(2023) 20:39
Iwasaki et al. Virology Journal
https://doi.org/10.1186/s12985-023-01995-3

Virology Journal

Open Access

RESEARCH

Oligonucleotide usage in coronavirus
genomes mimics that in exon regions in hostgenomes
Yuki Iwasaki1, Takashi Abe2 and Toshimichi Ikemura1*

Abstract
Background Viruses use various host factors for their growth, and efficient growth requires efficient use of thesefactors. Our previous study revealed that the occurrence frequency of oligonucleotides in the influenza virus genome
is distinctly different among derived hosts, and the frequency tends to adapt to the host cells in which they grow. We
aimed to study the adaptation mechanisms of a zoonotic virus to host cells.
Methods Herein, we compared the frequency of oligonucleotides in the genome of alpha- and betacoronavirus withthose in the genomes of humans and bats, which are typical hosts of the viruses.
Results By comparing the oligonucleotide frequency in coronaviruses and their host genomes, we found a statistically tested positive correlation between the frequency of coronaviruses and that of the exon regions of the hostfrom which the virus is derived. To examine the characteristics of early-stage changes in the viral genome, which areassumed to accompany the host change from non-humans to humans, we compared the oligonucleotide frequency
between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the beginning of the pandemic and theprevalent variants thereafter, and found changes towards the frequency of the host exon regions.
Conclusions In alpha- and betacoronaviruses, the genome oligonucleotide frequency is thought to change in
response to the cellular environment in which the virus is replicating, and actually the frequency has approached thefrequency in exon regions in the host.
Keywords SARS-CoV-2, Oligonucleotide frequency, Genome, Exon region, Zoonoses, Pandemic, Zinc-finger antiviralproteinBackground
Viruses have always posed threats to public health, ashighlighted by the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic [1, 2],*Correspondence:
Toshimichi Ikemura
t_ikemura@nagahama-i-bio.ac.jp
1
Department of Bioscience, Nagahama Institute of Bio-Scienceand Technology, Tamura‑Cho 1266, Nagahama‑Shi, Shiga‑Ken 526‑0829,
Japan
2
Department of Information Engineering, Graduate School of Scienceand Technology, Niigata University, Niigata‑Ken 950‑2181, Japanthe ebolavirus outbreak in West Africa in 2014 [3–6],
and the emerging and re-emerging nature of influenza
viruses [7]. To address the worldwide threats caused byzoonotic RNA viruses, which suddenly cause serious
outbreaks by invasion from non-human hosts, we must
understand the molecular evolutionary changes in theirgenome sequences from various aspects. Viral growthdepends on many host factors (e.g. nucleotide pools,
proteins, and RNAs), but human cells may not provide
ideal growth conditions for viruses invading non-humanhosts. Our previous time-series analyses of short andlong oligonucleotide compositions in the above-mentioned virus genomes showed directional changes in their© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Iwasaki et al. Virology Journal(2023) 20:39

composition after the invasion from non-human hosts,
that is, a time-series monotonic increase or decrease
trend [8–10]. In the case of human influenza A viruses,
common directional changes have been observed forthree subtypes (H1N1, H3N2, and H1N1pdm09), whichhave invaded the human population independently overlong intervals, such as several decades [9, 11]. Interestingly, the destination of these reproducible directionalchanges appears to be the composition of influenza B[11], which can currently infect humans but not birds.
The category, nature, and content of factors available
in host cells should differ among hosts; therefore, whena virus invades a new host population and causes an
epidemic, its genome is altered so that it can efficiently
utilize host factors and evade the host’s antiviral mechanisms [12–14]. A wide variety of oligonucleotides are
known to be functional motifs (e.g. those for binding tohost proteins and RNAs) [15–17], and thus, the bias ofhost-dependent oligonucleotide composition may provide insight into the molecular mechanism of host adaptation of the virus.
In this study, we analyzed coronaviruses, including SARS-CoV-2. Most viruses belonging to Coronaviridae use bats as their natural reservoir hosts, andsome can infect a wide range of mammals and birds[18]. In humans, four types (Alpha 229E, Alpha NL63,
Beta HKU1, and Beta OC43) are prevalent and known
to cause common colds [18]. In the past two decades,
viruses, such as SARS, MERS, and SARS-CoV-2, have
posed a serious threat to humans [1, 19, 20]. When thesecoronaviruses have invaded human populations from
non-human hosts, their genome sequences are likely tochange to efficiently utilize human cell factors and avoid
human antiviral functions. We previously examined
time-series changes in oligonucleotide frequencies in
SARS-CoV-2 genomes, mainly during the first year of thepandemic, and found that a variety of oligonucleotideschanged their frequency in a directional manner [21–23].
We believe that this study could facilitate the elucidationof the host adaptation mechanisms of the virus.

