Abstract
Host-virus interaction via host cellular components has been an
important field of research in recent times. RNA interference mediated
by short interfering RNAs and microRNAs (miRNA), is a widespread
anti-viral defense strategy. Importantly, viruses also encode their own
miRNAs. In recent times miRNAs were identified as key players in
host-virus interaction. Furthermore, viruses were shown to exploit the
host miRNA networks to suite their own need. The complex cross-talk
between host and viral miRNAs and their cellular and viral targets
forms the environment for viral pathogenesis. Apart from protein-coding
mRNAs, non-coding RNAs may also be targeted by host or viral miRNAs in
virus infected cells, and viruses can exploit the host miRNA mediated
gene regulatory network via the competing endogenous RNA effect. A
recent report showed that viral U-rich non-coding RNAs called HSUR,
expressed in primate virus herpesvirus saimiri (HVS) infected T cells,
were able to bind to three host miRNAs, causing significant alteration
in cellular level for one of the miRNAs. We have predicted protein
coding and non protein-coding targets for viral and human miRNAs in
virus infected cells. We identified viral miRNA targets within host
non-coding RNA loci from AGO interacting regions in three different
virus infected cells. Gene ontology (GO) and pathway enrichment
analysis of the genes comprising the ceRNA networks in the virus
infected cells revealed enrichment of key cellular signaling pathways
related to cell fate decisions and gene transcription, like Notch and
Wnt signaling pathways, as well as pathways related to viral entry,
replication and virulence. We identified a vast number of non-coding
transcripts playing as potential ceRNAs to the immune response
associated genes; e.g., APOBEC family genes, in some virus infected
cells. All these information are compiled in HumanViCe
([29]http://gyanxet-beta.com/humanvice), a comprehensive database that
provides the potential ceRNA networks in virus infected human cells.
Keywords: virus, microRNA, host-virus interaction, lncRNA, circRNA,
ceRNA, immune response, APOBEC
Introduction
microRNAs (miRNA) are small non coding RNAs (21–24 nucleotides) that
play a major role in post-transcriptional gene regulation. Following
their discovery in Caenorhabditis elegans (Lee et al., [30]1993; Lau et
al., [31]2001), research on miRNAs progressed rapidly in the past two
decades. miRNAs have been identified to be involved in regulation of a
variety of cellular processes such as development, differentiation,
growth, pluripotency, immune activation, apoptosis, and host-viral
interaction (Griffiths-Jones, [32]2004; Cullen, [33]2006; German et
al., [34]2008; Zhang and Su, [35]2008; Bartel, [36]2009; Xiao and
Rajewsky, [37]2009). As of now, ~24,000 miRNA precursors and ~30,000
mature miRNAs have been annotated from 97 species, and that includes
2042 Homo sapiens mature miRNAs (Griffiths-Jones et al., [38]2008).
miRNAs are known to play a significant role in antiviral defense in
most organisms. During viral infection, interaction of viral
transcripts or proteins with host cellular components mediates factors
like virulence, viral replication, spread of infection, and host immune
response (Berkhout and Haasnoot, [39]2006; Ghosh et al., [40]2009). It
has been observed that cellular miRNAs play important roles on
host-viral interaction (Gottwein and Cullen, [41]2008). Viruses too are
found to encode their own miRNAs to exploit host gene silencing
machinery (Pfeffer et al., [42]2004; Dunn et al., [43]2005; Cui et al.,
[44]2006; Nair and Zavolan, [45]2006; Skalsky et al., [46]2007; Umbach
et al., [47]2008). Initially the existence of viral miRNAs in
Epstein-Barr virus (EBV) was reported by Tuschl group (Pfeffer et al.,
[48]2004). Till now, mirBASE lists 295 miRNA genes identified in 27
viruses; amongst them, 56 miRNA genes identified in 11 viruses
infecting human cells. These viruses are from the families of
Herpesvirus, Polyomaviridae, Adenoviridae, and Retrovirus
(Griffiths-Jones et al., [49]2008). Several virus-encoded miRNAs have
been reported to target host transcripts for their own advantage. There
are reports of viral miRNAs targeting antiviral signaling molecules,
e.g., EBV encoded miR-BHRF1-3 downregulates CXC-chemokine ligand 11
(CXCL11), an interferon (IFN)-inducible T-cell chemoattractant (Xia et
al., [50]2008). The host gene Thrombospondin 1 (THBS1) has been
reported to be targeted by multiple KSHV miRNAs, identified through
gene expression profiling of cells engineered to stably express 10 KSHV
pre-miRNAs (Samols et al., [51]2007). Another example of cellular mRNA
targeted by viral miRNA is PUMA gene targeted by EBV miR-BART5 (Choy et
al., [52]2008). Viral miRNAs may mimic the seed-region sequences of
host cellular miRNAs. It has been reported that miR-K12-11 encoded by
KSHV, shares the first eight nucleotides with human miRNA-155 (Gottwein
et al., [53]2007; Skalsky et al., [54]2007) and it is also observed
that BACH-1 has been targeted by both human miRNA-155 and KSHV encoded
miRNA miR-K-12-11 (Skalsky et al., [55]2007). Recent studies suggest
that cellular miRNA can target other non-coding RNA like long
non-coding RNA (lncRNA) and circular RNA (circRNA) (Jeggari et al.,
[56]2012; Bhartiya et al., [57]2013; Ghosal et al., [58]2013; Hansen et
al., [59]2013; Paraskevopoulou et al., [60]2013; Li et al., [61]2014).
