Behavior and population structure of Anopheles darlingi in Colombia

    Behavior and population structure of Anopheles darlingi in Colombia Nelson Naranjo-D´ıaz, Jan E. Conn, Margarita M. Correa PII: DOI: Reference:

S1567-1348(16)30003-X doi: 10.1016/j.meegid.2016.01.004 MEEGID 2594

To appear in: Received date: Revised date: Accepted date:

29 September 2015 1 December 2015 4 January 2016

Please cite this article as: Naranjo-D´ıaz, Nelson, Conn, Jan E., Correa, Margarita M., Behavior and population structure of Anopheles darlingi in Colombia, (2016), doi: 10.1016/j.meegid.2016.01.004

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ACCEPTED MANUSCRIPT Behavior and population structure of Anopheles darlingi in Colombia Nelson Naranjo-Díaz1, Jan E. Conn2, 3, Margarita M. Correa1*

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1. Grupo de Microbiología Molecular, Escuela de Microbiología, Universidad de Antioquia, Medellín, Colombia. 2. Wadsworth Center, New York State Department of Health, Albany, NY, USA. 3. Department of Biomedical Sciences, School of Public Health, State University of New York, Albany, NY, USA.

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N. Naranjo-Díaz: [email protected] Jan E. Conn: [email protected] *Margarita M. Correa: [email protected] (Corresponding author)

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Abstract

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Anopheles darlingi is a widely distributed and important malaria vector in Colombia. Biogeographical and ecological heterogeneity across the Colombian distribution led to the hypothesis of behavioral and genetic differentiation among An. darlingi populations. A total of 2,017 An. darlingi specimens were collected during 222 h of sampling. This vector was the most abundant anopheline species in most of the localities sampled. Subdivision between samples collected west and east of the Andes was indicated by 1) mitochondrial COI and nuclear CAD sequences from NW-W and CE-S populations (COI ΦST = 0.48761 0.81974, CAD FST= 0.11319 - 0.21321), 2) a COI haplotype network, and 3) SAMOVA. Endo- and exophagy were detected in populations west of the Andes, whereas exophagy was evident in PTG, a locality east of the Andes. Isolation by resistance was significant for COI and explained 26% of the genetic differentiation. We suggest that at a macrogeographic scale, the Andes influence the differentiation of An. darlingi in Colombia and may drive divergence, and, at a microgeographic scale, ecological differences have a significant impact on structure. These data could constitute a baseline for the design of effective vector interventions, locality-specific for the east and similar for panmictic populations west of the Andes. Keywords Population genetics; Isolation by resistance; Isolation by distance; Anopheles darlingi; ecoregions; Andes Mountains

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1. Introduction

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In the Neotropics, Anopheles darlingi is one of the most important malaria vectors, distributed from southern Mexico to northern Argentina (Loaiza et al., 2009; Manguin et al., 1996; Mirabello and Conn, 2006; Rubio-Palis and Zimmerman, 1997). It is characterized by high anthropophily in many areas and an ability to adapt to environmental changes (Hiwat and Bretas, 2011; Vittor et al., 2006). Ample evidence for its major role in malaria transmission in many countries has been detected (Galardo et al., 2009; Moreno et al., 2009, 2015; Naranjo-Diaz et al., 2013). In past decades Colombia reported an average of 100,000 malaria cases per year, but since 2011 the number of annual registered cases has decreased substantially, to above 50,000 (INS, 2014, 2013). Nonetheless, in Latin America, Colombia reports 12% of cases, next only to Brazil and Venezuela (WHO, 2014).

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In Brazil, Anopheles darlingi is now considered a complex of at least three putative species (Emerson et al., 2015). In other countries, there is evidence of divergence, structure and behavioral differentiation that together indicate the likelihood of additional species or extreme plasticity (Charlwood, 1996; Hiwat and Bretas, 2011; Kreutzer et al., 1972; Manguin et al., 1999; Mirabello et al., 2008; Pedro and Sallum, 2009). In South America, An. darlingi population subdivision has been hypothesized to be driven by distance-IBD, differences in effective population size, geographic barriers including the Amazon and coastal mountain ranges, and seasonal variation (Angêlla et al., 2014; Conn et al., 2006; Mirabello and Conn, 2006; Mirabello et al., 2008; Motoki et al., 2012; Pedro and Sallum, 2009; Pedro et al., 2010). In Colombia, An. darlingi is widely distributed (Gonzalez and Carrejo, 2009; Olano et al., 2001), in lowland regions with different ecological conditions (IGAC, 2002) on either side of the Andes (Olano et al., 2001). In the first of two studies of the genetic structure of An. darlingi in Colombia, conspecific populations and high gene flow between the W and NW and moderate genetic differentiation between E and W populations were supported by AFLP loci and attributed to biogeographic differences (González et al., 2007). Results of the second study, conducted in NW Colombia, using COI and microsatellites markers, established that An. darlingi is a unique taxon locally, with low genetic differentiation and high gene flow among populations (Gutiérrez et al., 2010). Mitochondrial COI has revealed the population structure of several Anopheles vector species such as An. darlingi (Mirabello and Conn, 2006; Pedro and Sallum, 2009; Gutiérrez et al., 2010) and Anopheles albimanus (Gutiérrez et al., 2009a; Loaiza et al., 2010). COI sequences were significantly different between northwestern and southeastern Colombian Anopheles triannulatus (Rosero et al., 2012). In other taxa, such as Lutzomyia anduzei, high genetic differences between northern and central Brazil Amazonian populations were 2

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detected using COI (Scarpassa et al., 2015). To test the hypothesis of An. darlingi divergence, we also chose to analyze a nuclear marker, the single-copy gene CAD. This marker has been used primarily in phylogenetic inferences (Foster et al., 2013; Wiegmann et al., 2009); however, high polymorphism was detected within different Anopheles taxa (Foster et al., 2013), as in other single-copy nuclear markers (Du et al., 2014). Therefore, we hypothesized that CAD sequences would be useful in inferring population processes.

