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Article

Genetic Diversity of the Fall Armyworm Spodoptera frugiperda (J.E. Smith) in the Democratic Republic of the Congo

by
Matabaro Joseph Malekera
1,2,
Damas Mamba Mamba
2,
Gauthier Bope Bushabu
2,
Justin Cishugi Murhula
2,
Hwal-Su Hwang
1,3 and
Kyeong-Yeoll Lee
1,3,4,*
1
Department of Plant Medicine, College of Agriculture and Life Sciences, Kyungpook National University, Daegu 41566, Republic of Korea
2
Department of Plants Protection, Ministry of Agriculture, Kinshasa 8722, Democratic Republic of the Congo
3
Research Institute for Dok-do and Ulleung-do Island, Kyungpook National University, Daegu 41566, Republic of Korea
4
Institute of Plant Medicine, Kyungpook National University, Daegu 41566, Republic of Korea
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(8), 2175; https://doi.org/10.3390/agronomy13082175
Submission received: 29 July 2023 / Revised: 15 August 2023 / Accepted: 17 August 2023 / Published: 19 August 2023
(This article belongs to the Special Issue Invasive Lepidoptera: Monitoring, Distribution, Taxonomy, Evolution, and Management of Agricultural and Forest Pests)
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Abstract

:
In 2016, the fall armyworm (FAW), Spodoptera frugiperda, invaded western Africa and rapidly spread in sub-Saharan Africa, causing significant losses in yields of corn, a major food crop in Africa. Although the Democratic Republic of the Congo (DRC) is a large corn-growing country, the impact of FAW has not been investigated. This study was designed to expand investigations on the genetic diversity of FAW populations in the DRC. We collected FAW individuals from eight provinces across the country, for analysis of genetic variation. Based on the partial sequences of both mitochondrial cytochrome oxidase subunit I (COI) and nuclear triosephosphate isomerase (Tpi) genes, we compared polymorphic features of the COI haplotype and Tpi single nucleotide polymorphisms. The results revealed that most (84%) of the analyzed individuals were heterogeneous hybrids Tpi-corn/COI-rice (Tpi-C/COI-R), whereas 16% were homogenous Tpi-corn/COI-corn (Tpi-C/COI-C). Further analysis of the fourth exon/intron sequences of the Tpi gene identified two subgroups, TpiCa1 and TpiCa2, constituting 80% and 20%, respectively, of the collected individuals. Analysis of genetic variation among native and invasive populations indicated significant genetic differences (10.94%) between the native American and DRC populations, whereas both the DRC and African populations were genetically closer to Asian than American populations. This study provides important information on FAW genetic diversity in the DRC, which can be used for effective management of FAW.

1. Introduction

The fall armyworm (FAW), Spodoptera frugiperda (Lepidoptera, Noctuidae), is a devastating agricultural pest in the tropical and subtropical regions [1,2]. Although FAW is native to south America, since the first outbreak outside its native region in 2016, its global distribution range has swiftly expanded throughout Africa, Asia, and recently, Oceania [3,4,5]. This rapid range expansion is probably due to its capacity to adapt to a wide range of temperature conditions and its polyphagy [6,7]. FAW is a highly polyphagous species that can feed on at least 76 plant families, mainly Poaceae, Asteraceae, and Fabaceae [8]. However, FAW has a strong propensity for corn (Poaceae), which is the main food crop for more than 200 million African smallholder farmers [2]. FAW larvae damage corn plants by feeding on leaves, stems, and reproductive parts, thus destroying their growth potential [9]. When their population is large, they develop an “armyworm” behavior and disperse in large numbers, attacking almost all vegetation in their path [10].
According to host plant preference, FAW consists of two strains, namely, the corn strain and rice strain [11]. These strains are morphologically similar but genetically different with 2.09% genome divergence. They also exhibit variation in developmental, physiological, and ecological features such as host plant preference, and sex pheromones [11,12,13]. The rice strain is typically associated with rice Oryza sativa L., sugar cane Saccharum officinarum L., and grass species, such as Johnson grass Sorghum halepense (L.), Bermuda grass Cynodon dactylon (L.), whereas the corn strain is associated with corn Zea mays L. sorghum Sorghum bicolor (L.), soybean (Glycine max), and cotton Gossypium hirsutum (L.) [13,14].
Considering the intra- and interspecific variation among FAW strains, reliable strain identification is essential in field studies of FAW populations. The two strains of FAW are identified mainly based on polymorphisms in the mitochondrial gene cytochrome c oxidase subunit 1 (COI) and the nuclear gene triosephosphate isomerase (Tpi) [15,16]. In the western hemisphere, the relationship between the COI and Tpi markers is important for identifying FAW strains [17]. However, in the invaded regions of Africa and Asia, strain identification using the two markers has shown discordant results. Overall, the host association in invasive populations was accurately predicted by Tpi but not COI [18,19]. The discordance between the COI and Tpi markers indicates the hybrid nature of FAW populations that invaded Africa [17]. This hybrid nature of invasive populations was lately confirmed by whole-genome sequencing studies [20,21]. The identification of two COI-based haplotypes and a small number of Tpi haplotypes showed that genetic diversity was low in the invasive populations of FAW [17,22,23]. The low genetic diversity and small number of haplotypes observed in most invaded locations indicate a possible recent introduction from a common source of the FAW population and could affect the monitoring of the crops at risk in these locations [17,19,23].
This study was conducted to expand investigations on the genetic diversity of FAW populations in the DRC. Thus, additional samples collected in new locations in the DRC were combined and compared with those from earlier studies [19,22] to provide a more representative picture of the country-wide genetic structure of FAW in the DRC. The genetic characterization of FAW in the DRC during the first six years of invasion can predict changes in the populations as they rebalance and respond to pest management measures. Additionally, the comparison of the populations of FAW in the DRC with those from both native and other invaded regions can provide the phylogeographic patterns and relationships of FAW haplotypes in the DRC and could be used in understanding the possible route of the FAW population that invaded the DRC.