Moreover, cellular transcripts like mRNAs, pseudogenes, lncRNA,
circRNAs, that harbor miRNA response elements (MRE) for one or more
common miRNA, can compete with each other for the limited pool of
cellular miRNAs and thus affect the competing RNAs level (Sarver and
Subramanian, [62]2012; Li et al., [63]2014). Competing Endogenous RNAs
or ceRNAs have been found to have important roles in a variety of
cellular processes like cell cycle control and tumor suppression, e.g.,
PTEN-P1 blocking miR-19b and miR-20a from binding to PTEN tumor
suppressor (Karreth et al., [64]2011; Sumazin et al., [65]2011; Tay et
al., [66]2011). ceRNAs can modulate self-regulation in hepatocellular
carcinoma, e.g., HULC lncRNA acts as ceRNA of the protein coding gene
PRKACB that induces activation of CREB which in turn is involved in
upregulation of HULC (Wang et al., [67]2010). It is also observed that
ceRNAs have important role in developmental stages e.g., linc-MD1
blocking miR-133 from binding to transcription factors involved in
myogenic differentiation (Cesana et al., [68]2011) and H19 blocking the
miRNA let-7 to affect muscle differentiation in vitro (Kallen et al.,
[69]2013). Importantly, there is evidence for the viral strategy of
exploiting host gene regulatory circuit by ceRNA effect. Cazalla and
his group have reported that viral U-rich non-coding RNAs called HSUR
expressed in primate virus herpesvirus saimiri (HVS) infected T cells
are able to bind to three host miRNAs. They also noted that this
activity resulted in striking alteration of the cellular levels of one
of these miRNAs, miRNA-27. This phenomenon leads to the regulation of
expression of the host-cell genes targeted by this miRNA. Hence, the
potential of viral miRNAs to exploit the host gene regulatory network
via the ceRNA effect is suggested. As viral miRNAs have already been
reported to interact with host cellular factors, they could have
potential interaction with cellular non-coding RNAs. We identified
potential lncRNA and circRNA targets for both host and viral miRNAs
from AGO PAR-CLIP datasets in some virus infected cells including EBV
infected lymphoblastoid cell lines (LCLs), Human cytomegalovirus (HCMV)
infected primary human fibroblast cells and two latently Kaposi's
sarcoma associated herpesvirus (KSHV) infected primary effusion
lymphoma (PEL) cell lines, BCBL-1, and BC-3. We developed a repository
of the putative viral and host (human) miRNA interaction with cellular
protein-coding RNA, lncRNA, and circRNA and modeled the potential ceRNA
functions in virus infected cells in human. Gene ontology (GO) and
pathway enrichment analysis of the genes comprising the ceRNA networks
in the virus infected cells revealed enrichment of key cellular
signaling pathways related to cell fate decisions and gene
transcription as well as pathways related to viral entry, replication,
and virulence. These cellular pathways are known to be frequently
manipulated by virus to facilitate their own spreading. Our database
HumanViCe provides the users with the potential ceRNA networks in virus
infected human cells that are instrumental in fine tuning of gene
expressions to aid the host defense against viruses. We believe this
database to be an important resource for exploration of the role of
host ceRNAs in viral infection.
Methods
Sequence data collection
We collected human and viral mature miRNA sequence data from miRBase
database (Griffiths-Jones et al., [70]2008). We collected miRNA
sequences from 11 virus species known to infect human. These 11 virus
species included Epstein barr virus (EBV), Herpes Simplex Virus 1
(HSV1), Herpes Simplex Virus 2 (HSV2), BK polyomavirus (BKV), HCMV,
Human immunodeficiency virus 1 (HIV1), JC polyomavirus miRNAs (JCV),
Kaposi sarcoma-associated herpesvirus (KSHV), Merkel cell polyomavirus
(MCV), Simian virus 40 (SV40) and Human herpesvirus 6B. Human lncRNAs
data were collected from the current version of GENCODE human, GENCODE
19. GENCODE 19 annotated 13220 lncRNA genes (Derrien et al., [71]2012).
The human circRNA dataset consisting of 1953 predicted human circRNAs
was collected from the study of Memczak et al (Memczak et al.,
[72]2013).
Prediction of host and viral miRNA targets on host
The dataset for viral miRNA targets on human protein coding transcripts
was collected from vHot database where targets were predicted by
TargetScan, miRanda, RNAhybrid, microT, and PITA (Kim et al.,
[73]2012). We collected putative human miRNA targets on human protein
coding transcripts from Targetscan (Lewis et al., [74]2005).
For prediction of host and viral miRNA targets on human lncRNAs and
circRNAs, we developed a custom algorithm for seed matched target
finding coupled with favorable duplex stability. For Seed
complementarity search, a modified Smith-Waterman algorithm was used
and for prediction of different types of miRNA target sites (6-mer,
7-mer, 7-merA1, and 8-mer). We considered transcripts with seed
complementarity as well as one base mismatch tolerance (in position 2–8
or 2–7) in the seed region with 3′ compensatory complementarities. To
reduce runtime involved in computation of target sites on huge set of
transcripts (~25000 lncRNA+circRNA), we first searched for sites
containing perfect 6-mer complementarity with miRNA seed from either
miRNA 5′ end (2–7th base) or 3′ end (13–18th base). In case a match was
found, then a 25 base window around the seed-matched site (starting
from two base preceding seed-matched site in case of miRNA 5′ end seed
match, starting from 14 base preceding seed-matched site in case of
miRNA 3′ end seed match) was used for further alignment by the modified
Smith-Waterman algorithm.
For calculating duplex energy of the predicted miRNA-target duplex we
used the Vienna RNA package (Hofacker et al., [75]1994). We calculated
target accessibility for each miRNA-target by the following:
[MATH:
Ed=E
mil−E<
mrow>ln<−20<
mtext>kcal/mol, :MATH]
(1)
where,
E[mil] = Duplex energy of the putative miRNA-target pair (calculated
using the cofold routine in Vienna RNA package)
E[ln] = Free energy of the target lncRNA (calculated using the fold
routine in Vienna RNA package).
Validation of miRNA-lncRNA interaction predicted by our custom algorithm
For partial validation and providing a measure of accuracy in our
prediction of miRNA targets on host lncRNAs, we verified our miRNA
target finding program on the dataset of Nam et al NCBI GEO accession
no. [76]GSE52530 (Nam et al., [77]2014). This dataset consisted of
RNA-Seq expression profiling of cellular transcripts in four different
cell lines (HeLa, HEK293, Huh7, and IMR90) after transfection of
miR-124 and miR-155 respectively, compared to mock transfection. The
transcripts included non-coding transcripts also (with RefSeq
annotation prefix NR_). From the dataset of HEK293 cells, out of 4789
non-coding (NR_) transcripts, 1617 and 1552 transcripts showed
decreased expression (log 2 fold change<-1) upon transfection of
miR-124 and miR-155 respectively compared to the mock transfected
control. The log 2 fold changes were calculated using the R package
foldchange and foldchange2logratio. We regarded these 1617 and 1552
non-coding transcripts as positive control for miR-124 and miR-155
respectively and the rest (3172 and 3537 transcripts) were regarded as
negative control. From these two datasets, we predicted targets of
miR-124 and miR-155 respectively to measure the accuracy of our
algorithm in terms of false discovery rate (FDR). FDR measures the
specificity of an algorithm in terms of its false positive detections.
FDR = FP/FP+TN, where FP = number of false positives and TN = number of
true positives.
