48617(G) - 98 93.8 94 93.32 46.6+1.6 48.7 46.3 44.9 46.5 - QIG55953(G) QIQ54056(G) QIA48630(G) QIA48656(G) SARS-CoV-2 Wuhan-Hu-1 Delta Delta1 Delta2 Omicron Omicron BA1 Omicron XBB 100 99.1 99.1 - 98.7 99.1 5.9 5.9+0.01 5.9 5.9 5.7+0.4 5.4 YP009724393(G) QUX81285(G) QUX81285(G) - UFO59282(G) WBI50320(G) 100 99.1 - 98.6 48.2 47.1+0.5 46.8 47.5 44.5+0.4 44.8 44.2 YP009724397(G) QYM89997(G) QYM89845(G) - UFO692871(G) WIL50325 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 3.7. Phylogenetic Trees Using M Reveal a More Intimate Relationship Between Pangolin-CoVs and SARS-CoV-2: Another Sign of a Pangolin Footprint We have seen that a hard M is the hallmark of all thus-known COVID-19 viruses. Being hard or ordered usually entails a more conserved protein. Based on this, M is very likely to be highly conserved among all COVID-19 related viruses. Such a feature makes M more ideal for phylogenetic studies, as it is known that recombinations could cause gross errors in phylogenetic calculations. Interestingly, phylogenetic calculations yielded results that are different from those using the entire genome or other proteins [41-48,52,55]. Figure 5A,C uses M to show that pangolin-CoVs have a much closer relationship to SARS-CoV-2, not seen when other protein such N (Figure 5B) or entire genome is used. Figure 5A is different from Figure 5C as they involve slightly different algorithms. In any case, both show that pangolin-CoVs have intimate relationships with SARS-CoV-2. It is interesting to note that the two viruses that have the greatest sequence similarities to SAR-CoV-2 are bat-CoVs, RaTG13 and BANAL-52, with 96.1% and 96.8% respectively [51-52]. What is intriguing, however, is that in Figure 3A, it can be seen that BANAL-52 and Pang2019 have the similarly closest relationship to SARS-CoV-2, in contrast to RaTG13. How can this be when RaTG13 has a 96.1% sequence identity to SARS-CoV-2, compared to about 90% for Pang2019? Sequence identity does not offer the full picture because of the possibility of recombination occurring. Furthermore, phylogenetic genetic algorithms tend to make mistakes when recombination had taken place [94]. The abnormally hard M found in all COVID-19-related viruses entails a structural and genetic conservation, which means that the likelihood of any recombination having taken place is much lower. Therefore, we believe that Figure 5A,C presents the most accurate phylogenetic study by avoiding the chances of recombination. Yet another odd feature can be seen in Figure 5C, where Omicron is more closely related to the pangolin-CoVs than to the other variants [40,52,55]. While this may seem odd, we know that Omicron itself is shrouded in mystery. When Omicron was first sequenced, it was found to have mutations that are unlike any other variants. The question that quickly arises is: Where has Omicron been hiding all along? [95]. Why didn't the medical and scientific community notice it if it was in the human population? There were a few suggestions. A few scientists suggested that the virus was hiding in a small group of immunocompromised people such HIV or cancer patients [96]. Others have suggested that the virus had been hiding in an animal such as rat or pangolin. One paper has suggested that Omicron had been hiding among mice based on the mutations of its S and mouse ACE-2 [96]. Disorder studies on Omicron do suggest that it could be hiding in a burrowing animal such as pangolin as the first Omicron variant BA1 had an even harder M than other SARS-CoV-2 variants (M PIDs: 5.4% Vs 5.8%) [55]. The idea that Omicron had been hiding in mice actually does not contradict the suggestion in the previous statement since mice are also burrowing animals. A complication arises, however, when we examine the evolution of mice and rats. While rats and mice dwell in burrows in the countryside, rats and mice in the urban settings have evolved to live in the homes of human (Schmidt-Holmes et al) [97]. Therefore, depending on the species, they could have characteristics of both burrowing and non-burrowing animals. The phylogenetic tree (Figure 5C) that shows an unusually close relationship between Omicron and pangolin-CoVs, in contrast to the other variants, adds an important clue towards solving the puzzle. The phylogenetic tree is suggesting that it was literally hiding within a population of a burrowing animal such as pangolins. 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 677 Figure 5. A CoV Phylogenetic Study Trees Using M. A. Phylogenetic study of CoVs using M via CLUSTAL OMEGA [89]. B. Phylogenetic study of CoVs using N via CLUSTAL OMEGA [98-99] . C. Phylogenetic study of COVID-19-related viruses using M (via CLUSTALW [100]). Viruses related to SARS-CoV-2/1 are shaded in blue in (A) and (B). 4. The Shell Disorder Models and Reproducibility: M and N Proteins 4.1. The Problem with the S Protein: Limitations and Potentials When COVID-19 frst struck in Wuhan it spread globally with a ferocity not seen since SAR-CoV-1, despite efforts to control it. Almost immediately, many scientists began to zero in on S as the protein responsible for its high infectivity [9-11]. A computational model suggested that the SARS-CoV-2 S binds more effciently to the ACE-2 receptor than SARS-CoV-1 [9-11]. Furthermore, a specifc polybasic sequence known as the furin cleavage (FCS) can only be found in SARS-CoV-2 but not in the COVID-19-related bat-CoVs and pangolin-CoV and SARS-CoV-1 [101-103]. Many scientists hailed S, as well as FCS, as the holy grail of COVID-19 transmissibility and virulence knowledge [102-103]. The S protein has been widely studied [104] since it plays important roles with many implications. It is intimately involved in viral entry, which presents an opportunity for the discovery of drugs and vaccines that block the attachment of the virus to the host cells [104]. Furthermore, being a surface protein, it is being widely studied for the way its mutations help evade the host immune system. It is therefore not diffcult to see why S is the most studied CoV protein. S is, however, so intensively studied that it is easy to get the impression that it is the only protein that matters, or that no other CoV protein exists. This paradigm is, of course, nonsensical as it violates a basic tenet of biochemistry: each protein plays individual but important roles [105]. Even in the debate involving COVID’s origin, many scientists seem to imply that the secret of COVID infectiousness and virulence lies in the S protein and that the moment that the secret of S is unlocked, the origin of 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 COVID-19 will be uncovered as well [102-103]. Contrary to the popular notion, there is mounting clinical and experimental evidence that the S protein may not be the main underlying cause of COVID-19 infectiousness and virulence. One major piece of evidence for this lies in a comprehensive clinical study conducted by Wolfel et al [29]. In this study, it was found that COVID-19 patients shed much more infectious particles than SARS-CoV-1 patients. If we look closely, the question that comes to mind is: why does SARS-CoV-2 need much more particles to be more infectious if its S has a 10 or 1000 times [9-11] greater binding affnity to ACE-2 than the latter? Keeping in mind that it requires much more energy to produce these extra infectious particles and nature does not, as a rule, usually waste energy to do redundant things. One could, however, attempt to get around this paradox by claiming that the S affnity to ACE-2 is responsible for the more rapid replication of the virus. Unfortunately, this suggestion contradicts what we know about the life-cycle of the virus, as viral entry represents only an initial stage of a long series of process that includes RNA replication, protein production, assembly, packaging, and the budding of viral particles [106-109]. There are those who suggest that it is possible that SARS-CoV-1 S binds more effciently to the ACE-2 in cells in the lower respiratory in contrast to SARS-CoV-2 [12]. There are a number of issues that arise when such as argument is made. Firstly, the argument is made without any considering current physiological knowledge, namely the Mucociliary Clearance system (MCC), which allows viral particles produced throughout the respiratory system to be transported upwards towards the nasal area so that they could be expelled and shed [88-91]. Secondly, to our knowledge, attempts