Methods
Genome sequences of coronaviruses prevalent in humans
or bats

The complete sequences of two types of human coronaviruses (Human-CoV), alphacoronaviruses (27 229E
and 55 NL63 strains) and betacoronaviruses (18 HKU1
and 138 OC43 strains), were obtained from the NCBI
virus
database(https://​www.​ncbi.​nlm.​nih.​gov/​labs/​
virus/). The complete genome sequences of two typesof bat coronavirus (Bat-CoV), alphacoronaviruses (87strains) and betacoronaviruses (79 strains, including 34
SARS-CoV), isolated from three bat species (Chiroptera,

Page 2 of 12

Vespertilionidae, and Rhinolophidae), were obtained
from the NCBI virus database. The poly(A) tail ofeach sequence was removed prior to the analysis. The
strains that were used are listed in (see Additional file 2:
Table S1).
Human and bat genome sequences and annotation
information

Human genome sequence (GRCh38) and annotation
data, including exon–intron information, were obtained
from the Ensembl Genome Browser (https://​asia.​ensem​
bl.​org/​Homo_​sapie​ns/​Info/​Index). Six bat genome
sequences and annotation data, including exon–intron
information, were obtained from Bat1k (https://​bds.​mpi-​
cbg.​de/​hille​rlab/​Bat1K​Pilot​Proje​ct/) [24].
SARS‑CoV‑2 genome sequences

The complete genome sequences of five variants (alpha,beta, delta, gamma, and omicron) of SARS-CoV-2 wereobtained from the GISAID database (https://​www.​gisaid.​org/); sequences that were complete, with high coverage, and from humans were downloaded on November
29, 2021. For each variant, the number of isolates permonth was counted, and only the genome data from themonth with the highest number of isolates were used forthe present analysis after removing the poly(A) tail. Thesestrains are listed in (see Additional file 3: Data S1, S2, S3,
S4, S5, and S6 ).
Statistical analyses

To compare the frequency of oligonucleotides between
human- and bat-CoVs, we calculated the frequency ofeach viral genome; the program to calculate frequency ofoligonucleotides can be obtained from a GitHub repository (https://​github.​com/​yukak​okats​ura/​BLSOM). To
test whether there was a significant difference in oligonucleotide frequency between human- and bat-CoV, at-test was performed using the t-test function in R. The
p values obtained from the tests were corrected using the
BH method [25]. For comparison of the oligonucleotide
frequency in exon (or intron) regions between humans
and bats, the regions were extracted from each gene, thenumber of each oligonucleotide was summed up for eachgene, and the statistical analysis of the frequency was
conducted as described above. For comparison of the oligonucleotide frequency between the genomes of humans
and bats, the genome sequence of each species was fragmented into 100-kb pieces, the frequency was calculated
for each fragment, and statistical analysis was conducted
as described above. Similar results were obtained when
the fragment size was set to 1 Mb.

Iwasaki et al. Virology Journal(2023) 20:39

Degree of adaptation

We characterized the degree of adaptation of each oligonucleotide to the human cellular environment of SARSCoV-2 using the following two-step method:
In the first step, we calculated the midpoint between
human-CoV and bat-CoV frequencies to be used as abaseline for examining whether the human or bat cellularenvironment is more suitable for each oligonucleotide.
The baseline was calculated using the following
formula:Xi = (Hi + Bi)/2
where Xi is the baseline of oligonucleotide i, and Hi and
Bi are the frequencies of oligonucleotide i of human-CoV
and bat-CoV, respectively.
In the second step, for each oligonucleotide, the ­log2
fold change in frequency of the baseline was calculatedusing the following formula:

FCi = log2 (Ci/Xi)
where FCi is the l­og2 fold change of oligonucleotide i, andCi is the frequencies of oligonucleotide i of SARS-CoV-2.

Results
Comparison of human and bat coronaviruses

The genome sequence of coronaviruses that cause humancommon colds, which have been prevalent in humans fora long time, may be well-adapted for growth and transmission in the human population. Thus, their comparison

Page 3 of 12with the genomes of coronaviruses isolated from batswill provide clues regarding the molecular mechanisms
involved in adaptation to human cells. Here, the occurrence frequency of short oligonucleotides (di- to tetranucleotides) in the genome sequences of 238 human-CoV(27 strains of Alpha 229E, 55 strains of Alpha-NL63, 18
strains of Beta-HKU1, and 138 strains of Beta-OC43) and166 bat-CoV (87 strains of Alpha-CoV and 79 strains of
Beta-CoV) strains were compared (see Additional file 2:
Table S2). This comparison showed that the frequencyof 87 oligonucleotides (6 di-, 18 tri-, and 63 tetra-nucleotides) was significantly higher (t-test; FDR ≤ 0.05) inhuman-CoV, while the frequency of 224 oligonucleotides(10 di-, 41 tri-, and 173 tetra-nucleotides) was significantly higher in bat-CoV (Table 1). This host-dependent
preference may be related to molecular mechanisms supporting coronaviruses to efficiently replicate in humanor bat cells and escape the antiviral mechanisms of eachhost.
It is clear from Table 1 that human-CoV has a lower frequency of C-containing oligonucleotides and a higher frequency of U-containing oligonucleotides. Several studies,including ours, have found that a large portion of mutations in SARS-CoV-2 are C to U mutations, which arethought to be the result of RNA editing by APOBEC3G[21, 26, 27]. The effect of APOBEC3G is thought to berelated to an antiviral mechanism in humans. However, if
a certain C-to-U mutation is favorable to the virus, rather
than unfavorable or neutral, this advantageous mutation
will rapidly expand its frequency in the viral population.

