Transmitted HIV-1 Drug resistance and the Role of Herpes Simplex Virus -2 Coinfection among Fishermen along the Shores of Lake Victoria, Kisumu, Kenya

Victor Mburu Macharia1, Caroline Ngugi1, Raphael Lihana2and Musa Otieno Ngayo3

1College of Health Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

2Centre for Virus Research, Kenya Medical Research Institute, Nairobi, Kenya

3Centre for Microbiology Research, Kenya Medical Research Institute, Nairobi, Kenya

*Corresponding Author:
Musa Otieno Ngayo
Centre for Microbiology Research
Kenya Medical Research Institute: Nairobi, Kenya
Tel: 254720607890
Email: [email protected]

Received date: October 17, 2016; Accepted date: October 24, 2016; Published date: October 29, 2016

Citation: Macharia VM, Ngugi C, Lihana R, et al. Transmitted HIV-1 drug resistance and the role of herpes simplex virus-2 co-infection among fishermen along the shores of lake Victoria, Kisumu, Kenya, J HIV Retrovirus. 2016, 2:3.

Copyright: © 2016 Macharia VM, 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

Introduction: Herpes simplex virus type 2 (HSV-2) infection has been associated with a 3-fold risk of HIV-1 acquisition. The prevalence of HIV-1 and HSV-2 in the fishing communities along the shores of Lake Victoria in Kisumu have been reported to be high. This may contribute to the growing HIV epidemic in Kenya including the spread of transmitted drug resistance (TDR). We report data on the association of HSV2/HIV-1 co-infection and TDR in this antiretroviral (ARV)-naïve population. Methods: Blood samples were obtained from 249 consenting fishermen from 5 beaches and a detailed sociodemographic questionnaire was administered. Blood samples were analyzed for HIV-1/HSV2 co-infection. The HSV-2 serology was performed using Kalon HSV type 2 enzyme-linked immunosorbent assay (ELISA). The HIV-1 counselling and serology were carried out according to local standards of practice in Kenya, using two parallel rapid assays (Alere Determine HIV-1/2 and Trinity Biotech Uni- Gold), with a third ELISA-Vironostika HIV Uni-Form II Ag/Ab for resolving discrepancies. All HIV positive samples were tested for TDR using an in house HIV-1 pol-RT genotyping protocol. Results: Of the 249 recruited fishermen (mean age 35.1 years), 134 (53.8%) were positive for HSV-2, 59 (23.7%) were HIV positive while 48 (19.3%) were HIV/HSV-2 coinfected. Twenty-three of 59 (38.9%) HIV positive men had TDR, with the majority (19/23, 82.6%) in HIV/HSV-2 coinfected fishermen. Among the 48 HIV/HSV-2 co-infected fishermen, 9 had nucleoside reverse-transcriptase inhibitor (NRTI) resistance mutations with NRTI- associated mutations [NAMS], M184V (77.8%) and K65R (11.1%) being the highest. Nineteen (19) fishermen had Non-NRTI (NNRTIs) mutations including; four (21.1%) each of K103N, Y181C and G190A. Three (15.7%) V179T, two V901V and single A98G and Y188L mutations. Among the 11 fishermen who had HIV-1 mono-infection, four (36.4%) had drug resistant mutations. One fisherman had NRTI resistance mutation M184V. In addition, three men (3/4) had NNRTI resistance; K103N, G190A and Y181C mutations each. In the regression model, HIV/HSV-2 co-infection was independently associated with TDR [OR 4.1 (95% CI 1.4 to 11.9)]. Conclusion: The level of TDR to NNRTIs in these ARV-naive fishermen was significantly high especially among those coinfected with HSV-2. HSV-2 infection may increase the risk of TDR in this population.

Keywords

HIV; AIDS; HIV-1; Drug resistance

Introduction

Fishing communities along the shore of Lake Victoria in Kenya comprise young, highly migratory men who spend long periods away from their families and local communities and engage in high risk sexual behaviour [1-3]. This community has one of the highest prevalence of sexually transmitted infection (STI) and HIV in East Africa [4]. In Kenya, higher HIV prevalence 25.6% and 19.6% HSV-2/HIV co-infection was reported among fishermen along Lake Victoria in Kisumu [5, 6] compared to the national prevalence of 5.3% and 16%, respectively [7, 8]. This fishing community therefore qualifies as a high priority groups for HIV intervention programs [1].

