Next Article in Journal
Differential Bacterial Community of Bee Bread and Bee Pollen Revealed by 16s rRNA High-Throughput Sequencing
Next Article in Special Issue
Survey of Candidatus Liberibacter Solanacearum and Its Associated Vectors in Potato Crop in Spain
Previous Article in Journal
Integrated Biological Control Using a Mixture of Two Entomopathogenic Bacteria, Bacillus thuringiensis and Xenorhabdus hominickii, against Spodoptera exigua and Other Congeners
Previous Article in Special Issue
Turnip Mosaic Virus Infection Differentially Modifies Cabbage Aphid Probing Behavior in Spring and Winter Oilseed Rape (Brassica napus)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Insects
Volume 13
Issue 10
10.3390/insects13100861
insects-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Mitochondrial Genetic Diversity of Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) Associated with Cassava in Lao PDR

by
Ana M. Leiva
1,
Khonesavanh Chittarath
2,
Diana Lopez-Alvarez
3,
Pinkham Vongphachanh
2,
Maria Isabel Gomez
1,
Somkhit Sengsay
2,
Xiao-Wei Wang
4,
Rafael Rodriguez
1,
Jonathan Newby
5 and
Wilmer J. Cuellar
1,*
1
Cassava Program, Crops for Nutrition and Health, International Center for Tropical Agriculture (CIAT), The Americas Hub, Km 17 Recta Cali-Palmira, Cali 763537, Colombia
2
Plant Protection Center (PPC), Department of Agriculture, Ministry of Agriculture and Forestry, Vientiane P.O. Box 811, Laos
3
Department of Biological Sciences, Universidad Nacional de Colombia UNAL-Palmira, Palmira 763533, Colombia
4
Institute of Insect Sciences, Zhejiang University, Hangzhou 310058, China
5
Cassava Program Asia Office, Crops for Nutrition and Health, International Center for Tropical Agriculture (CIAT), Laos Country Office, Vientiane P.O. Box 783, Laos
*
Author to whom correspondence should be addressed.
Insects 2022, 13(10), 861; https://doi.org/10.3390/insects13100861
Submission received: 1 August 2022 / Revised: 14 September 2022 / Accepted: 15 September 2022 / Published: 22 September 2022
(This article belongs to the Special Issue Insect Vectors of Plant Diseases)
Browse Figures
Versions Notes

Abstract

:

Simple Summary

The whitefly species Bemisia tabaci is a known pest of cassava and a vector of cassava geminiviruses in Africa and India, but its role in the recent spread of Cassava Mosaic Disease (CMD) in Southeast Asia is not well known. This is in part due to a lack of data on the occurrence and distribution of B. tabaci in this region. We show here the first results of any country-wide survey and identification of B. tabaci colonizing cassava in Lao PDR.

Abstract

Cassava Mosaic Disease (CMD) caused by Sri Lankan cassava mosaic virus (SLCMV), has rapidly spread in Southeast Asia (SEA) since 2016. Recently it has been documented in Lao PDR. Previous reports have identified whitefly species of B. tabaci as potential vectors of CMD in SEA, but their occurrence and distribution in cassava fields is not well known. We conducted a countrywide survey in Lao PDR for adult whiteflies in cassava fields, and determined the abundance and genetic diversity of the B. tabaci species complex using mitochondrial cytochrome oxidase I (mtCOI) sequencing. In order to expedite the process, PCR amplifications were performed directly on whitefly adults without DNA extraction, and mtCOI sequences obtained using nanopore portable-sequencing technology. Low whitefly abundances and two cryptic species of the B. tabaci complex, Asia II 1 and Asia II 6, were identified. This is the first work on abundance and genetic identification of whiteflies associated with cassava in Lao PDR. This study indicates currently only a secondary role for Asia II in spreading CMD or as a pest. Routine monitoring and transmission studies on Asia II 6 should be carried out to establish its potential role as a vector of SLCMV in this region.

1. Introduction

Cassava (Manihot esculenta Crantz) is an important smallholder cash crop and a staple food in the tropics of Latin America, Africa, Asia and Southeast Asia (SEA) [1,2,3]. However, cassava is affected by many diseases, critically including Cassava Mosaic Disease (CMD), caused by single or mixed infections of up to 10 species of viruses belonging to the genus Begomovirus in Africa, and two in Asia [4,5]. In 2015, CMD was first reported in SEA in Cambodia and its causal agent was identified as an isolate of Sri Lankan cassava mosaic virus (SLCMV) [6]. Since then, the disease has been reported from Vietnam, China, Thailand and, most recently, it has been documented in Lao PDR, creating a phytosanitary emergency throughout SEA [5,6,7,8,9,10]. Since cassava is a vegetatively propagated crop, infectious diseases can be easily spread through sharing infected planting material (stakes), under limited phytosanitary regulation [11,12] and this could explain the rapid expansion of CMD observed in SEA. Nevertheless, CMD is also a vector-borne disease, transmitted by various species of the Bemisia tabaci (Gennadius) whitefly complex (Hemiptera: Aleyrodidae) [11,13].
Whiteflies are vectors for at least 320 species of plant viruses belonging to the genera Begomovirus, Carlavirus, Crinivirus, Ipomovirus, Polerovirus and Torradovirus [14,15,16,17]. Notably, the whitefly species Bemisia tabaci is the sole vector of begomoviruses [18,19] and is considered a complex of at least 44 reproductively isolated but morphologically indistinguishable cryptic species [20]. Assigning the status of individual species within the complex classification of B. tabaci currently relies on network analysis of its mitochondrial cytochrome oxidase I (mtCOI) gene. This method has been widely used in diversity studies of B. tabaci [21,22,23,24,25] and is recommended as a barcoding system for whiteflies [26,27].
In several Asian countries such as Bangladesh China, Nepal, Thailand, Malaysia and Vietnam, different studies using mtCOI have identified B. tabaci species diversity and their potential as virus vectors [17,28,29,30,31,32]. The relevance of characterizing B. tabaci diversity has increased in SEA due to increasing importance of cassava and the recent introduction of CMD in this region [3,5].
Lao PDR is the sixth largest exporter of cassava in the world, with an estimated annual production of 2.3 M tons and USD 116 M in exports [33]. However, there is limited information on genetic diversity of B. tabaci populations in Laotian cassava-growing regions and their association with CMD. B. tabaci genetic diversity does not change significantly over a farm-scape [34], although it can vary between regions, and regional populations can differ in their ability to transmit begomoviruses. Therefore, the main objectives of this study were to provide the first detailed description of the diversity of the B. tabaci complex colonizing cassava in Lao PDR and its potential relation with the recently reported occurrence of CMD in this country [10]. For this, we carried out a country-wide field survey in the main cassava growing regions of Lao PDR and calculated the relative abundance of adult B. tabaci per province (10 provinces). To identify the genetic diversity, we sequenced 867 nt of the 3′ half of the mtCOI gene using a rapid and high throughput genotyping tool based on Oxford Nanopore Technologies (ONT). Two indigenous cryptic species, Asia II 1 and Asia II 6, were identified, which were grouped into 14 haplotypes distributed throughout the country. This is the first study on whitefly diversity in Lao PDR. Furthermore, unlike previous studies relying on DNA extraction, PCR amplicon cloning and subsequent sequencing of the mtCOI gene, the current study used more efficient and less cumbersome nanopore sequencing. The methodology employed in this study will simplify future B. tabaci genetic diversity studies and monitoring of regional abundance. Such studies will be pivotal in developing effective regional management programs for B. tabaci.

