key: cord-0711114-fkik557p
authors: Hassan, Sk Sarif; Basu, Pallab; Redwan, Elrashdy M.; Lundstrom, Kenneth; Choudhury, Pabitra Pal; Serrano-Aroca, Angel; Azad, Gajendra Kumar; Aljabali, Alaa A.A.; Palu, Giorgio; El-Aziz, Tarek Mohamed Abd; Barh, Debmalya; Uhal, Bruce D.; Adadi, Parise; Takayama, Kazuo; Bazan, Nicolas G.; Tambuwala, Murtaza; Lal, Amos; Chauhan, Gaurav; Baetas-da-Cruz, Wagner; Sherchan, Samendra P.; Uversky, Vladimir N.
title: Periodically aperiodic pattern of SARS-CoV-2 mutations underpins the uncertainty of its origin and evolution
date: 2021-09-22
journal: Environ Res
DOI: 10.1016/j.envres.2021.112092
sha: d3cb86a302a3d7bd6d9d7ce8a8754283e5a383cf
doc_id: 711114
cord_uid: fkik557p

Various lineages of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have contributed to prolongation of the coronavirus disease 2019 (COVID-19) pandemic. Several non-synonymous mutations in SARS-CoV-2 proteins have generated multiple SARS-CoV-2 variants. In our previous report, we have shown an evenly uneven distribution of unique protein variants of SARS-CoV-2 is geo-location or demography-specific. However, the correlation between the demographic transmutability of the SARS-CoV-2 infection and mutations in various proteins remains unknown due to hidden symmetry/asymmetry in the occurrence of mutations. This study tracked how these mutations are emerging in SARS-CoV-2 proteins in six model countries and globally. In a geo-location, considering the mutations having a frequency of detection of at least five hundred in each SARS-CoV-2 protein; we studied the country-wise percentage of invariant residues. Our data revealed that since October 2020, highly frequent mutations in SARS-CoV-2 have been observed mostly in the Open Reading Frames (ORF) 7b and ORF8, worldwide. No such highly frequent mutations in any of the SARS-CoV-2 proteins were found in the UK, India, and Brazil, which does not correlate with the degree of transmissibility of the virus in India and Brazil. However, we have found a signature that SARS-CoV-2 proteins were evolving at a higher rate, and considering global data, mutations are detected in the majority of the available amino acid locations. Fractal analysis of each protein's normalized factor time series showed a periodically aperiodic emergence of dominant variants for SARS-CoV-2 protein mutations across different countries. It was noticed that certain high-frequency variants have emerged in the last couple of months, and thus the emerging SARS-CoV-2 strains are expected to contain prevalent mutations in ORF3a, membrane, and ORF8 proteins. In contrast to other beta-coronaviruses, SARS-CoV-2 variants have rapidly emerged based on demographically dependent mutations. Characterization of the periodically aperiodic nature of the demographic spread of SARS-CoV-2 variants in various countries can contribute to the identification of the origin of SARS-CoV-2.

Various lineages of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have contributed to prolongation of the coronavirus disease 2019 pandemic. Several non-synonymous mutations in SARS-CoV-2 proteins have generated multiple SARS-CoV-2 variants. In our previous report, we have shown an evenly uneven distribution of unique protein variants of SARS-CoV-2 is geo-location or demography-specific. However, the correlation between the demographic transmutability of the SARS-CoV-2 infection and mutations in various proteins remains unknown due to hidden symmetry/asymmetry in the occurrence of mutations. This study tracked how these mutations are emerging in SARS-CoV-2 proteins in six model countries and globally. In a geo-location, considering the mutations having a frequency of detection of at least five hundred in each SARS-CoV-2 protein; we studied the country-wise percentage of invariant residues. Our data revealed that since October 2020, highly frequent mutations in SARS-CoV-2 have been observed mostly in the Open Reading Frames (ORF) 7b and ORF8, worldwide. No such highly frequent mutations in any of the SARS-CoV-2 proteins were found in the UK, India, and Brazil, which does not correlate with the degree of transmissibility of the virus in India and Brazil. However, we have found a signature that SARS-CoV-2 proteins were evolving at a higher rate, and considering global data, mutations are detected in the majority of the available amino acid locations. Fractal analysis of each protein's normalized factor time series showed a periodically aperiodic emergence of dominant variants for SARS-CoV-2 protein mutations across different countries. It was noticed that certain high-frequency variants have emerged in the last couple of months, and thus the emerging SARS-CoV-2 strains are expected to contain prevalent