to reproduce the argument involving SARS-CoV-1 S and ACE-2 has not been conducted experimentally, probably, because of the current diffculty in obtaining the now extinct SARS-CoV-1 to conduct comparative experiment alongside SARS-CoV2. There are, however, also similar arguments made to account for the differences in virulence of Omicron and non-Omicron variants Hui et al [78]. This difference is that COVID-19 pandemic provides us with a deluge of clinical and experimental data. In fact, attempts to reproduce this argument has been made, which we will discuss at length later. The clinical study of Wolfel et. al. [29] is just the tip of the iceberg, as there is also a deluge of other evidence that shows that the nature of S is not what many scientists have made it out to be. Nor does S alone provide for a coherent conceptual framework of COVID-19 infectiousness and virulence, unlike M and N. This is also the case with long COVID. We will examine these in greater details in later sections. This is not to say that S is unimportant or that it does not play any part in infectiousness or virulence. It is important, but we must also fully comprehend its true potential and limitation. A more complete understanding of the true limitations and potentials of S will come when we study other important proteins such as N and M more thoroughly, which is challenging even if the majority of research is oriented towards S. 4.2. SDMs and Reproducibility We have seen that SDMs are highly reproducible by their ability to accurately predict and explain certain phenomena that are otherwise diffcult to account for. Unlike alternative explanations, they are explained in a coherent manner using a logical and unifed paradigm that is consistent with current knowledge of physiology and biochemistry [52]. There is also important clinical and experimental evidence that reproduces many of the predictions of SDMs. While a previous experiment was not able to detect any statistical difference in the ability of SARS-CoV-2 to last on various surfaces under presence of light when compared to SARS-CoV-1, Riddell et al. [21] conducted a similar experiment, devoid of light, and found that SARS-CoV-2 lasts much longer on external surfaces than the control CoVs. SDMs predict that SARSCoV-2 is more persistent than most virus as its unusually hard M protects against the environment and harsh antimicrobial enzymes, and this experiment reaffrms SARS-CoV-2's resilience in the absence of light. Reproducibility of SDMs is not confned to computational and experimental research, but also extends to clinical studies. One such study involved an investigation into the infectiousness of SARS-CoV-19. It was found 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 that COVID-19 patients shed a much larger amount of infectious particles than the 2003-SARS patients [29]. This contradicts all other paradigms set forth. For instance, if S is fully responsible and has a much greater affnity for human ACE-2, why does SARS-CoV-2 need to expunge such higher quantities of particles in order to be more infectious? SDMs provide for a much more elegant explanation: the virus is more resistant to the salivary and mucosal anti-microbial enzymes because of the abnormally hard SARS-CoV-2 outer shell (low M PID). This clinical observation raises more questions than it answers. More specifcally, how is the virus able to make the host expunge such a large amount of particles without being more virulent to the body? If we assume that the body is shedding more viral particles because it is producing more virus copies, then vital organs such as the lungs should be fooded with the virus, thus making the latter more dangerous, but this is apparently not happening in the case of COVID-19. Adding to the paradox, Ogando et al. [79] found that SARS-CoV-1 has a higher viral growth VERO-E6 cells than SARS-CoV-2 under the same conditions. Even if this is consistent with the greater pathogenesis of SARSCoV-2, how do we then reconcile this result with the previously mentioned clinical observation? If SARS-CoV-2 S has a 10 or 1000 times [9-11] greater affnity for ACE-2 than the affnity between SARS-CoV-1 S and ACE-2, how is this possible? It seems that we need to look elsewhere for answers by examining N and M more closely. The SDMs explain that the reason that SARS-CoV-1 is producing higher levels of particles has to do with the higher N disorder that allows greater effciencies in its replication process but, conversely, lesser infectious particles are shed by the body as the higher M PID does not provide suffcient protection against the antimicrobial enzymes. Yet another enigma involves long COVID [110-111], and SDMs are able to explain the persistence of the virus among COVID-19 patients even months after infection. Again, we return to the theme of hard M that protects the virus from antimicrobial enzymes. This time the focus is not only on mucosal and salivary enzymes as in the case of infectivity, but also on antimicrobial enzymes in the immune system. Further discussion on the antimicrobial enzymes found in the immune system can be found in the long COVID section below. 4.3 More Reproducibility: Omicrons and Pangolin-CoVs There are many other predictions that SDMs make involving N and M. One such prediction pertains to pangolin-CoV. When SDMs were applied to pangolin-CoVs, it became obvious that the 2017 pangolin-CoV (Pang2017) isolate from Guangxi is attenuated because of its low N PID (~44%) [40,52,5556]. There are several implications for this fnding. If this or a similar virus had entered the human population, it is likely that it would had easily moved quietly among humans as a mild cold without the notice of the medical communities [18]. The predicted attenuation has been independently reproduced by several laboratories [52,55,74-77]. Animals models have seen milder manifestation of symptoms upon Pang2017 infection, in contrast of the Wuhan-Hu-1 strain. The SDMs prediction pertaining to Pang2017 was published before the arrival of Omicron. Omicron was frst detected in South Africa around November 2021. Omicron was clinically and, later, experimentally observed to be milder than previous variants [71-78]. Once again, SDMs have much to say. Based on the initial Omicron subvariant, BA.1, the N and M PIDs are 43.65% and 5.4%, which are both lower than previous variants [49]. The smaller than usual M PID (5.9% vs. 5.4%) could suggest that the virus had a recent origin involving a burrowing animal, whereas the lower N PID predicts that Omicron is likely attenuated to similar levels as Pang2017. The prediction involving N PIDs was experimentally replicated when it was shown that the viral growth of VEROE6 cells infected by Pang2017 and Omicron respectively are very similar, and when it was shown that the viral damage on cells by the two viruses are similar [55] . This presents evidence of further reproducibilities for both Pang2017 and Omicron. Omicron has also been shown to be attenuated with lower growth than Wuhan-Hu-1 under viral titration [55,75,77,79]. 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 The correlations between N disorder and viral titer or virulence can be found in SARS-CoV-2 related virus data that include Pang2019 [71-73,75] and the Laotian bats-CoV (BANAL) [50-51]. Furthermore, while Pang2017 has been predicted and reaffrmed to be attenuated, this is not the case with Pang2019. Instead, SDMs have to predict Pang2019 to be non-attenuated with its N PID at 48.2% [18,52,55-56]. Huo et al. [81] were able to obtain levels of viral titer from Pang2019 similar to those of Wuhan-Hu-1 in VERO-E6 cells. Several laboratories were also able to independently observe that Pang2019 is able to infict severe disease in at least one strain of mice. In contrast, virulence was not observed in Pang2017 [55,74-79]. All these are again consistent with the predictions of SDMs. We know that all of these SARS-CoV-2 related viruses do not possess FCS, unlike SARS-CoV-2. The question then becomes: how does Pang2019 possess similar infectivity and virulence as SARS-CoV-2 without FCS, when FCS-mutant non-Omicron SARS-CoV-2 is largely not transmissible among ferrets? 