From this dataset, our algorithm detected only 95 false positives (FP)
out of a total of 2363 negative sets (FP+TN), which gave an FDR of
0.04. The low FDR was due to the stringency of the algorithm that
required perfect seed matches (either from miRNA 5′ end or 3′ end) and
a filtering for favorable miRNA-target duplex. Though the stringent
conditions resulted in much false negative detection in the current
dataset, we kept these conditions to ensure a low false positive rate
that was particularly required for the present work.
Collection of miRNA expression data in virus infected cells
We used host (human) miRNA expression profiling data from EBV, HCMV,
HIV1, and KSHV infected cells, collected from NCBI GEO database. miRNA
expression profile from small RNA deep sequencing of EBV B95.8-infected
LCLs was collected from GEO series accession no. [78]GSE41437,
high-throughput profiling of smRNAs, Ago1, and Ago2-associated miRNAs
from HCMV-infected fibroblast cells was collected from [79]GSE33584,
miRNA expression profiles in the peripheral blood mononuclear cells
(PBMCs) after HIV1 infection was collected from [80]GSE44332, Ago
HITS-CLIP in KSHV-infected PEL cell lines BCBL-1 and BC-3 was collected
from [81]GSE41357.
Calculating the probability of ceRNA pair to cross-regulate each other
We implemented a measure to assess the likelihood of a ceRNA pair to
regulate each other via shared miRNAs. This approach was similar to
what has been used in the study of Sumazin et al. ([82]2011) and in
StarBase v2.0 (Li et al., [83]2014). We calculated the p-value for each
potential ceRNA pair by hypergeometric test considering the number of
shared miRNAs between a ceRNA pair against the number of miRNAs
targeting individual components of the ceRNA pair. The p-value was
measured as:
[MATH: p=∑i=
mcmin<
mrow>(mp,<
mi>mn)(
mni)(
MT−m<
mi>nm
p−i)(MT<
msub>mp
), :MATH]
(2)
where,
M[T] = Total number of miRNAs in the human genome
m[p] = Number of miRNAs interacting with the first ceRNA
m[n] = Total number of miRNAs interacting with the second ceRNA
m[c] = Number of miRNAs shared between the ceRNA pair.
Implementation
The miRNA target finding algorithm was implemented in JAVA and the web
interface of the database HumanViCe was developed using PHP-mySql. The
collected dataset of viral miRNA-host mRNA interactions and host
miRNA-host miRNA interactions along with our generated dataset of viral
miRNA-host lncRNA and circRNA and host miRNA-host lncRNA interactions
were stored in a mySQL database. The dataset of host miRNA expression
profiles in virus infected cells were also stored in the database (for
EBV, HIV1, HCMV and KSHV). For these four viruses, only the targets of
host miRNAs expressed in the virus infected cells were displayed.
Browsing of the database HumanViCe for viral miRNA targets or ceRNAs of
a target transcript by users is enabled by mySql queries from PHP.
Figure [84]1 shows the flowchart for development of the database
HumanViCe.
Figure 1.
[85]Figure 1
[86]Open in a new tab
The flow-diagram of the steps involved in development of the database
HumanViCe. The predicted interaction of the host miRNA-host target and
viral miRNA-host target was collected from Targetscan and VHot
databases respectively. The interaction of host lncRNA and circRNA with
viral miRNA was predicted by our custom algorithm. We also stored the
miRNA expression profiling data from virus infected cells collected
from NCBI GEO ([87]GSE41437, [88]GSE33584, [89]GSE44332, and
[90]GSE41357). All the miRNA target interaction data (host miRNA-host
target and viral miRNA-host target), along with miRNA expression
profiles are stored in a mySQL database which can now be queried for
potential ceRNAs of a transcript in a given virus infected cell.
Results
Database contents
Presently HumanViCe contains targets for a total of 144 viral miRNAs
encoded by 10 viruses known to infect human cells (see Table [91]1).
Other than the viral miRNA targets, HumanViCe also lists host cellular
miRNA targets to present a more comprehensive picture of the miRNA
mediated regulations in virus infected cells. Like host cellular
miRNAs, viral miRNAs were also predicted to target host non-coding
transcripts like lncRNA and circRNAs. From our prediction, 144 viral
miRNAs were found to potentially target 6257 human lncRNA transcripts
(from the set of 23898 annotated lncRNA transcripts in GENCODE 19
version) resulting in 10262 putative host lncRNA-viral miRNA
interactions. Viral miRNAs were also found to potentially target 1277
human circRNA transcripts among the set of 1954 circRNA candidates from
the study of Memczak et al. ([92]2013). As previously reported, these
non-coding transcripts bearing MREs can act as miRNA sponge to regulate
the availability of the targeting miRNAs. Especially circRNAs are
reported to contain extensive miRNA binding sites for effective miRNA
sponge activity. From our study we identified some circRNAs containing
large number of binding sites for some viral miRNAs, e.g., human
circRNA transcript of ANKRD11 (circRNA id: hsa_circ_002048) was found
to bear putative 36 sites for EBV encoded miRNA ebv-miR-BART20-5p.
Table 1.
miRNAs encoded by viruses known to infect human.
