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The Zika Virus sfRNA Secondary Structure Reveals a miR-147a Homologue that Targets Neurofascin as a Potential Cause of its Neurologic Syndromes

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ABSTRACT Pathologies associated with Zika virus infection include partial progressive paralysis (Guillan Barre Syndrome) and microcephaly in Brazil and Panama. We identified an 85 nucleotide stem loop structure in the 3' end of Zika virus sfRNA (position (343-428) with the canonical structural and sequence characteristics of a miRNA. In comparison, the West Nile Virus has been previously demonstrated to contain a miRNA in its 3’ untranslated (UTR) sfRNA region. The West Nile Virus miRNA, KUN-mir-1, was found to be 81 nucleotides in length (492-573) and 58% conserved overall as to the corresponding Zika virus stem loop nucleotide sequence. The majority on the sequence homology was in the terminal loop (78%). The 40 nucleotide (nt) 3p arm demonstrated 60% homology, decreasing at the distal segment. The 5p arm demonstrates on 42% sequence homology. The Zika virus stem loop in this study was identified to contain a hairpin structure in the 3’ end of the Zika virus sfRNA 3’ UTR segment predicted to contain a miRNA with significant homology to human hsa-mir-147a. Target analysis identified 241 human transcripts which include, but not limited to, neurofascin, synaptic vesicle glycoprotein 2A, neurofibromin 1, SAM and SH3 domain containing 1, neurogenin 2, in addition to multiple immune transcripts. Autoantibodies to neurofascin have been reported in in Guillain-Barre Syndrome (GBS), but have not yet been reported in or after Zika virus infection. A significant 9 mer match exists between the Zika virus miRNA 3p arm and the Neurofascin 3’UTR seed region, CCACACA. In contrast to West Nile Virus, GATA4 was not predicted to be, nor was identified in the MirBaseDB, to be a target of the Zika virus miRNA. These computational results indicate highly plausible mechanisms explaining the neurologic syndromes such a Guillan-Barre Syndrome and microcephaly associated with Zika virus infection, and warrant further investigation.
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1
The Zika Virus sfRNA Secondary Structure Reveals a miR-147a Homologue that Targets
1
Neurofascin as a Potential Cause of its Neurologic Syndromes
2
Robert Ricketson, MD
3
James Lyons-Weiler, PhD
4
Institute of Pure and Applied Knowledge
5
Allison Park, PA 15101
6
March 21, 2016
7
8
ABSTRACT
9
Pathologies associated with Zika virus infection include partial progressive paralysis (Guillan Barre Syndrome) and
10
microcephaly in Brazil and Panama. We identified an 85 nucleotide stem loop structure in the 3' end of Zika virus sfRNA
11
(position (343-428) with the canonical structural and sequence characteristics of a miRNA. In comparison, the West Nile
12
Virus has been previously demonstrated to contain a miRNA in its 3’ untranslated (UTR) sfRNA region. The West Nile
13
Virus miRNA, KUN-mir-1, was found to be 81 nucleotides in length (492-573) and 58% conserved overall as to the
14
corresponding Zika virus stem loop nucleotide sequence. The majority on the sequence homology was in the terminal
15
loop (78%). The 40 nucleotide (nt) 3p arm demonstrated 60% homology, decreasing at the distal segment. The 5p arm
16
demonstrates on 42% sequence homology.
17
The Zika virus stem loop in this study was identified to contain a hairpin structure in the 3’ end of the Zika virus sfRNA 3’
18
UTR segment predicted to contain a miRNA with significant homology to human hsa-mir-147a. Target analysis identified
19
241 human transcripts which include, but not limited to, neurofascin, synaptic vesicle glycoprotein 2A, neurofibromin 1,
20
SAM and SH3 domain containing 1, neurogenin 2, in addition to multiple immune transcripts.
21
Autoantibodies to neurofascin have been reported in in Guillan-Barre Syndrome (GBS), but have not yet been reported
22
in or after Zika virus infection. A significant 9 mer match exists between the Zika virus miRNA 3p arm and the
23
Neurofascin 3’UTR seed region, CCACACA. In contrast to West Nile Virus, GATA4 was not predicted to be, nor was
24
identified in the MirBaseDB, to be a target of the Zika virus miRNA.
25
These computational results indicate highly plausible mechanisms explaining the neurologic syndromes such a Guillan-
26
Barre Syndrome and microcephaly associated with Zika virus infection, and warrant further investigation.
27
28
INTRODUCTION
29
Between January 2007 and February 2016, a total of 41 countries and territories reported local transmission of Zika
30
virus; this includes 36 countries which reported local transmission between 2015 and 2016. Six countries (Brazil, French
31
Polynesia, El Salvador, Venezuela, Colombia and Suriname) have reported an increase in the incidence of cases of
32
microcephaly and/or Guillain-Barré syndrome (GBS) following a Zika virus outbreak. Puerto Rico and Martinique have
33
also reported cases of GBS associated with Zika virus infection, but without evidence of an overall increase in the
34
incidence of GBS. A number of concurrent events have occurred in Brazil, each of which provides an alternative
35
hypothesis for microcephaly and Guillan-Barre Syndrome worth consideration.
36
2
We previously reported on several hypotheses as to the pathogenesis in Zika virus neurologic associations in this
37
outbreak. Here, we further append the discussion towards the identification of a potential virus encoded miRNA in the
38
subgenomic Flavivirus (sfRNA) 3’ UTR in Zika virus. We identified specific plausible mechanisms for microencephaly,
39
including:
40
1. Direct Zika-related microcephaly through unspecified mechanisms;
41
2. Molecular mimicry of Bordetella pertussis peptides in tetanus-diphtheria-acellular pertussis (TdaP)
42
Vaccine and whole-cell Bordetella pertussis vaccine (wP);
43
3. Pestivus virus contamination in locally produced whole-cell Bordetella pertussis vaccine;
44
4. Glyphosate toxicity in bovine products in TdaP or wP vaccine (via interactions w/aluminum in the vaccine;
45
5. Zika p53-BAX induced apoptosis, as in Rubella virus;
46
6. Use of paracetamol (Acetaminophen) to reduce fever in pregnancy and in newborns;
47
7. Horizontal transfer of piggyBAC transposon from released GMO mosquitos;
48
8. Interactions among any of the above.
49
Importantly, microcephaly has not yet been reported due to Zika infection outside of Brazil, except one report of one
50
case in Panama. There have been thousands of pregnancies outside of Brazil with Zika virus infection but no sign of
51
microcephaly.
52
The Zika virus belongs to the genus Flavivirus, and is related to the Dengue, Yellow Fever, Japanese encephalitis,
53
Chikungunya, and West Nile viruses. Like other Flaviviruses, Zika virus (ZIKV) contains a nonsegmented, single-stranded,
54
positive-sense, single-stranded RNA genome. The open reading frame of the Zika virus reads as follows: 5′ UTR-Capsid
55
[C], prM-E-NS1-NS2A-NS2B-NS3-NS4A-NS4B-NS5 (RNA-dependent RNA polymerase)-3′ UTR. The polyprotein is
56
subsequently cleaved into capsid (C), precursor membrane (prM), envelope (E), and non-structural proteins. The
57
structural proteins encapsulate the viral genome. The replicated RNA strand is held within a nucleocapsid formed from
58
12-kDa protein blocks; the capsid is contained within a host-derived membrane modified with two viral glycoproteins.