4.7. Measuring Virulence Using CFR, Animal Models and Cell/Tissue Damage Observation While Figure 1 shows correlation between Inner Shell Disorder and CFR, it must be admitted that CFR is not necessarily the most ideal representation of virulence for at least two reasons. Firstly, CFR figures are often extrapolated out of necessity [103]. Secondly, CFR is applied only to human, not animals. Furthermore, virulence may vary among different animals even within a single virus or variant. There are, however, other methods of measuring virulence such as animal models, viral titration and indications of cell/tissue damage after infection. We have mentioned some of the animal models. It must be noted that attenuation or aggressiveness of SARS-CoV-2 variants, Pang2017-CoV and Pang2019-CoV were reproduced by viral titrations, animal models and inspections of cell/tissue damage after infecting cells or animals were conducted by at least two independent laboratories for each virus or variant [55,74-79]. The animal models included, at least, hamsters and several strains of mice. In addition, the severity of Pang2019-CoV infection was also observed in pangolins under laboratory conditions [80-82]. It can also be argued that a SARS-CoV-2 related virus may infect different types of cells in different manner, which could potentially make interpretation of viral titration data more challenging. This is related to the argument made by some scientists that SARS-CoV-1 could infect the lower respiratory tract more efficiently in comparison to SARS-CoV-2. There are, however, hints that the arguments may not present a great difficulty in the interpretation of viral titration data. Viral titrations have been made a variety of cells including Vero E6, Calu3 and Caco2 [77] . The data show subtle but definite differences in the viral titers using the three different cell lines that is the result of differences in S-ACE-2 binding mechanism. The differences, however, are not so huge that it would make interpretation of viral titration data challenging as the variations viral titers among cell types are not great and follows a trend. Besides, as we see later, the S of SARS-CoV-2 related viruses typically become quickly more efficient to binding to the S of different cell types after several passages.With all these in mind, it is possible to present a consistent picture of virulence nad infectivity. In fact, a previous paper [55] was able to find a positive correlation between virulence and N PIDs based on a combination of data from viral titer, animal model and cell plaques. 5. Comparative Roles of S, M, and N Proteins 5.1. The S Protein, Omicron, and Pangolins We have seen that the use of S is unable to explain some crucial experimental and clinical data. This just the tip of the iceberg, as there is more experimental and clinical data that S alone simply cannot explain or account for and, thus far, attempts to do so are not reproducible, or can be shown to be inherently fawed as we have argued. There are also important biological reasons for this. For us to understand this, we need to examine the basic fundamental biology of the virus. While S is defnitely an important protein in terms of viral entry, there is no fundamental reason that S should be the most important protein in COVID-19 infectiousness and virulence, as much as many scientists would like to believe. Viral entry, though important, is just the frst of the many steps in the virus' life-cycle [109]. It must be emphasized that 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 SARS-CoV-2 encodes 19 proteins, even though not all are major proteins [109]. S is not the only major protein, and neither is it the most abundant protein. On the contrary, the N and M are the most abundant major structural proteins found in the cell and virion respectively [96-100]. For this reason alone, we should not be surprised if N and M have greater infuence on the infectivity and pathogenesis of the virus. Further sets of perplexing evidence that contradict the “S-alone” paradigm can be found in the data that pertains to pangolin-CoVs and Omicron. We need to keep in mind that all variants of SARS-CoV-2 have FCS that is absent in all COVID-19-related bat and pangolin viruses. Many scientists thought that they have found FCS to be the true cause of COVID-19 infectiousness and virulence when it was found that SARS-CoV-2 with FCS intact is more aggressive in respiratory cells, in contrast to the FCS-mutated one, by promoting cell to cell fusion. Furthermore, it has been shown that while non-Omicron SARS-CoV-2 wild-type can be transmitted experimentally via aerosol between hamsters, no transmission was seen when FCS-mutant was used on ferrets [113]. Again, how can this be the case, given that pangolin-CoV has no FCS and is easily transmitted between hamsters [74-75,81] ? 5.2. The S and Omicron Conundrum When Omicron arrived, it presented an enigma by being infectious and yet attenuated. How did Omicron achieve its attenuation given that it has FCS just like all other variants? Animal studies have shown that rats are unable to transmit via aerosol in the laboratory. How could this be so when Omicron has been clinically shown to be highly infectious? More importantly, how can this be happen when Omicron has effcient FCS [114-115], in which its presence in other variants has been shown to induce greater transmissibility in ferrets [113]? These discrepancies point to the probability that other factors are in play. In fact, all these, as we have shown, are consistent with the workings of N and M, as summarized in Figure 5. An attempt to get around this “S-Omicron” paradox is the hypothesis that claims Omicron infects the cells in the upper respiratory system more easily than the lungs. Hui et al. [78] were able to isolate bronchial and lung tissues, which were infected with Omicron and previous COVID-19 variants. They were able to qualitatively see the greater presence of viral particles and concluded that Omicron is attenuated because it replicates more easily in the lungs than in the bronchi. There are, however, a number of problems with this interpretation. Firstly, this observation has not, to our knowledge, been reproduced in other laboratories. In fact, several laboratories have observed effcient replications in both lungs and upper respiratory systems [77,116-117]. Furthermore, several other independent laboratories have noticed that many subvariants of Omicron are even more adapted to the human ACE-2 than the non-Omicron variants [116-118]. If this is the case, why hasn't Omicron achieved greater virulence and infectivity like its predecessors? 5.3. Evidence of the Different Roles of N and M in Experimental Data The story is actually even more complicated than what Hui et al. had envisaged [78]. In reality, the N and M proteins, together with knowledge of MCC, provide a more consistent and reproducible explanation for their results. In our previous publications, we have shown that the initial waves of Omicron ha lower M PIDs than previous variants. This feature is likely a tell-tale sign of its recent interactions with a burrowing animal, possibly, the pangolin. In any case, it explains why Hui et al. were able to see more viral particles in the bronchi. It is because Omicron is more resistant to the anti-microbial mucosal enzymes that it encounters as it is being transported upwards by the hair-like structures in the network of mucus-covered ciliary cells. The lung has no ciliary cells or mucus but has surfactants that, even though it is anti-microbial, is not as harsh, unlike the variety of enzymes present. The apparent lack of viral particles is likely an indication of particles still trapped in the tissues as there would not be MCC to bring it to the top in the lungs [88-92] . Viral titrations were also made using Omicron, Delta, and Wuhan-Hu-1 in lungs and bronchial tissues in the above-mentioned experiment. The data were used to support the hypothesis of differentiated replication of Omicron in the 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 lungs and bronchi. In one of our previous articles, we managed to use their data to perform multivariate analysis (Figure 6).The viral titration data were taken from publicly available paper of Hui et al, while N and M PIDs based on variants and sub-variants were taken from our curated disorder database (Table 2). Regression (Multivariate) Analysis was performed using R package. The regression analysis provides us with the correlations between viral titer and N/M PIDs is measured as correlation coeffcients (r) or coeffcients of determination (r2). The total n (sample size) for the entire statistical experiment as seen in Figure 6 is 24 (p < 00,1, n =24, r2~ 0.9). Statistical results were computed using R package that is available publicly [119-120]. We found strong correlations between N/M PIDs and viral titers. We were also able to observe peculiar changes in the signs of the correlations when moved from lung tissues to bronchial ones [40] . We were able to obtain a positive correlation (r = + 0.96, Figure 6A) especially on the N PID and viral titer (VT= A * PIDN +B * Time + C ,where A,B,C = coeffcients and VT = viral titer). It was, however, very startling when we received a negative correlation (r - -0,93, Figure 6B) between PIDM/PIDN and viral titer ( VT = A * PIDM+ B * PIDN + C * Time + D where VT = viral titer, A,B,C = coeffcients and D = Y-intercept) [44]. What is remarkable and puzzling is the change between the positive and negative signs. It is not only reproducing the SDMs but also instructing us on how to use the SDMs. The change in the sign is an indication that the greater presence of particles in the lungs is dependent on the greater N disorder (higher PIDN), whereas the greater viral presence in the bronchi is dependent on lower disorder in N and M PID (lower M PID and N PID). The change in the direction of the slopes (correlations) can be observed in Figure, 6C-D. We will also see that this change in slope (correlation) is completely absent when viral titration is conducted in an animal model, instead of tissues (Figure. 7), as virus particles can easily travel upward via MCC in the case of animal models. 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 Figure 6. Multivariate Analysis of M/N Disorder and Viral Titer. A. Regression analysis reveals a positive correlation between N PID and the viral titer from human lung tissues and N PID. (Model: VT = A*(N PID) + B*Time + C where VT = Viral Titer, A,B = Coeffcients , C = Y-Intercept.). B. Regression analysis found a negative correlation between viral Titer from human bronchial tissues and M/N PIDs. (Regression model: VT = A*(M PID) + B (NPID) + C*Time + D where VT = Viral Titer, A,B.C = Coeffcients , D = Y-Intercept). C. The three dimensional plane with the 95% confdence interval as applied to the regression analysis involving viral titration on the lung tissues. D. Three dimensional plane with 95% confdence interval related to the the viral titration using bronchial tissues. As (C) and (D) provide only three dimensional representations (Model: VT = A*(N PID) + B*Time + C where VT = Viral Titer, A,B = Coeffcients , C = Y-Intercept.), they do not offer a complete picture that can only be found in four dimensions(Regression model: VT = A*(M PID) + B (NPID) + C*Time + D where VT = Viral Titer, A,B.C = Coeffcients , D = Y-Intercept). The analysis is a statistical extension of the experiment of Hui et al. [40,43]. Data pertaining to viral titration and PIDs are from the the experiment of Hui et al and available in Table 2 (which can also be found in previous publications) respectively. A negative slope (correlation) with respect to the N PID-Titer axes can be found in (D), unlike (C). 1016 1017 1018 1019 1020 1022 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 Prior to this investigation, we focused on the hard outer shell (low M PID) in resisting the onslaught of mucosal antimicrobial enzymes and on greater N disorder in its ability to assist in providing more effcient viral replication [1820] , but the experiment also showed us that N plays a role in protecting the virion from damage [52,55]. This is something that we had forgotten about, even though we had evidence from the very beginning that harder inner shells do protect the virus from harm in viruses such as EIAV, DENV, and rabies [3435,55-56,65-67]. We conducted a similar regression study as seen in Figure 7 using the experimental data of Guo et al. [75]. Again, we were able to obtain the titration data from the published paper of Guo et al with the respective N and M PIDs,, which depends on the SARS-CoV-2 variant and pangolin-CoV isolate, obtained from our curated database As with the data from Hui et al, we did a regression analysis to obtain the correlation coeffcients (r) and coeffcients of determination (r2) grouped by the location in the respiratory system the samples were obtained (Figure 7, Total n = 30, p < 0.01, r2 ~ 0.9). Guo et al. used Pang2017 and hamsters in lieu of Omicron and tissue cultures respectively [65]. Therefore, M becomes an unreliable independent variable as there is hardly any difference between the M PID of Pang2017 and nonOmicron SARS-CoV-2. We also expected to see the full effect of MCC since Guo et al. [75] used an animal model, in contrast to the use of tissues by in Hui et al. [78] i.e. the virus particles are more able to move freely between different parts of the respiratory system via MCC. Positive correlations between N PID and viral titer were seen in samples collected from all three areas of the respiratory system as seen in Figure 7, which helps validate SDMs and the results described in the previous sections. Figure 7B affrms a positive slope (correlation) when view with respect to the N PID-Titer plane. 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 room for conformation changes. Therefore, an M PID of 0 % means that the virus is not functional. The data in Table 2 and Figure 2 seem to reinforce this idea by the existence of the 4% minimum cutoff point. While a lower M PID offers greater protection to the virus in its native state, it could also imply that M could have less efficiency in undergoing conformational changes. We don't, however, know how much impact this has on the efficiency of conformational changes that are necessary for processes such as viral entry and protein assembly. Extrapolating from the efficiency of SARS-CoV-2 (M PID ~ 6.3%) in replication, it is likely not that much, but, again, this is just an extrapolation that has to be confirmed by experimental results. 5.12. A Comparative Analysis of SARS-CoV-2, Pangolin-CoVs, and Laotian BatCoV Experiments Using S, N, and M A more comprehensive analysis can be accomplished when examining M, N, and S in experimental data for SARS-CoV-2, bat-CoVs, and pangolin-CoVs. We have seen that all COVID-19-related viruses are potentially infectious because of their abnormally hard M. We have also seen that Pang2019, BANAL: (Laotian bat-CoV) and Wuhan-Hu-1 are all potentially virulent because of their high N PIDs (~48%), whereas Pang2017 and Omicron are attenuated as a result of their relatively low N PIDs (~44%). These have been largely reproduced. Cells infected with Pang2017 or Omicron have signs of lower viral growth and cytopathic effects [55,74]. Similarly, animal models have shown less severity. In contrast, however, viral titers of cells infected by Pang2019 [80-82] and Wuhan-Hu-1 [75,79] are higher than those infected by Pang2017 or Omicron [55,74-76], just as predicted by the SDMs. In contrast, mice were observed to be severely sickened by Pang2019 [81-82]. We need to keep in mind that this trend is true despite the fact that FCS is found only in SARS-CoV-2 including Omicron. Therefore, the stark differences in the results of the various experiments can only be accounted for when the roles of N and M are considered. 5.13. Evidence of the Potentials and Limitations of S: Viral Entry and Replication The data for the Laotian bat-CoVs present an enigma [50-51]. It was shown that BANAL S binds to ACE2 in a different manner from SARS-CoV-2 even as the BANAL viruses bind effciently to human cells. Ironically, even though the N PID of BANAL is close to those of Wuhan-Hu-1 and Pang2019, no severe disease was detected in mice infected by BANAL-236. This seemed inconsistent with what SDMs predicted until the data were studied very carefully. If we inspect the viral titration data, we can see that the viral titers in VERO-E6 cells is high and comparable to Wuhan-Hu-1, but this is not the case in CALU-3 cells, where the difference is larger; it should also be kept in mind that a VERO-E6 cell is of kidney origin, whereas CALU cells are respiratory. This implies that BANAL-236 is more adapted to kidney cells than respiratory ones presumably because of its S structure. We also need to keep in mind that it has been observed that the Laotian bat-CoV S binds to ACE-2 in a different manner [51]. While this serves as evidence that S does play a role in infectivity, it does not show that SDM results are wrong or not reproducible. Instead, it points to the correct way that SDMs should be interpreted. Firstly, the experimental data remind us that SDMs predict potential virulence, not necessarily actual virulence, and this potential virulence arises from the high viral load in at least one organ, which is populated with cells that the virus can enter more easily. A second lesson to be learned from the data is that SDMs predict virulence in general, not just humans, since N PID correlates best with highest viral titers among various cell types [55,74-79] , and high viral load is associated with organ failures. While human COVID-19 fatality is mainly associated with pulmonary (lung) failures, this may not be necessarily so for other animals. Therefore, SDMs are predicting potential virulence in general, not just in humans. 