\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}








\begin{document}

\title{The signature of SARS-CoV-2 evolution reflects selective pressures
within human guts}



\author[1]{Ancha Baranova}%
\author[2]{Zhiyanov A.}%
\author[2]{Shkurnikov M.}%
\author[2]{Nersisyan A.}%
\author[3]{Cai H.}%
\author[2]{Tonevitsky A.}%
\affil[1]{George Mason University School of Systems Biology}%
\affil[2]{Nacional'nyj issledovatel'skij universitet Vyssaa skola ekonomiki Fakul'tet biologii i biotehnologii}%
\affil[3]{Sun Yat-sen University - Shenzhen Campus}%


\vspace{-1em}



  
  \date{May 23, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
In somatic cells, microRNAs (miRNAs) bind to the genomes of RNA viruses
and influence their translation and replication. Here we demonstrate
that a significant number of miRNA binding sites locate in the NSP4
region of the SARS-CoV-2 genome, and the intestinal human miRNAs exert
evolutionary pressure on this region. Notably, in infected cells, NSP4
promotes the formation of double-membrane vesicles, which serve as the
scaffolds for replication-transcriptional complexes and protect viral
RNA from intracellular destruction. In three years of selection, the
loss of many miRNA binding sites, in particular, those within the NSP4,
has shaped the SARS-CoV-2 genomes to promote the descendants of the BA.2
variants as the dominant strains that define current momentum of the
pandemics. Findings highlight the possibility that intestinal tissue may
significantly impact evolution of the SARS-CoV-2 genome and may play a
pivotal role in the long COVID.%
\end{abstract}%



\sloppy


The signature of SARS-CoV-2 evolution reflects selective pressures
within human guts

Zhiyanov A.\textsuperscript{1,+,*}, Shkurnikov M.\textsuperscript{1,*},
Nersisyan A.\textsuperscript{1}, Cai H.\textsuperscript{2}, Baranova
A.\textsuperscript{3,4, +}, and Tonevitsky
A.\textsuperscript{1,5,6}\emph{\textsuperscript{1} Faculty of Biology
and Biotechnology, HSE University, Moscow 101000,
Russia}\textsuperscript{2} \emph{Sun Yat-Sen University,
Nanoengineering, 66 Gongchang Lu, Guangming District, Shenzhen 518107,
China}\textsuperscript{3} \emph{School of Systems Biology, George Mason
University, Fairfax 22030, VA, United States} \textsuperscript{4}
\emph{Research Centre for Medical Genetics, Moscow 115478, Russia}5
\emph{Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian
Academy ofSciences, Moscow 117997, Russia}\textsuperscript{6} \emph{Art
Photonics GmbH, Berlin 12489, Germany}+ \emph{Correspondence:
azhiyanov@hse.ru, abaranov@gmu.edu}* \emph{Equal contribution}

\section*{Abstract}

{\label{abstract}}

In somatic cells, microRNAs (miRNAs) bind to the genomes of RNA viruses
and influence their translation and replication. Here we demonstrate
that a significant number of miRNA binding sites locate in the NSP4
region of the SARS-CoV-2 genome, and the intestinal human miRNAs exert
evolutionary pressure on this region. Notably, in infected cells, NSP4
promotes the formation of double-membrane vesicles, which serve as the
scaffolds for replication-transcriptional complexes and protect viral
RNA from intracellular destruction. In three years of selection, the
loss of many miRNA binding sites, in particular, those within the NSP4,
has shaped the SARS-CoV-2 genomes to promote the descendants of the BA.2
variants as the dominant strains that define current momentum of the
pandemics. Findings highlight the possibility that intestinal tissue may
significantly impact evolution of the SARS-CoV-2 genome and may play a
pivotal role in the long COVID.

\section*{1 Introduction}

{\label{introduction}}

The (+)RNA genome of the pandemic SARS-CoV-2 virus is about 30,000
nucleotides long {[}1{]}. Since December 2019, when the original strain
(WuhanHu-1) has emerged, the SARS-CoV-2 acquired more than 10,000
recorded nucleotide mutations {[}2{]}, with an estimated mutation rate
between 0.0004 and 0.002 mutations per nucleotide per year {[}3{]}. The
current release of the GISAID database contains more than 14 million
SARS-CoV-2 sequences {[}4{]} and allows the tracking of the evolution of
the virus with unprecedented precision. As widely discussed, SARS-CoV-2
is being shaped by the pressure exerted by neutralizing antibodies and
applied upon the receptor-binding domain of the Spike (S) protein
{[}5{]}. It should be, however, noted that antibodies are far from being
the only adversarial factor encountered by the virus upon infection. In
particular, the binding of host miRNA to other (+)RNA viruses provides a
substantial evolutionary pressure {[}6{]} as these molecules suppress
both the translation and the replication of virus along with altering
some aspects of their pathogenesis {[}7--9{]}.