59
Prior studies have demonstrated that the highly conserved secondary structures within the subgenomic Flavivirus RNA
60
(sfRNA) in the West Nile Virus 3’ UTR were resistant to host nucleases and responsible for the observed cytopathology
61
and pathogenicity. It was subsequently demonstrated that the 3' end of the West Nile Virus sfRNA encoded a miRNA,
62
KUN-miR-1, processed in the cytoplasm via Dicer-1 which increased viral RNA replication by inducing GATA4. As both
63
West Nile Virus and Zika virus share similar sequences homology in the region of the sfRNA, we investigated the
64
possibility that the Zika virus could similarly encode a miRNA with different human targets leading to a potential cause of
65
the neurologic syndromes reported in Zika virus infection.
66
Here, as part of our attempts to exhaust possible causal hypotheses of Zika virus pathogenesis, we explore the possibility
67
that Zika virus encodes one or more miRNA’s in the subgenomic Flavivirus (sfRNA) 3’ UTR in Zika virus involved in
68
Guillan-Barre Syndrome.
69
The Zika virus belongs to Flaviviridae and the genus Flavivirus, and is related to the Dengue, Yellow fever, Japanese
70
encephalitis, Chikungunya, and West Nile viruses. Like other flaviviruses, Zika virus is enveloped and has a
71
3
nonsegmented, single-stranded, positive-sense, single-stranded RNA genome. The open reading frame of the Zika virus
72
reads as follows: 5′ UTR-Capsid [C], prM-E-NS1-NS2A-NS2B-NS3-NS4A-NS4B-NS5 (RNA-dependent RNA polymerase)-3′
73
UTR. The polyprotein is subsequently cleaved into capsid (C), precursor membrane (prM), envelope (E), and non-
74
structural proteins. The structural proteins encapsulate the viral genome. The replicated RNA strand is held within a
75
nucleocapsid formed from 12-kDa protein blocks; the capsid is contained within a host-derived membrane modified with
76
two viral glycoproteins [1].
77
Pijman et al in 2008 [2] demonstrated that the highly conserved secondary structures within the subgenomic Flavivirus
78
RNA (sfRNA) of the 3’ UTR were resistant to host nucleases and responsible for the observed cytopathology and
79
pathogenicity. Furthermore, the sfRNA of Flaviviruses accumulated in the brain, was not packaged into virions, and was
80
not a product of RNA self-cleavage. The sfRNA was readily detected in both replicating and nonreplicating transfected
81
cells. This appeared to demonstrate that neither viral RNA replication, viral proteins, nor the 5’ UTR were essential for
82
sfRNA generation. The full length sfRNA was shown to produce cytopathology in cell culture but cell death (apoptosis)
83
only occurred in the presence of viral infection, alluding to addition factors such as p53-BAX/Caspace pathways we have
84
recently reported 37].
85
Hussain et al in 2012 [36] identified a viral-encoded miRNA within the sfRNA segment of West Nile Virus, KUN-miR-1, in
86
the 3’ end of the sfRNA. Levels of KUN-miR-1 were significantly reduced after silencing Dicer-1 and replication of the
87
virus was reduced upon inhibition of KUN-miR-1. KUN-miR-1 was subsequently found to increased accumulation of
88
GATA4 mRNA and virus RNA replication. The WNV miRNA was identified to be produced by cytoplasmic pathway,
89
thereby utilizing only Dicer and independent of the nuclear endonuclease Drosha.
90
As both West Nile Virus and Zika virus share similar sequence and secondary structural homology in the region of the
91
sfRNA, we investigated the possibility that the Zika virus could similarly encode a miRNA with different human targets
92
leading to a potential cause of the neurologic syndromes reported in Zika virus infection.
93
MicroRNAs (miRNAs) are small. ~22 nt in length RNAs that are involved in regulation of gene expression. MiRNAs silence
94
gene expression by directing repressive protein complexes to the 3′ untranslated region (UTR) of target messenger RNA
95
(mRNA) transcripts. The first miRNAs were discovered in Caenorhabditis elegans [19] and have been extensively
96
identified to represent a large family of evolutionarily conserved genes among insects, nematodes, and humans [3-5]
97
In hostpathogen interactions, miRNAs play a role in regulating the innate immune response, adaptive immune cell
98
differentiation, metabolism, apoptosis, cell proliferation, cancer, and maintenance of homeostasis during stress.
99
Canonical miRNAs derive from longer precursor primary transcripts (pri-miRNAs. Pri-miRNAs contain at least one, but
100
often several, precursor(s) of imperfectly complementary stem-loop hairpin structures.
101
Precursor miRNAs (pre-miRNAs) are generated from the larger 32-35 base pair pri-miRNA via the endonuclease Drosha
102
with 3’ overhangs of 2 nucleotides [5]. Drosha, along with DGCR8, combine to form the microprocessor complex (6-8).
103
The Drosha-DGCR8 microprocessor complex binds to the pri-miRNA, at the base of the hairpin stem, typically a 5’ UG/AG
104
motif and a CNNC motif at the 3’ end. Fang et al reported in 2015 that the basal UG and apical UGU motifs at the
105
junctions of single-stranded and double-stranded RNA regions of the pri-miRNA that the Drosha-FDGR8 Microprocessor
106
complex recognizes either or both of these junctions [9, 10]. Biochemical analyses have shown that Drosha recognizes
107
the basal junction and the DGCR8 dimer recognizes the apical junction [11, 12]. Their analysis further determined the
108
following requirements:
109
1. A mismatched GUG motif in the basal stem region
110
2. A preference for maintaining or improving base pairing throughout the remainder of the stem
111
4
3. A stem length preference of ~35 base pairs
112
4. A basal U(A)G motif
113
5. An apical U(A)G motif in the terminal loop
114
115
FIGURE 1. Features of a Canonical miRNA
116
The 5’ flanking sequence contains a UG motif whereby the uracil is occasionally substituted by an adenine (A) with
117
continued processing towards a mature miRNA. A GUG mismatch motif is located in the proximal stem loop structure
118
duplex, and an apical (G)UG motif is identified in the 5p-terminal loop junction. The 3p arm usually terminates in a
119
CNNC motif (not shown).
120
The newly liberated 60-80 nucleotide hairpin pre-miRNA is then exported from the nucleus to the cytoplasm via the
121
RAN-GTPase Exportin 5. In the cytoplasm, the pre-miRNAs are cleaved by the endonuclease Dicer.
122
Dicer-mediated cleavage produces a transient 22 nt duplex RNA, of which one strand (guide strand) is incorporated
123
into the RNA-induced silencing complex (RISC). The “passenger” strand is less likely to associate with RISC. RISC is a
124
multiprotein complex including the Argonaute (Ago) protein. Ago-loaded miRNAs (miRISC) typically bind to target
125
transcripts and inhibit gene expression.
126
Recently, diverse DNA virus families, and some + RNA genomes, have been found to encode miRNAs (TABLE 1]. strand
127
RNA viruses have been less likely to form miRNA but have recently been reported in the Zaire ebolavirus [13].
128
Within the group of reported viral encoded miRNAs, two distinct groups exist. Some viral miRNAs mimic host miRNAs
129
and take advantage of conserved networks of host miRNA target sites while other viral miRNAs do not share common
130
target sites conserved for host miRNAs. RNA viruses represent a challenge in that their replication occurs in the
131
cytoplasm, and thereby bypassing the initial nuclear Drosha cleavage. In that regard, noncanonical miRNA biogenesis
132
pathways have been identified [14].