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 If there is evidence that S does play some role in infectivity and virulence via viral load, the question then becomes: how easily does S adapt to bind more effciently? It would seem that greater S ftness may be more easily acquired than many believe. Experiments have shown that COVID-19 viruses acquire greater ability to infect the lungs after passing to humanized mice several times [133-136]. Furthermore, a closer comparison of the two may provide clues. Given the fact that both BANAL-236 and Pang2019 have high N PIDs, why has Pang2019 been shown to be virulent to humanized mice, unlike BANAL-236? Evidently, Pang2019 S is more adapted than BANAL-236. To understand how this is the case, we need to look at the evolutionary differences between the two viruses. If we look at Figure 5B, which is a phylogenetic tree using M, we see that Pang2019 is much more closely related to SARS-CoV-2 than BANAL-236. This implies that Pang2019 was exposed to a similar range of hosts as Wuhan-Hu-1. What is also remarkable is that both BANAL-236 and Pangolin-CoVs, unlike SARS-CoV-2, do not have FCS. It is likely that Pang2019 split off from SARS-CoV-2 to infect mainly pangolins, and as a result lost its FCS while still maintaining much of the rest of its S structure. This may also have implications for Peacock et al.’s FCS-mutant experiment [113] It is possible that FCS-defcient S compensates for its defcit by binding to ACE-2 in a different way over the long run as in the case of Pang2019, and, as we will see, it is easy for S to quickly adapt to the ACE-2 of a particular species or cell type in a laboratory. It is evident that S has to be at least suffciently adapted to ACE-2 in order to even be infectious, but in order for SARS-CoV-2 to have any sustained infectivity or virulence, other factors must also come in crucial play, especially the roles of M and N, as seen in the experimental and clinical evidence pertaining to SARS-CoV-1/2. The question then becomes: how diffcult it is for S to gain suffcient adaptation to respiratory cells? The earlier section argues that it may not be that diffcult. Several studies have shown that COVID-19 viruses can easily acquire adaptability to human respiratory cells in the laboratory very quickly [133-136]. Acquiring abnormally hard M that provides for potentially high infectivity, on the other hand, may not be that easy. It is easy to fnd CoVs that effciently bind to S of respiratory cells [101,109,118] or have FCS [101,109,118] or that S easily adapts [108,131-132], but it is diffcult to fnd CoVs with the abnormally hard M, as most CoVs are not intimately associated with a burrowing animal unlike COVID-19 related viruses [4,1620,53-56] . 5.14. Greater Model Reproducibility and Reliability Come When More Proteins are Considered The greater reliability and reproducibility of SDMs, in large part, arise from the fact that they take into account the disorder of two major proteins, M and N, which are most abundant in the virion and infected cell respectively. In fact, SDMs become unreliable if M or N is omitted from consideration. SDMs are reliable and reproducible in most cases except in certain occasions when the role of S has to be taken into serious consideration as we have seen in the case of the Laotian bat-CoVs. For this reason, we cannot dismiss S or any other proteins as unimportant. In fact, as we have shown, SDMs become even more reliable and reproducible when S (or other protein) is taken into consideration. This trend is consistent with what we know about the biology of viral replication. Viral replication involves multiple proteins even if there are proteins that play more major roles than others. 5.15. Limitations and Potentials of N, M and SDMs We have already touched on the limitation of SDMs. We have seen that it cannot account some of the experimental results conducted using BANAL (Laotian bat-CoVs). They are, for example, unable to account for the differences in the the viral titrations of BANAL-236 and SARS-CoV-2 on different cell types (e.g. CALU, COCA, Vero-E6) [51-52,77]. The signifcance of the differences is, however, needs further investigation as other researchers have shown that SARS-CoV-2 becomes more effcient in replication as several passages are made in each cell type. Nevertheless, this points to the role of S, 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 which is a limitation of the SDMs. Currently, SDMs involves on the viral shell proteins, which, in the case of CoVs, are the N and M. Given that SARS-CoV-2 has 29 viral proteins and many of them are involved in the replication process [106-109], there will be limitation if only one or two proteins are used to study infectivity, virulence or long COVID. This is, of course, the case in SDMs, which currently use only M and N. In fact, as already mentioned, without either M or N, SDMs would be of limited reproducibility and reliability and would have much diffculty explaining the underlying cause of infectivity and virulence. Ironically, the question is then: How could M and N account for infectivity and virulence with even a decent level of reproducibility and reliability when the limitation of using only two proteins is considered? The answer could lie in two factors. The important roles of M and N, as seen above, are likely to be partly responsible. Secondly, a hint of an overwhelming importance of M and N can be seen by the massive abundance of the proteins, not seen in other COVID-19 viral proteins. This could also reinforce the idea that the two proteins are playing greater major roles in the functioning of the virus. While the focus of this review paper is on SDMs, N and M, it is not intended to dismiss the importance of S or any other viral protein. This review, however, attempts to underscore the importance of M and N in infectivity and virulence as exemplifed by the exercise seen in Figures 6-7. The results seen in Figures 67 do not invalidate the role of S, as Hui et al attempted to demonstrate but, rather, suggest that the currently understudied roles of M and N could even overshadow that of S with respect to infectivity, virulence and, potentially, long COVID. Of course, SDMs would become more reproducible and reliable if more viral proteins such as S and NSP7 are considered in the models, but, currently, SDMs have not reach the stage where the roles of more proteins can be incorporated. 6. Pangolin Footprints and Long COVID 6.1. The Long COVID Enigma and Pangolin Footprints One unusual characteristic that is often found among some COVID-19 patients is long COVID, which is when symptoms persist for weeks, months, or even years after the initial infection {57-58,110-111]. While the cause of long COVID remains largely a mystery, SDMs offer the most logical and plausible explanation yet. We have seen that all COVID-19 related viruses have extraordinarily hard outer shell (M) that is not found in any CoVs except those associated with burrowing animals. We have also seen how SDMs explain that the hard M allows for greater infectiousness of COVID-19, by providing more resistance to mucosal and salivary antimicrobial enzymes, often without greater virulence that is associated with greater N disorder [18-20,52,55-56]. Just as a hard M provides resistance to antimicrobial enzymes, it will almost certainly also provide resistance to other destructive mechanisms offered by other aspects of the host immune system. This is possible as clinical studies have shown that large amounts of the virus tend to remain in the body even after months [110-111]. We will further explore this link by a further examination of the various known aspects of immunology related to this matter as seen in the following subsections. 