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the Coronavirus Disease (COVID-19) (Pedersen et al., 2020; Setti et al., 2020; Domingo et al., 2020) . SARS-CoV-2 has spread rapidly and has evolved prolonging pandemic and precarious clinical entity (Health, 2020; Tapper and Asrani, 2020) . Since the beginning of the pandemic, SARS-CoV-2 has increasingly accumulated various mutations leading to patterns of genomic diversity (van Dorp et al., 2020; Zhou et al., 2020) . The wide SARS-CoV-2 variations were scattered across the various geolocations, and it can underlie geographically specific etiological effects (Mercatelli and Giorgi, 2020) . It was expected that these mutations could be of use to to monitor the spread of the virus, and to identify sites putatively under selection as SARS-CoV-2 potentially adapts to its new human host. SARS-CoV-2 may be evolving towards higher transmissibility as it may not yet fully adapt to its human host. The most plausible mutations under putative natural selection are those which have emerged repeatedly and independently (van Dorp et al., 2020) . It was reported that 198 sites in the SARS-CoV-2 genome appear to have already undergone recurrent, independent mutations (van Dorp et al., 2020) . Various SARS-CoV-2 missense mutations are the key evolving factors affecting the infectivity, and virulence, and pathogenicity of the virus (Mercatelli and Giorgi, 2020) . Several SARS-CoV-2 variants have significantly strengthened their infectivity as reported previously . It was previously reported that the rate of SARS-CoV-2 mutations are relatively low compared to other RNA viruses, such as influenza virus (Kupferschmidt, 2020; Khan et al., 2020; Gómez-Carballa et al., 2020) . The low SARS-CoV-2 mutation rate might relate to its proofreading ability, which is a unique embedded function of SARS-CoV-2 (Romano et al., 2020; Ogando et al., 2020) .Thus far, although several mutations have been detected, SARS-CoV-2 seem not to be drifting antigenically (Yuan et al., 2021; Williams and Burgers, 2021) . However, the mechanism of SARS-CoV-2 evolution or developing gain of function variations have remained unclear . A non-uniform mutation pattern in the viral proteins was recently reported, and further emerging of the questions of the origin of SARS-CoV-2 (Hassan et al., 2021b) .

The rapidly evolving data on mutations and various strains of SARS-CoV-2 makes it vulnerable to firmly assert whether SARS-CoV-2 results from a zoonotic emergence or from an accidental escape of a laboratory (Sallard et al., 2021; Pipes et al., 2021; Nadeau et al., 2021; Seyran et al., 2021) . This issue of origin needs to be resolved because it has important consequences on the risk/benefit balance of human interactions with ecosystems, on intensive breeding of wild and domestic animals, on some laboratory practices and on scientific policy and bio-safety regulations (Sallard et al., 2021) . Despite these recent investigations, several issues related to the evolutionary patterns and origin of the COVID-19 pandemic remain to be fully characterized (Liu et al., 2020 (Liu et al., , 2019 Domingo, 2021a,b) . No direct correlation was observed in the mutation pattern of SARS-CoV-2 from the infection rate in the first and second waves of COVID-19 (Ko et al., 2021; Lv et al., 2020; Kumar et al., 2020) .

In this study, the potential embedded mutation pattern of S, E, M, N, ORF3a, ORF6, ORF7a, ORF7b, ORF8, and ORF10 proteins of SARS-CoV-2 are analyzed in six model countries viz. USA, UK, Brazil, Germany, India, South Africa (SA), and globally.

The majority of publicly available SARS-CoV-2 genomic sequences were sourced from GISAID, NCBI, and CNGB. The SARS-CoV-2 sequence data was taken from the GISAID database (Shu and McCauley, 2017) . Mutation data with their respective details were collected from the CoVal database. In this study, we focused on spike (S), membrane (M), envelope (E), nucleocapsid (N), ORF3a, ORF6, ORF7a, ORF7b, ORF8, and ORF10 proteins of SARS-CoV-2. We also considered mutations within the protein of our interest from all geo-locations available in the CoVal database. In particular, a set of six model countries, the USA, UK, South Africa, India, Germany, and Brazil was selected.

