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ISSN: 2153-0602
Journal of Data Mining in Genomics & Proteomics
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Imex Based Analysis of Repeat Sequences in Flavivirus Genomes, Including Dengue Virus

Chaudhary Mashhhood Alam1,2, Asif Iqbal1, Babita Thadari2 and Safdar Ali2*

1PIRO Technologies Private Limited, New Delhi-110025, India

2Department of Biomedical Sciences, SRCASW, University of Delhi, Vasundhara Enclave, New Delhi-110096, India

*Corresponding Author:
Safdar Ali
Assistant Professor
Department of Biomedical Sciences
SRCASW, University of Delhi, New Delhi – 110096, India
Tel: 91-11-22623503
Fax: 91-11-22623504
E-mail: [email protected]; [email protected]

Received date: January 21, 2016 Accepted date: January 28, 2016 Published date: February 06, 2016

Citation: Alam CM, Iqbal A, Thadari B, Ali S (2016) Imex Based Analysis of Repeat Sequences in Flavivirus Genomes, Including Dengue Virus. J Data Mining Genomics Proteomics 7:187. doi:10.4172/2153-0602.1000187

Copyright: © 2016 Alam CM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Simple sequence repeats (SSRs), also known as microsatellites, are 1-6 nucleotides repeat motif, present in varying number of iterations, across coding and non-coding regions of prokaryotes, eukaryotes and viruses. Present study focuses on simple sequence repeats (SSRs) in 27 Flavivirus genomes, which includes dengue virus. The comparative viral genomics in the light of SSRs would help us understand the diversity and adaptability to new hosts. A total of 1164 SSRs and 53 cSSRs were uncovered from the 27studied genomes. Mononucleotide A was the most prevalent repeat motif with an average distribution of around 6. This was followed by G (average distribution of 2). Amongst the dinucleotides AG/GA repeat motif was the most prevalent with an average distribution of 14 across studied genomes. The Flavivirus genomes lacked two essential features responsible for genome evolution, dinucleotide repeat motif AT/TA (least represented with average distribution of ~0.5) and cSSR in non-coding regions, suggesting a stable genome or evolution by hitherto unexplained mechanisms. The unveiling of conserved sequences in the isolates of Dengue virus suggests a basis for biomarker development for viral diagnostics.

Keywords:

Flavivirus; Simple sequence repeats; Imperfect microsatellite extraction IMEx; dMAX

Abbreviations

SSR: Simple Sequence Repeat; cSSR: Compound Simple Sequence Repeat; IMEx: Imperfect Microsatellite Extraction; RD: Relative Density; RA: Relative Abundance

Introduction

Viruses utilize almost all spectra of the living world for their survival, as in host for infection and survival. The classification and evolution of viruses have been based either on the genome features (size/type) or on their host range [1,2]. A single viral genome encodes from 2 to about a thousand proteins [3,4]. Though a complete understanding of the evolutionary mechanisms driving evolution of viruses is underway, however, transposable elements and tandemly repetitive sequences are believed to play a crucial role [5,6].

Simple sequence repeats (SSRs), also known as microsatellites, are 1-6 nucleotides repeat motif, present in varying number of iterations, across coding and non-coding regions of prokaryotes, eukaryotes and viruses [7-9]. SSRs, being recombination hot spots aid in genome evolution, sometimes being the basis of diseases [10,11]. Functionally, these sequences are reported to be associated with gene regulation, transcription and protein function [12,13]. The incidence of SSRs may be influenced by the genome features like size and GC content [14-16]. However, this correlation is not universal, adding to the enigma of SSRs.

Present study focuses on simple sequence repeats (SSRs) in 27 Flavivirus genomes, which includes Dengue virus. Dengue is a mosquito borne viral infection found in tropical and sub-tropical regions of the world and is caused by one of the four serotypes of dengue viruses (DENV1-DENV4). An increase in infection has been seen in recent years due to many factors including urbanization and air travel. Over 2.5 billion people of the world’s population are now at risk for dengue. They may be asymptomatic or may give rise to undifferentiated fever, dengue fever, dengue haemorrhagic fever (DHF), or dengue shock syndrome.

Dengue virus infection has been counted among emerging and re-emerging diseases because of (1) the increasing number of patients, (2) the expansion of epidemic areas, and (3) severe clinical manifestation of dengue hemorrhagic fever (DHF)/dengue shock syndrome (DSS), which is often fatal if not properly treated In the meantime, there are no effective dengue control measures: a dengue vaccine is still under development and vector control does not provide a long-lasting effect. Early recognition and prompt initiation of appropriate treatment are vital if disease related morbidity and mortality are to be limited. Our study proposes a biomarker based on repeat sequences, which can be used as an effective mode for diagnosing different strains of Dengue virus.

Materials and Methods

Genome sequences

Complete genome sequences of 27 Flavivirus were assessed and downloaded in both GenBank and FASTA formats from NCBI and subsequently analyzed for simple and compound microsatellites. The Flaviviruses included in the study and their genome features have been summarized in Table 1. Flaviviruses have monopartite linear genome of about 10-11kb length.

Microsatellite extraction

The search for microsatellites was performed using Imperfect microsatellite extractor (IMEx) software. The analysis was done using the ‘Advance- Mode’ of IMEx with parameters as reported for analysis of HIV genomes; as in Type of Repeat: perfect; Repeat Size: all; Minimum Repeat Number: 6, 3, 3, 3, 3, 3; Maximum distance allowed between any two SSRs (dMAX) is 10 [7]. Two SSRs separated by a distance of less than or equal to 10bp would be thus treated as compound SSR (cSSR).

Statistical analysis

Microsoft Office Excel 2007 was used to perform regression analysis to predict correlation of Genome size and GC content on different parameters of SSR and cSSR such as incidence, relative abundance and relative density. Our sample size was 27 genomes, which we used in our analysis.

MATLAB based tools for SSR analysis

IMEx has been widely used to obtain the SSRs in a genome [17-22]. However, for subsequent analysis we developed two MATLAB based tools namely Identification of Gene Location from NCBI Nucleotide File (IGLNNF) and In-corporation of Gene Location in SSR File (IGLSF). IGLNNF was used to obtain the gene locations from Genbank directly but some manual help was needed for incorporation of gene position, because only starting and end point of polyprotein were mentioned in NCBI file for species of Flavivirus genomes whereas individual member of polyprotein were mentioned separately as misc_feature. It was further saved further into (.xlsx) format. IGLSF was used to incorporate the gene location in the SSRs file.

Results and Discussion

SSR/cSSR incidence

A total of 1164 SSRs and 53 cSSRs were uncovered from the 27studied genomes. Though Flaviviruses are known to have comparable genome sizes (10-11kb), the SSR incidence per genome is varying from 27 (F12) to 67 (F21) (Figure 1 and Table 1, Supplementary file 1). These variations cannot be attributed to genome size owing to small range and further highlighted by lack of co-linearity between genome size and SSR incidence. For instance, F11 with genome length of 10871bp has 65 SSRs as compared to F19 (10892bp) has just 37 SSRs (Table 1).

S.No Genus: Flavivirus Accession Number GS*(bp) GC**(%) SSRa cSSRa RAb RDc cRAb cRDc cSSR%d
F1 Apoi virus NC_003676.1 10116 48.3 42 3 4.15 23.03 0.30 4.05 7.14
F2 Banzi virus DQ859056 10182 50.4 34 1 3.34 21.70 0.10 1.18 2.94
F3 Bouboui virus DQ859057 10173 48.2 42 1 4.13 26.54 0.10 1.08 2.38
F4 Dengue virus AF326827 10618 47 53 4 4.99 30.80 0.38 5.46 7.55
F5 Edge Hill virus DQ859060 10206 47 39 5 3.82 24.99 0.49 8.72 12.82
F6 Japanese encephalitis virus AF221499 10976 51.3 36 2 3.28 20.68 0.18 4.83 5.56
F7 Jugra virus DQ859066 10173 48.2 39 1 3.83 24.38 0.10 1.57 2.56
F8 Kedougou virus DQ859061 10227 53 44 4 4.30 29.53 0.39 5.87 9.09
F9 Kyasanur Forest disease virus HM055369 10774 55.1 52 1 4.83 31.19 0.09 1.02 1.92
F10 Langat virus AF253420 10943 54.3 55 0 5.03 32.99 0.00 0.00 0.00
F11 Louping ill virus NC_001809 10871 54.8 65 3 5.98 37.72 0.28 4.14 4.62
F12 Modoc virus AJ242984 10600 45.5 27 0 2.55 17.26 0.00 0.00 0.00
F13 Montana myotisleukoencephalitis virus AJ299445 10690 44.1 48 0 4.49 27.60 0.00 0.00 0.00
F14 Murray Valley encephalitis virus KF751871 10953 48.9 40 2 3.65 23.01 0.18 4.29 5.00
F15 Powassan virus NC_003687 10839 53.3 57 1 5.26 34.41 0.09 1.57 1.75
F16 Rio Bravo virus JQ582840 10742 43.4 42 0 3.91 24.30 0.00 0.00 0.00
F17 Saboya virus DQ859062 10173 47.7 40 2 3.93 24.77 0.20 2.26 5.00
F18 Sepik virus DQ859063 10218 47.2 36 3 3.52 24.08 0.29 4.60 8.33
F19 St. Louis encephalitis virus KM267635.1 10892 49.78 37 1 3.40 23.60 0.09 1.01 2.70
F20 Tembusu virus KR061333.1 10278 48.97 34 3 3.31 22.57 0.29 5.25 8.82
F21 Tick-borne encephalitis virus NC_001672 11141 53.8 67 3 6.01 42.10 0.27 3.59 4.48
F22 Uganda S virus DQ859065 10182 46.9 37 2 3.63 23.87 0.20 3.04 5.41
F23 Usutu virus AY453411 11066 51 37 3 3.34 22.77 0.27 3.98 8.11
F24 Wesselsbron virus JN226796 10814 47.7 39 0 3.61 23.58 0.00 0.00 0.00
F25 West Nile virus NC_009942.1 11029 51.15 37 4 3.35 22.85 0.36 5.08 10.81
F26 Yellow fever virus KM388815 10236 50.1 40 1 3.91 25.60 0.10 1.86 2.50
F27 Zika virus DQ859059 10254 50.8 45 3 4.39 29.06 0.29 3.51 6.67

Table 1: Overview of simple and compound microsatellites in genus Flavivirus genome including dengue Virus.

data-mining-genomics-Incident-frequency

Figure 1: Incident frequency of SSRs, cSSR and cSSR%.

Two SSRs with a distance of <dMAX between them are considered as compound SSR (cSSR). Analysis of cSSR gives an insight into the uniformity in distribution of SSRs across genomes, wherein, a co-linearity between number of SSRs present and its conversion to cSSR would suggest existence of cSSR in an unbiased manner. However, the cSSR incident frequency ranged from zero to five (F5). A total of five species namely F10, F12, F13, F16 and F24 exhibited no cSSR in their genomes. These species had 55, 27, 48, 42 and 39 SSRs respectively. This variance in SSR to cSSR conversion across genomes is represented as cSSR% which reaches a maximum of 12.82% in F5 with 39 SSRs (Figure 1 and Table 1, Supplementary file 1).

These attributes highlight two aspects about repetitive sequences. First, the distribution of SSRs is non-uniform across genomes from which we can construe their emergence and maintenance in genomes to be based on functional and regulatory implications. Secondly, the variation in cSSR% across genomes is an outcome of differential clustering of SSRs in a genome which is suggestive of SSRs divergent roles in different genomes.

Relative abundance and relative density of SSR and cSSR

Relative abundance (RA) is number of SSRs present per Kb of genome while relative density (RD) is total SSR sequence per Kb of genome. The RA of SSR ranged from 2.55 (F12) to 6.01 (F21) and for cSSR it ranged from 0 (F10, F12, F13, F16, F24) to 0.49 (F5) (Table 1, Figure 2 and 3). The RD of SSR ranged from 17.26 (F12) to 42.10 (F21) and for cSSR it exhibited a maximum of 8.72 (F5) (Table 1, Figure 2 and 3). The range for RA and RD across Flavivirus genomes may be considered as a representative of potential for genome evolution.

data-mining-genomics-Relative-abundance

Figure 2: Relative abundance (Number of SSR per Kb of genome) and Relative density (Length occupied by SSR per Kb of genome) of SSRs.

data-mining-genomics-Relative-density

Figure 3: Relative abundance (Number of cSSR per Kb of genome) and Relative density (Length occupied by cSSR per Kb of genome) of cSSRs.

Correlation studies

We tested for correlation between genome size/GC content and number/relative abundance/relative density of SSRs and cSSRs. Incidence of SSRs is non-significantly correlated with genome size (R2=0.15, P>0.05) and GC content (R2=0.12, P>0.05). Similarly relative density (R2 =0.09, P>0.05) and relative abundance (R2=0.06, P>0.05) were non- significantly correlated with genome size and GC content respectively R2=0.36, P>0.05; and R2=0.27, P>0.05. The regression analysis of cSSR (R2=0.02, P>0.05), relative density (R2=0.02, P>0.05) and relative abundance (R2=0.04, P>0.05) shows non-significant correlation with genome size. Similarly GC content is also not significantly correlated for cSSR (R2=0.04, P>0.05), relative density (R2=0.02, P>0.05) and relative abundance (R2=0.03, P>0.05).

cSSR and dMAX

The uncovered cSSRs in present study were with a dMAX value of 10 as mentioned in section 2.2. However, IMEx has an option of varying the dMAX value between 0 and 50 [23]. So, in order to determine the impact of varying dMAX on cSSR incidence, five genomes F1, F7, F14, F21 and F27 were chosen at random and cSSR were extracted with increasing dMAX from 10 to 50. Expectedly, an increase in cSSRs% with higher dMAX values were observed as represented in Figure 4. However non-linearity in the increase further corroborates our initial suggestion of unequal distribution of SSRs, as in the distances between iterations differs across genomes. The ability of motifs to induce variations is often dependent on the proximity with other motifs and hence the differences therein would lead to different genome evolution potential. The repeat sequences induce variations by strand slippage and unequal recombination, chances of which are enhanced when different SSRs are in close proximity to one another [24].

data-mining-genomics-randomly-selected-genomes

Figure 4: Frequency of cSSR-% (Percentage of individual microsatellites being part of a compound microsatellite) in relation to varying dMAX (10 to 50) across 5 randomly selected genomes.

Motif types in iterations

We further looked into the divergence of repeat motifs extracted from Flavivirus genomes. The SSRs repeat motif ranged from mono- to penta-nucleotides. With the GC content in the genomes lying close to 50%, a bias in the iterations was not expected. However, in mononucleotides, A was the most prevalent repeat motif with an average distribution of around 6. This was followed by G (average distribution of 2). The least represented mononucleotide repeat motif was C as represented in Figure 5A. Amongst the dinucleotides AG/GA repeat motif was the most prevalent with an average distribution of 14 across studied genomes (Figure 5A). AAG/GAA was the most represented trinucleotide repeat as illustrated in Figures 5B.

data-mining-genomics-Average-distribution

Figure 5: Average distribution of repeat motifs. 5A) Mono- or dinucleotide repeat motifs. 5B) Tri-nucleotide repeat motifs.

Further, in terms of number of iterations present at a stretch, a maximum of 49 A repeat motif were observed in F21 followed by G repeat motif of 11(F15) and 10(F21) respectively. Whereas in di-nucleotide (AG/GA) repeat motifs maximum iteration were 5 in F10, F16 and F20. Tri-nucleotide maximum iteration repeats were found to be 4 in F1, F10, F11, F15, F21, F22 and F27 respectively.

Furthermore, the AT/TA dinucleotide motif were the least represented with average distribution of ~0.5. This motif is an established platform for SSR mutability and their low incidence is possibly suggestive of genome stability. Though repeats are known to be associated with copy number variations, strand slippage and polymorphisms accounting for genome evolution and adaptation; [6,25,26] their absence can lead to converse outcomes as well.

SSRs/cSSRs in coding regions

The distribution of SSRs across coding and non-coding regions of the genomes was accomplished by first extracting the locations of genes/proposed genes in the genomes in excel format using IGLNNF. A total of ~50 proteins were thus obtained. Subsequently, this data was simulated with the SSR data through IGLSF to get the distribution across coding and non-coding regions. For our analysis, we used 11 proteins present in most number of species (Figure 6). Coding regions accounting for over 80% of the total SSRs has been observed in earlier studies [17-22] across a diverse set of viruses suggestive of their role in gene expression, regulation and evolution. In the present study, interestingly, there was no cSSR present in the non-coding region (Figure 6). This further corroborates the idea that these repeat sequences have a role to play in gene regulation and expression when present in the coding regions. And when in non-coding regions they have a role to play in introducing variations leading to genome evolution. However, the present set of genomes have a low frequency of AT/TA repeat motifs as well as cSSRs are absent in non-coding regions suggestive of relatively stable genome of Flaviviruses.

data-mining-genomics-Differential-distribution-SSRs

Figure 6: Differential distribution of SSRs (%) in coding/non-coding regions of Flavivirus genomes.

Conserved motifs in dengue virus

The weak platform for genome evolution across Flavivirus genomes and the clinical significance of Dengue virus edged us to explore the possibility of conserved regions across the different isolates of Dengue viruses which can be used as biomarkers for diagnostics. The details of sequences of Dengue viruses used in the study have been listed in Table 2. These sequences were retrieved from NCBI (http://www.ncbi.nlm.nih.-gov/) and analyzed for conserved cSSR motifs. A total of 7 motifs were subsequently analyzed and the results have been summarized in Table 3. There are 2 such motifs which were present in all the 32 studied sequences whereas another 2 motifs were represented in all but one isolate sequences. We postulate the possibility of these sequences as candidate biomarkers for a common diagnostics of different isolates of Dengue viruses.

S. No Dengue virus isolate Accession No
D1 Dengue virus vector p4(Delta30) AY376438.1|
D2 Dengue virus type 4 recombinant clone 2Adel30 AF326826.1|
D3 Dengue virus type 4 vector p4 AY648301.1|
D4 Dengue virus type 4 recombinant clone rDEN4 AF326825.1|
D5 DENSTRA Dengue virus type 4 M14931.2|
D6  Dengue virus type 4 recombinant clone 2A AF375822.1|
D7  Dengue virus type 4 strain 814669 AF326573.1|
D8  Dengue virus strain Dakar HD 34460 KF907503.1|
D9  Dengue virus 4 strain 341750 GU289913.1|
D10  Dengue virus 4 strain H402276 JN559740.2|
D11  Dengue virus 4 isolate DENV-4/US/BID-V2429/1994 GQ199878.1|
D12  Dengue virus 4 isolate DENV-4/US/BID-V2437/1996 GQ199883.1|
D13  Dengue virus 4 isolate DENV-4/US/BID-V2440/1996 FJ850058.1|
D14  Dengue virus 4 isolate DENV-4/US/BID-V860/1994 FJ226067.1|
D15  Dengue virus 4 isolate DENV-4/US/BID-V2438/1996 GQ199884.1|
D16  Dengue virus 4 isolate DENV-4/US/BID-V2435/1996 GQ199881.1|
D17  Dengue virus 4 isolate Haiti73 JF262782.1|
D18  Dengue virus 4 isolate DENV-4/US/BID-V2439/1996 GQ199885.1|
D19 Dengue virus 4 isolate DENV-4/US/BID-V1094/1998 EU854297.1|
D20 Dengue virus 4 isolate DENV-4/US/BID-V2436/1996 GQ199882.1|
D21  Dengue virus 4 isolate INH6412 JF262781.1|
D22  Dengue virus 4 isolate DENV-4/VE/BID-V2163/1998 FJ639736.1|
D23  Dengue virus 4 isolate DENV-4/US/BID-V2446/1999 FJ882599.1|
D24  Dengue virus 4 isolate DENV-4/US/BID-V2448/1999 FJ882601.1|
D25  Dengue virus 4 isolate DENV-4/VE/BID-V2172/1999 FJ639744.1|
D26  Dengue virus 4 isolate DENV-4/CO/BID-V1600/1997 FJ024476.1|
D27 Dengue virus 4 isolate DENV-4/VE/BID-V2607/2006 JN819406.1|
D28  Dengue virus 4 strain H780120 JQ513341.1|
D29  Dengue virus 4 strain H772854 JN559741.2|
D30  Dengue virus 4 isolate Br246RR/10 JN983813.1|
D31  Dengue virus 4 strain H779228 JQ513338.1|
D32 Dengue virus 4 strain H772846 JQ513330.1|

Table 2: Details of Dengue virus sequences used in the study.

S. No Motif Motif present/total sequences analyzed Candidate Biomarker
1 (AG)3-x1-(AG)3 31/32 (Absent in D32) Yes
2 (AG)3-x3-(A)6 32/32 Yes
3 (AC)3-x4-(AG)3 32/32 Yes
4 (GA)3-x6-(GA)3 31/32(Absent in D26) Yes
5 (TG)3-x6-(C)6 2/32 No
6 (AG)4-x2-(TC)3 3/32 No
7 (AG)3-x9-(A)6 20/32 Maybe

Table 3: Conserved iterations in dengue virus.

Recent studies have demonstrated that discrete steps in the replication cycles of these viruses can be inhibited by pharmacological agents that target host factors mediating lipid synthesis, metabolism, trafficking, and signal transduction. Lipids are necessary for every step in the replication cycle of Hepatitis C virus (HCV) and Dengue virus (DENV), members of the family Flaviviridae. Despite this, targeting host lipid metabolism and trafficking as an antiviral strategy by blockade of entire biosynthetic pathways may be limited due to host toxicity therein highlighting the need for better diagnostics to counter the challenge of these viruses.

Conclusion

The comparative viral genomics in the light of SSRs would help us understand the diversity and adaptability to new hosts. The Flavivirus genomes lacked two essential features responsible for genome evolution, dinucleotide repeat motif AT/TA (least represented with average distribution of ~0.5) and cSSR in non-coding regions, suggesting a stable genome or evolution by hitherto unexplained mechanisms. The unveiling of conserved sequences in the isolates of Dengue virus suggests a basis for biomarker development for viral diagnostics.

Acknowledgement

We thank Department of Biomedical Sciences, Shaheed Rajguru College of Applied Sciences for Women, University of Delhi, Delhi-96, India and PIRO Technologies Private Limited, New Delhi-25, India for the financial and infrastructural support provided.

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