2. Materials and Methods

2.1. Plant Material and Whitefly Sampling

Between June and July 2020, 70 cassava fields were inspected for adult whiteflies (Bemisia tabaci) three to six months after planting to calculate the relative abundance of adult whiteflies, in 10 different provinces of Lao PDR (Table 1).
The relative abundance of whiteflies per field was measured as the total number of whiteflies observed in an inspected field divided by the total number of inspected plants within 1 ha. Previous analyses considering the crop density per ha, carried out at CIAT’s research station, indicated that a total of 30 observations was most efficient in terms of minimizing the standard error and time required to collect data per field (Figure S1). To obtain the data by province we used the average number of all the fields surveyed in each province. To count the whiteflies per plant we inspected every fourth cassava plant following a diagonal transect across a 1 ha cassava field (Figure S1) by gently turning and photographing the first fully expanded leaf of one of the branches; this is the preferred leaf for several whitefly species of cassava [35]. The number of adults settled on each leaf was later counted using the photographic record. Additionally, when possible, whitefly adults were collected by aspiration and then immediately stored in 96% ethanol. Due to the low whitefly population observed, whiteflies could only be collected for molecular analysis from 56 fields and 64 individuals were sequenced. The geographic coordinates per field were recorded using a handheld GPS unit, together with the name of the location (province, district, village). Data on CMD incidence in Lao PDR were collected as described in [10] and are stored in the PestDisPlace database [36].

2.2. Mitochondrial COI (mtCOI) DNA Amplification

Mitochondrial COI (mtCOI) DNA amplification was carried out by removing the whiteflies from the ethanol, and quickly dropping the insects into tubes containing the PCR reaction mix; there was no need to macerate the samples or to include any additional PCR steps. For amplification we used primers 2195-Bt-F (5′-TGRTTTTTTGGTCATCCRGAAGT-3′) and C012-Bt-sh2-R (5′-TTTACTGCACTTTCTGCC-3′) [37]. These primers amplify 867 bp of the 3′ half of the mtCOI gene. The PCR reaction mix was prepared with 12 μL 2X GoTaq Green Master Mix 2X (Promega Corp®), 0.5 μL of each primer (10 pmol) and 7 μL ultrapure water (Invitrogen, Life Technologies, CA, USA) to complete a final volume of 20 μL. PCR was carried out at 95 °C for 5 min (initial denaturation of template DNA), followed by 35 cycles at 94 °C for 40 s, 56 °C for 30 s for annealing, and 72 °C for 45 s for extension, with a final extension at 72 °C for 10 min. The PCR products were run on a 1% agarose gel in 1× TBE buffer stained with GelRedTM (Biotium, Fremont, CA, USA). DNA bands were visualized using a Gel Doc™ XR+ Gel Documentation System (BioRad, CA, USA). As no unspecific bands were observed, libraries for sequencing were prepared directly from the PCR reaction mix.

2.3. Nanopore Sequencing

The sequencing workflow is shown in Figure 1. Briefly, PCR reactions (4 µL each) were diluted with nuclease-free water to obtain 1 µg of total DNA, as determined by the Qubit DNA HS assay (Invitrogen, Life Technologies, CA, USA). Libraries were prepared using the 1D ligation sequencing kit (SQK-LSK109) and native barcodes for genomic DNA (EXP-NBD104 and EXP-NBD114), following manufacturer protocols (ONT). Amplicons were repaired and end-prepped by mixing with 1.75 μL NEBNext FFPE DNA Repair Buffer (New England Biolabs, MA, USA), 1 μL NEBNext FFPE DNA Repair Mix (New England Biolabs, MA, USA), 1.75 μL Ultra II End-prep reaction buffer (New England Biolabs) and 1.5 μL Ultra II End-prep enzyme mix (New England Biolabs). Then, the mixtures were cleaned-up using AMPure XP beads (Beckmann Coulter, CA, USA) as described in the ONT protocol and eluted in 31 μL of nuclease-free water. Adapters were afterwards ligated by mixing 30 μL of amplicons prepared in the previous step, 12.5 μL of ligation buffer (ONT), 5 μL of NEBNext Quick T4 DNA ligase (New England Biolabs) and 2.5 μL of adapter mix (ONT). The amplicons were cleaned-up again using AMPure XP beads and combined to 10 fmol of each sample in one tube. Using 125 μL of short fragment buffer (ONT) as described in the ONT protocol, the pellet was resuspended in 8 μL of elution buffer (ONT). A total volume of 5 μL was used for the sequencing mix, which was then mixed with 10 μL of loading beads (ONT) and 15 μL of sequencing buffer (ONT). We sequenced between 18 and 20 samples per assay, using a Flongle flow cell. The Flongle flow cell was primed with a mix of 117 μL of flush buffer with 3 μL of flush tether, followed by the addition of 30 μL of the sequencing mix. The Flongle was operated using MinKNOW and the total run time was 24 h using a MK1C device (ONT).

2.4. Bioinformatic Analysis

Raw FAST5 files pass produced by MK1C device were basecalled under high-accuracy mode using the ONT basecaller Guppy (version 6.0.6; used parameters: guppy_basecaller-input_path PATH-save_path PATH-qscore_filtering-min_qscore 7-flowcell FLO-MIN106-kit SQK-LSK109-cpu_threads_per_caller 4-num_callers 4). The FASTQ files were filtered with a quality Qscore ≥ 9 and only files with this quality were further processed using the next pipeline. The reads were assembled using reference sequences with Minimap2 long-read aligner with 99% accuracy [38]. Then, two different polishers were used for error correction: Pilon, which polishes assemblies created from long-read technologies by overlaying high-quality data to improve local base quality [39], and Medaka v.1.4.4, a neural network-based method to improve accuracy and speed as compared to nanopolish (ONT) [40]. Finally, quality was validated using Qualimap v.2.2.1 [41].

2.5. Statistical Analysis

The estimation of nucleotide (π) and haplotype (h) diversities, as well as the number and allocation of haplotypes, was determined using DnaSP 6.12.03 software [42] for sequences generated in this work. Sequence alignments were generated with MUSCLE implemented in Geneious Prime (Biomatters Ltd., Auckland, New Zealand) using 59 sequences generated in this work and 45 reference sequences collected from Genbank. Haplotype networks were determined with Network 10.2.0.0 software (www.fluxus-engineering.com/sharenet.htm (accessed on 31 July 2022)) using the Neighbor-Joining algorithm. On the other hand, to determine phylogenetic relationships, we used fourteen haplotypes from Lao PDR plus 104 sequences obtained from Genbank (https://www.ncbi.nlm.nih.gov/nuccore/?term=Bemisia+tabaci+coi (accessed on 31 July 2022)) representing all whitefly (Bemisia tabaci) cryptic species reported so far (Table S1). A phylogenetic tree was inferred applying the Maximum Likelihood optimization criterion using IQ-TREE v 2.0.3 [43] with the model TIM3+I+G4. Support values for each node were calculated based on bootstrapping with 1000 replicates. Finally, the tree was visualized using FigTree v1.4.4 (http://tree.bio.ed.ac.uk/software/figtree/ (accessed on 31 July 2022)). Additionally, to identify whether a population has undergone a recent population expansion event, the mutation frequencies in the sequences were calculated using the tests of Tajima’s D implemented in MEGA-X software [44]. This is determined by the difference between the average number of nucleotide differences and the number of segregating sites estimated from pairwise comparisons [45].