Table 1 The list of oligonucleotides with significant differences in frequency between human-CoV and bat-CoV
Preferred in human-CoV

Preferred in Bat-CoVDi

AA, AU, GU, UA, UG, UU

AC, AG, CA, CC, CG, CU, GA, GC, GG, UC

Tri

AAA, AAG, AAU, AGU, AUA, AUG, AUU, GAU, GUA, GUU, UAA, UAG,
UAU, UGA, UGU, UUA, UUG, UUU​

AAC, ACA, ACC, ACG, ACU, AGC, AGG, CAA, CAC, CAG, CAU, CCA, CCC,
CCG, CCU, CGA, CGC, CGG, CGU, CUA, CUC, CUG, CUU, GAA, GAC, GAG,
GCA, GCC, GCG, GCU, GGC, GGG, GUC, GUG, UAC, UCA, UCC, UCG, UCU,UGC, UUC​

Tetra AAAA, AAAG, AAAU, AAGC, AAGU, AAUA, AAUC, AAUG, AAUU, AGAU,
AGUA, AGUU, AUAA, AUAC, AUAG, AUAU, AUCU, AUGA, AUGG, AUGU,
AUUA, AUUC, AUUG, AUUU, CUAA, GAAU, GAUA, GAUC, GAUG,GAUU, GGAU, GGUA, GGUU, GUAA, GUAU, GUGU, GUUA, GUUG,
GUUU, UAAA, UAAG, UAAU, UACU, UAGA, UAGU, UAUA, UAUC, UAUG,
UAUU, UGAU, UGGA, UGGU, UGUA, UGUU, UUAA, UUAG, UUAU,UUGA, UUGG, UUGU, UUUA, UUUG, UUUU​

AAAC, AACA, AACC, AACG, AACU, AAGG, ACAA, ACAC, ACAG, ACAU,
ACCA, ACCC, ACCG, ACCU, ACGA, ACGC, ACGG, ACGU, ACUA, ACUC,
ACUG, ACUU, AGAC, AGAG, AGCA, AGCC, AGCG, AGCU, AGGC, AGGG,
AGGU, AGUC, AGUG, AUCC, AUCG, CAAA, CAAC, CAAG, CAAU, CACA,
CACC, CACG, CACU, CAGA, CAGC, CAGG, CAGU, CAUC, CAUG, CCAA,
CCAC, CCAG, CCAU, CCCA, CCCC, CCCG, CCCU, CCGA, CCGC, CCGG,
CCGU, CCUA, CCUC, CCUG, CCUU, CGAA, CGAC, CGAG, CGAU, CGCA,
CGCC, CGCG, CGCU, CGGA, CGGC, CGGG, CGGU, CGUA, CGUC, CGUG,
CGUU, CUAC, CUAG, CUAU, CUCA, CUCC, CUCG, CUCU, CUGA, CUGC,
CUGG, CUGU, CUUA, CUUC, CUUG, CUUU, GAAC, GAAG, GACA, GACC,GACG, GACU, GAGA, GAGC, GAGG, GCAA, GCAC, GCAG, GCCA, GCCC,
GCCG, GCCU, GCGA, GCGC, GCGG, GCGU, GCUC, GCUG, GCUU, GGAA,GGAC, GGAG, GGCA, GGCC, GGCG, GGCU, GGGA, GGGC, GGGU, GGUC,
GGUG, GUAC, GUCA, GUCC, GUCG, GUCU, GUGA, GUGC, GUGG, UAAC,
UACA, UACC, UACG, UCAA, UCAC, UCAG, UCAU, UCCA, UCCC, UCCG,
UCCU, UCGA, UCGC, UCGG, UCGU, UCUA, UCUC, UCUG, UGAC, UGAG,UGCA, UGCC, UGCG, UGCU, UGGC, UGGG, UGUC, UUAC, UUCA, UUCC,
UUCG, UUGC, UUUC​

Iwasaki et al. Virology Journal(2023) 20:39

This type of complexity was assumed for the effect of
APOBEC3G.
Host genome differences

When considering the molecular mechanisms of thehost-dependent preference for oligonucleotide usage
in coronaviruses, it would be interesting to compare itwith the usage frequency of host genomes and mRNAs.
Greenbaum et al. [28] reported that single-stranded RNA
viruses, including influenza viruses, mimic their dinucleotide frequency (especially CG frequency) to reflect thefrequency in their host genomes. In the present study, we
first calculated the oligonucleotide frequency in humanand bat genomes and compared it to that of coronaviruses isolated from these hosts (see Additional file 2:
Table S3); for the host genomes, exon and intron regions
were analyzed separately to obtain information on
mRNAs. For the bat, we intitailly analyzed Rhinolophus
ferrumequinum, a member of the Rhinolophidae family,
from which various coronaviruses, including SARS, havebeen isolated [29, 30]. Comparing oligonucleotide frequencies in the human and bat exon regions, 147 oligonucleotides (7 di-, 27 tri-, and 113 tetra-nucleotides) weremore frequent in humans, whereas 172 oligonucleotides(9 di-, 35 tri-, and 128 tetra-nucleotides) were more frequent in bats (Table 2). In the case of intron regions, 127oligonucleotides (5 di-, 26 tri-, and 96 tetra-nucleotides)
were more frequent in humans, but 186 oligonucleotides(7 di-, 34 tri-, and 145 tetra-nucleotides) were more frequent in bats (see Additional file 2: Table S4), showing a
difference between the two genomic regions.

