Vectorial capacity and genetic diversity of Anopheles annularis (Diptera: Culicidae) mosquitoes in Odisha, India from 2009 to 2011

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Vectorial capacity and genetic diversity of Anopheles annularis (Diptera: Culicidae) mosquitoes in Odisha, India from 2009 to 2011

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Biswadeep Das 1 , Aparna P. Patra 1 , Mumani Das, Namita Mahapatra, Harekrushna Tripathy, Santanu K. Kar, Rupenangshu K. Hazra ∗

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Regional Medical Research Centre, Chandrasekharpur, Bhubaneswar 751023, Odisha, India

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a r t i c l e

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a b s t r a c t

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Article history: Received 13 January 2014 Received in revised form 14 April 2014 Accepted 1 May 2014 Available online xxx

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Keywords: An. annularis Vectorial capacity Genetic diversity ISSR

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1. Introduction

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Anopheles annularis is one of the major vectors of malaria in Odisha, India. The present study was undertaken to determine the vectorial capacity and assess the genetic diversity of An. annularis collected from different endemic regions of Odisha. Mosquitoes were collected from thirteen endemic districts using standard entomological collection methods from 2009 to 2011. Sibling species of An. annularis were identified by PCR-RFLP and sequencing of D3 region of 28S ribosomal DNA (rDNA) region. Plasmodium falciparum (Pf) sporozoite rate and human blood fed percentage (HBF) were estimated by multiplex PCR using Pf and human specific primers. Genetic diversity of An. annularis was estimated by ISSR markers. Out of 1647 An. annularis collected, 1353 (82.15%) were collected by mechanical aspirators and 294 (17.85%) by light trap. 49 (2.97%) were positive for human blood and 18 (1.09%) were positive for Pf sporozoite. PCR-RFLP and sequencing analyses detected only An annularis A in the study areas. Overall genetic differentiation among An. annularis populations was moderate (FST = 0.048) and showed significant correlation between genetic distance and geographic distance (r = 0.882; P < 0.05). Angul population proved to be genetically unique and was highly divergent FST > 0.110) from other populations, suggesting low gene flow between them. The study indicated that only An. annularis A was found in Odisha with potential vectorial capacity that can play a major role in malaria transmission. ISSR markers proved to be useful molecular tools to evaluate genetic variability in An. annularis populations. © 2014 Published by Elsevier B.V.

Malaria is one of the most important vector borne disease in the world, with approximately 207 million cases (range 135–287 million) and 627,000 death cases in 2012 (WHO, 2013). The frequent outbreaks of malaria in India have been an insurmountable challenge to the public health programs. Odisha State of India contributed with nearly 24% cases and 17% deaths due to malaria in the country in 2011 (NVBDCP, 2011). Odisha is located in the Eastern part of India and has tropical climate with high temperature and rich rainfall, which is an ideal environment for malaria transmission. Most of the districts of the state report Plasmodium falciparum (Pf) malaria.

∗ Corresponding author. Tel.: +91 674 2301416/919778740543; fax: +91 6742301351. E-mail address: [email protected] (R.K. Hazra). 1 Equal contribution of both author.

Anopheles fluviatilis s.l., Anopheles culicifacies s.l. and Anopheles annularis s.l. are the three major species complexes comprising of potential vectors responsible for malaria transmission in Odisha Q2 (Mohanty et al., 2007; Swain et al., 2009; Das et al., 2013). Out of the three species complexes, An. annularis s.l. belongs to Neocellia Series of the subgenus Cellia and comprises four recognized species: An. annularis Van der Wulp 1884, An. pallidus Theobald 1901, An. philippinensis Ludlow 1902 and An. schueffneri Stanton 1915 (Harbach, 2013). Among the four species, An. annularis s.s. plays an important role in malaria transmission as a vector of local importance (Dash et al., 1982; Gunasekaran et al., 1989; Subbarao, 1998). It has a wide distribution throughout the Oriental region (Rao, 1984; Dev et al., 2004) and is considered as the major malaria vector in many states of India and neighboring countries like Sri Lanka, Bangladesh, Myanmar and Nepal (Bang, 1985; Gunasekaran et al., 1989; Ramasamy et al., 1992; Shrestha, 1985; Alam et al., 2007). This species is predominantly zoophilic but also shows affinity for human host (Dev et al., 2004). In India, its role in malaria transmission has been reported in Odisha, Assam, West Bengal and Andhra Pradesh. An. annularis s.s. consists of two sibling species

http://dx.doi.org/10.1016/j.actatropica.2014.05.002 0001-706X/© 2014 Published by Elsevier B.V.

Please cite this article in press as: Das, B., et al., Vectorial capacity and genetic diversity of Anopheles annularis (Diptera: Culicidae) mosquitoes in Odisha, India from 2009 to 2011. Acta Trop. (2014), http://dx.doi.org/10.1016/j.actatropica.2014.05.002

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Fig. 1. Map of Odisha showing the four different geo-physiographical regions (left view) and annual parasite incidence (API) status, number of deaths and An. annularis sampling locations in black squares (right view).