Several works have predicted interactions of the SARS-CoV-2
single-stranded

(+)RNA virus with multiple human miRNAs {[}10--13{]}. A majority of
studies have not, however, taken into account the ability of the
SARS-CoV-2 virus to evolve, and, accordingly, to selectively alter its
miRNA binding regions, especially these enriched in binding seeds. Only
a few published works utilized the sequences from various Variants of
Concern (VOCs) {[}14{]}. Human genomes encode many miRNAs, a majority of
which display differential expression in human tissues. It is obvious
that only miRNA species expressed in replication-permissive host cells
will be relevant to SARS-CoV-2 evolution. In humans, the SARS-CoV-2
virus predominantly replicates in the type 2 alveocytes of the lung. In
addition, significant clinical data indicate the presence of the virus
in the intestinal epithelium, which expresses both ACE2 and TMPRSS2
{[}15{]}. Moreover, intestinal viral persistence is commonly detected.
It is now recognized that long-term residence of SARS-CoV-2 in the human
gut supports low-intensity inflammation, which contributes to the
complications of COVID-19, collectively known as post-COVID or long
COVID {[}16{]}. For three years of the pandemic, the SARS-CoV-2 virus
has significantly mutated. Due to the degeneracy of the genetic code,
many mutations have not led to a change in the sequence of viral
proteins and, therefore, are classified as silent {[}17{]}.
Nevertheless, silent nucleotide changes have managed to get fixed in the
virus genome. In evolution, the fixation of the mutation may take place
either by chance, or under the influence of the selective forces acting
upon RNA itself rather than on the encoded proteins. The binding of the
cellular miRNAs to the viral genome may be capable of exerting such
pressure. The hypothesis that the evolutionary pressure exerted by
binding of miRNA species expressed in human lung and intestinal tissue
has shaped up the genome of the current variants of SARS-CoV-2 virus
dominating the globe was tested.

\section*{2 Materials and methods}

{\label{materials-and-methods}}

\subsection*{2.1 SARS-CoV-2 variants}

{\label{sars-cov-2-variants}}

A total of 14,472,180 SARS-CoV-2 RNA sequences were downloaded from the
GISAID database {[}18{]} on January 7\textsuperscript{th} 2023 and
annotated with Pangolin {[}19{]} (program's version 4.2, Pangolin's data
version 1.17). Among 28,420 variants associated with the City of Berlin
(Federal Republic of Germany), 3,755 ones satisfying the following
conditions were selected: viral RNA does not contain unrecognized
nucleotides; the difference between viral RNA length and the ``Wuhan''
reference variant (GISAID: EPI ISL 402125) is less than 5\%. All
selected RNA sequences were collected between the 14\textsuperscript{th}
January 2021 and 12\textsuperscript{th} December 2022. All sequences
were divided into two groups, ``Ancestral variants'' and ``Omicrons'',
according to their GISAID descriptions. ``Ancestral variants'' included
variants of ``Alpha'', ``Beta'', ``Delta'', and others (Table 1 of
Supplementary materials) and collected before the 30\textsuperscript{th}
January 2022, while all ``Omicrons'' species were sequenced after
1\textsuperscript{st} December 2021 (Figure 1).

\subsection*{2.2 Highly expressed miRNAs}

{\label{highly-expressed-mirnas}}

miRNA-seq read count tables were downloaded from the GDC portal at
https: //portal.gdc.cancer.gov/projects/ for colon (COAD) and lung
(LUAD) tissues. Following the corresponding clinical description, the
dataset samples were referred to as ``Normal'' (``Solid Tissue Normal'')
and ``Tumor'' (``Primary Tumor'') groups. Only ``Normal'' samples were
used to assess miRNA expression in the analysis: 8 entries in COAD
dataset and 46 in the LUAD project. Highly expressed miRNAs were
selected independently for each tissue by the following procedure: read
counts associated with a particular miRNA by the samples were summed up;
then, miRNAs were sorted by the total number of reads; finally, miRNAs
accounting for 95\% of all reads were selected; As a final expression of
the miRNAs, corresponding CPM expressions were averaged by the samples.

\subsection*{2.3 Binding model}

{\label{binding-model}}

To model the binding interaction between miRNA and viral mRNAs, the
regions of viral RNA that were a reverse complement to the seed region
of miRNA (nucleotides from 2 to 7 on the 5'-end of mature miRNA) were
mapped to SARSCoV-2 genomes. Following the common classification
{[}20{]}, such interactions are called ``6mer''; they lead to
translational inactivation of target mRNAs {[}21{]}. In addition to the
reverse complementarity of the seed region, miRNA target prediction
tools may rely on additional features. For example, TargetScan {[}22{]}
utilizes context++ score based on site type,
3\textsuperscript{0}-supplementary pairing, local AU content and others,
MirTarget {[}23{]} considers GC content of target site, and UTR length,
while RNA22 {[}24{]} predicts target sites by placing ``Target Islands''
within 3'-UTRs, 5'-UTRs or CDS.

Commonly, the overlap of the targets by different software is weak at
best {[}25,

26{]}. While RNA22 predicts more individual miRNA-mRNA interactions than
TargetScan, it misses the majority of classical seed region binding
sites in 3'-UTRs. Moreover, the prediction results heavily depend on the
tool-specific internal thresholds, with sensitivity and specificity
expected to vary depending on particular thresholds. To overcome these
limitations, a bottom-up approach based on the mapping of SARS-CoV-2
regions with reverse complement ``6mers'' matching the seed regions
within tissue-specific and relevant host miRNAs was utilized. All
SARS-CoV-2 RNA sequences were aligned to the reference ``Wuhan'' variant
using MAFFT tool {[}27{]}. After the alignment, we transformed viral
RNAs coordinates on the ``Wuhan'' variant and annotated protein-coding
regions using

``Wuhan'' annotation (https://www.ncbi.nlm.nih.gov/nuccore/1798174254).

\subsection*{2.4 Statistical analysis}

{\label{statistical-analysis}}

In all comparisons, the significances of the findings were evaluated in
the nonparametric Mann--Whitney U tests {[}28{]}. In addition, the
standard Student's t-test procedure {[}29{]} was used to test the
hypothesis that the Spearman correlation {[}30{]} was zero. The
Monte-Carlo sampling procedure was used to show that the decrease in the
occurrence of miRNA binding motifs in the ``Omicron'' sequence group was
specific to the highly expressed miRNAs. More precisely, the expression
profile of the highly expressed miRNAs was fixed and the miRNA sequences
were randomly replaced with other miRNAs expressed in a tissue. After
that, the weighted number of binding motifs in ``Omicrons'' and
``Ancestral'' groups were calculated: the number of binding motifs
within a particular miRNA was multiplied by the proportion of its read
counts, summed up and normalized by the total lengths of the compared
viral genomes. Then, for ``Ancestral variants'' and ``Omicrons'' groups,
the computed numbers were compared using the MannWhitney U test. The
described procedure was repeated 1,000 times. Thus, the distribution of
statistics characterizing the change in the number of binding motifs
between ``Ancestral variants'' and ``Omicrons'' was obtained. Finally,
using this distribution, the probability that the decrease in the number
of binding motifs for highly expressed miRNAs was greater than for
randomly selected ones was calculated.