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Host genome differences and host‑dependent differencesin viral genomes

We then compared host genome differences with hostdependent preferences in viral genomes. Of the 147 oligonucleotides that were more frequent in human exon
regions, 73 (5 di-, 15 tri-, and 53 tetra-nucleotides) weremore frequent in human-CoV. Of the 172 oligonucleotides that were more frequent in the bat exon regions,
149 (8 di-, 29 tri-, and 112 tetra-nucleotides) were more
frequent in bat-CoV (Fig. 1A, B), suggesting a higher
degree of host adaptation within the bat virus. Notably,in the case of intron regions, the preference differed fromthat of the exon regions. Of the 127 oligonucleotides thatwere more frequent in human intron regions, only 19 (1di-, 4 tri-, and 14 tetra-nucleotides) were more frequentin human-CoV, and of the 186 oligonucleotides that weremore frequent in bat intron regions, only 110 (3 di-, 18
tri-, and 89 tetra-nucleotides) were more frequent in batCoV (Fig. 1B). The finding that the viral genome is moresimilar to exonic regions than to intronic regions may berelated to the fact that viral RNAs are located only in thecytoplasm but not in the cell nucleus.
Next, we analyzed the frequency of oligonucleotides
in whole genomes of humans and bats, which included
intergenic regions (see Additional file 2: Table S5), andthe results were closer to those of the intron region
than the exon region (Fig. 1C). Collectively, the oligonucleotide frequencies in the coronavirus genomes weremore closely related to the frequency in exon regions;
therefore, we focused on exon regions in the following
analyses.

Table 2 Oligonucleotides with significant differences in frequency between human exon and bat exon
Preferred in human exon

Preferred in Bat exon

Di

AA, AU, CU, GU, UA, UC, UU

AC, AG, CA, CC, CG, GA, GC, GG, UG

Tri

AAA, AAU, ACU, AGG, AGU, AUA, AUU, CAU, CCU, CUA,
CUC, CUU, GGG, GGU, GUA, GUU, UAA, UAG, UAU, UCA,
UCU, UGA, UGU, UUA, UUC, UUG, UUU​

AAC, AAG, ACA, ACC, ACG, AGA, AGC, AUC, AUG, CAA, CAC, CAG,
CCA, CCC, CCG, CGA, CGC, CGG, CGU, CUG, GAA, GAC, GAG, GCA,
GCC, GCG, GCU, GGA, GGC, GUC, GUG, UAC, UCG, UGC, UGG​

Tetra

AAAA, AAAC, AAAU, AACU, AAGU, AAUA, AAUC, AAUG,
AAUU, ACAU, ACUA, ACUC, ACUU, AGGC, AGGG, AGGU,
AGUA, AGUC, AGUG, AGUU, AUAA, AUAC, AUAG, AUAU,
AUCU, AUGU, AUUA, AUUC, AUUG, AUUU, CAAA, CAAU,
CACU, CAGG, CAUA, CAUG, CAUU, CCUA, CCUC, CCUU,CUAA, CUAG, CUAU, CUCA, CUCC, CUCU, CUGU, CUUA,CUUG, CUUU, GAAU, GAGU, GAUA, GAUU, GCAU, GCCU,
GCUA, GCUU, GGGA, GGGU, GGUA, GGUU, GUAA, GUAG,GUAU, GUCU, GUGA, GUGU, GUUA, GUUG, GUUU, UAAA,
UAAC, UAAG, UAAU, UACA, UACU, UAGA, UAGC, UAGG,
UAGU, UAUA, UAUC, UAUG, UAUU, UCAU, UCCC, UCCU,UCUA, UCUC, UCUG, UCUU, UGAG, UGAU, UGGG, UGUA,UGUG, UGUU, UUAA, UUAC, UUAG, UUAU, UUCA, UUCC,
UUCU, UUGA, UUGC, UUGG, UUGU, UUUA, UUUC,UUUG, UUUU​AAAG, AACC, AACG, AAGA, AAGC, AAGG, ACAA, ACAC, ACAG,
ACCA, ACCC, ACCG, ACCU, ACGA, ACGC, ACGG, ACGU, AGAA,
AGAC, AGAG, AGCA, AGCC, AGCG, AGCU, AGGA, AUCA, AUCC,
AUCG, AUGA, AUGC, AUGG, CAAC, CAAG, CACC, CACG, CAGA,
CAGC, CAUC, CCAA, CCAC, CCAG, CCAU, CCCC, CCCG, CCGA,
CCGC, CCGG, CCGU, CCUG, CGAA, CGAC, CGAG, CGAU, CGCA,
CGCC, CGCG, CGCU, CGGA, CGGC, CGGG, CGGU, CGUA, CGUC,
CGUG, CGUU, CUAC, CUCG, CUGC, CUGG, CUUC, GAAA, GAAC,
GAAG, GACA, GACC, GACG, GAGA, GAGC, GAGG, GAUC, GAUG,
GCAA, GCAC, GCAG, GCCA, GCCC, GCCG, GCGA, GCGC, GCGG,
GCGU, GCUC, GCUG, GGAA, GGAC, GGAG, GGCA, GGCC, GGCG,
GGGC, GGUC, GGUG, GUAC, GUCA, GUCC, GUCG, GUGC, GUGG,
GUUC, UACC, UACG, UCAA, UCCA, UCCG, UCGA, UCGC, UCGG,
UCGU, UGAA, UGAC, UGCA, UGCC, UGCG, UGCU, UGGA, UGGC,
UGGU, UUCG​

Iwasaki et al. Virology Journal(2023) 20:39

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Fig. 1 Oligonucleotide frequencies in coronavirus genomes correlate with those of their host exon region. Venn diagrams of di-, tri-, andtetra-nucleotides that were specifically high in frequency in coronaviruses and host genomes. A Host exons, B Host introns, and C 100 kb fragmentsof human and bat genomes. In the comparison of oligonucleotide frequency between human- and bat-CoV, the oligonucleotide that was more
frequent in human-CoV is denoted by CoV_H, and the oligonucleotide that was more frequent in bat-CoV is denoted by CoV_B. In the comparisonof oligonucleotide frequency between human and bat exons, the oligonucleotide that was more frequent in human exons is denoted by Exon_H,
and the oligonucleotide that was more frequent in bat exons is denoted by Exon_B. In the comparison of oligonucleotide frequency between
human- and bat-introns, the oligonucleotide that was more frequent in human intron is denoted by Intron_H, and the oligonucleotide that wasmore frequent in bat intron is denoted by Intron_B. In the comparison of oligonucleotide frequency between 100 kb fragments of human andbat genomes, the oligonucleotide that was more frequent in the human genome is denoted by Host_H, and the oligonucleotide that was more
frequent in the bat genome is denoted by Host_B

Characteristics of exon regions of other bats

In the above analysis, we used the genome data of R.
ferrumequinum; however, coronaviruses have been
isolated from various bat species. To obtain a muchbroader perspective on the host-dependent adaptationof coronaviruses, we analyzed five additional species.