3. Results

3.1. Whitefly Abundance and CMD Incidence

In this study, the number and distribution of collection sites and number of whiteflies collected varied, depending on the area of cassava cultivated in each province and the relative abundance of B. tabaci in those fields (Figure 2). The localities with the highest number of fields sampled were Champasack and Borlikhamsai (representing the largest cassava cultivation areas in Lao PDR) (Table 1). In general, whitefly abundances were below 0.23 per leaf, which is 10 times lower than the minimum abundance values observed in cassava fields from Africa (Table 1). This sampling protocol allowed us to determine the relative abundance of whiteflies and the incidence of CMD symptoms at the sampled sites. CMD was only recorded in fields of the southern provinces of Champasack and Attapue, with incidences of 2.4% and 20.3%, respectively (Table S1). At 3–6 months after planting, a distinction is possible between infections derived from infected planting material versus whitefly-derived infections; if symptoms are observed only in the top leaves, this indicates that infection occurred after planting, suggesting whitefly transmission. When the planting material comes from infected plants, one also observes CMD symptoms in the older leaves [46]. 7.6 % of plants showed clear CMD symptoms only in the top young leaves (3–6 months after planting), while in most symptomatic plants, leaf symptoms were distributed along the whole plant.

3.2. Genetic Diversity

Out of 64 individuals sequenced with the MK1C (ONT) sequencer which had a coverage greater than 1000× (Table S1), 37 sequences (62.72%) corresponded to Asia II 1 while 22 sequences (37.28%) corresponded to Asia II 6. The average nucleotide frequencies found were 23.4% (A), 41.8% (T), 13.9% (C) and 23.4% (G) for 669 nucleotides aligned against available NCBI reference sequences. In total, 73 polymorphic sites (S) were identified, including two singleton sites and 71 parsimony-informative sites, important for classifying haplotypes (Table 2). In general, the haplotype diversity (h) of the entire population from Lao PDR was 0.743 and the nucleotide diversity (π) presented values of 0.04975. Tests for neutrality on the Asia II 1 data subset were significatively negative (Tajima’s D = −2.168 with p < 0.05), suggesting a recent population expansion. On the other hand, neutrality tests for Asia II 6 were close to zero (Tajima’s D = −0.644 with p > 0.05) and the sequences in this subgroup showed the highest values of average nucleotide differences (k) (Table 2).
When analyzed together with other available COI sequences detected in cassava from Asian countries (n = 45), the network confirmed the presence of haplotypes previously reported in Asia, and revealed a few haplotypes exclusively found in Lao PDR (Figure 3). Asia II 1 sequences were grouped into 12 haplotypes, where the most frequent was H1 (n = 58 sequences). This Asian haplotype was also found in eight provinces from Lao PDR, and it was the only one found in the northern provinces of LaoungNamTha, Saiyabouly, Xaysomboun and Khammoun.
Asia II 1 haplotypes H3, H5, H12 and H6 were exclusively found in Lao PDR and among these, haplotype H12 (Salavan province) had the highest number of genetic changes. In comparison, Asia II 6 sequences (n = 22) were grouped into eight haplotypes (Table S2) where haplotype H17 was the most frequently found in Lao PDR (four provinces) and where haplotypes H13, H15, H18, H20 and H21 were exclusively found in Lao PDR.

3.3. Phylogenetic Analysis

The phylogenetic reconstruction inferred using Maximum Likelihood (Figure 4) allowed separating and confirming the classification of the whitefly samples into two cryptic species. The Asia II 1 clade comprised 62.7% of sampled whiteflies of this study, and grouped with external references sequences from China, Pakistan, Syria, Thailand and Vietnam (reported in cassava, cotton, mung bean, soybean, sweet potato and tomato). On the other hand, the Asia II 6 clade was represented by 37.3% sequences and eight haplotypes, which were grouped into two clades: H16, H18, H20 and H21 are grouped with a bootstrap support of 51, while H163, H14, H15 and H17 with bootstrap support of 100. Overall, samples from Lao PDR were represented in 14 haplotypes (Figure 3; Table S2), which were included in the phylogenetic analysis, with 262 other sequences from the mtCOI region corresponding to representative samples of different cryptic species of B. tabaci (Figure 4).