\subsection*{2.5 Other technical details}

{\label{other-technical-details}}

The statistical analysis (hypothesis testing, correlation computation)
was performed with SciPy library {[}31{]}. All vector computations were
implemented using NumPy library {[}32{]} and parallelized using Parallel
tool {[}33{]}. Miscellaneous computations with tables were produced with
Pandas {[}34{]}. Finally, figures were made using Matplotlib {[}35{]}
and Seaborn {[}36{]} libraries. The source code of the analysis can be
found at https://github.com/zhiyanov/covid-miRNA-evolution.

\section*{3 Results}

{\label{results}}

In this study, both the tissue-specificity and expression volume
restrictions were applied to facilitate identification of the most
prominent interactions between host miRNAs and the viral genomes.
Firstly, the scope of the study was limited to the lungs and colon
tissues as the two main sites of SARS-CoV-2 replication in the human
body {[}15{]}. Secondly, only those miRNA species were analyzed that
represent the top 95\% of the miRNAome for their tissue type {[}37{]},
with the following rationale. In COVID-19, each infected cell expresses
from 1,500 to 15,000 copies of the virus {[}38{]} and from 100,000 to
200,000 miRNA molecules {[}39{]}. To visualize the kinetics of their
interactions, one should take into account that each miRNA species, in
addition to the virus, binds to 25-50 host mRNAs species, each expressed
as 50 to 100 copies per cell, thus, and therefore, being supplied with
more than 1,250 endogenous target molecules {[}21{]}. Because of that,
only highly represented miRNA species are likely to exert a significant
impact on the evolution of the viral genomes. For lung tissue, there
were 32 miRNAs (Figure 2 of Supplementary materials), and for the
intestines, 40 miRNAs (Figure 3) that fulfilled the abovementioned
criteria. Twenty-one of these miRNAs were found in both of these
tissues, and 19 were expressed in either one or another tissue. In lung
tissue, lung-specific miRNAs accounted for 81\% of all miRNAs, while in
the colon only 56\% of expressed miRNAs were colon-specific. In lungs,
the most prevalent miRNA was miR-143-3p, followed by miR-21-5p,
miR-22-3p and miR-30a-5p. In colon, miRNA expression profile had a
predominance of let-7b-5p, miR-92a-3p and miR-200c-3p. From the GISAD
database, a total of 3,755 SARS-CoV-2 sequences were retrieved. All of
them were tagged by location within the same metropolitan area (Berlin,
Germany) and a time stamp between the 1\textsuperscript{st} January 2020
and the 20\textsuperscript{th}December 2022 (Figure 1), see ``On
choosing a city of the study'' section of Supplementary materials for
more details). All selected sequences were divided into two groups:

``Ancestral variants'', which included ``Alpha'' (B.1.1.7-like, 759
variants), ``Beta'' (B.1.351-like, 4 variants), Delta (B.1.617.2-like,
841 variants; AY.4.2like, 12 variants) as well as 7 other strains (Table
1 of Supplementary materials). All of these sequences were collected in
January 2022 and earlier; ``Ancestral variants'' included ``Alpha''
(B.1.1.7-like, 759 variants), ``Beta'' (B.1.351-like, 4 variants), Delta
(B.1.617.2-like, 841 variants; AY.4.2like, 12 variants) and 7 other
strains (Table 1 of Supplementary materials) and belonged to the period
until January 2022;

``Omicrons'', which included 8 varieties of ``Omicron'' strain. All of
these were collected in December 2021 or later.

Figure 1: SARS-CoV-2 variants obtained from GISAID portal according to
Berlin, Germany geotag.

To assess the evolutionary pressure exerted by colon and lung-specific
miR-

NAs, the expression-normalized amounts of binding motifs numbers within
29,903 nucleotides of viral SARS-CoV-2 RNA were analyzed (see
``Materials and methods'' for more details). In this way, the regulatory
contribution of each miRNA was taken separately into account. As
compared to ``Ancestral variants'', Omicron variants contain strikingly
lesser amounts of binding sites for either colon miRNAs (Mann-Whitney U
test, \emph{p} [?] 4\emph{.}44 \selectlanguage{ngerman}· 10\selectlanguage{english}\textsuperscript{-254}, Figure 2, A) or
lung miRNAs (Mann-Whitney U test, \emph{p} \selectlanguage{english}[?] 1\emph{.}07 *
10\selectlanguage{english}\textsuperscript{-146}, Figure 1 of Supplementary materials). By
reshuffling the list of miRNAs with a predefined expression repertoire
specific to the highly expressed miRNAs, the proportion of random
shuffles for which the drop in the number of binding motifs was greater
than the drop detected for the highly expressed miRNAs was calculated
(the detailed description of the procedure can be found in ``Materials
and Methods'' section). This proportion turned to be lower than 5\% for
colon miRNAs (\emph{p} \selectlanguage{english}[?] 2\emph{.}50 * 10\selectlanguage{english}\textsuperscript{-2}), but not
for lung miRNAs (\emph{p} \selectlanguage{english}[?] 7\emph{.}79 * 10\selectlanguage{english}\textsuperscript{-2}). When
the significance of the miRNA influence was tested for other tissue
compartments, including the prostate, the breast, the bladder, and the
liver, the decrease in the number of observed motifs was not significant
for either of these tissues (Table 2 of Supplementary materials).
Further study of the colon-specific dropdown showed that it was mainly
observed within Omicron brunch (BA.2-like) (see Figure 2, A). However,
even after exclusion of Omicron (BA.2-like) from ``Omicrons'' group, the
indicated trend remained strong (Mann-Whitney U test, \emph{p} \selectlanguage{english}[?]
2\emph{.}57 * 10\selectlanguage{english}\textsuperscript{-69}). Moreover, the weighted number of
miRNA binding motifs decreased steadily according to time stamps
(Spearman correlation \selectlanguage{english}[?] \selectlanguage{english}-0\emph{.}34\emph{, p} \selectlanguage{english}[?] 7\emph{.}37 *
10\selectlanguage{english}\textsuperscript{-80}, Figure 2, B), thus, pointing at continuous
evolution of ``Omicron'' sequences within the intestinal niche.