Single mutation details were retrieved from the CoVal database by searching by the name of the model country and the SARS-CoV-2 protein of interest. For example, details of all single mutations in the SARS-CoV-2 S protein from the UK were retrieved, of which a snapshot is presented in Figure 1 . Likewise, for other model countries and SARS-CoV-2 proteins, details of single mutations were retrieved. Prior to proceeding into the result section, some definitions are recalled and redefined for easy reading.

Four different classes were defined on the date of the first detection and frequency of mutations in the SARS-CoV-2 proteins of our interest. Class-I contains all mutations detected in the proteins of SARS-CoV-2 across the world. Class-II contains only those mutations with a frequency more than or equal to 500 (a reasonably good frequency of a mutation detected in a geo-location) in SARS-CoV-2 in affected patients worldwide. Mutations that were detected after October 2020 belong to Class-III. Mutations with a frequency larger than 500 since October 2020 are the members of Class-IV.

Amino acid residues where no mutations were detected are termed "invariant residues". From the CoVal database, we first found distinct residue positions of mutations, and the total number of invariant residues (r) of each type. Furthermore, the percentage of invariant residues in each protein of length l was determined using the formula l−r × 100.

The percentages of invariant residues (Class-I) in SARS-CoV-2 proteins in various countries are listed in Table 1 .

J o u r n a l P r e -p r o o f Considering all mutations with amino acid changes in all available geo-locations, it was observed that, except from the SARS-CoV-2 structural proteins, the ORF proteins (ORF3a, ORF6, ORF7a, ORF7b, ORF8, and ORF10) possessed mutations at every residue position of the respective protein. On the other hand, it was found that an in-creasing order of the percentage of invariant residues in the structural proteins of SARS-CoV-2 turned out to be E(6.67) < N (10.74) < S(14.69) < M (24.32). In other words, the highest and lowest number of mutations were detected in the E and M proteins, respectively. Across six countries, the highest and lowest number of invariant residues were found in the M and ORF3a proteins, respectively. It was noted that the highest frequency of invariant residues in the E among all the proteins was observed in Germany. Among all the proteins, the highest number of mutations was detected in the ORF3a proteins in Germany, India, Brazil, and South Africa, whereas in the USA and UK, ORF7b possessed the highest number of mutations. Notably, it was observed that each protein in SARS-CoV-2 possessed an almost similar number of mutations in the USA and UK. Among six countries, the least amount of mutations across all proteins was found in COVID-19 patients from South Africa, whereas in the USA, the highest number of mutations in SARS-CoV-2 were detected.

An increasing-order (decreasing-order) of the six geo-locations based on the invariant residues (mutations) across all proteins was found as USA<UK<Germany<India<Brazil<SA (reverse order).

The percentages of invariant residues (Class-II) over the SARS-CoV-2 proteins in various countries are listed in Table 2 . While we considered only those mutations with a frequency at least 500, it was observed that the increasing order of the percentage of invariant residues in the SARS-CoV-2 structural proteins was N(80.19)<S(89.94)<E(92)< M (94.14). Therefore, the highest and lowest frequency of mutations were detected in the N and M proteins, respectively. Across six geo-locations, ORF3a and M proteins possessed the highest and lowest amount of amino acid changing mutations, respectively. It was further noticed that each SARS-CoV-2 protein possessed almost the same number of mutations in the USA and UK. Among six countries, the least number of mutations across all the proteins is found in the COVID-19 affected patients from the UK, whereas the highest number of mutations were detected in SARS-CoV-2 in India.

A decreasing order of the total number of mutations in proteins at the six geo-locations was the UK>USA>Germany>SA>Brazil>India (from highest to lowest). In other words, the highest number of different mutations across all these proteins were detected in the UK, and the lowest number of mutations was observed in India. In India, Brazil, and SA, no mutation with a frequency of at least 500 (originated in these countries) was detected in any SARS-CoV-2 protein, except one or two reported most frequent deleterious mutations D614G in S and Q57H in ORF3a. Note that in Germany, the S, N, ORF10, ORF3a, and ORF8 proteins possessed a couple of mutations, whereas E, M, ORF6, ORF7a, and ORF7b did not have any mutations originating from Germany, with a frequency of more than 500.