6.2. Hard M Resistance to Virolysis by Macrophages and Complement System In order to further examine the role of an abnormally hard M in long COVID, we need to look more closely at how the immune system gets rid of invading foreign particles, especially viruses. Our knowledge of immunology helps us to focus our attention on proteins produced by the complement system and lysosome [135-142]. These enzymes are experimentally shown to damage viruses and viral membranes. 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 Complement proteins are produced mainly by hepatocytes in the liver, even though they are secreted by monocytes, macrophages, and epithelial cells in the intestines. There are, at least, 30 types of complement proteins [139]. B and T cells, along with antibodies, can alert the complement system in the presence of a foreign matter such as bacteria or virus. Complementary proteins assemble and bind to a protein at the targeted membrane, and the protein complex punches holes in the membrane. Apoptosis occurs in the case of bacteria or infected cells, whereas, in the case of a virus, this is referred to as virolysis. It is at this point in COVID-19 and long COVID that the hard M may make it diffcult for the complement system to do its job, as the proteins are likely unable to penetrate the membrane since M spans the entire membrane that covers the virion. 6.3. Resistance to Virolysis Within a Macrophage May Provide the Virus a Place to Dwell: Possible Reservoir A second way that the immune system attempts to get rid of pathogens is to expose them to digestive enzymes [130-131,140]. The macrophage will frst engulf the microbe or microbial protein, and upon phagocytosis, the immune system will attempt to digest the particle via lysosomes [138-141]. The problem with this strategy, however, is that many pathogens are somehow resistant to the exposure of digestive enzymes and end up living in the macrophages themselves [140-141]. An ongoing enigma pertaining to long COVID involves the possibility of a reservoir that harbors the virus long after the initial infection of the patient and where the source actually is, since a hard outer shell could prevent the macrophage from destroying the virus and the virus ends up living in the macrophage. The observation of an abnormally hard M, along with our knowledge of immunology, suggests that the frst place to look at is none other than the macrophage itself, and current research supports this. Huot et al. [144] found the presence of SARS-CoV-2 in the lungs of patients with symptoms of long COVID, whereas several other research groups have found presence in organs and tissues throughout the body [110-111], which is consistent with the fact that macrophages can be found in nearly all organs in the body [139]. The nature of SARS-CoV-2 should not be confused with that of other viruses such as HIV and HSV. The fact that SARS-CoV-2 has among the hardest outer shell, not just among CoVs but also among all viruses, is a telltale sign that the virus is of a different nature from other viruses such as HSV and HIV-2 that are known to hide in places such as the brain only to show up later [109]. HIV and HSV-2 have one of the most disordered outer shells among viruses, unlike SARS-CoV-2. Their highly disordered outer shells help them penetrate and hide in organs, as greater disorder allows for more effcient protein-protein binding. SARS-CoV-2, on the other hand, has one of the hardest outer shells. If it does not have the beneft of a disordered outer shell, how does it hide? The answer has to lie in the mechanism of phagocytosis described above. There is research showing that infammatory responses may be responsible for long COVID. A hard M and inflammatory responses such as those caused by cytokines are not mutually exclusive [142-144]. Because of the extraordinarily hardness of M, it provides for a unique way for SARS-CoV-2 to hide in macrophages only to appear whenever the opportunity arises. Whenever the virus keeps reappearing, the immune system could respond in such a way that could result also in inflammation caused by cytokines. 6.4. Granuzymes: A Suspected Mechanism of M Resistance While the exposure of destructive enzymes has been shown to act against viruses in the complement system and macrophages, there are also other destructive enzymes available in the immune system. These involve a family of enzymes known as granuzymes. Upon entry of the virus, the immune system will initiate a variety of defensive actions that include Cytotoxic T-Cells and Natural Killer (NK) cells, which secrete substances that could potentially damage viral particles [136-139]. These cells secretes granuzymes inresponse to a foreign invader. One of the granuzymes is perforin, which binds to plasma membranes and punches holes that allows other granuzymes to enter to cause further damage to the bacterium or infected cell [145]. While current 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 experimental evidence has shown this to occur in membranes of bacteria and infected cells, there is currently no evidence that it damages viral membranes. Nevertheless, given the known biochemical capabilities of perforin , it can be construed that perforin can potentially damage virion in the same manner, since most animal viruses including SARS-CoV-2 have a protective outer membrane layer to protect themselves. In any case, perforin causes lysis in infected cells, and thereby allows the virus particles to be exposed to the complement system and macrophages. It is also known that, while T-Cells and NK cells secrete perforin, macrophages secrete perforin-2 (PFN2), which is similar to perforin, but it remains unclear if PFN2 affect viral membranes directly [144-145]. 6.5. Uniqueness of COVID-19 Strategy of Immune Evasion in Long COVID There are many mysteries involving long COVID that physicians and scientists are still struggling to solve so that we can come up better treatments. How does SARS-CoV-2 induce long COVID? What are the mechanisms? Is there a reservoir? If so, where is it? As we have seen, SDMs offer specifc answers to these questions. While N disorder modulates the amount of virus replicated, especially in vital organs, the unusually hard M provides resistance to antimicrobial enzymes. Therefore, the immune evasion strategy used by SARS-CoV-2 is drastically different from that of viruses such as HIV, HSV, and HCV [4,16-20,65-67], which have high disorder in the other shell that allows such viruses to hide in organs. Not only has no such high disorder been detected in SARS-CoV-2, the virus has one of the hardest outer shell among viruses, not just CoVs. That is why the macrophages offer the virus the opportunity to dwell and hide in them. Indeed, one laboratory showed the presence of the virus in the lung alveolar macrophages of patients who tested negative for the virus in the upper respiratory system [143]. What is even more puzzling is that several other laboratories have observed the presence of the virus in many organs of long COVID patients [111-112]. This raises the question, where is the reservoir? Again, if macrophages are the reservoir, the virus will be present in these various organs since macrophages are found in nearly every organ in the body. 6.6. Long COVID, long SARS and S Long COVID has been linked to the activation of the immune system, via, particularly, interferons (IFN-γ) and natural killer T-cells (NK cells), that results in infammation [142,144]. Many scientists point to S as the main underlying cause of the activation. While this postulation is very interesting and plausible, it raises more questions than it answers. For instance, it doesn't tell us the reason why some people get long COVID, whereas, others don't. It doesn't tell us if long COVID arises from a reservoir and, if so, what is the source of the reservoir i.e. the hiding place of the virus. We also need to remember that SARS-CoV-1 has a CoV S protein too. Because the two viruses are relatively closely related (80%), it is likely the S of both viruses are functionally similar including their ability to activate the immune system in similar ways. A conundrum is , however, seen when upon an examination of the differences between long COVID and long SARS. One stark difference is the fact that long SARS was usually associated with severe manifestation of the disease, whereas long COVID could come even with mild symptoms {64]. How could there be such a disparity if the S proteins of the two viruses are likely to be structurally similar and when it has been shown that S activates the immune system [142,144], which is likely to cause long COVID. SDMs offers a novel explanation. SDMs have observed that SARS-CoV-2 has a much harder M than that of SARS-CoV-1 as a result the virus is able to resist the immune enzymes and hide in the macrophage in the case of COVID. We are, therefore, likely to get a better picture when we consider the roles of M. N and S keeping in mind that N also plays a role as greater N disorder could allow greater production of viral particles even in the event of long COVID. 