R. ferrumequinum is a member of the Microchiroptera family, which includes Phyllostomus discolor, Myotis myotis, Pipistrellus kuhlii, and Molossus molossus,
and sequences and annotation data of the latter four
were also obtained from the Bat1K Project [24]. Rousettus aegyptiacus, a member of Megachiroptera, was also

Iwasaki et al. Virology Journal(2023) 20:39

analyzed. It should be noted that various coronaviruseshave been isolated from bats or their relatives [31]. As
conducted for R. ferrumequinum, we calculated the oligonucleotide frequency in the exon regions of each bat
and compared it with the frequency in human exon
regions (see Additional file 2: Table S6). In each comparison, there was a significant difference (t-test; FDR < 0.05)
in the frequency of approximately 300 oligonucleotides(Fig. 2A, B). Importantly, a large portion is common
among six bats; 127 oligonucleotides (6 di-, 22 tri-, and99 tetra-nucleotides) and 129 oligonucleotides (7 di-,23 tri-, and 99 tetra-nucleotides) were commonly foundat higher and lower frequencies, respectively, than in
human exon regions. The selection of common features
among the six bats slightly reduced the number of focal
oligonucleotides compared to the results obtained for R.
ferrumequinum alone. These oligonucleotides are considered to characterize exon regions in a wide phylogenetic
range of bats, differentially from human exons. Next, asshown in Table 1 and Fig. 1, we compared the results ofhost exon differences with host-dependent preferences inviral genomes.
As shown in Fig. 2C, D, from the 127 oligonucleotides
that were used more frequently in human exon regions,72 oligonucleotides (5 di-, 15 tri-, and 52 tetra-nucleotides) were more frequently used in human-CoV. Fromthe 129 oligonucleotides that were used more frequently
in exon regions of bats, 124 (7 di-, 22 tri-, and 95 tetranucleotides) were used more frequently in bat-CoV, suggesting a higher degree of host adaptation on the batvirus side. These 72 and 124 oligonucleotides, whichshow common features for viral genomes and host exon
regions, are thought to be landmarks for the adaptationof coronaviruses to their respective hosts. We definedeach oligonucleotide set as “Human- or Bat-type oligonucleotide” (Table 3). Bat-CoV appears to have a greater
degree of adaptation to its host than human-CoV, which
may be related to the fact that bats have been natural reservoir hosts of bat-CoV for a long period of evolution.
SARS‑CoV‑2 at the beginning of the epidemic

SARS-CoV-2, whose natural reservoir host is believed
to be bats, has recently invaded the human population,
possibly through an intermediate host animal [1]. We
examined the extent to which SARS-CoV-2 had humanor bat-type characteristics at the beginning of the pandemic. This may indicate the potentiality of initiating an

Page 6 of 12

outbreak in the human population and be useful in the
search for dangerous strains found in a non-human animal that are feared to cause a pandemic in the future.
First, for the SARS-CoV-2 population in the early epidemic stage (22 strains isolated in December 2019), we
examined whether the frequency of each focal oligonucleotide listed in Table 3 was similar to that of human- or
bat-CoV. In more detail, as described in “Degree of adaptation” in the Methods section, the midpoint between
the human- and bat-CoV frequencies was first calculated
for each focal oligonucleotide and assumed as a reference level. For each oligonucleotide, we calculated theaverage frequency of 22 SARS-CoV-2 strains isolated in
December 2019 (CoV-2_2019), although their genome
sequences were very similar. We next obtained the ratioof this average frequency to the above reference level andshowed the ­log2 fold change using an orange-filled circle
surrounded by a black line (Fig. 3). For confirmation, thefold-change of human- or bat-CoV to the reference levelis displayed in green or blue circles, respectively.
Human-type oligonucleotides, which were determinedby incorporating the characteristics of human mRNAs,
were examined (Fig. 3A (Di and Tri) and [see Additional
file 1: Fig. S1A (Tetra)]). Green circles for human-CoV
and blue circles for bat-CoV are located on the upper andlower side by definition, respectively. In the case of CoV2_2019, the majority of oligonucleotides were located on
the lower side and lacked human-type characteristics,
showing that the characteristics of bat-CoV prevailed at
the start of the SARS-CoV-2 epidemic even though thevirus was isolated from humans. Only about 1/4 of the 72
human-type oligonucleotides are human-CoV types, with
AA being a typical example of dinucleotides. The abundance of AA is also the case for tri- and tetranucleotides,
and that of U also contributes.
Bat-type oligonucleotides, which were determined byincorporating the characteristics of bat mRNAs, wereanalyzed (Fig. 3B (Di and Tri) and [see Additional file 1:
Fig. S1B (Tetra)]). Green circles for human-CoV (or batCov) are located on the lower (or upper) side by definition. Interestingly, for bat-type dinucleotides, there were
three cases with characteristics of human-CoV rather
than those of bat-CoV. Notably, in the case of CG (underlined in Fig. 3B), the difference in the vertical position
between the blue and green circles is large, showing thatthe difference between bat- and human-CoV is very large,
and the level of CoV-2_2019 is almost equal to that of(See figure on next page.)
Fig. 2 Correlation of oligonucleotide frequencies between virus genome and exon region of host animals. A, B Venn diagram showing di-, tri-, andtetra-nucleotides that were specifically high in frequency in host animals. The oligonucleotides that are (A) more or (B) less frequent in the humanexon region than the exon region in each bat. The notation in the Venn diagram is as follows. MOL: Molossus molossus, MYO: Myotis myotis, PHY:
Phyllostomus discolor, PIP: Pipistrellus kuhlii, RHI: Rhinolophus ferrumequinum, ROU: Rousettus aegyptiacus. C, D Venn diagram showing di-, tri-, andtetra-nucleotides that were specifically high in frequency in human-CoV and the human genome and in Bat-CoV and the bat genome, respectively