Virus Encoded miRNAs
Epstein Barr Virus ebv-miR-BART4-3p CACAUCACGUAGGCACCAGGUGU
ebv-miR-BART20-3p CAUGAAGGCACAGCCUGUUACC
ebv-miR-BHRF1-2-5p AAAUUCUGUUGCAGCAGAUAGC
ebv-miR-BART22 UUACAAAGUCAUGGUCUAGUAGU
ebv-miR-BART14-3p UAAAUGCUGCAGUAGUAGGGAU
ebv-miR-BART10-3p UACAUAACCAUGGAGUUGGCUGU
ebv-miR-BART7-3p CAUCAUAGUCCAGUGUCCAGGG
ebv-miR-BART16 UUAGAUAGAGUGGGUGUGUGCUCU
ebv-miR-BART15 GUCAGUGGUUUUGUUUCCUUGA
ebv-miR-BART9-3p UAACACUUCAUGGGUCCCGUAGU
ebv-miR-BART21-5p UCACUAGUGAAGGCAACUAAC
ebv-miR-BART3-3p CGCACCACUAGUCACCAGGUGU
ebv-miR-BART7-5p CCUGGACCUUGACUAUGAAACA
ebv-miR-BART1-3p UAGCACCGCUAUCCACUAUGUC
ebv-miR-BART11-5p UCAGACAGUUUGGUGCGCUAGUUG
ebv-miR-BART6-3p CGGGGAUCGGACUAGCCUUAGA
ebv-miR-BART13-5p AACCGGCUCGUGGCUCGUACAG
ebv-miR-BART1-5p UCUUAGUGGAAGUGACGUGCUGUG
ebv-miR-BART2-5p UAUUUUCUGCAUUCGCCCUUGC
ebv-miR-BART2-3p AAGGAGCGAUUUGGAGAAAAUAAA
ebv-miR-BHRF1-3 UAACGGGAAGUGUGUAAGCACA
ebv-miR-BART14-5p UACCCUACGCUGCCGAUUUACA
ebv-miR-BART18-5p UCAAGUUCGCACUUCCUAUACA
ebv-miR-BART4-5p GACCUGAUGCUGCUGGUGUGCU
ebv-miR-BART8-3p GUCACAAUCUAUGGGGUCGUAGA
ebv-miR-BHRF1-2-3p UAUCUUUUGCGGCAGAAAUUGA
ebv-miR-BART20-5p UAGCAGGCAUGUCUUCAUUCC
ebv-miR-BART13-3p UGUAACUUGCCAGGGACGGCUGA
ebv-miR-BART19-3p UUUUGUUUGCUUGGGAAUGCU
ebv-miR-BART8-5p UACGGUUUCCUAGAUUGUACAG
ebv-miR-BART5-5p CAAGGUGAAUAUAGCUGCCCAUCG
ebv-miR-BART17-3p UGUAUGCCUGGUGUCCCCUUAGU
ebv-miR-BART17-5p UAAGAGGACGCAGGCAUACAAG
ebv-miR-BHRF1-1 UAACCUGAUCAGCCCCGGAGUU
ebv-miR-BART19-5p ACAUUCCCCGCAAACAUGACAUG
ebv-miR-BART18-3p UAUCGGAAGUUUGGGCUUCGUC
ebv-miR-BART6-5p UAAGGUUGGUCCAAUCCAUAGG
ebv-miR-BART12 UCCUGUGGUGUUUGGUGUGGUU
ebv-miR-BART21-3p CUAGUUGUGCCCACUGGUGUUU
ebv-miR-BART3-5p ACCUAGUGUUAGUGUUGUGCU
ebv-miR-BART5-3p GUGGGCCGCUGUUCACCU
ebv-miR-BART9-5p UACUGGACCCUGAAUUGGAAAC
ebv-miR-BART10-5p GCCACCUCUUUGGUUCUGUACA
ebv-miR-BART11-3p ACGCACACCAGGCUGACUGCC
Herpes Simplex Virus 1 hsv1-miR-H8-5p UAUAUAGGGUCAGGGGGUUC
hsv1-miR-H13 UUAGGGCGAAGUGCGAGCACUGG
hsv1-miR-H1-5p GAUGGAAGGACGGGAAGUGGA
hsv1-miR-H6-5p GGUGGAAGGCAGGGGGGUGUA
hsv1-miR-H11 UUAGGACAAAGUGCGAACGC
hsv1-miR-H14-3p UCUGUGCCGGGCGCGUGCGAC
hsv1-miR-H7-5p AAAGGGGUCUGCAACCAAAGG
hsv1-miR-H26 UGGCUCGGUGAGCGACGGUC
hsv1-miR-H7-3p UUUGGAUCCCGACCCCUCUUC
hsv1-miR-H8-3p GCCCCCGGUCCCUGUAUAUA
hsv1-miR-H6-3p CACUUCCCGUCCUUCCAUCCC
hsv1-miR-H1-3p UACACCCCCCUGCCUUCCACCCU
hsv1-miR-H2-3p CCUGAGCCAGGGACGAGUGCGACU
hsv1-miR-H3-3p CUGGGACUGUGCGGUUGGGAC
hsv1-miR-H4-5p GGUAGAGUUUGACAGGCAAGCA
hsv1-miR-H17 UGGCGCUGGGGCGCGAGGCGG
hsv1-miR-H14-5p AGUCGCACUCGUCCCUGGCUCAGG
hsv1-miR-H4-3p CUUGCCUGUCUAACUCGCUAGU
hsv1-miR-H16 CCAGGAGGCUGGGAUCGAAGGC
hsv1-miR-H5-5p GGGGGGGUUCGGGCAUCUCUAC
hsv1-miR-H2-5p UCGCACGCGCCCGGCACAGACU
hsv1-miR-H12 UUGGGACGAAGUGCGAACGCUU
hsv1-miR-H18 CCCGCCCGCCGGACGCCGGGACC
hsv1-miR-H15 GGCCCCGGGCCGGGCCGCCACG
hsv1-miR-H5-3p GUCAGAGAUCCAAACCCUCCGG
hsv1-miR-H3-5p CUCCUGACCGCGGGUUCCGAGU
Herpes Simplex Virus 2 hsv2-miR-H11-3p UUAGGACAAAGUGCGAACGCUU
hsv2-miR-H7-5p AAAGGGGUCCGUAACCAAAGG
hsv2-miR-H23-3p ACGAGCUUCGCGGUACUACUC
hsv2-miR-H19 UUCGCUAGGCAAGCACGGACUG
hsv2-miR-H4-5p GAGUUCACUCGGCACGCAUGC
hsv2-miR-H3 UUUGGGAGUCUGCGGUUGGGAG
hsv2-miR-H20 UUUGGUUACGGACCCCUUUCU
hsv2-miR-H21 AUAACGUCAUGCUGUCUACGG
hsv2-miR-H9-3p UUCCCACCUCGGUCUCCUCCUC
hsv2-miR-H6-3p CCCAUCUUCUGCCCUUCCAUCCU
hsv2-miR-H23-5p AGGCCGUGGAGCUUGCCAGC
hsv2-miR-H7-3p UUUGGAUUCCGACCCCUCGUC
hsv2-miR-H11-5p AAGCGUUCGCACUUUGUCCUA
hsv2-miR-H5 GGGGGGGCUCGGGCCACCUGACC
hsv2-miR-H4-3p CCGUGCUUGCCUAGCGAACUC
hsv2-miR-H10 GGGUGCGGGGGUGGGCGG
hsv2-miR-H22 AGGGGUCUGGACGUGGGUGGGC
hsv2-miR-H25 CUGCGCGGCGGAGACCGGGAC
hsv2-miR-H13 UUAGGGCAAAGUGCGAGCACUG
hsv2-miR-H2 UCUGAGCCUGGGUCAUGCGCGA
hsv2-miR-H9-5p CUCGGAGGUGGAGUCGCGGU
hsv2-miR-H6-5p AAUGGAAGGCGAGGGGAUGC
hsv2-miR-H12 UUAGGACGAAGUGCGAACGCUU
hsv2-miR-H24 CUCCGGCGCCUUCCCCCCGCCCU
BK bkv-miR-B1-3p UGCUUGAUCCAUGUCCAGAGUC
Polyomavirus bkv-miR-B1-5p AUCUGAGACUUGGGAAGAGCAU
Human cytomegalovirus hcmv-miR-US25-1-3p UCCGAACGCUAGGUCGGUUCUC
hcmv-miR-US25-2-3p AUCCACUUGGAGAGCUCCCGCGG
hcmv-miR-UL36-3p UUUCCAGGUGUUUUCAACGUGC
hcmv-miR-US4 CGACAUGGACGUGCAGGGGGAU
hcmv-miR-UL70-3p GGGGAUGGGCUGGCGCGCGG
hcmv-miR-US25-2-5p AGCGGUCUGUUCAGGUGGAUGA
hcmv-miR-UL70-5p UGCGUCUCGGCCUCGUCCAGA
hcmv-miR-UL22A-5p UAACUAGCCUUCCCGUGAGA