7. Summary and Conclusion 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 7.1. Reproducibility and Reliability of SDMs: Experimental and Clinical Evidence Some of the clinical and experimental evidence of the reproducibility and reliability are as follows: .i) The extraordinary hardiness of SARS-CoV-2 was seen experimentally by an Australian group, Riddell et al [21]. They discovered that SARS-CoV-2 lasts much longer in the environment away from light than the CoV controls.This is consistent with the detection of an extraordinarily hard SARS-CoV-2 M by SDMs. ii) The Dutch group, Ogando et al [79] [69] conducted a viral titration of SARS-CoV-2 that was compared with SARS-CoV-1 and found that the viral titers of SARS-CoV-1 are far higher than those of SARS-CoV-2. This is consistent with the SDM pertaining to Virulence-Inner shell disorder, which predicts that SARS-CoV-1 will produce more viral particles in cells and vital organs and, thereby, making the virus more dangerous. iii) The German group Wolfel et al [29] found that COVID-19 patients shed much more viral particles than SARS-CoV-1 patients. Again SDMs show the greatest consistency in explaining this. How do we explain the discrepancy between (ii) and this discovered mechanism of infectivity? How can we reconcile greater infectivity with lesser virulence and vice-versa? The “Shypothesis” says that SARS-CoV-2 binds to ACE-2 with 10-1000 greater affnities. If so, how do you account for (ii) and (iii)? SDMs explains that even though SARS-CoV-2 does not replicate as effciently as SARS-CoV-1 as a result of its lower N disorder. Iit has a much harder M that is more resistant to the salivary and mucosal anti-microbial enzymes and thus greater amount of viral sheddings occur. iv) Why did Hui et al's attempt [78] to show that Omicron (S) binds more effciently to upper respiratory tract (ACE-2) instead shows high statistical correlations with M and N PIDs just as SDMs have predicted [86]? v) Why is Omicron much milder than previous variants but yet as infectious as other variants? Once again, SDMs provide an elegant explanation as Omicron has lower N PIDs and lower or similar M PID when compared to other variants. The “S-hypothesis” offers an explanation but there are problems with such an explanation as seen in (iv). vi) Why was Pang2019 found to be virulent in animal models, viral titrations and cell plaques, whereas the opposite was found in Pang2017? The Shypothesis has no answer especially since all pangolin-CoVs lack FCS. SDMs have already predicted these even before its discovery. vii) Why do viral titers of the various SARS-CoV-2 variants, SARS-CoV-2related viruses and SARS-CoV-1 correlate with their N PIDs? Why do attenuation and virulence of the various SARS-CoV-2 variants and SARS-CoV1 as determined by animal models and cell plaque studies correlate with N PID? viii) What is the nature of long COVID? Again the S-hypothesis has no answer, whereas SDMs have. ix) Why are SDMs able to tie the different manifestations of COVID-19 ie infectivity,virulence and long COVID under an umbrella of three closely related concepts? The S-hypothesis, on the other hand, still struggles to understand the role of S in the three manifestations. There are two factors that determine on how good a model is. These are reproducibility and reliability. Reproducibility refers to the ability of independent laboratories getting similar results, whereas reliability involves the ability to consistently explain and predict the results [146-147]. Therefore, a model that is unable 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 to be replicated in different independent laboratories is unreproducible, whereas, a model that is unable to explain a result is unreliable. (i)-(ix) summarizes both the reproducibility and reliability of SDMs. We would argue that there is definitely evidence of both as we have seen even though, as we have seen, there are certain occasions where other proteins such as S, have to be involved in the explanation. While the experimental work of Riddell et al is crucial in providing evidence that the abnormally hard M of SARS-CoV-2 is likely to provide greater protection to the virus, in contrast to other CoVs, there is more that could be done. For instance, there are a variety of anti-microbial enzymes found in the mucus and saliva but we know only how they in general can damage viruses . We still don't understand which specific enzymes can damage or kill viruses SARS-CoV-2 or CoVs [22-28]. Experiments need to be done on various CoVs with different levels M PIDs and various enzymes [22-28] so that we can have a better understanding of the mechanisms involved. 7.2. Links Between Virulence and Infectivity An important part of reproducibility and reliability of a model or paradigm is its ability to explain certain results or certain phenomena. We have seen how SDMs are not only able to explain virulence and infectivity but also are able to link infectivity and virulence. The two manifestations are related as virulence involves the ability of the virus to infect cells in vital organs such the virus is able to overwhelm vital organs especially the lungs, whereas infectivity pertains to virus' ability to leave the body to infect the cells of other hosts. We can see that the two manifestations are related but how are they exactly related is complex. A conundrum is quickly seen: Why is SARSCoV-2 less virulent than SARS-CoV-1 but is more infectious? How did the viruses accomplish this? The S-alone hypothesis is unable to provide a clear answer without further complications and questions (see (iv) in 7.1 and MCC), whereas the SDMs have coherent and clearcut answers to this. According to SDMs. when the virus have higher N disorder ( N PID), the virus is able to replicate more quickly because greater disorder at N provides for more efficient protein-protein/RNA/DNA/lipid binding and therefore more efficient and rapid replication. If that is the case, then why is SARS-CoV-1 less infectious than SARS-CoV-2 since MCC entail transportation of much of the viral particles to the nasal region to be expelled ? Again, SDMs have a novel answer and it has to do with the much harder outer shell (lower M PID) found in SARS-CoV-2 and related virus. This abnormally hard M protects the viral particles from the onslaught of anti-microbial enzymes found in the mucus and saliva even if all SARS-CoV-2 variants have lower N PIDs i.e. less efficiencies in replication of viral particles. This is consistent with Wolfel et al's clinical data showing that COVID-19 patients shed much larger amount of virus. As we can see, a more correct approach comes with a coordinated understanding of the roles of M, N and S. 7.3. Pangolin Footprints in the Evolution of COVID-19 The phrase “pangolin footprints” refers to a set of molecular signatures that were left behind by the intimate evolutionary interactions COVID-19 ancestral strains had with pangolins. These signatures that involve the abnormally hard M and the trend towards lower N disorder arose from the pangolins' burrowing habit, which entails a harder outer and often inner viral shell to facilitate oral-fecal transmission via buried feces. These features or pangolin footprints are clinically manifested in the symptoms, infectiousness, and attenuation/virulence of COVID-19. A review of current knowledge of immunology indicates that it is also manifested as long COVID. The abnormally hard M that facilitates greater infectiousness by being resistant to antimicrobial enzymes in the saliva and mucus could also resist attempts by the immune system to get rid of it in other ways, thus leading to long COVID. 