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\begin{document}

\title{Use molecular barcodes to study emerging infectious diseases}



\author[1]{Heng-Chang Chen}%
\affil[1]{Institut de Génétique Humaine}%


\vspace{-1em}



  
  \date{May 14, 2024}


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\selectlanguage{english}
\begin{abstract}
The appearance for viruses that evolve to adapt to a new living niche
often reflect on viral sequence changes. Fixation of these changes may
require a long time through repeated transmission, thereby rendering a
reduced size of an effective population harboring dominant alterations
in their sequence spaces. Those approaches, with which we can closely
monitor and survey the transient changes of viral sequences over the
longer timescales, thus become a requisite to better understand the
evolution of viral pathogenicity. Molecular barcodes are a powerful and
practical molecular tool to individually label sequences, allowing for
correcting sequencing errors and identifying true mutants of interest
with a single nucleotide resolution. Molecular barcoding has also been
implemented as a useful approach to study several zoonotic viruses. In
this review, the emphasis will not only be limited to summarize current
studies focusing on viral pathogenesis and fitness; we will also propose
ideas that molecular barcodes can be used to execute surveillance of
changes of viral sequences. We believe that this review will be helpful
for the readers to better understand the rationale and the usage of
molecular barcodes and the perspectives of what molecular barcodes can
do for fighting upcoming emerging infectious diseases.%
\end{abstract}%



\sloppy


\textbf{Introduction}

While looking back to our history, outbreaks of infectious diseases
always bring tragedy for humans. Even though we have been able to
conquer most of them, several remain to circulate in human populations
and emerge from time to time. Recent outbreak of novel coronavirus
diseases 2019 (COVID-19)\textsuperscript{{[}1--3{]}}caused by
coronaviruses has made us more alert to the emergence of infectious
diseases originating from animal reservoirs and transmitting between
animals and people (so called zoonotic diseases). It is known that
nearly two-thirds of emerging infectious diseases (EIDs) have their
origins in animals\textsuperscript{{[}4--6{]}}. In the U.S. zoonotic
diseases of most concern include zoonotic influenza, salmonellosis, West
Nile virus, plague, emerging coronaviruses, rabies, brucellosis and Lyme
disease\textsuperscript{{[}4--6{]}}. Other EIDs, such as human
immunodeficiency virus type 1 (HIV-1) infections, \emph{Escherichia}
coli O157:H7, hantavirus, dengue fever and the Zika virus are also a
significant burden on public health and global economies at present.
Therefore, the way how to closely surveil and to efficiently control
EIDs for pandemic prevention are urgent to acquire. In this review
article, we will focus on EIDs caused by viruses.

Relative to DNA viruses, RNA viruses have high rates of
mutation\textsuperscript{{[}7{]}}due to, in part, the high error-prone
and low-fidelity of the RNA-dependent RNA polymerases that replicate
their genomes\textsuperscript{{[}8{]}}, subsequently attributing to
viral sequence changes. These changes are somehow essential and
necessary for viruses to maintain their fitness, especially allowing
viruses to frequently undergo host switching under different selection
pressures\textsuperscript{{[}9{]}}. On a per-site level, it is known
that virus sequence change is often dominated by synonymous nucleotide
substitutions in coding regions; the protein sequence is thus
unaffected\textsuperscript{{[}10{]}}. In contrast, nonsynonymous
substitutions that change protein sequences frequently result in
changing physicochemical properties of amino acids, thereby bringing a
much greater effect on an individual. Take severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2) as an example, the most common type
of nonsynonymous mutation observed is alanine to valine (Ala -
Val)\textsuperscript{{[}11{]}}. Either way, substituted nucleotides that
retrain after repeated circulation within a population are sort of
\emph{imprints} , reflecting how viruses adapt as a host niche changes
throughout evolutionary timescales. It is important to stress the point
that these changes in the viral sequence space can however be not
straightforward; some substituted nucleotides may disappear during the
period of this evolutionarily transient process; others may remain until
the viruses can fully adapt to new hosts. Therefore, in the second part
of this review we will focus on the potential of the molecular barcoding
technology, a systematic and quantitative approach, with which we will
be able to experimentally follow up sequential changes of the viral
genomic sequences at a single-sequence level, being indispensable to
dissect the molecular basis of any present and upcoming EID caused by
emerging- and newly discovered viruses.

Molecular barcoding has been invented as a useful tool to investigate
population diversity. The molecular barcoding strategy has first been
proposed to solve the problems of PCR duplications and to improve the
accuracy of next generation sequencing
quantification\textsuperscript{{[}12--15{]}}. In the past, molecular
barcodes have been given various names, such as unique identifier,
unique molecular identifier (UMI)\textsuperscript{{[}16{]}}, primer
ID\textsuperscript{{[}17{]}}and duplex barcodes. Molecular barcodes are
commonly in the string form of random nucleotides, partially degenerate
nucleotides, or defined nucleotides. The concept of molecular barcodes
is that each original DNA or RNA fragment, within the same pool of the
samples, is tagged with a unique sequence of molecular
barcodes\textsuperscript{{[}18{]}}. Sequence reads that contain
different molecular barcodes illustrate different origins of molecules,
whereas sequence reads with the same molecular barcodes are the result
of PCR duplication from the same original
molecule\textsuperscript{{[}18{]}}. The length of molecular barcodes can
vary (normally 4 - 20 base pairs): with a longer sequence of molecular
barcodes we have a lower probability of identical barcodes present
between two or more sequence reads. By employing molecular barcodes, we
can thus possibly distinguish PCR artifacts from sequence variants
present in different original molecules\textsuperscript{{[}13,19{]}}.

In the past ten years, technological progress of the molecular barcoding
strategy has been made to reach the resolution at a single-molecule
level\textsuperscript{{[}12,13,16,17,20--22{]}}and detect low-frequency
and subclonal variations\textsuperscript{{[}23{]}}. This strategy can
now be applied to study viruses in many aspects, for example, viral
transmission\textsuperscript{{[}24{]}}, transcriptomics analyses of
viruses\textsuperscript{{[}25{]}}, evolutionary
dynamics\textsuperscript{{[}26{]}}, diagnostics of infectious
diseases\textsuperscript{{[}27{]}}, viral capsid
functions\textsuperscript{{[}28{]}}as well as the analysis of a viral
gene\textsuperscript{{[}17{]}}. In this review we summarize several
examples of studies, in which molecular barcodes are used to understand
the molecular bases and viral fitness\textsuperscript{{[}29--31{]}}of
zoonotic viruses with the emphasis placed on SARS-CoV-2, HIV-1,
influenza virus and Zika virus followed by elucidating our ideas about
how molecular barcodes can be applied to closely survey and predict
evolutionary of dynamic sequence changes of other emerging- and new
discovered viruses \emph{in vitro} .