Iwasaki et al. Virology Journal(2023) 20:39

Fig. 2 (See legend on previous page.)

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Iwasaki et al. Virology Journal(2023) 20:39

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Table 3 The list of human- and bat-type oligonucleotides
Human-type

Bat-type

Di

AA, AU, GU, UA, UU

AC, CA, CC, CG, GA, GC, GG

TriAAA​, AAU​, AGU, AUA, AUU, GUA, GUU, UAA, UAG​, UAU, UGA, UGU, UUA, ACC, ACG​, AGC​, CAC, CAG, CCA, CCC​, CCG​, CGA​, CGC​, CGG​, CGU​, GAC,UUG, UUU​
GAG, GCA​, GCC​, GCG​, GCU​, GGC​, GUC​, UCG​, UGC​

Tetra

AAAA​, AAAU​, AAGU, AAUA, AAUC​, AAUG, AAUU​, AGUA, AGUU, AUAA,
AUAC​, AUAG, AUAU, AUCU​, AUGU, AUUA​, AUUC​, AUUG, AUUU, CUAA​,GAAU​, GAUA, GAUU, GGUA, GGUU, GUAA, GUAU, GUUA, GUUG, GUUU,UAAA​, UAAG, UAAU, UACU​, UAGA​, UAGU, UAUA, UAUC, UAUG, UAUU,UGAU, UGUA​, UGUU, UUAA, UUAG, UUAU, UUGA, UUGG, UUGU, UUUA,UUUG, UUUU​AACG​, AAGG​, ACCA, ACCC, ACCG​, ACCU, ACGA​, ACGC​, ACGG​, ACGU​,
AGCA​, AGCC​, AGCG​, AUCG​, CAAC, CAAG, CACC, CACG, CAGA, CAGC​,
CAUC, CCAA, CCAC, CCAG, CCCC​, CCCG​, CCGA​, CCGC​, CCGG​, CCGU​,
CCUG​, CGAA, CGAC​, CGAG​, CGAU​, CGCA, CGCC​, CGCG​, CGCU​, CGGA​,
CGGC, CGGG​, CGGU​, CGUC​, CGUG​, CGUU​, CUAC, CUCG, CUGC, CUGG​,
CUUC, GAAC, GAAG, GACA, GACC, GACG​, GAGA, GAGC, GAGG​, GCAC,
GCAG​, GCCA​, GCCC​, GCCG​, GCGA​, GCGC​, GCGG​, GCGU​, GCUC​, GCUG,
GGAC, GGAG, GGCA​, GGCC​, GGCG​, GGGC, GGUC​, GUAC, GUCC, GUCG​,GUGC​, GUGG​, UACC, UACG​, UCCA, UCCG​, UCGA​, UCGC​, UCGG, UCGU​,UGCC​, UGCG​, UGCU​, UGGC​, UUCG​

Underlined oligonucleotides indicate those in SARS-CoV-2 with a frequency closer to human-CoV than to bat-CoV at the beginning of the epidemic

Fig. 3 Level of adaptation of each di- and tri-nucleotide to the human cellular environment in SARS-CoV-2. A, B The level of adaptation of thehuman- and bat-type oligonucleotides in the three virus types (human-CoV, bat-CoV, and SARS-CoV-2) is displayed. This level is the ratio of theaveraged oligonucleotide frequency in each virus to the baseline, which is the midpoint between human- and bat-CoV frequencies. The ratio of
SARS-CoV-2 for each oligonucleotide was displayed as an orange-filled circle surrounded by a black line, and those of human- or bat-CoV wasdisplayed as a green or blue circle, respectively

human-CoV. The trinucleotides, including CG (underlined in Fig. 3B), again showed a large difference in thevertical position between the green and blue circles, andthe orange circles of SARS-CoV-2 were close to the green
circles of human-CoV. Collectively, SARS-CoV-2 appears
to have lost appreciably of the characteristics of bat-type
oligonucleotides, even at the beginning of its outbreak in
the human population. This loss (e.g., low CG levels) may
reflect the changes that had already occurred in the nonhuman host prior to entry into the human population,and may suggest a possible requirement for initiating anefficient outbreak in the human population.
CG is known to be a target of zinc-finger antiviral protein (ZAP), one of the host antiviral systems, and a variety of RNA viruses, including coronaviruses, have beenshown to escape the antiviral action of ZAP by maintaining low CG levels [26]. In the present study, SARS-CoV-2
was found to reduce the frequency of CG-containing
oligonucleotides to almost the same level as that ofhuman-CoV even at the beginning of the epidemic. This
lower CG level would have most likely been achieved inIwasaki et al. Virology Journal(2023) 20:39

non-human intermediate SARS-CoV-2 hosts. However,there are examples of bat-type oligonucleotides, especially those unrelated to CG, that are not of the humanCoV type (Fig. 3B). In addition, there are more cases ofhuman-type oligonucleotides that are not of the humanCoV type (Fig. 3A). This suggests that the characteristics
of intermediate host mRNAs likely differ from those ofhuman mRNAs.
Changes in SARS‑CoV‑2 genomes