7.4. Clues Pointing to Pangolin Footprints in the Evolution of COVID-19 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 A unique and highly unusual property that involves all examined SARS-CoV-2related viruses is its hard outer shell (low M PID). This property is very rarely found even among the CoV family. What is the evolutionary origin of this peculiar characteristic? A retrospective search of our disorder database of CoVs provided us with a clue when it was found that very few CoVs have such exceptionally hard M and those found were associated with a burrowing animal such as rabbits. Furthermore, it can be seen that phylogenetic trees using M have a much closer relationship to SARSCoV-2 than previous phylogenetic trees have shown. How can this be even possible when RaTG13 has 96.4% to SARS-CoV-2, whereas pangolin-CoVs have approximately only 90% similarity to SARS-CoV-2? What is even more puzzling is that one of our trees shows that Omicron has a closer relationship to pangolin-CoVs than to the other variants. The answer has to do with the fact that phylogenetic algorithms do not handle recombinations well and M may be the best choice for phylogenetic studies since M is abnormally structured (low disorder), which means that it is likely to be highly conserved. Secondly, many scientists have been looking for an intermediary animal host for SARS-CoV-2 to no avail. It has obviously not occurred to many scientists that there is no intermediary animal host is likely because the ancestral virus has entered and re-entered the human population and other animal populations over a long time of time with a primary or secondary reservoir being pangolins. This could explain how all SARS-CoV-2 related virus have unusually hard M and SARSCoV-2 is highly infectious not just to humans but also to a wide range of animals. It takes time for the virus to become highly adapted to such a wide variety of animals. All these present clues of a more unique evolutionary relationship between pangolins and SARS-CoV-2 that needs to be further research as there are hints that the resulting characteristics are related to the behaviors and clinical manifestations of the virus. 7.5. Greater Reproducibility and Reliability When M, N and S are Used in Coordination There is thus far no current review article based on updated data attempting to demonstrate the reproducibility that S is the main protein or sole protein responsible for the infectivity or pathogenesis, especially when compared to other viral proteins such as M and N. As we have shown, there is indisputably important clinical and experimental evidence that shows that S cannot account for the difference in virulence and infectivity between SARSCoV-1 and SARS-CoV-2, even though there is defnitely some evidence of its role in modulating infectivity including antibody evasion via S mutation. The behaviors of N and M via SDMs can account for much of the clinical and experimental results and are highly reproducible even to the smallest details. There are important fundament biological reasons for this. While S is important for viral entry and antibody recognition [109], it is not the most abundant protein. N and M are the most abundant proteins in the infected cell and virion, respectively [109]. Furthermore, viral entry is only the initial, albeit highly important, step in the multistep process of the virus life-cycle. We have, however, also seen that the roles of S in infectivity and virulence become clearer and more consistent when we examine other proteins, especially M and N, alongside S. The interplaying roles among the various proteins have to be considered before we can fully understand the actual nature of these proteins and their impacts on infectivity and pathogenesis. Without S’s greater adaption to ACE-2, there would be ineffcient viral entry, if any, but without the hard M and varying N disorder there would not be any highly sustainable infectivity or the diverse levels of virulence respectively seen in COVID-19. Also, as mentioned, S alone cannot account for the differences in infectivity and virulence between SARS-CoV-1 and SARS-CoV-2 without further questions, given current clinical and experimental data. N and M, on the other hand, do provide a coherently novel explanation that should be further explored. It is also worth noting that many laboratories have shown that it easy for S to adapt to the ACE-2 even within respiratory cells [133-135], but it is extremely diffcult to fnd CoVs with such a hard M as in the case of SARS-CoV-2-related viruses [4,18-20,52-54]. Acquiring such hard M which is necessary for potential high infectivity had to take its time to evolve from the 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 interactions with a burrowing animal, i.e. pangolins, and none of the high COVID infectivity we have seen would have been possible with this crucial evolution. Furthermore, it has been argued that nothing is unique about SARSCoV-2, including its FCS, since it is not diffcult to fnd CoVs with similar properties. In our dataset, we are, however, unable to fnd CoVs with such an abnormally hard M among CoVs not associated with a burrowing animal, and there are very few CoVs associated with a burrowing animal, if we don't count COVID-19-related viruses. 7.6. Long COVID Furthermore, S is not able offer a plausible hypothesis for the cause of long COVID. The cause of long COVID has thus far remained a complete mystery, which is a great hindrance for the search for more effective treatments. The M and, to a much smaller extent, N via SDMS, however, offer an elaborate and highly plausible explanation using our current knowledge of immunology on the cause of long COVID: the hard outer shell of the virus is likely to make it diffcult for macrophages, T-Cells, and other such entities to get rid of the virus. As a result, there may be plenty of opportunities for the virus to dwell in the macrophages, which are a likely reservoir. 7.7 Clues for Further Research We have seen that SDMs offers a novel coherent that links virulence and infectivity with N and M via protein intrinsic disorder. Both experimental and computational evidence of the reliability and reproducibility [146-147] of SDMs as applied to COVID-19 is covered. We have tried to show that SDMs using N and M are by and large reproducible when experimental and clinical data especially from other laboratories are scrutinized. The reason for reproducibility and reliability of M and N can arguably be traced to the abundance of the major proteins and their important roles in the replication process. The role of M in infectivity is based on the observation of an abnormally hard SARS-CoV-2 M using AI. Interestingly, phylogenetic study of SARS-CoV-2 related viruses points to a closer relationship between pangolinCoVs and SARS-CoV-2. This has not been shown in any other phylogenetic study using other proteins or entire genome. While evidence of the reproducibility and reliability of SDMs as applied to COVID-19 using N and M has been presented, further experimental and clinical research is needed to reaffrm their reproducibility and reliability [146-147]. Even though M and N are the most abundant proteins in the virion and cell respectively, there are 29 CoV viral proteins, of which many are also involved in replication and virulence. For this reason, we have tried to show that much of the limitations of COVID-19 SDMs arise from this fact. A solution would be to include more proteins such as S and NSP7 as discussed. This is where further research is also necessary since the current SDMs incorporate only N and M. While we tried to argue using the existing framework of SDMs and current knowledge of immunology that SDMs as applied to long COVID is promising , the models as currently applied to long COVID are still preliminary or at its infancy and, therefore, rely on circumstantial evidence. Given that long COVID is still largely a mystery, it is imperative that long COVID be further researched into using novel means including SDMs. 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 ` Author Contributions: Conceptualization, G.K.-M.G.; data curation, G.K.-M.G. and V.N.U.; formal analysis, G.K.-M.G.; investigation, G.K.-M.G.; methodology, A.K.D. and G.K.-M.G.; resources, A.K.D. and J.A.F.; software, G.K.-M.G.; validation, G.K.-M.G.; visualization, A.K.D. and J.A.F.; writing—original draft, G.K.-M.G. ; writing—review and editing, V.N.U. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.. 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