2. Materials and Methods

2.1. Collection of FAW Samples

Samples were collected from corn fields at 21 locations in 8 provinces in the DRC during the five-year period from 2018 to 2022 (Table 1). After field collection, the larvae were preserved individually in 1.5 mL microcentrifuge tubes in 70% ethanol. The samples were transported to the plant clinic in Kinshasa, DRC and kept at −20 °C until they were sent to the Laboratory of Insect Molecular Physiology at Kyungpook National University, Republic of Korea, for DNA extraction and further genomic analysis.

2.2. DNA Extraction

Total genomic DNA was extracted from the head of single larva using a pure link genomic DNA mini kit (Invitrogen, Carlsbad, CA, USA) [22]. Each sample was homogenized in a 1.5 mL microcentrifuge tube containing 200 µL of digestion buffer and 20 µL of proteinase K (50 µg/mL) before it was incubated at 56 °C for 30 min. The DNA supernatant was collected in a genomic spin column and stored in a new 1.5 mL microcentrifuge tube at −20 °C until downstream analysis. DNA quality and concentration were assessed using a NanoPhotometer™ (Implen GmbH, Schatzbogen, Germany). The remaining portions of the samples were kept in 70% ethanol at −20 °C.

2.3. PCR Amplification and Sequence Analysis

DNA was subjected to PCR amplification using a SimpliAmp 96-Well Thermal Cycler (Applied Biosystems, Foster City, CA, USA). Each PCR reaction mixture of 30 µL contained 15 µL of Solg 2 × Taq PreMix (Solgent, Daejeon, Republic of Korea), 2 µL of each primer (10 pmol/µL), 2 µL of the DNA template, and 9 µL of sterile water. Partial COI (658 bp) and Tpi (444 bp) barcode regions of the FAW were amplified using the primer pairs LCO1490 (5′-GGTCAACAAATCATAAAGATATTGG-3′) and HCO2198 (5′-TAAACTTCAGGG TGACCAAAAAATCA-3′) for COI, and TPI412F (5′-CCGGACTGAAGGTTATCGCTTG-3′) and TPI1140R (5′-GCGGAAGCATTC GCTGACAACC-3′) for Tpi [23,24]. The thermo-cycling conditions for COI included an initial denaturation at 92 °C for 5 min, followed by 35 cycles of denaturation at 92 °C for 1 min, annealing at 55 °C for 1 min, and extension at 72 °C for 1 min. The Tpi gene thermo-cycling parameters included an initial denaturation at 94 °C for 1 min, followed by 33 cycles of denaturation at 92 °C for 30 s, annealing at 56 °C for 45 s, extension at 72 °C for 1 min, and final extension at 72 °C for 5 min. The amplified products were stained with ethidium bromide before they were visualized on 1% agarose gel under ultraviolet (UV) light. The amplified products were sequenced using the BigDye® Terminator Cycle Sequencing Kit and ABI Prism 3730XL DNA Analyzer (50 cm capillary) (DNA Sequencer) at the Celemics Sequencing Facility (Celemics, Seoul, Republic of Korea). The sequences generated in this study showed 100% similarity to those of FAW in the NCBI database. The sequences were submitted to the NCBI GenBank under accession numbers assigned to each specimen (Table 1).

2.4. DNA Polymorphism Analysis

COI sequences from our previous study [22] were aligned with the sequences generated in this study using ClustalW [24] and used for characterizing genetic diversity (Table 1). Furthermore, the diversity of the Congolese FAW population was compared with that from other geographic locations. The COI sequences reported from Africa (89), Asia (72), and America (126) (Table A1) were retrieved from GenBank database and trimmed to a length of 483 bp as this region was present in most the of sequences and was used for comparative polymorphism studies [25]. The dataset was classified into four main geographical categories: (1) Africa, (2) Asia, (3) America, and (4) the DRC. Descriptive statistics including nucleotide diversity, number of haplotypes (H), haplotype diversity (Hd), genetic neutrality, and mismatch distribution were generated using DnaSP ver. 6.12.03 [26]. Mismatch distribution curves which report the frequency of pairwise nucleotide-site differences between FAW sequences from the DRC, were generated using the constant population size model in DnaSP to further examine the demographic pattern of FAW in DRC.
The FAW COI and Tpi gene polymorphisms of the DRC samples were analyzed using previously published strain defining loci and polymorphic sites [27,28]. The single nucleotide polymorphisms (SNPs) (mCOI72, mCOI117, mCOI171, mCOI207, mCOI258, mCOI564, mCOI570, mCOI600, mCOI634, and mCOI663) that define the strain polymorphic sites of FAW found in the barcode region of the COI were used to distinguish between corn and rice strains of FAW in our previous study [22]. Additionally, the Tpi partial gene segment (444 bp), which contained 166 bp of the fourth exon (Tpi-E4) and 278 bp of the fourth intron (Tpi-I4), was used to identify the S. frugiperda host strain. The presence of the nucleotide base letters “C” or “T” at gTpi183, for the corn strain (Tpi-C) or rice strain (Tpi-R), respectively, allowed us to distinguish the FAW Tpi-based host strains.

2.5. Haplotype Network Plot and Phylogenetic Analysis

A haplotype network was constructed using the popART software ver. 1.7 [29]. Sequences were aligned and grouped within the four geographical regions using ClustalW [24] and Dnasp, respectively. The median joining network method was used to infer haplotype relationships. To generate the evolutionary relationship between the DRC FAW haplotypes, a phylogenetic tree for the COI gene was constructed using the maximum likelihood method implemented in MEGA 6.0 [30], with other Spodoptera species and FAW corn and rice strains retrieved from NCBI [31,32]. For each phylogeny, 1000 bootstrap replicates were used to assess robustness using the Hasegawa–Kishino–Yano (HKY850) model and gamma distribution rate of variation between sites [33].