The percentages of invariant residues (Class-III) in the SARS-CoV-2 proteins in various countries are presented in Table 3 . It was observed that the SARS-CoV-2 ORF3a protein was no longer a hotspot for dominant mutations. From October 2020 until June 2021, M and ORF8 owned the lowest (66.22%) and the highest (99.174%) number of mutations, respectively, in SARS-CoV-2 across the world. Notably, in the past, ORF3a possessed the highest number of mutations (Bianchi et al., 2021; Hassan et al., 2020) . Currently, it seems that ORF3a mutations in the USA, UK, and elsewhere are relatively rare (less than 10%). Since October 2020, the highest number of mutations have been detected in ORF3a in SA. In the UK, Germany, India, and SA, the E protein had the lowest frequency of mutations, whereas M protein possessed several mutations amounting to the highest among others in the USA and Brazil. The highest percentage of mutations were detected in the ORF8 protein, since October 2020 in Germany, India, and Brazil. The highest frequency of mutations in the USA and UK were observed in ORF7a and ORF7b, respectively.

An increasing order of the six geo-locations based on the variability of mutations in SARS-CoV-2 proteins (detected after September 2020 until June 2021) was India<SA<Brazil<Germany<USA<UK.

The percentages of invariant residues (Class-IV) in SARS-CoV-2 proteins in various countries are listed in Table 4 . Considering all mutations having a frequency of more than 500, from October 2020 until June 2021, it was observed that the ORF7b and ORF8 proteins owned the highest frequency of various mutations, whereas the M protein contained the lowest number of mutations. In fact, all four structural proteins S, E, M, and N contain more than 94% of invariant residues. No mutations that had a frequency of more than 500 since October 2020 in the UK, India, and Brazil were found in any of the aforementioned SARS-CoV-2 proteins. In SA, the proteins S, E, N, ORF3a, and ORF10 owned a couple of mutations with a frequency of more than 500 detected since October 2020. S, N, and ORF10 possessed a couple of mutations of this kind in Germany, whereas only in S and E proteins, a few mutations of this type were noticed.

The degree of fractality (fractal dimension) of the normalized factor time series (NFTS) for each SARS-CoV-2 protein in a given model country was quantified (Table 5) . Fractal dimension revealed any hidden self-organized self-similar patterns (if any) in the frequency of emerging mutations in each protein (Pilgrim and Taylor, 2018; Sánchez-Granero et al., 2012) . Here the normalized factor time series (NFTS) is defined as follows:

Normalized factor time series: For each mutation (m) in a given SARS-CoV-2 protein, a normalized factor is determined based on the presence of the mutation in various geo-locations (countries). Normalized factor NFm is defined as NFm = NGm TGm Here, NGm and TGm denote the number of genomes with this specific mutation (m) in this country, and the total number of genomes with this mutation (m) worldwide, respectively. It varies from 0 to 1. Here the normalized factor 0 denotes the spreading of the mutations uniformly across various geo-locations, whereas 1 denotes the detection of the mutation in a single geo-location (Gelly et al., 2011) . A series of detected mutations in a given protein lead to a normalized factor time series (NFTS). Table 5 it was concluded that FD of NFTS for SARS-CoV-2 proteins ranged from 0.5144 to 1.9771. This wide range of FD depicts the hidden non-uniform distribution of the frequency in various countries. We observed that the lowest FD for each SARS-CoV-2 protein was noticed in SA, whereas the highest fractality for all proteins were either found in the USA or UK. These model countries were ranked in increasing order based on FD for each protein (Table 6 ). The ranking of countries based on FD of NFTS for the S and N proteins was identical (Table 6) . Likewise, an identical ranking was noticed for the SARS-CoV-2 M and ORF3a proteins. Also, the country-ranking based on the FD of NFTS for the ORF6 and ORF7b were the same. Notably, the fractality of NFTS for E and ORF7a in India, Germany and Brazil did not follow any strict ordering.