The difference was calculated between the number of binding motifs in
the

``Wuhan'' and the subsequent ``Omicrons'' (Figure 3). It turned out that
the loss of the binding sites was primarily due to diminished binding to
hsa-let-7b-5p, hsa-let7a-5p and hsa-miR-93-5p. Notably, for
hsa-miR-194-5p and hsa-miR-29a-3p, the amounts of binding sites in the
``Omicrons'' have increased. The pairwise alignment of SARS-CoV-2 RNAs
with reference ``Wuhan'' variant allowed us to compare the distributions
of the miRNA seed regions within each variant of the virus. Further, the
binding distributions for individual miRNA were calculated. It was found
that the contribution of each miRNA in those distributions was
proportional to its expression in colon tissues. Finally, the
distributions were averaged over ``Ancestral variants'' and different
groups of ``Omicrons'', either including or excluding Omicron
(BA.2-like) variants, and analyzed. As seen from Figure 4, the decrease
in amounts of the complementary seeds observed in Omicron BA.2-like
variants was primarily due to a loss of hsa-let-7b5p binding motifs
located within NSP4. Indeed, C9861T nucleotide mutations occurred mostly
in Omicron BA.2-like sequences (872 variants of 887), while the
9861\textsuperscript{st} position in all ``Ancestral variants''
sequences retained the cytosine. Interestingly, that transition was
silent as it has not lead to amino acid mutation: both CTC and CTT
triplets encode for lysine.

Notably, a comparison of ``Ancestral variants'' and other ``Omicrons''
(Figure

3 of Supplementary materials) showed that the overall dropdown of miRNA
binding sites was not due to any specific mutation within a particular
miRNA binding motif. On the contrary, a gradual decrease in the number
of binding motifs was observed over time (Figure 2, B). To access the
contribution of virus RNA coding regions to the observed dropdown, the
weighted (by miRNA expression) amounts of miRNA binding motifs per
coding region were calculated for ``Ancestral variants'' and for
``Omicrons'' with or without Omicron (BA.2-like) sequences separately.
By comparing these amounts within ``Ancestral variants'' with
``Omicrons'' using Mann-Whitney U test (Table 1), it turned out that
Spike and NSP15 regions were changed the most. In addition, the
sequences classified as Omicron (BA.2-like) displayed an additional
region with accumulated changes, the one encoding for NSP4, while in
other ``Omicrons'' the third most divergent location was the region of
NSP12. Figure 2: Weighted number (by miRNA expression in colon tissues)
of binding motifs per 100 miRNA and 29,903 nucleotides \textbf{A}:
grouped by ``Ancestral variants'' and ``Omicrons'', \textbf{B}:
distributed over time. Spearman correlation between the weighted number
and number of days since the start of pandemics is \selectlanguage{english}[?] \selectlanguage{english}-0\emph{.}34,
(\emph{p} \selectlanguage{english}[?] 7\emph{.}37 * 10\selectlanguage{english}\textsuperscript{-80}). In this analysis,
Omicron (BA.2 like) variants were excluded. Figure 3: Amounts of the
binding motifs in analyzed SARS-CoV-2 variants and reference ``Wuhan''
variant differ. Here, miRNAs are sorted by the levels of their
expression in the intestine. The share of miRNome represented by
particular miRNA could be found adjacent to miRNA ID. Figure 4:
Distributions of miRNA binding motifs along to ``Wuhan'' RNA sequences
and averaged by \textbf{A}: ``Ancestral variants'',\textbf{B}:
difference between ``Ancestral variants'' and Omicron (BA.2-like)
variants, and \textbf{C}: Omicron (BA.2like) variants.