\textbf{Main text}

\textbf{Principle and design of molecular barcodes tagged on the viral
genome}

The molecular barcoding strategy has been successfully employed in
``omics'' techniques for virus studies (summarized in Table 1). The
principle of molecular barcoding is to tag the viral genome with a
string of random nucleotides in order to distinguish one virus from
another. Molecular barcodes can be introduced into a template by using
different approaches. First, molecular barcodes can be embedded into
sequencing adaptors while constructing sequencing libraries (Figure 1A).
A classical example was given by Schmitt et al.
(2012)\textsuperscript{{[}19{]}}. They constructed sequencing libraries
with two independent molecular barcodes associated at both ends of each
library to correct sequencing errors present on every sequencing read.
It is noteworthy to point out the difference between ``molecular
barcodes'' and ``sample barcodes (sample indexes)'' here. Although both
are common to be used in the same sequencing reads, molecular barcodes
aim to correct sequencing errors and increase sequencing accuracy or
define true mutations; whereas sample barcodes enable multiple samples
to be sequenced together, so called multiplexed into the same lane of
flow cells (Figure 1B). The second approach is to employ molecular
inversion probes (MIP) carrying molecular barcodes. A good example of
this approach is the single molecule Molecular Inversion Probes
method\textsuperscript{{[}23{]}}(Figure 1C), which combines the MIP
strategy for targeted capture\textsuperscript{{[}32--34{]}}at a
single-molecule level\textsuperscript{{[}12,13,16,17,21--22{]}}. Third,
molecular barcodes can also be introduced on a template by PCR
amplification with target-specific primers (Figure 1D).

\textbf{Estimation barcode collision}

Molecular barcode sequences can be altered during barcode synthesis,
primer ligation, PCR amplification and sequencing errors, leading to
incorrect sample identification. These errors can be either nucleotide
substitutions or small insertions and
deletions\textsuperscript{{[}35{]}}. It is thus critical to take barcode
complexity into account while generating a barcoded library in order to
diminish the negative impact on the accuracy of readouts when such
circumstances happen. In general, barcode complexity determines the
quality of sequencing libraries: the higher barcode complexity of a
library is, the better experimental outcomes can reflect natural
phenomena. It is also recommended to implement error correcting
algorithms and codes in every analytical pipeline while decoding barcode
sequences. Hamming codes\textsuperscript{{[}36,37{]}}and Levenshtein
codes\textsuperscript{{[}38{]}} are two popular methods for
error-correcting. Furthermore, evaluation of the influence of molecular
barcodes on the capacity of a virus to fulfill its life cycle, so-called
viral fitness\textsuperscript{{[}29--31{]}}should be considered as well.
Below we will detail approaches used to embed molecular barcodes in
SARS-CoV-2, HIV-1 and Simian immunodeficiency virus (SIV), Influenza
virus and Zika virus.

\textbf{Molecular barcodes designed for virus studies}

\emph{SARS-CoV-2}

SARS-CoV-2 continues to threaten our lives after more than two years of
the COVID-19 outbreak. Up to date, more than 500 millions cases of
COVID-19 have been reported, including more than six millions deaths
worldwide, numbers that increase daily\textsuperscript{{[}39{]}}. One of
the major challenges to suppress local spread of SARS-CoV-2 is due to
the fact that 33\% of people with SARS-CoV-2 infection are estimated to
be asymptomatic\textsuperscript{{[}40{]}}. Population-scale testing thus
appears to be very important for us to effectively detect people with
SARS-CoV-2 infections and rapidly place them in
quarantine\textsuperscript{{[}41--43{]}}at the first place. The success
of population-scale testing relies on how many individual samples can be
synchronously tested. To tackle this issue, several groups have applied
the barcode strategy on the high-throughput sequencing platform to make
their testing capacity as maximal as possible. Ludwig and colleagues
reported a high-throughput technology named LAMP-seq to sequence ten of
millions of individual samples at the same
time\textsuperscript{{[}27{]}}. LAMP-seq is derived from
Reverse-Transcription Loop-mediated Isothermal Amplification
(RT-LAMP)\textsuperscript{{[}44,45{]}}by employing molecular barcodes
specific for each sample. LAMP-seq can be started with an unpurified
swab sample\textsuperscript{{[}46{]}}followed by a single heating step,
extensive sample pooling, massively parallel RT-LAMP, and standard
computational analysis\textsuperscript{{[}27{]}}to identify infected
people. Six different LAMP primers {[}F3, B3, forward inner primer
(FIP), backward inner primer (BIP), LF, LB{]} were used in a RT-LAMP
reaction to target the SARS-CoV-2 N gene; a sequence of 10 base pairs
DNA barcode was incorporated in the FIP primer (primer sequence is
TCTGGCCCAGTTCCTAGGTAGTNNNNNNNNNNCCAGACGAATTCGTGGTGG, where Ns represent
a unique barcode sequence)\textsuperscript{{[}27,47,48{]}}(Figure 2A).
The authors further suggested using the combination of unique barcodes
per sample to form a compressed barcode space, where up to five barcodes
can be present in order to enlarge the capacity for sample pooling as
long as a small fraction of samples is expected to be positive during
population scale testing. The characterization of the sequencing library
is illustrated in Figure 2A. The authors calculated a minimum
Levenshtein distance to ensure that one to two insertion, deletion or
substitution errors between any pair of barcodes were detectable in
barcode sets, indicating that the molecular barcode system employed in
LAMP-seq was robust. It is important to note that the complexity of
molecular barcodes (1,000 - 10,000 barcode set) in LAMP-seq is
sufficient to cover the dynamic range of input viral loads and the
presence of molecular barcodes do not affect LAMP sensitivity, product
amounts, or downstream PCR amplification. Finally, this molecular
barcoding system was validated by employing a modified Bloom
filter\textsuperscript{{[}49{]}}based on a pool of 10,000 barcodes to
estimate the probability of false-positive and false-negative generated
by LAMP-seq. After computational simulation the authors concluded that
when using 5 barcodes per sample, 3 barcodes are detectable.

Bloom and colleagues proposed another high-throughput method named
Swab-Seq by employing molecular barcodes for a population-scale
testing\textsuperscript{{[}50,51{]}}. Two sets of 1,536 unique barcodes
were placed adjacent to the P5 and P7 adaptors in Illumina sequencing
primers (so-called i5 and i7 sample barcodes), respectively, rendering
the presence of two independent barcodes in each amplicon (Figure 2B).
Barcoded primers were designed to amplify the SARS-CoV-2 S gene. Every
barcode is 10 base pairs in length and does not contain homopolymer
repeats greater than 2 nucleotides. At least three nucleotides (a
minimum Levenshtein distance of 3)\textsuperscript{{[}52{]}}are
different between two barcodes present in the same amplicon, allowing
for demultiplexing even in the face of sequencing errors. Swab-Seq has
been validated on the bench by employing purified RNA nasopharyngeal
samples and extraction-free saliva specimens, showing that it has
extremely high sensitivity and specificity for the detection of viral
RNA\textsuperscript{{[}50,53{]}}. Swab-Seq has been scaled up since 2021
to support asymptomatic screening. To date, over 80,000 tests have been
performed by applying this high-throughput protocol, at a scale of
\textasciitilde{}10,000 testing samples per week. More
methods\textsuperscript{{[}53--55{]}}that employ the similar strategy of
molecular barcoding for sample multiplexing, rendering the feasibility
of initiating a population-scale testing have been expected to come up
one after another. At present, the molecular barcoding strategy used for
SARS-CoV-2 studies mainly focus on scaling up the testing capacity
efficiently. How to use molecular barcodes to gain more insight into the
SARS-CoV-2 Virology will be considerable for further investigation.