133
5
Table 1. Viral-Encoded miRNAs Identified in miRBase
134
Bovine foamy virus
2 precursors, 4 mature
Bovine herpesvirus 1
10 precursors , 12 mature
Bovine herpesvirus 5
5 precursors, 5 mature
BK polyomavirus
1 precursor, 2 mature
Bovine leukemia virus
5 precursors, 10 mature [K02120.1]
Bandicoot papillomatosis carcinomatosis virus type 1
1 precursor, 1 mature
Bandicoot papillomatosis carcinomatosis virus type 2
1 precursor, 1 mature
Duck enteritis virus
24 precursors, 33 mature
Epstein Barr virus
25 precursors, 44 mature [EMBL:AJ507799.2]
Herpes B virus
12 precursors, 15 mature [Refseq:NC_004812]
Human cytomegalovirus
15 precursors, 26 mature [EMBL:X17403.1]
Human herpesvirus 6B
4 precursors, 8 mature
Human immunodeficiency virus 1
3 precursors, 4 mature
Herpes Simplex Virus 1
18 precursors, 27 mature
Herpes Simplex Virus 2
18 precursors, 24 mature
Herpesvirus saimiri strain A11
3 precursors, 6 mature
Herpesvirus of turkeys
17 precursors, 28 mature
Infectious laryngotracheitis virus
7 precursors, 10 mature
JC polyomavirus (1 precursor
2 mature
Kaposi sarcoma-associated herpesvirus
13 precursors, 25 mature [EMBL:U75698.1]
Mouse cytomegalovirus
18 precursors, 29 mature
Merkel cell polyomavirus
1 precursor, 2 mature
Mareks disease virus type 1
14 precursors, 26 mature [EMBL:AF243438.1]
Mareks disease virus type 2
18 precursors, 36 mature
Mouse gammaherpesvirus 68
15 precursors, 28 mature [EMBL:U97553.1]
Pseudorabies virus
13 precursors, 13 mature
Rhesus lymphocryptovirus
36 precursors, 68 mature
Rhesus monkey rhadinovirus
7 precursors, 11 mature [EMBL:AF210726.1]
Simian virus 40
1 precursor, 2 mature
135
The majority of host and viral miRNAs are processed through the canonical pathway. Other viruses utilize noncanonical
136
mechanisms in the biogenesis of pre-miRNA molecules and may not demonstrate the classic structural properties. For
137
example, the validated Epstein-Bar miRNA, ebv-miR-BART1-5p MIMAT0000999, as shown below contain 26 bp, and an
138
apical loop of only 6 nt, without an apical GUG (FIGURE 2).
139
6
140
Figure 2. Secondary structure of ebv-mir-BART1
141
The secondary structure of the validated Epstein - Barr virus miRNA, ebv-mir-BART1, was generated with Mfold [15].
142
The 5’ terminus contains a UG motif and a UGU mismatch region. The Terminal loop is 6 nt in length,, and the GUG
143
motif is not predicted to be in the 5’ region of the terminal loop but in the downstream region of the 5’ arm. The 3’
144
arm terminates in a CCCG sequence, in contrast to the canonical CNNC motif. Despite these deviations from the
145
canonical sequence-structure definitions of a novel miRNA, the ebv-mir-BART1 miRNA is a valid Epstein-Barr virus
146
encoded miRNA.
147
The World Health Organization (WHO) recently determined 5 priority research areas for the Zika virus outbreak to be:
148
1. Causality Framework
149
2. Sexual Transmission
150
3. Mosquito Vector Control
151
4. Natural History of Zika Virus Disease
152
5. Infection Control
153
Towards causality of the neurologic deficits, particularly with regards to the incidence of Guillan-Barre Syndrome (GBS),
154
we investigated the possibility of a previously unreported viral-encoded miRNA. As of this report, no definite Flavivirus
155
stem-loop or mature miRNAs have been identified in miRBase. The miRBase database is a searchable database of
156
published miRNA sequences with annotation and target listing. Each entry in the miRBase Sequence database represents
157
a predicted hairpin portion of a miRNA transcript (termed mir), with information on the location and sequence of the
158
mature miRNA sequence (termed miR) as well as predicted targets in the 3’UTRs in additional public databases. Both
159
hairpin and mature sequences are available for analysis.
160
Materials and Methods
161
Secondary Structure Analysis
162
7
163
The 427 nt 3’ UTR of the ZIKV miRNA (gi|226377833:10367-10794 ) was obtained from the National Center of
164
Biotechnology Information (NCBI) database (5’
165
GCACCAAUUUUAGUGUUGUCAGGCCUGCUAGUCAGCCACAGUUUGGGGAAAG CUG
166
UGCAGCCUGUAACCCCCCCAGGAGAAGCUGGGAAACCAAGCUCAUAGUCAGGCCGAGAACGCCAUGGCACGGAAGAAGCCAUG
167
CUGCCUGUGAGCCCCUCAGAGGACACUGAGUCAAAAAACCCCACGCGCUUGGAAGCGCAGGAUGGGAAAAGAAGGUGGCGAC
168
CUUCCCCACCCUUCAAUCUGGGGCCUGAACUGGAGACUAGCUGUGAAUCUCCAGCAGAGGGACUAGUGGUUAGAGGAGACCC
169
CCCGGAAAACGCAAAACAGCAUAUUGACGUGGGAAAGACCAGAGACUCCAUGAGUUUCCACCACGCUGGCCGCCAGGCACAGA
170
UCGCCGAACUUCGGCGGCCGGUGUGGGGAAAUCCAUGGUUUCU-3’) and submitted to the Mfold web server [15] with
171
default parameters to identify any potential miRNA stem loop secondary structure as well as the online Vienna
172
webserver. Secondary structures were predicted with RNAFold, formatting the output to base pair probability and
173
positional entropy [16, 17]. Pseudoknots were predicted with RNAstructure, Version 5.8 [18].
174
175
Comparison of the secondary structures of the sfRNA 3’UTR of West Nile virus (WNV|gi|11528013:10390-10962),
176
Dengue Virus 1 (DENV1|gb|JQ045634.1|:10252-10699), Chikungunya virus (CHIKV|gb|GU301780.1|:11233-11757), the
177
Japanese encephalitis virus (JEV|gb|AY303791.1|:10395-10970), and Yellow fever virus (YFV|gb|AF094612.1|:10355-
178
10760) was similarly analyzed for potential pri-miRNA stem loop sequences and compared with the secondary structure
179
of ZIKV.
180
181
MiRNA Stem Loop and Mature miRNA Candidates
182
183
Candidate stem loop and mature sequences were then searched in both MiRBase [30-36] and Vir-MiR [19] and filtered
184
to search only for human miRNA homologues (hsa-miR) and their human targets. The Vir-MiR Database has 171
185
accumulated potential miRNA’s in Flavivirus with none seen in the closely related Spondweni virus and Dengue virus
186
group (Table 2). Zika virus is not listed under any taxonomy group. West Nile Virus (GenBank 11528013) is listed in the
187
Japanese encephalitis group with six potential candidates. Three are located in the 3’ untranslated region (10367-
188
10794). The other candidates are identified within the coding sequence and not identified to be within the 5’ UTR (1-
189
106) were excluded from consideration (Table 3).