2.6. Analysis of Molecular Variance (AMOVA)

AMOVA was performed using Arlequin ver. 3.5.2.2 [34]. The analysis was conducted with four geographic groups including the rest of Africa, Asia, America, and the DR Congo. Apart from the overall AMOVA, the COI sequences from the four geographical regions were examined in six combinations comprising DRC vs. America, DRC vs. Africa, DRC vs. Asia, America vs. Africa, America vs. Asia, and Africa vs. Asia. We observed variance differences among groups by a randomized test with 1000 permutations in a haplotype-based standard AMOVA.

3. Results

3.1. PCR Amplification and Sequence Analysis

We recovered 25 nucleotide sequences of both COI and Tpi genes from the represented FAW samples collected from 21 different regions of the DRC (Table 1). Sequence analysis of the partial COI fragment (658 bp) showed that the COI-R constituted 84%, whereas the COI-C constituted 16% of the sequences (Figure 1b). Additionally, analysis of Tpi sequences on the polymorphic locus gTpi183 (which was used to identify the rice and corn strains) and at the Tpi-E4 was C but not T, which indicated that all the samples were from the Tpi-C genetic group. Furthermore, two Tpi-based haplotypes were identified in the DRC’s FAW population, including the Tpi-Ca1 homozygous (in 80% of individuals), and Tpi-Ca2 homozygous (in 20% of individuals) (Figure 1c). The Tpi-R haplotype was not detected in any of the samples. Further analysis of the Tpi-Ca2 subgroup showed that the Tpi-Ca2a and Tpi-Ca2b genetic groups were detected in three and two individuals, respectively. Our results indicated that the nuclear Tpi marker consistently identifies the phenotypic feeding behavior of FAW on corn, which is the host plant of FAW in the DRC as well as in other African and Asian countries.

3.2. Polymorphism Analysis

The haplotype diversity of FAW in the DRC was analyzed using 657 bp of the COI barcode region from individuals collected from eight provinces (Figure 1a). Our findings indicated seven polymorphic sites and a nucleotide diversity of 0.00469 (Table 2). Two distinct haplotypes (the corn and rice strains) were identified from the DRC’s COI sequences with a haplotype diversity (Hd) of 0.324 (Table 2). Most (84%) of the COI sequences from this study belonged to a single rice haplotype (DRC_haplotype 1, submitted under GenBank accession number OP132901). The remaining 16% belonged to the corn strain haplotype (DRC_haplotye 2, submitted under GenBank accession number OP132898). Four sequences identified as corn strain were detected in specimens from four provinces of the DRC (Sud-ubangi, Tchuapa, Kongo central, and Kinshasa), and the rice strain sequences were detected throughout the country, suggesting that the distribution pattern of FAW haplotypes in the DRC was not region-specific.
The values of both the Fu’s Fs and Tajima’s D test statistic for the FAW population of the DRC were significantly positive (Table 2). Our results did not detect any nucleotide diversity within the strain populations. Genetic analysis of the FAW population in the DRC did not provide evidence of population expansion. Mismatch distribution analyses of the two strains indicated a bimodal curve, suggesting neutral evolution of FAW population in the DRC (Figure 2).

3.3. Comparative Genetic Analyses of the FAW Population in the DRC and Three Geographic Regions

Comparative analysis of the COI partial gene region (483 bp) common to all the sequences, revealed haplotype numbers of 29, 3, and 4 in FAW populations from America, Africa, and Asia, respectively (Table 2). The two DRC haplotypes (rice and corn strains) were identical to the predominant rice and corn haplotypes from America (GenBank Accession: U72977.1 and U72975.1, respectively), which are most likely to be the ancestors of all haplotypes in the invaded regions. The neutrality test statistics for the DRC and African FAW populations revealed that FAW populations in these regions are still evolving neutrally relative to the American and Asian FAW populations (Table 2).

3.4. Comparative Phylogenetic and Haplotype Network Analysis

The phylogenetic analysis, based on the maximum likelihood method, indicated that both the rice and corn strain haplotypes from the DRC were identical to haplotypes from American, Asian and other African regions (Figure 3). Haplotype network analysis showed that there were two ancestral strain haplotypes (DRC-RS and DRC-CS) in the FAW populations of the DRC (Figure 4). The network showed the presence of the two ancestral haplotypes in the four geographical regions, with the Tpi-C/COI-R group being the dominant haplotype in the invaded regions (Africa, Asia, and the DRC). However, the distribution of novel haplotypes in America, Africa, and Asia differed significantly. Our findings suggest the 29 distinct haplotypes in America with the corn strain (Tpi-C/COI-C) as the most prevalent haplotype, whereas in the two invaded regions, the rice strain haplotype (Tpi-C/COI-R) was the most prevalent, with 3 and 4 haplotypes in Africa and Asia, respectively (Figure 3). The COI marker information indicated that there was no evidence of multiple introductions in the DRC.

3.5. Population Structure of FAW

We performed seven single AMOVA analyses, including one comparing all the geographical regions and six different combinations of groups (DRC and Africa, DRC and America, DRC and Asia, Africa and America, Africa and Asia, and America and Asia) (Table 3). The findings showed significant genetic differences among all the geographical regions (12.70%). The analysis of genetic variation among native and invasive populations indicated significant genetic differences between the native American and DRC populations (10.94%), whereas both DRC populations and those from other parts of Africa were genetically closer to the Asian populations than to American populations (Table 3).