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2. Material and Methods

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Considering the ample distribution of An. darlingi in Colombia, we evaluated behavior and population structure to provide a more detailed analysis of its countrywide population dynamics. Compared with earlier studies of An. darlingi in Colombia, additional geographical sites and markers with different evolutionary rates and inheritance were employed. We hypothesized that geographic heterogeneity and ecological differences across the Colombian distribution reflect the impact of geographic barriers that promote genetic differentiation in An. darlingi. Information on genetic variation among anopheline populations may reveal differences in vector capacity (Coluzzi et al., 1979; Van Bortel et al., 2004; Wang et al., 2001), which can be used to guide the design of effective vector control strategies.

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2.1. Sampling sites. Specimens were collected in seven localities of five Colombian ecoregions (WWF, 2015) where An. darlingi has been previously reported (Figure 1). Localities sampled and the number of malaria cases per municipality during the year 2014 included, in the Magdalena-Urabá Moist Forest ecoregion-MUMF (NW Colombia), El Caño in San Pedro de Urabá-SPU municipality (n=331 malaria cases), La Capilla in El Bagre-BAG (n=2,264) and Juan Jose in Puerto Libertador-PTL (n=207) (INS, 2014). In the past decades, this region reported the highest number of malaria cases in Colombia (> 60%) (INS, 2013, 2014). The MUMF ecoregion is characterized by dry forest and wetland vegetation on flooded soils, currently highly impacted by anthropic activities. Dry and rainy seasons are conspicuous; annual average precipitacion is estimated at 3,000 mm (WWF, 2015). In the Chocó-Darien Moist Forest ecoregion-CDMF, sampling was conducted in San Antonio de Padua, Vigía del Fuerte municipality –VGF (n=145 cases) (INS, 2014), in western Colombia. The CDMF ecoregion is characterized by rainforest, with an annual average of 16,000 mm precipitation. This and the MUMF ecoregion are separated by a branch of the Colombian western Andean mountains (Figure 1). Cumaral municipality-CUM (during 2014 only one case was reported) (INS, 2014), in central-eastern Colombia is in the Apure Villavicencio Dry Forest ecoregion-AVDF, east of the Andes. AVDF is a transition zone between montane forests and extensive plains (or Llanos Orientales), consisting of a mosaic of premontane forest, dry forest, savanna and 3

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gallery forest with low annual precipitation (average 135 mm; WWF, 2015). Cecilia Cocha in Puerto Leguízamo-PTG (n=22 cases) (INS, 2014), southern Colombia and east of the Andes, is in the Napo Moist Forest ecoregion-NMF. NMF is characterized by lowlands dominated by flooded river plains, alternating with low hills. There is no demarcated dry season, only a decrease in rainfall; with an annual average of 1,600-3,000 mm precipitation (WWF, 2015). Santa Lucia in Tarapacá-TAR (n=330) (INS, 2014), in the extreme southern Colombia, east of the Andes, is located in the Solimões-Japurá Moist Forest ecoregionSJMF. This ecoregion is characterized by mosaic soils and high humidity, with annual rainfall higher than 3000 mm. The region is known for high species diversity and wide river systems (WWF, 2015).

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2.2. Data collection. Collections were conducted from January 2009 to July 2010, using animal bait and human landing-catches, under an informed consent agreement and collection protocol reviewed and approved by the University of Antioquia Institutional Review Board (Comité de Bioética Sede de Investigación Universitaria, CBEIH-SIU, UdeA, approval document 07-41-082). The collections were performed indoors and outdoors (within 10 m of a house), by a team of two people in each setting, from 18:00 to 00:00 h, during five to six days per field trip. BAG, PTL and VGF in NW-W were visited four times every three months, but SPU, PTG, TAR and CUM only once for logistical reasons.

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Differences in behavior by hour for An. darlingi in localities with several visits were evaluated using ANOVA; since no significant difference were found, all collections were treated equally, normalized by taking the total number of An. darlingi collected per hour divided by the number of collection days (Naranjo-Diaz et al., 2013; Naranjo-Díaz et al., 2014). Adult mosquitoes were identified using a morphology-based key (Gonzalez and Carrejo, 2009). A subsample of mosquitoes identified as An. darlingi were molecularly confirmed as such using a PCR-RFLP-ITS2 (Cienfuegos et al., 2008, 2011; Zapata et al., 2007). In addition, potential larval habitats within approximately 1 km of adult collection sites were inspected visually to identify the type of larval habitat (natural vs artificial) and temporality of habitat (temporal vs permanent). Positive larval habitats were sampled using the methodology proposed by PAHO (OPS/OMS, 2008). Third and fourth instar larvae were collected and stored preserved with ethanol (95%) for morphological identification (Gonzalez and Carrejo, 2009) and determination of An. darlingi immature stages related to specific larval habitats. 2.3. Detection of mosquitoes naturally infected with Plasmodium spp. Pools of up to five heads and thoraces of An. darlingi of the same collection date and locality were evaluated for natural infection with Plasmodium falciparum and Plasmodium vivax (VK210 and VK247) using ELISA (Gutiérrez et al., 2008; Wirtz et al., 1992, 1987). Positive ELISA pools were confirmed by a second ELISA and a nested species-specific PCR (Singh et al., 4

ACCEPTED MANUSCRIPT 1999), using DNA extracted from individual abdomens to identify each infected specimen (Rosero et al., 2013).