hcmv-miR-UL36-5p UCGUUGAAGACACCUGGAAAGA
hcmv-miR-US25-1-5p AACCGCUCAGUGGCUCGGACC
hcmv-miR-UL112 AAGUGACGGUGAGAUCCAGGCU
hcmv-miR-US33-5p GAUUGUGCCCGGACCGUGGGCG
hcmv-miR-UL148D UCGUCCUCCCCUUCUUCACCG
hcmv-miR-US5-2 UUAUGAUAGGUGUGACGAUGUC
hcmv-miR-US5-1 UGACAAGCCUGACGAGAGCGU
hcmv-miR-UL22A-3p UCACCAGAAUGCUAGUUUGUAG
hcmv-miR-US33-3p UCACGGUCCGAGCACAUCCA
Human Immunodeficiency virus hiv1-miR-H1 CCAGGGAGGCGUGCCUGGGC
hiv1-miR-N367 ACUGACCUUUGGAUGGUGCUUCAA
hiv1-miR-TAR-3p UCUCUGGCUAACUAGGGAACCCA
hiv1-miR-TAR-5p UCUCUCUGGUUAGACCAGAUCUGA
JC polyomavirus jcv-miR-J1-3p UGCUUGAUCCAUGUCCAGAGUC
jcv-miR-J1-5p UUCUGAGACCUGGGAAAAGCAU
Kaposi's sarcoma associated herpesvirus kshv-miR-K12-5-3p
UAGGAUGCCUGGAACUUGCCGGU
kshv-miR-K12-4-3p UAGAAUACUGAGGCCUAGCUGA
kshv-miR-K12-8-5p ACUCCCUCACUAACGCCCCGCU
kshv-miR-K12-10a-3p UAGUGUUGUCCCCCCGAGUGGC
kshv-miR-K12-5-5p AGGUAGUCCCUGGUGCCCUAAGG
kshv-miR-K12-8-3p CUAGGCGCGACUGAGAGAGCA
kshv-miR-K12-6-3p UGAUGGUUUUCGGGCUGUUGAG
kshv-miR-K12-6-5p CCAGCAGCACCUAAUCCAUCGG
kshv-miR-K12-3-5p UCACAUUCUGAGGACGGCAGCGA
kshv-miR-K12-2-5p AACUGUAGUCCGGGUCGAUCUG
kshv-miR-K12-12-3p UGGGGGAGGGUGCCCUGGUUGA
kshv-miR-K12-11-5p GGUCACAGCUUAAACAUUUCUAGG
kshv-miR-K12-1-3p GCAGCACCUGUUUCCUGCAACC
kshv-miR-K12-9-3p CUGGGUAUACGCAGCUGCGUAA
kshv-miR-K12-12-5p AACCAGGCCACCAUUCCUCUCCG
kshv-miR-K12-10b UGGUGUUGUCCCCCCGAGUGGC
kshv-miR-K12-3-3p UCGCGGUCACAGAAUGUGACA
kshv-miR-K12-11-3p UUAAUGCUUAGCCUGUGUCCGA
kshv-miR-K12-1-5p AUUACAGGAAACUGGGUGUAAGC
kshv-miR-K12-9-5p ACCCAGCUGCGUAAACCCCGCU
kshv-miR-K12-2-3p GAUCUUCCAGGGCUAGAGCUG
kshv-miR-K12-7-5p AGCGCCACCGGACGGGGAUU
Merkel cell polyomavirus mcv-miR-M1-5p UGGAAGAAUUUCUAGGUACACU
mcv-miR-M1-3p UGUGCUGGAUUCUCUUCCUGAA
Simian virus 40 sv40-miR-S1-3p GCCUGUUUCAUGCCCUGAGU
sv40-miR-S1-5p UGAGGGGCCUGAAAUGAGCCUU
[93]Open in a new tab
Analysis of the predicted host ceRNA networks for host-virus interaction
We built the predicted ceRNA network for host-virus interaction for 10
viruses which are known to infect human and which encode viral miRNAs
(see Table [94]1). These ceRNA networks consist of host mRNAs, lncRNAs
and circRNAs sharing common viral miRNAs or host miRNAs. Figure [95]2
shows a schematic diagram that depicts the typical connections in such
a ceRNA network. To get an insight into the biological significance of
these ceRNA networks for host-virus interactions, we performed GO
enrichment analysis by GORILLA (Eden et al., [96]2009) and pathway
enrichment analysis by KOBAS 2.0 (Xie et al., [97]2011) using pathway
databases KEGG pathway and Reactome. Interestingly, when we looked into
the top 20 most enriched pathways (p-value< 0.05) for ceRNA networks
corresponding to each virus, we found many common pathways to be
enriched in all these networks. The most common enriched pathways
include Axon Guidance (for all 10 viruses), signaling by NGF (all
viruses except JCV), Hippo signaling pathway (all viruses except SV40),
MAPK signaling pathway (all viruses except SV40), signaling by NOTCH
(all viruses except JCV and SV40), Proteoglycans in cancer (all viruses
except HSV2 and KSHV), Wnt signaling pathway (all viruses except HCMV,
JCV, and SV40). The key cellular signaling pathways related to cell
fate decisions and gene transcription, like Notch signaling and Wnt
signaling pathways are frequently utilized or manipulated by viruses to
suite their own need (Hayward, [98]2004; Shackelford and Pagano,
[99]2004). Our observation indicates that viruses may exploit these
pathways also via the ceRNA network around them. Moreover, the GO
process and pathway enrichment analysis of the ceRNA networks for all
the viruses revealed enrichment of pathways related to viral entry,
replication and virulence. The genes in the ceRNA networks of EBV,
HCMV, HIV1, HSV1, HSV2, and KSHV were found to be enriched for
endocytosis. The genes in the ceRNA networks of EBV, HIV1, HSV1, HSV2,
and KSHV were found to be enriched for Membrane trafficking (Tokarev
and Guatelli, [100]2011). This observation may provide further insight
into viral strategy as viruses like HIV are already known to manipulate
the intracellular membrane trafficking to facilitate their spreading.