\emph{HIV and SIV}

Retroviral zoonoses have received public health attention due to the
origin of HIV-1 and human immunodeficiency virus type 2 linked to
cross-species transmission of SIV from naturally infected primates, with
a prevalence ranging up to 36\%\textsuperscript{{[}56,57{]}}. Even
though viruses have been adapted in human society over thirty years, up
to 2020, the estimated number of people living with HIV was 37.7
million\textsuperscript{{[}58,59{]}}, indicating research on HIV/AIDS
(Acquired immunodeficiency syndrome) remains a top priority worldwide.
Jabara and colleagues employed the molecular barcoding strategy (so
called Primer ID in their article) to reveal HIV-1 dynamic changes in
genetic variation\textsuperscript{{[}17{]}}. A string of eight
nucleotides of a molecular barcode followed by a string of three
nucleotides of a sample barcode were incorporated in a reversed primer
which anneals downstream of the HIV-1 protease gene (Figure 3A). Both
barcodes can be followed through the PCR amplification and deep
sequencing and be used to calibrate artificial errors generated by PCR
amplification and high-throughput sequencing and then created a
consensus sequence for individual viral templates. After calibration by
using molecular barcodes, 80\% of the unique sequence polymorphisms were
removed in this dataset\textsuperscript{{[}17{]}}. Of note, the
distribution of identical molecular barcodes did not form a Gaussian
distribution, meaning that each viral genome was not equally amplified.
The authors assumed that this phenomenon was because the number of cDNA
templates amplified in each RNA cycle varies\textsuperscript{{[}17{]}}.

In 2017 the laboratory of Dr. Filion published a high-throughput method
named Barcoded HIV Ensembles (B-HIVE) to monitor the expression of
thousands of proviruses in a cell population\textsuperscript{{[}25{]}}.
The principle of B-HIVE is to tag the HIV genome with a molecular
barcode of 20 random nucleotides prior to generating infectious viral
particles (Figure 3B)\textsuperscript{{[}25,60{]}}. Advantages of
molecular barcodes implemented in B-HIVE are two-folds. First, molecular
barcodes were used to map HIV integration sites by generating a
high-throughput sequencing library, in which every chimeric DNA is
composed of the host local genomic region where provirus DNA inserts.
Second, molecular barcodes were used to quantify HIV transcription by
performing RT-PCR on viral RNA with the primers flanking the region of a
barcode\textsuperscript{{[}25,60{]}}. The authors verified the
insert-specific provirus expression measured by B-HIVE by using another
method called T7-PCR: a positive correlation between RNA barcode counts
measured by B-HIVE and RT-qPCR values measured by T7-PCR for the
selected barcodes were detected\textsuperscript{{[}25,60{]}}. It is
worthy noting that all the possible 20-mers represent enough complexity
(4\textsuperscript{20} distinct barcode combinations) so that the
probability of two viruses having the same barcode is negligible. The
authors also observed that the presence of the barcodes did not
interfere with the efficiency of HIV (HIV-based vector) infection; side
by side comparisons of infection rates between barcoded- and
non-barcoded viral particles showed that barcoded viral particles were
only \textasciitilde{}5\% less infectious\textsuperscript{{[}25{]}}.

Marsden and colleagues have successfully tagged molecular barcodes on
the genome of nearly full-length and replication-competent
HIV\textsuperscript{{[}61{]}}. This HIV construct is derived from NL4-3,
which has been modified to express an NL-hemagglutinin (HA) epitope in
place of Vpr for the purpose of enriching HIV-infected
cells\textsuperscript{{[}62{]}}. Molecular barcodes encompassing 21
nucleotides in length were introduced in the region right downstream of
the HA tag (Figure 3C); this region is not responsible for any gene
expression and lack for any cis elements as well as splice sites (Figure
3C). It is important to note that a thymidine was designed to be placed
in the sequence of barcodes with an interval of 2 nucleotides to avoid
random strings of consecutive GC bases, subsequently resulting in the
complexity of barcodes down to 4\textsuperscript{14} combinations
(Figure 3C). The authors verified the complexity of barcodes by
employing Illumina HiSeq sequencing, showing that around 18,000 unique
sequences of barcodes were homogeneous present in the original plasmid
preparation. The average number of different nucleotides between any two
barcodes was 11 base pairs, showing an appreciable diversity of
barcodes. Moreover, the frequency of barcodes present in virions
correlated with the frequency of barcodes present in the original
plasmid preparation, meaning that the complexity of barcodes is able to
be recapitulated in infectious particles\textsuperscript{{[}61{]}}.
Viral infectivity and replication were validated in GHOST CXCR4+CCR5+
cells and primary human peripheral blood mononuclear cells, showing that
barcoded viruses are capable of infecting and replicating in primary
cells without causing any bias in the presence of
barcodes\textsuperscript{{[}61{]}}.

Apart from HIV, Fennessey and colleagues first established a model of
rhesus macaques infected with barcoded SIV to study the dynamics of
viral reservoir establishment and viral rebound over
time\textsuperscript{{[}63{]}}. Molecular barcodes encompassing 10
random nucleotides in length were inserted between the stop codon of
\emph{vpx} and the start codon of\emph{vpr} in the SIVmac239 plasmid
(Figure 3D). Since only one restriction enzyme, MluI, was chosen for
cloning barcodes, insertion of each barcode can thus be bidirectional,
consequently doubling the complexity of the barcodes in viral stocks. In
order to accurately estimate the complexity of input barcodes named
viral clonotypes in the text, the authors reverse transcribed viral RNA
into cDNA, which was diluted down to 5,000 viral copies in total 168
aliquots in order to avoid any sequencing error. The authors were able
to nicely separate probability distributions between barcodes generated
from PCR-induced errors and true sequences of barcodes (9,336
sequences)\textsuperscript{{[}63{]}}. Every barcode contains an average
of seven distinct nucleotides. Furthermore, the authors also showed that
barcoded SIV did not cause any effect on viral infectivity and
replication.

\emph{Influenza virus}

In addition to HIV, Influenza virus A is another classic example that
the viruses have zoonotic origins with associated public health concern
of host adaptation, permanently establishing in the human
population\textsuperscript{{[}64{]}}. Varble and
colleagues\textsuperscript{{[}24{]}}first barcoded influenza A virus,
with which the authors attempted to understand the probability and
stochastic nature resulting from different transmission routes. Varble
and colleagues\textsuperscript{{[}24{]}}employed molecular barcodes on
the genome of a model influenza A virus to track viral transmission. The
challenge to insert molecular barcodes in the genome of influenza A
virus is because the eight single-stranded RNA segments are all required
for virion production; therefore there is little space for molecular
manipulation. Previously Varble and
colleagues\textsuperscript{{[}65{]}}have chosen to engineer segment 8,
encoding the nonstructural protein 1 (NS1) and the nuclear export
protein (NEP, also referred to as NS2) because of the shorter of two
viral transcripts, facilitating to give any genetic material. Since NS1
and NEP use different reading frames with the overlap between the
C-terminal transcript of NS1 and the N-terminal transcript of NEP, the
authors thus disrupted the endogenous splice acceptor site and placed it
after the stop codon of NS1\textsuperscript{{[}65{]}}(Figure 4A). This
modified segment 8 thus harbored a noncoding intergenic region where a
string of 22 nucleotides of a molecular barcode was inserted (Figure
4A). Barcodes were amplified from the 30 arm of a shRNA
library\textsuperscript{{[}66{]}}, along with 100 base pairs of common
flanking sequence (Figure 4A). The authors confirmed that inserted
molecular barcodes did not affect viral replication in human lung
epithelial cells.