Herpes simplex virus type-2 (HSV-2) is a chronic sexually transmitted infection [STI] responsible for genital ulcer disease worldwide [9, 10]. The HSV-2 infection constitutes a substantial public health problem because it increases the risk of HIV acquisition up to about four fold [11, 12]. Many studies have demonstrated a synergistic relationship between HSV-2 and HIV; HSV-2 infection increases the susceptibility to and transmission of HIV, while HIV infection increases the susceptibility to HSV-2 infection and HSV-2 genital shedding [10, 13]. HSV-2 therefore contributes in the HIV epidemic in Kenya and may also enhance the transmission of drug-resistant HIV variants to newly infected individuals termed transmitted drug resistance (TDR) [14].

Sub-Saharan African countries in the past decade, has been marked with substantial scale up in the access to antiretroviral therapy (ART) [15]. Unfortunately, significant increase in patients failing ART and HIV resistance to some ART medications is being observed [16]. Consequently, cases of ART-naïve individuals infected with TDR, associated with treatment failure [17, 18] are on the rise. Studies across Kenya are beginning to show widespread resistance in ART-naïve persons [19-21]. In this study, we determined the prevalence of drug-resistant mutations as well as evaluated the association of HSV-2 coinfection in the prevalence of TDR among ARV-naive men working in the fishing industries along the shores of Lake Victoria in Kisumu, Kenya.

Methods

Study design and population

This cross sectional study was conducted among men working in the fishing industry in the beaches along Lake Victoria in Kisumu Kenya. The description of these beaches has been described elsewhere [3]. Formula for estimating the population proportion with specified absolute precision by Lemeshow et al. [22] was used to determine the number of fishermen recruited in this study. Setting α at 0.05 and fishermen HSV-2/HIV coinfection rate of 19.6% [8], a total of 249 fishermen were recruited to achieve 0.95 power.

Four beaches, namely Nyamware, Dunga, Kichinjio and Kobudho were chosen based on the population size, the level of fishing activity and mobility. From each beach, the beach management unit (BMU) provided us with the list of registered boats and the number of fishermen on the boat. Men who were 18 years old or more and worked in the fishing industry for at least 3 months were eligible to participate. Informed consent was sought before they were enrolled in the study.

Counselling and sample collection

The enrolled fishermen underwent HIV counselling according to the guidelines in Kenya before blood samples were collected for testing. The risk and benefits of testing and meaning of test results were explained. About 5 ml of blood were collected for HIV-1 and herpes simplex virus (HSV) testing. This study was approved by the ethical review committee of Kenyatta National Hospital and University of Nairobi.

Laboratory procedures

Serology

Serology was carried out according to local standards of practice in Kenya, using two parallel rapid assays (Alere Determine HIV-1/2 and Trinity Biotech Uni-Gold), with a third enzyme-linked immunosorbent assay (ELISA) assay using Vironostika HIV Uni-Form II Ag/Ab (Biomerieux, Marcyl’Etoile, France) to resolve discrepancies. The HSV-2 serology was tested by Kalon HSV type 2 IgG ELISA (Kalon Biological Ltd, Surrey, United Kingdom) according to the kit manufacturer’s instructions [23].

Genotypic testing

All HIV positive samples were evaluated for genotypic drug resistance using an in-house population-based sequencing method as described previously [24]. Briefly, viral RNA was extracted from 140 μl of plasma using a QiAmp viral RNA kit (Qiagen, Valencia, CA) according to the manufacturer’s instructions. A nested reverse transcriptase-polymerase chain reaction (RT-PCR) was performed to amplify 645 base pairs of HIV-1 pol [24]. The PCR products of correct size were confirmed by gel electrophoresis and purified and sequenced by dideoxynucleoside-based analysis using a Big Dye terminator kit (Applied Biosystems) and ABI Prism 3100 equipment (Applied Biosystems, Foster City, US).