Interestingly, the genes in the ceRNA networks of EBV, HCMV, HSV2, and
MCV are found to be enriched for focal adhesion. Notably, EBV, HCMV,
and HSV2 belong to the Herpesvirus family which has been reported to
activate focal adhesion kinase (FAK), critical for the entry of
Herpesviruses into the target cell (Cheshenko et al., [101]2005). The
genes in the ceRNA networks of HCMV, HIV1, HSV1, and HSV2 are enriched
for Platelet activation, signaling and aggregation. Enriched pathways
in the ceRNA networks for each of the 10 viruses used in this study can
be viewed from the database url [102]http://gyanxet-beta.com/humanvice.
Figure 2.
[103]Figure 2
[104]Open in a new tab
Schematic diagram of the connections in a host-virus ceRNA network. A
viral miRNA (v-miR-1) targets three cellular transcripts (Hu-mR-1,
Hu-ln-1, and Hu-circ-1). These transcripts are also targeted by host
miRNAs Hu-miR-1, Hu-miR-2, and Hu-miR-3. The cellular transcripts
Hu-mR-2, Hu-mR-3, Hu-ln-2, Hu-ln-3 and Hu-circ-2 do not have a direct
connection with the viral miRNA but shares Hu-miR-1,2, and 3 with
Hu-mR-1, Hu-ln-1, and Hu-circ-1 and thus they are connected by a ceRNA
network.
Identification of viral miRNA targets on host lncRNAs from PAR-CLIP in virus
infected cells
We identified viral miRNA targets on host lncRNA loci from AGO PAR-CLIP
dataset in EBV, HCMV, and KSHV infected human cells. Datasets of
PAR-CLIP performed in EBV B95-8-infected LCLs was collected from NCBI
GEO database under GEO accession id [105]GSE41437. We identified 34
targets of 16 different EBV miRNAs on 29 different human lncRNA loci.
Similarly, we found 1 HCMV miRNA target on 1 human lncRNA loci from AGO
PAR-CLIP dataset from HCMV infected primary human fibroblast cells
([106]GSE33584). From the PAR-CLIP dataset on two latently KSHV
infected PEL cell lines, BCBL-1 and BC-3 ([107]GSE41357), we identified
69 targets of 16 different KSHV miRNAs on the genomic loci of 55
different human lncRNA loci (data downloadable from
[108]http://gyanxet-beta.com/humanvice). These results suggest that
host lncRNAs are likely to be targeted by viral miRNAs.
Predicted ceRNA activity mediated by host and viral miRNAs in kaposi's
sarcoma associated herpesvirus (KSHV) infected cells from PAR-CLIP dataset
Kaposi's sarcoma associated herpesvirus (KSHV) or Herrpesvirus 8
(HHV-8) is an oncovirus that causes Kaposi's sarcoma and PEL in human.
We identified viral and host miRNA targets from PAR-CLIP datasets in
KSHV infected PEL cells that may act as competing endogenous RNA
(ceRNA). Computational analysis identified 762 protein-coding and 144
non-coding targets of 1717 distinct human miRNAs and 19 distinct KSHV
miRNAs. We developed the whole predicted ceRNA network consisting of
host mRNAs, lncRNAs and circRNAs targeted by human and viral miRNAs in
KSHV infected cells (Figure [109]3). We did GO analysis on the set of
protein-coding targets of the host and viral miRNAs using Gorilla GO
enrichment analysis tool (Eden et al., [110]2009). Importantly, we got
enrichment for the GO cellular component phagocytic vesicle membrane.
We further studied the potential miRNA mediated regulation of host
immune response associated genes as identified from the AGO PAR-CLIP
dataset on KSHV infected PEL cell lines. From a list of 1535 immune
response associated genes downloaded from InnateDB (Breuer et al.,
[111]2013), 24 genes were found to be targeted by miRNAs in KSHV
infected PEL cell line. Table [112]2 lists the host immune response
related genes targeted by miRNAs in KSHV infected PEL cells. We looked
into the ceRNA network around these 24 host immune response associated
genes. A total of 246 transcripts (217 protein-coding and 29
non-coding), targeted by 21 distinct host miRNAs, were found to have
potential ceRNA effects on 24 host immune response associated genes.
Statistical analysis showed that the transcripts potentially targeted
by miRNAs in KSHV infected human PEL cells were enriched for having a
ceRNA effect on the host immune response associated genes (p-value <
0.01, calculated using hypergeometric test). The ceRNA network
consisting of only host immune response related targets of host and
viral miRNAs is shown in Figure [113]4.
Figure 3.
[114]Figure 3
[115]Open in a new tab
The ceRNA network in KSHV infected PEL cells incorporating predicted
cellular protein-coding, lncRNA and circRNA targets of both host
cellular and viral miRNAs identified from AGO PAR-CLIP data. The
network comprises of 762 protein-coding and 144 non-coding targets of
1717 distinct human miRNAs and 19 distinct KSHV miRNAs. As analyzed by
cytoscape, the average number of neighbors is 3.799 and network
centralization is 0.04.
Table 2.
Host immune response associated genes targeted by miRNAs in KSHV
infected PEL cells.