Russel and colleagues\textsuperscript{{[}67{]}}utilized three different
types of barcodes, cell barcodes, UMI and viral barcodes to distinguish
every single mRNA transcript based on the single-cell droplet-based
platform\textsuperscript{{[}67,68{]}}(Figure 4B). Cell barcodes were
used to distinguish one cell from another; UMIs incorporated in primers
were designed to separate each mRNA molecule during the process of
reverse transcription, allowing to quantify the number of molecules of
each mRNA that have been captured for each cell. The challenge here is
how to separate viral- and cellular mRNA generated in the same cell. The
authors thus engineered the viral genomic sequence where two synonymous
mutations were given in the vicinity of the 3' end of each transcript
(Figure 4B) for this discrimination. The transcription of the viral
hemagglutinin from the viruses carrying these synonymous mutations was
compatible with the wild-type viruses\textsuperscript{{[}67{]}},
indicating that synonymous mutations did not affect viral fitness. The
authors successfully used these viral barcodes to distinguish the viral
transcripts carrying mutations from the transcripts produced by the
wild-type viruses\textsuperscript{{[}67{]}}. By employing this
three-barcodes approach, the authors established a threshold determining
truly infected cells and quantitatively investigated stochastic gene
expression of influenza viruses among individual infected
cells\textsuperscript{{[}67{]}}.

\emph{Zika virus}

ZIKV is an arbovirus that consists of an envelope, positive-sense,
single-stranded RNA. Its genome encodes three structural proteins
(capsid, precursor of membrane and envelope) and seven non-structural
proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5) responsible for RNA
replication\textsuperscript{{[}69{]}}. Aliota and colleagues introduced
a string of eight degenerate codons where the third nucleotide of each
codon is degenerated into a region of the NS2A protein to generate as
many synonymous mutations as possible (Figure 5). This barcoded Zika
virus was named ZIKV-BC-1.0\textsuperscript{{[}26{]}}. A multiplex-PCR
approach was applied to sequence the entire coding genome of the
ZIKV-BC-1.0 library. Compared with its original strain, ZIKV-IC, three
single nucleotide mutations (site 1964, 8488 and 9581) were detected
outside of the barcode region in ZIKV-BC-1.0\textsuperscript{{[}26{]}}.

Aliota and colleagues further used three different approaches to define
trustable barcodes referred to as authentic barcodes in their study.
Among these three approaches, the third approach (Approach `C'), with
which the authors acquired the highest threshold frequency (0.57\%) was
placed to be the minimum threshold to identify authentic barcodes. By
using this threshold, 20 sequences in the sequencing output list were
considered to be authentic\textsuperscript{{[}26{]}}. Intriguingly, the
first three barcodes (Zika\_BC01, Zika\_BC02 and Zika\_BC03) were more
dominant than the rest of 17 barcodes when limited input viral RNA was
used for amplification\textsuperscript{{[}26{]}}; however at these stage
it is still quite challenging to explain whether barcode heterogeneity
comes from a potential bias introduced by a barcode itself or the
evolutionary selection. The authors also claimed that the absence of
barcodes in sequencing reads could be due to the fact that either
barcodes were not present in certain biological samples or the copy
number of the barcodes were not sufficient to be detected by sequencing,
meaning that the presence of each barcode in ZIKV-infected animals was
not homogeneous. Viral infectivity and replication of ZIKV-BC-1.0 showed
no difference \emph{in vitro} compared with Zika infectious
clone-derived- and wild-type viruses\textsuperscript{{[}26{]}},
indicating that \emph{in vitro} additional degenerate nucleotides in the
viral genome did not cause a significant effect on viral
fitness.\emph{In vivo} three rhesus macaques were inoculated with
ZIKV-BC-1.0 to validate its replication capacity. Viruses were
detectable in plasma one day post inoculation (dpi) and viral load
elevating between two and four dpi was comparable to ZIKV-IC and
wild-type viruses\textsuperscript{{[}26{]}}, indicating that viral
infectivity and replication was not damaged by molecular barcodes
present in ZIKV-BC-1.0 \emph{in vivo} , neither.

\textbf{What have we learned from molecular barcodes in virus studies?}

\emph{Measure single virus transcriptomics}

It is known that transcription measured by standard methods from a bulk
sample of materials is typically lost upto 20\% variants present in a
population\textsuperscript{{[}70,71{]}}, thereby causing a strong bias
for investigating HIV gene expression which is known for its stochastic
phenotype of transcription\textsuperscript{{[}72,73{]}}. Barcoded HIV
thus provides us a power matter to quantitatively measure
insert-specific provirus transcription at a single-virus level. Taking
the B-HIVE technology as an example, the authors first found that the
transcriptional phenotype of stochastic HIV expression falls under the
concept of `position effects' first discovered by Joseph M\selectlanguage{ngerman}üller in
1930\textsuperscript{{[}74{]}}when he observed that the translocation of
the \emph{Drosophila} white gene to the centromere caused its expression
to fluctuate. This phenomenon remained elusive until the discovery of
histone modifications. On the basis of the concept of position effects,
it turns out that proviruses that integrate in the proximity of active
enhancers (defined by histone H3 lysine 27 acetylation, H3K27ac)
displayed a better transcriptional level compared with those present far
from active enhancers\textsuperscript{{[}25{]}}. This observation was
supported by the previous study, showing that HIV that integrated in
different chromosomal regions displayed different transcription levels
in Jurkat clonal cell lines\textsuperscript{{[}75{]}}and the study
conducted by Vansant and colleagues\textsuperscript{{[}76{]}}, showing
that integration sites retargeted by
`LEDGINs'\textsuperscript{{[}77,78{]}}harbored low HIV transcription
measured by B-HIVE. Another example was given by Russel and
colleagues\textsuperscript{{[}67{]}}, who attempted to study how well
influenza virus genes are expressed in hundreds of individual mammalian
cells in a single-cell scale. By employing the three-barcodes approach
described in the previous section, the authors statistically quantified
viral transcription from every infected cell by using the method called
the Gini coefficient\textsuperscript{{[}79{]}}, a measure of the
difference between a given distribution of variables to represent an
inequality within a group. The authors calculated the Gini coefficient
value that was at least 0.64 from viral mRNA per infected cell,
indicating a detectable variation present in viral gene expression and
concluded that such transcriptional variation was caused by both
cellular factors and inherent stochasticity in addition to viral
transcription itself\textsuperscript{{[}80--82{]}}, which only partially
explained this phenotype.

\emph{Monitor behavior of latent proviruses in the frame of
antiretroviral therapy}

In many cases of viruses, the status of viral transcription shows a
strong correlation to their pathogenesis. Viruses, such as hepatitis B
virus, herpes virus and HIV\textsuperscript{{[}83--85{]}}require a
transcriptionally silent state, so-called latency throughout their life
cycles, allowing viruses to persist in host cells for a long time and
establish chronic infections. In the case of HIV, viral rebound
frequently occurs when the antiretroviral therapy is interrupted,
subsequently leading to the AIDS symptoms resurface. It thus becomes an
obvious obstacle for curing HIV-1 infection. In the previous section we
talked about molecular barcodes used to quantitatively measure single
virus transcription. Here we are going to give several examples that
illustrate how molecular barcodes help us to interfere with translation
research involving antiretroviral therapy in addition to the measurement
of viral transcription.