4. Discussion

In referring to comparative studies on whitefly abundance in cassava fields [49], reports from Africa indicate an average of 100 adults per leaf per plant in Uganda, where severe whitefly-transmitted CMD is endemic [50]. This is in sharp contrast to our observations in Lao PDR (Table 1). There are no other reports on whitefly abundance in the region; however, colleagues in Cambodia and Vietnam report preliminary whitefly abundance data ranging from 0.02 to 0.23 adults/leaf per plant (not shown). Interestingly, provinces in Lao PDR with the relative highest abundance of whiteflies correspond to fields located in the southern provinces where CMD also occurs (Table S2) [10]; nevertheless, abundance of adult whiteflies cannot be directly related to incidence of CMD as it can take several weeks for the symptoms to develop after whitefly transmission [46,49,50]. Data was collected 3–6 months after planting as recommended in previous works, a time where populations of whiteflies are most numerous and we can still distinguish between infections propagated by distributing infected planting material versus whitefly-derived infections [46,49]. The results show abundance values significantly lower compared to those in Tanzania and Nigeria, where the superabundance of whiteflies is considered the main factor for the transmission of CMD-causing viruses [50,51,52]. Furthermore, the presence of CMD symptoms in the older leaves of most affected plants suggest at this point a major role for distribution of infected planting material, rather than whiteflies, in the transmission of the disease in Lao PDR.
At this moment we cannot consider cassava as a major host of Asia II in SEA. In comparison, cassava-infesting B. tabaci in sub-Saharan Africa (SSA) is rarely found on other hosts under field conditions, unlike other non-cassava B. tabaci in Africa such as MED, Indian Ocean and MEAM1, which do not colonize cassava under field conditions [53]. In contrast, Asia II has been reported in other crops such as cotton and several solanaceous hosts in Asia (see below). Higher whitefly abundances in other crops near cassava fields suggest that cassava is not the main host of Asia II, implying a host effect on the low abundance of B. tabaci in cassava in SEA [53].
Direct PCR amplification of the mtCOI region, without DNA extraction (Figure 1), produced clear bands and no PCR inhibition was observed We obtained the expected amplification products (867 bp) from all the samples collected in the different cassava fields and no unspecific band was observed. Sequences obtained using ONT Flongle cells had high levels of coverage, confirming the efficiency of this protocol for the rapid, effective and low-cost sequencing of fragments derived from PCR [54,55]. The protocol described here allowed for the rapid sequencing and bioinformatic analysis of barcoded samples in a single run, offering a more efficient alternative to Sanger sequencing [56]. Although the main problem with nanopore sequencing is its still relatively high per-base error rate in raw reads compared to other sequencing techniques, these are offset by the high coverages obtained and good bioinformatics handling [56]. Previous reports indicate that a 100× coverage is adequate to establish an accurate genotype; furthermore, advances in nanopore sequencing technology, in combination with a dedicated data analysis pipeline and good coverage, have proven to be comparable in performance to conventional Sanger sequencing applied to PCR amplicons, as is the case in this work [57,58].
Cryptic whitefly species Asia II 1 and Asia II 6 have been previously identified as indigenous species in cassava fields in southern Vietnam, Thailand and China [29,59]. The haplotype network reconstructed on the analysis of 14 haplotypes from Lao PDR and 15 haplotypes from various regions of the world clearly showed differences among the indigenous cryptic species of B. tabaci Asia II 1 and Asia II 6 (Figure 3), in agreement with the phylogenetic analysis results (Figure 4). The difference between the two cryptic species is given by 59 mutational changes according to the network of haplotypes. A similar range has been observed between Asia II 1 and Asia I in cotton crops in India, where 57 mutations were detected between the two cryptic species [60]. In addition, they show a sequence divergence greater than 3.5%, a value assigned for the separation of cryptic species in B. tabaci [25]. Species status of Asia II 1 and Asia II 6 is also supported by previous studies, where mating crosses have shown evidence of reproductive incompatibilities [61,62,63].
Asia II 1 was the most frequent species found in the cassava fields, and is the one found also as the dominant species in cotton in Pakistan and in soybean in India [64,65] Although Asia II 1 was more dominant in Lao PDR, Asia II 6 showed a higher haplotype diversity (0.88). Interestingly, Asia II 6 in China forms at least two separate networks, suggesting a distinct evolutionary history for this species in that country [22]. Network analysis does not involve many of the assumptions of phylogenetic reconstruction methods, such as the existence of an ancestral sequence or branching relationships [66]; therefore, haplotype network analysis is useful as an empirical prediction of boundaries within a species complex [67,68,69]. The neutrality test was negative for both species, indicating that their expansion was limited within the local area. Meanwhile the positive value of the Tajima’s D statistics with total population indicated balanced selection [45].
Asia II 1 is an efficient vector of SLCMV, in comparison to MED and MEAM species [32], and is also a vector for Multan cotton leaf curl virus, a pathogen causing significant yield losses in cotton [70]. The phytosanitary importance of Asia II 1 is further highlighted by its recorded resistance to insecticides [71,72]. On the other hand, the Asia II 6 cryptic species occurring in beans, cassava, chili pepper, cotton, cucumber and sweet potato, from China, India, Malaysia, Japan, Thailand and Vietnam [20,29,30,73], as well as in cassava (this work), has not yet been identified as a vector of SLCMV. Furthermore, a study in Japan found this species to be sensitive to the majority of the tested pesticides [74].

5. Conclusions

Our study presents the first report on the genetic diversity of B. tabaci whitefly populations collected in Lao PDR, at the haplotype level (fourteen groups of B. tabaci haplotypes). The two whitefly cryptic species (Asia II 1 and Asia II 6) are found throughout the country, including the southern provinces where CMD has been reported. Cryptic species Asia II 1, a known vector of SLCMV, are widespread in the region; however, in Lao PDR, low abundances of whiteflies and the distribution of CMD symptoms in infected plants suggest, at this time, a secondary role for Asia II in the dissemination of CMD or as a pest, as compared to CMD transmission by infected planting material. However, this situation will likely evolve in the near future. Haplotypes unique to Lao PDR have been identified and are useful in studying and tracking the spread of whiteflies at a regional level in parallel with CMD. Further transmission studies with Asia II 6 should be carried out to establish its potential role as a vector of SLCMV and routine monitoring is recommended for timely detection of an increase in abundance of whitefly populations in Lao PDR.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/insects13100861/s1, Figure S1: Field survey protocol; Table S1: Survey data; Table S2: List of COI sequences used.

Author Contributions

Conceptualization, A.M.L., K.C., M.I.G. and W.J.C.; validation, A.M.L., K.C., D.L.-A., P.V., S.S. and W.J.C.; formal analysis, A.M.L. and D.L.-A.; investigation, all authors; data curation, A.M.L., K.C., M.I.G., R.R. and W.J.C.; writing—original draft preparation, A.M.L. and W.J.C.; writing—review and editing, A.M.L., X.-W.W. and W.J.C.; supervision, A.M.L., D.L.-A., X.-W.W., J.N. and W.J.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Australian Centre for International Agricultural Research (ACIAR), and the United States Agency for International Development (USAID). The research was undertaken as part of, and funded by, the CGIAR Research Program on Roots, Tubers and Bananas (RTB) and supported by CGIAR Trust Fund contributors. We acknowledge financial support from the CGIAR Plant Health (PHI) and Accelerated Breeding (ABI) initiatives and from the National Natural Science Foundation of China (32161143008).

Data Availability Statement

The data presented is available in the supplementary tables included with this work.