Gene name Gene symbol Transcript accession Targeting miRNA name
Annexin A11 ANXA11 [116]NM_145868 hsa-miR-1913
Amyloid beta (A4) precursor protein APP [117]NM_001136016 hsa-miR-128
Basigin (Ok blood group) BSG [118]NM_001728 hsa-miR-338-3p
Complement component 4A (Rodgers blood group) C4A [119]NM_007293
hsa-miR-769-3p
Caspase 3, apoptosis-related cysteine peptidase CASP3 [120]NM_032991
hsa-miR-513b
CD4 molecule CD4 [121]NM_001195017 hsa-miR-139-5p
CASP8 and FADD-like apoptosis regulator CFLAR [122]NM_001202516
hsa-miR-548a-3p
ELK1, member of ETS oncogene family ELK1 [123]NM_001114123
hsa-miR-3667-3p
Glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase GNE
[124]NM_001128227 hsa-miR-605
Major histocompatibility complex, class I, B HLA-B [125]NM_005514
hsa-miR-129-5p
Itchy E3 ubiquitin protein ligase ITCH [126]NM_001257137 hsa-miR-760
Integrin, beta 1 (fibronectin receptor, beta polypeptide, antigen CD29
includes MDF2, MSK12) ITGB1 [127]NM_133376 hsa-miR-338-3p
Promyelocytic leukemia PML [128]NM_033247 hsa-miR-215
Proteasome (prosome, macropain) subunit, alpha type, 4 PSMA4
[129]NM_002789 hsa-miR-324-5p
Proteasome (prosome, macropain) 26S Subunit, non-ATPase, 12 PSMD12
[130]NM_174871 hsa-miR-1249
Ribosomal protein L3 RPL3 [131]NM_000967 hsa-miR-1976
Superoxide dismutase 2, mitochondrial SOD2 [132]NM_001024466
hsa-miR-1270
Secreted protein, acidic, cysteine-rich (osteonectin) SPARC
[133]NM_003118 hsa-miR-296-3p
Serglycin SRGN [134]NM_002727 hsa-miR-769-3p
Transcription factor 4 TCF4 [135]NM_003199 hsa-miR-941
Tissue factor pathway inhibitor (lipoprotein-associated coagulation
inhibitor) TFPI [136]NM_006287 hsa-miR-3605-3p
TSC22 domain family, member 3 TSC22D3 [137]NM_198057 hsa-miR-142-3p
X-box binding protein 1 XBP1 [138]NM_001079539 hsa-miR-142-3p
X-linked inhibitor of apoptosis XIAP [139]NM_001167 hsa-miR-139-3p
[140]Open in a new tab
Figure 4.
[141]Figure 4
[142]Open in a new tab
The ceRNA network around the 24 host immune response associated genes
(listed in Table [143]2). This network comprises of 246 transcripts
(217 protein-coding and 29 non-coding), targeted by 21 distinct host
miRNAs, which were found to have potential ceRNA effects on 24 host
immune response associated genes. Compared to the whole ceRNA network
in KSHV infected PEL cells (Figure [144]3), this network has increased
centralization index 0.112.
Predicted ceRNA activity in human immunodeficiency virus 1 (HIV1) infected
peripheral blood mononuclear cells (PBMC)
We checked for the predicted targets of host and viral miRNAs expressed
in HIV1 infected human Peripheral blood mononuclear cells (PBMC). From
a dataset of miRNA expression profiling in HIV1 infected PBMC
([145]GSE44332), out of 339 host miRNAs expressed in HIV1 infected
PBMCs, we found 87 miRNAs potentially targeting 1370 human immune
response associated gene transcripts out of total 1535 immune response
associated gene transcripts listed in InnateDB (Breuer et al.,
[146]2013). Our analysis further revealed extensive ceRNA networks
around these host immune response associated transcripts in HIV1
infected PBMC cells (data downloadable from
[147]http://gyanxet-beta.com/humanvice). This suggested an immensely
complex regulatory circuit around host immune response associated genes
in HIV1 infected PBMCs. As the presence of ceRNAs was known to have a
diluting effect on the miRNA mediated regulation of a gene, the
extensive predicted ceRNA network around the immune response related
genes targeted by miRNAs in HIV1 infected PBMCs should have significant
effects on fine tuning of their expressions in these cells. APOBEC
family genes have been shown to inhibit HIV1 replication as part of the
innate immune system. We looked into the miRNA target network around
APOBEC family genes. Many of the miRNAs expressed in HIV1 infected
PBMCs were predicted to target APOBEC family genes including APOBEC1,
APOBEC2, APOBEC3G, and APOBEC4 (see Table [148]3) and they have an
extensive potential ceRNA network around them. The presence of the
ceRNA network around APOBEC family genes suggests of an alternative
strategy of the host immune system toward antiviral defense.
Table 3.
Targets of miRNAs expressed in HIV infected PBMCs on human APOBEC
family genes.
Gene symbol Transcript accession (RefSeq) Targeting miRNAs
APOBEC2 [149]NM_006789
hsa-miR-324-3p,hsa-miR-329,hsa-miR-107,hsa-miR-378,hsa-miR-770-5p,hsa-m
iR-508-3p,hsa-miR-508-3p
APOBEC3G [150]NM_021822 hsa-miR-520g
APOBEC3D [151]NM_152426
hsa-miR-508-5p,hsa-miR-125a-5p,hsa-miR-32,hsa-miR-423-5p,hsa-miR-1,hsa-
miR-206,hsa-miR-129-5p,hsa-miR-129-5p,hsa-miR-107,hsa-miR-210,hsa-miR-5
12-5p,hsa-miR-615-3p
APOBEC4 [152]NM_203454
hsa-miR-125a-5p,hsa-miR-298,hsa-miR-22,hsa-miR-142-5p,hsa-miR-199a-3p,h
sa-miR-301b,hsa-miR-372,hsa-miR-372,hsa-miR-494,hsa-miR-496,hsa-miR-520
g,hsa-miR-93,hsa-miR-484
APOBEC1 [153]NM_001644 hsa-miR-329,hsa-miR-526a
APOBEC3A [154]NM_145699
hsa-miR-372,hsa-miR-520g,hsa-miR-93,hsa-miR-129-5p,hsa-miR-129-5p,hsa-m
iR-433
APOBEC3F [155]NM_145298
hsa-miR-508-5p,hsa-miR-1197,hsa-miR-125a-5p,hsa-miR-298,hsa-miR-22,hsa-
miR-32,hsa-miR-423-5p,hsa-miR-671-5p,hsa-miR-1,hsa-miR-206,hsa-miR-450b
-5p,hsa-miR-494,hsa-miR-527,hsa-miR-760,hsa-miR-875-3p,hsa-miR-93,hsa-m
iR-129-5p,hsa-miR-208a,hsa-miR-208b,hsa-miR-299-3p,hsa-miR-484,hsa-miR-
296-3p,hsa-miR-342-3p,hsa-miR-210,hsa-miR-512-5p,hsa-miR-615-5p,hsa-miR
-485-3p
APOBEC3C [156]NM_014508
hsa-miR-125a-5p,hsa-miR-423-5p,hsa-miR-1,hsa-miR-206,hsa-miR-875-3p,hsa
-miR-875-3p,hsa-miR-107
APOBEC3H [157]NM_001166003, [158]NM_001166002, [159]NM_181773
hsa-miR-372,hsa-miR-520g,hsa-miR-93
APOBEC3H [160]NM_001166004 hsa-miR-770-5p
[161]Open in a new tab
Utility of humanViCe
Users can browse for viral miRNA targets on host protein-coding and
non-coding genes by choosing a particular virus. The resulting page
lists miRNAs encoded by the chosen virus along with its mRNA, lncRNA,
and circRNA targets in host cells. Users can view details of the
targets by choosing a particular viral miRNA. There is also provision
for checking the host cellular miRNA targets on a particular viral
miRNA target transcript. The users can check the potential ceRNAs for a
chosen transcript. The resulting page shows the potential ceRNA
candidates that share one or more common cellular or viral miRNA(s),
sorted by their probability (p-value) to act as ceRNA to the chosen
transcript. The list of ceRNAs is sorted by the number of shared
miRNAs. The miRNA targets for a particular lncRNA, mRNA, or circRNA can
also be browsed in our database by choosing a particular
lncRNA/mRNA/circRNA from the ceRNA list. The usage of HumanViCe is
described in Figure [162]5. The users can download the results of
pathway enrichment analysis for the genes comprising the ceRNA networks
for each of the 10 viruses included in HumanViCe after searching by a
particular virus name.