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2.4. Entomological parameters. Biting activity was calculated as the number of An. darlingi collected per hour, expressed in percentage. The infection rate (IR) was calculated as the percentage of Plasmodium positive mosquitoes out of the total number of An. darlingi analyzed. Confidence intervals (CI, 95%) were calculated under the assumption of a binomial distribution using EPIDAT v. 3.1 (OPS/OMS 2006). Differences in hourly biting behavior among localities were evaluated using ANOVA, and endophagic vs exophagic biting values were estimated using the Mann-Whitney U test in SPSS v. 21 (IBM Corp.); the Bonferroni test was used to correct the significance of multiple comparison. The summed hourly abundance data of mosquitoes collected on human landing catches by locality was used to calculate the human biting rate-HBR or the number of bites per person per night-b.p.n., estimated as the total number of An. darlingi in each collection divided by the total number of collection days and the average number of collectors. The annual EIR index, corresponding to the number of infective bites that a person may receive in one year in each site, was estimated by multiplying the average HBR by the IR by 365 days.

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2.5. DNA extraction, mitochondrial and nuclear DNA amplification. DNA was extracted from the An. darlingi abdomens using a salt precipitation protocol (Rosero et al., 2010). A 1,300 bp fragment of the COI gene was amplified after optimizing a PCR using primers C1-J-1718 5’-GGAGGATTTGGAAATTGATTAGTTCC-3’ (Simon et al., 1994) and COI-r 5’-TATGAAGCTTAAATTCATTGCAC-3’ (Pedro and Sallum, 2009). Final PCR concentrations were: 1X buffer, 0.2 mM dNTPs, 1.5 mM MgCl2, 0.4 µM Primers and 1 U of Taq. Cycling conditions included an initial denaturation to 95º C for 5 min, 36 cycles of denaturation at 94ºC for 1 min, annealing 51.2ºC for 1 min and extension at 72ºC for 1 min; the final extension was at 72ºC for 10 min. To amplify a region of the nuclear gene carbamoyl phosphate synthetase, aspartate transcarbamylase and dihydroorotaseCAD, a set of primers was designed using the An. darlingi and Anopheles nuneztovari CAD gene sequences downloaded from GenBank. The primers CAD-F 5’GARCTGTTYGTBAACCTGAACG-3’ and CAD-R 5’-GCAAAYCCKGACCCAAGCCC -3’ amplified a 761 bp CAD fragment. Optimized amplification conditions were: 1X buffer, 0.2 mM dNTPs, 2 mM MgCl2, 0.4 µM Primers, 1 U of Taq and 5% of DMSO. Cycling conditions included an initial denaturation to 95º C for five min, 35 cycles of denaturation at 94ºC for 30 sec, annealing 59.7ºC for 20 sec and extension at 72ºC for 40 sec; the final extension was at 72ºC for 10 min. Amplified products were sequenced in both directions; chromatograms were edited and trimmed using Geneious Pro v. 6.1 (Kearse et al., 2012). Sequences were aligned using MUSCLE (Edgar, 2004), in Geneious Pro and misaligned nucleotides were manually revised and adjusted. Analyses of CAD included coding 5

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heterozygous sites using IUPAC ambiguity codes, inferring their gametic phases using the Bayesian algorithm implemented in PHASE v. 2.1 (Stephens et al., 2001). Recombination was evaluated using three different tests. The PHI test implemented in SPLITSTREE v. 4.11.3 (Huson and Bryant, 2006), distinguishes recombination events from homoplasies (Bruen et al., 2006), and the tree-based methods, SBP that searches for evidence of recombination on alignment, and GARD (Kosakovsky Pond et al., 2006), that locates recombination breakpoints, both in the Datamonkey webserver (Delport et al., 2010).

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2.6. Genetic analyses. For CAD and COI gene sequences, haplotype and nucleotide diversities were calculated in ARLEQUIN v. 3.1.1 (Excoffier et al., 2005) and DnaSP v. 5.0 (Librado and Rozas, 2009). Haplotype relationships were estimated in a haplotype network constructed in TCS v. 1.21 (Clement et al., 2000). Mutation-drift equilibrium was evaluated by the neutrality tests Tajima's D (Tajima, 1989), Fu's FS (Fu, 1997), Fu and Li's D and F (Fu and Li, 1993), implemented in ARLEQUIN and DnaSP 5.0. In addition, the unimodal or multimodal frequency distributions among haplotypes were evaluated using the mismatch distribution test in DnaSP.

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Spatial configuration of An. darlingi population groups was estimated using Spatial Analysis of Molecular Variance–SAMOVA (Dupanloup et al., 2002). To define the optimal number of groups (K), the performance of the FCT index was evaluated with K= 2 to 4; groups with single populations were excluded (Magri et al., 2006). Differences among groups defined by SAMOVA were tested with Analysis of Molecular Variance-AMOVA (Excoffier et al., 1992). The ΦST and FST index of genetic differentiation among populations were calculated using ARLEQUIN software and tested by permutation tests using 10,000 replicates. The influence of geographic distance and environmental aspects on genetic structure were evaluated together and independently; IBD was calculated by the Mantel test (Mantel, 1967), using matrices of pairwise estimates of genetic differentiation among An. darlingi populations and geographic distances, in IBDWS v. 3.23 (Jensen et al., 2005). The isolation by resistance-IBR hypothesis was used to evaluate the effect of environmental aspects on genetic structure (McRae, 2006). For this, 19 bioclimatic layers obtained for the study area from WorldClim database (www.worldclim.org) (Hijmans et al., 2005) and georeferenced An. darlingi occurrence data from this study and available in the literature (Supplementary material S1) were used to create a current spatial distribution model (SDM) of An. darlingi in Colombia, inferred using the maximum entropy method implemented in MAXENT program v. 3.3.3 (Phillips et al., 2006). This SDM was used as a habitat suitability model to perform a pairwise matrix of the average resistance among An. darlingi populations, calculated in Circuitscape v. 3.5.8 (Shah and McRae, 2008). Finally, IBR was evaluated using pairwise estimate matrices of genetic differentiation and resistance among An. 6