To understand the emergence of new strains and which SARS-CoV-2 strain dominates, we looked at the dates of the detection of various mutations in each SARS-CoV-2 protein and their respective frequencies (in logarithmic scale) ( Table  7) . A negative correlation signifies that the early detected mutations since the beginning of the COVID-19 pandemic, are more dominant in the present scenario (Lee Rodgers and Nicewander, 1988) . We noticed precisely that the pattern in the J o u r n a l P r e -p r o o f USA, UK, and Germany was that each correlation coefficient was negative (Mukaka, 2012) . The similar pattern was seen in SA and Brazil. We detected newer ORF3a mutations that were dominating in SA, and some new ORF10 mutations were seen. The trend with newer ORF10 mutations was more dominant in Brazil. Mutations in E and S in Brazil also showed the emergence of new strains. To understand the emergence of newer variants, we plotted the date and frequencies (log) of mutations in the SARS-CoV-2 E, M, ORF3a, and ORF8 ( Figure 2 ). Outlier points (in red) in Figure 2 showed the emergence of newer mutations in the four SARS-CoV-2 proteins.

The density of outlier points in ORF3a implies the emergence of new variants of SARS-CoV-2 with a significant number of missense mutations. These new SARS-CoV-2 variants would also contain various mutations of M and ORF8, whereas it was observed that mutations in E were no longer dominant.

The first twenty dominant (with regards to frequency) mutations worldwide are plotted for all proteins in It seems that M had more versatile dominant mutants over the globe (Figure 3 ). For ORF10, only one dominant mutant P10 globally. Two mutants (S24, R52) dominate globally for ORF8. For E and S proteins, there was one dominant in each protein and many sub-dominant mutants. The N protein showed an intermediary pattern between M and ORF8, ORF10, ORF6, ORF7a and it also falls into the common pattern of a few dominant and a dozen or so sub-dominant mutants.

We studied and tracked how mutant variants emerged in SARS-CoV-2 S, E, M, N, ORF10, ORF3a, ORF6, ORF7a, ORF7b, ORF8, and ORF10 proteins from six model countries. While we considered all SARS-CoV-2 protein mutations since the beginning of the COVID-19 pandemic, it was noticed that the least number of invariant residues in each SARS-CoV-2 protein was observed in SA. On the other hand, the prevalent missense mutations were detected in the UK and USA. Considering the mutations in each protein having a frequency of more than 500, it was found that the increasing order of the model countries based on the percentage of invariant residues is UK<USA<Germany<SA<Brazil<India. Interestingly, India had the lowest number of protein variants with high frequent mutations (frequency more than 500). Furthermore, the ample protein variations in the USA and UK were ascribed to the presence of highly frequent mutations in each SARS-CoV-2 protein. Since October 2020, high-frequent mutations in SARS-CoV-2 have been found only in ORF7b and ORF8, worldwide. No such highly frequent mutations in any of the SARS-CoV-2 proteins were found in the UK, India, and Brazil, which does not seem to correlate with the transmutability of the virus in India and Brazil. Not every high-frequency mutation necessarily affects the life cycle of the virus, both, ORF7b and ORF8, proteins are not essential for the virus life cycle as previously shown like Spike and/or ORF3a (Hassan et al., 2021a,b) . Of note, the majority of Manaus (Brazil) citizens had been reinfected with the P.1 strain bearing these mutations, which may offer a further capability of immune escape (Faria et al., 2021) . The combination of E484K+∆140, leading to a complete abolition of neutralizing antibodies. Of these, observation questions if there is any ethnic correlation with SARS-CoV-2 infection? Ethnic/racial disparities in the SARS-CoV-2 infection, incidence, and COVID-19 severity and deaths are reported (Hollis et al., 2021; McCoy et al., 2020; Vahidy et al., 2020; Shoily et al., 2021; Sze et al., 2020) . In the context of correlation between ethnic/racial origin and SARS-CoV-2 infections the interaction between the S protein and the ACE2-solute carrier family 6-member 19 (SLC6A19) dimer was investigated applying a quantitative dynamics cross-correlation matrix . Forty-seven potential functional missense variants were identified from genomic databases within ACE2/SLC6A19/TMPRSS2 justified genomic enrichment studies in COVID-19 patients. Although the variants occurred at ultra-low frequency, two ACE2 non-coding variants (rs4646118 and rs143185769) found in 9% of African Americans may be involved in the regulation of ACE2 expression and may contribute to enhanced risk of COVID-19. Additionally, studies have demonstrated that ACE2 expression levels relate to the rs2285666 polymorphism significantly affecting SARS-CoV-2 susceptibility in Asian and European populations (Asselta et al., 2020; Srivastava et al., 2020) . Moreover, it was discovered that SARS-CoV-2 cell entry depended on polymorphism in the ACE2, TMPRSS2, TMPRSS11A, cathepsin L (CTSL), and elastase (ELANE) genes in American, African, European, and Asian populations (Vargas-Alarcón et al., 2020) .