\section*{4 Discussion}

{\label{discussion}}

In this study, the hypothesis that host cell miRNAs may have influenced
the evolution of SARS-CoV-2 was tested. Two sets of miRNAs expressed in
the main sites of virus reproduction, the lungs and the intestine, were
considered. It was found that those sets significantly differed in the
representation of individual miRNAs. On one hand, hsa-miR-143-3p,
hsa-miR-21-5p, hsa-miR-22-3p and hsa-miR-30a-5p were specific for the
lungs, with large predominance of miR-1433p expression (41.5\%). On the
other hand, the intestine was characterized by a more uniform profile
with the presence of tissue-specific let-7b-5p, miR-92a-3p and
miR-200c-3p. During pandemics, SARS-CoV-2 genomes lost many miRNA
binding sites specific for the lung and intestinal tissue expressed
miRNAs. Importantly, MonteCarlo simulations showed that observed changes
were significant only for the set\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Region\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Length\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Ancestral variants\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Omicron BA.2-like\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Omicron BA.2-like\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Other Omicrons\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Other Omicrons\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Diff\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
FDR\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Diff\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
FDR\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NSP4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1500\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
962\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
464\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.1127\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Spike\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3822\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
709\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
21\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NSP15\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1038\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
376\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
21\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NSP9\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
339\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1518\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NSP3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
5835\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
467\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.7554\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NSP6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
870\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1162\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NSP12\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2769\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
356\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.1789\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NSP14\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1581\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1781\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.8104\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.0129}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
N\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1260\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
683\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.4540\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.0123}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NS7a\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
366\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
223\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.5782\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.0458}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NSP13\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1803\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1659\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NS9b\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
294\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
444\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.9704\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.0040}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
E\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
228\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2338\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.1030\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.0486}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NSP2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1914\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
455\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.3511\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.0015}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NS9c\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
222\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2474\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.1000\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-7\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.0166}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NS3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
828\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
243\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.0002}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NSP1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
540\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
498\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-7\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{0.0001}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3P\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
370\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
188\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-9\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.9165\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
RNA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
29903\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
744\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{\textless{} 0.0001}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{\textless{}} \textbf{0\emph{.}0001}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 1: The differences in the weighted number of the binding motifs
for colon-specific miRNA within various coding regions of SARS-CoV-2 RNA
genome. All weighted numbers are averaged by variants of the groups and
normalized by ``Wuhan'' RNA length. ``Diff'' columns contain differences
between the averaged values of the corresponding groups. ``FDR'' columns
contain adjusted p-values of Mann-Whitney U tests comparing the weighted
numbers of virus variants of the corresponding groups. of miRNAs
expressed in the intestinal tissue (\emph{p} [?] 2\emph{.}50 \selectlanguage{ngerman}·
10\selectlanguage{english}\textsuperscript{-2}). Coronaviruses are generally perceived as Acute
Respiratory Infections (ARIs), and the viral production in the lungs
provides for a reservoir underlining the virus spread. In addition to
the lungs, SARS-CoV-2 is capable of replication in many other organs and
tissues {[}40{]}, the main of which is an intestine, where the virus may
reside for up to three month {[}41{]}. Prolonged period of intestinal
replication provides sufficient time for evolutionary forces to work on
the population of the viruses within the same host, with the host
factors serving as the drivers for such evolution. Arguably, binding of
host miRNAs represents such a force. Resultant viral populations may be
returned to circulating pool of the viruses either through the
fecal-oral route {[}42{]} or during secondary viremia in the
immunocompromised hosts capable of supporting internal reinfection of
the lungs {[}43,44{]}, and subsequent airborne spread. It has been shown
that the initial version of Omicron variant shared a high similarity
with the variants of the SARS-CoV-2 virus found in rodents, particularly
in mice {[}45{]}. Therefore, it has been hypothesized that one of the
ancestral variants of SARS-CoV-2, from approximately mid-2020 {[}46{]},
has entered the population of rodents and has evolved within these
animals for quite some time till reemerging in humans {[}47,48{]}. An
alternative theory suggests a longterm virus evolution within an
immunocompromised patient, predominantly in the intestines {[}49{]}. An
observation described in this study highlights the significance of the
intestinal site for the pathogenesis of COVID-19. Although pneumonia and
respiratory complications account for the most of the morbidity and
mortality of COVID-19, extrapulmonary manifestations of COVID-19
infection include prominently diarrhea, as well as nausea, emesis,
anorexia, abdominal pain, and heartburn {[}41{]}. These complications
are observed in 10\%-20\% of patients with COVID-19 {[}50{]}, which does
not exclude the possibility of asymptomatic gastrointestinal infections
with SARS-CoV-2. Moreover, the latent persistence of SARS-CoV-2 in the
intestine may be one of the contributors to the long COVID {[}51{]}. A
dramatic decrease in miRNA binding sites was especially pronounced in
BA.2-like SARS-CoV-2 variants. To a large extent, this drop was due to
the mutations within the region encoding the NSP4 protein, which
promotes the formation of double-membrane vesicles serving as a scaffold
for replication/transcriptional complexes {[}52{]} of the virus.
Additionally, attachment to NSP4 protects viral RNA from intracellular
immunity {[}53{]}. Along with dramatic changes in S-protein, the
miRNA-driven alterations of NSP4, acquired during prolonged replication
with the intestine, may have contributed to the current global dominance
of the descendants of the BA.2 variant {[}54{]}. Findings highlight the
possibility that intestinal tissue may significantly impact evolution of
the SARS-CoV-2 genome and may play a pivotal role in the long COVID.

\section*{5 Acknowledgements}

{\label{acknowledgements}}

The authors thank Dr. Stepan Nersisyan from Computational Medicine
Center at Thomas Jefferson University for useful comments and
discussions.

\textbf{6 Conflict of interest}

Dr. Alexander Tonevitsky is an employee of Art Photonics GmbH.

\section*{7 Funding}

{\label{funding}}

The research was performed within the framework of the ``Creation of
Experimental Laboratories in the Natural Sciences Program'' and Basic
Research Program at HSE University.

\section*{References}

{\label{references}}

M. Lotfi, M. R. Hamblin, and N. Rezaei, ``Covid-19: Transmission,
prevention, and potential therapeutic opportunities,'' \emph{Clinica
chimica acta}, vol. 508, pp. 254--266, 2020.