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3. Results

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3.1. Abundance and distribution. A total of 4,568 Anopheles mosquitoes were collected in seven Colombian localities; of those, 2,017 (44.15%) were An. darlingi. This vector was dominant in six of the seven localities, with a relative abundance higher than 60%, but notably, in VGF and CUM, its abundance was above 90%; a low relative abundance (1.5%) was only detected in PTL (Table 1). Comparing An. darlingi abundances among localities, PTG exhibited the highest (21.17%) and PTL the lowest (1.34%). The total number of An. darlingi collected per night did not show a normal distribution (Shapiro-Wilk W= 0.55, p< 0.001), with the highest number registered in SPU locality, ranging from 7 to 144 (Mean = 78.6, SD ± 50.49) and the lowest in PTL, 0 to 5 (Mean = 1, SD ± 1.4) (Table 1). In addition, inspection of larval habitats for An. darlingi indicated that in BAG they constituted mainly abandoned open-sky mining holes near forest fragments; in VGF and PTG, flooded forest margins and also in VGF, inundated and unplanned rice cultivation areas served as habitats.

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3.2. Biting activity. Biting activity for An. darlingi was only evaluated in BAG, SPU, VGF and PTG, because of the low number of mosquitoes collected in the other localities. In general, An. darlingi was active during the entire collection time (18:00 to 00:00 h), however in PTG, biting activity started at 19:00 h (Figure 2). Endophagy and exophagy were observed in BAG, SPU and VGF localities and there was not a significant preference for biting in any of these settings; however, in PTG exophagy was significant (z = -5.39, p< 0.001, n = 24). In BAG the highest biting activity peak was exophagic from 22:00 to 23:00 h, in SPU from 20:00 to 21:00 h, in VGF an endophagic peak from 19:00 to 20:00 h was followed by an exophagic peak from 20:00 to 22:00 h. and in PTG the highest biting activity peak was exophagic from 21:00 to 22:00 h (Figure 2). 3.3. Natural infection, HBR and EIR. Two An. darlingi specimens from a total of 2,017 were infected with Plasmodium. In BAG, one An. darlingi was infected with P. vivax VK210 (Naranjo-Diaz et al., 2013) and in VGF, with P. falciparum (Naranjo-Díaz et al., 2014) (Table 1). The highest An. darlingi HBR was observed in PTG (47.4 b.p.n.) and the lowest in PTL (1.5 b.p.n.). High differences in HBRs were detected among northwestern localities, for example, BAG as compared to SPU or PTL. In southern Colombia localities, differences in HBRs were detected between PTG and TAR (Table 1). EIR values indicated high malaria intensities in VGF and BAG, with 5 and 4 infective bites per year, respectively (Table1). 7

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3.4. Genetic diversity. A 1,180 bp COI sequence was obtained for 190 specimens representing the seven Colombian localities. A total of 45 haplotypes (GenBank accession numbers: KT831902-KT831946) and 65 variables sites were detected (Table 2); of these, 58 were synonymous, 7 non-synonymous and 47 parsimony informative. Haplotype and nucleotide diversity were 0.878 and 0.00796, respectively. The lowest haplotype diversity was detected in PTG (0.414) and the highest in SPU (0.833) (Table 2); low nucleotide diversities were observed in NW and W localities (Table 2). The neutrality tests Tajima’s D and Fu’s Fs, Fu and Li's D*, Fu and Li's F* were non-significant for most localities, except Tajima’s D for PTG and Fu´s Fs for PTL (Table 2). A bimodal mismatch distribution was detected for all populations, whereas a multimodal distribution was observed for TAR and CUM located S and CE, respectively (Figure 3).

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For CAD, a 658 bp fragment was obtained for 80 specimens representing the localities sampled. A total of 71 haplotypes (GenBank accession numbers: KU180732-KU180802) and 83 variables sites were detected (Table 2); of these, 77 were synonymous, 16 nonsynonymous and 82, parsimony informative. Few haplotypes were shared, evidence of high haplotype and nucleotide diversity, 0.988 and 0.02732, respectively (Table 2). High nucleotide diversity values were observed for each locality; however in concordance with COI results, the southern Colombia localities had the highest values (Table 2). Recombination was not detected by PHI (p = 0.1299) and GARD tests, however, SBP detected recombination at position 335. In contrast to results for COI, the positive and significant values of Fu and Li's D* and Fu and Li's F* tests denoted violation of neutrality for all populations, except PTG in the Fu and Li's F* test. 3.5. Haplotype network and population analysis. The TCS network identified two divergent COI clades (A and B), separated by at least seven mutational steps with no haplotype sharing (Figure 4), also confirmed by the SAMOVA results. Clade A included all specimens west of the Andes (from NW and W localities), with haplotypes shared among localities (Figure 4). Clade B included specimens east of the Andes (from S and CE localities) with no haplotypes shared among them; each clade was separated by several mutational steps (Figure 4). The CAD network was poorly resolved with numerous loops (data not shown). The results did not improve even when two subnetworks were implemented using alignments that included from 1 to 334 nucleotide positions and from 336 to 658 positions, avoiding the recombination position at 335. In the COI SAMOVA test to identify population genetic groups, K=2 was selected using the FCT value (Heuertz et al., 2004) and no single-population group was formed; populations were grouped by geographic localizations. As above, localities west and east of the Andes each formed a separate group. Based on the previous organization, AMOVA found most variance between groups (60.14%); a significant differentiation index (ΦCT= 0.63) confirmed this subdivision (Table 3). 8

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Both pairwise ΦST and FST estimates showed differences between populations on either side of the Andes (Table 4). The highest differences between regions were detected with COI, ΦST = 0.48761 to 0.81974, whereas CAD FST values ranged from 0.11319 to 0.21321. Most pairwise comparisons among localities were significant. Low genetic differentiation was detected among NW and W localities and most comparisons were not significant, ranging from -0.00308 to 0.11477 for COI and 0.07567 to 0.09925 for CAD (Table 4). Low to moderate differentiation values were detected among S and CE localities, ranging from 0.05072 to 0.34288 for COI and 0.03027 to 0.14983 for CAD. The Mantel test was nonsignificant for both COI (r = 0.289 p = 0.097) and CAD (r = -0.0426 p = 0.44). The association between population variation and environmental differences, as measured by the IBR test that incorporates landscape heterogeneity and identify resistance pathways that limit gene flow among populations, showed a significant association between environmental and genetic differences for COI, when controlling for geographic distance (r = 0.514 p = 0.04); this indicated that 26% of genetic differentiation is caused by landscape heterogeneity.