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i.e. A and B (Atrie et al., 1999; Norris, 2002; Surendran et al., 2011), which can be distinguished from each other through different banding patterns in the arm 2 of polytene chromosome of ovary (Atrie et al., 1999; Surendran et al., 2011). However, identification of sibling species of An. annularis s.s. based on cytogenetics and taxonomical methods has its own limitations viz., requires intact specimens, half gravid females, skilled personnel, etc. In contrast, molecular methods for species identification are relatively fast, accurate and avoid such requirements (Das et al., 2012). Vectorial attributes like Pf sporozoite rate and human blood feeding habit are important criteria for the determination of vectorial potential in mosquitoes (Swain et al., 2009). Therefore, this information is particularly important in this part of the world, where data on such parameters is very limited. DNA based molecular markers are used to study the genetic diversity in a wide range of taxa. Inter-simple sequence repeats (ISSR) marker analyses is an efficient tool to analyze differentiation of geographically and genetically isolated populations (Abbot, 2001; Deshpande et al., 2001; Gorrochotegui et al., 2000). The ISSR marker analysis is based on PCR amplification of DNA fragments delimited by two inverted microsatellites (SSR) (Zietkiewicz et al., 1994). ISSR markers are informative at various levels of genetic variations and reveal the polymorphisms without complicated protocols (Hess et al., 2000). These markers are stable, cost effective, rapid and very sensitive for assessing genetic variations among species (Paduan et al., 2006). ISSR markers are highly variable within a species and have advantage in utilizing longer primers high reproducibility and have high levels of polymorphism. It is particularly important for intraspecific studies on a small geographic scale to identify genetically distinct populations. Success of any vector control programme depends on the accurate identification of mosquito vector species (Das et al., 2012) along with exploring the genetic diversity of vector populations. A precise knowledge on genetic variation of vectors and gene flow among the populations is a key factor for understanding population structure and implementing control strategies (Tabachnik

and Black, 1995). Since An. annularis plays an important role in malaria transmission in Odisha, studies on the distribution, vectorial attributes and patterns of genetic diversity in this species complex are needed and important parameters to help developing appropriate control measures.

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2. Materials and methods

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2.1. Selection of study site

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Odisha (17◦ 34 N and 87◦ 31 E) is located along the coast of Bay of Bengal in the eastern part of India. It is divided into four distinct geophysiographical regions i.e. Northern Plateau, Central Tableland, Coastal Tract and Eastern Ghats. Thirteen districts belonging to the above geographical regions were selected for the study based on their high Annual Parasite Index (API) (NVDCP, 2011).

2.2. Entomological studies 2.2.1. Collections and identification of mosquitoes Collections took place in 30 different locations of the above mentioned districts (Fig. 1), between 2009 and 2011, during summer (April–June), rainy (July–August) and winter seasons (November–January). Adult mosquitoes were collected from human dwellings and cattle shed from 6.00 a.m. to 9.00 a.m. and 6.00 p.m. to 10.00 p.m. using mechanical aspirators and CDC light traps (Table 1). The wild caught mosquitoes were identified according to the standard keys of identification (Christophers, 1933; Nagpal et al., 2005) and An. annularis were separated. After identification, each mosquito was dissected into head-thoracic and the abdominal regions, which were kept separately in two different micro centrifuge tubes and used for DNA isolation (Das et al., 2013).

Please cite this article in press as: Das, B., et al., Vectorial capacity and genetic diversity of Anopheles annularis (Diptera: Culicidae) mosquitoes in Odisha, India from 2009 to 2011. Acta Trop. (2014), http://dx.doi.org/10.1016/j.actatropica.2014.05.002

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Table 1 An. annularis samples collected from different localities of Odisha along with their geographic coordinates.

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Geographical Region

District

Name of the location (Code)

Coordinates

Northern Plateau Region

Mayurbhanj Mayurbhanj Keonjhar Mayurbhanj Keonjhar Mayurbhanj Mayurbhanj Keonjhar Keonjhar Keonjhar Keonjhar Keonjhar Keonjhar

Badampahar (BAD) Jasipur (JAS) Kanjipani (KAN) Karanjia (KAR) Baghamari (BAG) Baripada (BAR) Rairangpur (RAI) Ghatagaon (GHA) Champua (CHA) Anandpur (ANA) Jhumpura (JHU) Joda (JOD) Hatadihi (HAT)

22◦ 10 N, 86◦ 10 E 21◦ 16 N, 85◦ 40 E 23◦ 24 N, 85◦ 23 E 21◦ 45 N, 87◦ 07 E 23◦ 24 N, 85◦ 23 E 21◦ 56 N, 86◦ 46 E 22◦ 27 N, 86◦ 17 E 20◦ 71 N, 85◦ 11 E 22◦ 08 N, 85◦ 67 E 22◦ 56 N, 87◦ 40 E 21◦ 82 N, 85◦ 57 E 22◦ 06 N, 85◦ 32 E 21◦ 20 N, 84◦ 10 E

Central Tableland Region

Angul Boudh Boudh Boudh

Angul (ANG) Khajuripada (KHA) Phiringia (PHI) Nuapada (NUA)

20◦ 49 N, 85◦ 06 E 20◦ 44 N, 84◦ 13 E 20◦ 54 N, 84◦ 23 E 20◦ 84 N, 84◦ 30 E

Eastern Ghat Region

Malkangiri Malkangiri Rayagada Kalahandi Kandhamala Kalahandi Koraput Koraput Gajapati Bolangir Bolangir Sonepur Nawarangpur