J. Chen, R. Wang, M. Wang, and G.-W. Wei, ``Mutations strengthened

sars-cov-2 infectivity,'' \emph{Journal of molecular biology}, vol. 432,
no. 19, pp. 5212-- 5226, 2020.

\begin{enumerate}
\tightlist
\item
  K. Tao, P. L. Tzou, J. Nouhin, R. K. Gupta, T. de Oliveira, S. L.
  Kosakovsky Pond, D. Fera, and R. W. Shafer, ``The biological and
  clinical significance of emerging sars-cov-2 variants,'' \emph{Nature
  Reviews Genetics}, vol. 22, no. 12, pp. 757--773, 2021.
\item
  S. Elbe and G. Buckland-Merrett, ``Data, disease and diplomacy:
  Gisaid's innovative contribution to global health,'' \emph{Global
  challenges}, vol. 1, no. 1, pp. 33--46, 2017.
\item
  Y. Cao, J. Wang, F. Jian, T. Xiao, W. Song, A. Yisimayi, W. Huang, Q.
  Li, P. Wang, R. An, \emph{et al.}, ``Omicron escapes the majority of
  existing sars-cov-2 neutralizing antibodies,'' \emph{Nature}, vol.
  602, no. 7898, pp. 657--663, 2022.
\item
  T. K. Scheel, J. M. Luna, M. Liniger, E. Nishiuchi, K. Rozen-Gagnon,
  A. Shlomai, G. Auray, M. Gerber, J. Fak, I. Keller, \emph{et al.}, ``A
  broad rna virus survey reveals both mirna dependence and functional
  sequestration,'' \emph{Cell host \& microbe}, vol. 19, no. 3, pp.
  409--423, 2016.
\item
  S. Yekta, I.-h. Shih, and D. P. Bartel, ``Microrna-directed cleavage
  of hoxb8 mrna,'' \emph{Science}, vol. 304, no. 5670, pp. 594--596,
  2004.
\item
  H. Ingle, S. Kumar, A. A. Raut, A. Mishra, D. D. Kulkarni, T.
  Kameyama, A. Takaoka, S. Akira, and H. Kumar, ``The microrna mir-485
  targets host and influenza virus transcripts to regulate antiviral
  immunity and restrict viral replication,'' \emph{Science signaling},
  vol. 8, no. 406, pp. ra126--ra126, 2015.
\item
  J. Huang, F. Wang, E. Argyris, K. Chen, Z. Liang, H. Tian, W. Huang,
  K. Squires, G. Verlinghieri, and H. Zhang, ``Cellular micrornas
  contribute to hiv-1 latency in resting primary cd4+ t
  lymphocytes,''\emph{Nature medicine}, vol. 13, no. 10, pp. 1241--1247,
  2007.
\item
  S. A. Nersisyan, S. Myu, O. Ai, and V. Vi, ``Role of ace2/tmprss2
  genes regulation by intestinal microrna isoforms in the covid-19
  pathogenesis,'' \emph{Bulletin of Russian State Medical University},
  no. 2, pp. 16--18, 2020.
\item
  W. J. Lukiw, ``microrna heterogeneity, innate-immune defense and the
  efficacy of sars-cov-2 infection---a commentary,'' \emph{Non-coding
  RNA}, vol. 7, no. 2, p. 37, 2021.
\item
  M. A.-A.-K. Khan, M. R. U. Sany, M. S. Islam, and A. B. M. M. K.
  Islam, ``Epigenetic regulator mirna pattern differences among
  sars-cov, sars-cov-2, and sars-cov-2 world-wide isolates delineated
  the mystery behind the epic pathogenicity and distinct clinical
  characteristics of pandemic covid-19,'' \emph{Frontiers in genetics},
  vol. 11, p. 765, 2020.
\item
  M. D. S. Demirci and A. Adan, ``Computational analysis of
  micrornamediated interactions in sars-cov-2 infection,'' \emph{PeerJ},
  vol. 8, p. e9369, 2020.
\item
  K. J. Capistrano, J. Richner, J. Schwartz, S. K. Mukherjee, D. Shukla,
  and A. R. Naqvi, ``Host micrornas exhibit differential propensity to
  interact with sars-cov-2 and variants of concern,'' \emph{Biochimica
  et Biophysica Acta (BBA)-Molecular Basis of Disease}, vol. 1869, no.
  2, p. 166612, 2023.
\item
  J.-Q. Zhou, G.-X. Liu, X.-L. Huang, and H.-T. Gan, ``The importance of
  fecal nucleic acid detection in patients with coronavirus disease
  (covid-19): A systematic review and meta-analysis,'' \emph{Journal of
  Medical Virology}, vol. 94, no. 6, pp. 2317--2330, 2022.
\item
  A. Zollner, R. Koch, A. Jukic, A. Pfister, M. Meyer, A. Ro"ssler, J.
  Kimpel, T. E. Adolph, and H. Tilg, ``Postacute covid-19 is
  characterized by gut viral antigen persistence in inflammatory bowel
  diseases,'' \emph{Gastroenterology}, vol. 163, no. 2, pp. 495--506,
  2022.
\item
  S. Nersisyan, A. Zhiyanov, M. Shkurnikov, and A. Tonevitsky, ``T-cov:
  a comprehensive portal of hla-peptide interactions affected by
  sars-cov-2 mutations,'' \emph{Nucleic acids research}, vol. 50, no.
  D1, pp. D883--D887, 2022.
\item
  S. Khare, C. Gurry, L. Freitas, M. B. Schultz, G. Bach, A. Diallo, N.
  Akite, J. Ho, R. T. Lee, W. Yeo, \emph{et al.}, ``Gisaid's role in
  pandemic response,'' \emph{China CDC Weekly}, vol. 3, no. 49, p. 1049,
  2021.
\item
  A. O'Toole, E. Scher, A. Underwood, B. Jackson, V. Hill, J. T.
  McCrone,' R. Colquhoun, C. Ruis, K. Abu-Dahab, B. Taylor, \emph{et
  al.}, ``Assignment of epidemiological lineages in an emerging pandemic
  using the pangolin tool,'' \emph{Virus evolution}, vol. 7, no. 2, p.
  veab064, 2021.
\item
  A. Grimson, K. K.-H. Farh, W. K. Johnston, P. Garrett-Engele, L. P.
  Lim, and D. P. Bartel, ``Microrna targeting specificity in mammals:
  determinants beyond seed pairing,'' \emph{Molecular cell}, vol. 27,
  no. 1, pp. 91--105, 2007.
\item
  D. Baek, J. Vill'en, C. Shin, F. D. Camargo, S. P. Gygi, and D. P.
  Bartel, ``The impact of micrornas on protein output,'' \emph{Nature},
  vol. 455, no. 7209, pp. 64--71, 2008.
\item
  V. Agarwal, G. W. Bell, J.-W. Nam, and D. P. Bartel, ``Predicting
  effective microrna target sites in mammalian mrnas,'' \emph{elife},
  vol. 4, p. e05005, 2015.
\item
  W. Liu and X. Wang, ``Prediction of functional microrna targets by
  integrative modeling of microrna binding and target expression
  data,''\emph{Genome}
\end{enumerate}

\emph{biology}, vol. 20, pp. 1--10, 2019.

\begin{enumerate}
\tightlist
\item
  K. C. Miranda, T. Huynh, Y. Tay, Y.-S. Ang, W.-L. Tam, A. M. Thomson,
  B. Lim, and I. Rigoutsos, ``A pattern-based method for the
  identification of microrna binding sites and their corresponding
  heteroduplexes,'' \emph{Cell}, vol. 126, no. 6, pp. 1203--1217, 2006.
\item
  S. Nersisyan, A. Gorbonos, A. Makhonin, A. Zhiyanov, M. Shkurnikov,
  and A. Tonevitsky, ``isomirtar: a comprehensive portal of pan-cancer
  5'-isomir targeting,'' \emph{PeerJ}, vol. 10, p. e14205, 2022.
\item
  S. Nersisyan, A. Zhiyanov, N. Engibaryan, D. Maltseva, and A.
  Tonevitsky,
\end{enumerate}

``A novel approach for a joint analysis of isomir and mrna expression
data reveals features of isomir targeting in breast
cancer,''\emph{Frontiers in Genetics}, vol. 13, 2022.

\begin{enumerate}
\tightlist
\item
  K. Katoh, K. Misawa, K.-i. Kuma, and T. Miyata, ``Mafft: a novel
  method for rapid multiple sequence alignment based on fast fourier
  transform,'' \emph{Nucleic acids research}, vol. 30, no. 14, pp.
  3059--3066, 2002.
\item
  H. B. Mann and D. R. Whitney, ``On a test of whether one of two random
  variables is stochastically larger than the other,'' \emph{The annals
  of mathematical statistics}, pp. 50--60, 1947.
\item
  J. H. Zar, ``Significance testing of the spearman rank correlation
  coefficient,'' \emph{Journal of the American Statistical Association},
  vol. 67, no. 339, pp. 578--580, 1972.
\item
  C. Spearman, ``The proof and measurement of association between two
  things,'' \emph{The American Journal of Psychology}, vol. 100, no.
  3/4, pp. 441--471, 1987.
\item
  P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D.
  Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright,\emph{et
  al.}, ``Scipy 1.0: fundamental algorithms for scientific computing in
  python,'' \emph{Nature methods}, vol. 17, no. 3, pp. 261--272, 2020.
\item
  C. R. Harris, K. J. Millman, S. J. Van Der Walt, R. Gommers, P.
  Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J.
  Smith,\emph{et al.}, ``Array programming with numpy,'' \emph{Nature},
  vol. 585, no. 7825, pp. 357--362, 2020.
\item
  O. Tange \emph{et al.}, ``Gnu parallel-the command-line power
  tool,''\emph{The USENIX Magazine}, vol. 36, no. 1, pp. 42--47, 2011.
\item
  W. McKinney \emph{et al.}, ``Data structures for statistical computing
  in python,'' in \emph{Proceedings of the 9th Python in Science
  Conference}, vol. 445, pp. 51--56, Austin, TX, 2010.
\item
  J. D. Hunter, ``Matplotlib: A 2d graphics
  environment,''\emph{Computing in science \& engineering}, vol. 9, no.
  03, pp. 90--95, 2007.
\item
  M. L. Waskom, ``seaborn: statistical data
  visualization,''\emph{Journal of Open Source Software}, vol. 6, no.
  60, p. 3021, 2021.
\item
  B. D. Brown and L. Naldini, ``Exploiting and antagonizing microrna
  regulation for therapeutic and experimental
  applications,''\emph{Nature Reviews Genetics}, vol. 10, no. 8, pp.
  578--585, 2009.
\item
  R. Suzuki, D. Yamasoba, I. Kimura, L. Wang, M. Kishimoto, J. Ito, Y.
  Morioka, N. Nao, H. Nasser, K. Uriu, \emph{et al.}, ``Attenuated
  fusogenicity and pathogenicity of sars-cov-2 omicron
  variant,''\emph{Nature}, vol. 603, no. 7902, pp. 700--705, 2022.
\item
  U. Bissels, S. Wild, S. Tomiuk, A. Holste, M. Hafner, T. Tuschl, and
  A. Bosio, ``Absolute quantification of micrornas by using a universal
  reference,'' \emph{Rna}, vol. 15, no. 12, pp. 2375--2384, 2009.
\item
  J. Van Cleemput, W. van Snippenberg, L. Lambrechts, A. Dendooven, V.
  D'Onofrio, L. Couck, W. Trypsteen, J. Vanrusselt, S. Theuns, N.
  Vereecke, \emph{et al.}, ``Organ-specific genome diversity of
  replication-competent sars-cov-2,'' \emph{Nature communications}, vol.
  12, no. 1, p. 6612, 2021.
\item
  M. H. Cha, M. Regueiro, and D. S. Sandhu, ``Gastrointestinal and
  hepatic manifestations of covid-19: A comprehensive
  review,''\emph{World journal of}
\end{enumerate}

\emph{gastroenterology} , vol. 26, no. 19, p. 2323, 2020.

\begin{enumerate}
\tightlist
\item
  Y. Akiyama, N. Kinoshita, K. Sadamasu, M. Nagashima, I. Yoshida, Y.
  Kusaba, T. Suzuki, M. Nagashima, M. Ishikane, J. Takasaki, \emph{et
  al.}, ``A pilot study on viral load in stool samples of patients with
  covid-19 suffering from diarrhea,'' \emph{Japanese Journal of
  Infectious Diseases}, vol. 75, no. 1, pp. 36--40, 2022.
\item
  D. A. Schwartz, S. B. Mulkey, and D. J. Roberts, ``Sars-cov-2
  placentitis, stillbirth, and maternal covid-19 vaccination:
  clinical--pathologic correlations,'' \emph{American journal of
  obstetrics and gynecology}, 2022.
\item
  F. Facchetti, M. Bugatti, E. Drera, C. Tripodo, E. Sartori, V.
  Cancila, M. Papaccio, R. Castellani, S. Casola, M. B.
  Boniotti,\emph{et al.}, ``Sars-cov2 vertical transmission with adverse
  effects on the newborn revealed through integrated
  immunohistochemical, electron microscopy and molecular analyses of
  placenta,''\emph{EBioMedicine}, vol. 59, p. 102951, 2020.
\item
  C. Wei, K.-J. Shan, W. Wang, S. Zhang, Q. Huan, and W. Qian,
  ``Evidence for a mouse origin of the sars-cov-2 omicron
  variant,''\emph{Journal of genetics}
\end{enumerate}

\emph{and genomics}, vol. 48, no. 12, pp. 1111--1121, 2021.

\begin{enumerate}
\tightlist
\item
  D. P. Martin, S. Lytras, A. G. Lucaci, W. Maier, B. Gru"ning, S. D.
  Shank, S. Weaver, O. A. MacLean, R. J. Orton, P. Lemey, \emph{et al.},
  ``Selection analysis identifies clusters of unusual mutational changes
  in omicron lineage ba. 1 that likely impact spike
  function,''\emph{Molecular biology and evolution}, vol. 39, no. 4, p.
  msac061, 2022.
\item
  Y. Sun, W. Lin, W. Dong, and J. Xu, ``Origin and evolutionary analysis
  of the sars-cov-2 omicron variant,'' \emph{Journal of biosafety and
  biosecurity}, vol. 4, no. 1, pp. 33--37, 2022.
\item
  S. Mallapaty, ``Where did omicron come from? three key
  theories.,''\emph{Nature}, pp. 26--28, 2022.
\item
  B. Choi, M. C. Choudhary, J. Regan, J. A. Sparks, R. F. Padera, X.
  Qiu, I. H. Solomon, H.-H. Kuo, J. Boucau, K. Bowman, \emph{et al.},
  ``Persistence and evolution of sars-cov-2 in an immunocompromised
  host,'' \emph{New England Journal of Medicine}, vol. 383, no. 23, pp.
  2291--2293, 2020.
\item
  F. A. F. d. Silva, B. B. d. Brito, M. L. C. Santos, H. S. Marques, R.
  T. d. Silva Ju'nior, L. S. d. Carvalho, E. S. Vieira, M. V. Oliveira,
  and F. F. d. Melo,
\end{enumerate}

``Covid-19 gastrointestinal manifestations: a systematic
review,''\emph{Revista da Sociedade Brasileira de Medicina Tropical},
vol. 53, 2020.

\begin{enumerate}
\tightlist
\item
  H. Crook, S. Raza, J. Nowell, M. Young, and P. Edison, ``Long
  covid---mechanisms, risk factors, and management,'' \emph{bmj}, vol.
  374, 2021.
\item
  D. E. Gordon, G. M. Jang, M. Bouhaddou, J. Xu, K. Obernier, K. M.
  White, M. J. O'Meara, V. V. Rezelj, J. Z. Guo, D. L. Swaney, \emph{et
  al.}, ``A sars-cov-2 protein interaction map reveals targets for drug
  repurposing,'' \emph{Nature}, vol. 583, no. 7816, pp. 459--468, 2020.
\item
  K. Vermeire, T. W. Bell, V. Van Puyenbroeck, A. Giraut, S. Noppen, S.
  Liekens, D. Schols, E. Hartmann, K.-U. Kalies, and M. Marsh, ``Signal
  peptide-binding drug as a selective inhibitor of co-translational
  protein translocation,'' \emph{PLoS biology}, vol. 12, no. 12, p.
  e1002011, 2014.
\item
  J. Zou, C. Kurhade, S. Patel, N. Kitchin, K. Tompkins, M. Cutler, D.
  Cooper, Q. Yang, H. Cai, A. Muik, \emph{et al.}, ``Neutralization of
  ba. 4--ba. 5, ba. 4.6, ba.
\end{enumerate}

2.75. 2, bq. 1.1, and xbb. 1 with bivalent vaccine,'' \emph{New England
Journal of Medicine}, 2023.

\textbf{Hosted file}

\verb`berlin-covid-weighted-plus-time-1200-cmyk-compressed.tiff`  available at \url{https://authorea.com/users/604266/articles/634237-the-signature-of-sars-cov-2-evolution-reflects-selective-pressures-within-human-guts}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/berlin-miRNA-covid-1200-cmyk-compressed/berlin-miRNA-covid-1200-cmyk-compressed}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`berlin-seed-distribution-early-omicron2-1200-cmyk-compressed.tiff`  available at \url{https://authorea.com/users/604266/articles/634237-the-signature-of-sars-cov-2-evolution-reflects-selective-pressures-within-human-guts}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/berlin-strains-1200-cmyk-compressed/berlin-strains-1200-cmyk-compressed}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