190
191
Table 2. Flavivirus miRNA Vir-miR database
192
Flavivirus (171 )
0
37
0
5
0
5
7
0
0
89
18
0
10
193
194
195
Table 3. West Nile Virus Potential miRNA segment-Vir-MiR database 2016
196
8
ID
strand
start
length
sRNA loop score
core MFE.
NP ORF
Known viral miRNA
3377
+
189
89
24.5
-26.1
NP_041724
None
3378
+
7267
90
20.5
-32.8
NP_041724
None
3379
+
10884
79
22
-29.6
None
None
3380
-
8371
89
21
-25.3
NP_041724
None
3381
-
10604
89
23
-24.9
None
None
3382
-
10961
77
22.5
-32.5
None
None
197
Multiple Sequence Analysis
198
The full length sfRNA 3’ UTR nucleotide sequences from Zika virus (ZIKV (gi|226377833:10367-10794), West Nile virus
199
(WNV|gi|11528013:10390-10962), Dengue Virus 1 (DENV1|gb|JQ045634.1|:10252-10699), Chikungunya virus
200
(CHIKV|gb|GU301780.1|:11233-11757), the Japanese encephalitis virus (JEV|gb|AY303791.1|:10395-10970), and
201
Yellow fever virus (YFV|gb|AF094612.1|:10355-10760) were then analyzed using Clustal Omega within Jalview 2.9.0b2
202
using default parameters [20].
203
Hybridization
204
Potential miRNA-target hybrids were analyzed using the RNAHybrid webserver Bielefeld Bioinformatics Server [21, 22].
205
RNAHybrid predicts multiple potential binding sites of miRNAs in large target RNAs by finding the energetically most
206
favorable hybridization sites of a small RNA in a large RNA sequences.
207
RESULTS
208
Secondary Structure of the ZIKV sfRNA
209
Mfold revealed a stem loop structure with significant positional entropy of ~87 base pairs from position 341-428 in the
210
terminal 3’ region of the ZIKV sfRNA (FIGURE 3). The 5p arm contains an AG motif as well as a UG motif in the 5’
211
overhang that could be recognized by Drosha to cleave the stem loop. There does appear to be a GUG mismatch motif in
212
the proximal 5’ region in the stem loop. The 3p arm terminates in a stable paired construct without overhang. The
213
terminal loop is a variable 9 nt in length and does not appear to contain the GUG motif (FIGURE 4). However, as
214
mentioned earlier, this may impair efficient recognition and processing, other known viral-encoded miRNAs also do not
215
contain that motif suggesting the GUG motif is not a requisite for processing to a pre-miRNA.
216
9
217
Figure 3. Secondary Structure of the ZIKV 3’ UTR sfRNA
218
The secondary structure of the Zika virus sfRNA of the 3’ UTR (428 nt) was generated in Mfold using default
219
parameters. The stem loop structure (MFE -167.68) from 343-428 is highlighted. Pseudoknots cannot be predicted in
220
Mfold and predictions of peudoknots were obtained with RNAStructure and visualized with VARNA [23].
221
222
10
Figure 4. ZIKV Predicted Stem Loop Structure
223
The 5’ end on the predicted stem loop (343-428) contains a basal AG motif similar to the West Nile Virus miRNA, a
224
GUG mismatch in the downstream 5p arm, a 9 nucleotide terminal loop with an apical AG motif similar to the West
225
Nile Virus miRNA, but does not have a 3p CNNC motif. All flavivirus sfRNAs terminate at the 3’ end without a CNNC
226
motif and the West Nile Virus miRNA has been demonsrated to bypass the endonucease Drosha and be processed in
227
the cytoplasm.
228
Pseudoknots cannot be predicted in Mfold. A circular structure diagram of the partition function to predict potential
229
pseudoknots was obtained using RNAStructure, version 5.8 (Figure 5) and the secondary structure was generated with
230
VARNA version 3.93 [23] (FIGURE 6). 5 regions of pseudoknot formation were predicted ({1-3->26-28}; {31-32->67-68},
231
{91-92->159-160}; 107-110->196-200}; {237-243->302-308}) from position 1-308. No pseudoknot was predicted to lay
232
within the stable stem loop located within the the 3’ region of the ZIKV sfRNA.
233
234
Figure 5. Circular plot of potential pseuknots in ZIKV sfRNA (RNAStructure)
235
A partition function, maximum free energy (MFE) structure, and pseudoknot prediction was performed using
236
RNAStructure. The resultant sequence-structure was submitted to RNAFold for secondary structure visualization for
237
calculation of positional entropy. All predicted pseudoknots were upstream from the stable, conserned stem loop
238
structure in the 3’ region 383-428 in the Zika virs sfRNA.
239
11
240
241
Figure 6. Pseudoknot Secondary Structure Prediction of ZIKV (Natal, 2016)
242
A pseudoknot dot-plot structure was generated in RNAStructure and RNAFold. 5 regions of pseudoknot formation
243
were predicted ({1-3->26-28}; {31-32->67-68}, {91-92->159-160}; 107-110->196-200}; {237-243->302-308}) from
244
position 1-308 in the Zika virus sfRNA. The 3’ stem loop was downstream and was not involved in a predicted
245
pseudoknot conformation. The secondary structure was visualized utilizing Pseudoviewer 2.5 (Panel A) [46-49] and
246
the Visualization Applet for RNA (VARNA-Panel B) [23].
247
SECONDARY STRUCTURE COMPARISON OF THE sfRNA IN FLAVIVIRUS
248
Base pair probability secondary structures were obtained and visualized with RNAFold. A conserved secondary stem
249
loop within the 3’ region was observed in ZIKV, WNV, JEV, and YFV (Figure 7). ZIKV demonstrated the best base pair
250
probability and positional entropy within the predicted stem loop as compared to WNV, JEV, and YFV. The long stem
251
loop was not observed in either CHIKV or DENV1.
252
12
253
13
FIGURE 7. Comparative Secondary Structures in Flavivirus
254
Secondary structures of the sfRNA from ZIKV, WNV, DENV1, CHIKV, Japanese encephalitis virus, and Yellow Fever
255
Virus were generated with RNAFold, formatted for base pair probability and positional entropy. A highly conserved 3’
256
stem loop was identified between ZIKV (383-428), WNV (492-573), Japanese encephalitis virus (497-576), and Yellow
257
Fever Virus (318-406). The 3’ region was significantly truncated in both DENV1 and CHIKV with respect the ZIKV sfRNA
258
sequence.
259
MULTIPLE SEQUENCE ALIGNMENT IN FLAVIVIRUS
260
A global MSA of representative sequences from Zika virus (ZIKV (gi|226377833:10367-10794 ), West Nile virus
261
(WNV|gi|11528013:10390-10962), Dengue Virus 1 (DENV1|gb|JQ045634.1|:10252-10699), Chikungunya virus
262
(CHIKV|gb|GU301780.1|:11233-11757), the Japanese encephalitis virus (JEV|gb|AY303791.1|:10395-10970), and
263
Yellow fever virus (YFV|gb|AF094612.1|:10355-10760) were then analyzed using Clustal Omega within Jalview, version
264
2.9. The coloring was set to percent identity for region of conservation. The length of the sfRNA ranged from 406 nt (5’
265
truncated in YFV) to 576 nt (JEV). The regions of conservation were identified at positions 15-27, 56-78. 137-146, 213-
266
222, 242-265, 277-302, 316-341, and 378-390. (Figure 8)
267
Within the stem loop of ZIKV, the 3’ region and the terminal loop demonstrated the greatest overall conservation as
268
compared to ZIKV (FIGURE 9). CHIKV was poorly conserved as to ZIKV overall. JEV and DENV1 were both truncated at the
269
3’ arm which would affect base pairing in a miRNA model as suggested by the comparative secondary structures. The
270
West Nile Virus sequence-structure homology was strongest sequence homology was in the terminal loop (78%). The 40
271
nt 3p arm demonstrated 60% sequence homology, decreasing at the distal segment. The 5p arm demonstrates only 42%
272
sequence homology (Figures 8 and 9). This would suggest that any potential viral-encoded mature miRNA generated by
273
ZIKV would be unique to the species.
274
275
14
276
FIGURE 8. MSA sfRNA with Zika virus, West Nile Virus, Dengue virus 1 (DENV1), Dengue virus 2 (DENV2), Cikungunya
277
virus (CHIKV), Japanese encephalitis virus, and Yellow fever virus
278
The length of the sfRNA ranged from 406 (Yellow Fever Virus)-573 (West Nile Virus0. With respect to the sfRNA of
279
ZIKV, the overall percent identity with West Nile Virus was 54.88%, DENV1 63.98%, CHIKV 46.55%, Japanese
280
encephalitis virus 54.34%, and Yellow fever virus 55.69%. The 3’ region from position 208-423 of ZIKV demonstrated
281
the greatest homology. The Dengue virus sfRNA and Chikungunya sfRNA were 3’ truncated with respect to the Zika
282
virus stem loop, West Nile Virus, Japanese encephalitis virus, and Yellow Fever Virus sfRNA sequences.
283
284
285
15
286
FIGURE 9. MSA ZIKV Stem Loop with West Nile Virus, Dengue virus 1 (DENV1), Dengue virus 2 (DENV2), Chikungunya
287
virus (CHIKV), Japanese encephalitis virus, and Yellow fever virus
288
Computational studies identified an 84 nucleotide stem loop structure in the 3' end of Zika virus sfRNA (position (343-
289
428), 6 nucleotides (nt) downstream from the Zika virus sfRNA 5’ flanking sequence, UGGAA. DENV2, DENV1, and
290
CHIKV sfRNA sequences were truncated at the 3’ region with respect to ZIKV.
291
The West Nile virus stem loop was 81 nucleotides in length (492-573), and 58% conserved overall as to sequence. The
292
majority on the sequence homology with West Nile Virus was in the terminal loop (78%). The 40 nt 3p arm
293
demonstrated 60% homology, decreasing at the distal segment. The 5p arm demonstrates on 42% sequence
294
homology.
295
ZIKV miRNA Candidates in MiRBase
296
The predicted miRNA sequence from ZIKV obtained from the secondary structure analysis ( 5’-UGGGAAAGACCAGAGAC
297
UCCAUGAGUUUCCACCACGCUGGCCGCCAGGCACAGAUCGCCGAACUUCGGCGGCCGGUGUGGGGAAAUCCAUGGUUUCU-3’)
298
was studied using MiRBase in search of human hsa--mir homologues. The 5’ end was extended 7 nt in order to include
299
the flanking sequence that included the nearest UG motif.
300
An 84 single stem loop sequence corresponding to hsa-mir-147a (accession number MI0000262) + strand (E-value 7.0)
301
was obtained in the 3’ terminal loop extending into the 3p 56-90 arm. The overall sequence homology was greatest in
302
the 3p arm (5’-GGCCGGUGUGGGGAAAUCCAUGGUUUCU-3’) and not in the terminal loop (5’-CGCCGAACUUCGGC-3’).
303
304
ZIKV|3'UTR|gi|226377833/1-54 CCAGGCACAGAUCGCCGAACUUCGGCGGCCGGUGUGGGGAAAUCCAUGGUUUCU--
305
||||| |||||| | ||||
306
hsa-mir-147a|mature/1-20 -------------------------------GUGUGUGGAAAUGC-----UUCUGC
307
308
Hsa-mir-147 is a member of gene family MIPF0000105, mir-147. The mature sequence is 5’-
309
GUGUGUGGAAAUGCUUCUGC-3’ corresponding to the region of homology with 3p arm of the ZIKV stem loop, position
310
75-90, upstream from the essential GUG motif mismatch (Figure 10).
311
16
312
Figure 10. Mir-147 mature miRNA (MIMAT0000251) secondary structure.
313
The stem loop sequence of the validated hsa-mir-147 was visualized with Mfold. The mature hsa-mir-miRNA
314
sequence, 5’-GUGUGGGGAAAUCCAU-3’ and hybridizes to the 3’ UTR target sequence, CCACACA, is highlighted in
315
green.
316
317
318
17
TARGETS
319
There are 251 predicted targets for hsa-miR-147a in miRDB (See Supplemental Files). Table 4 lists those targets with a
320
score greater than 90. Neurofascin, rank number 7, functions include cell adhesion, ankyrin-binding protein which may
321
be involved in neurite extension, axonal guidance, synaptogenesis, myelination and neuron-glial cell interactions. Most
322
importantly, anti-neurofascin autoantibodies have been reported in Guillan-Barre Syndrome.
323
Table 4. miRDB Targets of hsa-mir-147a
324
Target
Rank
Target
Score
miRNA Name
Gene
Symbol
Gene Description
1
99
hsa-miR-147a
BMP4
bone morphogenetic protein 4
2
98
hsa-miR-147a
CREBRF
CREB3 regulatory factor
3
98
hsa-miR-147a
KIAA1549L
KIAA1549-like
4
98
hsa-miR-147a
ACLY
ATP citrate lyase
5
98
hsa-miR-147a
C2orf61
chromosome 2 open reading frame 61
6
97
hsa-miR-147a
BCOR
BCL6 corepressor
7
96
hsa-miR-147a
NFASC
Neurofascin
8
96
hsa-miR-147a
PCMT1
protein-L-isoaspartate (D-aspartate) O-methyltransferase
9
96
hsa-miR-147a
PDE4D
phosphodiesterase 4D, cAMP-specific
10
95
hsa-miR-147a
CYP2S1
cytochrome P450, family 2, subfamily S, polypeptide 1
11
94
hsa-miR-147a
IGLON5
IgLON family member 5
12
93
hsa-miR-147a
SV2A
synaptic vesicle glycoprotein 2A
13
93
hsa-miR-147a
HDLBP
high density lipoprotein binding protein
14
92
hsa-miR-147a
ZEB2
zinc finger E-box binding homeobox 2
15
92
hsa-miR-147a
C11orf87
chromosome 11 open reading frame 87
16
92
hsa-miR-147a
NR3C1
nuclear receptor subfamily 3, group C, member 1
(glucocorticoid receptor)
17
91
hsa-miR-147a
NF1
neurofibromin 1
18
91
hsa-miR-147a
PLXNA2
plexin A2
19
91
hsa-miR-147a
DCLK3
doublecortin-like kinase 3
325
Three of these (BMP4, NF1, and ZEB2) were found to be involved in telencephalon development (GO:0021537) using
326
DAVID (Huang et al., 2009a; Huang et al., 2009b) Telencephalon development is the paired anteriolateral division of the
327
prosencephalon and the lamina terminalis from which the olfactory lobes, cerebral cortex, and subcortical nuclei are
328
derived. The same three proteins are also involved in forebrain development, including the cerebral hemispheres, the
329
18
thalamus, the hypothalamus, and the sensory and associative information processing, visceral functions, and voluntary
330
motor function control centers [42, 43, 44].
331
Hsa-mir-147a appears to target the CCACACA motif in the 3’ UTR of neurofascin (seed location 5851-5857; 5937-5943).
332
The mir-147a binding sequence, GGUGUGU, has significant homology to ZIKV 3p arm motif, GGUGUGG, suggesting a
333
ZIKV miRNA could also bind in a similar fashion to repress the Neurofascin transcript.
334
>Neurofascin 3’UTR| NM_015090
335
5821 GACCCCGGAG GACCTCCTGC CCCGCCCCCA CCACACACCC ATATCCCCCA CCATTCCAAT
336
5881 TTGTTCTTTC CCGTGGGGAA TTTTTTTTCC CAGCGTCTCC ATCCCTTCCT ACATATCCAC
337
5941 ACACACACAA ATTGGTCTGA TCTTTTTTCC ATTGGTTAAA CATTTAACTC CATGCCAGAC
338
339
ZIKV HYBRID with Neurofascin
340
To evaluate a hybrid with Neurofascin, a 4620 nt sequence of the 3’ UTR including the CCACACA seed region was
341
analyzed with a 21 nt sequence from the ZIKV 3’ arm (5’-GGCCGGUGUGGGGAAAUCCAU-3’). A significant 9 mer
342
predicted binding region was identified downstream from the original seed region, also an 8 mer match. Both seed
343
regions were separated by a single nucleotide (Figure 11). This would effectively be predicted to be capable of
344
repressing transcription of the NFASC gene and cause a Guillan-Barre Syndrome similar to the anti-neurofascin
345
autoantibodies reported to cause GBS.
346
347
Figure 11. Predicted ZIKV-NAFSC Hybrid
348
The predicted ZIKV mature miRNA sequence was hybridized with the predicted 3’ UTR sequence from Neurofascin
349
and analyzed with RNAHybrid. The ZIKV sequence, 5’-GGCCGGUGUGGGGAAAUCCA-3’, in 3p stem loop arm was found
350
to have a significant hybrid match to the NFASCN 3’ UTR, 5’-CUGGAUUUCCUCCAACACC-3’.
351
DISCUSSION
352
The World Health Organization (WHO) has declared Causality Framework as a priority towards understanding the
353
pathogenicity of Zika virus in lieu of the reported incidences of microcephaly and Guillan-Barre Syndrome. To answer
354
this, we append our list to include a potential miRNA in the sfRNA of Zika virus.
355
As of this report, only one miRNA has been reported in Flaviviridae, the closely related West Nile Virus. The Vir-MiR
356
database has 171 potential candidates, but none for ZIKV. The ZIKV sfRNA 3’ UTR that appears to contain a miRNA at the
357
3’ end needs conclusive laboratory evidence of its existence in that the neurofascin target could very well be responsible
358
19
for the reported incidence on GBS in adults infected with Zika virus, and microcephaly due to gestational Zika virus
359
infection.
360
The Neurofascin (NFASC) gene encodes three glial and neuronal isoforms of neurofascin (NFASC-140, NFASC-155, and
361
NFASC-186) with key functions in assembling the nodal macromolecular complex, cell adhesion, ankyrin-binding protein
362
(which may be involved in neurite extension), axonal guidance, synaptogenesis, myelination and neuron-glial cell
363
interactions . NFASC-140 is a neuronal protein strongly expressed during mouse embryonic development. Expression of
364
NFASC-140 persists but declines during the initial stages of node formation, in contrast to NFASC-155 and NFASC-186,
365
which increase. Nevertheless, NFASC-140 can cluster voltage-gated sodium channels (Nav) at the developing node of
366
Ranvier and can restore electrophysiological function independently of NFASC-155 and NFASC-186. This suggests that
367
NFASC-140 complements the function of Nfasc155 and Nfasc186 in initial stages of the assembly and stabilization of the
368
nodal complex. NFASC-140 is reexpressed in demyelinated white matter lesions of postmortem brain tissue from human
369
subjects with multiple sclerosis. This expands the critical role of the NFASC gene in the function of myelinated axons and
370
reveals further redundancy in the mechanisms required for the formation of this crucial structure in the vertebrate
371
nervous system. [24].
372
Anti-neurofascin autoantibodies have been reported in Guillan-Barre Syndrome, combined central and peripheral
373
demyelination disease (CCPD), Multiple Sclerosis (MS), and encephalitis [25-29]. As such, a virus-encoded miRNA from
374
ZIKV with significant homology to hsa-mir-147 that is known to target neurofascin, is a compelling concept and worthy
375
of further investigation. GuillainBarré Syndrome is a transient, post-infectious disorder with onset at 10 days and
376
normally resolves within 30 days, it would be imperative to consider how a miRNA from Zika virus might contribute in
377
the pathogenicity. The highly conserved secondary structures within the subgenomic Flavivirus RNA (sfRNA) of the 3’
378
UTR were resistant to host nucleases and responsible for the observed cytopathology and pathogenicity. A contained,
379
functioning Zika virus miRNA therefore could result in emergence of GBS if appropriate therapy is not directed towards
380
that segment and may not respond to conventional immune therapies.
381
The next phase of research should initially validate this Zika virus encoded-miRNA, similar to the West Nile Virus, and
382
validate all identified targets (neurofascin, synaptic vesicle glycoprotein 2A, neurofibromin 1, SAM and SH3 domain
383
containing 1, neurogenin 2, others) that have been identified in the course of this study. The same miRNA-like small RNA
384
in a 3' untranslated region is found in West Nile virus and has been shown to up-regulate GATA4 expression. GATA4, a
385
zinc finger transcription factor, has been shown to be expressed during neural crest formation .GATA4 resides in 8p23; a
386
8p23 deletion syndrome is known as a malformation syndrome with clinical symptoms of facial anomalies,
387
microcephaly, mental retardation, and congenital heart defects (CHD), however CNV variation in GATA4 appears
388
unrelated to CHD (Guida et al., 2010 [41]). MCPH1 is also nearby (Bhatia et al., 1999), and co-localized genes are often
389
co-regulated. We have elsewhere explored the plausibility of MCPH1 involvement in p53-BAX mediated apoptosis [37].
390
GATA4 also regulates Nf-kB and has been shown in excess to move cells into a phase of senescence (Kang et al., 2015
391
[38]).Additional co-factors remain unexplored and should be included in epidemiological studies (Lyons-Weiler et al., in
392
review [37]).Additionally, mutations in either the mature miRNA segment or host target genes may exist that make
393
binding more likely in some individuals and should be examined as a potential co-factor.
394
The risk of microcephaly due to Zika infection has been reported to be to 1% at this time (Cao-Lormeau et al., 2016)[45].
395
Our study has lessons for future studies of the etiology and mechanisms of pathologies of viruses, including Zika;
396
miRNAs can have multiple divergent and convergent effects on genome regulation. We propose that miRNA-targeted
397
treatments may be therefore considered keystone treatments that may be more effective than treatments that target
398
individual proteins. Treatments aimed at silencing this Zika virus encoded-miRNA may be effective in the prevention of
399
Flavivirus-induced pathologies.
400
20
References
401
1. Chambers TJ, Hahn CS, Galler R, Rice CM. Flavivirus genome organization, expression, and replication. Annu Rev
402
Microbiol. 1990;44:649-88. Review. PubMed PMID: 2174669.
403
2. Pijlman GP, Funk A, Kondratieva N, Leung J, Torres S, van der Aa L, Liu WJ, Palmenberg AC, Shi PY, Hall RA,
404
Khromykh AA. A highly structured,nuclease-resistant, noncoding RNA produced by flaviviruses is required for
405
pathogenicity. Cell Host Microbe. 2008 Dec 11;4(6):579-91. doi: 10.1016/j.chom.2008.10.007. PubMed PMID:
406
19064258.
407
3. Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T (2001) Identification of novel genes coding for small
408
expressed RNAs. Science 294: 85385 doi:.1126/science.1064921
409
4. Lau NC, Lim LP, Weinstein EG, Bartel DP (2001) An abundant class of tiny RNAs with probable regulatory roles in
410
caenorhabditis elegans. Science 294: 858862. doi: 10.1126/science.1065062
411
5. Lee RC, Ambros V (2001) An extensive class of small RNAs in caenorhabditis elegans. Science 294: 862864. doi:
412
10.1126/science.1065329
413
6. Denli AM, Tops BB, Plasterk RH, Ketting RF, Hannon GJ. Processing of primary microRNAs by the Microprocessor
414
complex. Nature. 2004 Nov 11;432(7014):231-5. Epub 2004 Nov 7. PubMed PMID: 15531879.
415
7. Gregory RI, Yan KP, Amuthan G, Chendrimada T, Doratotaj B, Cooch N, Shiekhattar R. The Microprocessor
416
complex mediates the genesis of microRNAs. Nature. 2004 Nov 11;432(7014):235-40. Epub 2004 Nov 7. PubMed
417
PMID: 15531877.
418
8. Landthaler M, Gaidatzis D, Rothballer A, et al. Molecular characterization of human Argonaute-containing
419
ribonucleoprotein complexes and their bound target mRNAs. RNA. 2008;14(12):2580-2596.
420
doi:10.1261/rna.1351608.
421
9. Han J, Lee Y, Yeom KH, Nam JW, Heo I, Rhee JK, Sohn SY, Cho Y, Zhang BT, Kim VN. Molecular basis for the
422
recognition of primary microRNAs by the Drosha-DGCR8 complex. Cell. 2006 Jun 2;125(5):887-901. PubMed
423
PMID: 16751099.
424
10. Auyeung, Vincent C. Beyond Secondary Structure: Primary-Sequence Determinants License Pri-miRNA Hairpins
425
for Processing. Cell , Volume 152 , Issue 4 , 844 - 858
426
11. Fang W, Bartel DP. The Menu of Features that Define Primary MicroRNAs and enable De Novo Design of
427
MicroRNA Genes. Mol Cell. 2015 Oct 1;60(1):131-45. doi: 10.1016/j.molcel.2015.08.015. Epub 2015 Sep 24.
428
PubMed PMID: 26412306; PubMedCentral PMCID: PMC4613790
429
12. Nguyen TA, Jo MH, Choi YG, Park J, Kwon SC, Hohng S, Kim VN, Woo JS. Functional Anatomy of the Human
430
Microprocessor. Cell. 2015 Jun 4;161(6):1374-87. doi: 10.1016/j.cell.2015.05.010. Epub 2015 May 28. PubMed
431
PMID: 26027739.
432
13. R Ricketson, SM Christensen. Computational Evidence For A Viral Encoded Mirna In The Intergenic, Untranslated
433
Region Of The Zaire Ebolavirus Nucleoprotein Gene May Explain Its Differential Virulence: A Potential Pandora
434
Element. WebMedCentralPlus, March 15, 2015, Article ID: WMCPLS00512; ISSN 2051-0799]
435
14. RP Kincaid, CS Sullivan. Virus-Encoded microRNAs: An Overview and a Look to the Future. PLOS Pathogens,
436
December 20, 2012. DOI: 10.1371/journal.ppat.1003018
437
15. M. Zuker. Mfold web server for nucleic acid folding and hybridization prediction.. Nucleic Acids Res. 31 (13),
438
3406-15, (2003)
439
16. Gruber AR, Lorenz R, Bernhart SH, Neuböck R, Hofacker IL. The Vienna RNA Websuite. Nucleic Acids Res. 2008.
440
Lorenz, R. and Bernhart, S.H. and Höner zu Siederdissen, C. and Tafer, H. and Flamm, C. and Stadler, P.F. and
441
Hofacker, I.L. “ViennaRNA Package 2.0
442
17. RNAFold. Algorithms for Molecular Biology, 6:1 page(s): 26, 2011
443
18. Matthews Lab; . Reuter, J. S., & Mathews, D. H. (2010). RNAstructure: software for RNA secondary structure
444
prediction and analysis. BMC Bioinformatics. 11,129.
445
19. Li S-C, Shiau C-K, Lin W. Vir-Mir db: prediction of viral microRNA candidate hairpins. Nucleic Acids Research.
446
2008;36(Database issue):D184-D189. Doi:10.1093/nar/gkm610
447
20. Waterhouse, A.M., Procter, J.B., Martin, D.M.A, Clamp, M. and Barton, G. J. (2009) “Jalview Version 2 – a
448
multiple sequence alignment editor and analysis workbench” Bioinformatics25 (9) 1189-1191 doi:
449
10.1093/bioinformatics/btp033
450
21
21. RNAHybrid. http://bibiserv.techfak.uni-bielefeld.de/rnahybrid
451
22. Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R. Fast and effective prediction of microRNA/target
452
duplexes. RNA. 2004 Oct;10(10):1507-17. PubMed PMID: 15383676; PubMed Central PMCID: PMC1370637
453
23. VARNA. http://varna.lri.fr
454
24. Zhang, A; Desmazieres, A; Zonta, B; Melrose, S; Campbell, G; Mahad, D; Li, Q; Sherman, DL; Reynolds, R; Brophy,
455
PJ, Ng, King Man (2012): Anti-neurofascin antibodies: assay development and analysis of inflammatory diseases
456
in the peripheral and central nervous system. Dissertation, LMU München: Graduate School of Systemic
457
Neurosciences (GSN)]
458
25. Willison H. Neurofascin as target of autoantibodies in GuillainBarré syndrome. Brain May 2011, 134 (5) e174;
459
DOI: 10.1093/brain/awq375
460
26. Kawamura N, Yamasaki R, Yonekawa T, Matsushita T, Kusunoki S, Nagayama S,Fukuda Y, Ogata H, Matsuse D,
461
Murai H, Kira J. Anti-neurofascin antibody in patients with combined central and peripheral demyelination.
462
Neurology. 2013 Aug 20;81(8):714-22. doi: 10.1212/WNL.0b013e3182a1aa9c. Epub 2013 Jul 24. PubMed PMID:
463
23884033.
464
27. Mathey EK, Derfuss T, Storch MK, Williams KR, Hales K, Woolley DR, Al-Hayani A, Davies SN, Rasband MN, Olsson
465
T, Moldenhauer A, Velhin S, Hohlfeld R, Meinl E,
466
28. Linington C. Neurofascin as a novel target for autoantibody-mediated axonal injury. J Exp Med. 2007 Oct
467
1;204(10):2363-72. Epub 2007 Sep 10. PubMed PMID: 17846150; PubMed Central PMCID: PMC2118456.,
468
29. Lindner M, Ng JK, Hochmeister S, Meinl E, Linington C. Neurofascin 186 specific autoantibodies induce axonal
469
injury and exacerbate disease severity in experimental autoimmune encephalomyelitis. Exp Neurol. 2013
470
Sep;247:259-66. doi: 10.1016/j.expneurol.2013.05.005. Epub 2013 May 18. PubMed PMID: 23688679
471
30. Kozomara A, Griffiths-Jones S. NAR 2014 42:D68-D73 miRBase: integrating microRNA annotation and deep-
472
sequencing data.
473
31. Kozomara A, Griffiths-Jones S.NAR 2011 39:D152-D157miRBase: tools for microRNA genomics.
474
32. Griffiths-Jones S, Saini HK, van Dongen S, Enright AJ.NAR 2008 36:D154-D158miRBase: microRNA sequences,
475
targets and gene nomenclature.
476
33. Griffiths-Jones S, Grocock RJ, van Dongen S, Bateman A, Enright AJ.NAR 2006 34:D140-D144The microRNA
477
Registry. Griffiths-Jones S. NAR 2004 32:D109-D111
478
34. Ambros V, Bartel B, Bartel DP, Burge CB, Carrington JC, Chen X, Dreyfuss G, Eddy SR, Griffiths-Jones S, Marshall
479
M, Matzke M, Ruvkun G, Tuschl T. A uniform system for microRNA annotation. RNA 2003 9(3):277-279
480
35. Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, Bowman JL, Cao X, Carrington JC, Chen X, Green PJ,
481
Griffiths-Jones S, Jacobsen SE, Mallory AC, Martienssen RA, Poethig RS, Qi Y, Vaucheret H, Voinnet O, Watanabe
482
Y, Weigel D, Zhu JK. Criteria for annotation of plant MicroRNAs. Plant Cell. 2008 20(12):3186-3190
483
36. Hussain M, Torres S, Schnettler E, et al. West Nile virus encodes a microRNA-like small RNA in the 3′
484
untranslated region which up-regulates GATA4 mRNA and facilitates virus replication in mosquito cells. Nucleic
485
Acids Research. 2012;40(5):2210-2223. doi:10.1093/nar/gkr848.
486
37. Lyons-Weiler, J., R. Ricketson, E.F. Fogarty and G. Macgregor-Skinner. in review. Areas of Research and
487
Preliminary Evidence on Microcephaly, Guillain-Barré Syndrome and Zika Virus Infection in the Western
488
Hemisphere. PLOS One, PONE-D-16-09495
489
38. Kang C et al., 2015. The DNA damage response induces inflammation and senescence by inhibiting autophagy of
490
GATA4. Science. 2015 Sep 25;349(6255):aaa5612. doi: 10.1126/science.aaa5612.
491
39. Bhatia SN et al., 1999. Prenatal detection and mapping of a distal 8p deletion associated with congenital heart
492
disease. Prenat Diagn. 19(9):863-7.
493
40. http://eriba.umcg.nl/wp-content/uploads/2016/01/Demaria-2015-09-25-Science.pdf
494
41. Guida V, Lepri F, Vijzelaar R, De Zorzi A, Versacci P, Digilio MC, Marino B, De Luca A, Dallapiccola B. 2010.
495
Multiplex ligation-dependent probe amplification analysis of GATA4 gene copy number variations in patients
496
with isolated congenital heart disease. Dis Markers. 28(5):287-92. doi: 10.3233/DMA-2010-0703.
497
42. Huang DW, Sherman BT, Lempicki RA. 2009a. Systematic and integrative analysis of large gene lists using DAVID
498
Bioinformatics Resources. Nature Protoc. 4(1):44-57.
499
43. Huang DW, Sherman BT, Lempicki RA. 2009b. Bioinformatics enrichment tools: paths toward the comprehensive
500
functional analysis of large gene lists. Nucleic Acids Res. 37(1):1-13.
501
22
44. Marin, O. and Rubenstein, JL. 2003. Cell migration in the forebrain. Annual Review of Neuroscience [2003,
502
26:441-483]
503
45. Cao-Lormeau VM, Blake A, Mons S, Lastère S, Roche C, Vanhomwegen J, Dub T, Baudouin L, Teissier A, Larre P,
504
Vial AL, Decam C, Choumet V, Halstead SK, Willison HJ, Musset L, Manuguerra JC, Despres P, Fournier E, Mallet
505
HP, Musso D, Fontanet A, Neil J, Ghawché F. 2016. Guillain-Barré Syndrome outbreak associated with Zika virus
506
infection in French Polynesia: a case-control study. Lancet. pii: S0140-6736(16)00562-6. Doi: 10.1016/S0140-
507
6736(16)00562-6.
508
46. Y. Byun and K. Han, PseudoViewer3: generating planar drawings of large-scale RNA structures with pseudoknots,
509
Bioinformatics, Vol. 25, 1435-1437, 2009.
510
47. Y. Byun and K. Han, PseudoViewer: web application and web service for visualizing RNA pseudoknots and
511
secondary structures, Nucleic Acids Research, Vol.34, W416-W422, 2006.
512
48. K. Han and Y. Byun, PseudoViewer2: visualization of RNA pseudoknots of any type, Nucleic Acids Research, Vol.
513
31, No. 13, 3432-3440, 2003.
514
49. K. Han, Y. Lee and W. Kim, PseudoViewer: Automatic Visualization of RNA Pseudoknots, Bioinformatics, Vol. 18,
515
S321-S328, 2002.
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... Our analyses of the genomic sequences of ZIKV obtained from the publicly available databases in search of plausible specific molecular mechanisms of microcephaly also yielded a finding of an miRNA-like hairpin secondary structure located at the 3' terminus of the 3' untranslated region (UTR) in ZIKV [29]. This structure located within the sfRNA of ZIKV was found to have considerable structural and sequence homology with a West Nile Virus miRNA in the 3' UTR. ...
... A stem loop and mature human miRNA homolog, hsa-miR-147a, was identified. 251 predicted human target transcripts included Neurofascin (NFASC), Synaptic Vesicle Glycoprotein 2A (SV2A), and Neurofibromin 1 (NF1) were identified as neurotropic targets [29]. ...
... A novel miRNA in ZIKV is also a plausible candidate, especially given it targeting of neurotropic transcripts such as neurofascin, synaptic vesicle glycoprotein 2A, neurofibromin 1, SAM and SH3 domain containing 1, and neurogenin 2 [29]. ...
Chapter
An increasingly diverse literature has established that microRNAs (miRNAs) are expansively engaged in typical brain development, function, and dysfunction. They are comprehensively involved in the genesis of the hippocampus, frontal cortex, cerebellum, midbrain, and other nervous tissues. miRNA dysfunction has been demonstrated to lead to nerve-related pathologies. Deciphering the roles of miRNAs in response to diseases, such as Zika, provides an approach to discover key pathways connected to illness, including the role of essential genes that may be silenced, repressed, activated, and/or enhanced.
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Sir, The letter by Pruss and colleagues on the detection antibodies to neurofascin in Guillain–Barre syndrome raises a number of important questions and highlights exciting recent developments in the field of novel autoantibodies in both peripheral and central nervous system disorders. As such, there is a compelling case for investigating this area in much more detail. Neurofascin is one of a large number of potential nodal complex protein candidates to be explored, as Pruss et al . point out. While in principle this sounds straightforward, in practice it is complicated in terms of technical issues …
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