Padmagiri (PADM) Padia (PADI) Chandragiri (CHAN) Bhawanipatna (BHA) Daringibadi (DAR) Kesinga (KES) Boriguma (BOR) Laxmipur (LAX) Mohana (MOH) Kantabanjhi (KANT) Luisinga (LUI) Sonepur (SON) Nawarangpur (NAW)

17◦ 45 N, 81◦ 23 E 18◦ 35 N, 81◦ 9 E 19◦ 10 N, 83◦ 23 E 19◦ 54 N, 83◦ 10 E 19◦ 34 N, 84◦ 40 E 20◦ 12 N, 83◦ 23 E 18◦ 82 N, 82◦ 72 E 19◦ 54 N, 82◦ 23 E 18◦ 45 N, 84◦ 27 E 20◦ 40 N, 83◦ 30 E 20◦ 54 N, 84◦ 23 E 19◦ 36 N, 83◦ 64 E 19◦ 14 N, 82◦ 72 E

2.3. Molecular studies 2.3.1. DNA isolation DNA isolation was carried out from head-thoracic region and the abdominal region of single adult mosquito by phenol chloroform method as described by Coen et al., 1982 with slight modification. The isolated DNA was stored at −20 ◦ C until further use. 2.3.2. Sibling species specific PCR-RFLP and sequencing analysis For the determination of sibling species of the An. annularis s.s. complex, the D3 region of 28S rDNA was amplified and the PCR product was digested with the enzymes Alw26 1 and Kpn 1 separately (Alam et al., 2007). 25 ␮l of the D3 PCR product was subjected to ethidium bromide stained agarose gel electrophoresis with 1× TBE buffer for visualization. The amplified PCR products were excised from the gel, purified using QIAquick Spin Column (Qiagen, Hilden, Germany) and sequenced in an automated DNA sequencer (Genetic Analyser XL 3130, Applied Biosystems, Foster City, CA, USA) with either the D3 forward or D3 reverse primer following the manufacturer’s instructions. 2.3.3. Detection of human blood fed percentage (HBF) and P. falciparum (Pf) sporozoite In order to detect the presence of human blood in the abdominal region and Pf sporozoite in the head-thoracic region, a multiplex PCR assay was carried out as described in Mohanty et al. (2007). 2.3.4. ISSR PCR amplification For ISSR amplification, some anchored and non-anchored microsatellite primers (Sigma Life Science, USA) were selected. Twelve out of twenty five ISSR primers were chosen for the analysis because they produced highly reproducible bands. Each reaction mixture of 25 ␮l contained 30 ng mosquito template DNA, 1× PCR buffer (10 mM Tris HCl pH 8.3, 50 mM KCl and 0.1% gelatin pH 9), 1.5 mM MgCl2 , 0.25 mM each dNTPs, 20 ␮M primer and 0.5U Taq

DNA polymerase (Bangalore Genei, Bangalore). The reaction was cycled at 94 ◦ C for 4 min. for initial denaturation, 40 cycles of 94 ◦ C for 30 s, 55–59 ◦ C for 45 s for annealing and 72 ◦ C for 1 min, followed by 72 ◦ C for 7 min in a thermo cycler (Applied Biosystem). For consistency, the experiment was repeated thrice. The PCR product was subjected to electrophoresis in a 1.8% agarose gel stained with ethidium bromide and TBE buffer .The gel was visualized and photographed by using gel documentation system (AlphaImager, EC). Fragment size was estimated by comparison with a 1 kb ladder (Fermentas, USA). 2.3.5. ISSR scoring Polymorphism among the field populations was detected by scoring the polymorphic and monomorphic bands. Size of all the amplified products were estimated by 1 kb ladder (Fermentas, Germany) and scored as discrete variables. (1) is used to indicate presence and (0) to indicate absence of a band and binary data matrices of ISSR loci were assembled for further analysis. The gel score was converted to allele frequencies assuming that (a) two alleles segregate in each locus, including a dominant allele that amplifies a band of a given molecular size and recessive allele that does not; (b) co-migrating bands are the products of the same loci; (c) recessive alleles are identical in state and (d) populations are in Hardy Weinberg-equilibrium. 2.3.6. ISSR-primer polymorphism analysis Resolving power (RP) of the ISSR primers was calculated as RP = IB {IB = Band informativeness = 1 − [2 (0.5 − P)]}, P, being the proportion of the 30 anopheline specimens containing the band. Polymorphic information content (PIC) and primer index (PI) was calculated for evaluating the discriminatory power of the molecular markers (Liu et al., 2010). The PI was calculated as the sum of the PIC values for all selected markers produced by particular primer (Roldan-Ruiz et al., 2000). The PIC value was calculated using the formula PIC = 1 − pi 2 , where pi is the frequency of the

Please cite this article in press as: Das, B., et al., Vectorial capacity and genetic diversity of Anopheles annularis (Diptera: Culicidae) mosquitoes in Odisha, India from 2009 to 2011. Acta Trop. (2014), http://dx.doi.org/10.1016/j.actatropica.2014.05.002

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Table 2 Summary of districts surveyed, collection sites, collection methods, Human Blood Fed %, Pf % and type of sibling species of An. annularis s.l. identified by multiplex PCR in Odisha from 2009 to 2011. Sl no.