Furthermore, it was observed that the percentage of invariant residues was inversely proportional to the fractal dimension of NFTS of any SARS-CoV-2 protein. In other words, the fractal dimension near to 2 of an NFTS associated witha SARS-CoV-2 protein signifies wide variations of the protein. So, the widest variations in each SARS-CoV-2 protein variant were observed in the USA and UK since October 2020. On the contrary, the second wave of COVID-19 infection rate has seen an uptick in Brazil and India, quite significantly. The emerging SARS-CoV-2 strains are expected to contain prevalent mutations in ORF3a, M, and ORF8 proteins ( J o u r n a l P r e -p r o o f proteins were evolving at a high rate and considering global data mutations detected in the majority of the available amino acid locations. It was found that the most prominent variants across the globe have one mutation in all proteins. Fractal analysis of NFTS of each protein showed a periodically aperiodic emergence of dominant variants for SARS-CoV-2 protein mutations across various countries. This hidden periodically aperiodic phenomenon of mutations has a strong similarity with forest-fire models, where periodic emergence of fire happens after a brief period of non-activity (Drossel and Schwabl, 1992; Grass-berger, 1993) . This possibly keeps SARS-CoV-2 apart from other beta-coronaviruses. Furthermore, it was noticed that there is a negative correlation between the frequency of a variant and when it was first recorded in country-wise data. This signifies that older variants are by and large more dominant, although there were many notable exceptions, and certain variants have quickly become dominant in the last couple of months.

The origin of SARS-CoV-2 has continued to receive much attention even after almost two years since the outbreak. Recombinant events and inevitable mutations keep developing during the pandemic (Danchin and Timmis, 2020) . As a consequence of Muller's ratchet, mutations will deplete the genome in its cytosine content, which leads to attenuation of the virus (Muller, 1964) . However, this process is counteracted by recombination, which can occur when different coronaviruses or mutants/variants co-infect the same host creating an enormous evolutionary landscape for viruses to explore (Joffrin et al., 2020) . It has been postulated that SARS-CoV-2 might have been active well before the outbreak in Wuhan and either through zoonosis or accidental release from a laboratory, mutations in the SARS-CoV-2 spike protein dramatically increased its transmissibility leading to the COVID-19 pandemic (Platto et al., 2021) . For this reason and to limit the emergence and impact on the pandemic, it is of utmost importance to evaluate SARS-CoV-2 mutants/variants in light of the origin of SARS-CoV-2 (Otto et al., 2021) .

In conclusion, the periodically aperiodic nature of the spread of mutant SARS-CoV-2 proteins is a unique feature of SARS-CoV-2 among beta-coronaviruses, which can contribute to the identification of the origin of SARS-CoV-2.

Ace2 and tmprss2 variants and expression as candidates to sex and country differences in covid-19 severity in italy

Sars-cov-2 orf3a: mutability and function

Mutations strengthened sars-cov-2 infectivity

Sars-cov-2 variants: Relevance for symptom granularity, epidemiology, immunity (herd, vaccines), virus origin and containment

Sars-cov-2/covid-19: Natural or laboratory origin?