4. Discussion

This study aimed to analyze the genetic diversity and distribution of the FAW population that invaded the DRC. The findings suggest low genetic variability in the Congolese FAW population as only two haplotypes from each of the genes (COI and Tpi) were recovered. Most (84%) of the samples were COI-R, whereas COI-C occurred in 16%. These findings were consistent with those of a recent study conducted in Uganda, a neighboring country [32], and a previous report from 11 sub-Saharan African countries, including two provinces of the DRC [19]. Based on the COI marker, both corn and rice strains were detected in FAW specimens collected from corn fields, despite the known association of strains to their host plant [35,36]. Similar findings have been reported in several African and Asian countries [19,22,32]. These findings suggested that the discordance between the COI marker and host plants may be due to FAW’s plasticity in plant choice or the inability of the marker to reliably discriminate between the corn and rice strains of FAW.
The COI-based analysis of population genetics test statistics revealed that the FAW populations in the DRC, like those from the rest of Africa, are evolving in a neutral pattern. This neutral pattern was further supported by the absence of novel haplotypes and the low genetic diversity in FAW populations from the DRC. In contrast, the haplotype network of FAW populations in America indicates that the populations are still expanding. Thus, our findings indicate that America is still the main point of FAW population expansion. Our findings corroborate those of previous studies which also recorded that the FAW populations in Africa and America were still evolving neutrally and expanding, respectively [25,32].
Analysis of the partial sex-linked Tpi nuclear gene showed an 84% detection discrepancy between the COI and Tpi markers in the DRC FAW population, an observation that corroborated previous findings from other invaded regions of Africa and Asia [19,22]. In this study, we observed a dominance of the hybrid Tpi-C/COI-R individuals over the homogeneous Tpi-C/COI-C individuals among specimens collected from the corn host plant, suggesting that the Tpi marker is more accurate in determining the FAW–host strain association than the COI marker. Previously, Nagoshi et al. [17] and Nayer et al. [25] found that the hybrid Tpi-C/COI-R and the homogenous Tpi-C/COI-C were equally distributed in Central Africa, whereas in eastern and southern Africa and India, the hybrid strain predominated. Our study did not detect Tpi-R/COI-R homogenous individuals in the DRC FAW population, which occur in the western hemisphere but are rare in Africa [37]. These results are similar to those of previous studies showing that the homogeneous strain was more marginally distributed in invaded regions than the hybrid strain [19,25,32]. This hybrid strain is expected to result from the small initial interstrain mating populations explained by the admixture regularly seen during invasive events [38]. However, interstrain hybrids may have a large fitness advantage and become more prevalent in the invasive population, including in the FAW population from the DRC, eventually leading to the extinction of one or both strains in favor of more unique hybrid genotypes. These findings, combined with those of Nagoshi et al. [19] suggest that the unique African rice strain Tpi haplotype of the FAW found in several African regions may be rare in the DRC. In fact, Nagoshi et al. [19] detected the Tpi-Ra1 in the FAW specimens from the Haut-Katanga region of the DRC but not in those from the Sud-Ubangi region, which is in line with our results. These results have implications for the assessment of the crops at risk and the design of FAW management measures in the DRC. Further assessments are needed in other regions of the DRC.
Analysis of the fourth exon and intron regions of the nuclear Tpi gene showed the existence of two subgroups of the Tpi-Ca genetic group, including the predominant Tpi-Ca1, and minor Tpi-Ca2 subgroups. Further analysis showed the presence of two polymorphic variants, Tpi-Ca2a and Tpi-Ca2b, which have A or C at nucleotide 148 of Tpi-I4. The predominance of Tpi-Ca1a over Tpi-Ca2a and Tpi-Ca2b in invading FAW populations was also observed in Uganda [32], India [25], and several African and Asian regions [19,22].
As a highly polyphagous crop pest with a larval stage able to feed on the aerial parts of a wide range of plants, FAW can develop and establish for several generations in the DRC owing to its favorable biodiversity [39]. However, our findings combined with those of previous studies indicate that the FAW population in the DRC feeds mainly on corn plants and rarely on other plants [17,22]. This observation calls into question the nature of the hybrid genotype (Tpi-C/COI-R) detected in this study. Thus, at the molecular level, it seems that corn preference is more associated with the Tpi gene marker than the COI gene marker. This finding is not completely new because previous studies in invaded regions of Africa and Asia recorded the same genetic pattern in FAW [17,32]. These similarities in genotype features between invading populations of FAW provide evidence of a common origin and should be used for further evolutionary studies that include the FAW whole genome sequence to better understand FAW population dynamics and subsequent dissemination in the DRC.
In summary, this study aimed to analyze the genetic diversity and distribution of the FAW population six years after the first reports of FAW invasion in the DRC. Our findings suggest that the FAW population that invaded the DRC is still evolving neutrally with a low number of haplotypes based on the COI gene marker. The observed low numbers of haplotypes and the hybrid nature of the FAW population in the DRC could be explained by a single introduction followed by a rapid dispersion through natural and trade-related processes. This finding combined with further studies on the migration dynamics may serve as important tools for the management of this crop pest in the DRC. Additionally, our findings showed that the nuclear Tpi gene marker was more accurate in determining the host association of FAW than the COI gene marker. Based on both the COI and Tpi markers, our study detected three genetic groups in the DRC’s FAW populations, including the hybrids Tpi-Ca1/COI-R, Tpi-Ca2/COI-R, and the homogeneous Tpi-Ca1/COI-C. These results will serve as baseline resource for future studies on how the invading FAW population may change to adapt to the DRC’s bio-environment and in the design of management measures. However, additional genotype surveys in other regions of the country combined with more evolutionary studies are required to refine knowledge of the FAW population dynamics and subsequent spreading routes of the pest.

Author Contributions

Conceptualization, M.J.M. and K.-Y.L.; methodology, M.J.M., D.M.M., G.B.B., J.C.M., H.-S.H. and K.-Y.L.; formal analysis, M.J.M.; investigation, M.J.M.; validation, K.-Y.L.; writing—original draft preparation, M.J.M.; writing—review and editing, M.J.M., D.M.M., G.B.B., J.C.M., H.-S.H. and K.-Y.L.; funding acquisition, K.-Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) and funded by the Ministry of Education (NRF-2016R1A6A1A05011910).

Data Availability Statement

Not applicable.