Ethical considerations

This study was conducted according to the Declaration of Helsinki and International Conference on Harmonization Guideline on Good Clinical Practice (ICH-GCP). The protocol and informed consent form were reviewed and approved by the Kenyatta National Hospital and University of Scientific Steering Committee and the Ethical Review Committee prior to commencement of field activities (KNH-ERC/RR/707- P545/08/2015 on 16th September 2015). Written informed consent was obtained from each participant. Confidentiality was maintained by assigning all participants with a unique identification number and all paper research records stored in a locked cabinet stationed in a secured room only accessible to the principal investigator. This research adhered to the STROBE guidelines for observational studies as outlined.

Drug resistance mutation analysis and statistical methods

Genotypic resistance was defined as the presence of resistance mutations associated with impaired drug susceptibility using the Stanford Genotypic Resistance Interpretation Algorithm. Codons included major NNRTI mutations at K103N, Y188L, Y181C, and G190A, and major NRTI mutations, M184V, K65R, and thymidine analog mutations (TAMs). HIV-1 subtypes were determined using the NCBI subtyping tool and phylogenetic trees were constructed from pol sequences with PAUP version 4.0 Beta10 [25] by creating a neighbour-joining phylogenetic tree with reference sequences from the Los Alamos National Laboratory HIV Database.

Data were analysed using STATA 13 (StataCorp, College Station, TX, USA). Descriptive statistics, frequency (%), mean, standard deviation was used to present the quantitative data. Bivariate and multivariate analyses were done using Poisson regression to evaluate factors that were associated with TDR (at P ≤ 0.05).

Results

Characteristics of the study participants

Between September 2015 and February 2016, 249 fishermen were enrolled. The characteristics of the fishermen are shown in Table 1. The mean age (SD) was 35.1 (7.8) years. The HIV infected 34.7 (8.3) years, HSV-2 infected 35.6 (8.98) years and HIV-1/HSV-2 34.3 (7.49) years. Overall, the majority (68.3%) of participants were married and 78.7% worked as fishermen. Majority (93.2%) had their age of sexual debut below 18 years, 85.9% were uncircumcised, while 42.2% had more than one sexual partner. For the fishermen who travelled away from their fishing beaches, 11.6% had at least one sexual act and 20.9% used condoms. Of the 249 fishermen, 134 (53.8%) were HSV-2 positive, 59 (23.7%) were HIV-1 positive while 48 (19.3%) were HIV/HSV-2 co-infected.

Variable Mono or co-infection
Total HIV-1 HSV-2 HIV-1/HSV-2
N % N % N % N %
Beach
Dunga 63 25.3 20 33.9 39 29.1 20 41.7
Kichinjio 62 24.9 15 25.4 24 17.9 6 12.5
Kobudho 62 24.9 7 11.9 17 12.7 5 10.4
Nyamware 62 24.9 17 28.8 54 40.3 17 35.4
Age
Mean (SD) (Years) 35.1 7.8 34.7 8.3 35.6 8.98 34.3 7.49
Range (Years) 40 26 to 66 36 26 to 66 40 26 to 66 31 26 to 66
21-30 80 32.1 24 40.7 46 34.3 20 41.7
31-40 127 51 24 40.7 60 44.8 19 39.6
>41 42 16.9 11 18.6 28 20.9 9 18.8
Marital status
Single 71 28.5 19 32.2 42 31.3 18 37.5
Married 170 68.3 38 64.4 90 67.2 29 60.4
Divorced/Widowed 8 3.2 2 3.4 2 1.5 1 2.1
No. of wives
1 160 64.3 35 59.3 83 61.9 26 54.2
2 10 4 3 5.1 7 5.2 3 6.3
Not applicable 79 31.7 21 35.6 44 32.8 19 39.6
Education Level
Primary 144 57.8 32 54.2 69 51.5 22 45.8
Secondary 104 41.8 27 45.8 64 47.8 26 54.2
Tertiary 1 0.4 0 0 1 0.7 0 0
Occupation
Fisherman 196 78.7 33 55.9 93 69.4 25 52.1
Fish trader 53 21.3 26 44.1 41 30.6 23 47.9
Income (Ksh)
<10000 223 89.6 53 89.8 117 87.3 43 89.6
>10001 26 10.4 6 10.2 17 12.7 5 10.4
Age sex debut
Mean (SD) (Years) 15.1 2.6 14.7 1.9 15.3 2.7 14.8 1.999
Range (Years) 19 7 to 26 8 10 to 18 19 7 to 26 8 10 to 18
<18 232 93.2 59 100 123 91.8 48 100
>18 17 6.8 0 0 11 8.2 0 0
Circumcised
Yes 35 14.1 12 20.3 25 18.7 10 20.8
No 214 85.9 47 79.7 109 81.3 38 79.2
No of sexual partner
1 140 56.2 32 54.2 76 56.7 23 47.9
>1 105 42.2 23 54.2 54 40.3 21 43.8
None 4 1.6 4 6.8 4 3 4 8.3
No travelled in past month
1 58 23.3 14 23.7 34 25.4 13 27.1
>1 72 28.9 20 33.9 40 29.9 14 29.2
None 119 47.8 25 42.4 60 44.8 21 43.8
Sexual acts during last travel
None 220 88.4 51 86.4 116 86.6 40 83.3
At least once 29 11.6 8 13.6 18 13.4 8 16.7
Sexual partner
Girlfriend 124 49.8 28 47.5 71 53 22 45.8
Wife 5 2 3 5.1 4 3 3 6.3
Casual partner 106 42.6 21 35.6 48 35.8 17 35.4
Not applicable 14 5.6 7 11.9 11 8.2 6 12.5
Condom use last two act
Yes 52 20.9 19 32.2 33 24.6 18 37.5
No 183 73.5 33 55.9 90 67.2 24 50
Not applicable 14 5.6 7 11.9 11 8.2 6 12.5