Acknowledgments

We are thankful to Jenyfer Jimenez, Juan Manuel Pardo, Viviana Dominguez and Harrison Moran from the Cassava Program team and to the Plant Pathology group at PPC in Vientiane, for their help with laboratory and field activities. The authors acknowledge the editorial support of Vincent Johnson, on behalf of the Bioversity-CIAT Alliance Science writing service. We are thankful to Luis A. Becerra Lopez-Lavalle and Joe Tohme for supporting the study.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Alene, A.D.; Abdoulaye, T.; Rusike, J.; Labarta, R.; Creamer, B.; del Río, M.; Ceballos, H.; Becerra, L.A. Identifying crop research priorities based on potential economic and poverty reduction impacts: The case of cassava in Africa, Asia, and Latin America. PLoS ONE 2018, 13, e0201803. [Google Scholar] [CrossRef] [PubMed]
  2. Howeler, R. Cassava in Asia: Trends in Cassava Production, Processing and Marketing. In Proceedings of the Partnership in Modern Science to Develop a Strong Cassava Commercial Sector in Africa and Appropriate Varieties by 2020, Bellagio, Italy, 2–6 May 2006; Available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.497.2007&rep=rep1&type=pdf (accessed on 1 March 2022).
  3. Malik, A.I.; Kongsil, P.; Nguyễn, V.A.; Ou, W.; Srean, P.; Sheela, M.N.; Becerra, L.A.; Utsumi, Y.; Lu, C.; Kittipadakul, P.; et al. Cassava breeding and agronomy in Asia- 50 years of history and future directions. Breed Sci. 2020, 20, 145–166. [Google Scholar] [CrossRef] [PubMed]
  4. Legg, J.P.; Kumar, P.L.; Makeshkumar, T.; Tripathi, L.; Ferguson, M.; Kanju, E.; Ntawuruhunga, P.; Cuellar, W. Cassava virus diseases: Biology, epidemiology, and management. Adv. Virus Res. 2015, 91, 85–142. [Google Scholar]
  5. Siriwan, W.; Jimenez, J.; Hemniam, N.; Saokham, K.; Lopez-Alvarez, D.; Leiva, A.M.; Martinez, A.; Mwanzia, L.; Becerra, L.A.; Cuellar, W.J. Surveillance and diagnostics of the emergent Sri Lankan cassava mosaic virus (Fam. Geminiviridae) in Southeast Asia. Virus Res. 2020, 285, 197959. [Google Scholar] [CrossRef]
  6. Wang, H.L.; Cui, X.Y.; Wang, X.W.; Liu, S.S.; Zhang, Z.H.; Zhou, X.P. First Report of Sri Lankan Cassava Mosaic Virus Infecting Cassava in Cambodia. Plant Dis. 2016, 100, 1029. [Google Scholar] [CrossRef]
  7. Uke, A.; Hoat, T.X.; Quan, M.V.; Liem, N.V.; Ugaki, M.; Natsuaki, K.T. First Report of Sri Lankan Cassava Mosaic Virus Infecting Cassava in Vietnam. Plant Dis. 2018, 102, 2669. [Google Scholar] [CrossRef]
  8. Wang, D.; Yao, X.M.; Huang, G.X.; Shi, T.; Wang, G.F.; Ye, J. First Report of Sri Lankan Cassava Mosaic Virus Infected Cassava in China. Plant Dis. 2019, 103, 1437. [Google Scholar] [CrossRef]
  9. Leiva, A.M.; Siriwan, W.; Lopez-Alvarez, D.; Barrantes, I.; Hemniam, N.; Saokham, K.; Cuellar, W.J. Nanopore-based complete genome sequence of a Sri Lankan cassava mosaic virus (Geminivirus) strain from Thailand. Microbiol. Resour. Announc. 2020, 9, 1–3. [Google Scholar] [CrossRef]
  10. Chittarath, K.; Jimenez, J.; Vongphachanh, P.; Leiva, A.M.; Sengsay, S.; Lopez-Alvarez, D.; Bounvilayvong, T.; Lourido, D.; Vorlachith, V.; Cuellar, W.J. First report of Sri Lankan cassava mosaic virus and Cassava Mosaic Disease in Laos. Plant Dis. 2021, 105, 1861. [Google Scholar] [CrossRef]
  11. Legg, J.; Somado, E.A.; Barker, I.; Beach, L.; Ceballos, H.; Cuellar, W.; Elkhoury, W.; Gerling, D.; Helsen, J.; Hershey, C. A global alliance declaring war on cassava viruses in Africa. Food Secur. 2014, 6, 231–248. [Google Scholar] [CrossRef]
  12. Delaquis, E.; Andersen, K.F.; Minato, N.; Cu, T.T.L.; Karssenberg, M.E.; Sok, S.; Karssenberg, M.E.; Sok, S.; Wyckhuys, K.A.; Newby, J.C.; et al. Raising the stakes: Cassava seed networks at multiple scales in Cambodia and Vietnam. Front. Sustain. Food Syst. 2018, 2, 73. [Google Scholar] [CrossRef]
  13. Maruthi, M.N.; Colvin, J.; Seal, S.; Gibson, G.; Cooper, J. Co-adaptation between cassava mosaic geminiviruses and their local vector populations. Virus Res. 2002, 86, 71–85. [Google Scholar] [CrossRef]
  14. Navas-Castillo, J.; Fiallo-Olive, E.; Sanchez-Campos, S. Emerging virus diseases transmitted by whiteflies. Annu. Rev. Phytopathol. 2011, 49, 219–248. [Google Scholar] [CrossRef] [PubMed]
  15. Ghosh, S.; Kanakala, S.; Lebedev, G.; Kontsedalov, S.; Silverman, D.; Alon, T.; Mor, N.; Sela, N.; Luria, N.; Dombrovsky, A.; et al. Transmission of a New Polerovirus Infecting Pepper by the Whitefly Bemisia tabaci. J. Virol. 2019, 93, e00488-19. [Google Scholar] [CrossRef] [PubMed]
  16. Costa, T.M.; Inoue-Nagata, A.K.; Vidal, A.H.; Ribeiro, S.G.; Nagata, T. The recombinant isolate of cucurbit aphid-borne yellows virus from Brazil is a polerovirus transmitted by whiteflies. Plant Pathol. 2020, 69, 1042–1050. [Google Scholar] [CrossRef]
  17. Acharya, R.; Shrestha, Y.K.; Khatun, M.F.; Lee, K.Y. Identification of Begomoviruses from Three Cryptic Species of Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) in Nepal. Agronomy 2021, 11, 2032. [Google Scholar] [CrossRef]
  18. Hogenhout, S.A.; Ammar, E.D.; Whitfield, A.E.; Redinbaugh, M.G. Insect vector interactions with persistently transmitted viruses. Annu. Rev. Phytopathol. 2008, 46, 327–359. [Google Scholar] [CrossRef]
  19. Wang, X.; Blanc, S. Insect transmission of plant single-stranded DNA viruses. Annu. Rev. Phytopathol. 2021, 66, 389–405. [Google Scholar] [CrossRef]
  20. Kanakala, S.; Ghanim, M. Whitefly-transmitted begomoviruses and advances in the control of their vectors. In Genes, Genetics and Transgenics for Virus Resistance in Plants; Pali, B., Ed.; Caister Academic Press: Poole, UK, 2018; pp. 201–220. [Google Scholar]
  21. Munguti, F.M.; Kilalo, D.C.; Nyaboga, E.N.; Wosula, E.N.; Macharia, I.; Mwango’mbe, A.W. Distribution and molecular diversity of whitefly species colonizing cassava in Kenya. Insects 2021, 12, 875. [Google Scholar] [CrossRef]
  22. Hu, J.; Zhang, X.; Jiang, Z.; Zhang, F.; Liu, Y.; Li, Z.; Zhang, Z. New putative cryptic species detection and genetic network analysis of Bemisia tabaci (hempitera: Aleyrodidae) in China based on mitochondrial COI sequences. Mitochondrial DNA A 2018, 29, 474–484. [Google Scholar] [CrossRef] [PubMed]
