Theory Biosci. DOI 10.1007/s12064-015-0211-9
ORIGINAL PAPER
In-silico search of virus-specific host microRNAs regulating avian influenza virus NS1 expression V. N. Muhasin Asaf1 • Amod Kumar1 • Ashwin Ashok Raut2 • Sandeep Bhatia2 Anamika Mishra2
•
Received: 28 May 2014 / Accepted: 28 April 2015 Ó Springer-Verlag Berlin Heidelberg 2015
Abstract Avian influenza is a highly contagious viral infection caused by avian influenza virus type A of the family Orthomyxoviridae primarily affecting the avian species. The non-structural protein 1 (NS1) encoded by the NS1 gene of the virus is critical in establishing the infection. NS1 protein acts to suppress the virus-induced host interferon response and also inhibit Protein kinase R activation thereby helping the virus to establish the infection. MicroRNAs (miRNA) are small regulatory endogenous non-coding RNAs of *22 nucleotides in length located within introns of coding and non-coding genes, exons of non-coding genes or inter-genic regions. miRNAs can target the gene at various sites and effectively reduce or shut down its expression. In this study, set of differentially expressed chicken miRNA identified by deep sequencing H5N1 infected and SPF chicken lung were computationally analyzed, to identify targets in the NS1 gene. 300 differentially expressed miRNAs were then analyzed individually for target sites in gi|147667147|gb|EF362422.1| influenza A virus (A/chicken/India/NIV33487/06(H5N1)) segment 8, complete sequence using RNAhybrid 2.2. The analysis yielded gga-miR-1658* as the potential miRNA which is targeting the NS1 gene of H5N1 genome. Keywords Avian influenza NS1 miRNA RNAhybrid H5N1
& Anamika Mishra [email protected] 1
Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
2
High Security Animal Disease Laboratory, Bhopal, India
Introduction Highly pathogenic avian influenza (HPAI) is a contagious viral infection, primarily affecting avian species which is characterized by gastrointestinal, respiratory, and/or nervous signs particularly in avian species. HPAI is caused by avian influenza virus type A of the family Orthomyxoviridae. The family Orthomyxoviridae contains four genera: influenza A, B, and C viruses, and thogotovirus (Lamb and Krug 2001). HPAI was one of the first viral diseases to be described in poultry and still remains a major area of concern. Influenza type A viruses have been important with respect to genetic variations and pandemics it has caused. Influenza viruses can undergo genetic changes either by single mutations or by recombination with the genetic material of other flu strains (Petsko 2005). MicroRNAs (miRNA) are small, regulatory, endogenous, non-coding RNAs of *22 nucleotides in length located within introns of coding and non-coding genes, and exons of non-coding genes or inter-genic regions (Kim and Nam 2006). The miRNAs are reported to be involved in many biological processes and are having great impacts on computational and traditional biology (Min and Yoon 2010). miRNAs are the key regulators of numerous genes in biological processes that ranges from developmental timing to apoptosis (Boehm and Slack 2005; Sokol and Ambros 2005). Metazoan miRNA function has been mainly for target sites in 30 untranslated regions (UTRs), but they are also found to target the 50 UTRs and open reading frames (ORFs) (Bartel 2009). Depending on the complementarity between miRNA and target mRNA, there are two known mechanisms of miRNAs action on mRNAs, target mRNA degradation and translational inhibition; however, in animals, they function mainly by preventing translation without mRNA degradation. When the miRNA is near-perfectly
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complementary with target mRNA, then deadenylation and subsequent degradation of the target mRNA occur (Giraldez et al. 2006). Translational inhibition occurs when the miRNA is having partial complementary to its target mRNA (Doench and Sharp 2004). These two processes are independent to each other but may overlap sometimes (Gu and Kay 2010), but the modulation of one event can also result a corresponding change in the other (Coller and Parker 2004). The mRNA degradation occurs by removing the cap at 50 end and deadenylation of the poly-A tail (Wu et al. 2006). Translational repression is always followed by mRNA degradation and translational repression is the initial event which is followed by a secondary event of mRNA degradation (Djuranovic et al. 2012). MicroRNAs which can regulate the gene expression can be used as candidates for developing novel biotherapeutics against viruses (Giglio and Vecchione 2010). Segment 8 of H5N1 genome encodes the non-structural protein 1 (NS1) protein. The NS1 protein is a regulatory protein with numerous functions (Krug et al. 2003) which is a regulator of both mRNA splicing and translation, and also plays a critical role in competing against the cell’s antiviral defense. NS1 protein is directly related to the pathogenicity of the influenza strain (Palese 2006). The NS1 protein which is highly expressed in infected cells acts to suppress the virus-induced host interferon (IFN) response and due to this property NS1 gene is also called as IFN antagonist (Garcia-Sastre 2005). NS1 has double-stranded (ds) RNAbinding function by which it prevents the synthesis of IFN mRNA or to destabilize IFN mRNA. NS1 binds dsRNA of virus during its replication which prevents it from undergoing degradation mediated by RNA interference pathway of host cell. Protein kinase R (PKR) is activated in the presence of dsRNA during virus infection and is responsible for the phosphorylation of the eIF2a subunit, which causes stoppage of protein translation whereby preventing further viral replication. Influenza virus blocks the activation of PKR by two mechanisms viz., by sequestering its activator, dsRNA (Hatada et al. 1999) and NS1-induced block in IFN synthesis will reduce the levels of PKR in infected cells. Lam et al. (2011) has extensively reviewed the effects of NS1 protein regarding influenza some of which are interaction with the cellular protein phosphatidylinositol-3kinase (PI3-kinase) which may cause a delay in virus-induced apoptosis. Interacting with the cellular protein retinoic acid-inducible gene product I (RIG-I) prevents the maturation of human primary dendritic cells, thereby limiting host Tcell activation and thereby preventing the host cell antiviral responses by blocking the activation of RNaseL. To sum up, NS1 is very critical to the virus in establishing the H5N1 infection. Hence by targeting the NS1 gene we can intervene the pathogenesis of avian influenza virus and for attaining this microRNA is an attractive tool.
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In this study, we have computationally analyzed the host miRNAs that are targeting the NS1 gene of H5N1 virus. The bioinformatics prediction can help us to scale down time and cost by targeting a few of them which have surpassed the in-silico analysis for biological validation.
Materials and methods Datasets A total of 300 miRNAs expressed in chicken lungs (Kumar et al. 2014) were analyzed for having targets in segment 8 of H5N1 genome. Influenza A virus (A/chicken/India/ NIV33487/06(H5N1)) segment 8, complete sequence (GenBank accession number EF362422.1) was used as the reference sequence. Additionally a list of 66 different nucleotide sequences of segment 8 of H5N1 spanning different time periods and geographical areas were retrieved from NCBI database for performing multiple sequence alignment for conservation analysis. The step by step procedures which were performed in the analysis are explained below. MicroRNA target prediction The first step of the experiment was finding the miRNAs which were targeting the segment 8 of the H5N1 genome. The 300 miRNAs were individually analyzed to predict their targets in influenza A virus (A/chicken/India/NIV33487/ 06(H5N1)) segment 8, complete sequence using RNAhybrid 2.2 (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid?id= rnahybrid_view_submission, Rehmsmeier et al. 2004). The target prediction was performed by fixing the minimum-free energy (MFE) for heteroduplex at -24 kcal/mol and 2 hits per target keeping others as default parameters of the software. This forms the first filter in screening out miRNA target prediction to reduce the false positives. Demarcation of miRNA target sites The targets which were predicted by RNAhybrid 2.2 were then subjected to the second filter, which is demarcation of the miRNA target sites into to canonical, marginal, and atypical sites (Bartel 2009). According to this, the miRNA target sites were grouped into the above categories and those which do not fall into any of these categories were removed. This steps acts as a strong filter, which increases the probability of the miRNA target being functional in the biological system. Target site conservation analysis The third filter used in sorting out the miRNA target site was conservation analysis. For this, the segment 8
Theory Biosci.
sequences of 66 different H5N1 isolates of time periods spanning from 1959 to 2011 and covering 44 geographical areas were retrieved from NCBI database and were aligned using Clustal W algorithm using MEGA5.2 software (Tamura et al. 2011). The target sites at which the miRNA seed sequence was binding were analyzed. Only those sequences with at least 80 % conservation for target sites of seed sequence were selected for further analysis, while the rest which were not conserved were discarded. Secondary structure prediction and accessibility The secondary structure of the segment 8 of H5N1 was predicted using mfold Web Server RNA Folding Form (http://mfold.rna.albany.edu/?q=mfold/RNA-Folding-Form, Zuker 2003). This step acted as the fourth filter in screening out the miRNAs by predicting the accessibility of the miRNA to the target site. Secondary structure prediction was performed in two steps viz., global secondary structure in which the whole sequence was included and local secondary structure in which only the sequences lying 100 bases either side of the target site was included. This filter further increases the credibility of the miRNA target site being functional in the biological system. Accessibility energy and ranking A constraint folding of the mRNA was performed in which the target site of seed sequences were forced to unpair and the MFE of constraint folding were used for calculation of the accessibility energy. DDG was calculated using the formula as given under (Yue et al. 2009). The miRNAs which have surpassed the target site conservation analysis were then ranked based on the accessibility energy.
have done this analysis by setting the MFE as -24 kcal/mol and 2 hits per target. This was the first filter used in the experimental analysis. Demarcation of miRNA target sites A total of 15 miRNAs have surpassed this filter by categorizing the target sites into canonical, marginal, and atypical sites. Functional targets are generally fully complementary to nucleotides 2–7 or 2–8, at the 50 end of the miRNA is the miRNA seed sequence (Bartel 2009). This filter increases the probability of the particular miRNA target being functional in the biological system. Many of the target sites predicted by RNAhybrid had G-U wobble pairs in the seed region, and this will significantly interrupt the miRNA-target interaction (Brennecke et al. 2005). Multiple sequence alignment A total of 8 miRNAs has surpassed the multiple sequence alignment filter. They were gga-miR-15c*, gga-miR-181a, gga-miR-1706, gga-miR-1573, gga-miR-1658*, gga-miR1680*, gga-miR-1662, and gga-miR-1793 (Fig. 1), and these miRNA target sites were at least 80 % conserved when aligned by clustal W using MEGA 5.2 (Table 1). The miRNAs were filtered for target site conservation as AIV is known to show genetic drift hence before a miRNA can be identified to target its gene it was important that it should be conserved. Also change in the sequence at the target site would alter the thermodynamics of hybridization as well as seed sequence complementarities thus affecting the effectiveness of the miRNA target site.
DGopen ¼ DGfree DGunpair and; DDG ¼ DGduplex DGopen DGduplex values were obtained while target prediction by RNAhybrid. DGfree was obtained by local folding (native) by mfold by taking only the 100 bases either side of the seed sequence. DGunpair was obtained by forcibly making the seed sequence to unpair using the constraint folding option in mfold RNA folding form (constraint folding).
Results MicroRNA target prediction A total of 105 miRNAs were found to be targeting the segment 8 of H5N1 genome. Many of the miRNAs were having more than one site as target in the segment. We
Fig. 1 The miRNAs which have surpassed MFE of -24 kcal/mol and target site demarcation filters for NS1 gene. A lower the free energy indicated a firmer binding structure which is more likely it suggests the true binding between the miRNA and the target sequence
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Host species
Chicken
Whooper swan
Mallard
Whooper swan
Duck
Peregrine falcon
Muscovy duck Duck
Chicken
Chicken
Goose
Duck
Chicken
Chicken
Mute swan
Goose
Chicken
Falcon
Duck
Chicken
Chicken
Chicken Chicken
Teal
Great crested-grebe
Chicken
Mallard
Crow
Chicken
Mallard duck
Goose
Quail
Goose/
Turkey
Chicken
Accession no.
EF362422.1
123
AB265204.1
AB530996.1
AB610976.1
AB612905.1
AB629717.1
AB636528.1 AB700639.2
AF009898.1
AF098570.1
AF144307.1
AF468844.1
AF509076.1
AM503037.1
AM773788.1
AY075032.1
CY030000.1
CY035245.1
CY046138.1
CY047487.1
CY048415.1
CY057199.1 CY061306.1
CY061889.1
CY063322.1
CY066008.1
CY095486.1
DQ083624.1
DQ321190.1
DQ321202.1
DQ354062.1
DQ354063.1
DQ676842.1
DQ683027.1
EF605601.1
Russia_Krasnodar
Israel
Krasnoozerka
Yunnan
Jiangsu
Vietnam
Hong Kong
Bangkok/Thailand
Ontario
Bhutan
Qinghai
Germany
Egypt West Bengal
Nigeria
Reshoty
France
Saudi Arabia
Kuwait
Hong Kong
France
Burkina Faso
Hong Kong
Anyang
Guangdong
Hong Kong
Scotland
Vietnam Vietnam
Aomori
Hokkaido
Hokkaido
Hokkaido
Mongolia
India
Country/region
2007
2006
2005
2002
2003
2005
2002
2007
2010
2009
2005
2007 2010
2007
2006
2005
2007
2007
2000
2007
2006
2001
2001
1996
1997
1959
2011 2011
2011
2010
2009
2009
2006
2006
Year
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
gga-miR15C*
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
gga-miR181a
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
gga-miR1706
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
gga-miR1573
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
gga-miR1658*
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
gga-miR1680*
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
gga-miR1662
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
gga-miR1793
Table 1 Multiple sequence alignment of miRNAs with 66 various viral isolates with EF362422.1 (? indicates that the sequence is conserved at the concerned seed sequence target site)
Theory Biosci.
Domestic goose
Cygnus olor
Cygnus olor
Turkey
Chicken
Goose
Chicken
Duck
Chicken
Chicken
Chicken
Grebe
Mallard
Turkey Goose
Goose
Goose
Duck
Chicken
Chicken
Duck
Muscovy duck
Chicken
Chicken
Chicken
Duck
Chicken
Chicken
Chicken Duck
Mallard
Turkey
EU213069.1
EU443585.1
EU443588.1
EU871824.1
FJ390032.1
FJ445246.1
FJ750823.1
FJ784865.1
FM164808.1
FM177131.1
FR687294.1
GQ386149.1
GU051305.1
GU051961.1 GU052031.1
GU052109.1
GU052461.1
GU052482.1
GU066393.1
GU083633.1
GU183415.1
GU183416.1
GU183426.1
GU272002.1
GU354077.1
HQ141896.1
HQ200528.1
HQ200589.1
JN808092.1 JQ714231.1
U85380.1
U85447.1
England
Wisconsin
Korea Cambodia
Cambodia
Cambodia
Eastern China
West Bengal
West Bengal
West Java
West Java
Jakarta
West Bengal
Assam
Hong Kong
Hong Kong
Viet Nam
Virginia Hong Kong
Minnesota
Tyva
Egypt
Germany
Nigeria
Hunan
Sukhothai
Hungary
Astana
Ontario
Czech Republic
Czech Republic
Pavlodar
Country/region
1991
1975
2010 2011
2007
2005
2008
2008
2009
2008
2007
2006
2008
2008
1998
2002
2001
2007 1999
2000
2009
2010
2007
2007
2007
2008
2006
2005
1983
2007
2006
2005
Year
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR15C*
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR181a
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR1706
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR1573
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR1658*
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR1680*
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR1662
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR1793
Segment 8 sequences of 66 different H5N1 isolates spanning from 1959 to 2011, covering 44 geographical areas were retrieved from NCBI database were aligned using Clustal W algorithm using MEGA5.2 software. Only those with at least 80 % conserved target sites were selected for further analysis while the rest which were not conserved were discarded
Host species
Accession no.
Table 1 continued
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Theory Biosci. Fig. 2 Global folding as observed by mfold analysis. miRNA target sites of gga-miR15c*, gga-miR-181a, gga-miR1706, gga-miR-1573, gga-miR1658*, gga-miR-1680*, ggamiR-1662, and gga-miR-1793 (highlighted green). Global secondary structure prediction is done by including the whole target sequence. This is done to ascertain the accessibility of the miRNA at the target sequence where it is binding. At the target site where the seed sequence is binding, at least three consecutive bases should be unpaired in the secondary structure so that the miRNA is accessible (Color figure online)
Secondary structure prediction and accessibility This particular analysis was done in different steps. The accessibility of the target site was evaluated based on the secondary structure of the target mRNA using mfold Web Server RNA Folding Form. On analyzing the global secondary structure of the miRNA target sites it was observed that the target sites of gga-miR-1658* and gga-miR-1793 were found to be accessible (Fig. 2). When the local secondary structure was analyzed, it was found that the target sites of gga-miR181a and gga-miR-1658* were accessible (Fig. 3). Various authors have set different criteria for accessibility of target site like only three consecutive nucleotide complementary to the seed (Robins et al. 2005) or some suggested that four consecutive nucleotide complementary to any part of miRNA are required for initiation of the binding process (Long et al. 2007;
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Marı´n and Vanı´cek 2011). In this analysis, we have set the parameter that at least three consecutive nucleotides complementary to the seed should be unpaired. Accessibility energy and ranking The accessibility energy is critical to rank the miRNAs. The miRNAs were then ranked based on the DDG values. Lower the accessibility energy, better will be the chance that miRNA target will functional in the biological system. DGfree values ranged from -76.74 kcal/mol to -33.07 kcal/mol and DGunpair values ranged from -73.72 kcal/mol to -31.16 kcal/mol (Fig. 4). DDG values were calculated from the formula explained above, and it was found that gga-miR1658* ranked first while gga-miR-1706 was ranked the last (Fig. 5). Target accessibility is a critical factor in microRNA
Theory Biosci. Fig. 3 Local Folding observed by mfold analysis. miRNA target sites of gga-miR-15c*, gga-miR-181a, gga-miR-1706, gga-miR-1573, gga-miR-1658*, gga-miR-1680*, gga-miR-1662, and gga-miR-1793 (highlighted green). In prediction of local secondary structure only the sequences lying 100 bases either side of the target site was included. Local folding along with global folding further increases the credibility of the miRNA target being functional in the biological system (Color figure online)
function. Energy-based secondary structure prediction algorithms have good accuracy and can be used for these kinds of studies. The variability that are observed in the biological experiments are due to differences in accessibility imposed by the sequence surrounding the target and also the site accessibility is as important as sequence match in the seed for determining the efficacy of microRNA-mediated translational repression (Kertesz et al. 2007). miRNA–mRNA interaction is positively correlated with physical accessibility of the target sites (Robins et al. 2005).
Discussion Avian influenza has been the prime focus owing to its importance in human health and the economic losses it creates. Asian countries have been mainly affected by the H5N1 virus where the disease is enzootic. A loss of 0.4 % change in GDP occurred in South Asian region due to Avian influenza outbreaks (World Bank 2006). The current control strategies against Avian Influenza are stamping and vaccination. Both these policies suffer from various
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Fig. 4 Comparison of MFE value (DG) of secondary structure (native) and secondary structure (constraint) to prohibit base pairing at the miRNA target site. DDG is calculated from the equation DDG = DGduplex - DGopen, where DGopen = DGfree - DGunpair. DGduplex values are those obtained during the target prediction by RNAhybrid, DGfree from local folding (native) and DGunpair from the constraint folding
Fig. 5 Ranking of miRNAs based on their DDG values (kcal/mol). Lower the accessibility energy, better will be the chance that miRNA target will be functional in the biological system
drawbacks. The stamping out policy leads to loss of genotype, which will results in selection of low-disease resistance birds and selection of high virulent virus. Stamping out policy disrupts the dynamics of host-pathogen interactions (Shim and Galvani 2009). Vaccination may result the emergence of more virulent variants of the virus. Vaccination also suffers from drawbacks like antigenic drift in field viruses by inactivated vaccines, decreased efficacy when used for a long periods etc. (Ausvetplan 2008). These factors points towards the need for an alternative strategy to control Avian influenza for which a better understanding of the disease process is necessary. Targeting NS1 directly would be an option in devising a control strategy against H5N1. Integrating bioinformatics with gene expression data is an attractive strategy in enabling to propose new therapeutic strategies to combat avian influenza viruses in a very cost effective manner. Very many software’s are
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currently available in predicting miRNA targets and each of them have different algorithms and different parameters. Various authors have used a cutoff-free energy of -17 kcal/mol for predicting miRNA targets in PB1 gene of H1N1 (Song et al. 2010) and a threshold free energy of -15 kcal/mol while predicting the binding site of miRNA to NS1 gene of H1N1 and H5N1 (Koparde and Singh 2010). miRNAs can target 50 -UTR, ORF or 30 -UTR but ORF targeting is less frequent and less effective than 30 UTR targeting but still much more frequent than 50 -UTR targeting (Bartel 2009). ViTA, the prediction tool for host microRNAs that target the viruses uses a default MFE of -10 kcal/mol and utilizes Miranda and TargetScan algorithms for prediction (Hsu et al. 2007). We have used a value of -24 kcal/mol for the analysis as the lower the free energy, the firmer the binding structure is and the more likely it suggests the true binding (Yue et al. 2009). The potential of these miRNAs as therapeutic agent can only be ensured if the target site is conserved across various viral isolates. It is conventional to screen for conservation of the target site in majority of target prediction databases like TargetScan, Miranda etc. Accessibility criteria are important as the particular target site should be accessible to the miRNA so as to be functional in the biological system. There may be targets but may not be accessible and unless they are accessible they will not have any impact in the system. At the target site where the miRNA is binding, at least three consecutive bases should be unpaired in the secondary structure to have the accessibility to the target site (Robins et al. 2005), and this improves the efficiency of the prediction. The closely related miRNA and RNAi pathways are important regulators of virus–host cell interactions. RNAinduced silencing complex (RISC) functions in a cooperative manner (Doench et al. 2003; Doench and Sharp 2004). Multiple target sites in the same 30 UTR can greatly increase the degree of translational suppression and also enhances the specificity of gene regulation (Min and Yoon 2010). Thus the miRNAs that are targeting the NS1 gene may have a cumulative effect in RNAi pathway. But how far they have been targeting the NS1 gene in a cumulative way is still not clear. miRNA acts like a switch like mechanism that can change the whole process in either way. The miRNAs which are targeting the NS1 gene is present in the host system itself indicating the chances of possible host resistance against the virus. These miRNAs which are identified during the analysis are the possible candidates for developing novel biotherapeutics against viruses. The analysis has helped us to scale down from a very large list into potentially a few, which can be validated biologically thereby saving time and cost. As bioinformatics prediction experiments are not substitutes for biological experiments, the obtained
Theory Biosci.
bioinformatics results have to be confirmed by biological validation. Acknowledgments The authors wish to acknowledge the help and support rendered by Director, Indian Veterinary Research Institute, Bareilly, India and Joint-Director, High Security Animal Disease Laboratory, Bhopal, India for providing necessary facilities to carry out this work.
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therapeutic development. J Data Min Genomics Proteomics 1:104. doi:10.4172/2153-0602.1000104 Krug RM, Yuan W, Noah DL, Latham AG (2003) Intracellular warfare between human influenza viruses and human cells: the roles of the viral NS1 protein. Virology 309:181–189 Kumar A, Vn MA, Raut AA, Sood R, Mishra A (2014) Identification of chicken pulmonary miRNAs targeting PB1, PB1-F2, and N40 genes of highly pathogenic avian influenza virus H5N1 in silico. Bioinform Biol Insights 8:135–145 Lam WY, Yeung ACM, Chan PKS (2011) Apoptosis, cytokine and chemokine induction by non-structural 1 (NS1) proteins encoded by different influenza subtypes. Virol J 8:554 Lamb RA, Krug RM (2001) Orthomyxoviridae: the viruses and their replication. In: Knipe DM, Howle PM (eds) Fields virology. Lippincott Williams & Wilkins, Philadelphia, pp. 1487–1532 Long D, Lee R, Williams P, Chan CY, Ambros V, Ding Y (2007) Potent effect of target structure on microRNA function. Nat Struct Mol Biol 14:287–294 Marı´n RM, Vanı´cek J (2011) Efficient use of accessibility in microRNA target prediction. Nucleic Acids Res 39(1):19–29 Min Y, Yoon S (2010) Got target? Computational methods for microRNA target prediction and their extension. Exp Mol Med 42(4):233–244 Palese P (2006) Making better influenza virus vaccines? Emerg Infect Dis 12(1):61–65 Petsko GA (2005) H5N1. Genome Biol 6:121 Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R (2004) Fast and effective prediction of microRNA/target duplexes. RNA 10:1507–1517 Robins H, Li Y, Padgett RW (2005) Incorporating structure to predict microRNA targets. Proc Natl Acad Sci USA 102(11):4006–4009 Shim E, Galvani AP (2009) Evolutionary repercussions of avian culling on host resistance and influenza virulence. PLoS One 4(5):e5503 Sokol NS, Ambros V (2005) Mesodermally expressed Drosophila microRNA-1 is regulated by Twist and is required in muscles during larval growth. Genes Dev 19(19):2343–2354 Song L, Liu H, Gao S, Jiang W, Huang W (2010) Cellular microRNAs inhibit replication of the H1N1 influenza A virus in infected cells. J Virol 84(17):8849–8860 Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28:2731–2739 World Bank (2006) http://www.worldbank.org/gdf2006. Accessed 8 Aug 2013 Wu L, Fan J, Belasco JG (2006) MicroRNAs direct rapid deadenylation of mRNA. Proc Natl Acad Sci USA 103:4034–4039 Yue D, Liu H, Huang Y (2009) Survey of computational algorithms for MicroRNA target prediction. Curr Genomics 10(7):478–492 Zuker M (2003) Mfold web server for nucleic acid folding and hybridization prediction. Nucleic Acids Res 31(13):3406–3415
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ORIGINAL PAPER
In-silico search of virus-specific host microRNAs regulating avian influenza virus NS1 expression V. N. Muhasin Asaf1 • Amod Kumar1 • Ashwin Ashok Raut2 • Sandeep Bhatia2 Anamika Mishra2
•
Received: 28 May 2014 / Accepted: 28 April 2015 Ó Springer-Verlag Berlin Heidelberg 2015
Abstract Avian influenza is a highly contagious viral infection caused by avian influenza virus type A of the family Orthomyxoviridae primarily affecting the avian species. The non-structural protein 1 (NS1) encoded by the NS1 gene of the virus is critical in establishing the infection. NS1 protein acts to suppress the virus-induced host interferon response and also inhibit Protein kinase R activation thereby helping the virus to establish the infection. MicroRNAs (miRNA) are small regulatory endogenous non-coding RNAs of *22 nucleotides in length located within introns of coding and non-coding genes, exons of non-coding genes or inter-genic regions. miRNAs can target the gene at various sites and effectively reduce or shut down its expression. In this study, set of differentially expressed chicken miRNA identified by deep sequencing H5N1 infected and SPF chicken lung were computationally analyzed, to identify targets in the NS1 gene. 300 differentially expressed miRNAs were then analyzed individually for target sites in gi|147667147|gb|EF362422.1| influenza A virus (A/chicken/India/NIV33487/06(H5N1)) segment 8, complete sequence using RNAhybrid 2.2. The analysis yielded gga-miR-1658* as the potential miRNA which is targeting the NS1 gene of H5N1 genome. Keywords Avian influenza NS1 miRNA RNAhybrid H5N1
& Anamika Mishra [email protected] 1
Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, India
2
High Security Animal Disease Laboratory, Bhopal, India
Introduction Highly pathogenic avian influenza (HPAI) is a contagious viral infection, primarily affecting avian species which is characterized by gastrointestinal, respiratory, and/or nervous signs particularly in avian species. HPAI is caused by avian influenza virus type A of the family Orthomyxoviridae. The family Orthomyxoviridae contains four genera: influenza A, B, and C viruses, and thogotovirus (Lamb and Krug 2001). HPAI was one of the first viral diseases to be described in poultry and still remains a major area of concern. Influenza type A viruses have been important with respect to genetic variations and pandemics it has caused. Influenza viruses can undergo genetic changes either by single mutations or by recombination with the genetic material of other flu strains (Petsko 2005). MicroRNAs (miRNA) are small, regulatory, endogenous, non-coding RNAs of *22 nucleotides in length located within introns of coding and non-coding genes, and exons of non-coding genes or inter-genic regions (Kim and Nam 2006). The miRNAs are reported to be involved in many biological processes and are having great impacts on computational and traditional biology (Min and Yoon 2010). miRNAs are the key regulators of numerous genes in biological processes that ranges from developmental timing to apoptosis (Boehm and Slack 2005; Sokol and Ambros 2005). Metazoan miRNA function has been mainly for target sites in 30 untranslated regions (UTRs), but they are also found to target the 50 UTRs and open reading frames (ORFs) (Bartel 2009). Depending on the complementarity between miRNA and target mRNA, there are two known mechanisms of miRNAs action on mRNAs, target mRNA degradation and translational inhibition; however, in animals, they function mainly by preventing translation without mRNA degradation. When the miRNA is near-perfectly
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complementary with target mRNA, then deadenylation and subsequent degradation of the target mRNA occur (Giraldez et al. 2006). Translational inhibition occurs when the miRNA is having partial complementary to its target mRNA (Doench and Sharp 2004). These two processes are independent to each other but may overlap sometimes (Gu and Kay 2010), but the modulation of one event can also result a corresponding change in the other (Coller and Parker 2004). The mRNA degradation occurs by removing the cap at 50 end and deadenylation of the poly-A tail (Wu et al. 2006). Translational repression is always followed by mRNA degradation and translational repression is the initial event which is followed by a secondary event of mRNA degradation (Djuranovic et al. 2012). MicroRNAs which can regulate the gene expression can be used as candidates for developing novel biotherapeutics against viruses (Giglio and Vecchione 2010). Segment 8 of H5N1 genome encodes the non-structural protein 1 (NS1) protein. The NS1 protein is a regulatory protein with numerous functions (Krug et al. 2003) which is a regulator of both mRNA splicing and translation, and also plays a critical role in competing against the cell’s antiviral defense. NS1 protein is directly related to the pathogenicity of the influenza strain (Palese 2006). The NS1 protein which is highly expressed in infected cells acts to suppress the virus-induced host interferon (IFN) response and due to this property NS1 gene is also called as IFN antagonist (Garcia-Sastre 2005). NS1 has double-stranded (ds) RNAbinding function by which it prevents the synthesis of IFN mRNA or to destabilize IFN mRNA. NS1 binds dsRNA of virus during its replication which prevents it from undergoing degradation mediated by RNA interference pathway of host cell. Protein kinase R (PKR) is activated in the presence of dsRNA during virus infection and is responsible for the phosphorylation of the eIF2a subunit, which causes stoppage of protein translation whereby preventing further viral replication. Influenza virus blocks the activation of PKR by two mechanisms viz., by sequestering its activator, dsRNA (Hatada et al. 1999) and NS1-induced block in IFN synthesis will reduce the levels of PKR in infected cells. Lam et al. (2011) has extensively reviewed the effects of NS1 protein regarding influenza some of which are interaction with the cellular protein phosphatidylinositol-3kinase (PI3-kinase) which may cause a delay in virus-induced apoptosis. Interacting with the cellular protein retinoic acid-inducible gene product I (RIG-I) prevents the maturation of human primary dendritic cells, thereby limiting host Tcell activation and thereby preventing the host cell antiviral responses by blocking the activation of RNaseL. To sum up, NS1 is very critical to the virus in establishing the H5N1 infection. Hence by targeting the NS1 gene we can intervene the pathogenesis of avian influenza virus and for attaining this microRNA is an attractive tool.
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In this study, we have computationally analyzed the host miRNAs that are targeting the NS1 gene of H5N1 virus. The bioinformatics prediction can help us to scale down time and cost by targeting a few of them which have surpassed the in-silico analysis for biological validation.
Materials and methods Datasets A total of 300 miRNAs expressed in chicken lungs (Kumar et al. 2014) were analyzed for having targets in segment 8 of H5N1 genome. Influenza A virus (A/chicken/India/ NIV33487/06(H5N1)) segment 8, complete sequence (GenBank accession number EF362422.1) was used as the reference sequence. Additionally a list of 66 different nucleotide sequences of segment 8 of H5N1 spanning different time periods and geographical areas were retrieved from NCBI database for performing multiple sequence alignment for conservation analysis. The step by step procedures which were performed in the analysis are explained below. MicroRNA target prediction The first step of the experiment was finding the miRNAs which were targeting the segment 8 of the H5N1 genome. The 300 miRNAs were individually analyzed to predict their targets in influenza A virus (A/chicken/India/NIV33487/ 06(H5N1)) segment 8, complete sequence using RNAhybrid 2.2 (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid?id= rnahybrid_view_submission, Rehmsmeier et al. 2004). The target prediction was performed by fixing the minimum-free energy (MFE) for heteroduplex at -24 kcal/mol and 2 hits per target keeping others as default parameters of the software. This forms the first filter in screening out miRNA target prediction to reduce the false positives. Demarcation of miRNA target sites The targets which were predicted by RNAhybrid 2.2 were then subjected to the second filter, which is demarcation of the miRNA target sites into to canonical, marginal, and atypical sites (Bartel 2009). According to this, the miRNA target sites were grouped into the above categories and those which do not fall into any of these categories were removed. This steps acts as a strong filter, which increases the probability of the miRNA target being functional in the biological system. Target site conservation analysis The third filter used in sorting out the miRNA target site was conservation analysis. For this, the segment 8
Theory Biosci.
sequences of 66 different H5N1 isolates of time periods spanning from 1959 to 2011 and covering 44 geographical areas were retrieved from NCBI database and were aligned using Clustal W algorithm using MEGA5.2 software (Tamura et al. 2011). The target sites at which the miRNA seed sequence was binding were analyzed. Only those sequences with at least 80 % conservation for target sites of seed sequence were selected for further analysis, while the rest which were not conserved were discarded. Secondary structure prediction and accessibility The secondary structure of the segment 8 of H5N1 was predicted using mfold Web Server RNA Folding Form (http://mfold.rna.albany.edu/?q=mfold/RNA-Folding-Form, Zuker 2003). This step acted as the fourth filter in screening out the miRNAs by predicting the accessibility of the miRNA to the target site. Secondary structure prediction was performed in two steps viz., global secondary structure in which the whole sequence was included and local secondary structure in which only the sequences lying 100 bases either side of the target site was included. This filter further increases the credibility of the miRNA target site being functional in the biological system. Accessibility energy and ranking A constraint folding of the mRNA was performed in which the target site of seed sequences were forced to unpair and the MFE of constraint folding were used for calculation of the accessibility energy. DDG was calculated using the formula as given under (Yue et al. 2009). The miRNAs which have surpassed the target site conservation analysis were then ranked based on the accessibility energy.
have done this analysis by setting the MFE as -24 kcal/mol and 2 hits per target. This was the first filter used in the experimental analysis. Demarcation of miRNA target sites A total of 15 miRNAs have surpassed this filter by categorizing the target sites into canonical, marginal, and atypical sites. Functional targets are generally fully complementary to nucleotides 2–7 or 2–8, at the 50 end of the miRNA is the miRNA seed sequence (Bartel 2009). This filter increases the probability of the particular miRNA target being functional in the biological system. Many of the target sites predicted by RNAhybrid had G-U wobble pairs in the seed region, and this will significantly interrupt the miRNA-target interaction (Brennecke et al. 2005). Multiple sequence alignment A total of 8 miRNAs has surpassed the multiple sequence alignment filter. They were gga-miR-15c*, gga-miR-181a, gga-miR-1706, gga-miR-1573, gga-miR-1658*, gga-miR1680*, gga-miR-1662, and gga-miR-1793 (Fig. 1), and these miRNA target sites were at least 80 % conserved when aligned by clustal W using MEGA 5.2 (Table 1). The miRNAs were filtered for target site conservation as AIV is known to show genetic drift hence before a miRNA can be identified to target its gene it was important that it should be conserved. Also change in the sequence at the target site would alter the thermodynamics of hybridization as well as seed sequence complementarities thus affecting the effectiveness of the miRNA target site.
DGopen ¼ DGfree DGunpair and; DDG ¼ DGduplex DGopen DGduplex values were obtained while target prediction by RNAhybrid. DGfree was obtained by local folding (native) by mfold by taking only the 100 bases either side of the seed sequence. DGunpair was obtained by forcibly making the seed sequence to unpair using the constraint folding option in mfold RNA folding form (constraint folding).
Results MicroRNA target prediction A total of 105 miRNAs were found to be targeting the segment 8 of H5N1 genome. Many of the miRNAs were having more than one site as target in the segment. We
Fig. 1 The miRNAs which have surpassed MFE of -24 kcal/mol and target site demarcation filters for NS1 gene. A lower the free energy indicated a firmer binding structure which is more likely it suggests the true binding between the miRNA and the target sequence
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Host species
Chicken
Whooper swan
Mallard
Whooper swan
Duck
Peregrine falcon
Muscovy duck Duck
Chicken
Chicken
Goose
Duck
Chicken
Chicken
Mute swan
Goose
Chicken
Falcon
Duck
Chicken
Chicken
Chicken Chicken
Teal
Great crested-grebe
Chicken
Mallard
Crow
Chicken
Mallard duck
Goose
Quail
Goose/
Turkey
Chicken
Accession no.
EF362422.1
123
AB265204.1
AB530996.1
AB610976.1
AB612905.1
AB629717.1
AB636528.1 AB700639.2
AF009898.1
AF098570.1
AF144307.1
AF468844.1
AF509076.1
AM503037.1
AM773788.1
AY075032.1
CY030000.1
CY035245.1
CY046138.1
CY047487.1
CY048415.1
CY057199.1 CY061306.1
CY061889.1
CY063322.1
CY066008.1
CY095486.1
DQ083624.1
DQ321190.1
DQ321202.1
DQ354062.1
DQ354063.1
DQ676842.1
DQ683027.1
EF605601.1
Russia_Krasnodar
Israel
Krasnoozerka
Yunnan
Jiangsu
Vietnam
Hong Kong
Bangkok/Thailand
Ontario
Bhutan
Qinghai
Germany
Egypt West Bengal
Nigeria
Reshoty
France
Saudi Arabia
Kuwait
Hong Kong
France
Burkina Faso
Hong Kong
Anyang
Guangdong
Hong Kong
Scotland
Vietnam Vietnam
Aomori
Hokkaido
Hokkaido
Hokkaido
Mongolia
India
Country/region
2007
2006
2005
2002
2003
2005
2002
2007
2010
2009
2005
2007 2010
2007
2006
2005
2007
2007
2000
2007
2006
2001
2001
1996
1997
1959
2011 2011
2011
2010
2009
2009
2006
2006
Year
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
gga-miR15C*
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
gga-miR181a
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
gga-miR1706
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
gga-miR1573
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
gga-miR1658*
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
gga-miR1680*
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
gga-miR1662
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
gga-miR1793
Table 1 Multiple sequence alignment of miRNAs with 66 various viral isolates with EF362422.1 (? indicates that the sequence is conserved at the concerned seed sequence target site)
Theory Biosci.
Domestic goose
Cygnus olor
Cygnus olor
Turkey
Chicken
Goose
Chicken
Duck
Chicken
Chicken
Chicken
Grebe
Mallard
Turkey Goose
Goose
Goose
Duck
Chicken
Chicken
Duck
Muscovy duck
Chicken
Chicken
Chicken
Duck
Chicken
Chicken
Chicken Duck
Mallard
Turkey
EU213069.1
EU443585.1
EU443588.1
EU871824.1
FJ390032.1
FJ445246.1
FJ750823.1
FJ784865.1
FM164808.1
FM177131.1
FR687294.1
GQ386149.1
GU051305.1
GU051961.1 GU052031.1
GU052109.1
GU052461.1
GU052482.1
GU066393.1
GU083633.1
GU183415.1
GU183416.1
GU183426.1
GU272002.1
GU354077.1
HQ141896.1
HQ200528.1
HQ200589.1
JN808092.1 JQ714231.1
U85380.1
U85447.1
England
Wisconsin
Korea Cambodia
Cambodia
Cambodia
Eastern China
West Bengal
West Bengal
West Java
West Java
Jakarta
West Bengal
Assam
Hong Kong
Hong Kong
Viet Nam
Virginia Hong Kong
Minnesota
Tyva
Egypt
Germany
Nigeria
Hunan
Sukhothai
Hungary
Astana
Ontario
Czech Republic
Czech Republic
Pavlodar
Country/region
1991
1975
2010 2011
2007
2005
2008
2008
2009
2008
2007
2006
2008
2008
1998
2002
2001
2007 1999
2000
2009
2010
2007
2007
2007
2008
2006
2005
1983
2007
2006
2005
Year
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR15C*
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR181a
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR1706
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR1573
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR1658*
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR1680*
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR1662
? ?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
?
gga-miR1793
Segment 8 sequences of 66 different H5N1 isolates spanning from 1959 to 2011, covering 44 geographical areas were retrieved from NCBI database were aligned using Clustal W algorithm using MEGA5.2 software. Only those with at least 80 % conserved target sites were selected for further analysis while the rest which were not conserved were discarded
Host species
Accession no.
Table 1 continued
Theory Biosci.
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Theory Biosci. Fig. 2 Global folding as observed by mfold analysis. miRNA target sites of gga-miR15c*, gga-miR-181a, gga-miR1706, gga-miR-1573, gga-miR1658*, gga-miR-1680*, ggamiR-1662, and gga-miR-1793 (highlighted green). Global secondary structure prediction is done by including the whole target sequence. This is done to ascertain the accessibility of the miRNA at the target sequence where it is binding. At the target site where the seed sequence is binding, at least three consecutive bases should be unpaired in the secondary structure so that the miRNA is accessible (Color figure online)
Secondary structure prediction and accessibility This particular analysis was done in different steps. The accessibility of the target site was evaluated based on the secondary structure of the target mRNA using mfold Web Server RNA Folding Form. On analyzing the global secondary structure of the miRNA target sites it was observed that the target sites of gga-miR-1658* and gga-miR-1793 were found to be accessible (Fig. 2). When the local secondary structure was analyzed, it was found that the target sites of gga-miR181a and gga-miR-1658* were accessible (Fig. 3). Various authors have set different criteria for accessibility of target site like only three consecutive nucleotide complementary to the seed (Robins et al. 2005) or some suggested that four consecutive nucleotide complementary to any part of miRNA are required for initiation of the binding process (Long et al. 2007;
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Marı´n and Vanı´cek 2011). In this analysis, we have set the parameter that at least three consecutive nucleotides complementary to the seed should be unpaired. Accessibility energy and ranking The accessibility energy is critical to rank the miRNAs. The miRNAs were then ranked based on the DDG values. Lower the accessibility energy, better will be the chance that miRNA target will functional in the biological system. DGfree values ranged from -76.74 kcal/mol to -33.07 kcal/mol and DGunpair values ranged from -73.72 kcal/mol to -31.16 kcal/mol (Fig. 4). DDG values were calculated from the formula explained above, and it was found that gga-miR1658* ranked first while gga-miR-1706 was ranked the last (Fig. 5). Target accessibility is a critical factor in microRNA
Theory Biosci. Fig. 3 Local Folding observed by mfold analysis. miRNA target sites of gga-miR-15c*, gga-miR-181a, gga-miR-1706, gga-miR-1573, gga-miR-1658*, gga-miR-1680*, gga-miR-1662, and gga-miR-1793 (highlighted green). In prediction of local secondary structure only the sequences lying 100 bases either side of the target site was included. Local folding along with global folding further increases the credibility of the miRNA target being functional in the biological system (Color figure online)
function. Energy-based secondary structure prediction algorithms have good accuracy and can be used for these kinds of studies. The variability that are observed in the biological experiments are due to differences in accessibility imposed by the sequence surrounding the target and also the site accessibility is as important as sequence match in the seed for determining the efficacy of microRNA-mediated translational repression (Kertesz et al. 2007). miRNA–mRNA interaction is positively correlated with physical accessibility of the target sites (Robins et al. 2005).
Discussion Avian influenza has been the prime focus owing to its importance in human health and the economic losses it creates. Asian countries have been mainly affected by the H5N1 virus where the disease is enzootic. A loss of 0.4 % change in GDP occurred in South Asian region due to Avian influenza outbreaks (World Bank 2006). The current control strategies against Avian Influenza are stamping and vaccination. Both these policies suffer from various
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Fig. 4 Comparison of MFE value (DG) of secondary structure (native) and secondary structure (constraint) to prohibit base pairing at the miRNA target site. DDG is calculated from the equation DDG = DGduplex - DGopen, where DGopen = DGfree - DGunpair. DGduplex values are those obtained during the target prediction by RNAhybrid, DGfree from local folding (native) and DGunpair from the constraint folding
Fig. 5 Ranking of miRNAs based on their DDG values (kcal/mol). Lower the accessibility energy, better will be the chance that miRNA target will be functional in the biological system
drawbacks. The stamping out policy leads to loss of genotype, which will results in selection of low-disease resistance birds and selection of high virulent virus. Stamping out policy disrupts the dynamics of host-pathogen interactions (Shim and Galvani 2009). Vaccination may result the emergence of more virulent variants of the virus. Vaccination also suffers from drawbacks like antigenic drift in field viruses by inactivated vaccines, decreased efficacy when used for a long periods etc. (Ausvetplan 2008). These factors points towards the need for an alternative strategy to control Avian influenza for which a better understanding of the disease process is necessary. Targeting NS1 directly would be an option in devising a control strategy against H5N1. Integrating bioinformatics with gene expression data is an attractive strategy in enabling to propose new therapeutic strategies to combat avian influenza viruses in a very cost effective manner. Very many software’s are
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currently available in predicting miRNA targets and each of them have different algorithms and different parameters. Various authors have used a cutoff-free energy of -17 kcal/mol for predicting miRNA targets in PB1 gene of H1N1 (Song et al. 2010) and a threshold free energy of -15 kcal/mol while predicting the binding site of miRNA to NS1 gene of H1N1 and H5N1 (Koparde and Singh 2010). miRNAs can target 50 -UTR, ORF or 30 -UTR but ORF targeting is less frequent and less effective than 30 UTR targeting but still much more frequent than 50 -UTR targeting (Bartel 2009). ViTA, the prediction tool for host microRNAs that target the viruses uses a default MFE of -10 kcal/mol and utilizes Miranda and TargetScan algorithms for prediction (Hsu et al. 2007). We have used a value of -24 kcal/mol for the analysis as the lower the free energy, the firmer the binding structure is and the more likely it suggests the true binding (Yue et al. 2009). The potential of these miRNAs as therapeutic agent can only be ensured if the target site is conserved across various viral isolates. It is conventional to screen for conservation of the target site in majority of target prediction databases like TargetScan, Miranda etc. Accessibility criteria are important as the particular target site should be accessible to the miRNA so as to be functional in the biological system. There may be targets but may not be accessible and unless they are accessible they will not have any impact in the system. At the target site where the miRNA is binding, at least three consecutive bases should be unpaired in the secondary structure to have the accessibility to the target site (Robins et al. 2005), and this improves the efficiency of the prediction. The closely related miRNA and RNAi pathways are important regulators of virus–host cell interactions. RNAinduced silencing complex (RISC) functions in a cooperative manner (Doench et al. 2003; Doench and Sharp 2004). Multiple target sites in the same 30 UTR can greatly increase the degree of translational suppression and also enhances the specificity of gene regulation (Min and Yoon 2010). Thus the miRNAs that are targeting the NS1 gene may have a cumulative effect in RNAi pathway. But how far they have been targeting the NS1 gene in a cumulative way is still not clear. miRNA acts like a switch like mechanism that can change the whole process in either way. The miRNAs which are targeting the NS1 gene is present in the host system itself indicating the chances of possible host resistance against the virus. These miRNAs which are identified during the analysis are the possible candidates for developing novel biotherapeutics against viruses. The analysis has helped us to scale down from a very large list into potentially a few, which can be validated biologically thereby saving time and cost. As bioinformatics prediction experiments are not substitutes for biological experiments, the obtained
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bioinformatics results have to be confirmed by biological validation. Acknowledgments The authors wish to acknowledge the help and support rendered by Director, Indian Veterinary Research Institute, Bareilly, India and Joint-Director, High Security Animal Disease Laboratory, Bhopal, India for providing necessary facilities to carry out this work.
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