Two years have passed since the start of the SARS-CoV-2
pandemic, many mutations have accumulated in the viralgenomes, and five highly infectious variants, termedvariants of concern (VOC), have emerged: Alpha, Beta,
Gamma, Delta, and Omicron. These five prevalent variants are considered to have adapted more successfully tothe human cellular environment than other less prevalentvariants. To obtain information on sufficiently accumulated mutations in these prevalent variants, the number
of isolates per month was first counted for each variant,and only the genome data from the month with the highest number of isolates were used as a representative viral
population that has accumulated sufficient mutations.
Although we used sequences that were complete andhad high coverage, there were slight differences in thelength and presence of undetermined nucleotides (Ns).

Page 9 of 12

After normalization of the sequence length without N
for each viral genome, the occurrence frequency of eachfocal oligonucleotide was calculated, and the average ofthe occurrence frequency was obtained for each variant.
Its differences from the average frequency in the population at the beginning of the pandemic (CoV-2_2019)is displayed in Fig. 4. As above mentioned, Fig. 3 showsthat at the beginning of the SARS-CoV-2 pandemic, thefrequencies of some focal oligonucleotides were alreadycloser to that of human-CoV than that of bat-CoV, andthese oligonucleotides were specified as having alreadyachieved adaptation to the human host, distinguishingthem from unachieved oligonucleotides (Fig. 4).
Almost all unachieved human-type dinucleotides(Fig. 4Ai) increased their frequency regardless of the variant type, indicating increased adaptation to the humanhost. In the case of the achieved human-type dinucleotide (Fig. 4Aii), its increase level was not high, and one
variant decreased. For unachieved bat-type dinucleotides (Fig. 4Bi), there was a clear tendency to reduce thefrequency of these oligonucleotides and avoid their use,regardless of the variant type. In contrast, the frequencies of the achieved bat-type dinucleotides (Fig. 4Bii), andthe degree of their increase or decrease, were small andvaried in different directions for different variants, most
likely because their frequency was sufficiently low even at

Fig. 4 Changes observed for five variants of SARS-CoV-2. The cumulative bar chart shows the difference in the number of oligonucleotide loci per
30 kb (approximate length of the viral genome) from that of SARS-CoV-2 isolated in December 2019. The numbers of changes in alpha, beta, delta,
gamma, and omicron are shown in dark blue, orange, gray, yellow, and light blue, respectively. A Change in the number of oligonucleotide loci for(Ai) unachieved and (Aii) achieved human-type dinucleotides. B Change for (Bi) unachieved and (Bii) achieved bat-type dinucleotides. C, D Changefor (Ci, Di) unachieved and (Cii, Dii) achieved human- or bat-type trinucleotides, respectively

Iwasaki et al. Virology Journal(2023) 20:39

the start of the outbreak. A similar trend was observed
for trinucleotides, but there was also a marked increase
in UUU (Fig. 4Ci) and a marked decrease in CCC(Fig. 4Dii), which seems to be an effect of APOBEC3G inhuman cells [21, 26, 27].
In this study, by analyzing the usage frequency of shortoligonucleotides, we have analyzed the evolutionarychanges of SARS-CoV-2 and focused mainly on the variant-independent changes. However, when considering
the SARS-CoV-2 evolution, it is also important to study
the variant-specific changes. In particular, regarding the
emergence of the Omicron variant [32], peculiar evulutiobnary processes in immunosuppressed humans oreven in non-human hosts are hypothesized [33, 34], andthe relationship of its emergence with the specific host’s
cellular environment is of particular interest. Whenfocusing on long oligonucleotides, the relationship withfunctions may become clearer while the types of target
variables become large, and variant-specific changes canbe characterized. Independently of the present study, ourgroup have shown that variant-specific clustering, andthus variant-specific feature extraction, is possible byusing an unsupervised AI suitable for analysis of a large
number of variables and thus of long oligonucleotides[22, 23, 35]. By analyzing the usage of long oligonucleotides (e.g.,20-mers) in Omicron and other variants, wehave recently characterized advantageous mutations thatspread convergently in multiple lineages [36].

Discussion
The oligonucleotide composition of SARS-CoV-2 at the
beginning of the outbreak is thought to reflect the cellular environment of the hypothesized intermediate host[14]. Thus, some changes that have accumulated during
the progression of the current pandemic may reflect,
at least in part, the differences in cellular environment
between the intermediate host and humans. CG-containing oligonucleotides were sufficiently low even at the outbreak start indicating that the ZAP system [37] may havebeen fully functional in the intermediate host. The low
CG level at the beginning of the current pandemic may
be one cause of the SARS-CoV-2 outbreak and seemes tobe one guideline for searching for dangerous coronavirus strains that may cause a future pandemic from nonhuman hosts.
The marked decrease in C-rich oligonucleotides andincrease in U-rich oligonucleotides after the outbreakstart indicates that APOBEC3G [26, 27] may have beenless functional in the intermediate host than in humans.
In the present study, tetranucleotides were also analyzed,
but the results are mainly shown as supplementary data.
Although the general trend is similar to that of di- andtrinucleotides, some cases show different trends among

Page 10 of 12

variants. In the case of tetranucleotides, some are functional motifs, such as binding motifs for RNA-binding
proteins [14, 15]. Mutations that occur within such functional sequences may significantly alter the fitness of themutated strain, leading to variant-specific differences.