Table 1: Participants characteristics by infection type (Data on beach, marital status, No of wives, education, occupation, income, circumcision, No. of sexual partner, No. travelled in past month, sexual acts during last travel, sexual partner and condom use last two act was presented as absolute numbers (n) and percentages (%) while age and Age of sexual debut was shown as mean ± standard deviation (SD) in years).

Genotypic Profiles

A genotype result was obtained for all the 59 HIV infected fishermen. Subtype analysis of the pol region showed that HIV-1 subtype A was most common 28/59 (47.5%) [78.6% in HIV-1/ HSV-2 co-infection verses 21.4% in HIV-1 mono-infection], followed by subtype D 16/59 (27.1%) [93.8% in HIV-1/HSV-2 coinfection verses 6.3% in HIV-1 mono-infection], subtype C 3/59 (5.1%) [66.7% in HIV-1/HSV-2 co-infection verses 33.3% in HIV-1 mono-infection], subtype B 3/59 (5.1%) [All in HIV-1/HSV-2 coinfection], subtype G 2/59 (3.4%) and possible unique recombinants 7/59 (11.9%) (Table 2).

Age Beach Subtype NRTI NNRTI Age Beach Subtype NRTI NNRTI
31 Dunga A Susc V179T 29 Kichinjio G Susc V90I, K103N, F227FL
47 Kobudho B K65KR V106AV, F227FL, M230I 62 Kichinjio CRF01_AE Susc Y181CY
30 Nyamware D M184V V106A 57 Kichinjio C M184V Susc
35 Dunga C M184V K103KN 29 Kichinjio A Susc G190A
30 Dunga C M184V K103KN 37 Kichinjio CRF01_AE Susc Susc
30 Nyamware A Susc K103KN 32 Kobudho D Susc Susc
40 Dunga D M184V A98G, Y181C, H221HY 32 Kobudho A Susc Susc
28 Dunga D M184V Y188L 36 Kichinjio A Susc Susc
26 Dunga D Susc G190AG 33 Kichinjio A Susc Susc
42 Kichinjio A Susc K103N 30 Kichinjio A Susc Susc
38 Dunga CRF01_AE Susc G190AG 29 Kichinjio A Susc Susc
28 Dunga CRF01_AE M184V E138Q, G190A
33 Nyamware D Susc V90IV
36 Kichinjio A M184V G190A
37 Nyamware D Susc V179DV
31 Nyamware D V75MV Y181CY
30 Nyamware CRF01_AE Susc V90IV, Y181FINY
33 Kobudho G Susc Y181C, H221Y
35 Nyamware A Susc V179T
32 Dunga A Susc Susc
28 Dunga A Susc Susc
41 Dunga A Susc Susc
31 Dunga D Susc Susc
30 Kobudho D Susc Susc
30 Dunga B Susc Susc
37 Dunga D Susc Susc
37 Dunga A Susc Susc
30 Kobudho A Susc Susc
29 Dunga A Susc Susc
41 Dunga A Susc Susc
43 Dunga A Susc Susc
38 Nyamware D Susc Susc
35 Dunga D Susc Susc
45 Kichinjio A Susc Susc
28 Dunga A Susc Susc
27 Nyamware A Susc Susc
33 Kichinjio D Susc Susc
28 Nyamware B Susc Susc
28 Nyamware A Susc Susc
28 Nyamware A Susc Susc
32 Kichinjio A Susc Susc
27 Nyamware A Susc Susc
56 Kichinjio A Susc Susc
26 Nyamware D Susc Susc
51 Nyamware A Susc Susc
27 Nyamware CRF01_AE Susc Susc
32 Nyamware D Susc Susc
57 Kobudho CRF01_AE Susc Susc

Table 2: HIV-1 Subtype and Transmitted Drug Resistance Mutations (TDRM) among Antiretroviral - naive HIV mono and HIV/HSV-2 co-infected fishermen.

Twenty-three of 59 (38.9%) sequenced samples had TDR. Majority, 19/23 (82.6%) were among HIV/HSV-2 co-infected fishermen. Among the 59 HIV infected fishermen, 10 had NRTI resistance mutations with NAMS M184V (80%) and K65R (10%) were among mutations observed (about 90% found in the HIV/ HSV-2 co-infected fishermen). Twenty-two (22) had NNRTI mutations; five (22.7%) fishermen had K103N, Y181C and G190A each. Three13.6%) fishermen had V179T, two fishermen had V901V while A98G, Y188L mutations were also detected. About 81.8% of these NNRTI mutations were found in the HIV/HSV-2 co-infected fishermen (Table 3).

Variable Sample size TDR infected fishermen Bivariate Multivariate
Frequency Percentage cOR (95% CI) aOR (95% CI)
HSV-2 infection NS
Positive 134 19 14.2 4.1(1.4-11.9)
Negative 115 4 3.5 1
Beach NS
Dunga 63 8 12.7 1.1(0.4-3.1)
Kichinjio 62 6 9.7 0.9(0.3-2.6)
Kobudho 62 2 3.2 0.3(0.1-1.4)
Nyamware 62 7 11.3 1
Age NS
21-30 80 9 11.3 0.8(0.3-2.7)
31-40 127 10 7.9 0.7(0.3-1.7)
>41 42 4 9.5 1
Marital status NS
Single 71 5 7
Married 170 18 10.6 ND
Divorced/Widowed 8 0 0
No of wives NS
1 160 17 10.6 1.7(0.6-4.6)
2 10 1 10 1.5(0.2-13.5)
Not applicable 79 5 6.3 1
Education Level ND ND
Primary 144 8 5.6
Secondary 104 15 14.4
Tertiary 1 1 100
Occupation NS
Fisherman 196 15 7.7 1.9(0.8-4.7)
Fish trader 53 8 15.1 1
Income (Ksh) NS
<10000 223 21 9.4 1.2(0.3-5.2)
>10001 26 2 7.7 1
Age sex debut ND ND
<18 232 23 9.9
>18 17 0 0
Circumcised NS
Yes 35 5 14.3 1.7(0.6-4.6)
No 214 18 8.4 1
No of sexual partner NS
1 140 16 11.4 0.5(0.06-3.4)
>1 105 6 5.7 0.2(0.03-1.9)
None 4 1 25 1
No travelled in past month NS
1 58 5 8.6 1.8(0.3-9.9)
>1 72 8 11.1 0.6(0.1-2.6)
None 119 10 8.4 1
Sexual acts during last travel NS
At least once 29 3 10.3 0.9(0.3-2.9)
None 220 20 9.1 1
Sexual partner
Girlfriend 124 13 10.5 0.7(0.2-3.3)
Wife 5 0 0 ND NS
Casual partner 106 8 7.5 0.5(0.1-2.4)
Not applicable 14 2 14.3 1
Condom use last two act NS
Yes 52 7 13.5 1.7(0.7-4.1)
No 183 14 7.7 1

Table 3: Factors associated with TDR variant.

Table 3 summarizes the bivariate and multivariate analysis of factors associated with the prevalence of TDR. In the regression model, HSV-2 co-infection was the only factor independently associated with TDR; OR 4.1 (95% CI 1.4-11.9).