Acknowledgments

The Authors thank James Bafurume and his team for their support in sample collection. We are grateful to the farmers and extension officers for allowing different team access to their fields and guiding the team, respectively, during sample collection.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Details of FAW COI gene sequences used in the present study.
Table A1. Details of FAW COI gene sequences used in the present study.
(A)  COI gene sequences from America
No.GenBank AccessionLocationYear Submitted
1.KX281221.1Canada2017
2.U72978.1USA1996
3.U72977.1USA1996
4.U72976.1USA1996
5.U72975.1USA1996
6.U72974.1USA1996
7.KT809294.1Brazil2018
8.KT809293.1Brazil2018
9.KT809292.1Brazil2018
10.KT809291.1Brazil2018
11.KT809290.1Brazil2018
12.KT809289.1Brazil2018
13.KT809288.1Brazil2018
14.KT809287.1Brazil2018
15.KT809286.1Brazil2018
16.KT809285.1Brazil2018
17.KT809284.1Brazil2018
18.KT809283.1Brazil2018
19.KT809282.1Brazil2018
20.KT809281.1Brazil2018
21KT809280.1Brazil2018
22.KT809279.1Brazil2018
23.KT809278.1Brazil2018
24.KT809277.1Brazil2018
25.KT809276.1Brazil2018
26.KT809275.1Brazil2018
27.KT809274.1Brazil2018
28.KT809273.1Brazil2018
29.KT809272.1Brazil2018
30.KT809271.1Brazil2018
31.KT809270.1Brazil2018
32.KT809269.1Brazil2018
33.KT809268.1Brazil2018
34.KT809267.1Brazil2018
35.KT809266.1Brazil2018
36.KT809265.1Brazil2018
37.KT809264.1Brazil2018
38.KT809263.1Brazil2018
39.KT809262.1Brazil2018
40.KT809261.1Brazil2018
41.KT809260.1Brazil2018
42.KT809259.1Brazil2018
43.KT809258.1Brazil2018
44.KT809257.1Brazil2018
45.KT809256.1Brazil2018
46.KT809255.1Brazil2018
47.KT809254.1Brazil2018
48.KT809253.1Brazil2018
49.KT809252.1Brazil2018
50.KT809251.1Brazil2018
51.KT809250.1Brazil2018
52.KT809249.1Brazil2018
53.KT809248.1Brazil2018
54.KT809247.1Brazil2018
55.KT809246.1Brazil2018
56.KT809245.1Brazil2018
57.KT809244.1Brazil2018
58.KT809243.1Brazil2018
59.KT809242.1Brazil2018
60.KT809241.1Brazil2018
61.KT809240.1Brazil2018
62.KT809239.1Brazil2018
63.KT809238.1Brazil2018
64.KT809237.1Brazil2018
65.KT809236.1Brazil2018
66.KT809235.1Brazil2018
67.KJ634298.1Suriname2014
68.KJ634297.1Honduras2014
69.MK318422.1Mexico2019
70.MK318420.1Mexico2019
71.MK318377.1Puerto Rico2019
72.MK318373.1Puerto Rico2019
73.MK318372.1Mexico2019
74.MK318311.1Mexico2019
75.MK318297.1Dominican 2019
76.GU439151.1Ontario2018
77.GU439150.1Puslinch2018
78.GU439149.1Puslinch2018
79.GU439148.1Puslinch2018
80.GU439147.1Puslinch2018
81.GU090724.1Puslinch2018
82.GU090723.1Puslinch2018
83.GU095403.1New Brunswick2018
84.GU094756.1Puslinch2018
85.GU094755.1Puslinch2018
86.GU094754.1Puslinch2018
87.KJ388147.1Quebec2018
88.HM102314.1USA2016
89.KJ641998.1Guano2015
90.KJ641997.1Guano2015
91.KF624877.1Roraima2014
92.KF624876.1Roraima2014
93.JQ559528.1Costa Rica2012
94.JQ554012.1Costa Rica2012
95.JQ572603.1Costa Rica2012
96.JQ571459.1Costa Rica2012
97.JQ547900.1Costa rica2012
98.JQ577923.1Costa Rica2012
99.JF854747.1Campina Grande2012
100.JF854746.1Morretes2012
101.JF854745.1Morretes2012
102.JF854744.1Campina Grande2012
103.JF854743.1Morretes2012
104.JF854741.1Morretes2012
105.JF854740.1Morretes2012
106.HQ964527.1Massachusetts2012
107.HQ964487.1Massachusetts2012
108.HQ964486.1Massachusetts2012
109.HQ964485.1Massachusetts2012
110.HQ964443.1Massachusetts2012
111.HQ964441.1Massachusetts2012
112.HQ964442.1Massachusetts2012
113.HQ964440.1Massachusetts2012
114.HQ964439.1Massachusetts2012
115.HQ964394.1Massachusetts2012
116.HQ964393.1Massachusetts2012
117.HQ964352.1Massachusetts2012
118.HQ964351.1Massachusetts2012
119.GU159435.1Costa Rica2012
120.GU159434.1Costa Rica2012
121.GU159433.1Costa Rica2012
122.GU159432.1Costa Rica2012
123.GU159431.1Costa Rica2012
124.GU159430.1Costa Rica2012
125.GU159429.1Costa Rica2012
126.GU658451.1Alvaro Obregon2019
(B) COI gene sequences from Africa
No.GenBank AccessionLocationYear Submitted
1.MF593258.1South Africa2018
2.MF593257.1South Africa2018
3.MF593256.1South Africa2018
4.MF593255.1South Africa2018
5.MF593254.1South Africa2018
6.MF593253.1South Africa2018
7.MF593252.1South Africa2018
8.MF593251.1South Africa2018
9.MF593250.1South Africa2018
10.MF593249.1South Africa2018
11.MF593248.1South Africa2018
12.MF593247.1South Africa2018
13.MF593246.1South Africa2018
14.MF593245.1South Africa2018
15.MF593244.1South Africa2018
16.MF593243.1South Africa2018
17.MF593242.1South Africa2018
18.MF593241.1South Africa2018
19MK493020.1South Africa2019
20.MK493019.1South Africa2019
21.MK493018.1South Africa2019
22.MK493017.1South Africa2019
23.MK493016.1South Africa2019
24.MT933058Tanzania2020
MT103348Tanzania
25.MT103346.1Zimbabwe2020
MT103347Zimbabwe
26.KX580619.1Nigeria2016
27.KX580618.1Nigeria2016
28.KX580617.1Nigeria2016
29.KX580616.1Nigeria2016
30.KX580615.1Sao-Tome, 2016
31.KX580614.1Sao-Tome2016
32.MT641267.1Uganda2020
33.MF278659.1Tanzania2018
34.MF278658.1Tanzania2018
35.MF278657.1Tanzania2018
36.MH190448.1Kenya2018
37.MH190447.1Kenya2018
38.MH190446.1Kenya2018
39.MH190445.1Kenya2018
40.MH190444.1Kenya2018
41.KY472255.1Ghana2017
42.KY472254.1Ghana2017
43.KY472253.1Ghana2017
44.KY472252.1Ghana2017
45.KY472251.1Ghana2017
46.KY472250.1Ghana2017
47.KY472249.1Ghana2017
48.KY472248.1Ghana2017
49.KY472245.1Ghana2017
50.KY472244.1Ghana2017
51.KY472242.1Ghana2017
52.KY472241.1Ghana2017
53.KY472240.1Ghana2017
54.MG993205.1Malawi: Sande2018
55.MF197867.1Uganda2018
56.MK493006.1Kenya2019
57.MK493000.1Kenya2019
58.MK492996.1Kenya2019
59.MK493010.1Kenya2019
60.MK493009.1Kenya2019
61.MK493008.1Kenya2019
62.MK493007.1Kenya2019
63.MK493004.1Kenya2019
64.MK493003.1Kenya2019
65.MK493002.1Kenya2019
66.MK493001.1Kenya2019
67.MK492999.1Kenya2019
68.MK492998.1Kenya2019
69.MK492997.1Kenya2019
70.MK492995.1Kenya2019
71.MK492994.1Kenya2019
72.MK492993.1Kenya2019
73.MK492992.1Kenya2019
74.MK492991.1Kenya2019
75.MK492990.1Kenya2019
76.MK492989.1Kenya2019
77.MK492988.1Kenya2019
78.MK492987.1Kenya2019
79.MK492986.1Kenya2019
80.MK492985.1Kenya2019
81.MK492984.1Kenya2019
82.MK492983.1Kenya2019
83.MK492982.1Kenya2019
84.MK492981.1Kenya2019
85MK492972.1Uganda2018
86MK492971.1Uganda
87MK492970.1Uganda2022
88MK492969.1Uganda2022
89MK492958.1Tanzania2020
(C) COI gene sequences from Asia
No.GenBank AccessionLocationYear Submitted
1.MT103344.1Bangladesh: Dhaka2020
2.MT103343.1Bangladesh: Dhaka2020
3.MT103342.1South Korea: Gyeongsan2020
4.MT103341.1Viet Nam: Ninh binh2020
5.MT103340.1Viet Nam: Ninh binh2020
6.MT103339.1Viet Nam: Ha noi2020
7.MT103338.1Viet Nam: Vinh phuc2020
8.MT103336.1Viet Nam: Hanoi2020
9.MT103335.1Viet Nam: Vinh Phuc2020
10.MT103334.1Viet Nam: Ninh Binh2020
11.MT641270.1South Korea: Gyeongsan2020
12.MT641269.1South Korea: Jeju2020
13.MT641268.1South Korea: Campus2020
14.LC546868.1Japan: Aomori2020
15.LC546867.1Japan: Aomori2020
16.LC546866.1Japan: Iwate2020
17.LC546865.1Japan: Kanagawa2020
18.LC546864.1Japan: Chiba2020
19.LC546863.1Japan: Fukushima2020
20.LC546862.1Japan: Ibaraki2020
21LC546861.1Japan: Ibaraki2020
22.LC546860.1Japan: Miyazaki2020
23.LC546859.1Japan: Miyazaki2020
24.LC546858.1Japan: Miyazaki2020
25.LC546857.1Japan: Okinawa2020
26.LC546856.1Japan: Okinawa2020
27.LC546855.1Japan: Okinawa2020
28.LC546854.1Japan: Kagoshima2020
29.LC546853.1Japan: Kagoshima2020
30.LC546852.1Japan: Kagoshima2020
31.LC546851.1Japan: Kagoshima2020
32.LC546850.1Japan: Kagoshima2020
33.LC546849.1Japan: Kagoshima2020
34.LC546848.1Japan: Kagoshima2020
35.LC546847.1Japan: Kagoshima2020
36.LC546846.1Japan: Kagoshima2020
37.MK913648.1Viet Nam: Nghe An2019
38.MK913647.1Viet Nam: Nghe An2019
39.MK913646.1Viet Nam: Ha Noi2019
40.MK860942.1China: Tengchong, Yunnan2019
41.MK860941.1China: Tengchong, Yunnan2019
42.MK860940.1China: Tengchong, Yunnan2019
43.MK860939.1China: Tengchong, Yunnan2019
44.MK860938.1China: Tengchong, Yunnan2019
45.MK860937.1China: Tengchong, Yunnan2019
46.MK860936.1China: Ruili, Yunnan2019
47.MK860935.1China: Ruili, Yunnan2019
48.MK860934.1China: Ruili, Yunnan2019
49.MK860933.1China: Ruili, Yunnan2019
50.MK860932.1China: Ruili, Yunnan2019
51.MK860931.1China: Ruili, Yunnan2019
52.MK860930.1China: Ruili, Yunnan2019
53.MK860927.1China: Ruili, Yunnan2019
54.MK860926.1China: Ruili, Yunnan2019
55.MK860925.1China: Ruili, Yunnan2019
56.MK860924.1China: Ruili, Yunnan2019
57.MK860923.1China: Mangshi, Yunnan2019
58.MK860922.1China: Mangshi, Yunnan2019
59.MK860921.1China: Mangshi, Yunnan2019
60.MK860920.1China: Mangshi, Yunnan2019
61.MK860919.1China: Mangshi, Yunnan2019
62.MK860918.1China: Mangshi, Yunnan2019
63.MK713974.1Myanmar2019
64.MN075831.1China2019
65.MN075830.1China2019
66.MK913645.1Viet Nam: Ninh Binh2019
67.MT073263.1Bangladesh: Gazipur2020
68.MT180097.1Pakistan2020
69.OP132904.1South Korea 2020
70.MT073264.1Bangladesh: Bogura2020
71.MT073266.1Bangladesh: Jamalpur2020
72.MT073265.1Bangladesh: Rangpur2020

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Agronomy 13 02175 g001
Figure 1. Distribution pattern of haplotypes of the fall armyworm Spodoptera frugiperda populations in the DRC based on collected locations (a), mitochondrial COI (b), and Tpi (c) partial gene markers.
Figure 1. Distribution pattern of haplotypes of the fall armyworm Spodoptera frugiperda populations in the DRC based on collected locations (a), mitochondrial COI (b), and Tpi (c) partial gene markers.
Agronomy 13 02175 g001
Agronomy 13 02175 g002
Figure 2. COI mismatch distribution curve indicating the observed (solid red line) and expected (dotted blue line) pairwise nucleotide site divergences computed with DnaSP.
Figure 2. COI mismatch distribution curve indicating the observed (solid red line) and expected (dotted blue line) pairwise nucleotide site divergences computed with DnaSP.
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Figure 3. A phylogenetic tree derived from a maximum likelihood analysis comparing the two DRC COI haplotypes with those from Spodoptera frugiperda host strains of other invaded and native regions. For each phylogeny, 1000 bootstrap replicates were used to assess robustness using the Hasegawa–Kishino–Yano (HKY850) model, and gamma distribution rates of variation between sites were used to construct the phylogenetic tree in MEGA6.
Figure 3. A phylogenetic tree derived from a maximum likelihood analysis comparing the two DRC COI haplotypes with those from Spodoptera frugiperda host strains of other invaded and native regions. For each phylogeny, 1000 bootstrap replicates were used to assess robustness using the Hasegawa–Kishino–Yano (HKY850) model, and gamma distribution rates of variation between sites were used to construct the phylogenetic tree in MEGA6.
Agronomy 13 02175 g003
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Figure 4. Median-joining haplotype network of the fall armyworm Spodoptera frugiperda mtCOI gene partial sequences from four geographical groups (DRC, Africa, America, and Asia). Each pie represents a distinct haplotype, with the radius equal to the number of sequences belonging to that haplotype.
Figure 4. Median-joining haplotype network of the fall armyworm Spodoptera frugiperda mtCOI gene partial sequences from four geographical groups (DRC, Africa, America, and Asia). Each pie represents a distinct haplotype, with the radius equal to the number of sequences belonging to that haplotype.
Agronomy 13 02175 g004
Table 1. List of fall armyworm Spodoptera frugiperda samples and the locations from which they were collected from the DRC.
Table 1. List of fall armyworm Spodoptera frugiperda samples and the locations from which they were collected from the DRC.
No.Sample IDProvince/Territory/VillageLocation Collection Date
(Day/Month/Year)		Accession Number Genetic Group
COITpiCOITpi
1Congo11Sud-Kivu/Kabare/ Katana2°22′51″ N 28°82′35″ E29 November 2018MT103350MT894220COI-RSTpi-Ca1a
2Congo42Sud-Kivu/Walungu/Nduba2°63′73″ N 28°69′63″ E15 December 2018MT103349MT894225COI-RSTpi-Ca1a
3Congo3Sud-Kivu/Kalehe/Bunyakiri1°99′49″ N 28°54′62″ E29 November 2018OQ612484OQ632453COI-RSTpi-Ca1a
4Congo41Sud-Kivu/Uvira/Sange3°06′10″ N 29°08′55″ E15 December 2018MT933055MT894224COI-RSTpi-Ca2b
5Congo31Sud-Kivu/Uvira/Luvungi2°89′15″ N 28°97′12″ E15 December 2018MT933054MT894223COI-RSTpi-Ca2a
6Congo21Sud-Kivu/Kalehe/Minova1°74′73″ N 28°98′78″ E29 November 2018MT933053MT894222COI-RSTpi-Ca2a
7Congo12Sud-Kivu/ Kabare/Miti2°33′06″ N 28°76′69″ E29 November 2018MT933052MT894221COI-RSTpi-Ca2b
8K1 Lomami/Kabinda/Kabinda6°07′48″ S 24°28′48″ E18 July 2020OP132901OQ468459COI-RSTpi-Ca1a
9Gem1Sud-ubangi/Gemena/Gemena13°14′56″ N 19°46′36″ E15 July 2020OP132892OQ468451COI-RSTpi-Ca1a
10Bkd Sud-ubangi/Gemena/Bokunda3°12′39″N 19°46′29″ E15 July 2020OP132899OQ468460COI-RSTpi-Ca1a
11Bsg1 Sud-ubangi/Gemena/Bosengwen3°13′50″N 19°42′57″ E18 July 2020OP132898OQ468458COI-CSTpi-Ca1a
12Bbw1 Sud-ubangi/Gemena/Bombawuli3°13′48″ N 19°53′51″ E18 July 2020OP132896OQ468455COI-RSTpi-Ca1a
13Mtf1 Tanganyika/Kalemie/Kalemie5°52′08″ S 29°10′14″ E21 July 2020OP132894OQ468453COI-RSTpi-Ca1a
14Tshb1 Tshuapa/Boende/Boende1 0°17′13″ S 20°52′24″ E18 July 2020OP132895OQ468454COI-RSTpi-Ca1a
15Blk1 Tshuapa/Boende/Baliko0°18′05″ S 20°52′30″ E18 July 2020OP132897OQ468456COI-CSTpi-Ca1a