Conclusion
In this study, we first found that the oligonucleotide
frequency in coronaviruses with host-dependent chractersitics was clearly correlated with the frequency inexon regions of the host animal. Changing the oligonucleotide frequency in the viral genome is considered as a
crucial host adaptation process after changing the host,and based on this perspective, we examined the oligonucleotide frequency in SARS-CoV-2 and found thatthe frequencies of some oligonucleotides expected to be
associated with the adaptation to humans were alreadyclose to those of human-CoV even at the beginning of
the pandemic, suggesting that they had been ready toadapt to the human cell environment before the invasionof the human population; these oligonucleotides may
include ZAP- and other possible host immunity-related
oligonucleotide motifs. In addition, investigating theoligonucleotides that were rather closer in frequency to
bat-CoV at the beginning of the pandemic revealed that
their frequencies approached those of human-CoV during the course of the present pandemic, showing that
SARS-CoV-2 is trying to further mimic the frequency ofexon regions of humans. These oligonucleotides may also
play an important role in the efficient survival of coronaviruses in the human cellular environment.
Abbreviations
SARS-CoV-2		Severe acute respiratory syndrome coronavirus 2
Human-CoV	Human coronavirus
Bat-CoV		Bat coronavirus
CoV-2_2019	SARS-CoV-2 isolated in Dec. 2019
VOCs		Variants of concern
ZAP		Zinc-finger antiviral protein

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12985-​023-​01995-3.
Additional file 1. Figure S1: Level of adaptation of each tetranucleotide
to the human cellular environment in SARS-CoV-2. Level of adaptationof the human- and bat-type tetranucleotides for the three virus types,
human-CoV, bat-CoV, and SARS-CoV-2. This level is the ratio of the averaged oligonucleotide frequency in each virus type to the baseline, whichis the midpoint between the human- and bat-CoV frequencies. The valueof SARS-CoV-2 is displayed as an orange-filled circle surrounded by ablack line, and that of human- or bat-CoV is displayed as a green or blue
circle, respectively. Figure S2: Changes in human-type tetranucleotides
observed for five variants of SARS-CoV-2. The cumulative bar chart shows
the difference in the number of loci per 30 kb for human-type nucleotides
from that of SARS-CoV-2 isolated in December 2019; the following fiveIwasaki et al. Virology Journal(2023) 20:39

variants were considered. The numbers of changes of alpha, beta, delta,
gamma, and omicron variants are represented by dark blue, orange, gray,yellow, and light blue, respectively. Figure S3: Changes in bat-type tetranucleotides in SARS-CoV-2. The cumulative bar chart shows the diffrencein the number of loci per 30 kb for bat-type tetranucleotides from that of
SARS-CoV-2 isolated in December 2019, as displayed in Fig. S2.
Additional file 2. Table S1: The list of strains of human- and bat-CoV.
Table S2: Comparison of the frequency of di-, tri-, and tetra-nucleotides
between human- and bat-CoV. Table S3: Comparison of the frequency
of di-, tri-, and tetra-nucleotides between human and bat genomes.
Table S4: The list of oligonucleotides with significant differences in frequency between human and bat introns. Table S5: The list of oligonucleotides with significant differences in frequency between human and bat
genomes. Table S6: Comparisons of the frequency of oligonucleotides
between human exons and those of additional five bat species. Table S7:
Level of adaptation of each oligonucleotide. Table S8: The number ofisolates per month of each SARS-CoV-2 variant. Table S9: Changes in theoligonucleotide locus number of SARS-CoV-2.
Additional file 3. Data S1: The list of accession numbers of CoV-2_2019
used in this study. Data S2: The list of accession numbers of alpha variants
used in this study. Data S3: The list of accession numbers of beta variants
used in this study. Data S4: The list of accession numbers of delta variants
used in this study. Data S5: The list of accession numbers of gamma variants used in this study. Data S6: The list of accession numbers of omicron
variants used in this study.
Acknowledgements
We gratefully acknowledge the authors who have submitted their sequences
to the GISAID Database. We would like to thank Editage (www.​edita​ge.​com)for English language editing. We also thank Mr. Daiki Ando and Mr. Yukito
Saikawa of Nagahama Institute of Bio-Science and Technology for valuable
discussions.
Author contributions
YI conceived the approach and conducted this analysis. TA developed thealgorithm. TI supervised this study. All author(s) read and approved the final
manuscript.
Funding
This work was supported by JST CREST Grant Number JPMJCR20H1 and JSPS
KAKENHI Grant Number 18K07151, Japan.
Availability of data and materials
The accession numbers of all sequences analyzed in this study are listed in
Additional File 2 or 3, and the corresponding sequence data are available in
the GISAID database (https://​www.​gisaid.​org/) or the NCBI virus database(https://​www.​ncbi.​nlm.​nih.​gov/​labs/​virus/). Other numerical data generated
or analyzed during this study are included either in this published article or in
the additional files.

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 28 April 2022 Accepted: 19 February 2023

Page 11 of 12

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