Discussion

The prevalence of HIV TDR in this population was 38.9%. According to WHO surveillance criteria, this level is high [26]. This is not surprising because this region is not only marked with highest prevalence of HIV in Kenya but has also experienced significant ART treatment roll-out over the last 10 years. The emergence of TDR in communities has been largely attributed to the longer availability of ARVs [27]. It has also been stipulated that the higher the ARV coverage, the higher the risks of the emergence and spread of TDR [28]. Previous studies of TDR in Kenya showed a prevalence ranging from 1.1% to as high as 13.3% [20, 29, 30]. In Uganda a prevalence of 6% TDR among fishermen along the shore of L. Victoria in Uganda has been reported [31].

The mutations seen in our study were associated with resistance to both NNRTIs (K103N, Y181C, V106A and G190A) and NRTI (M184V and K65KR). These mutations confer resistance to drugs used as standard first line ART regimens in Kenya. Reports show that some NNRTI mutants are relatively fit and may therefore be more likely to be transmitted and to persist over time [32]. Other studies among ART naïve individuals in Kenya have identified either only NNRTI mutation [20] or mutations to NRTIs and PIs [21]. A multisite study in 6 sub-Saharan Africa countries (Kenya, Nigeria, South Africa, Uganda, Zambia, and Zimbabwe), identified K103N, thymidine analogue mutations, M184V, and Y181C/I as the most common drug-resistance mutations [27]. With the initiation of ART in Kenya, levels of HIVDR seem to increase with time [33], a trend that is yet to be confirmed everywhere [34].

Most TDR mutations seen in our study were among fishermen who were co-infected with HSV-2. The synergistic relationship between HIV and HVS-2 has been established [35]. Studies consistently demonstrate higher plasma viral loads and increased genital tract HIV during episodes of HSV-2 reactivation, which may increase the risk for sexual and motherto- child transmission and accelerate HIV disease progression [36, 37]. The HIV/HSV-2 coinfection has been shown to potentiate the clinical severity and infectiousness of the two viruses [38]. Whether it’s the synergistic relationship between HIV and HVS-2 or TDR viral fitness that was associated with the high prevalence of TDR in HVS-2 infected men, is an area for further investigation.

Our findings of high TDR in HSV-2 infected fishermen, poses a significant challenge not only in treatment but also the need for drug resistant testing prior to ART initiation among the naïve individuals [39]. Further, it is important to intensify monitoring the development and propagation of drug resistance. Intensive health education, counseling and monitoring of patients on ART are some of the suggested interventions to achieve maximal ART adherence [31]. Currently, monitoring of TDR in developing countries is often opportunistic and depends on individual initiatives. Surveillance strategies are needed to systematically screen populations (especially the high risk) in Kenya and other countries that are currently rolling out and or scaling up ART programs.

Some of the limitations in our study include, first the population sequencing as used in our study. Resistance variants are likely to remain undetected. Secondly, although this study was conducted prior to the increasing availability of ARVs, we were not able to report increases in TDRs over time. A longitudinal design would give better data.

Conclusion

In conclusion, the level of TDR in this high risk population was high (38.9%). This is a concern given their high mobility which limit access to healthcare services. With increasing ARV therapy availability in sub-Saharan Africa and the recent WHO recommendations for earlier treatment, it follows that the prevalence of TDRs will also increase, highlighting the importance of TDR monitoring. Although the synergetic relationship between HIV and HSV-2 has been continuously reported, the role of HSV-2 co-infection in the transmission of TDR require further investigation.

Acknowledgement

We acknowledge all of the volunteers who participated in this study. We would also like to thank Brian Khasimwa of Nairobi University for offering technical support during HIV genotyping. This study was funded by student’s loan.

Author’s Contributions

This work was part of Master of Science degree for VMM in Medical Virology at the Jomo Kenyatta University of Agriculture and Technology. MON and VMM conceived and designed the study. MON and VMM conducted field work and laboratory assays. MON conducted data analysis. RL and CN guided the design of the study and provided general supervision. All authors read and approved the final manuscript.

Nucleotide Sequence Accession Numbers

The gene sequences determined in this study were deposited in Gen Bank under accession numbers KX505314-KX505372.

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