What we know and what we need to know about the origin of sars-cov-2

Influence of airborne transmission of sars-cov-2 on covid-19 pandemic. a review

No evidence for increased transmissibility from recurrent mutations in sars-cov-2

Self-organized critical forest-fire model. Physical review letters 69, 1629

Genomics and epidemiology of the p. 1 sars-cov-2 lineage in manaus, brazil

ipba: a tool for protein structure comparison using sequence alignment strategies

Mapping genome variation of sars-cov-2 worldwide highlights the impact of covid-19 super-spreaders

On a self-organized critical forest-fire model

Sars-cov-2 (covid-19) structural and evolutionary dynamicome: Insights into functional evolution and human genomics

Molecular conservation and differential mutation on orf3a gene in indian sars-cov2 genomes

Non-uniform aspects of sars-cov-2 intraspecies evolution reopen questions on its origin

Covid-19 puts societies to the test

Racial and ethnic disparities in incidence of sars-cov-2 infection, 22 us states and dc

Bat coronavirus phylogeography in the western indian ocean

Comparative genome analysis of novel coronavirus (sars-cov-2) from different geographical locations and the effect of mutations on major target proteins: An in silico insight

Molecular characterization and the mutation pattern of sars-cov-2 during first and second wave outbreaks in hiroshima, japan

Comparative genomic analysis of rapidly evolving sars-cov-2 reveals mosaic pattern of phylogeographical distribution

The pandemic virus is slowly mutating. but does it matter?

Thirteen ways to look at the correlation coefficient

Viral metagenomics revealed sendai virus and coronavirus infection of malayan pangolins (manis javanica)

Comparative genomic analysis revealed specific mutation pattern between human coronavirus sars-cov-2 and bat-sarsr-cov ratg13

Racial variations in covid-19 deaths may be due to androgen receptor genetic variants associated with prostate cancer and androgenetic alopecia. are anti-androgens a potential treatment for covid-19

Geographic and genomic distribution of sars-cov-2 mutations

A guide to appropriate use of correlation coefficient in medical research

The relation of recombination to mutational advance

The origin and early spread of sars-cov-2 in europe

The enzymatic activity of the nsp14 exoribonuclease is critical for replication of mers-cov and sars-cov-2

The origins and potential future of sars-cov-2 variants of concern in the evolving covid-19 pandemic

Sars-cov-2: a storm is raging

Fractal analysis of time-series data sets: methods and challenges, in: Fractal analysis

Assessing uncertainty in the rooting of the sars-cov-2 phylogeny

History of the covid-19 pandemic: origin, explosion, worldwide spreading

A structural view of sars-cov-2 rna replication machinery: Rna synthesis, proofreading and final capping

Tracing the origins of sars-cov-2 in coronavirus phylogenies: a review

Introducing fractal dimension algorithms to calculate the hurst exponent of financial time series

Sars-cov-2rna found on particulate matter of bergamo in northern italy: first evidence

Questions concerning the proximal origin of sars-cov-2

Disparities in covid-19 severities and casualties across ethnic groups around the globe and patterns of ace2 and pir variants

Gisaid: Global initiative on sharing all influenza data-from vision to reality

Genetic association of ace2 rs2285666 polymorphism with covid-19 spatial distribution in india

Ethnicity and clinical outcomes in covid-19: a systematic review and meta-analysis

Covid-19 pandemic will have a long-lasting impact on the quality of cirrhosis care

Racial and ethnic disparities in sars-cov-2 pandemic: analysis of a covid-19 observational registry for a diverse us metropolitan population

Variability in genes related to sars-cov-2 entry into host cells (ace2, tmprss2, tmprss11a, elane, and ctsl) and its potential use in association studies

Intra-host variation and evolutionary dynamics of sars-cov-2 populations in covid-19 patients

Sars-cov-2 evolution and vaccines: cause for concern?

Structural and functional ramifications of antigenic drift in recent sars-cov-2 variants

A pneumonia outbreak associated with a new coronavirus of probable bat origin

We gratefully acknowledge the authors from the originating laboratories responsible for obtaining the specimens and the Submitting laboratories where genetic sequence data were generated and shared via the GISAID Initiative, on which this research is based.

SSH designed the study. SSH, PB, KL, and VNU contributed to the implementation of the research, to the analysis of the results. SSH and PB wrote the initial draft of the manuscript. SSH, KL, VNU, EMR, PPC, ASA, GKA, AAAA, TMAE, DB, PA, KT, MT, AL, GC, and SPS reviewed and edited. GP, BDU, WBC, and NGB provided constructive reviews and suggestions. All authors read the final version and approve.

J o u r n a l P r e -p r o o f

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:J o u r n a l P r e -p r o o f