16Bde1 Tshuapa/Boende/Boende3 0°16′39″ S 20°53′05″ E15 July 2020OP132898OQ468457COI-RSTpi-Ca1a
17Isi1 Haut-Uélé/Isiro/Isiro 2°45′57″ N 27°36′32″ E8 August 2020OP132893OQ468452COI-RSTpi-Ca1a
18M1 Kongo central/Matadi/Matadi5°47′58″ S 13°26′26″ E18 July 2020OP132900OQ632454COI-RSTpi-Ca1a
19Kst1 Kongo central/Kisantu/Kisantu15°13′82″ S, 15°09′08″ E15 December 2022OQ427278OQ468462COI-RSTpi-Ca2a
20Kst2 Kongo central/Kisantu/Kisantu25°13′82″ S, 15°09′08″ E15 December 2022OQ427279OQ468466COI-CSTpi-Ca1a
21Kst3Kongo central/Kisantu/Kisantu35°13′82″ S, 15°09′08″ E15 December 2022OQ427280OQ857569COI-RSTpi-Ca1a
22Plaba1Kinshasa/Plateau de Bateke1 4°20′72″ S, 15°84′48″ E20 December 2022OQ427282OQ468463COI-RSTpi-Ca1a
23Plaba2Kinshasa/Plateau de Bateke2 4°20′72″ S, 15°84′48″ E20 December 2022OQ427284OQ468464COI-CSTpi-Ca1a
24Kimw1Kinshasa/Kimwenza14°47′11″ S, 15°30′14″ E20 December 2022OQ427281OQ468461COI-RSTpi-Ca1a
25Kimw2 Kinshasa/Kimwenza24°47′11″ S, 15°30′14″ E20 December 2022OQ427283OQ468465COI-RSTpi-Ca1a
Table 2. Summary of the genetic diversity of the fall armyworm Spodoptera frugiperda populations analyzed on the basis of partial mtCOI gene from four geographical locations.
Table 2. Summary of the genetic diversity of the fall armyworm Spodoptera frugiperda populations analyzed on the basis of partial mtCOI gene from four geographical locations.
DRCAfricaAmericaAsiaTotal
No. of sequences258912672308
No. of sites483483482483482
No. of polymorphic sites7834937
No. of mutations7838941
No. of haplotypes2329432
Haplotype diversity0.3240.3440.7420.3780.562
Nucleotides diversity0.004690.004780.008550.005200.00735
Fu’s Fs statistic6.0126.837−9.9665.134−9.841
Fu and Li’s D × test statistic1.296270.47452−3.82406 **−0.08303−5.46527 **
Fu and Li’s F × test statistic1.147340.79287−3.33095 **0.30287−4.28883 **
Tajima’s D0.534891.13421−1.255180.92310−1.28326
**: significant at p < 0.02.
Table 3. Results of analysis of molecular variance (AMOVA) among the four fall armyworm Spodoptera frugiperda geographical groups.
Table 3. Results of analysis of molecular variance (AMOVA) among the four fall armyworm Spodoptera frugiperda geographical groups.
GroupSource dfSSVariance ComponentTotal Variancep-Value
All Among groups371.4110.236912.700.0001
Among populations within groups2294.0820.290015.54
Within populations283379.0081.339271.76
Total308544.5021.86629
DRC and AfricaAmong groups10.019−0.04839−4.330.17595
Among populations within groups1219.4290.075436.75
Within populations96104.7441.0910897.58
Total109124.1911.11811
America and DRCAmong groups118.3250.2595710.940.0001
Among populations within groups1186.9420.6355426.79
Within populations142209.7591.4771862.27
Total154315.0262.37228
Asia and DRCAmong groups10.154−0.06807−4.440.1700
Among populations within groups1022.8700.115547.54
Within populations81120.2451.4845196.90
Total92143.2691.53197
Africa and AmericaAmong groups15.4730.0376911.170.0001
Among populations within groups1210.1070.0436612.94
Within populations20652.7570.2561075.89
Total21968.3360.33745
America and AsiaAmong groups138.8210.2511411.510.0001
Among populations within groups1194.2170.5423624.85
Within populations190263.8591.3887363.64
Total202396.8972.18223
Africa and AsiaAmong groups10.132−0.04126−3.480.0400
Among populations within groups1226.8950.112849.52
Within populations147163.6941.1135693.96
Total160190.7201.18514
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Malekera, M.J.; Mamba, D.M.; Bushabu, G.B.; Murhula, J.C.; Hwang, H.-S.; Lee, K.-Y. Genetic Diversity of the Fall Armyworm Spodoptera frugiperda (J.E. Smith) in the Democratic Republic of the Congo. Agronomy 2023, 13, 2175. https://doi.org/10.3390/agronomy13082175

AMA Style

Malekera MJ, Mamba DM, Bushabu GB, Murhula JC, Hwang H-S, Lee K-Y. Genetic Diversity of the Fall Armyworm Spodoptera frugiperda (J.E. Smith) in the Democratic Republic of the Congo. Agronomy. 2023; 13(8):2175. https://doi.org/10.3390/agronomy13082175

Chicago/Turabian Style

Malekera, Matabaro Joseph, Damas Mamba Mamba, Gauthier Bope Bushabu, Justin Cishugi Murhula, Hwal-Su Hwang, and Kyeong-Yeoll Lee. 2023. "Genetic Diversity of the Fall Armyworm Spodoptera frugiperda (J.E. Smith) in the Democratic Republic of the Congo" Agronomy 13, no. 8: 2175. https://doi.org/10.3390/agronomy13082175

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