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4. Discussion

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Dominance and abundance of An. darlingi in most of the NW localities seems to be favored by ecological perturbations resulting from various human activities. For example in BAG, An. darlingi immature stages were detected in abandoned, open mining pits within 500 m of forest fragments (Naranjo-Diaz et al., 2013). In SPU, a higher proportion of An. darlingi mosquitoes were collected resting in livestock corrals; but in PTL, a locality characterized by intensive livestock activity that has contributed to progressive deforestation, An. darlingi was detected in low abundance and An. nuneztovari, a species more tolerant of environmental modifications, was more common (Naranjo-Diaz et al., 2013). These results in PTL agree with findings of lower An. darlingi abundance under such circumstances in Brazil and Peru (Linthicum, 1988; Moutinho et al., 2011; Vittor et al., 2009, 2006). Forest cover and river and stream margins near human settlements offer ideal ecological conditions for An. darlingi (Hiwat and Bretas, 2011; Linthicum, 1988; Vittor et al., 2009). VGF-W, PTG and TAR-S and CUM-CE Colombia, localities where An. darlingi dominated, are all characterized by tropical forest (IGAC, 2002). Only in VGF and PTG was An. darlingi detected in immature stages mainly in flooded forest margins, as previously reported in other Latin American countries (Hudson, 1984; Rozendaal, 1992; Rubio-Palis et al., 2005). In VGF, unplanned inundated rice plantations were also found to be viable larval habitats (Naranjo-Díaz et al., 2014). In most localities, An. darlingi had no clear preference for endo- versus exophagic behavior; however, in the single collection from PTG, where people received insecticide9

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treated mosquito nets (NN-D, personal observation), its behavior was exophagic. This may or may not be related to the local use of ITNs, since there are no prior comparative data, and other populations of An. darlingi in Brazil, French Guiana, Surinam and Peru with no history of ITN use are primarily exophagic (Charlwood, 1996; Girod et al., 2008; Moreno et al., 2015; Rozendaal, 1992).

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Various reports indicate that An. darlingi exhibits biting activity throughout the night (Girod et al., 2008; Moreno et al., 2007; Tadei and Dutary-Thatcher, 2000; Zimmerman et al., 2013). In the present work, An. darlingi was active during the entire collection period (18:00 to 00:00 h), and in most localities its highest activity was after 20:00 or 21:00 h, the time at which the risk for vector-human contact increases because people are in their houses involved in leisure activities. In general, biting times for An. darlingi were similar to those reported in Venezuela and Suriname, where low activity was observed at sunset but increased after 20:00 h (Girod et al., 2008; Gutiérrez et al., 2009b; Moreno et al., 2007). However, in other Colombian regions, i.e., Villavicencio, and Leticia, Tarapacá and Puerto Nariño in the Amazon Department, and in Rondônia State, Brazil, a biting peak around sunset has been reported for An. darlingi (Ahumada et al., 2013; Rodríguez et al., 2009; Tadei and Dutary-Thatcher, 2000). Together these data make it clear that local information on peak biting times must be determined for planning vector control measures directed to decrease human-vector contact (Korgaonkar et al., 2012).

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HBRs estimated for An. darlingi in NW localities varied (1.5-14.1 b.p.n), and were higher than those previously recorded in this region (<1 b.p.n) (Gutiérrez et al., 2009b). Remarkably, in SPU, An. darlingi was present in high abundance, however, its HBR was low, perhaps influenced by the high number of An. darlingi collected biting animals. Such opportunistic behavior is not uncommon for An. darlingi in Amapá State, Brazil (Zimmerman et al., 2006). HBR values for TAR and CUM Colombia, were similar but in PTG, An. darlingi exhibited the highest HBR (47.4 b.p.n), though this cannot be taken as characteristic for this locality given the small sample size. PTG’s location on the banks of the river Caucayá in a rainforest (WWF, 2015) likely influences both the abundance and high HBR exhibited. Similar HBRs (53.9 to 837.7 b.p.n.) were been reported for An. darlingi from rural riverine villages of Amapá State, Brazil, and in Loreto Department, Peru (Galardo et al., 2007; Moreno et al., 2015). The EIR values in BAG and VGF indicate that a person may receive one infective bite every two months in VGF and every three in BAG, confirming the important role of An. darlingi in malaria transmission in these localities, as shown for other NW Colombian localities (Gutiérrez et al., 2009b). Previous studies of Colombian An. darlingi showed no genetic structure but collection sites were relatively limited in geographic scope (González et al., 2007; Gutiérrez et al., 2010). The present study included additional sites in malaria endemic areas and different markers to facilitate the comprehension of population evolutionary processes (Zhang and Hewitt, 10

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2003). Both mitochondrial COI and nuclear CAD markers detected low nucleotide diversity and moderate to high haplotype diversity in localities west of the Andes with respect to those to the east which may indicate recent population expansion or a short evolutionary population history (Avise, 2000; Frankham, 1996). COI nucleotide diversity values for An. darlingi from NW localities were similar to those of a previous study (Gutiérrez et al., 2010), suggesting genetic homogeneity for An. darlingi in NW Colombia. In contrast, high nucleotide diversities detected for the CE and S populations are more similar to those reported for Peru and Brazil Amazonian populations (Mirabello and Conn, 2006; Pedro and Sallum, 2009). As previously indicated, diversity is higher in populations near the proposed origin (Avise, 2000), which for An. darlingi is central Amazonia (Mirabello and Conn, 2006; Pedro and Sallum, 2009).