Figure 5.
[163]Figure 5
[164]Open in a new tab
The navigation of HumanViCe is depicted. (A) Users can search by a
virus name. (B) Resulting page shows the list of miRNAs encoded by the
chosen virus along with the number of mRNA, lncRNA and circRNA targets
of each of the viral miRNAs. Searching for targets of a particular
transcript type (mRNA, lncRNA or circRNA) for a particular viral miRNA
from the list results in (C) a page listing all the targets of the
chosen type of the chosen viral miRNA. (D) The users can search for
host miRNA targets on a particular transcript from the list. The
resulting page includes only interactions with the host miRNAs those
are expressed cells infected with the particular virus. (E) The users
can search for potential ceRNAs of a chosen host transcript. The ceRNAs
may share common host or viral miRNAs expressed in virus infected
cells.
Discussions
It has been observed that both cellular and viral miRNAs (miRNAs
encoded by virus) play important roles on host-viral interaction. The
complex cross-talk between host and viral miRNAs and their cellular and
viral targets form the environment for viral pathogenesis. The current
study is aimed at unraveling the cross-talk-network of the viral and
host miRNA targets in virus infected cells in human.
In recent years, it has been observed that other than protein-coding
transcripts, cellular miRNAs can also target other non-coding RNA like
lncRNA and circRNA. Cellular transcripts (mRNAs, pseudogenes, lncRNAs,
or circRNAs) sharing target sites for one or more common miRNAs compete
with each other for the limited pool of cellular miRNAs and thus affect
the competing RNA's level, a phenomenon known as ceRNA effect. This
ceRNA effect is known to play significant roles in important biological
processes including many disease pathogenesis as crucial new
determinants of gene expression regulation. As viral miRNAs have
already been reported to interact with host cellular factors, and viral
miRNAs may have potential interaction with cellular non coding RNAs as
well. Furthermore, it is highly likely that viral miRNAs may exploit
the host gene regulatory network via the ceRNA effect. A virus
exploiting the cellular miRNA mediated gene regulatory network via
ceRNA effect has been already reported. In a previous study Cazalla and
his group reported that viral U-rich non-coding RNAs called HSUR
expressed in primate virus HVS infected T cells are able to bind to
three host miRNAs resulting in a striking alteration of the cellular
levels of one of these miRNAs, miRNA-27, which in turn may impair the
regulation of the cellular targets of that miRNA in HVS infected cells.
In our study, we identified viral miRNA targets on host lncRNA loci
from AGO PAR-CLIP dataset in EBV, HCMV, and KSHV infected human cells.
circRNAs are reported to contain extensive miRNA binding sites for
effective miRNA sponge activity. From our study we identified some
circRNAs containing large number of binding sites for some viral
miRNAs; e.g., human circRNA transcript of ANKRD11 (circRNA id:
hsa_circ_002048) was found to bear 36 sites for EBV encoded miRNA
ebv-miR-BART20-5p. Our computational analysis identified many
protein-coding and non-coding transcripts targeted by common viral or
host miRNAs that points to a possible ceRNA effect in the virus
infected cells. When we looked into the predicted ceRNA networks for
all the 10 viruses used in current study, we found genes enriched for
cellular signaling pathways commonly exploited by viruses. The most
common enriched pathways for all the viruses include axon guidance,
NOTCH signaling pathway, MAPK signaling pathway, Wnt signaling pathway.
These pathways are related to cell differentiation state, gene
transcription and intracellular signaling and prone to be manipulated
by viruses after infection. Also the enrichment of pathways related to
viral entry, replication and virulence was observed. The genes in the
ceRNA networks of EBV, HCMV, HIV, HSV1, HSV2, and KSHV were found to be
enriched for endocytosis. The genes in the ceRNA networks of EBV, HIV,
HSV1, HSV2, and KSHV were found to be enriched for membrane
trafficking. ceRNA networks of EBV, HCMV, HSV2, and MCV were found to
be enriched for focal adhesion. The genes in the ceRNA networks of
HCMV, HIV, HSV1, and HSV2 were enriched for platelet activation,
signaling and aggregation.
We especially looked into the possible ceRNA networks around human
immune response associated genes in KSHV and HIV infected cells. Our
analysis suggested that transcripts potentially targeted by miRNAs in
both KSHV infected human PEL cells and HIV infected PBMCs were enriched
for having a ceRNA effect on the host immune response associated genes.
Furthermore, we identified a vast number of non-coding transcripts
playing as potential ceRNAs to the immune response associated genes in
these virus infected cells. Importantly, we identified seed matched
targets sites of 87 miRNAs expressed in HIV infected PBMCs on 1370 host
immune response associated genes out of the total 1535 genes listed in
InnateDB. Furthermore, extensive predicted ceRNA network was identified
around these host immune response associated genes, which suggested of
a complex regulatory circuit around host immune response associated
genes in HIV infected PBMCs. Working toward understanding the
regulatory effect of the ceRNA network in HIV infected cells will be
our future direction.
We developed HumanViCe, a repository of the putative viral and host
(human) miRNA interaction with cellular protein-coding RNA, lncRNA, and
circRNAs in virus infected cells in human. Potential ceRNAs in
different virus infected cells can be browsed in HumanViCe, where we
have also calculated a ceRNA score (measured using the number of shared
miRNAs between a pair) for each potential ceRNA pair to show the
likelihood for them to act as ceRNAs. Put together, HumanViCe can be a
very useful tool for researchers working on host-virus interactions to
understand the dynamics of host and viral miRNA mediated regulations in
virus infected cells.
Database availability
HumanViCe is available freely from
[165]http://gyanxet-beta.com/humanvice
Conflict of interest statement
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
References