  23. Ellango, R.; Singh, S.T.; Rana, V.S.; Priya, N.G.; Raina, H.; Chaubey, R.; Naveen, N.C.; Mahmood, R.; Ramamurthy, V.V.; Asokan, R.; et al. Distribution of Bemisia tabaci genetic groups in India. Environ. Entomol. 2015, 44, 1258–1264. [Google Scholar] [CrossRef] [PubMed]
  24. Ovalle, T.M.; Parsa, S.; Hernández, M.P.; Lopez-Lavalle, L.A.B. Reliable molecular identification of nine tropical whitefly species. Ecol. Evol. 2014, 4, 3778–3787. [Google Scholar] [CrossRef] [PubMed]
  25. Dinsdale, A.; Cook, L.; Riginos, C.; Buckley, Y.M.; de Barro, P.J. Refined global analysis of Bemisia tabaci (Hemiptera: Sternorrhyncha: Aleyrodoidea: Aleyrodidae) mitochondrial cytochrome oxidase 1 to identify species level genetic boundaries. Ann. Entomol. Soc. Am. 2010, 103, 196–208. [Google Scholar] [CrossRef]
  26. Hebert, P.D.N.; Cywinska, A.; Ball, S.L.; DeWaard, J.R. Biological identifications through DNA barcodes. Proc. Biol. Sci. 2003, 270, 313–321. [Google Scholar] [CrossRef]
  27. Shatters, R.G.; Powell, C.A.; Boykin, L.; Liansheng, H.; McKenzie, C.L.; Shatters, R.G.; Powell, C.A.; Boykin, L.; Liansheng, H.; McKenzie, C.L. Improved DNA barcoding method for Bemisia tabaci and related Aleyrodidae: Development of universal and Bemisia tabaci biotype-specific mitochondrial cytochrome c oxidase I polymerase chain reaction primers. J. Econ. Entomol. 2009, 102, 750–758. [Google Scholar] [CrossRef] [PubMed]
  28. Chen, L.; Yuan, Y.A.O.; Rongjiang, W.; Fengming, Y.; Dunxiao, H.; Zhili, Z. The use of mitochondrial cytochrome oxidase I (mt COI) gene sequences for the identification of biotypes of Bemisia tabaci (Gennadius). Acta Entomol. Sin. 2022, 45, 757–763. [Google Scholar]
  29. Götz, M.; Winter, S. Diversity of Bemisia tabaci in Thailand and Vietnam and indications of species replacement. J. Asia Pac. Entomol. 2016, 19, 537–543. [Google Scholar] [CrossRef]
  30. Shadmany, M.; Boykin, L.M.; Muhamad, R.; Omar, D. Genetic diversity of Bemisia tabaci (Hemiptera: Aleyrodidae) species complex across Malaysia. J. Econ. Entomol. 2019, 112, 75–84. [Google Scholar] [CrossRef]
  31. Khatun, M.; Jahan, S.; Lee, S.; Lee, K.-Y. Genetic diversity and geographic distribution of the Bemisia tabaci species complex in Bangladesh. Acta Trop. 2018, 187, 28–36. [Google Scholar] [CrossRef]
  32. Chi, Y.; Pan, L.-L.; Bouvaine, S.; Fan, Y.-Y.; Liu, Y.-Q.; Liu, S.-S.; Seal, S.; Wang, X.-W. Differential transmission of Sri Lankan cassava mosaic virus by three cryptic species of the whitefly Bemisia tabaci complex. Virology 2020, 540, 141–149. [Google Scholar] [CrossRef]
  33. FAOSTAT Production Data. Available online: https://www.fao.org/faostat/es/#data/QCL (accessed on 1 March 2022).
  34. Gautam, S.; Crossley, M.S.; Dutta, B.; Coolong, T.; Simmons, A.M.; da Silva, A.; Snyder, W.E.; Srinivasan, R. Low Genetic Variability in Bemisia tabaci MEAM1 Populations within Farmscapes of Georgia, USA. Insects 2020, 11, 834. [Google Scholar] [CrossRef] [PubMed]
  35. Arias, B. Estudio Sobre el Comportamiento de la "Mosca Blanca" Aleurotrachelus Socialis Bondar (Homoptera: Aleyrodidae) en Diferentes Genotipos de Yuca Manihot Escalenta Crantz. Tesis de Maestría; Universidad Nacional de Colombia: Palmira, Colombia, 1995; p. 181. [Google Scholar]
  36. Cuellar, W.J.; Mwanzia, L.; Lourido, D.; Martinez, A.F.; Rodriguez, R.; Garcia, C. PestDisPlace, version 3.0; Monitoring the Distribution of Pests and Diseases; International Center for Tropical Agriculture (CIAT): Palmira, Colombia, 2018; Available online: https://pestdisplace.org (accessed on 25 July 2022).
  37. Mugerwa, H.; Seal, S.; Wang, H.L.; Patel, M.V.; Kabaalu, R.; Omongo, C.A.; Alicai, T.; Tairo, F.; Ndunguru, J.; Sseruwagi, P.; et al. African ancestry of New World, Bemisia tabaci-whitefly species. Sci. Rep. 2018, 8, 2734. [Google Scholar] [CrossRef] [Green Version]
  38. Li, H. Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics 2018, 1, 7. [Google Scholar] [CrossRef] [PubMed]
  39. Walker, B.J.; Abeel, T.; Shea, T.; Priest, M.; Abouelliel, A.; Sakthikumar, S.; Cuomo, C.A.; Zeng, A.; Wortman, J.; Young, S.K.; et al. Pilon: An integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS ONE 2014, 9, e112963. [Google Scholar] [CrossRef] [PubMed]
  40. Wang, Y.H.; Zhao, Y.; Bollas, A.; Wang, Y.R.; Au, K.F. Nanopore Sequencing Technology, Bioinformatics and Applications. Nat. Biotechnol. 2021, 39, 1348–1365. [Google Scholar] [CrossRef] [PubMed]
  41. Okonechnikov, K.; Conesa, A.; García-Alcalde, F. Qualimap 2: Advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 2015, 32, 292–294. [Google Scholar] [CrossRef]
  42. Librado, P.; Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 2009, 25, 1451–1452. [Google Scholar] [CrossRef]
  43. Nguyen, L.-T.; Schmidt, H.A.; Von Haeseler, A.; Minh, B.Q. IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. Mol. Biol. Evol. 2014, 32, 268–274. [Google Scholar] [CrossRef]
  44. Kumar, S.; Stecher, G.; Li, M.; Knyaz, C.; Tamura, K. MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms. Mol. Biol. Evol. 2018, 35, 1547–1549. [Google Scholar] [CrossRef] [PubMed]
  45. Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 1989, 123, 585–595. [Google Scholar] [CrossRef] [PubMed]
  46. Sseruwagi, P.; Sserubombwe, W.S.; Legg, J.P.; Ndunguru, J.; Thresh, J.M. Methods of surveying the incidence and severity of cassava mosaic disease and whitefly vector populations on cassava in Africa: A review. Virus Res. 2004, 100, 129–142. [Google Scholar] [CrossRef] [PubMed]
  47. Tajebe, L.S.; Boni, S.B.; Guastella, D.; Cavalieri, V.; Lund, O.S.; Rugumamu, C.P.; Rapisarda, C.; Legg, J.P. Abundance, diversity and geographic distribution of cassava mosaic disease pandemic-associated Bemisia tabaci in Tanzania. J. Appl. Entomol. 2015, 139, 627–637. [Google Scholar] [CrossRef]
  48. Nwezeobi, J.; Onyegbule, O.; Nkere, C.; Onyeka, J.; van Brunschot, S.; Seal, S.; Colvin, J. Cassava whitefly species in eastern Nigeria and the threat of vector-borne pandemics from East and Central Africa. PLoS ONE 2020, 15, e0232616. [Google Scholar] [CrossRef]
  49. Macfayden, S.; Paull, C.; Boykin, L.M.; de Barro, P.; Maruthi, M.N.; Otim, M.; Kalyebi, A.; Vassão, D.G.; Sseruwagi, P.; Tay, W.T.; et al. Cassava whitefly, Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) in East African farming landscapes: A review of the factors determining abundance. Bull. Entomol Res. 2018, 108, 565–582. [Google Scholar] [CrossRef]