The ``shock and kill'' antiretroviral therapy has been the most
attractive strategy for purging HIV latent infection as it was first
proposed in 2012\textsuperscript{{[}86{]}}. The concept of this strategy
is to give HIV-1-infected individuals latency reversing agents (LRAs)
such as histone deacetylase inhibitors to deliberately reactivate
proviral transcription in latently infected cells, rendering reactivated
cells destroyed by the host immune system. The success of this strategy
relies on the drugs used to reactivate latent proviruses, yet several
exploratory clinical trials have already shown limited evidence of
efficacy\textsuperscript{{[}87{]}}. Since molecular barcodes allow us to
quantitative measure HIV transcription at a single-virus level, Chen and
colleagues thus attempted to use them for estimating the spectrum of
vorinostat, an histone deacetylase inhibitor used in clinical
trials\textsuperscript{{[}88{]}}as a proof of concept. Comparing with
phytohemagglutinin, a strong activator for T cells, it turned out that
the same provirus can either respond more strongly to phytohemagglutinin
than vorinostat, or\emph{vice versa} , indicating that vorinostat fully
reactivate only a subset of the proviruses in a cell population.
Proviruses that were prone to be reactivated by vorinostat were more
frequently in the vicinity of active regulatory elements (H3K27ac and
histone H3 trimethylated at K4 and monomethylated at
K4)\textsuperscript{{[}25{]}}. This observation was later confirmed by
Battivelli and colleagues\textsuperscript{{[}89{]}}using a second
generation of full-length dual-fluorescence reporter HIV,
HIV\textsubscript{GKO}, to investigate the reactivation potential of
various LRAs, including panobinostat\textsuperscript{{[}90{]}},
JQ1\textsuperscript{{[}91--95{]}}and
bryostatin\textsuperscript{{[}96,97{]}}in pure latent populations. They
showed that less than 5\% of the latently infected primary CD4+ T cells
were able to be reactivated by LRAs and confirmed that the proviruses
refractory to be reactivated appeared to be present closer to
heterochromatin (histone H3 trimethylated at K27 and at K9) and
non-accessible regions (DNase
hyposensitivity)\textsuperscript{{[}89{]}}.

\emph{In vivo} barcoded HIV has also been used to track viral rebounds
in animal models. Marsden and
colleagues\textsuperscript{{[}61{]}}infected bone marrow-liver-thymus
mice, an animal model that can represent the human immune
system\textsuperscript{{[}98{]}}, with barcoded nearly full-length and
replication-competent HIV and measured viral rebound after giving a
synthetic bryostatin analog, SUW133\textsuperscript{{[}99{]}}, which is
a PKC inhibitor\textsuperscript{{[}100{]}}. It was observed that mice
treated with SUW133 showed a delay of viral rebound after antiretroviral
treatment was stopped compared with vehicle control. In addition,
barcode diversity present in reactivated viruses showed a decrease
compared with the mice treated with vehicle control, meaning that the
administration of SUW133 can eliminate a subset of latently infected
cells in this \emph{in vivo} model.

Nonhuman primates are another useful animal model to study retroviruses.
SIV-infected nonhuman primates are similar in their pathophysiology and
viral dynamics to humans infected with
HIV-1\textsuperscript{{[}101--105{]}}and therefore make ideal models for
HIV studies in many aspects. Fennessey and
colleagues\textsuperscript{{[}63{]}}successfully employed the model of
rhesus macaques infected with barcoded SIV to identify and quantify the
dynamics of the establishment of viral reservoir and viral rebound after
combination antiretroviral therapy administration and interruption,
demonstrating that the time of initiation and duration of therapy
administration in nonhuman primates can alter the size of the reservoir.
Soon afterwards Khanal and
colleagues\textsuperscript{{[}106{]}}generated a derivative based on
this barcoded SIV. At present barcoded SIV continues to be applied on
animal models of rhesus macaques to tackle several important questions
in the field of HIV/AIDS in many
aspects\textsuperscript{{[}107--111{]}}.

\emph{Examine evolutionary bottlenecks to depict viral fitness
landscapes}

An evolutionary bottleneck is the event that reduces the size of a
population, limiting the genetic diversity of the species. It occurs
when viruses transmit from one host to another, rendering the
functionally dominant populations remaining in the circulation among
hosts. Molecular barcodes thus become a powerful tool, with which we can
simply trace transmission paths of a population of the viruses over
different hosts at a single-virus level. Influenza virus is one of the
viruses that has subsisted in the human community over a long history.
The most remarkable pandemics caused by influenza virus is the so-called
Spanish flu, or the 1918 H1N1 pandemic, infecting \textasciitilde{}30\%
of the population\textsuperscript{{[}112,113{]}}. Influenza virus A is
endemic in waterfowl and is usually nonpathogenic in these
birds\textsuperscript{{[}114{]}}until the emergence of H5N1 outbreaks in
Asia\textsuperscript{{[}115--117{]}}. Soon afterwards H5N1 influenza
virus is known to be zoonoses, which can transmit from its natural host
to humans\textsuperscript{{[}118--122{]}}. To date, in addition to avian
influenza virus A(H5N1), several zoonotic influenza viruses, such as
avian influenza virus A(H7N9)\textsuperscript{{[}123,124{]}}and
A(H5N6)\textsuperscript{{[}125--127{]}}as well as swine influenza virus
A(H1N1)\textsuperscript{{[}128--131{]}}and
A(H3N2)\textsuperscript{{[}132,133{]}}and its variant
(H3N2v)\textsuperscript{{[}134{]}}have been reported. Rapid antigenic
variation that enables the viruses to escape from neutralizing
antibodies\textsuperscript{{[}135{]}}is frequently observed in influenza
viruses, rendering vaccines not fully effective. It thus a requisite to
understand which variations of the viruses could go through the pressure
of selection bottlenecks in different cross-species transmission routes
before developing a better strategy against influenza virus infection.

With barcoded influenza A virus in hand, Varble and colleagues examined
viral transmission bottlenecks under the selection forces present in
four different experimental systems, including Madin-Darby canine kidney
cells, embryonated eggs, direct-contact transmission in guinea pigs and
ferrets and respiratory-droplet transmission in
ferrets\textsuperscript{{[}24{]}}. In the context of population dynamics
and evolution, the bottleneck concept is often used in reference to
events that limit population size: only individuals which successfully
pass through the bottleneck retrain in a population in a new
environment. The authors detected that bottleneck selection occurred
only when embryonated-chicken eggs were infected with barcoded viruses:
5 - 13 viral clones harboring unique barcodes were successfully
amplified in each egg\textsuperscript{{[}24{]}}. Intriguingly, such
bottlenecks observed in individual recipient animals can be independent
from viral genome sequences. Furthermore, the authors observed that
viral transmission routes affect bottleneck stringency: survival of
barcoded viruses transmitting via contact- and airborne infection showed
a dramatic decrease compared with direct
inoculation\textsuperscript{{[}24{]}}. As such, the authors concluded
that viruses like H5N1 have to overcome two distinct bottlenecks before
the emergence of pandemic. The first one lies on the level of the viral
genome sequence. Viruses acquire genetic mutations and/or reassortments
to maintain replication fitness in recipient
hosts\textsuperscript{{[}136{]}}. Another is dependent on viral
transmission routes. This barcoded influenza A virus has further been
applied by Muñoz-Moreno and colleagues to reveal the evolutionary
relationship between influenza NS1 genotypic variants and host
tropism\textsuperscript{{[}137{]}}.