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In general, neutrality tests with COI were non-significant, indicating demographic equilibrium; however, negative values for the NW suggest a population expansion at least in PTL, also supported by the diversity index values. In addition, the bimodal mismatch distributions detected for NW localities suggest that the populations may have undergone a previous bottleneck or colonization events (Excoffier et al., 1992; Rogers and Harpending, 1992; Rogers et al., 1996). In TAR and CUM, neutrality tests were positive. This together with a multimodal mismatch distribution may indicate that, 1) the populations are influenced by migration, 2) diminishing population sizes, 3) subdivision, or 4) they have undergone a historical contraction (Marjoram and Donnelly, 1994; Ray et al., 2003). A negative and significant Tajima’s D test for PTG indicates a population expansion (Tajima, 1989), not supported by the bimodal mismatch distribution (Excoffier et al., 1992; Rogers and Harpending, 1992; Rogers et al., 1996). Positive and significant values of the Fu and Li's D* and Fu and Li's F* tests together with high molecular diversity estimates suggest that populations represented by CAD sequences may be experimenting balancing selection (Charlesworth, 2006; Fu and Li, 1993); the retention in excess of low frequency haplotypes may indicate accumulation of new mutations after a selective sweep such as a bottleneck, with posterior rapid expansion (Yednock and Neigel, 2014). The detection of two main clusters west and east of the Andes is consistent with the structure detected by the ΦST index that demonstrated a reduction in gene flow between clades. In addition, IBR results suggested that the Andes at a macrogeographic, and ecological differences at a microgeographic scale influence the genetic structure of An. darlingi in Colombia. Contrary to these findings, genetic differentiation for An. darlingi from NW and E Colombia was attributed to biogeographic differences; however, the authors suggested an overestimation of differentiation because of the high mutational rate in the RAPD makers analyzed (González et al., 2007). In Brazil, the Amazon River constituted an important barrier for population differentiation (Motoki et al., 2012; Pedro 11

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and Sallum, 2009). In southeastern Brazil, coastal mountains explain differentiation in An. darlingi and An. triannulatus (Pedro and Sallum, 2009; Pedro et al., 2010). Differentiation of three clusters of Brazilian An. darlingi putative species was attributed to biogeographical differences (Emerson et al., 2015).

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5. Conclusions

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The COI ΦST and CAD FST pairwise comparisons did not detect differentiation among NW populations, consistent with previous reports of NW An. darlingi (Gutiérrez et al., 2010). The localities evaluated in the NW are in the MUMF ecoregion with characteristics of forested lowlands (WWF, 2015). Hence, the absence of obvious geographic barriers may contribute to the maintenance of An. darlingi population homogeneity. The low to moderate differentiation in VGF-W with respect to NW localities could be the result of the effects of ecoregion differences (CDMF vs. MUMF ecoregions) (WWF, 2015). Similarly, a reduction in gene flow was reported for N and W Colombian An. albimanus populations, attributed to their distribution in different biogeographic provinces (Gutiérrez et al., 2009a). Moderate genetic differentiation between PTG and CUM with respect to TAR could signal population subdivision, also supported by the multimodal mismatch distribution. TAR is located in SJMF ecoregion characterized by the presence of vast river systems (WWF, 2015), such as the Putumayo and Caquetá rivers, that may act as barriers.

Genetic differentiation of Colombian An. darlingi into two main groups was partly the result of isolation by resistance, probably due to ecoregion differences. However, the differentiation pattern for this species is complex and results of the neutrality tests for both COI and CAD markers suggest the influence of historical and demographic aspects. The behavioral variation in biting activity, which requires additional samples for confirmation, could have a genetic basis (Lounibos and Conn, 2000), or be the result of IRS or ITNs, or both (Charlwood, 1996; Moreno et al., 2015; Rozendaal, 1987; Zimmerman and Voorham, 1997). Behavioral and genetic information is crucial for the design of vector control strategies (Lounibos and Conn, 2000), for example the populations west of the Andes are panmictic; therefore, similar vector control strategies could be appropriate. Furthermore, the release of genetically modified mosquitoes refractory to Plasmodium infection to decrease malaria case numbers, have the potential to replace the native populations in this region. East of the Andes, in additional to the use of insecticide-treated mosquito nets and because of the exophagic behavior registered, alternative vector control measures should be implemented to reduce human-vector contact.

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This study was supported by Comité para el Desarrollo de la Investigación-CODI, Universidad de Antioquia-UdeA, Project code No. CIMB 049-2012, and received partial support from Estrategia para la Sostenibilidad de Grupos de Investigación 2016-2017, UdeA.

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Zapata, M.A., Cienfuegos, A. V, Quirós, O.I., Quiñones, M.L., Luckhart, S., Correa, M.M., 2007. Discrimination of seven Anopheles species from San Pedro de Uraba, Antioquia, Colombia, by polymerase chain reaction-restriction fragment length polymorphism analysis of its sequences. Am. J. Trop. Med. Hyg. 77, 67–72. Zhang, D.-X., Hewitt, G. M., 2003. Nuclear DNA analyses in genetic studies of populations: practice, problems and prospects. Mol. Ecol., 12, 563–84. Zimmerman, R.H., Galardo, A.K.R., Lounibos, L.P., Arruda, M., Wirtz, R., 2006. Bloodmeal hosts of Anopheles species (Diptera: Culicidae) in a malaria-endemic area of the Brazilian Amazon. J. Med. Entomol. 43, 947–56. Zimmerman, R.H., Lounibos, L.P., Nishimura, N., Galardo, A.K.R., Galardo, C.D., Arruda, M.E., 2013. Nightly biting cycles of malaria vectors in a heterogeneous transmission area of eastern Amazonian Brazil. Malar. J. 12, 262. Zimmerman, R.H., Voorham, J., 1997. Use of insecticide-impregnated mosquito nets and other impregnated materials for malaria control in the Americas. Rev. Panam. Salud Publica 2, 18–25.

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PT

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n (%)

06°28´N 75°33´W 07°35´N 74°49´W

393 (66.84) 358 (70.18)

07°43’N 75°51´W

27 (1.53)

HBR

2.9 14.1

0 - 5 (1; ± 1.4)

1.5

8 - 66 (36.17; ± 20.27)

13.8

84 (97.67)

2 - 63 (42; ± 29.69)

10.5

02°53´S 69°45´W

392 (69.38)

3 - 166 (43.56; ± 48.12)

13.1

00°10´S 74°53´W

427 (66.72)

15 - 118 (75.67; ± 43.93)

47.4

MA

04°13´N 73°19´W

PT ED

Central-East Meta CUM

336 (96.76)

AC

06° 17´N 76°45´W

NU

7 - 144 (78.6; ± 50.49) 30 - 115 (56.33; ± 31)

West Antioquia VGF

South Amazonas TAR Putumayo PTG

No of mosquitoes/night** (mean; SD)

SC

Coordinates

CE

Region/ Department/ Municipality* Northwest Antioquia SPU BAG Córdoba PTL

RI

Table 1. Data on abundance, HBR, IR and EIR for Anopheles darlingi.

IR % (CI)

Annual EIR

0.087 Pv VK210 (0.002-0.485)

3.7

0.036 Pf (0.001–0.203)

5.2

n: Total number of An. darlingi collected. % Relative abundance expressed in percentage. **Minimum and maximum number of mosquitoes collected per night. SD: standard deviation. HBR: human biting rate (Average of mosquito bites/person/night calculated for each site). IR: Infection rate (No. of positive An. darlingi/ No. of total analyzed) × 100. CI: IR confidence interval. Pv: Plasmodium vivax, Pf: Plasmodium falciparum. EIR: Entomological inoculation rate or the number of potential infective mosquito bites per species per year. *Municipality for the localities evaluated. Magdalena-Urabá Moist Forest ecoregion: San Pedro de Uraba-SPU, El Caño. El Bagre-BAG, La Capilla. Puerto Libertador-PTL, Juan Jose. Choco-Darien Moist Forest ecoregion: Vigía del Fuerte-VGF, San Antonio de Padua. Apure Villavicencio Dry Forest ecoregion: Cumaral-CUM, Cumaral. Solimões-Japurá Foist Forest ecoregion: Tarapacá-TAR, Santa Lucia. Napo Moist Forest ecoregion: Puerto Leguízamo-PTG, Cecilia Cocha.

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ACCEPTED MANUSCRIPT Table 2. Genetic diversity indices for An. darlingi populations. S

K

SPU

33

1 0

1 3

3.3 7

NW

BAG

30

7

1 0

2.4 6

NW

PTL

22

1 0

1 4

3

W

VGF

34

8

1 1

CE

CUM

16

TAR

S

SW

TE

D

2.4 3

7

2 7

7.4 2

25

8

2 9

10. 14

PTG

30

6

2 8

3.1 8

Total

19 0

4 5

6 5

9.3 9

SPU

11

1 0

3 1

12. 27

π (SD)

0.83 3 (0.04 3) 0.66 9 (0.05 6) 0.80 1 (0.07 2) 0.53 1 (0.09 7) 0.80 0 (0.09 0) 0.83 0 (0.05 4) 0.41 4 (0.11 1) 0.87 8 (0.01 7)

0.0028 6 (0.000 22) 0.0020 9 (0.000 18) 0.0025 5 (0.002 4) 0.0020 6 (0.000 47) 0.0062 9 (0.001 53) 0.0085 9 (0.000 47) 0.0026 9 (0.001 09) 0.0079 6 (0.000 34)

0.93 5 (0.02 5)

0.0186 6 (0.000 95)

MA

NW

Hd (SD)

Neutrality tests Tajim a´s D

Fu´s Fs

0.1660 7

1.4483 2

PT

h

RI

n

SC

Localit ies

NU

Regi on

AC CE P

Mark er COI

Fu and Li's D* 0.7704 9

Fu and Li's F* 0.5565 9

0.0779 5

0.2099 3

1.8242 6

15058 6

0.7785 0

2.3892 2.2361 6‡ 7

2.1000 3

0.3078 6

0.3978 0

0.6381 9

0.6267 2

0.3638 3

2.2521 3

0.8675 1

0.6013 4

1.1949 0

4.8928 8

1.0555 1

1.2883 5

- 1.9704 1.9784 5 9†

2.0376 7

2.3774 6

1.5365 4

1.6829 1‡

1.9140 0‡

CAD NW

1.5799 7

23

ACCEPTED MANUSCRIPT 14. 99

NW

PTL

3

3

2 8

14. 93

W

VGF

14

1 1

3 3

12. 24

CE

CUM

17

1 4

4 9

17. 31

S

TAR

10

9

4 5

16. 82

SW

PTG

14

1 6

D 5 9

17. 95

8 3

17. 98

TE AC CE P Total

80

7 1

0.95 2 (0.01 9) 0.80 0 (0.12 2) 0.92 1 (0.02 7) 0.94 8 (0.01 5) 0.92 6 (0.02 9) 0.95 2 (0.02 1) 0.98 8 (0.00 2)

0.0227 9 (0.001 15) 0.0227 0 (0.003 48) 0.0186 1 (0.001 30) 0.0263 1 (0.000 94) 0.0255 6 (0.001 16) 0.0272 8 (0.001 58) 0.0273 2 (0.000 55)

1.1803 6

2.8465 8

1.7232 4‡

1.8207 4‡

1.3768 1

1.7930 4

1.7166 2‡

1.7964 5‡

1.3361 4

0.7473 2

1.7478 6‡

1.7478 6‡

1.4181 3

1.1894 1

1.8664 6‡

2.0310 4‡

0.8761 2

0.3980 4

1.7203 4‡

1.7104 8‡

0.6235 2

0.8682 2

1.4891 3‡

1.4238 1

PT

3 9

RI

1 1

SC

11

NU

BAG

MA

NW

n: number of sequences. h: number of haplotypes S: number of polimorfic sites. K: average number of nucleotide differences. Hd: haplotipic diversity. π: nucleotide diversity SD: standard deviation. ‡ p < 0.05 †p < 0.01. SPU: El Caño. BAG: La Capilla. PTL: Juan Jose. VGF: San Antonio de Padua. CUM: Cumaral. TAR: Santa Lucia. PTG: Cecilia Cocha. NW: northwest. W: west. CE: central-east. S: south. SW: southwest.

24

ACCEPTED MANUSCRIPT Table 3. Analysis of Molecular Variance-AMOVA based on COI for An. darlingi populations.

314.592 50.957

0.31531

305.690 671.240

1.65238 5.32922

ΦCT 0.63‡

5.92

31.01

AC CE P

TE

D

MA

NU

‡ p < 0.05; significance level based on 1,023 permutations

Percentage of variation 63.08

PT

Between NW-W and CE-SW-S regions Among populations within regions Within populations Total

Variance components 3.36154

RI

Sum of squares

SC

Source of variation

25

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Table 4. Differentiation statistics based on pairwise estimates of COI ΦST (below diagonal) and CAD FST (above diagonal) for An. darlingi populations. VGF 0.08657 0.09925† NA 0.71213† 0.52083† 0.81263†

CUM 0.16801† 0.16291† NA 0.16147† 0.17957† 0.05072

TAR 0.20520† 0.16613† NA 0.21321† 0.14983† 0.34288†

PT

PTL NA NA 0.08422 0.67608† 0.48761† 0.79985†

RI

BAG 0.07567 -0.00308 0.11477‡ 0.71767† 0.53357† 0.81974†

SC

SPU BAG PTL VGF CUM TAR PTG

SPU -0.02942 -0.01408 0.05962 0.68652† 0.51241† 0.79151†

PTG 0.16556† 0.11319† NA 0.12152† 0.03027 0.08227† -

AC CE P

TE

D

MA

NU

‡ p < 0.05; † p < 0.001. For CAD only populations with more than five specimens were analyzed. SPU-El Caño. BAG-La Capilla. PTL-Juan Jose. VGF-San Antonio de Padua. CUM-Cumaral. TAR-Santa Lucia. PTG-Cecilia Cocha.

26

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SC

RI

PT

Figure 1. Ecoregions and sampling sites for An. darlingi. Sampling was conducted in localities of important malaria endemic regions of Colombia. Magdalena-Urabá Moist Forest ecoregion, northwest Colombia: El Caño in the San Pedro de Urabá-SPU municipality, Juan Jose in Puerto Libertador-PTL and La Capilla in El Bagre-BAG. ChocoDarien Moist Forest ecoregion, west of the country: San Antonio de Padua in Vigía del Fuerte-VGF. Apure Villavicencio Dry Forest ecoregion, central-east: Cumaral-CUM. Napo Moist Forest ecoregion, south: Cecilia Cocha in Puerto Leguízamo-PTG. Solimões-Japurá Moist Forest ecoregion, south: Santa Lucia in Tarapacá-TAR.

MA

NU

Figure 2. Biting activity of An. darlingi. Biting activity was evaluated from 18:00 to 00:00 h. A) La Capilla in the El Bagre-BAG, B) El Caño in San Pedro de Uraba-SPU, C) San Antonio de Padua in Vigía del Fuerte-VGF and D) Cecilia Cocha in Puerto Leguízamo-PTG. A 100% value will correspond to the total number of endophagic and exophagic mosquitoes collected from 18:00 to 00:00 h.

AC CE P

TE

D

Figure 3. COI mismatch distribution analyses for An. darlingi. Localities: La CapillaBAG, El Cañon-SPU and Juan Jose-PTL, San Antonio de Padua-VGF, Cumaral-CUM, Cecilia Cocha-PTG and Santa Lucia-TAR. X-axis: pairwise difference numbers, Y-axis: frequency.

Figure 4. Parsimony network based on COI haplotypes for Colombian An. darlingi. Each circle contains information for the haplotype and the number of specimens of the haplotype in the localities. Circle size is proportional to the frequency of each haplotype. Black filled circles represent mutational events or haplotypes not sampled. Clade A is delimited by dashed lines and includes BAG, SPU, PTL and VGF. Clade B: CUM, PTG and TAR.

27

MA

NU

SC

RI

PT

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AC CE P

TE

D

Fig. 1

28

AC CE P

Fig. 2

TE

D

MA

NU

SC

RI

PT

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29

Fig. 3

AC CE P

TE

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

30

MA

NU

SC

RI

PT

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AC CE P

TE

D

Fig. 4

31

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AC CE P

TE

D

MA

NU

SC

RI

PT

Graphical abstract

32

ACCEPTED MANUSCRIPT Highlights

TE

D

MA

NU

SC

RI

PT

Two clades of An. darlingi, east and west of the Colombian Andes, were detected. An. darlingi populations west of the Andes constituted a panmictic group. Populations east of the Andes did not share haplotypes. Andes Mountains and ecoregions influence the genetic structure of An. darlingi.

AC CE P

   

33