  50. Ally, H.M.; el Hamss, H.; Simiand, C.; Maruthi, M.N.; Colvin, J.; Omongo, C.A.; Delatte, H. What has changed in the outbreaking populations of the severe crop pest whitefly species in cassava in two decades? Sci. Rep. 2019, 9, 14796. [Google Scholar] [CrossRef]
  51. Mugerwa, H.; Rey, M.E.; Alicai, T.; Ateka, E.; Atuncha, H.; Ndunguru, J.; Sseruwagi, P. Genetic diversity and geographic distribution of Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) genotypes associated with cassava in East Africa. Ecol. Evol. 2012, 2, 2749–2762. [Google Scholar] [CrossRef] [PubMed]
  52. Szyniszewska, A.M.; Busungu, C.; Boni, S.B.; Shirima, R.R.; Bouwmeester, H.; Legg, J.P. Spatial Analysis of Temporal Changes in the Pandemic of Severe Cassava Mosaic Disease in North-Western Tanzania. Phytopathology 2017, 107, 1229–1242. [Google Scholar] [CrossRef] [PubMed]
  53. Milenovic, M.; Wosula, E.N.; Rapisarda, C.; Legg, J.P. Impact of Host Plant Species and Whitefly Species on Feeding Behavior of Bemisia tabaci. Front. Plant Sci. 2019, 10, 1. [Google Scholar] [CrossRef] [PubMed]
  54. Vereecke, N.; Bokma, J.; Haesebrouck, F.; Nauwynck, H.; Boyen, F.; Pardon, B.; Theuns, S. High Quality Genome Assemblies of Mycoplasma Bovis Using a Taxon-Specific Bonito Basecaller for MinION and Flongle Long-Read Nanopore Sequencing. BMC Bioinform. 2020, 21, 517. [Google Scholar] [CrossRef]
  55. Grädel, C.; Miani, M.A.T.; Barbani, M.T.; Leib, S.L.; Suter-Riniker, F.; Ramette, A. Rapid and Cost-Efficient Enterovirus Genotyping from Clinical Samples Using Flongle Flow Cells. Genes 2019, 10, 659. [Google Scholar] [CrossRef]
  56. Cao, M.D.; Ganesamoorthy, D.; Cooper, M.A.; Coin, L.J. Realtime analysis and visualization of MinION sequencing data with npReader. Bioinformatics 2016, 32, 764–766. [Google Scholar] [CrossRef] [PubMed]
  57. Whitford, W.; Hawkins, V.; Moodley, K.S.; Grant, M.J.; Lehnert, K.; Snell, R.G.; Jacobsen, J.C. Proof of concept for multiplex amplicon sequencing for mutation identification using the MinION nanopore sequencer. Sci. Rep. 2022, 12, 8572. [Google Scholar] [CrossRef] [PubMed]
  58. Schmidt, J.; Berghaus, S.; Blessing, F.; Herbeck, H.; Blessing, J.; Schierack, P.; Rödiger, S.; Roggenbuck, D.; Wenzel, F. Genotyping of familial Mediterranean fever gene (MEFV)—Single nucleotide polymorphism—Comparison of Nanopore with conventional Sanger sequencing. PLoS ONE 2022, 17, e0265622. [Google Scholar] [CrossRef] [PubMed]
  59. Hu, J.; Chen, Y.-D.; Jiang, Z.-L.; Nardi, F.; Yang, T.-Y.; Jin, J.; Zhang, Z.-K. Global haplotype analysis of the whitefly Bemisia tabaci cryptic species Asia I in Asia. Mitochondrial DNA A 2015, 26, 232–241. [Google Scholar] [CrossRef] [PubMed]
  60. Kranthi, S.; Kumar, R.; Suke, R.; Chawla, S.; Kranthi, K.R. Mitochondrial COI based genetic diversity and phylogeographic structure of whitefly Bemisia tabaci (Gennadius) on cotton in India. Int. J. Trop. Insect Sci. 2021, 41, 1543–1554. [Google Scholar]
  61. De Barro, P.J.; Liu, S.-S.; Boykin, L.M.; Dinsdale, A.B. Bemisia tabaci: A Statement of Species Status. Annu. Rev. Entomol. 2011, 56, 1–19. [Google Scholar] [CrossRef] [PubMed]
  62. Moya, A.; Guirao, P.; Cifuentes, D.; Beitia, F.; Cenis, J.L. Genetic diversity of Iberian populations of Bemisia tabaci (Hemiptera: Aleyrodidae) based on random amplified polymorphic DNA-polymerase chain reaction. Mol. Ecol. 2001, 10, 891–897. [Google Scholar] [CrossRef]
  63. Liu, S.-S.; de Barro, P.J.; Xu, J.; Luan, J.-B.; Zang, L.-S.; Ruan, Y.-M.; Wan, F.-H. Asymmetric mating interactions drive widespread invasion and displacement in a whitefly. Science 2007, 318, 1769–1772. [Google Scholar] [CrossRef]
  64. Prasanna, H.C.; Kanakala, S.; Archana, K.; Jyothsna, P.; Varma, R.K.; Malathi, V.G. Cryptic species composition and genetic diversity within Bemisia tabaci complex in soybean in India revealed by mtCOI DNA sequence. J. Integr. Agric. 2015, 14, 1786–1795. [Google Scholar] [CrossRef]
  65. Mahmood, M.A.; Ahmed, N.; Hussain, S.; Muntaha, S.T.; Amin, I.; Mansoor, S. Dominance of Asia II 1 species of Bemisia tabaci in Pakistan and beyond. Sci. Rep. 2022, 12, 1528. [Google Scholar] [CrossRef]
  66. Gentile, G.; Torre, A.D.; Maegga, B.; Powell, J.R.; Caccone, A. Genetic differentiation in the African malaria vector, Anopheles gambiae ss, and the problem of taxonomic status. Genetics 2002, 161, 1561–1578. [Google Scholar] [CrossRef] [PubMed]
  67. Hart, M.W.; Sunday, J. Things fall apart: Biological species form unconnected parsimony networks. Biol. Lett. 2007, 3, 509–512. [Google Scholar] [CrossRef] [PubMed]
  68. Chen, H.; Strand, M.; Norenburg, J.L.; Sun, S.; Kajihara, H.; Chernyshev, A.V.; Maslakova, S.A.; Sundberg, P. Statistical parsimony networks and species assemblages in cephalotrichid nemerteans (Nemertea). PLoS ONE 2010, 5, e12885. [Google Scholar] [CrossRef]
  69. De Barro, P.J.; Ahmed, M.Z. Genetic networking of the Bemisia tabaci cryptic species complex reveals pattern of biological invasions. PLoS ONE 2011, 6, e25579. [Google Scholar] [CrossRef]
  70. Pan, L.-L.; Cui, X.-Y.; Chen, Q.-F.; Wang, X.-W.; Liu, S.-S. Cotton leaf curl disease: Which whitefly is the vector? Phytopathology 2018, 108, 1172–1183. [Google Scholar] [CrossRef]
  71. Ahmed, M.Z.; de Barro, P.J.; Greeff, J.M.; Ren, S.X.; Naveed, M.; Qiu, B.L. Genetic identity of the Bemisia tabaci species complex and association with high cotton leaf curl disease (CLCuD) incidence in Pakistan. Pest Manag. Sci. 2011, 67, 307–317. [Google Scholar] [CrossRef]
  72. Naveen, N.C.; Chaubey, R.; Kumar, D.; Rebijith, K.B.; Rajagopal, R.; Subrahmanyam, B.; Subramanian, S. Insecticide resistance status in the whitefly, Bemisia tabaci genetic groups Asia-I, Asia-II-1 and Asia-II-7 on the Indian subcontinent. Sci. Rep. 2017, 7, 40634. [Google Scholar] [CrossRef]
  73. Thangavel, T. First report of Asia II 6 cryptic species from India and Asia I cryptic species of whiteflies, Bemisia tabaci (Gennadius), in Ash gourd. In Proceedings of the ESA Confex Entomology, Vancouver, BC, Canada, 11–14 November 2018; Available online: https://esa.confex.com/esa/2018/meetingapp.cgi/Paper/131026 (accessed on 1 March 2022).