Zika virus is another example of zoonotic diseases that is through
mosquito-borne transmission. It was first discovered in a Rhesus monkey
and in \emph{Aedes africanus} mosquitoes during a yellow fever study in
the Zika forest of Uganda in 1947 and
1948\textsuperscript{{[}138,139{]}}. Through the past fifteen years
several outbreaks of Zika virus were seen, bringing this virus to the
world's attention. Even though the World Health Organization declared
the end of the Zika virus epidemic in 2016\textsuperscript{{[}140{]}},
it still represents a highly significant and long-term problem. The
virus induces a mild flu-like disease in about one in five infected
individuals\textsuperscript{{[}141--144{]}}; in other words,
approximately 80\% of Zika infections are asymptomatic. Pathologically
speaking, Zika virus can also infect the placenta, transmitting from
pregnant women to fetuses\textsuperscript{{[}145{]}}in addition to
blood-brain barrier endothelial cells, neurons and neural stem
cells\textsuperscript{{[}146,147{]}}. Even though the frequency of
vertical transmission is not clear, current data suggest that it may be
a very common case, especially if infection occurs in the period of the
first trimester\textsuperscript{{[}148{]}}. At least 5\% of babies
contaminated by Zika virus during maternal pregnancy suffered a
collection of neurological, visual, auditory, and developmental birth
defects, so called congenital Zika syndrome\textsuperscript{{[}145{]}}.
It is now also known that Zika virus infection is closely associated
with diseases. In Brazil the outbreak of Zika virus correlated with an
increase in cases of infant microcephaly\textsuperscript{{[}149,150{]}},
and in other parts of the Amercias there has been a significant increase
in Guillain-Barré syndrome coinciding with Zika
infections\textsuperscript{{[}151{]}}.

In order to better understand the \emph{in vivo} replication and
evolutionary dynamics of Zika virus infection, Aliota and
colleagues\textsuperscript{{[}26{]}}employed barcoded Zika viruses to
track and monitor individual viral lineages during acute infection in
pregnant and nonpregnant macaques. The authors first confirmed that
viral RNA from barcoded Zika virus was detectable in placentae. Fetal
histology also showed several pathological symptoms relevant to Zika
virus infections\textsuperscript{{[}26{]}}, meaning that this animal
model with barcoded Zika virus infection can most likely bring us the
picture representing Zika virus infections in humans. Although no
significant changes in the frequency of barcode distribution was
observed in nonpregnant macaques, a bottleneck effect appeared after
seven dpi in pregnant macaques\textsuperscript{{[}26{]}}: the prevalence
of the dominant viral lineages identified by unique barcodes changes
mainly after day 8 post infection. Aliota and colleagues further used
mosquitos fed with \emph{Aedes aegypti} vector competence for barcoded
Zika virus to infect the pregnant macaque. Only one viral lineage,
Zika\_BC02, representing the second most dominant barcode present in the
early period of infection was successfully transmitted via mosquitoes.

\textbf{How molecular barcodes can bring us one step closer for better
understanding and preventing from EIDs}

As mentioned in the previous section that evolutionary bottlenecks
filter out those functionally recessive viruses; the viruses that are
able to pass through the selection pressure of bottlenecks usually
emerge certain genotypic changes, to be more specific, nucleotide
substitutions, allowing the viruses to adapt to a new host. Virus
sequence changes caused by evolutionary constraints are critical for
zoonotic viruses to jump across the barrier between different species;
thereby owning experimental evolution models (Figure 6), with which we
can track dynamic changes of viral sequences in the short term followed
by testing pathogenic phenotypes associated with their genetic
robustness, visualizing fitness landscapes and predicting their
long-term evolutionary paths at a single-sequence level becomes
irreplaceable to better understand pathogenesis of emerging viruses and
to set up antiviral strategies in advance. Below we will share two of
our ideas about what molecular barcodes can bring us one step closer to
dissecting emerging infectious diseases.

\emph{Unveil critical mutations responsible for antiviral drug
resistance, contributing to precision medicine}

Previously Jabara and colleagues have employed molecular barcodes to
create a consensus sequence for individual viral templates amplified
from HIV-1infected individuals and sought for the emergence of multiple
single-nucleotide polymorphisms (SNPs) responsible for
drug-resistance\textsuperscript{{[}17{]}}. In such a way only SNPs
present in the time as materials were collected can be identified;
whether these SNPs have been present from the beginning of the primary
infection or they were the SNPs derived from several rounds of
nucleotide substitutions over the period of the treatment is however not
traceable. Indeed, some substituted nucleotides in virus sequences may
not be essential for the final phenotype and could be replaced due to
the effects from different constraints during the selection process. A
genotype-traceable model for monitoring the dynamics of virus sequence
changes is thus important to recapitulate evolutionary trajectories at a
single-sequence level.

Our idea is that, by using barcoded viruses to infect host cells present
in the selection pressure of antiviral drugs, after several rounds of
passages we will then look for the variants that emerge mutations,
rendering the viruses capable of surviving in the presence of antiviral
drugs. Similar ideas have been applied on mosquito Infection models used
to recapitulate the emerging variant of chikungunya virus that occurred
in the Indian Ocean islands\textsuperscript{{[}152{]}}. At that stage,
molecular barcodes, accompanying high-throughput sequencing, allow us to
segregate every dominant variant at a single-sequence level and decode
corresponding genotypes. This approach can be done with different sorts
of combinations of antiviral drugs to build up a database, which
systematically associates different antiviral drug-resistant variants
with corresponding mutations. Since viruses used in this approach are
barcoded, we can also construct phylogenetic trees for individual
antiviral drug-resistant variants, allowing us to monitor whether or not
any intermediate nucleotide substitutions are required during the course
of the evolutionary process to result in final antiviral drugs-resistant
phenotypes. If this is the case, such information could be used as a
guideline for clinical diagnostics. In other words, after unveiling the
genotypes of viruses present in patients, based on experimental outputs
released from this genotype-traceable model, medical doctors can foresee
that an indicated patient might exhibit antiviral drug tolerance to
certain drugs and adjust antiviral regimens for treatments to satisfy
the need for every individual.

\emph{Unveil fitness landscapes of newly discovered zoonotic viruses,
contributing to preventive medicine}

Having this experimental evolution model mentioned above in hand, we can
further use this system, especially while dealing with emerging- and
newly discovered zoonotic viruses, to \emph{in vitro} generate mutations
by reiterating several times of infection in natural host cells/animals
followed by testing viral fitness landscapes of variant strains in human
cells, thereby helping us to understand and predict the possible impact
of variant strains derived from these emerging- and newly discovered
zoonotic viruses on humans. Retrospective studies of constructing
evolutionary trajectories based on phylogenetic analyses should be
performed; in such a way, we can closely monitor the sequential
transition of virus sequence changes reshaped by evolutionary
constraints throughout every passage of cells or every filial generation
in animal models at a single-sequence level and estimate the
evolutionary timescale that requires viruses to reprogram their
tropisms. This experimental evolution model is inspired by the
evolutionary approach named retrovolution\textsuperscript{{[}153{]}},
with which the authors studied the human deoxycytidine
kinase\textsuperscript{{[}153{]}}. Here under the framework of this
approach with modifications, we import the idea of incorporating
molecular barcodes embedded in our viruses of interest, thereby bringing
our investigation down to the resolution of a single virus sequence.
Most importantly, in line with the procedure designed in this model we
will be able to immediately assay viral fitness enhanced by emerging
mutations that can be traced through phylogenetic trees. In other words,
this barcoding evolution model enables us to associate genotypic changes
occurring in natural host cells/animals with predictive phenotypes in
human cells in every single evolutionary event in order to accelerate
our understanding of newly discovered zoonotic viruses and benefit to
develop antiviral strategies in advance. It is worthy to stress the
point that in addition to different experimental approaches can be used
for predicting the likelihood of predictive
mutations\textsuperscript{{[}152,154--156{]}}, we also propose to use
\emph{in silico} approaches for this purpose: we suggest that
algorithms, like generative adversarial
networks\textsuperscript{{[}157{]}}, an architecture for training
generative models, can be for instance applied for predicting
evolutionary paths when the number of input sequences is sufficient.
Eventually virus sequences with predictive mutations will also be
necessary to test for fitness in human cells.

\textbf{Perspectives of molecular barcoding for virus research}

Undoubtedly, molecular barcodes should strengthen their power in the
context of molecule quantification. Together with precise instruments,
such as the microfluidic system to delicately detect individual
molecules and deep learning algorithms, dedicated to accurately
interpret, calculate and classify each sequence of barcodes, we can
foresee that molecular barcodes will continue to be one of the most
attractive methods applied for quantitative biology. Additional
advantage of the molecular barcoding approach is that we will be able to
track any intermediate product relevant to the origin of genomic DNA,
such as RNA isoforms and antisense RNA or the functional unit which is
absent following mRNA processing; few examples have already shown the
feasibility of molecular barcoding for tackling these
questions\textsuperscript{{[}28,158,159{]}}. Furthermore, no matter
barcoding DNA- or RNA viruses, molecular barcodes are presently embedded
in genomic DNA or cDNA. However welcome, it is known that many zoonotic
viruses causing EIDs belong to RNA viruses. Direct barcoding and
sequencing native viral RNA molecules without conversion to cDNA will be
expected to bring us a more informative picture concerning chemical RNA
modifications present in the native RNA molecules and the whole
transcriptome. Example given by Smith and
colleagues\textsuperscript{{[}160{]}}demonstrated the potential for
barcoding directly on native RNA molecules by using the platform offered
by Oxford Nanopore Technologies.

\textbf{Conclusions}

\emph{It is said ``we all live in the past.'' It is probably true. If
so, how we can use the past to foresee the future could then be our task
at present.}

While preparing this manuscript, SARS-CoV-2 still continues circulating
in human populations and threatening public health. This pandemic has
further stressed the importance of closely monitoring the emergence of
infectious diseases originating from animal reservoirs and transmitted
between animals and people. It is obvious that this pandemics will not
be the last; a powerful experimental evolution approach allowing us to
closely survey the dynamics of genetic mutations in a population of
circulating zoonotic viruses in the short term and possibly predict
upcoming variants is therefore pivotal to improve the scientific,
medical and societal response to future zoonoses.

Molecular barcoding has a long history in yeast
genetics\textsuperscript{{[}161{]}}. And to date, it has made great
progress in investigating EIDs. Concerning RNA viruses, as mentioned in
the article, molecular barcodes have been used as a tracer to follow the
consequence of individual viruses encountering evolutionary selection
forces \emph{in vivo} , rendering us better understanding viral
transmission fitness and evolutionary dynamics. Molecular barcoding on
SARS-CoV-2 are focusing on sample multiplexing thus far; application of
molecular barcodes on coronaviruses to gain better understanding of
cross-species transmission fitness over time will be extremely
important. Whereas molecular barcodes in retroviruses (SIV and
HIV)\textsuperscript{{[}17,25,61,63{]}}have been applied to identify
different polymorphisms on the viral gene, study single provirus
transcriptomics and track viral rebound in animal models. Collectively,
molecular barcodes provide us a good opportunity for virus studies in a
quantitative manner.

The key to using molecular barcodes as universal identifiers for either
identifying single molecules or tracing individual viruses is to have a
very large initial collection of barcodes. Good complexity of barcodes
diminish the probability that two independent molecules harbor the same
barcodes. Several approaches used to validate the quality of barcoded
libraries, such as calculation of a minimum Levenshtein
distance\textsuperscript{{[}38{]}} or establishment of a threshold for
sorting out trustable barcodes\textsuperscript{{[}26{]}}have been
mentioned previously. In general, barcode duplications are recommended
to be kept at a frequency below 0.1\%\textsuperscript{{[}60{]}}. It is
also important to note that imbalance in the representation of the
barcodes in the library has been reported; a thorough investigation into
a bias of barcode representation will be required in the future.
Eventually, the impact of molecular barcodes on viral infectivity and
replication should be always taken into account; fitness of barcoded
viruses is recommended to be verified for the usage of every new
prepared barcoded library.

\textbf{Acknowledgements}

I would like to thank Dr. Benkirane (L'institut de Génétique Humaine,
Montpellier, France) and Dr. Negroni (Institut de biologie moléculaire
et cellulaire, Strasbourg, France) for his frequent exchanges and
constructive discussions. I would like to thank Dr. Horiuchi (Yokohama
City University, Yokohama, Japan) and Dr. Bechara (Center for Human
Technology, Istituto Italiano di Tecnologia, Genoa, Italy) for their
critical feedback on the manuscript. This manuscript was financially
supported by the \selectlanguage{polish}Ł\selectlanguage{english}ukasiewicz--PORT Polish Center for Technology
Development (startup funding package).

\textbf{Data availability statement:} Data sharing is not applicable to
this article as no new data were created or analyzed.

\textbf{Conflict-of-interest statement}

The authors declare that they have no conflict-of-interest.

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\textbf{Tables}

Table 1. Virus studies employed molecular barcodes\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Virus}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Viral genome}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Disease}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Zoonotic diseases}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Main reservoirs}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Usual mode of transmission to humans}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Studies employed molecular barcodes}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Adeno-associated viruses (AAV)}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Linear, single-stranded DNA of about 48,000 nucleotides\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AAV are not currently known to cause disease\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
No\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Humans\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Direct contact\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{{[}28,159,162,163{]}}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Bacteriophage\textsuperscript{a}}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Linear, double-stranded DNA of about 44,000 nucleotides (SP6), 38,000
nucleotides (T3) and 40,000 nucleotides (T7)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
The concept that bacteriophages as human pathogens has been
proposed\textsuperscript{{[}164{]}} Bacteriophages may trigger and
worsen a number of human diseases\textsuperscript{{[}165--167{]}}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
No\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Direct contact\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textsuperscript{{[}158{]}}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Human enterovirus} \textbf{(HEV)\textsuperscript{b}}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Linear, single-stranded RNA of about 7,400 nucleotides (coxsackievirus
B3)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Human myocarditis or dilated cardiomyopathy\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
No\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Humans\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
The oral-fecal route\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textsuperscript{{[}168{]}}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{HIV}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Linear, single-stranded RNA of about 9,700 nucleotides\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AIDS\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Yes\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Humans, chimpanzees, gorillas\textsuperscript{c}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Direct contact with certain body fluids (blood, semen and pre-seminal
fluid, rectal fluids, vaginal fluids, breast milk)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{{[}17,25,61{]}}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Influenza A virus}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Linear, single-stranded RNA of about 14,000 nucleotides (Influenza A
virus)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Influenza\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Yes\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Wild birds, poultry, swine\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Aerosols, faeces\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{{[}24,67{]}}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Poliovirus}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Single-stranded RNA of about 7,500 nucleotides\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Poliomyelitis, infantile paralysis\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
No\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Humans\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
The oral-fecal route\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{{[}169,170{]}d}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{SARS-CoV-2}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Single-stranded RNA of about 30,000 nucleotides\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
SARS\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Yes\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Bats\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Direct, indirect, or close contact with infected people Airborne
transmission\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textsuperscript{{[}27,51,53--55{]}}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{SIV}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Linear, single-stranded RNA of about 9,500 nucleotides\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
No\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Chimpanzees, sooty mangabeys\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bites and scratches\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{{[}63,107,108,110,111{]}}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Yellow fever virus (YFV)}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Linear, single-stranded RNA of about 11,000 nucleotides\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Yellow fever, yellow jack, yellow plague\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Yes\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Humans, monkeys\textsuperscript{e}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Mosquitoes\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{{[}171{]}f}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Zika virus}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Linear, single-stranded RNA of about 11,000 nucleotides\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Mild fever and rash, with possible links to microcephaly and Guillain
Barr\selectlanguage{ngerman}é syndrome\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Yes\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Humans, other primates\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Mosquitoes\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textsuperscript{{[}26,172{]}}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{a)}This indicated study, in which the molecular
barcoding strategy was employed, focused on the bacteriophage promoters
SP6 (SP6p32), T3 (class 3, phi13) and T7 (class 3,
phi10)\textsuperscript{{[}158{]}}.

\textsuperscript{b)}This indicated study, in which the molecular
barcoding strategy was employed, focused on coxsackievirus B3
(CVB3)\textsuperscript{{[}168{]}}.

\textsuperscript{c)}Chimpanzees and gorillas are the original but humans
are now the primary reservoir.

\textsuperscript{d)}The barcoding strategy used by Kuss et al.
(2008)\textsuperscript{{[}169{]}}relied on site-directed mutagenesis
(point mutations) on the viral genome to distinguish each of the ten
viruses from one another.

\textsuperscript{e)}Monkeys are the original but humans are now the
primary reservoir.

\textsuperscript{f)}The barcoding strategy used by Erickson and Pfeiffer
(2013)\textsuperscript{{[}171{]}}relied on site-directed mutagenesis
(point mutations) on the viral genome to distinguish each of the ten
viruses from one another.

\textbf{Figure Legends}

\textbf{Figure 1. Schematic representation of three principal approaches
to prepare barcoded libraries.} (A, C and D) Molecular barcodes can be
introduced to a template by ligating sequencing adaptors (A) or
hybridizing molecular inversion probes (C) or directly given by PCR
amplification with target-specific primers (D). Of note, both forward-
and reverse adaptors were synthesized followed by adaptor extension and
A tailing. (B) Schematic representation of the different applications
between the usage of molecular barcodes and sample barcodes. Molecular
barcodes used in pooled sample 1 aims to correct sequencing errors: a
misreading nucleotide, guanosine (G) for example, is corrected in final
consensus sequences. Molecular barcodes used in pooled sample 2 aims to
identify true mutations (marked by red triangles); a mistaken mutation
(marked by a blue triangle) is eventually removed in final consensus
sequences. Panel (A) is modified based on Figure 1 in Schmitt et al.
(2012)\textsuperscript{{[}19{]}}; panel (C) is modified based on Figure
1 in Hiatt et al. (2013)\textsuperscript{{[}23{]}}.

\textbf{Figure 2. Schematic representation of the molecular barcoding
strategies used for a population-scale testing to screen
SARS-CoV-2-infected individuals.} (A) A sequence of 10 base pairs DNA
barcodes named LAMP barcodes was incorporated in the FIP primer while
performing RT-PCR. PCR barcodes adjacent to Illumina P5 and P7 sequences
flanked the both ends of the library. Annotated amplicon sequence is
modified based on Figure 1b in Ludwig et al.
(2021)\textsuperscript{{[}27{]}}. (B) Two sets of unique barcodes named
i5 and i7 sample barcodes were placed adjacent to the P5 and P7 adaptors
in Illumina sequence primers in the stage of PCR amplification.
Illustration is modified based on Figure 1b in Bloom et al.
(2021)\textsuperscript{{[}51{]}}.

\textbf{Figure 3. Schematic representation of the molecular barcoding
strategies applied in HIV and SIV.} (A) A swarm of cDNA synthesis
primers containing a string of eight degenerate nucleotides named Primer
ID and a three nucleotides sample barcode were used to PCR amplify the
HIV-1 protease (\emph{pro} ) gene. (B) A sequence of 20-nucleotides
molecular barcodes was used to tag the region downstream the HIV 5' long
terminal repeat in the HIV-based vector. After infection, viral DNA
containing molecular barcodes were inserted in the host genome. Inverse
PCR performed on genome DNA isolated from the infected cells identifies
provirus insertion sites; RT-PCR performed mRNA of barcoded proviruses
measures viral transcription driven by the HIV 5' long terminal repeat.
(C) The genomic characterization of nearly full-length HIV and the
composition of the molecular barcode sequence. A 21 nucleotides barcode
sequence was inserted in a non-expressed region upstream of the HA tag
overlapped with the HIV \emph{vpr} gene in a nearly full-length and
replication-competent HIV genome. The original sequence in the barcode
region was given in this illustration. Each third nucleotide was
replaced by a thymidine in the sequence of barcodes. Pink stick marks
the region where a molecular barcode is inserted. Illustration is
modified based on Figure 1A in Marsden et al.
(2020)\textsuperscript{{[}61{]}}. (D) The genomic characterization of
SIV and the composition of the molecular barcode sequence. Molecular
barcodes encompassing 10 random nucleotides in length were inserted
between the stop codon of the SIV\emph{vpx} gene and the start codon of
the SIV \emph{vpr} gene in the SIVmac239 plasmid. Illustration is
modified based on Figure 1A in Fennessey et al.
(2017)\textsuperscript{{[}63{]}}.

\textbf{Figure 4. Schematic representation of the molecular barcoding
strategies applied in Influenza A virus.} (A) A string of 22 nucleotides
molecular barcodes were carried by a shRNA library with
amplification\textsuperscript{{[}66{]}}. Amplified products encompassing
molecular barcodes were inserted between the Influenza A virus genes
encoding NS1 and NEP, which have been manipulated in their previous
work\textsuperscript{{[}65{]}}. Illustration is modified based on Figure
1A in Varble et al. (2014)\textsuperscript{{[}24{]}}. (B) Three sorts of
barcodes, including cell barcodes, UMI and viral barcodes were applied
on viral mRNA to measure single mRNA transcript in cells infected with
Influenza A viruses. Annotated amplicon was illustrated based on Figure
1C in Russell et al. (2018)\textsuperscript{{[}67{]}}.

\textbf{Figure 5. Schematic representation of the genomic
characterization of Zika virus and the composition of the molecular
barcode sequence.} Molecular barcode consisting of eight degenerate
codons (24 nucleotides) was embedded into the gene encoding the NS2A
protein. Pink stick marks the region where a molecular barcode is
inserted. Nucleotides written in pink color are referred to as
degenerate nucleotides.

\textbf{Figure 6. Schematic representation of the rationale design of
experimental evolution models proposed in this review article.} Proposed
experimental evolution models are composed of two parts: the reservoir
of natural host cells/animals used to experimentally generate a swarm of
laboratory-produced variant strains and the validation stage, in which
we will predict phenotypes that are caused by genotypic changes and
survey their impacts on human cells at a single-sequence level.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 6}

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