Name of the districts

Locations

Total number of An. annularis collected

Collection methods

Aspirators 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Mayurbhanj Angul Mayurbhanj Keonjhar Mayurbhanj Keonjhar Mayurbhanj Mayurbhanj Keonjhar Keonjhar Keonjhar Keonjhar Keonjhar Keonjhar Boudh Boudh Malkangiri Malkangiri Rayagada Kalahandi Kandhamala Kalahandi Koraput Koraput Bolangir Gajapati Bolangir Sonepur Boudh Nawarangpur

Badampahar Angul Jasipur Kanjipani Karanjia Baghamari Baripada Rairangpur Ghatagaon Champua Anandpur Jhumpura Joda Hatadihi Phiringia Nuapada Padmagiri Padia Chandragiri Bhawanipatna Daringibadi Kesinga Boriguma Laxmipur Kantabanjhi Mohana Luisinga Sonepur Khajuripada Nawarangpur Total

180 181 182 183 184

185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207

107 54 78 85 43 69 58 39 132 88 94 141 69 72 32 39 47 51 34 45 17 26 24 30 14 26 30 18 57 28 1647

93 45 64 69 37 59 49 32 104 72 76 116 58 57 24 34 38 40 28 38 14 19 18 26 12 19 25 15 48 24

2.3.7. Genetic diversity analysis The effective number of alleles, Shannon’s index and percentage of polymorphic loci were calculated by population genetics software POPGENE version 1.31 (Yeh et al., 1999) for all the loci and for each population separately. Inter-population genetic diversity was determined by Nei’s gene diversity (H). H was calculated at the population (Hpop ) and species levels (Hsp ) (Nei, 1973). Genetic differentiation of all populations was assessed by the fixation index, FST in each population using Arlequin v3.11 (Schneider et al., 2000). Analysis of molecular variation (AMOVA) was undertaken in Arlequin v3.11 using pairwise FST as the distance measure, with 10,000 randomizations and missing data for loci set at 10%. The model for analysis partitioned variation among geographical regions and among populations. Since geographical isolation is often considered as the main driving force for population differentiation, the correlation between pairwise genetic differentiation and geographic distance were assessed by the regression of genetic distance (Wright, 1943). The logarithm of geographical distances [FST /(1 − FST )] was calculated between sampling sites using a Mantel test as implemented in the web service Isolation by Distance (Jensen et al., 2005) using 10,000 randomizations. Significance level of multiple testing was corrected using sequential Bonferroni’s procedures (Holm, 1979).

No. of Pf +ve (%)

An. annularis sibling sp A

Light trap 14 9 14 16 6 10 9 7 28 16 18 25 11 15 8 5 9 11 6 7 3 7 6 4 2 7 5 3 9 4

1353 (82.15%)

ith allele. High, medium and low polymorphism was respectively PIC > 0.5, 0.5 > PIC > 0.25 and PIC < 0.25 (Smith et al., 1997). The similarity matrices were converted into distance matrices (distance = 1 − similarity). Most informative primers were calculated by Mantel Z statistics (Mantel, 1967).

No. of human blood fed (HBF %)

294 (17.85%)

4 (3.73) 2 (3.7) 2 (2.56) 3 (3.52) 1 (2.3) 2 (2.89) 2 (3.44) 0 (0) 5 (3.78) 2 (2.27) 2 (2.13) 6 (4.25) 3 (4.34) 3 (4.16) 1 (3.12) 1 (2.56) 2 (4.25) 2 (3.92) 0 0 0 0 0 1 (3.3) 0 1 (3.84) 0 1 (5.5) 2 (3.5) 1 (3.57)

2 (1.87) 1 (1.85) 1 (1.28) 1 (1.17) 0 1 (1.44) 1 (1.72) 0 3 (2.27) 0 1 (1.06) 2 (1.41) 1 (1.14) 1 (1.38) 0 0 1 (2.12) 1 (1.9) 0 0 0 0 0 0 0 0 0 0 1 (1.75) 0

49 (2.97%)

18 (1.09%)

107 54 78 85 43 69 58 39 132 88 94 141 69 72 32 39 47 51 34 45 17 26 24 30 14 26 30 18 57 28 1647 (100%)

2.3.8. Clustering analysis A pairwise matrix of distance between samples collected was determined for the cumulative ISSR data. Jaccard’s coefficient of similarity was calculated by phylogram based on similarity coefficients generated by neighbor joining (NJ) method using 1000 bootstrap replications (Sneath and Sokal, 1973). The entire analysis was performed using the statistical package NTSYS 2.0e (Rohlf, 2002). Principal Co-ordinate Analysis (PCA) was performed using the NTSYS-pc version 2.10 software used to examine the resolving power of the ordination to retrieve information about the clustering pattern of different populations (Rohlf and Sokal, 1981; Wolfe and Liston, 1998). Additionally, a Bayesian approach (Holsinger et al., 2002) was used to infer the number of clusters (K) in the data set without prior information of the sampling locations using STRUCTURE 2.3 (Pritchard et al., 2000). A model where the genetic distance correlated within populations was assumed ( was set at 1, the default value) and the software was run with the option of admixture, allowing for some mixed ancestry within individuals. Twenty independent runs were performed for each assumed number of populations (K = 1–20), with a burn-in period of 100,000 iterations and 500,000 replications. The method of Evanno et al. (2005) was used to determine the most likely number of clusters.

208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229

3. Results

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3.1. Morphological identification of mosquitoes

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A total of 7610 mosquitoes were collected, out of which 1647 (21.64%) were identified as An. annularis s.l. Out of 1647 An. annularis s.l., 1353 (82.15%) were collected by mechanical aspirators and

Please cite this article in press as: Das, B., et al., Vectorial capacity and genetic diversity of Anopheles annularis (Diptera: Culicidae) mosquitoes in Odisha, India from 2009 to 2011. Acta Trop. (2014), http://dx.doi.org/10.1016/j.actatropica.2014.05.002

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Fig. 2. FST values between pairs of An. annularis populations collected from Odisha (2009–2011). Values in bold are statistically significant at P < 0.05 (after Bonferroni correction).

235 236

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238 239 240 241 242

243

244 245 246 247

294 (17.85%) with light traps from both human dwellings and cattle sheds (Table 2).

3.4. Genetic diversity of ISSR markers

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Out of 12 ISSR primers used for the analysis, the resolving power and primer index for (GACA)4 was maximum. Maximum number of bands (12) were resolved by the primer (GACA)4 and lowest number of bands (7) were resolved by (AC)8 G. The highest number of polymorphic loci was detected by primer (GACA) 4 . Total number of bands produced by ISSR markers was 115. The total numbers of polymorphic bands were 96 and the mean value of polymorphism is 84%. The average number of bands amplified per primer was 9.5. Resolving power of primer (GACA)4 was maximum 8.94 and in case (AC)8 G it was lowest, 4.87. Maximum PI was 5.86 in case of (TG)8 G and minimum was 2.88 in case of (AC)8 C. The mean value of PIC for ISSR primers was 0.398, with the maximum value for (GACA) 4 primer (0.528) and the minimum value for (AC)8 G primer (0.296). The highest number of effective alleles was obtained for the (GACA)4 primer (1.899) while the lowest value was obtained for the (GT)8 A primer (1.053). The mean value of Shannon index as a measure of genetic diversity was 0.398 for all primers, with the highest

3.2. Sibling species identification Digestion with the enzyme Alw261 showed two fragments (200 bp and 210 bp) in all An. annularis s.l. samples, which were identified as sibling species A. No digestion was detected with KpnI enzyme, which precluded the presence of sibling species B in Odisha (Supplementary figure S1).

3.3. Vectorial capacity Out of 1647 field collected An. annularis s.l., 49 (2.97%) showed band at 519 bp for human blood and 18 (1.09%) showed band at 205 bp for the presence of Pf sporozoite by multiplex PCR (Supplementary figure S1 and Table 2).

Table 3 List of ISSR primers with their resolving power, primer index, PIC, polymorphism percentage, Shannon index and heterozygosity used for assessing the genetic diversity in An. annularis s.l. populations of Odisha. ISSR Primers

Resolving power

Primer Index

PIC

Na

P%

I

Ne

H

(CAA)5 (GACA)4 (AC)8 G (GT)8 A (CA)8 G (GA)8 C (ATG)6 (TG)8 G (CT)8 A (AC)8 C (GT)8 T (AT)8 G

6.40 8.94 4.87 6.56 5.69 7.36 6.25 6.32 5.46 5.58 6.46 6.92

4.75 4.67 3.75 5.34 3.74 4.36 4.88 5.86 4.58 2.88 3.74 3.96

0.413 0.528 0.296 0.391 0.385 0.423 0.383 0.446 0.375 0.328 0.391 0.428

8 13 9 6 5 10 10 11 6 5 6 8

76.01 99.32 71.23 75.32 71.21 96.25 87.25 97.12 75.98 77.23 86.25 95.13

0.388(0.291) 0.608(0.111) 0.147(0.191) 0.484(0.201) 0.453(0.161) 0.542(0.091) 0.582(0.027) 0.588(0.116) 0.168(0.291) 0.218(0.156) 0.498(0.208) 0.109(0.291)

1.447(0.312) 1.899(0.416) 1.488(0.410) 1.053(0.419) 1.428(0.443) 1.734(0.248) 1.472(0.287) 1.500(0.410) 1.177(0.343) 1.454(0.230) 1.399(0.302) 1.330(0.304)

0.278(0.157) 0.481(0.211) 0.278(0.201) 0.035(0.085) 0.237(0.224) 0.210(0.100) 0.295(0.145) 0.283(0.208) 0.102(0.192) 0.384(0.090) 0.257(0.151) 0.214(0.180)

Mean

6.4

4.37

0.398

84.00

0.398

1.448

0.254

8.08

PIC = Polymorphic information content, Na = number of alleles; P% = polymorphism%; I = Shannon index; Ne = number of effective alleles; H = Nei’s genetic diversity; number in parenthesis (standard deviation).

Please cite this article in press as: Das, B., et al., Vectorial capacity and genetic diversity of Anopheles annularis (Diptera: Culicidae) mosquitoes in Odisha, India from 2009 to 2011. Acta Trop. (2014), http://dx.doi.org/10.1016/j.actatropica.2014.05.002

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Table 4 Summary of genetic parameters (polymorphism percentage, Shannon index, no. of effective alleles, Nei’s genetic diversity, population and species genetic diversity) among An. annularis s.l. populations based on ISSR marker analysis. Population

P%

I

Ne

H pop

Hpop /Hsp

(Hsp − Hpop )/Hsp

Badampahar Angul Jasipur Kanjipani Karanjia Baghamari Baripada Rairangpur Ghatagaon Champua Anandpur Jhumpura Joda Hatadihi Phiringia Nuapada Padmagiri Padia Chandragiri Bhawanipatna Daringibadi Kesinga Boriguma Laxmipur Kantabanjhi Mohana Luisinga Sonepur Khajuripada Nawarangpur

66.86 72.79 44.75 20.35 42.37 62.71 42.37 52.54 28.81 56.84 51.68 33.64 31.55 41.36 53.62 32.37 49.32 34.59 56.42 43.58 68.53 54.31 21.28 47.39 39.57 35.72 42.54 42.55 57.44 55.28

0.342(0.276) 0.434(0.387) 0.251(0.294) 0.111(0.224) 0.233(0.292) 0.377(0.310) 0.233(0.292) 0.322(0.319) 0.188(0.300) 0.201(0.165) 0.223(0.176) 0.141(0.183) 0.212(0.113) 0.233(0.270) 0.266(0.213) 0.211(0.183) 0.255(0.634) 0.243(0.384) 0.231(0.147) 0.253(0.179) 0.182(0.263) 0.248(0.371) 0.266(0.201) 0.122(0.181) 0.219(0.228) 0.263(0.195) 0.122(0.254) 0.331(0.265) 0.232(0.272) 0.329(0.214)

1.386(0.369) 1.488(0.383) 1.299(0.386) 1.122(0.265) 1.278(0.380) 1.480(0.428) 1.278(0.380) 1.414(0.437) 1.253(0.409) 1.275(0.258) 1.277(0.272) 1.188(0.275) 1.211(0.154) 1.254(0.163) 1.370(0.317) 1.169(0.271) 1.296(0.327) 1.168(0.329) 1.295(0.251) 1.274(0.271) 1.341(0.279) 1.164(0.244) 1.237(0.161) 1.176(0.264) 1.132(0.241) 1.146(0.252) 1.261(0.206) 1.258(0.160) 1.185(0.216) 1.272(0.218)

0.227(0.195) 0.324(0.202) 0.170(0.205) 0.073(0.151) 0.158(0.204) 0.261(0.221) 0.158(0.204) 0.224(0.227) 0.133(0.213) 0.116(0.183) 0.113(0.101) 0.263(0.104) 0.162(0.241) 0.135(0.212) 0.150(0.110) 0.247(0.113) 0.172(0.238) 0.224(0.125) 0.149(0.286) 0.119(0.202) 0.314(0.248) 0.147(0.191) 0.115(0.198) 0.245(0.132) 0.224(0.271) 0.221(0.214) 0.172(0.213) 0.251(0.137) 0.2240.138) 0.141(0.210)

0.650 0.938 0.635 0.275 0.550 0.909 0.550 0.780 0.463 0.761 0.727 0.524 0.164 0.332 0.807 0.441 0.659 0.357 0.794 0.628 0.521 0.352 0.652 0.612 0.413 0.253 0.221 0.612 0.331 0.543

0.150 0.861 0.364 0.524 0.452 0.090 0.452 0.219 0.536 0.241 0.051 0.253 0.613 0.250 0.181 0.341 0.243 0.428 0.246 0.255 0.271 0.341 0.184 0.361 0.128 0.425 0.138 0.154 0.142 0.354

P% = polymorphism%; I = Shannon index; Ne = number of effective alleles; H = Nei’s genetic diversity; number in paranthesis (standard deviation).

277

value for the (GACA)4 primer (0.608) and the lowest value for the (AT)8 G primer (0.109). The highest value for Nei’s genetic diversity (H) was found in (GACA)4 primer (0.481) and the lowest value was observed in (GT)8 A primer (0.035) (Table 3). Among the thirty An. annularis s.l. populations, the Angul population showed the highest value of intrapopulation polymorphism, the highest number of effective alleles, Shannon index and Nei’s genetic diversity. The lowest values of percentage polymorphism, number of effective alleles, Shannon index and Nei’s genetic diversity were obtained for the Sonepur population (Table 4).The genetic diversities highly varied within (Hpop /Hsp ) and among (Hsp − Hpop /Hsp ) An. annularis s.l. populations of Angul and other locations.

278

3.5. Genetic differentiation

266 267 268 269 270 271 272 273 274 275 276

279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297

Genetic differentiation over all populations was moderate (FST = 0.048), except when comparison was done between Angul population and any other population, high genetic differentiation was observed (FST > 0.110). Maximum genetic differentiation was observed between An. annularis s.l. populations of Nawarangpur (Nawarangpur District) and Angul (Angul District) (FST = 0.195). Lowest genetic differentiation was observed between the Kantabanjhi (Bolangir District) and Luisanga (Bolangir District) populations (FST = 0.009) (Fig. 2). The AMOVA carried out using ISSR data showed significant differences (P < 0.0001) among the An. annularis s.l. populations. Variation among regions was low (17.76%), whereas the variance between populations was 54.67% (P < 0.0001) (Table 5), which thus indicated high interpopulation genetic differentiation among the An. annularis s.l. populations of Odisha. A positive and significant correlation was found between genetic and logarithm of geographic distance (r = 0.882; Mantel test, P < 0.05) among the An. annularis s.l. populations across different districts of the state, suggesting that geographic distance between sites is responsible for part of the differentiation observed

(Fig. 3). However, the correlation was not significant (Mantel test, P > 0.05) when Angul population was excluded from the analysis. 3.6. Population structure The dendrogram analysis of ISSR makers by NJ clustering represented three clades. Clade I comprised An. annularis s.l. collected

Fig. 3. Correlation between genetic distance (FST /(1 − FST ) and logarithm of geographic distance between collection sites for pairwise comparisons of An. annularis populations from Odisha, India. Angul population showed high positive correlation between genetic distance and logarithm of geographic distance.

Please cite this article in press as: Das, B., et al., Vectorial capacity and genetic diversity of Anopheles annularis (Diptera: Culicidae) mosquitoes in Odisha, India from 2009 to 2011. Acta Trop. (2014), http://dx.doi.org/10.1016/j.actatropica.2014.05.002

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Table 5 Summary of analysis of molecular variance (AMOVA) results showing genetic variations among geographical regions and among populations of An. annularis s.l. of Odisha from 2009 to 2011. Genetic variation

df

MSD

Variance

% Variation

P

Among geographical regions Among populations

4 28

232.213 675.137

202.478 617.897

17.76 54.67

0.00001 0.00001

Fig. 4. Dendrogram of An. annularis generated by NJ clustering using Jaccard’s coefficient of similarity on ISSR markers showing three distinct clusters: I (An. annularis populations of Northern Plateau), II (An. annularis population of Angul District) and III (An. annularis populations of both Eastern Ghats and Central Tableland) among the species. In horizontal axis scale represents genetic distance.

303 304 305 306 307

from of Northern Plateau, which showed more genetic similarity among each other. Clade II consisted of An. annularis collected from Angul District (Central Tableland), which was genetically unique as compared with other populations. Clade III comprised An. annularis s.l. collected from Eastern Ghats and Central Tableland which

shared some genetic similarity (Fig. 4). The Bayesian cluster analysis too divided the populations into three clusters (posterior probability of Bayesian clustering Ln(D) likelihood score optimal for k = 3 clusters) (Fig. 5). As depicted by dendrogram analyses, Angul population was assigned to a distinct cluster. The clustering of 30

Fig. 5. Bayesian cluster analysis using STUCTURE representing the data set for the most likely cluster (K = 3), where each color corresponds to a suggested cluster and each individual is represented by a vertical bar. The X-axis corresponds to population codes. The Y-axis presents the probability of assignment of an individual to each cluster.

Please cite this article in press as: Das, B., et al., Vectorial capacity and genetic diversity of Anopheles annularis (Diptera: Culicidae) mosquitoes in Odisha, India from 2009 to 2011. Acta Trop. (2014), http://dx.doi.org/10.1016/j.actatropica.2014.05.002

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Fig. 6. Graph based on Principal Component Analysis (PCA) showing three distinct clustering pattern: I (An. annularis populations of Northern Plateau), II (An. annularis population of Angul District of Central Tableland) and III (An. annularis populations of both Eastern Ghats and Central Tableland) among the species by ISSR markers. Numericals code: 1-BAD, 2-ANG, 3-JAS, 4-KAN, 5-KAR, 6-BAG, 7-BAR, 8-RAI, 9-GHAT, 10-CHA, 11-ANA, 12-JHU, 13-JOD, 14-HAT, 15-PHI, 16-NUA, 17-PADM, 18-PAD, 19-CHAN, 20-BHA, 21-DAR, 22-KES, 23-BOR, 24-LAX, 25-KANT, 26-MOH, 27-LUI, 28-SON, 29-KHA, 30-NAW.

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samples collected from different districts of Odisha by ISSR markers was confirmed using PCA that revealed similar result as obtained from UPGMA and Bayesian clustering (Fig. 6).

316

4. Discussion

313 314

317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346

Knowledge on sibling species distribution, vectorial attributes and genetic diversity of important malaria vectors like An. annularis is crucial for assessing the magnitude of regional malaria risk before implementing control programmes. The present study revealed that An. annularis A was the only sibling species found throughout the surveyed areas and further confirmed by molecular methods, which thus proved it to be one of the major malaria vectors in Odisha. An. annularis A was found to be profusely distributed in the surveyed areas, which were mainly collected from foothill areas, also documented in earlier studies (Surendran et al., 2011). Human blood index and presence of Pf sporozoite were two essential criteria analyzed in this study for the assessment of vectorial capacity of An. annularis. This study demonstrated that the anthropophily (HBF) and Pf sporozoite rate of An. annularis A was nearly 3% and 1%, respectively. Although these values do not represent high levels for a main vector like Anopheles dirus, however, for a secondary vector like An. annularis A, such values are high enough to transform it to a major vector of malaria in future. Our analyses indicated a significant level of genetic diversity by ISSR markers in the An. annularis s.l. populations of Odisha. Within populations, genetic diversity was low, thereby indicating extensive gene flow between them. Correlation analyses indicated that geographic distance played a major role in shaping the genetic diversity of An. annularis s.l. in Odisha, however its role was minimized when Angul population was excluded from the analysis. Therefore, ecological factors other than geographical distance may also play a role in modulating the genetic architecture of An. annularis s.l. populations in Odisha. Comparison among different populations revealed high genetic differentiation (FST > 0.110) between Angul population and other populations (Fig. 2), thus

indicating low gene flow between Angul and other populations. AMOVA analyses indicated great interpopulation genetic differentiation among the populations between the regions (mainly between Angul and other populations). These findings indicated that the Angul population was genetically more divergent than the other populations. Such high genetic diversity in the Angul population can be attributed to the ecological landscapes (surrounding forests, hills) that form geographical barriers and separate it from other populations and also by distinct introduction events due to migration/immigration of An. annularis s.l., which can lead to the evolution of unique mosquito lineage. Such findings have also been reported in other malaria vectors, which revealed the circulation of unique mosquito lineage due to genetic differentiation among vector populations (Choi et al., 2012; Michel et al., 2005). The recent increase in malaria cases in Angul District can be attributed to the circulation of such unique mosquito lineage, which is an important factor in mediating malaria transmission. Thus Angul District can be rightly considered as one of the high risk areas for malaria transmission in Odisha state in the present scenario. The present study reported the usefulness of ISSR primers in demonstrating genetic diversity of An. annularis populations (Hess et al., 2000). We found high allelic variation with 12 ISSR markers. Among them, four ISSR primers gave very high values for allele polymorphism (>95%), which is valuable for wild An. annularis s.l. population discrimination. The high level of polymorphism at ISSR loci revealed by polymorphism rate and PIC indicates high genetic variability in An. annularis s.l. populations. The mean value of the PIC of all ISSR markers used in this study was 0.398, indicating that the primers could develop medium polymorphism, which can be useful for assessing genetic variation of An. annularis s.l. populations. ISSR data analyses showed more inter population genetic diversity in wild An. annularis s.l. populations of Odisha. Within populations, genetic diversity was maximum in Angul population as compared with other populations and was grouped into a unique clade after cluster analysis. The FST pair wise analysis of molecular data performed between Angul and other populations showed significant

Please cite this article in press as: Das, B., et al., Vectorial capacity and genetic diversity of Anopheles annularis (Diptera: Culicidae) mosquitoes in Odisha, India from 2009 to 2011. Acta Trop. (2014), http://dx.doi.org/10.1016/j.actatropica.2014.05.002

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genetic differences (Fig. 2). These findings suggest two implications: First, migration rates or gene flow between Angul population and other populations is low, thereby indicating that mutation-drift equilibrium, which is so common to analyses of population genetic structure, is not acting on Angul population. Second, it suggests a shared history due to the interaction between human population expansion, associated anthropogenic modifications to the environment, and the correlated rise of An. annularis s.l. species and malaria cases, which provide mechanistic insights into the rapid ecological adaptation and lineage splitting in the vector population. Such high genetic divergence in Angul District may be due to the occurrence of different mutations including insertions/deletions and lack of genetic exchange in Angul population and other populations (Van et al., 2011). However, similarity observed between some of the An. annularis s.l. species belonging to different populations (based on cluster and PCO analyses) shows some gene flow between these populations. The results suggest that An. annularis A was the only sibling species found in Odisha with anthropophily and Pf sporozoite rates potentially high enough to become an important malaria vector in future. Genetic analysis depicted that An. annularis s.l. populations from most parts of Odisha state were structured with high genetic exchanges. Exception was Angul population, which was observed to be genetically distinct from other populations. This is the first study to assess the genetic diversity of An. annularis in Odisha, India using ISSR primer polymorphisms. The ISSR marker analyses were simple and effective methods to assess the bio geographic relationships and genetic diversity within the populations of An. annularis s.l. in Odisha. Such genetic variations in An. annularis s.l. species can influence the patterns of mosquito-mediated host parasite dynamics in Odisha state because of its widespread distribution with human host preference. Furthermore, genetic structure of An. annularis s.l. populations can help to understand differences in disease transmission due to genetically distinct vector populations and to predict the spread of genes of interest, like those involved in insecticide resistance or refractoriness. Our findings will provide a benchmark to the State vector control authorities on the vectorial attributes and pattern of genetic diversity in An. annularis s.l., which is an important malaria vector in Odisha. Therefore the next step will be the effective management of An. annularis s.l. populations, which is of prime importance to public health and welfare. Hence the study warrants the need for development and implementation of effective vector control measures like sustained vector population suppression technologies at regional/local scale and second generation transgenic technologies (Marinotti et al., 2013; Isaacs et al., 2012), which have been successfully evaluated for genetic elimination of other malaria vectors and controlling parasite transmission.

Uncited references Peakall and Smouse (2006), Reid (1968) and Tripathy et al. (2010).

Acknowledgements This study was supported financially by Council of Scientific and Industrial Research (CSIR), Govt of India. Authors are thankful to S. Biswal and Dr. K.C. Samal, Assoc Professor, Odisha University of Agriculture and Technology, Bhubaneswar for technical assistance. The authors acknowledge the entomological collection assistance provided by B. Pradhan, C.S. Tripathy and S.S. Beuria.

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Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.actatropica.2014.05.002.

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