  74. Kijima, K.; Ohno, S.; Ganaha-Kikumura, T. Effect of several commercial pesticides on the survival of the Nauru biotype of Bemisia tabaci (Hemiptera: Aleyrodidae). Jpn. J. Appl. Entomol. Zool. 2012, 56, 9–12. [Google Scholar] [CrossRef] [Green Version]
Insects 13 00861 g001 550
Figure 1. Workflow employed in this work. 1—collected sample, 2—direct PCR using the whole whitefly, 3—confirm the PCR by electrophoresis, 4—library preparation, 5—sequencing using Nanopore Tech, 6—data analysis. Created with Biorender.com (Toronto, ON, Canada).
Figure 1. Workflow employed in this work. 1—collected sample, 2—direct PCR using the whole whitefly, 3—confirm the PCR by electrophoresis, 4—library preparation, 5—sequencing using Nanopore Tech, 6—data analysis. Created with Biorender.com (Toronto, ON, Canada).
Insects 13 00861 g001
Insects 13 00861 g002 550
Figure 2. Map of Lao PDR showing abundance of cassava whiteflies per surveyed province. https://pdptest.ciat.cgiar.org/projects/PDP_00088/map?provinces-4.79/15.34/98.13. Accessed on 15 March 2021.
Figure 2. Map of Lao PDR showing abundance of cassava whiteflies per surveyed province. https://pdptest.ciat.cgiar.org/projects/PDP_00088/map?provinces-4.79/15.34/98.13. Accessed on 15 March 2021.
Insects 13 00861 g002
Insects 13 00861 g003 550
Figure 3. Haplotypes obtained from Median Joining with Network Software. Twenty-four mitochondrial cytochrome oxidase I haplotypes from 104 individuals of Asia II 1 and Asia II 6 are observed. The area of the circles is proportional to haplotype frequency in the dataset. The geographical region of each haplotype is color-coded and the small black circles indicate mutational changes.
Figure 3. Haplotypes obtained from Median Joining with Network Software. Twenty-four mitochondrial cytochrome oxidase I haplotypes from 104 individuals of Asia II 1 and Asia II 6 are observed. The area of the circles is proportional to haplotype frequency in the dataset. The geographical region of each haplotype is color-coded and the small black circles indicate mutational changes.
Insects 13 00861 g003
Insects 13 00861 g004 550
Figure 4. Maximum Likelihood phylogenetic analysis of all consensus sequences from the region. mtCOI using IQTree with 1000 bootstrap replications involving 669 nt of sequences obtained in this study (in red) and reference sequences from whiteflies occurring in Asia. Detailed information on the sequences used in this analysis are included in Table S1.
Figure 4. Maximum Likelihood phylogenetic analysis of all consensus sequences from the region. mtCOI using IQTree with 1000 bootstrap replications involving 669 nt of sequences obtained in this study (in red) and reference sequences from whiteflies occurring in Asia. Detailed information on the sequences used in this analysis are included in Table S1.
Insects 13 00861 g004
Table
Table 1. List of surveyed provinces and abundance data for whiteflies associated to cassava in Lao PDR. Comparative whitefly abundance data from Tanzania and Nigeria is included. SD = standard deviation. The asterisks indicate provinces where CMD was observed. Asia II 1 and its haplotypes are indicated in red.
Table 1. List of surveyed provinces and abundance data for whiteflies associated to cassava in Lao PDR. Comparative whitefly abundance data from Tanzania and Nigeria is included. SD = standard deviation. The asterisks indicate provinces where CMD was observed. Asia II 1 and its haplotypes are indicated in red.
CountryProvinceNo. of FieldsAbundance (Per Leaf)SDCryptic SpeciesHaplotypeRef.
Lao PDRLaoungNamTha20.1500.012Asia II 1H1This work
Saiyabouly90.0410.021Asia II 1H1
Vientiane50.0140.012Asia II 1/Asia II 6H1, H2, H3, H14, H17
Xaysomboun30.0780.040Asia II 1H1
Borlikhamsai110.1450.034Asia II 1/Asia II 6H1, H5, H20, H13
Khammoun10.1660.000Asia II 1H1
Salavan70.1290.031Asia II 1/Asia II 6H12, H17, H15, H16
Sekong10.1670.000Asia II 6H18
Champasack *250.1770.043Asia II 1/Asia II 6H1, H2, H6, H17, H16, H14, H21
Attapue *60.2330.072Asia II 1/Asia II 6H1, H17, H16
Tanzania 2.35–71.99 (14.3)0.86–22.07SSA1-SG1-SG2-SG3ND[47]
Nigeria 242.34–265.5 SSA1-SG5, SSA3, SSA1-SG1-Bemisia afer, MED-ASLND[48]
Table
Table 2. Distribution and frequency of cryptic species found in Lao PDR. n = number of sequences; S = number of polymorphic sites; NS = Singleton’s number; Pi = parsimony-informative sites; k = average number of nucleotide differences; h = haplotypic diversity; π = nucleotide diversity; Tajima’s D = neutrality test.
Table 2. Distribution and frequency of cryptic species found in Lao PDR. n = number of sequences; S = number of polymorphic sites; NS = Singleton’s number; Pi = parsimony-informative sites; k = average number of nucleotide differences; h = haplotypic diversity; π = nucleotide diversity; Tajima’s D = neutrality test.
Cryptic SpeciesnSNSPiKHπTajima’s D
Asia II 13710940.7270.4170.00109−2.168
Asia II 6226211.4330.7750.00214−0.644
Total597327133.2810.7430.049753.923
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Leiva, A.M.; Chittarath, K.; Lopez-Alvarez, D.; Vongphachanh, P.; Gomez, M.I.; Sengsay, S.; Wang, X.-W.; Rodriguez, R.; Newby, J.; Cuellar, W.J. Mitochondrial Genetic Diversity of Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) Associated with Cassava in Lao PDR. Insects 2022, 13, 861. https://doi.org/10.3390/insects13100861

AMA Style

Leiva AM, Chittarath K, Lopez-Alvarez D, Vongphachanh P, Gomez MI, Sengsay S, Wang X-W, Rodriguez R, Newby J, Cuellar WJ. Mitochondrial Genetic Diversity of Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) Associated with Cassava in Lao PDR. Insects. 2022; 13(10):861. https://doi.org/10.3390/insects13100861

Chicago/Turabian Style

Leiva, Ana M., Khonesavanh Chittarath, Diana Lopez-Alvarez, Pinkham Vongphachanh, Maria Isabel Gomez, Somkhit Sengsay, Xiao-Wei Wang, Rafael Rodriguez, Jonathan Newby, and Wilmer J. Cuellar. 2022. "Mitochondrial Genetic Diversity of Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) Associated with Cassava in Lao PDR" Insects 13, no. 10: 861. https://doi.org/10.3390/insects13100861

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop